"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2023.79.87 \n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization \nof Cotton Jassid and Their Response Against Relatively Newer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-293X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.02.2023.79.87 \n\n\n\n\n\n\n\nMOLECULAR CHARACTERIZATION OF COTTON JASSID AND THEIR RESPONSE \nAGAINST RELATIVELY NEWER PESTICIDES IN TWO COTTON VARIETIES OF \nBANGLADESH \nShazzad Hossaina, Md. Mamunur Rahmana*, Haider Iqbal Khanb, Md. Ahsanul Haquea Rayhanur Jannatc and Jahidul Hassand \n\n\n\naDepartment of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh \nbDepartment of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh \ncDepartment of Plant Pathology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh \ndDepartment of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh \n*Corresponding Author Email: mamun@bsmrau.edu.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 09 July 2023 \nRevised 12 August 2023 \nAccepted 22 September 2023 \nAvailable online 25 September 2023 \n\n\n\n Cotton, known as the \"monarch of fibers\" for meeting global textile demands, faces a decline in production \n\n\n\ndue to natural threats like the cotton jassid insect. In Bangladesh, where this insect has caused production \n\n\n\nlosses of 20% to 50%, government support for upland cotton cultivation is challenged. Two experiments at \nBSMRAU's entomological field in 2021-2022 aimed to address this issue. The first confirmed the presence of \n\n\n\nthe jassid through molecular analysis, while the second studied how different cotton varieties responded to \n\n\n\nbiopesticides. Three different treatment combinations using different dosages of biorational insecticides on \nCB-12 and CB-14 cotton varieties were implied to see the interactions for revealing the biopesticidal effects \n\n\n\non species cotton cultivars. The study identified the cotton jassid insect by analyzing its mitochondrial DNA, \n\n\n\ntargeting cytochrome oxidase subunit-1 gene. The resulting nucleotide sequences were assigned accession \nnumbers ranging from OR362770 to OR362772. A phylogenetic analysis further confirmed the insect's \n\n\n\nidentity as the cotton jassid, with strong support indicated by a 94% bootstrap value. This study delves into \n\n\n\nthe impact of various treatments and different cotton plant varieties on chlorophyll and anthocyanin levels \n\n\n\nin parts of plants infested by the jassid. Principal component analysis (PCA) demonstrated the significant role \nof two environmentally-friendly insecticides in controlling the cotton jassid across two distinct cotton \n\n\n\nvarieties. Notably, there were significant differences observed in chlorophyll and anthocyanin levels among \n\n\n\nthe treatments and cotton varieties. Variety CB-12 consistently exhibited higher levels of chlorophyll and \nanthocyanin, suggesting it may possess a greater resilience to pests. These findings underscore the \n\n\n\nimportance of selecting appropriate cotton varieties and employing effective treatment strategies to manage \n\n\n\njassid infestations and enhance crop productivity. This research provides valuable insights for the promotion \nof sustainable cotton cultivation for supporting textile industry in Bangladesh.  \n\n\n\nKEYWORDS \n\n\n\nAnthocyanin, biopesticide, Cotton cultivars, chlorophyll, PCA, phylogeny  \n \n\n\n\n1.   INTRODUCTION \n\n\n\nCotton is considered to be the \u201cking of fibre\u201d, and commercially grown in \nmore than 50 countries of the world. In the 2021 calendar year, \nBangladesh imported 8.5 million bales of cotton spending more than $3 \nbillion. This year in 2023, Bangladesh, followed by China, has become the \nsecond largest importer of cotton. US-based international cotton trade \nanalyst Cotton Connect has conducted several studies on cotton \nproduction potentialities in Bangladesh. The studies suggest that \nBangladesh can possibly increase cotton production from 150,000 to 1 \nbillion bales minimum. \n\n\n\nCotton productivity and quality are influenced by factors such as climate, \ncrop variety, pest infestation, harvest frequency, and ginning processes \n(Azad et al., 2011). To support the textile industry's demand for cotton \nfiber in Bangladesh, the government has been providing subsidies for \nupland cotton cultivation (Gossypium hirsutum L.) since 1977. However, \ncotton farmers have shown reluctance to engage in cotton farming due to \nissues related to insect pests. These pests encompass a range of organisms, \n\n\n\nincluding insects, mites, nematodes, and others, which can diminish the \nquality and yield of cotton crops. They pose a significant threat to cotton \nproduction, leading to both reduced crop yields and quality. Among these \nbiotic stresses, the cotton jassid (Amrasca biguttula) stands out as a major \npest that inflicts significant damage on cotton crops (Jaber and Ownley, \n2018). \n\n\n\nCotton jassid is a polyphagous pest that feeds on various plant species, \nincluding cotton, okra, and hibiscus, and has a wide geographical \ndistribution. The pest causes damage by piercing and sucking the sap from \nthe plants, which leads to stunted growth, leaf curling, and ultimately \nreduced yield. Cotton jassid infestation has been reported in many cotton-\ngrowing countries, including India, Pakistan, China, Egypt, and the United \nStates (Lopez and Sword, 2015). The pest is known to cause yield losses \nranging from 20% to 50%, depending on the severity of the infestation and \nthe cotton cultivar. In addition, cotton jassid infestation can also increase \nthe susceptibility of cotton plants to other diseases, such as cotton leaf curl \nvirus (CLCuV). Management of cotton jassid infestation is challenging due \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\nto the pest's ability to rapidly develop resistance to insecticides (Farooq et \nal., 2011). \n\n\n\nGenetic analysis serves as a valuable tool for understanding how genes are \n\n\n\ninherited and determining the number of genes involved in a particular \n\n\n\nmechanism. To decipher the genetics of traits, researchers commonly \n\n\n\nemploy segregating populations. In modern species classification and \n\n\n\nidentification, the mitochondrial cytochrome C oxidase subunit I (COX1) \n\n\n\ngene region is frequently used as a genetic marker (Zhao et al., 2014). \n\n\n\nDNA-based methods are generally considered faster and more precise \n\n\n\nthan alternative techniques and are applicable to all cell types. \n\n\n\nControlling cotton pests involves a range of strategies, including cultural \n\n\n\npractices such as crop rotation, the cultivation of resistant cultivars, and \n\n\n\neffective irrigation management. Chemical and biological control methods \n\n\n\nare also employed. Synthetic pesticides, used in chemical control, are the \n\n\n\npredominant means of managing cotton pests but have raised concerns \n\n\n\ndue to their potential environmental and human health impacts (Roy, \n\n\n\n2016). Overreliance on chemical pesticides has led to the development of \n\n\n\npest resistance in jassid populations and adverse effects on both the \n\n\n\nenvironment and human health. Biological control, which involves the use \n\n\n\nof natural enemies like parasitoids and predators, has been explored as an \n\n\n\nalternative to chemical pesticides. However, its effectiveness can be \n\n\n\ninfluenced by factors such as the availability of natural enemies and \n\n\n\nenvironmental condition (Allegrucci et al., 2017). Neonicotinoids are \n\n\n\nconsidered a promising option due to their lower toxicity to mammals, \n\n\n\nreduced issues with pest resurgence, environmental friendliness, \n\n\n\nselectivity in pest management, and minimal harm to natural enemies. \n\n\n\nMicrobial control involves the use of various microorganisms such as \nbacteria, fungi, viruses, and nematodes to control pest populations. These \nmicroorganisms have specific modes of action and are highly target-\nspecific, making them an effective and safe alternative to chemical \ninsecticides. Mietkiewski observed spectacular mortality of Galleria \nmellonella larvae occurred due to B. bassiana, applied to soils (Mietkiewski \net al., 1997). Several studies have reported the use of entomopathogenic \nfungi such as Metarhizium anisopliae and Beauveria bassiana for the \ncontrol of jassids. Among these fungi, Beauveria bassiana has garnered \nsignificant attention for its effectiveness against various insect pests, \nincluding jassids. These fungi work by infiltrating the insect's outer \ncovering, germinating inside their bodies, ultimately leading to their \ndemise9. Furthermore, they have the potential to hinder jassids' \nreproductive capabilities, further reducing their population. Research by \nMantzoukas  revealed that applying B. bassiana during the early stages of \na jassid infestation is more efficient than later applications (Mantzoukas et \nal., 2015). Importantly, these fungi pose minimal harm to non-target \norganisms, are biodegradable, highly specific to insects, and are \nconsidered safe for both the environment and human health. In laboratory \nand greenhouse settings, Imidacloprid and other neonicotinoids have \nbeen found to enhance the efficacy of B. bassiana against white grub \n(Popillia japonica) larvae, although this synergy may not extend to field \nconditions (Morales- Rodriguez and Peck, 2009). \n\n\n\nIn Bangladesh, there have been limited studies on the use of B. bassiana \nfor cotton jassid control. Its potential as a microbial control agent against \ncotton jassid in Bangladesh is promising B. bassiana has demonstrated \neffectiveness in managing jassids across various agricultural scenarios, \nshowcasing its versatility and adaptability. Utilizing B. bassiana as a \nbiological control agent offers several advantages over traditional \nchemical insecticides. One study even reported a significant reduction in \njassid populations and an increase in cotton yields when employing B. \nbassiana (Rondot and Reineke, 2018). Nevertheless, there remains a need \nfor further research to optimize the application of B. bassiana within the \ncontext of cotton farming in Bangladesh and to develop more sustainable \nand environmentally friendly pest management practices for the country's \ncotton industry.  \n\n\n\nIn this study, our primary objectives were threefold. Firstly, we aimed to \ndetermine the identity of the cotton jassid through a comprehensive \nanalysis of mitochondrial DNA. Secondly, we sought to investigate and \nquantify the effectiveness of two distinct biopesticides in mitigating the \nprevalence of jassid infestations in cotton crops. Lastly, we endeavored to \nuncover the varying responses of different cotton varieties to jassid \ninfestations when treated with these biopesticides. These objectives \ncollectively contribute to a deeper understanding of cotton jassid \nmanagement strategies and provide valuable insights for sustainable \ncotton cultivation practices. \n\n\n\n2.   MATERIALS AND METHODS \n\n\n\nThe study comprised two distinct experiments conducted in the \nDepartment of Entomology at Bangabandhu Sheikh Mujibur Rahman \nAgricultural University, Gazipur, Bangladesh. The initial experiment was \ndedicated to the molecular confirmation of the Jassid species present in \nthe experimental field. This involved the meticulous collection of Jassid \nspecimens using standardized sampling techniques, followed by DNA \nextraction, PCR amplification of a specific mitochondrial DNA region (COI \ngene), sequencing of amplified DNA fragments, and subsequent sequence \nanalysis by comparing them to reference sequences in the NCBI GenBank \ndatabase.  \n\n\n\nIn contrast, the second experiment was designed to investigate how two \n\n\n\ndifferent cotton varieties, CB-12 and CB-14, responded to the application \n\n\n\nof biopesticides. The experimental setup included controlled plots for each \n\n\n\ncotton variety, the application of selected biopesticides (Imidacloprid and \n\n\n\nB. bassiana) at recommended dosages and timings, regular monitoring of \n\n\n\nJassid populations, and the quantification of chlorophyll and anthocyanin \n\n\n\ncontent in the cotton plants. Statistical data analysis was employed to \n\n\n\nevaluate the effectiveness of the biopesticides and assess varietal \n\n\n\nresponses. The study was conducted over ten months, from July 2021 to \n\n\n\nApril 2022, in a subtropical climate with specific soil characteristics. The \n\n\n\nexperimental field underwent meticulous land preparation, including soil \n\n\n\nconditioning and leveling, followed by the application of recommended \n\n\n\nmanure and fertilizer doses.  \n\n\n\nThe experimental design involved a randomized complete block design \n\n\n\n(RCBD) with three replications, subdividing the field into 18 plots. The \n\n\n\nthree treatments, including a control, were applied to the two cotton \n\n\n\nvarieties to assess their efficacy in managing Jassid infestations. The \n\n\n\nresearch encompassed essential agricultural practices such as seed \n\n\n\ncollection, treatment, intercultural operations, irrigation, and weeding to \n\n\n\nensure healthy crop growth. Harvesting was conducted in five pickings, \n\n\n\nwith cotton bolls harvested at their peak ripeness. Overall, the \n\n\n\ncomprehensive methodology employed in this study provides a robust \n\n\n\nfoundation for reliable and replicable research findings in the field of \n\n\n\ncotton Jassid management and varietal responses to biopesticides. \n\n\n\n2.1   Monitoring and Collection of Cotton Jassid \n\n\n\nAll plants in the plots were closely observed every day for the purpose of \n\n\n\nstudying the incidence of Jassid. The jassids were collected from \n\n\n\nexperimental site. The occurrence of pests was observed through visual \n\n\n\nsearch in all plots and their number per plot was noted. For recording \n\n\n\nJassid incidence, underside of leaves was considered and number of Jassid \n\n\n\nper selected leaves. At this stage, the observation was made once a week. \n\n\n\nThe collected specimens were preserved in 99.9% Ethanol prior to DNA \n\n\n\nextraction.  \n\n\n\n2.2 Molecular Identification Of Jassid Species Collected From \n\n\n\nExperimental Site. \n\n\n\nThe molecular analyses were conducted in the Advanced Entomology \n\n\n\nLaboratory of Bangabandhu Sheikh Mujibur Rahman Agricultural \n\n\n\nUniversity, Bangladesh. The molecular identification was done by the \n\n\n\nfollowing method: \n\n\n\n2.3   Extraction of DNA \n\n\n\nGenomic DNA was extracted from recently collected jassid specimens by \n\n\n\nusing QAGEN DNeasy Blood and Tissue kit, following the manufacturer\u2019s \n\n\n\ninstructions. Samples were vortexed with adding 180 \u00b5l Buffer ATL and 20 \n\n\n\n\u00b5l proteinase K. Sample was incubated at 55\u00b0 C for 48 hours. DNA \n\n\n\nextraction was completed by adding two wash buffer AW1 and AW2 with \n\n\n\nBuffer AE and lysis buffer AL incorporated with elusion buffer AE, as per \n\n\n\nmanufacturer instruction. All centrifugation steps were completed at \n\n\n\nroom temperature. The colony mates of the specimens used for DNA \n\n\n\nanalysis were preserved in the Advanced Entomology Laboratory, \n\n\n\nBSMRAU after DNA extraction  \n\n\n\n2.4   PCR (Polymerase Chain Reaction) \n\n\n\nAmplification of DNA was done by polymerase chain reaction (PCR) \n\n\n\nTaKaRa Ex Taq PCR kit, according to the manufacturer\u2019s instructions. The \n\n\n\nkit contains 10x Ex Taq Buffer (20 mM Mg2+ plus) and dNTP mixture (2.5 \n\n\n\nmM each). The storage buffer contains 20 m MTris-HCl (pH 8.0), 100 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\nmMKCl, 0.1 mM EDTA, 1mM DTT, 0.5 % Tween 20, 0.5 % Nonidet P-40 and \n\n\n\n50 % glycerol. dNTP mixtures contain TAPS, KCl, MgCl2, DTT, dATP, dGTP, \n\n\n\ndCTP with activated salmon sperm DNA. Reaction mixtures for PCR for \n\n\n\nconsisted of TaKaRa Ex Taq (0.25 \u00b5l), 10x Ex Taq Buffer (5 \u00b5l), dNTP \n\n\n\nmixture   (4 \u00b5l),   a     pair    of    oligonucleotide   primers   (0.2-1.0 \u00b5M).  For  \n\n\n\n conducting the PCR, PCR machine (GTQ-Cycler 96) was used from the \n\n\n\nDepartment of Horticulture of BSMRAU. The detailed primer configuration \n\n\n\nand PCR status are presented in Table 1 and Figure 1 respectively. (Zhao \n\n\n\net al., 2014).  \n\n\n\nTable 1: Primer Name and Corresponding Position \n\n\n\nRegion Name Direction Sequence (5\u2019-3\u2019) a Position Annealing Temperatu-re(\u00b0C) \n\n\n\nMitochondrion Cytochrome \noxidase Subunit -1 \n\n\n\nCO1 1-3b Forward ATAATTTTTTTTATAGTTATACC 1981-2002 54\u00b0C \n\n\n\nCO1 2-4b Reverse TCCTAAAAAATGTTGAGGAAA 3063-3083  \n\n\n\n a (1) Crozier et al., 1994, b Used for both PCR and sequence  \n\n\n\n\n\n\n\nFigure 1: Operational Status of PCR for COI Gene \n\n\n\n2.5  Sequencing And Submission To NCBI Genbank, USA For Accession \n\n\n\nThe PCR products were sequenced by using the facilities from GENEWIZ, \n(Azanta life science from China). Upon completing all the steps as gel \nelectrophoresis, Exostar, cycle sequencing and ethanol precipitation, the \nnucleotide sequence data have been received. The obtained sequences \nwere then submitted to the National Center for Biotechnology Information \n(NCBI), USA for receiving the accession number. Upon receiving the \naccession numbers from NCBI of all the bee samples, the nucleotide \nsequence data have been processed for further analysis. \n\n\n\n2.6   Measurement Of Biochemical Content Of Healthy And Infested \nCotton Leaf \n\n\n\nIn our study, the measurement of biochemical content in both healthy and \ninfested cotton leaves played a crucial role in assessing the impact of pest \ninfestations on plant health. We focused on several key biochemical \nparameters to make comparisons between the two conditions. \n\n\n\nFirstly, we determined the chlorophyll content in the leaves. This involved \ntaking fresh leaf samples weighing 200 mg and placing them in small vials \ncontaining 5 ml of 80% acetone, which were then covered with aluminum \nfoil and kept in the dark for 24 hours. The supernatant was adjusted to a \nfinal volume of 10 ml, and the absorbance of the extract was measured at \n470, 645, and 663 nm using a UV-visible spectrophotometer. We used 80% \nacetone as a blank for reference. The chlorophyll content, including both \nchlorophyll-a and chlorophyll-b, as well as carotenoid content, was \ncalculated using specific equations and expressed as milligrams per gram \nof fresh weight (mg/g FW) based on the methodology outlined by \nLichtenthaler in 1987. \n\n\n\nAdditionally, we assessed the anthocyanin content in the leaves using a \nspectrophotometric approach, following the pH differential method as \ndescribed by Lee et al. in 2005. This method involved using two buffer \nsystems, potassium chloride buffer (pH 1.0, 0.025 M) and sodium acetate \nbuffer (pH 4.5, 0.4 M). Leaf samples weighing 0.2 grams were ground with \n1 ml of extraction buffer (a mixture of methanol, water, and concentrated \nHCl solution), followed by centrifugation at 14,000 rpm. The supernatant \nwas then mixed with the respective buffers, and absorbance \nmeasurements were taken at 510 nm and 700 nm wavelengths using a UV-\nvisible spectrophotometer. Anthocyanin content was quantified using a \nspecific formula. \n\n\n\nTo analyze our data, we employed rigorous statistical methods. Molecular \nnucleotide sequences were analyzed using MEGA 11 software. The \nrecorded biochemical data were systematically compiled and organized \n\n\n\nfor statistical analysis, and all statistical procedures were conducted using \ncomputer software programs, particularly the statistical R package. This \ncomprehensive approach ensured the reliability and scientific rigor of our \nresearch findings, allowing for a detailed understanding of the \nbiochemical responses of cotton leaves to pest infestations and the \nmolecular identification of the involved species. \n\n\n\n3. RESULTS \n\n\n\nIn this chapter, the findings of the present study are presented through \ntwo distinct sections. The initial portion of the experiment was dedicated \nto verifying the Jassid species on a molecular level, while the latter part \nconcentrated on assessing how two different cotton varieties responded \nto biopesticide treatments. \n\n\n\n3.1   Molecular identification of Amrasca biguttula biguttula \n\n\n\nThe identification of Cotton jassid species was done by mitochondrial DNA \nanalysis of cytochrome oxidase subunit-1 gene. The nucleotide sequences \nwere submitted to NCBI GenBank, USA and received the accession \nnumber.  \n\n\n\nTable 2: Sampled Cotton jassid with assigned nucleotide accession \nnumber from NCBI GenBank \n\n\n\nSI No. Name GenBank Accession No \n\n\n\n01 Amrasca biguttula voucher OR362770 \n\n\n\n02 Amrasca biguttula voucher OR362771 \n\n\n\n03 Amrasca biguttula voucher OR362772 \n\n\n\n3.2   Phylogenetic Study Of Amrasca Biguttula Voucher \n\n\n\nThe phylogenetic analysis of the Jassid sample from BSMRAU was \nconducted using MEGA 11 software, as detailed by Tamura (Tamura et al., \n2004). The evolutionary history was inferred through the application of \nthe Neighbor-Joining method, as originally described by Saitou and Nei \n(Saitou and Nei, 1987). To assess the robustness of the phylogenetic tree, \na bootstrap test with 500 replicates was performed, and the percentage of \nreplicate trees in which the associated taxa clustered together is indicated \nadjacent to the branches, following Felsenstein's work (Felsenstein, \n1985). Evolutionary distances were calculated using the Maximum \nComposite Likelihood method, based on Tamura's methodology (Tamura \net al., 2004). Ambiguous positions in each sequence pair were eliminated, \nusing the pairwise deletion option, resulting in a final dataset comprising \na total of 487 positions. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\n\n\n\n\nFigure 2: Molecular phylogenetic analysis of Amrasca biguttula by Neighbor-Joining method. (The taxon with green triangles denotes sample from \nBsmrau, Bangladesh and others are the reference sequence from Genbank) \n\n\n\nThe phylogenetic tree indicates that the Jassid sample BSMRAU 2 and \nJassid sample BSMRAU 3 are closely related to each other with bootstrap \nvalue 49. But they are distantly related to Jassid sample BSMRAU 1 and are \nin different clades with bootstrap value 44. All the jassid of BSMRAU are \nclosely related to KX813734.1 Amrasca biguttula biguttula isolate DW12-\nKarnataka (5) with the bootstrap value 86. \n\n\n\n3.3   Biochemical Analysis Of Infected And Healthy Cotton Leaves \n\n\n\nAnalyzing the biochemical composition of cotton leaves offers valuable \ninsights into the physiological and metabolic condition of these crucial \nplant components. This comprehensive assessment commonly \nencompasses the measurement of chlorophyll content, a critical \nparameter for evaluating photosynthetic activity, along with the \nquantification of anthocyanin levels, which serves as an indicator of the \nplant's response to stress. The biochemical analysis of cotton leaves \nprovides essential data that can be used to fine-tune agricultural \n\n\n\ntechniques, ultimately leading to improvements in both crop yield and \nquality. \n\n\n\n3.4   Correlation Matrix Among Cotton Leaf Sample Variables \n\n\n\nCorrelation among the variables was examined through correlation \nmatrices, where shades of blue to white indicated positive correlations, \nand transitions from white to red indicated negative correlations among \nthe leaf sample variables (as illustrated in Figure 3). In the graph, it's \nevident that the chlorophyll content of fresh cotton leaves displayed a \nrobust negative correlation with jassid infestation in the top, lower, and \nmiddle leaves. Moreover, the chlorophyll content of infected cotton leaves \nexhibited a negative correlation with jassid infestation in the top and \nlower leaves, although no significant correlation was observed with jassid \ninfestation in the middle leaves. Interestingly, a contrasting trend emerged \nwhere jassid infestation in the top leaves and jassid infestation in the \nlower and middle leaves showed a positive correlation. \n\n\n\n\n\n\n\nFigure 3: Correlation matrix among the variables of cotton leaf samples. \n\n\n\nThere was a notable positive correlation between jassid infestation and \nthe number of jassids per plant in the lower and middle leaves. However, \nthere was no significant correlation identified between jassid infestation \nin the top, middle, or lower leaves and the number of jassids per plant \nwhen considering anthocyanin content in both fresh and infected cotton \nleaves. Furthermore, no observable correlation was detected between \nchlorophyll content in fresh and infected leaves and the anthocyanin \ncontent in fresh and infected leaves, as shown in Figure 3. \n\n\n\n3.5 Exploring Principal Component Analysis (PCA) For \nUnderstanding Cotton Jassid Infestation Variables \n\n\n\nPrincipal component analysis (PCA) was done among the healthy and \ninfested cotton leaf sample variables of two cotton variety and it was found \nthat the first two components could explain more than 82% of the \nvariation presented in Figure 4. Where dimension 1 contributed 54% and \ndimension 2 contributed 28.4%. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\n\n\n\n\nFigure 4: Principal Component analysis (PCA) among the healthy and infested leaf sample variables of two cotton variety. \n\n\n\nThe results of a Principal Component Analysis (PCA) involving various \nvariables related to anthocyanin and chlorophyll content, as well as jassid \ninfestation in leaves. The PCA results are described here under the \nfollowing subheadings: \n\n\n\n1. Positive Relationship with Dimension 1 and 2 for Anthocyanin \nContent in Infected and Fresh Leaves: This suggests that \nanthocyanin content in both infected and fresh leaves contribute \npositively to both principal components (dimensions 1 and 2) and \nmight be important factors in explaining the variability.  \n\n\n\n2. Negative Relationship with Dimension 1 and 2 for Chlorophyll \nContent in Infected and Fresh Leaves: Conversely, chlorophyll \ncontent in infected and fresh leaves is negatively correlated with both \ndimension 1 and 2. This indicated that lower chlorophyll levels are \nassociated with higher values of both principal components. \n\n\n\n3. Positive Correlation with Dimension 1 and Negative Correlation \nwith Dimension 2 for Jassid Infestation Metrics: The number of \njassids per plant and jassid infestation in different parts of the leaves \n(top, middle, and lower) are positively correlated with dimension 1 \nbut negatively correlated with dimension 2. This implies that \ndimension 1 might represent a factor associated with increased jassid \ninfestation. \n\n\n\n4. Strong Positive Correlation among Jassid Infestation Parameters, \nParticularly Anthocyanin: Among the various parameters related to \njassid infestation, anthocyanin content shows a strong positive \ncorrelation with other variables. This suggested that anthocyanin \ncontent is highly interrelated with other jassid-related factors in your \ndataset. \n\n\n\n5. Strong Negative Correlation for Chlorophyll Content in Infected \nand Fresh Leaves: Chlorophyll content in both infected and fresh \nleaves showed a strong negative correlation in both dimensions. This \nindicated that lower chlorophyll content is associated with higher \nvalues of both dimension 1 and dimension 2. \n\n\n\n6. Possible Variability in Correlations due to Jassid Infestation Rate: \nThe correlations among these variables may differ based on the \ninfestation rate of jassids. This suggested that the relationships \nbetween these variables might change or become more pronounced \nunder different levels of jassid infestation. \n\n\n\nThe PCA analysis resulted Anthocyanin content and jassid infestation \nseem to be important factors, while chlorophyll content has a negative \ninfluence on the two principal dimensions. The specific correlations and \ntheir strengths can provide valuable insights into the underlying \nrelationships within the varieties, which can be useful for further analysis \nor experimentation. \n\n\n\nFigure 5 displays the variance contributions in a PCA. In this analysis, \nanthocyanin levels in both infected and fresh leaves exhibit positive \nassociations with both the first and second dimensions, whereas \nchlorophyll content in infected and fresh leaves demonstrates negative \ncorrelations with both dimensions. Furthermore, the number of jassids \nper plant and jassid infestation in different leaf sections (top, middle, and \nlower) are positively linked to the first dimension but negatively tied to \nthe second dimension. Among the eight parameters related to jassid \ninfestation, anthocyanin displays a strong positive correlation with the \nother variables. Conversely, chlorophyll content in infected and fresh \nleaves exhibits a strong negative correlation across both dimensions. It's \nimportant to note that these correlations among the variables might vary \ndepending on the infestation rate of jassids. \n\n\n\n\n\n\n\nFigure 5: Principal component analysis representing different leaves variable for two varieties of cotton. \n\n\n\nThe contribution of different variables of cotton samples from Bangladesh in PC loadings plot analysis is presented with 2 dimensions in Figure 6. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\n\n\n\n\nFigure 6: Principal component Loading plot representing contribution of different variables of cotton leaf samples \n\n\n\nAnthocyanin content of both fresh and infected leaf was found to have \nstrong positive correlation in dimension 1 and dimension 2. On the other \nhand, Chlorophyll content of both fresh and infected leaf had strong \n\n\n\nnegative correlation with both dimensions. Whereas the number of jassid \nper plant, jassid infestation in top, middle and lower leaves had positive \ncorrelation with dimension 1, but negatively correlated with dimension 2. \n\n\n\n\n\n\n\nFigure 7: Biplot generated through principal component analysis corresponding with different treatments \n\n\n\nFigure 7 illustrated how various factors related to cotton leaves are \ninvolved in a PCA under different treatment conditions. In this specific \nstudy of cotton leaf samples, we observed the following patterns: \n\n\n\n\u2022 Under the T1-control treatment, which occupied the largest portion of \nboth dimensions, there was a positive correlation between anthocyanin \ncontent in fresh leaves and anthocyanin content in infected leaves in \nboth dimension 1 and dimension 2. \n\n\n\n\u2022 In the case of the T2-treated seed treatment, which also covered both \ndimensions, all the parameters displayed positive correlations with \nboth dimension 1 and 2, except for the chlorophyll content of the leaves. \n\n\n\n\u2022 With the T3-routine pesticide spray treatment, which also extended \nacross both dimensions, anthocyanin content in fresh and infected \nleaves exhibited positive correlations in both dimensions, while \nchlorophyll content in infected and fresh leaves showed negative \ncorrelations in both dimensions. \n\n\n\n\n\n\n\nFigure 8: Biplot generated through principal component analysis corresponding with different varieties of cotton. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\nThe engrossment of cotton leaf variables in PCA with its different varieties \nof cotton is presented in Figure 8. In this study of cotton leaf samples: \n\n\n\n\u2022 Variety-1 (V1), represented by CB 12, occupies the largest portion of \nboth dimensions in the PCA plot. Here, there is a positive correlation \nbetween anthocyanin content in fresh leaves and anthocyanin content \nin infected leaves in both dimension 1 and dimension 2. \n\n\n\n\u2022 Variety-2 (V2), represented by CB 14, exhibits a negative correlation in \ndimension 2. Additionally, chlorophyll content shows a \n\n\n\nnegativecorrelation in both dimension 1 and 2, while the other \nvariables display positive correlations on dimension 1. \n\n\n\n\u2022 The data provided in Table-3 reveals significant differences among the \ntreatments when it comes to jassid infestation in cotton plants. In the \ncontext of agricultural practices, it is crucial to assess how different \ntreatments impact a plant's response to environmental stressors, \nparticularly pests. Among these pests, jassids play a substantial role in \naffecting plant growth and overall health. This section explores the \ninfluence of treatments, specifically T1, T2, and T3, on essential plant \nparameters in areas exposed to jassid infestations. \n\n\n\nTable 3: Effects of different treatments on different parameters of cotton leaf \n\n\n\nTreatments Top leaf Middle leaf Lower leaf Jassid/ plant CHL Fresh CHL Infected Anthocyanin Fresh Anthocyanin Infected \n\n\n\nT1 11.61a 10.28ab 3.28a 8.53a 39.33b 7.41b 24.44a 98.47a \n\n\n\nT2 11.16a 13.78a 4.78a 9.84a 20.61c 3.28c 24.43a 98.45a \n\n\n\nT3 4.48a 4.43b 1.42a 3.85a 59.95a 10.70a 24.43a 98.46a \n\n\n\nData expressed as mean \u00b1 SE. Means within a row followed by same \nletter(s) are not significantly different according to TukeyHSD mean \ncomparison test- at < 0.01*, * T1: Control Treatment; T2: Seed treatment \nwith Beauvaria bassiana; T3: Routine foliar spray \n\n\n\nData were collected to quantify chlorophyll content (CHL) and \nanthocyanin content in both fresh and infected plant parts. Additionally, \nobservations were recorded regarding the presence of jassids on the \nplants. The plant segments analyzed included the top leaf, middle leaf, and \nlower leaf. Treatment T2 displayed the highest presence of jassids, with a \nrecorded value of 9.84a, while Treatment T3 exhibited the lowest \npresence at 3.85a. Notably, Treatment T2 generally showed higher \nchlorophyll content, particularly in the middle and lower leaf segments, \ncompared to T1 and T3. In terms of anthocyanin content, minor variations \nwere observed between the treatments in both fresh and infected states, \nwith all treatments displaying similar values. Treatment T1 had an \nanthocyanin content of 24.44a in the fresh state and 98.47a in the infected \nstate, Treatment   T2   exhibited   24.43a   and   98.45a, and   Treatment   T3 \n\n\n\n showed 24.43a and 98.46a in the respective fresh and infected states. \nThese findings underscore the influence of different treatments on \nchlorophyll content and jassid presence across various plant segments, \nhighlighting Treatment T2 as distinctive for its higher chlorophyll levels \nand increased jassid presence. \n\n\n\nThese findings underscore the significant impact of different treatments \non chlorophyll content and the presence of jassids across various plant \nsegments. Treatment T2, in particular, demonstrated higher chlorophyll \ncontent and a greater presence of jassids compared to the other \ntreatments. However, anthocyanin content exhibited minimal variation \nbetween the treatments. In summary, this section illuminates the \ninfluence of various treatments on chlorophyll and jassid presence in \ndifferent plant segments. Notably, treatment T2 emerged as a distinctive \ntreatment, displaying higher chlorophyll content and a greater presence of \njassids. These results hold implications for agricultural strategies and pest \nmanagement approaches. \n\n\n\nTable 4: Effects of Different Varieties On Different Parameters Cotton Leaf \n\n\n\nVariety Top leaf Middle leaf Lower leaf Jassid/ plant CHL Fresh CHL Infected Anthocyanin Fresh Anthocyanin Infected \n\n\n\nV1 10.32a 8.47a 3.36a 7.33a 34.44b 5.51b 36.98a 145.54a \n\n\n\nV2 7.85a 10.52a 2.95a 7.48a 45.48a 8.75a 11.89b 51.38b \n\n\n\nData expressed as mean \u00b1 SE. Means within a row followed by same \nletter(s) are not significantly different according to TukeyHSD mean \ncomparison test- at < 0.01*, *V1: Cotton Board 12; V2: Cotton Board 14 \n\n\n\nBased on the data presented in Table 4, there were no significant \ndifferences observed between the two different varieties when it came to \nthe number of jassid per plant and jassid infestation in the top, middle, and \nlower leaves. A fundamental aspect of modern agriculture involves \ndiscerning how different plant varieties respond to environmental \nstressors, particularly pest infestations. This study aims to explore the \nimpact of two distinct varieties, denoted as V1 and V2, on several critical \nparameters within jassid-infested plant parts. Data collection involved the \nmeasurement of chlorophyll content (CHL) and anthocyanin content in \nboth fresh and infected plant parts for varieties V1 and V2. Additionally, \nthe presence of jassids on the plants was recorded. Analysis of the data \nrevealed notable differences in chlorophyll content between the two \nvarieties across all plant segments. Variety V1 consistently exhibited \nhigher CHL content compared to V2. Similarly, V1 displayed higher \nanthocyanin content in both fresh and infected states. Conversely, the \npresence of jassids did not significantly differ between the two varieties. \nThe findings underscore the substantial impact of varietal selection on \nchlorophyll and anthocyanin content in jassid-affected plant parts. Variety \nV1 demonstrated a heightened capacity for chlorophyll and anthocyanin \nproduction, suggesting  its    potential    advantage    in   dealing   with   pest  \n\n\n\npressure. These results hold significant implications for crop management \nand varietal selection strategies. In conclusion, this research elucidates the \npronounced influence of plant variety on chlorophyll and anthocyanin \nproduction in the presence of jassid infestations. Variety V1 exhibited \nsuperior performance in these parameters, implying its potential \nsuitability for pest-prone agricultural environments. This study \ncontributes valuable insights to the ongoing discourse on varietal \nselection and pest management, facilitating informed decisions for \nenhancing crop productivity and sustainability.  \n\n\n\nThe data presented in Table-5, which focuses on the interaction between \ntreatment and varietal factors, highlights notable disparities in the \nchlorophyll and anthocyanin content of both infected and fresh leaves \nacross various treatment and variety combinations. In the realm of \ncontemporary agriculture, a comprehensive comprehension of the \nramifications of various treatments and varietal interactions on plant \nhealth stands as a paramount necessity for the optimization of crop yield \nand quality. Pests, exemplified by jassids, manifest the potential to exert \nsubstantial influence over plant growth and overall vitality. Consequently, \nthe principal objective of this investigation is to scrutinize the \nrepercussions of diverse treatments denoted as T1, T2, and T3, as well as \nvarieties represented by V1 and V2, on distinct segments of the plant when \nsubjected to the presence of jassids. \n\n\n\nTable 5: Effect of different treatments and varietal interaction in different jassid affected part \n\n\n\nInteraction Top leaf Middle leaf Lower leaf Jassid/ plant CHL Fresh CHL Infected Anthocyanin Fresh Anthocyanin Infected \n\n\n\nV1:T1 14.33a 6.56a 4.23a 8.31a 35.83d 5.46d 36.97a 145.57a \n\n\n\nV1:T2 11.10a 13.90a 4.23a 9.65a 15.83f 2.80f 36.97a 145.50a \n\n\n\nV1:T3 5.53a 4.96a 1.63a 4.02a 51.66b 8.26c 36.99a 145.55a \n\n\n\nV2:T1 8.90a 14.00a 2.33a 8.74a 42.83c 9.36b 11.91a 51.36b \n\n\n\nV2:T2 11.23a 13.66a 5.33a 10.02a 25.40e 3.76e 11.90b 51.39b \n\n\n\nV2:T3 3.43a 3.90a 1.21a 3.69a 68.23a 13.13a 11.88b 51.38b \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\nData expressed as mean \u00b1 SE. Means within a row followed by same \nletter(s) are not significantly different according to TukeyHSD mean \ncomparison test- at < 0.01*,   * T1: Control Treatment; T2: Seed treatment \nwith Beauvaria bassiana; T3: Routine foliar spray; V1: Cotton Board 12; \nV2: Cotton Board 14 \n\n\n\nData was systematically gathered from plant specimens that underwent a \nspectrum of treatments and belonged to different varieties. The \ncomponents of the plant subjected to analysis encompassed the top leaf, \nmiddle leaf, and lower leaf. Notably, meticulous attention was directed \ntowards recording the presence of jassids on the plant, alongside the \ncollection of measurements pertaining to chlorophyll content (CHL) and \nanthocyanin content within both uninfected, fresh plant parts and those \nafflicted by jassid infestation. The findings presented herein disclose a \nspectrum of effects precipitated by distinct treatments and varietal \ninteractions on chlorophyll and anthocyanin content, thus elucidating \nvariances across diverse plant segments. Employing a statistically sound \nframework, these variances have been systematically unveiled through \nthe utilization of letter-based annotations, designating the statistical \nsignificance observed within and between treatments and varieties. For \ninstance, within the \"Jassid/plant\" column, a uniform grouping denoted by \nthe letter 'a' signifies the absence of significant differences concerning \njassid presence between the various treatments and varieties. \n\n\n\nThe ramifications unearthed herein offer profound insights into the plant's \nresponse to jassid infestation, as well as the efficacy of distinct treatments \nand varietal choices in ameliorating these impacts. The profound \nunderstanding derived from these interactions possesses the potential to \nwield significant influence over agricultural practices, directly \ncontributing to the enhancement of crop resilience and the augmentation \nof overall yield, particularly in the face of pest pressures. In summation, \nthis empirical inquiry unequivocally underscores the pivotal role that \ntreatment and varietal selection play in molding the plant's response to \njassid infestation, with a pronounced emphasis on the modulation of \nchlorophyll and anthocyanin content within various plant segments. \nThese empirically derived insights offer substantial contributions to the \nongoing discourse surrounding pest management strategies, thereby \nfurnishing valuable guidance to both agricultural practitioners and \nresearchers, enabling them to make informed decisions aimed at elevating \ncrop productivity and ensuring long-term sustainability. \n\n\n\n4.   DISCUSSIONS \n\n\n\nIn our study, we utilized mitochondrial DNA to trace parental inheritance \nin jassid species, a method recognized for its dependability in this context. \nWhile attempts have been made to employ nuclear DNA, mitochondrial \nDNA's maternal trait inheritance makes it the preferred choice. Our \nmolecular analysis based on the COI gene within mitochondrial DNA \nresulted in a comprehensive phylogenetic tree, which strongly supported \nthe jassid species under investigation, consistent with previous findings \n(Kranthi et al., 2002). \n\n\n\nStudying a plant's canopy throughout its growth cycle provided valuable \ninsights into its response to environmental factors. We observed specific \nareas of the canopy exhibiting reddish-purple or red colors, indicative of \npotential anthocyanin accumulation. Our research, in line with Table-5, \nrevealed that variety-1 (CB-12) demonstrated a greater capacity for \nanthocyanin production compared to variety-2 (CB-14). \n\n\n\nWe also explored the relationship between leaf reddening, induced by \nvarious environmental stresses, and anthocyanin synthesis, a pigment \nresponsible for this phenomenon. Our findings supported the notion that \nbiotic factors, such as jassids, can induce leaf reddening and downward \ncupping effects (Praharaj , 2010). However, it's important to note that \nreddened foliage is less photostable due to reduced light utilization and \nincreased risk of photodamage (Close and Beadle, 2003). This suggests \nthat anthocyanin might act as a protective screen, reducing \nphotoinhibition and facilitating efficient resorption of nutrients ( Hoch et \nal., 2001). \n\n\n\nWhile foliar anthocyanin has been associated with plant resistance to \nherbivory, our findings suggest that anthocyanin accumulation alone may \nnot be solely responsible for induced resistance (Costa- Arbulu et al., 2001; \nColey and Kursor, 1996). The observed reddening was attributed to \nincreased anthocyanin and decreased chlorophyll levels, possibly related \nto rootlet death impacting water and nutrient uptake. Furthermore, \nreductions in chlorophyll content, such as the significant decreases in \nchlorophyll a, chlorophyll b, and total chlorophyll observed in jassid-\ninfested cotton plants, indicated a negative impact on photosynthetic \ncapabilities (Reddall et al., 2007). This aligns with previous studies \ndemonstrating a decrease in photosynthetic pigments due to insect \ndamage (Hung et al., 2013). \n\n\n\nOur findings, consistent with Golawska and Huang emphasize higher \nconcentrations of photosynthetic pigments in non-infested leaves \ncompared to jassid-infested plants(Golawska et al., 2010; Huang et al., \n2014). This reduction in pigments could potentially lead to slower plant \ngrowth and reduced yield (Almeselmani et al., 2006). While B. bassiana has \nshown promise as an endophyte in enhancing plant traits in previous \nstudies our focus was on its impact on jassid infestations, not on the \nspecific growth-promoting mechanisms (Jaber and Ownley, 2018). The \npresence of B. bassiana decreased jassid infestation rates, although we \nacknowledge that higher concentrations were not tested due to funding \nlimitations. Imidacloprid, a neonicotinoid insecticide, effectively \ncontrolled jassid populations in cotton fields, with the foliar application \nyielding particularly positive results. This aligns with prior research \nsupporting the efficacy of imidacloprid in pest control (Almeselmani et al., \n2006; Elbert et al., 1998). \n\n\n\nIn conclusion, our study provides essential groundwork for utilizing B. \nbassiana as a biopesticide in integrated pest management strategies for \ncotton jassid control. Future research should focus on developing suitable \nformulations and delivery methods for larger-scale assessments under \nmore authentic field conditions. \n\n\n\n5.   CONCLUSIONS \n\n\n\nThis study had a dual focus, aiming to uncover the molecular \ncharacteristics of cotton jassids and assess the response of two cotton \nvarieties to biopesticides. The key findings of our research can be \nsuccinctly summarized as follows: \n\n\n\nFirstly, we successfully determined the genetic identity of cotton jassids \nby analyzing the COI gene in mitochondrial DNA. Our molecular analysis \nunequivocally confirmed the presence of the Amrasca biguttula biguttula \nspecies of cotton jassids, aligning with nucleotide sequence data available \nin the NCBI GenBank under accession numbers OR362770, OR362771, \nand OR362772.  \n\n\n\nSecondly, our investigation into the impact of two biopesticides on jassid \npopulations yielded noteworthy results. Imidacloprid emerged as the \nmore effective choice compared to B. bassiana in reducing jassid \ninfestations within the cotton field. Imidacloprid-treated plots displayed \nfewer instances of jassid infestations and exhibited higher levels of \nchlorophyll content. It is important to note, however, that anthocyanin \ncontent was observed to be lower in Imidacloprid-treated plots when \ncontrasted with those treated with B. bassiana. \n\n\n\nLastly, our varietal response analysis shed light on significant differences \nbetween the two cotton varieties, CB-12 (variety-1) and CB-14 (variety-2). \nNotably, variety-1 experienced a significant reduction in chlorophyll \ncontent compared to variety-2, while the accumulation of anthocyanin \ncontent displayed an inverse trend between the two varieties. These \nvariations were attributed to jassid infestation. Importantly, despite \nreceiving identical management practices and fertilization, the distinct \nresponses of the two cotton varieties underscore the critical importance \nof selecting the appropriate variety to effectively address pest-related \nchallenges. Overall, these findings contribute valuable insights to the field \nof cotton pest management and varietal selection for sustainable cotton \ncultivation practices. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe author would like to express her sincere gratitude and deepest \nappreciation to her major professor Dr. Md. Mamunur Rahman, Professor, \nDepartment of Entomology, Bangabandhu Sheikh Mujibur Rahman \nAgricultural University, Gazipur, who has shown the creative attitude and \nsubstance of a genius throughout the study and thesis preparation. He \ncontinually and persuasively conveyed a spirit of adventure in regards to \nresearch and National Science and Technology fellowship and excitement \nin regard to acquired knowledge. Heartfelt respect and sincere gratitude \nare extended to Dr. Md. Ahsanul Haque Swapon, Professor, Department of \nEntomology and of minor Department Dr. Rayhanur Jannat, Associate \nProfessor, Department of Plant pathology, BSMRAU, Gazipur, for their \ngenerous guidance and valuable suggestions in research work and thesis \nreport preparation. The author also owes to Dr. Jahidul Hasan, Professor, \nDepartment of Horticulture. Finally, the author would like to thank her \nparents and all the well-wishers for their blessings and mental supports \nduring the study. \n\n\n\nREFERENCES \n\n\n\nAllegrucci, N., Velazquez, M.S., Russo, M.L., P\u00e9rez, M.E., Scorsetti, A.C., 2017. \nEndophytic colonisation of tomato by the entomopathogenic \nfungus Beauveria bassiana: the use of different inoculation \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 79-87 \n\n\n\n\n\n\n\n \nCite The Article: Shazzad Hossain, Md. Mamunur Rahman, Haider Iqbal Khan, Md. Ahsanul Haque, Rayhanur Jannat and Jahidul Hassan  (2023). Molecular Characterization of \n\n\n\nCotton Jassid and Their Response Against Relatively N ewer Pesticides in Two Cotton Varieties of Bangladesh . Malaysian Journal of Sustainable Agricultures, 7(2): 79-87. \n\n\n\ntechniques and their effects on the tomato leafminer Tuta absoluta \n(Lepidoptera: Gelechiidae). \n\n\n\nAlmeselmani, M., Deshmukh, P.S., Sairam, R.K., Kushwaha, S.R., Singh, T.P., \n2006. Protective role of antioxidant enzymes under high \ntemperature stress. Plant science. 2006 Sep 1;171(3): Pp. 382-8. \n\n\n\nAlmeselmani, M., Deshmukh, P.S., Sairam, R.K., Kushwaha, S.R., Singh, T.P., \n2006. Protective role of antioxidant enzymes under high \ntemperature stress. Plant science. Sep 1;171(3):Pp. 382-8. \n\n\n\nAzad, H.M., Amin, M.R., Tithi, D.A., Hossain, S.M.,2011. Performances of \nthree cotton varieties cultivated under economic threshold level-\nbased insecticide sprayed and non-sprayed conditions. Our \nnature. 9(1):Pp. 21-5. \n\n\n\nClose,  D.C., Beadle, C.L., 2003. The ecophysiology of foliar anthocyanin. \nThe Botanical Review. 2003 Apr;69(2): Pp. 149-61. \n\n\n\nColey, P.D., Kursor, T.A., 1996. Anti-herbivore defenses of young tropical \nleaves: physiological constraints and ecological trade-offs. In \nTropical forest plant ecophysiology  Pp. 305-336. Boston, MA: \nSpringer US. \n\n\n\nCosta-Arbul\u00fa, C., Gianoli, E., Gonz\u00e1les, W.L., Niemeyer, H.M., 2001. Feeding \nby the aphid Sipha flava produces a reddish spot-on leaves of \nSorghum halepense: an induced defense. Journal of Chemical \nEcology. Feb; 27:Pp. 273-83. \n\n\n\nElbert, A., Nauen, R., Leicht, W., 1998. Imidacloprid, a novel chloronicotinyl \ninsecticide: biological activity and agricultural importance. \nInsecticides with novel modes of action: mechanisms and \napplication.Pp. 50-73. \n\n\n\nFarooq, A., Farooq, J., Mahmood, A., Shakeel, A., Rehman, K.A., Batool, A., \nRiaz, M., Shahid, M.T., Mehboob, S., 2011. An overview of cotton \nleaf curl virus disease (CLCuD) a serious threat to cotton \nproductivity. Australian Journal of Crop Science. Jan 1;5(13):Pp. \n1823-31. \n\n\n\nFelsenstein, J.,1985. Confidence limits on phylogenies: an approach using \nthe bootstrap. evolution. Jul 1;39(4): Pp. 783-91. \n\n\n\nGolawska, S., Krzyzanowski, R., Lukasik, I., 2010. Relationship between \naphid infestation and chlorophyll content in Fabacea species. Acta \nBiologica Cracoviensia. Series Botanica. 52(2). \n\n\n\nHoch, W.A., Zeldin, E.L., McCown, B.H., 2001. Physiological significance of \nanthocyanins during autumnal leaf senescence. Tree physiology. \nJan 1;21(1): Pp. 1-8. \n\n\n\nHuang, J., Zhang, P.J., Zhang, J., Lu, Y.B., Huang, F., Li, M.J., 2013. Chlorophyll \ncontent and chlorophyll fluorescence in tomato leaves infested \nwith an invasive mealybug, Phenacoccus solenopsis (Hemiptera: \nPseudococcidae). Environmental Entomology. Oct 1;42(5): Pp. \n973-9. \n\n\n\nHuang, T.I., Reed, D.A., Perring, T.M., Palumbo, J.C., 2014. Feeding damage \nby Bagrada hilaris (Hemiptera: Pentatomidae) and impact on \ngrowth and chlorophyll content of Brassicaceous plant species. \nArthropod-Plant Interactions. Apr; 8:Pp. 89-100. \n\n\n\nJaber, L.R., Araj, S.E., 2018. Interactions among endophytic fungal \nentomopathogens (Ascomycota: Hypocreales), the green peach \n\n\n\naphid Myzus persicae Sulzer (Homoptera: Aphididae), and the \naphid endoparasitoid Aphidius colemani Viereck (Hymenoptera: \nBraconidae). Biological Control.  Jan 1; 116:Pp. 53-61. \n\n\n\nJaber, L.R., Ownley, B.H.,2018. Can we use entomopathogenic fungi as \nendophytes for dual biological control of insect pests and plant \npathogens? Biological control. Jan 1; 116: Pp. 36-45. \n\n\n\nKranthi, K.R., Jadhav, D.R., Kranthi, S., Wanjari, R.R., Ali, S.S., Russell, D.A., \n2002. Insecticide resistance in five major insect pests of cotton in \nIndia. Crop protection. Jul 1;21(6):Pp. 449-60. \n\n\n\nLopez, D.C., Sword, G.A.,2015. The endophytic fungal entomopathogens \nBeauveria bassiana and Purpureocillium lilacinum enhance the \ngrowth of cultivated cotton (Gossypium hirsutum) and negatively \naffect survival of the cotton bollworm (Helicoverpa zea). \nBiological Control. Oct 1; 89:Pp. 53-60. \n\n\n\nMantzoukas, S., Chondrogiannis, C., Grammatikopoulos, G., 2015. Effects of \nthree endophytic entomopathogens on sweet sorghum and on the \nlarvae of the stalk borer Sesamia nonagrioides. Entomologia \nExperimentalis et Applicata. Jan;154(1):Pp. 78-87. \n\n\n\nMietkiewski, R.T., Pell, J.K., Clark, S.J., 1997. Influence of pesticide use on \nthe natural occurrence of entomopathogenic fungi in arable soils \nin the UK: field and laboratory comparisons. Biocontrol Science \nand Technology. Dec 1;7(4):Pp. 565-76. \n\n\n\nMorales-Rodriguez, A., Peck, D.C., 2009.Synergies between biological and \nneonicotinoid insecticides for the curative control of the white \ngrubs Amphimallon majale and Popillia japonica. Biological \ncontrol. 2009 Oct 1;51(1):Pp. 169-80. \n\n\n\nPraharaj, C.S.,2010. Sankaranarayanan K. Red leaf symptoms in cotton its \ncauses and management options. Indian Farming;60(9). \n\n\n\nReddall, A.A., Wilson, L.J., Gregg, P.C., Sadras, V.O., 2007. Photosynthetic \nresponse of cotton to spider mite damage: interaction with light \nand compensatory mechanisms. Crop science. Sep;47(5):Pp. \n2047-57. \n\n\n\nRondot, Y., Reineke, A., 2018. Endophytic Beauveria bassiana in grapevine \nVitis vinifera (L.) reduces infestation with piercing-sucking \ninsects. Biological Control. Jan 1; Pp. 116:Pp. 82-9. \n\n\n\nRoy, A.,2016. Abstracts of the 8th international geminivirus symposium \nand the 6th international ssDNA comparative virology workshop, \n7\u201310th November 2016, New Delhi. Virus Disease.  Dec;27(4):Pp. \n405-59. \n\n\n\nSaitou, N., Nei, M., 1987. The neighbor-joining method: a new method for \nreconstructing phylogenetic trees. Molecular biology and \nevolution. Jul 1;4(4):406-25. \n\n\n\nTamura, K., Nei, M., Kumar, S., 2004. Prospects for inferring very large \nphylogenies by using the neighbor-joining method. Proceedings of \nthe National Academy of Sciences. Jul 27;101(30):Pp. 11030-5. \n\n\n\nZhao, W., Tan, K., Zhou, D., Wang, M., Cheng, C., Yu, Z., Miao, Y., He, S., 2014. \nPhylogeographic analysis of Apis cerana populations on Hainan \nIsland and southern mainland China, based on mitochondrial DNA \nsequences. Apidologie. Jan; 45: Pp. 21-33. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA)  3(2) (2019) 31-34  \n\n\n\nCite The Article: Mahmuda Islam, Abdullah Al Nayeem, Ahmad Kamruzzaman Majumder, Khandokar Tanjim Elahi (2019). Study On The Status Of Roof Top Gardening In \nSelected Residential Areas Of Dhaka City, Bangladesh. Malaysian Journal of Sustainable Agriculture, 3(2) : 31-34. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 February 2019 \nAccepted 14 March 2019 \n\n\n\nAvailable online 12 April 2019 \n\n\n\nABSTRACT\n\n\n\nABSTRACT \n\n\n\nThe rapid increment of low and middle-income consumers is exerting pressure on the food supply in urban areas. \nThe objective of the study was to identify the present status of rooftop gardening. The study was conducted in the 4 \nselected residential areas of Dhaka city through plot to plot interview by using questionnaire. Land use nature is \ndivided into four categories like residential, commercial, educational and mixed. Field survey was conducted on \n1376 buildings in Dhanmondi, 391 buildings in Lalmatia, 272 buildings in Mohakhali Defense Officers Housing \nSociety (DOHS) and 697 buildings in Uttara 13 no. Sector. Study found that, 39.1%, 59.2%, 36.6 % and 22.2% \nbuildings have rooftop gardening in those selected locations respectively. The study reveals that, nearly one-third \nof the buildings (36.4%) contain rooftop gardening which basically depends on the aesthetic sense and moral values \nof individuals. Government should appreciate initiatives and consider proper planning policy to motivate citizen of \nthe urban areas for planting fruit plants and vegetable in their roof. RTG system may also contribute to achieving \nthe Sustainable Development Goals (SDGs). The proposed study identifies the need for long-term policy measures \nfor rooftop gardening that can become the basis for a sustainable approach for urban agriculture.  \n\n\n\n KEYWORDS \n\n\n\nUrban Agriculture, Green City, Ecological Balance, Food Security. \n\n\n\n1. INTRODUCTION \n\n\n\nOn the verge of the rapid growth of urbanization in today\u2019s world the \n\n\n\nsustainable agriculture become a challenge. Due to the pull factors of cities \n\n\n\nover 50 % of the world population is now living in urban areas which \n\n\n\nwould be 70% by the end of 2030 [1]. In case of developing world this \n\n\n\nproportion will be 80% [1, 2]. More people mean more food production \n\n\n\nwhich needs more arable land and it has been found that, 109 million \n\n\n\nhectares of new land would be required to feed the world population in \n\n\n\n2050 by conventional farming [3,4]. But a study shows that the \n\n\n\nagricultural area decreased by 0.19% in between 2005 and 2011 [5]. It is \n\n\n\na common practice to use the suburb area to satisfy the daily food demand \n\n\n\nof city dwellers basically fruits and vegetable. As the rate of urbanization \n\n\n\nincreases over time, food production sites should be increasingly located \n\n\n\nnear main consumption centers [6]. Because of urban sprawl and \n\n\n\nsettlement scheme for the growing population, the rate of land \n\n\n\ntransformation in these areas is very high which is posing a great threat to \n\n\n\nmeet the demand of urban inhabitants with sufficient nutritious food [7]. \n\n\n\nDhaka the capital of Bangladesh, is such a one of these cities which will be \n\n\n\na megacity by 2030 with 27 million people and by 2050 with 35 million \n\n\n\nhaving a present population density of 50000 per square kilometer [8,9]. \n\n\n\nWith this rapid as well as unplanned urbanization, incidence of urban \n\n\n\npoverty and food insecurity has been escalating alarmingly in Dhaka [10]. \n\n\n\nIn a consequence urban agriculture is getting relevance like all over the \n\n\n\nworld and it is more important to adopt new approaches to ensure the \n\n\n\nfood supply and food security of those who live-in urban atmospheres \n\n\n\n[11]. Moreover urban agriculture is very efficient since its potential yields \n\n\n\nis up to 50 kg per m2 per year and more which can be even 4.5 times \n\n\n\ncompare to the production from conventional farming as seen in Havana \n\n\n\n[1,2,4 ]. \n\n\n\nWith the recent development of urban centers two very important \n\n\n\nconcepts are also emerged to make the development sustainable; \n\n\n\necological citizenship and ecological footprint [12]. The concept of \n\n\n\necological citizenship uses the metaphor of \u2018ecological footprint\u2019 in which \n\n\n\neach of us is responsible for taking up a certain amount of ecological \n\n\n\n\u2018space\u2019 (both for resource use and capacity burden), expressed as a \n\n\n\npersonal footprint left on the Earth [13,14]. Although it is assumed that an \n\n\n\nequal allocation of the available space on Earth would result in 1.8 \n\n\n\navailable global hectares per person, the footprint of the average European \n\n\n\nresident is actually 4.9 ha while in the USA up to 9.2 ha and for Bangladeshi \n\n\n\nit is only 0.5 ha [15-17]. \n\n\n\nA number of studies have found that, the vital role played by urban \n\n\n\nvegetable gardens in improving human well-being through the provision \n\n\n\nof both ecosystem services and food supply to the city dwellers [11,18,19]. \n\n\n\nThroughout the city area, urban agriculture and green spaces can be linked \n\n\n\nto one another, forming a network of Green Infrastructures (GIs) [20]. The \n\n\n\npossible green cover of most of the bare areas of a city could be a potential \n\n\n\necological frontier and could become a reality in many cities [21-23]. GIs \n\n\n\nmay reduce a city\u2019s Ecological Footprint (EF) by reduction of pollution and \n\n\n\nnoise, the absorption of CO2emissions and the control of the Urban Heat \n\n\n\nIsland (UHI) effect by shading [13, 24]. Rooftop gardening (RTG) can be an \n\n\n\neffective method in ensuring food supply and satisfying nutritional needs \n\n\n\nof the inhabitants as well as can reduce the expense of heating and cooling \n\n\n\nand at the same time improving urban air quality [21,25]. Furthermore, \n\n\n\nRTGs, while being aesthetically appealing, can play a vital role to \n\n\n\nbiodiversity conservation in the urban environment, achieving sustainable \n\n\n\ncities, including those necessary for the production of food and improve \n\n\n\nthe overall quality of urban life [26-29]. RTGs can produce an average of \n\n\n\n19.5 kg m\u22122 year\u22121 against 1.3 kg m\u22122 year\u22121 found in conventional urban \n\n\n\ngardens [23]. In a study conducted in South Delhi of India, it is seen that on \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.02.2019.31.34 \n\n\n\n STUDY ON THE STATUS OF ROOF TOP GARDENING IN SELECTED RESIDENTIAL \nAREAS OF DHAKA CITY, BANGLADESH \n\n\n\nMahmuda Islam, Abdullah Al Nayeem*, Ahmad Kamruzzaman Majumder, Khandokar Tanjim Elahi \n\n\n\nDepartment of Environmental Science, Stamford University Bangladesh, Dhaka- 1209 \n*Corresponding Author Email: nayeem.env58@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)  3(2) (2019) 31-34 \n\n\n\nCite The Article: Mahmuda Islam, Abdullah Al Nayeem, Ahmad Kamruzzaman Majumder, Khandokar Tanjim Elahi (2019). Study On The Status Of Roof Top Gardening In \nSelected Residential Areas Of Dhaka City, Bangladesh. Malaysian Journal of Sustainable Agriculture, 3(2) : 31-34. \n\n\n\nan average size of roof with one to three plants of each vegetable can give \n\n\n\n6 kg of vegetable and the common varieties are tomatoes, brinjal, bitter \n\n\n\ngourd, capsicum, corn, zucchini and lady finger [30]. \n\n\n\nRTG, although is being practiced in the city in many form for years in the \n\n\n\npast, there have been hardly any concerted effort on part of the \n\n\n\nGovernment, community organizations along with the general residents to \n\n\n\nintegrate it to urban agriculture. Taking into consideration the above \n\n\n\ncircumstance, a study of the rooftop gardens of densely populated Dhaka \n\n\n\nis a crying need. The present study focuses on the status of roof top garden \n\n\n\nand tries to find out the influencing factor and their relationship with the \n\n\n\nRTGs which can help to identify the long-term policy measures for RTG that \n\n\n\ncan become the basis for a sustainable approach for urban agriculture. \n\n\n\n2. STUDY AREA \n\n\n\nTo study the status of RTG in Dhaka city four residential areas are selected. \n\n\n\nThey are Dhanmondi, Lalmatia (Mohammadpur), Mohakhali DOHS and 13 \n\n\n\nno. sector of Uttara. Though the main land use of these areas is residential, \n\n\n\nthey also have some commercial uses of lands except in Mohakhali DOHS. \n\n\n\nFigure 1: Map of the Study Area \n\n\n\n3. METHODOLOGY\n\n\n\nIn this study a plot to plot population survey method was adopted. All 1376 \n\n\n\nbuildings of the Dhanmondi Residential Area, 391 buildings of the Lalmatia \n\n\n\nresidential area, 272 buildings of the Mohakhali DOHS and 697 buildings \n\n\n\nof the Uttara Sector 13 residential area were surveyed for the study. Data \n\n\n\non rooftop garden, geographic and demographic characteristics, year of \n\n\n\nbuilding, arrangement mainly rooftop garden and building expenditure in \n\n\n\ndifferent sector and stove design etc. were collected through interview by \n\n\n\nusing questionnaire. Data processing and analysis, the coding, data entry \n\n\n\nand required analysis were done by using SPSS and Microsoft Excel. \n\n\n\n4. RESULTS \n\n\n\nThe survey finds out that among all 2736 buildings in Dhanmondi, \n\n\n\nLalmatia, Mohakhali DOHS and Uttara 13 no. Sector, only 36.4% (997) \n\n\n\nbuilding has the RTG and most of them (19.66% of total building surveyed) \n\n\n\nfound in the Dhanmondi which isa very old and well known residential \n\n\n\narea though now a day some buildings are also used for commercial \n\n\n\npurposes (Table 1). If we see the area wise proportion, it is found that \n\n\n\nMohakhali DOHS is in better position having 59.20% of buildings with RTG \n\n\n\nand the poorest condition is in the Uttara 13 no. Sector with only 22.20% \n\n\n\nof buildings having RTG in compare to other three. \n\n\n\nTable 1: Status of Roof Top Gardening \n\n\n\nStudy Area \n\n\n\nNumber of \n\n\n\nBuilding \n\n\n\nFrequency Percentage \nPercentage (in Total) \n\n\n\nYes No Yes No Yes No \n\n\n\nDhanmondi 1376 538 838 39.10% 60.90% 19.66% 30.63% \n\n\n\nLalmatia 391 143 248 36.60% 63.40% 5.22% 9.06% \n\n\n\nDoHS 272 161 111 59.20% 40.80% 5.88% 4.06% \n\n\n\nUttara 13 \n\n\n\nno. Sector \n697 155 542 22.20% 77.80% 5.66% 19.81% \n\n\n\nTotal 2736 997 1739  ---------  --------- 36.4 63.6 \n\n\n\nFigure 2 represents the nature of land use in these four areas. From this \n\n\n\nfigure it can be said that, among the four types of the land use the  \n\n\n\npercentage of having garden in the rooftop is more in the residential land \n\n\n\nwhich is 61.5% and then the commercial one having percentage of 45.9%. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)  3(2) (2019) 31-34 \n\n\n\nCite The Article: Mahmuda Islam, Abdullah Al Nayeem, Ahmad Kamruzzaman Majumder, Khandokar Tanjim Elahi (2019). Study On The Status Of Roof Top Gardening In \nSelected Residential Areas Of Dhaka City, Bangladesh. Malaysian Journal of Sustainable Agriculture, 3(2) : 31-34. \n\n\n\nFigure 2: Nature of Land Use and Roof Top Gardening \n\n\n\nAgain in case of the type of ownership, RTG is mostly seen in independent \n\n\n\nhousing (60.0%) and regarding to type of building the highest percentage \n\n\n\nof RTG is seen in other types of building that means and the rest two; \n\n\n\nindividual and apartment is very nearer having percentage of 39.3% and \n\n\n\n34.0% (Table 2). \n\n\n\nTable 2: Prevalence of roof top gardening regarding to type of ownership \n\n\n\nand type of buildings \n\n\n\nYes \n\n\n\nCategory Total \n\n\n\nFrequency Percentage \n\n\n\nType of \n\n\n\nDeveloper 1185 377 31.8% \n\n\n\nOwnership \n\n\n\nIndependent 1551 931 60.0% \n\n\n\nIndividual 1232 484 39.3% \n\n\n\nType of \n\n\n\nApartment 1500 510 34.0% \n\n\n\nBuilding \n\n\n\nOthers 4 3 75.0% \n\n\n\n5. DISCUSSIONS \n\n\n\nThe table 1 shows that the prevalence of RTG is more in Mohakhali DOHS \n\n\n\nhaving the percentage of 59.00% which is a part of housing scheme for \n\n\n\nretired army officer and taken as one of the posh area of Dhaka city. The \n\n\n\nrest three areas (Dhanmondi, Lalmatia and Uttara 13 no. Sector) have the \n\n\n\nRTG in less than 50% buildings. Therefore, it can be said that having \n\n\n\nrooftop garden may be affected by the type of ownership of the buildings \n\n\n\nwhich also can be depicted from table 2. Some literature has been found on \n\n\n\nRTG in Bangladesh conducted in Dhaka, Chittagong and Sylhet \n\n\n\nmetropolitan city [31-34]. But most of them concentrated on the finding \n\n\n\nout of the variety of fruits and vegetables produced in the RTG, the benefits \n\n\n\nand the constraints.  Furthermore, no international standard is found in \n\n\n\nthis regard. Therefore, it is not possible to evaluate the status of the RTG of \n\n\n\nDhaka city.  \n\n\n\nFrom table 2 it is seen that the percentage of rooftop garden is two times \n\n\n\nmore in the independent housing than the developer one. Sometimes lack \n\n\n\nof accessibility to the roof is a great barrier to gardening for the tenants. \n\n\n\nAgain the persons are now in any type of service they don\u2019t have enough \n\n\n\ntime to engage themselves into gardening. This is very much relevant with \n\n\n\nthe findings of Uddin, 2016 which says that businessman, retired person \n\n\n\nand housewives are mainly contributing to the RTG in Dhaka and \n\n\n\nChittagong city [33]. But in Sylhet city the RTG is mostly done by \n\n\n\ngovernment job holders (25.56%) and then the retired employee (17.33%) \n\n\n\n[32]. Furthermore, RTG has a strong positive and significant relationship \n\n\n\n(Table 3) with the nature of land use which can also be depicted from figure \n\n\n\n1. Hence another reason of having more RTG in Mohakhali DOHS is that it \n\n\n\nis fully residential but the other three are not like that. They are more or \n\n\n\nless mixed area having some extent of commercial and educational uses of \n\n\n\nland. Type of buildings might not have any influence on the RTG since their \n\n\n\nrelationship is not significant (Table 3). Overall it can be said that having \n\n\n\ngarden at the rooftop may also get influenced by the aesthetic sense, moral \n\n\n\nand ethical values and personal likings of the individuals. This is also \n\n\n\nparallel to the findings of the Rahman, 2014 where it is seen that people \n\n\n\nare interested in RTG mainly for mental satisfaction (95.3%), leisure time \n\n\n\nactivity (87.8%), aesthetic value (82.9%) and environmental amelioration \n\n\n\n(54.9%) [32]. \n\n\n\nTable 3: Relationship of roof top gardening with land use nature \n\n\n\nand type of building \n\n\n\nYes \n\n\n\nCategory \n\n\n\nDegree of \n\n\n\nR value P value Comment \n\n\n\nFreedom \n\n\n\nLand Use \n\n\n\n0.99 3 0.0003 \n\n\n\nStrong positive \n\n\n\nrelationship and significant \n\n\n\nNature \n\n\n\nType of \n\n\n\n0.99 2 0.0784553 \n\n\n\nStrong positive \n\n\n\nrelationship but not \n\n\n\nsignificant \n\n\n\nBuilding \n\n\n\n6. CONCLUSION \n\n\n\nThe necessity of urban agriculture in ensuring a sustainable and secured \n\n\n\nfood supply is now approved by worldwide. It is a very fact for a city of a \n\n\n\ndeveloping country like Dhaka where the rate of urbanization is very high \n\n\n\nbut the quantity of arable land to ensure the sufficient food supply is \n\n\n\nbecoming less. Among the different models of urban agriculture RTG is the \n\n\n\nsuitable for densely populated Dhaka city as many buildings do not have \n\n\n\nspace for the other types of gardening. Hence this study was done to find \n\n\n\nout the present situation of the RTGs in Dhaka city. Here sampled areas are \n\n\n\nselected purposively by assuming that residential areas would have more \n\n\n\nRTG than commercial or mixed areas. The study finds out that the \n\n\n\nfrequency of RTG in comparatively newly developed and organized \n\n\n\nresidential area is better than the older residential area in which some \n\n\n\nbuildings are now also used for mixed purposes. Hence the conditions of \n\n\n\ncommercial and mixed area might be worse than the residential areas. \n\n\n\nThis is also analogous to the findings that more than 50% of the residential \n\n\n\nbuildings have the RTGs. In most of the cases there is a restriction on the \n\n\n\nusing of roof in developers building which lead to the conclusion that \n\n\n\nindividual owners are favorable to rooftop gardening. In this sector \n\n\n\ngovernment can play an important role. Though some initiatives have \n\n\n\nbeen taken by the City Corporation and Department of Environment of \n\n\n\nBangladesh but it is not enough. RTG and urban agriculture should be \n\n\n\nincorporated into urban planning. Roof Top Gardening (RTG) can be an \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)  3(2) (2019) 31-34 \n\n\n\nCite The Article: Mahmuda Islam, Abdullah Al Nayeem, Ahmad Kamruzzaman Majumder, Khandokar Tanjim Elahi (2019). Study On The Status Of Roof Top Gardening In \nSelected Residential Areas Of Dhaka City, Bangladesh. Malaysian Journal of Sustainable Agriculture, 3(2) : 31-34. \n\n\n\neffective method in ensuring food supply and satisfying nutritional needs \n\n\n\nof the inhabitants as well as reducing ecological foot print and providing \n\n\n\nwith ecosystem services. The policy makers and urban planner have to \n\n\n\nthink radically to make this dusty Dhaka as a beautiful green city. For this \n\n\n\na long term policy to go for green is needed which not only lead the city \n\n\n\ntowards green city but also help to achieve the 11th, 12th and 13th goal of \n\n\n\nSDGs. To accomplish this, studies on the scope of RTG, the rate of food \n\n\n\nproduction and varieties, nutritious value of the growing foods in the \n\n\n\ncontext of Dhaka city are needed. Studies should also be carried on the \n\n\n\nenvironmental aspect of RTG like as a medium of carbon sequestration, \n\n\n\necological footprint reduction, temperature and air pollution reduction \n\n\n\netc. to encourage the citizen of Dhaka for getting involved in RTG rather \n\n\n\nwasting their spaces. \n\n\n\nREFERENCES \n\n\n\n[1] Eigenbrod, C., Gruda, N. 2015. Urban vegetable for food security in \ncities. A review. Agronomy for Sustainable Development, 35 (2), 483-498. \n\n\n\n[2] Bakker, N., Dubbeling, M., Guendel, S., Koschella, U.S., deZeeuw, H. \n2000. Growing Cities, Growing Food, Urban Agriculture on the Policy \nAgenda. \n\n\n\n[3] Dickson, D. 2013. Vertical farming infographics. Retrieve from: \nhttps://verticalfarming.net/vertical-farming/vertical-farming-\ninfographics/ (From FAO and NASA. \n\n\n\n[4] Juniawati, Hayuningtyas, M. 2017. Urban Agriculture Development: A \nStrategy to Support Food Security Juniawati. 2nd International Conference \non Sustainable Agriculture and Food Security: A Comprehensive \nApproach, KnE Life Sciences, 701\u2013713. \n\n\n\n[5] Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., \nJohnston, M., Zaks, D. P. 2011. Solutions for a cultivated planet. \nInternational Journal of Science, 478, 337\u2013342. \nhttps://doi.org/10.1038/nature10452 \n\n\n\n[6] Grigoletti, G.D.C., Pereira, M.F.B. 2014. Carbon dioxide emissions of \ngreen roofing \u2013 case study in southern Brazil. In 30th International Plea \nConference, 1\u20138). \n\n\n\n[7] Thornbush, M. 2015. Urban agriculture in the transition to low carbon \ncities through urban greening. AIMS Environmental Science, 2 (3), 852\u2013\n867. https://doi.org/10.3934/environsci.2015.3.852 \n\n\n\n[8] Bdnews24. 2014. Dhaka will be sixth largest mega city in 2030. \nRetrieve from: https://bdnews24.com/bangladesh/2014/12/09/dhaka-\nwill-be-sixth-largest-mega-city-in-2030-scientist \n\n\n\n[9] Daily Star. 2019. The dark side of Dhaka\u2019s urbanization. Retrieve from:  \nhttps://www.thedailystar.net/supplements/28th-anniversary-\nsupplements/avoiding-urban-nightmare-time-get-planning-\nright/news/the-dark-side-dhakas-urbanisation-1703425. \n\n\n\n[10] Choguill, C.L. 1995. Urban Agriculture and Cities in the Developing \nWorld. Habitat International, 19 (2), 149-235. \n\n\n\n[11] Orsini, F., Gasperi, D., Marchetti, L., Piovene, C., Draghetti, S., \nRamazzotti, S., Bazzocchi, G., Gianquinto, G. 2014. Exploring the \nproduction capacity of rooftop gardens (RTGs) in urban agriculture\u202f: the \npotential impact on food and nutrition security , biodiversity and other \necosystem services in the city of Bologna. Food Security.  \n\n\n\n[12] Bastianoni, S., Galli, A. Niccolucci V.,  Pulselli, R.M. 2006. The ecological \nfootprint of building construction. The Sustainable City IV: Urban \nRegeneration and Sustainability, 93,345\u2013356. \nhttps://doi.org/10.2495/SC060331 \n\n\n\n[13] Wackernagel, M., Rees, W. 1996. Our Ecological Footprint: Reducing \nHuman Impact on the Earth. ISBN: 0-86571-312-X. New Society \nPublishers, Philadelphia. \n\n\n\n[14] Cubi, E., Zibin, N.F., Thompson, S.J., Bergerson, J. 2015. Sustainability \nof Rooftop Technologies in Cold Climates. Journal OfIndustrial Ecology, 20 \n(2), 249\u2013262. https://doi.org/10.1111/jiec.12269 \n\n\n\n[15] Global Footprint Network. 2005. Retrieve from: \nhttps://www.footprintnetwork.org/. Access in: 4 March, 2018. \n\n\n\n[16] Seyfang, G. 2006. Ecological citizenship and sustainable consumption: \nExamining local organic food networks. Journal of Rural Studies, 22, 383\u2013\n395.  \n\n\n\n[17] Metcalfe, I. 2015. Environmental Concerns for Bangladesh. South \nAsia: Journal of South Asian Studies, XXVI (3), 423\u2013438. \nhttps://doi.org/10.1080/0085640032000178961. \n\n\n\n[18] McClintock, N. 2010. Why farm the city? Theorizing urban agriculture \nthrough alens of metabolic rift. Cambridge Journal of Regions, Economy \nand Society, 3(2), 191\u2013207. \n\n\n\n[19] Brown, K.H., Jameton, A.L. 2000. Public health implications of urban \nagriculture. Journal of Public Health Policy,21, 20\u201339. \n\n\n\n[20] Mahmoud, N., Mohammed, G. 2012. Green infrastructure connectivity \nwithin social sustainability: Case studies around the world. Canadian \nJournal on Computing in Mathematics, Natural Sciences, Engineering and \nMedicine, 3, 225\u2013232. \n\n\n\n[21] Peck, S. 2003.Towards an integrated green roof infrastructure \nevaluation for Toronto. The Green Roof Infrastructure Monitor, 5, 4\u20137. \n\n\n\n[22] Kaethler, T.M. 2006. Growing space: the potential for urban \nagriculture in the city of Vancouver, Retrieve from https://community-\nwealth.org/sites/clone.community-wealth.org/files/downloads/report-\nkaethler.pdf. Access in: 25 May, 2014 \n\n\n\n[23] Grewal, S.S., Grewal, P.S. 2012. Can cities become self-reliant in food? \nCities, 29, 1\u201311. \n\n\n\n[24] Wilby, R.L. 2003.Past and projected trends in London\u2019s urban heat \nisland. Weather, 58, 251\u2013260. \n\n\n\n[25] Walters, S.A., Midden, K.S. 2018. Sustainability of Urban Agriculture: \nVegetable Production on Green Roofs. Agriculture, 8, 1\u201316. \nhttps://doi.org/10.3390/agriculture8110168 \n\n\n\n[26] Miller, J.R. 2005. Biodiversity conservation and the extinction of \nexperience. Trends in Ecology and Evolution, 20 (2), 430\u2013434. \n\n\n\n[27] Maas, J., Verheij, R.A., Groenewegen, P.P., DeVries, S., Spreeuwenberg, \nP. 2006. Green space, urbanity, and health: How strong is the relation? \nJournal of Epidemiol Community Health, 60, 587\u201359. \n\n\n\n[28] Khandaker, M.S.I. 2004. Rooftop gardening as a strategy of urban \nagriculture for food security: The case of Dhaka City, Bangladesh. Acta \nHortic, 643, 241\u2013 247. \n\n\n\n[29] Sany\u00e9-Mengual, E., Oliver-Sol\u00e01, J., Montero, J.I., Rieradevall, J. 2015. \nAn environmental and economic life cycle assessment of Rooftop \nGreenhouse (RTG) implementation in Barcelona, Spain. The International \nJournal of Life Cycle Assessment, 20 (3), 350\u2013366.  \n\n\n\n[30] Gupta, G., Mehta, P. 2017. Roof Top Farming a Solution to Food \nSecurity and Climate Change Adaptation for Cities. Climate Change \nResearch at Universities, 19\u201336. https://doi.org/10.1007/978-3-319-\n58214-6 \n\n\n\n[31] Islam, K.M.S. 2004. Rooftop Gardening as a Strategy of Urban \nAgriculture for Food Security: The Case of Dhaka City, Bangladesh. Proc. IC \non Urban Horticulture, 241-247. \n\n\n\n[32] Rahman, A., Nabi, N.A.K.M., Amin, S.A.M., Abu, S.M., Rahman, S., \nHossain, P.R., Rahman, M.H., Hossaine, S.M.M., Mirza, M. 2014. Building \nUrban Resilience: Assessing Urban and Peri-urban Agriculture in Dhaka, \nBangladesh. United Nations Environment Programme (UNEP), Nairobi, \nKenya. \n\n\n\n[33] Uddin, D.M.J., Khondaker, D.N.A., Hossain, M.M.A., Das, D.A.K., Masud, \nM.A.T.M.D. 2016. Baseline Study on Roof Top Gardening in Dhaka and \nChittagong City of Bangladesh. A final technical report under the project of \n\u201cEnhancing Urban Horticulture Production to Improve Food and Nutrition \nSecurity\u201d (TCP/BGD/3503) funded by Food and Agriculture Organization \nof the United Nations. \n\n\n\n[34] Safayet, M., Arefin, M.F., Hasan, M.U. 2017. Present practice and future \nprospect of rooftop farming in Dhaka city: A step towards urban \nsustainability. Journal of Urban Managment, 6, 56\u201365. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 24-28 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.24.28 \n\n\n\nCite The Article: Md. Zablul Tareq, Arif Mohammad Mojakkir, Mir Mehedi Hasan, Md. Jewel Alam , Md. Abu Sadat(2021). Moisture Content And Variety Of Jute Seed Is \nAffected By Long Term Seed Storage. Malaysian Journal Of Sustainable Agriculture, 5(1): 24-28.\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.01.2021.24.28 \n\n\n\nMOISTURE CONTENT AND VARIETY OF JUTE SEED IS AFFECTED BY LONG TERM \nSEED STORAGE \nMd. Zablul Tareqa, Arif Mohammad Mojakkirb, Mir Mehedi Hasanc, Md. Jewel Alamd, Md. Abu Sadata* \n\n\n\na Basic and Applied Research on Jute project, Bangladesh Jute Research Institute, Dhaka-1207, Bangladesh. \nb Department of Agricultural Extension, Khamarbari, Dhaka, Bangladesh.  \nc Adaptive Research Division, Bangladesh Rice Research Institute, Gazipur, Bangladesh.  \nd Department of Entomology, Bangladesh Agricultural University, Mymensingh, Bangladesh. \n*Corresponding Author E-mail: sadat@snu.ac.kr\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction\nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 November 2020 \nAccepted 09 December 2020 \nAvailable online 21 December 2020\n\n\n\nSeed perform a vital role in agricultural sector for crop production as well as seed business. Scarcity of healthy \nseed hinder not only the crop production but also the quality of seed. Storing of healthy seed with proper \nstoring condition is one of the suitable methods to maximize production however, healthy seed also lose its \nquality during seed storage. Seed remains viable for long time if the seed stored by maintaining seed moisture \ncontent, storage temperature with storage container. So, this experiment was carried out to observe the \nquality parameters of jute seed during long term storing. To find out the storage effect an experiment was \nconducted on march, 2020 at seed laboratory, Jute Agriculture Experimental Station, Jagir, Manikganj, \nBangladesh during the period of January 2016 to March, 2020. Plastic pot was used in this experiment as a \nstorage container to store jute seeds. Three tossa jute (C. olitorius L.) varieties viz., O-795 (V1), O-9897 (V2) \nand OM-1 (V3) were used in this study. Result revealed that storage period and jute variety showed significant \neffect on different seed quality parameters. The highest seed germination, field emergence, seed vigour and \nthe lowest 1000-seed weight, moisture content were recorded in T5 (2019-20) treatment. On the other hand, \nthe lowest seed germination, field emergence, seed vigour and the highest 1000-seed weight, moisture \ncontent were recorded in T1 (2015-2016) treatment. Furthermore, seed germination, field emergence, seed \nvigour  was negatively but 1000-seed weight was positively correlate with moisture content. Results revealed \nthat extended storage period caused the decreasing seed quality and seed can be stored for three years in \nplastic container without hampering the seed quality.  \n\n\n\nKEYWORDS \n\n\n\nTossa jute seed, storage period, germination, field emergence, 1000-seed weight, vigour, moisture content. \n\n\n\n1. INTRODUCTION\n\n\n\nJute grows abundantly in Bangladesh having best quality of fiber in \ncomparison with that of India (Zakaria and Sayed, 2008). Jute and jute \nproducts earn a lot of foreign currency and jute alone contributes about \n5.5% to the GDP in Bangladesh national economy (Sikder et al., 2008). For \nthe foreign earning, jute is called golden fiber of Bangladesh.  In 2016 - \n2017, about 8.39 million tons of jute fibre were produced from 10.89 \nmillion acre of land and covered about 2.80% of the total cropped area \n(BBS, 2018). It has been reported that jute covers almost 6.0 billion of taka \nas export materials in the fiscal year 2018-2019 (Akter et al., 2020). Jute \ncrop also significantly improves the soil fertility and soil quality by adding \norganic matter to the soil through the decomposition of dropped leaves \nand plant debris (Islam and Ali, 2017). In addition, jute and jute goods have \nbeen recognized as being ecofriendly to the environment globally \n(Abdullah, 2002). \n\n\n\nJute is mostly grown in the Indo-Bangladesh region and in some countries \nof Southeast Asia (Rahman et al., 2017). Among the jute growing countries, \nBangladesh ranks the second position globally in respect of fiber \nproduction (Islam, 2007). For jute cultivation, Bangladesh require about \n5000 to 5500 tons of jute seeds for every year, however only 15-20% of \n\n\n\nrequired seed are supplied by institutional resources (Islam, 2019). The \nrest of the seeds, about 85% or more of the requirement, are produced and \nmanaged and stored by farmer\u2019s level. In most cases, the quality of those \nstored seeds are not confirmed and hamper jute production (Sikder et al., \n2008).  It was reported that quality seed of an improved variety can itself \nprovide 20% additional jute yield (Hossain et al., 1994). \n\n\n\nDifferent factors including duration of storage, temperature, seed \nmoisture, oxygen pressure, and pests and diseases infestation affect seed \nquality of stored seed (Pradhan and Badola, 2012). These factors \nsignificantly vary with the type of storage method used for seed storing. \nFarmers use various storage containers such as earthen pots, polythene \nbags, glass containers, and plastic jars, which influence seed quality \ndifferently depending on the crop species (Haque et al., 2014).  Studies \nshowed that different containers had distinct effect to maintain seed \nquality of different crop seed (Bakhtavar et al., 2019). However, the \nnormal polythene bags do not achieve the same effect as the custom-made \nplastic bags (Camann et al., 2011).  The longer a seed is stored the more \nthe seed deteriorates due to the slow respiration that occurs in all stored \nseeds.  Respiration depletes food reserves that are required for seed \ngermination (Ali and Elozeiri, 2017). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 24-28 \n\n\n\nCite The Article: Md. Zablul Tareq, Arif Mohammad Mojakkir, Mir Mehedi Hasan, Md. Jewel Alam, Md. Abu Sadat(2021). Moisture Content And Variety Of Jute Seed Is \nAffected By Long Term Seed Storage. Malaysian Journal Of Sustainable Agriculture, 5(1): 24-28.\n\n\n\nHence, the production and quality of healthy jute seed as well as its quality \nof storage is highly essential to ensure the higher yield of quality fiber in \norder to meet the challenging need for natural fiber. Here, moisture is also \none of the major factors contributing to the deterioration during storage \nespecially in the tropics and sub-tropics (Suma et al., 2013). The lower \nranges of moisture probably help to maintain the seed quality during the \nstorage period (Ali et al., 2014).  \n\n\n\nMoisture percent, germination and vigour were differed significantly due \nto storage container and storage condition applied except 1000-seed \nweight (Islam et al., 2002). Storage of seed is an important factor on which \nthe seed quality greatly depends (Islam, 2016). Very few researches were \ndone on stored jute seed quality on prolong storage period. From the \nabove fact the experiment was designed to find out tossa jute (Corchorus \nolitorius L.) seed quality on extended storage period. \n\n\n\n2. MATERIALS AND METHODS\n\n\n\nThe experiment was performed at the seed laboratory of Jute Agriculture \nExperimental Station (JAES) of Bangladesh Jute Research Institute (BJRI), \nJagir, Manikganj. The crops were sown on August of 2015, 2016, 2017, \n2018, 2019 and harvested at the last week of January in 2016, 2017, 2018, \n2019, and 2020, respectively at full maturity. Land was prepared properly \nand crops were grown with proper agronomic management during crop \ngrowing season according to some study (Chowdhury and Hassan, 2013). \nThe seed was dried in the sun on a triple set on the cemented floor for \nmaking moisture content as per experimental specification and stored in \nthe plastic pot containers. Each container was completely filled with seed \nas per experimental specification and then made air tight. The room \ntemperature was maintained 18-20oC. The experiment was laid out in \ncompletely randomized design (CRD) with four replications.  \n\n\n\nThree types of tossa jute (Corchorus olitorius L.) varieties, viz. O-795(V1), \nO-9897(V2) and OM-1(V3) were used in this experiment. Data on \ngermination, field emergence, 1000-seed weight, vigour and moisture \ncontent were recorded on March, 2020. Germination and field emergence \ndata were collected according to a study (Mollah, 2014). 1000-seed weight \nwas measured by using digital electric balance. Vigour was calculated \naccording to the process described by Islam, 2016. Seed moisture was \nrecorded by using digital grain moisture meter (model: GMK-303). The \ndata were analyzed statistically by using R Statistics Software version \n3.5.3. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nQuality of seeds is considered as a vital factor for increasing total yield of \na crop. Seed must have to be varietally pure, disease and insect free as well \nas with proper moisture content. However, seed quality can be \ndeteriorated in various ways and storage period is one of them (Anderson, \n1970; Wang et al., 2018). In the present research, the effect of storage \nperiods was evaluated on tossa variety (Corchorus olitorius) seed quality.  \n\n\n\n3.1 Effect of storage on tossa (C. olitorius) seed quality \n\n\n\nAnalysis found that seed quality was significantly affected by applying \ndifferent treatments (Table 1). T1 showed the highest (23.30%) and T5 \nshowed the lowest (8.44%) moisture contents compare to the other \ntreatments. Similarly, thousand seed weight was higher (1.89 g) in T1 \ntreatment and T4 and T5 showed the lower seed weight (1.7 g). On the \ncontrary, percentage of seed germination and field emergence as well as \nseed vigour was higher in T4 and T5 treatments and lower in T1 treatment. \nTreatment T2 and T3 showed the intermediate results in all analyzed \nparameters (Table 1). It was also observed that percent of seed \ngermination and field emergence was higher up to three year of seed \nstoring. This result indicated that long term seed storing in plastic pot \nallowed seed to absorb moisture from the environment. These results \nclearly indicated that moisture content positively regulate seed weight \nhowever, seed germination, field emergence and seed vigour was \nnegatively affected by moisture. \n\n\n\nMoisture content of seed determine the seed quality and physical maturity \nhowever, specific seed moisture content is required for seed maturity in \nthe field (McDonald, 2007). Moisture of seed is always changeable because \nof hygroscopic nature of seed that allow them to absorb and desorb water \nfrom environment (Khaldun and Haque, 2009; Stanwood and McDonald, \n1989). In addition, seed moisture content is directly related to relative \nhumidity of the atmosphere and seed weight also depends on the moisture \nabsorption from the environment of storage condition (Copeland and \nMcDonald 2005; Delouche et al., 1973). In this research, higher seed \nweight was observed in those seeds having higher moisture content (Table \n1). Similar results were also reported by different scientists where storage \nperiods affected jute seed quality (Islam et al., 2002; Tareq et al., 2015). \nHigher moisture content might be resulted from the long-term seed \nstoring which also might affect seed germination and field emergence of \njute seed. Different physical and chemical properties of seed changes \nduring long the term seed storage, however short-term seed storage had \nno effect on seed germination, seedling growth (Silveira et al., 2014). \nSimilarly, short term seed storage (up to three years) had no effect on seed \nquality of C. olitorius seed storage in this present study.  \n\n\n\nTable 1: Effect of storage period on tossa jute (Corchorus olitorius) seed qualities \n\n\n\nStorage period Germination (%) Field emergence (%) 1000-seed weight (g) Vigour (%) Moisture (%) \n\n\n\nT1 48.33 e 33.33 e 1.89 a 18.33 d 23.30 a \nT2 72.67 d 62.00 d 1.81 b 26.33 c 19.85 b \nT3 85.00 c 74.00 c 1.76 c 30.33 b 16.16 c \nT4 94.00 b 83.67 b 1.72 d 36.67 a 10.42 d \nT5 96.00 a 91.33 a 1.71 d 37.00 a 8.44 e \n\n\n\nLevel of Significance *** *** *** *** *** \n\n\n\nCV (%) 2.12 2.82 0.74 4.87 6.45 \nLSD 1.62 1.87 0.012 1.39 0.97 \n\n\n\nSE (\u00b1) 0.79 0.92 0.61 0.68 0.47 \n\n\n\nIn column, means followed by different letters are significantly different.  \n***means at 0.1% level of probability \n\n\n\nResults are showing the average of different parameters of three tossa \nvarieties (Corchorus olitorius) (O-795, O-9897 and OM-1) in each year. T1: \nfive years old seed (2015-2020), T2: four years old seed (2016-2020), T3: \nthree years old seed (2017-2020), T4: two years old seed (2018-2020) and \nT5: one-year old seed (2019-2020). \n\n\n\n3.2 Effect of storage on seed qualities of tossa (Corchorus olitorius L.) \njute varieties \n\n\n\nResults revealed the significant variation on seed germination, field \nemergence, seed weight and seed vigour in same moisture content of \ndifferent tossa variety seeds in five year of seed storing (Table 2). Tossa \njute seed V2 (O-9897) showed the best results in all tested parameters \nincluding seed weight compare to the V1 (O-795) and V3 (OM-1) variety. V3 \ntossa variety showed the lowest percentage of seed germination (74%), \nfield emergence (63%) and seed vigour (28.4%).  Thousand seed weight \nof V1 and V3 was same in five year of seed storing however, lower than the \nV2. This result pointing that seed size of V2 (O-9897) is comparatively \nbigger than the V1 (O-705) and V3 (OM-1). Tossa variety V1 gave the \n\n\n\nintermediate results in all tested parameters (Table 2). From the above \nresults it can be predicted that size of tossa variety seed might helped to \nreserve more nutrient during long term storage than the other two tossa \nseeds.  \n\n\n\nSeed germination is highly depended on the source of seed collection \n(Gallagher and Wagenius, 2015; Rashid et al., 2007). It has been reported \nthat jute seed germination varies from variety to variety and even differ in \nlocation to location (Roy et al., 2011). Bangladesh Jute Research Institute \n(BJRI) has developed several tossa (C. olitorius) jute varieties having some \nunique features (Akter et al., 2009). In this experiment, tossa variety V2 (O-\n9897) showed the best results for having good quality of seeds even after \nfive year of storage compare to the V1 (O-795) and V3 (OM-1) (Table 2). \nFrom the results of this research, it is highly likely that tossa jute variety \nhad significant effect on seed quality. Tossa variety V2 might have more \nability to protect the higher respiration rate of seed during long term seed \nstoring. The higher rate of respiration is related to the loss of reserve \nnutrient of seed which in turn reduced the seed weight as well as seed \ngermination and field emergence (Ali and Elozeiri, 2017; Gupta and Aneja \n2004). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 24-28 \n\n\n\nCite The Article: Md. Zablul Tareq, Arif Mohammad Mojakkir, Mir Mehedi Hasan, Md. Jewel Alam, Md. Abu Sadat(2021). Moisture Content And Variety Of Jute Seed Is \nAffected By Long Term Seed Storage. Malaysian Journal Of Sustainable Agriculture, 5(1): 24-28.\n\n\n\nTable 2: Effect of storage periods on seed qualities of tossa (Corchorus olitorius L.) jute varieties. \nVarieties Germination (%) Field emergence (%) 1000-seed weight (g) Vigour (%) Moisture (%) \n\n\n\nV1 80.20 b 70.40 b 1.767 b 29.60 b 15.32 a \nV2 83.00 a 73.20 a 1.78 a 31.20 a 15.77 a \n\n\n\nV3 74.40 c 63.00 c 1.77 b 28.40 c 15.82 a \n\n\n\nLevel of Significance *** *** * *** NS \n\n\n\nCV (%) 2.12 2.82 0.74 4.87 6.45 \nLSD 1.25 1.45 4.25 1.08 0.75 \n\n\n\nSE(\u00b1) 0.61 0.71 1.30 0.52 0.36 \n\n\n\nIn column, means followed by different letters are significantly different.  \nIn column, means followed by same letters aren\u2019t significantly different.   \n***means at 0.1% level of probability  \n*means at 5% level of probability\nNS means non-significant. \n\n\n\nResults are showing the average of five years of three tossa (Corchorus \nolitorius) varieties. V1:(O-795), V2 (O-9897) and V3 (OM-1). \n\n\n\n3.3 Interaction effect of tossa jute seed variety with period of storing \n\n\n\nResults of interaction between different storage periods and tossa seed \nvarieties are summarized in table 3. Germination percentage was higher \nin five individual interactions (T5 \u00d7 V1, T5 \u00d7 V2, T5 \u00d7 V3, T4 x V1 and T4 x V2) \n(Table 3). On the other hand, interaction between T1 \u00d7 V3 showed the \nlowest seed germination percentage (40%). In case of field emergence \npercentage, again interaction of T5 \u00d7 V1, T5 \u00d7 V2 and T5 \u00d7 V3 showed the \nhigher percentage and similarly T1 \u00d7 V3 showed the lower field emergence. \nInterestingly, T1 \u00d7 V1 and T1 \u00d7 V2 interaction had highest value in thousand \nseed weight (1.9 g) and T5 \u00d7 V1 had the lowest seed weight (1.7 g). These \nresults clearly indicated that storing longer period in plastic pot might \nhelp seed to absorb moisture resulted higher seed weight. Analysis also \nrevealed that moisture content of seed is highly related to the seed vigour \npercentage (Table 3). Seeds having lower moisture from T5 \u00d7 V1, T5 \u00d7 V2 \nand T5 \u00d7 V3 interaction resulted in higher seed vigour percentage in the \nsame interaction. From the above analysis it is evident that storage period \nrather than seed variety had significant effect on tossa jute seed quality. \n\n\n\nProper moisture content and seed weight is one of the most important \nindicators for the good quality of seeds (Bekele et al., 2019). Jute seeds \ncontaining higher moisture normally poor at seed germination leading to \n\n\n\nless plant in the field (Masum et al., 2010). Moisture content of jute are \nhighly dependent on the jute variety which may also significantly affect \nseed quality of jute (Tareq et al., 2015). In this experiment, jute variety and \nextended storage period showed significant effect on seed germination in \nthe similar moisture content of jute seed (Table 1 and Table 2). So it was \nexpected that combination of jute variety with seed storage periods might \nplay important role for keeping seed quality and similar results were \nfound in this experiment (Table 3).  \n\n\n\n3.4 Correlation coefficient analysis between moisture and other seed \nquality characters \n\n\n\nStatistical analyses were carried out to understand the associations among \nmoisture and seed quality characters of jute seed. Analysis found the \nsignificant, and negative correlation of moisture with the seed \ngermination, field emergence, seed weight and seed vigour (Table 4). \nGermination percentage was significantly and positively related with the \nfield emergence and seed vigour, however negatively related with \nthousand seed weight. In addition, field emergence of seed negatively \ncorrelated with thousand seed weight but positively related with seed \nvigour. These results clearly pointing that moisture content of seed during \nseed preservation has significant role of determining tossa seed quality.  \nMoisture of seed is a critical influencing factor for seed germination, filed \nemergence and seed weight (Bakhtavar et al., 2019). Various mechanism \ninvolved in the loss of seed viability leading to seed quality deterioration \nand lipid peroxidation is one of them (Da Silva et al., 2018). Seed moisture \nis increased during long term seed storing that increase lipid peroxidation \nresult in less seed germination (Cai et al., 2011; Sung, 1996). Correlation \nof moisture with other parameters also indicating the significance of \nmoisture as one of the major reasons for seed quality assurance (Table 4). \n\n\n\nTable 3: Effect of Storage period \u00d7 Varieties interaction on tossa jute (Corchorus olitorius L.) seed qualities \n\n\n\nStorage period \u00d7 Varieties Germination Field emergence 1000-seed weight Vigour Moisture \nT1 \u00d7 V1 50.00 i 35.00 g 1.90 a 18.00 gh 22.68 a \n\n\n\nT1 \u00d7 V2 55.00 h 40.00 f 1.91 a 20.00 g 23.66 a \n\n\n\nT1 \u00d7 V3 40.00 j 25.00 h 1.85 b 17.00 h 23.56 a \nT2 \u00d7 V1 75.00 f 68.00 d 1.79 c 26.00 f 19.55 b \nT2 \u00d7 V2 79.00 e 70.00 d 1.80 c 27.00 ef 19.86 b \n\n\n\nT2 \u00d7 V3 64.00 g 48.00 e 1.81 c 26.00 f 20.15 b \nT3 \u00d7 V1 85.00 d 76.00 c 1.75 de 30.00 cd 15.80 c \nT3 \u00d7 V2 89.00 c 78.00 c 1.76 d 32.00 c 16.80 c \nT3 \u00d7 V3 81.00 e 68.00 d 1.76 d 29.00 de 15.89 c \nT4 \u00d7 V1 95.00 a 83.00 b 1.71 fg 37.00 ab 10.05 def \nT4 \u00d7 V2 95.00 a 85.00 b 1.72 f 38.00  a 10.15 de \n\n\n\nT4 \u00d7 V3 92.00 b 83.00 b 1.73 ef 35.00 b 11.05 d \nT5 \u00d7 V1 96.00 a 90.00 a 1.70 g 37.00 ab 8.50 efg \nT5 \u00d7 V2 97.00 a 93.00 a 1.72 f 39.00 a 8.36 g \nT5 \u00d7 V3 95.00 a 91.00 a 1.71 fg 35.00 b 8.45 fg \n\n\n\nLevel of Significance *** *** * * * \nCV (%) 2.12 2.82 0.74 4.87 6.45 \n\n\n\nLSD 2.81 3.25 0.02 2.42 1.68 \nSE(\u00b1) 1.37 1.58 0.01 1.18 0.82 \n\n\n\nIn column, means followed by different letters are significantly different.  \n***means at 0.1% level of probability  \n*means at 5% level of probability.\n\n\n\nTable 4: Correlation co-efficient between moisture and seed quality related characters \nMoisture (%) Germination (%) Field emergence (%) 1000 seed weight (g) Vigour (%) \n\n\n\nMoisture (%) 1 \nGermination (%) -0.90*** 1 \n\n\n\nField emergence (%) -0.90*** 0.99*** 1 \n1000 seed weight (g) -0.91*** -0.91*** -0.92*** 1 \n\n\n\nVigour (%) -0.93*** 0.95*** 0.94*** -0.92*** 1 \n\n\n\n***means at 0.1% level of probability \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 24-28 \n \n\n\n\n \nCite The Article: Md. Zablul Tareq, Arif Mohammad Mojakkir, Mir Mehedi Hasan, Md. Jewel Alam, Md. Abu Sadat(2021). Moisture Content And Variety Of Jute Seed Is \n\n\n\nAffected By Long Term Seed Storage. Malaysian Journal Of Sustainable Agriculture, 5(1): 24-28. \n \n\n\n\n4. CONCLUSION \n \nRequirement of jute seed is increasing day by day but the total production \nof jute is hampered due to the unavailability of quality seed in Bangladesh. \nMajor jute seed is supplied from the farmer\u2019s house but the seed quality is \nquestionable. Moreover, preserving seed for long time affect the seed \nquality due to the absorption of water from the storage environment. In \nthis research it was evident that storing tossa jute (Corchorus olitorius) \nseed in plastic pot can maintain seed quality up to three years. This \nresearch will help to maintain seed quality in the farmer\u2019s level.  \n \n\n\n\nACKNOWLEDGEMENT \n \nThis research did not receive any significant grant from any funding \nagencies in the public, commercial, or not-for-profit sectors. \n \n\n\n\nCONFLICT OF INTEREST \n \nThe authors declare no conflict of interest. \n \n\n\n\nREFERENCES \n \nAbdullah, J., 2002. Environment friendly and sustainable development \n\n\n\nwith jute products. CCIFB Newsletter no. 3rd quarter.  \n \nAkter, N., Islam, M.M., Begum, H.A., Alamgir, A., Mosaddeque, H.Q.M., 2009. \n\n\n\nAn improved variety of Corchorus olitorius L. Eco-Friendly \nAgricultural Journal, 2 (10), Pp. 864-869.     \n\n\n\n \nAkter, S., Sadekin, M.N., Islam, N., 2020. Jute and jute products of \n\n\n\nBangladesh: contributions and challenges. Asian Business Review, 10, \nPp. 143-152. \n\n\n\n \nAli, A.S., Elozeiri, A. A., 2017. Metabolic process during seed germination. \n\n\n\nAdvances in Seed Biology, \nhttp://dx.doi.org/10.5772/intechopen.70653. \n\n\n\n \nAli, M.R., Rahman, M.M., Ahammad, K.U., 2014. Effect of relative humidity, \n\n\n\ninitial seed moisture content and storage container on soybean \n(Glycine max L. Meril.) seed quality. Bangladesh Journal of Agricultural \nResearch, 39 (3), Pp. 461-469.   \n\n\n\n \nAnderson, J.D., 1970. Physiological and biochemical differences in \n\n\n\ndeteriorating barley seed. Crop Science, 10 (1), Pp. 36-39. \n \nBakhtavar, M.A., Afzal, I., Basra, S.M.A., 2019. Moisture absorption \n\n\n\nisotherms and quality of seed stored in conventional packaging \nmaterials and hermetic super bags. PLoS One, 14, Pp. e0207569. \nhttps://doi. org/10.1371/journal.pone.0207569. \n\n\n\n \nBBS (Bangladesh Bureau of Statistics). 2018. Statistical year Book of \n\n\n\nBangladesh. Bangladesh Bureau of Statistics, Statistics Division, \nMinistry of Planning, Govt. of the people\u2019s Republic of Bangladesh. \n\n\n\n \nBekele, N., Tesso, B., Fikre, A., 2019. Assessment of seed quality parameters \n\n\n\nin different sources of chickpea (Cicer arietinum (L.). African Journal \nof Agricultural Research, 14 (33), Pp. 1649-1658. \n\n\n\n \nCai, F., Mei, L.J., An, X.L., Gao, S., Tang, L., Chen, F., 2011. Lipid peroxidation \n\n\n\nand antioxidant responses during seed germination of Jatropha \ncurcas. International Journal of Agriculture and Biology, 13, Pp. 25-30. \n\n\n\n \nCamann, A., Dragsbaek, K., Krol, S., Sandgren, J., Song, D., 2011. Properties, \n\n\n\nRecycling and Alternatives to PE Bags. An Interactive Qualifying \nProject Report Submitted to the Faculty of Worcester Polytechnic \nInstitute. \n\n\n\n \nChowdhury, M.A.H., Hassan, M.S., 2013. Hand book of agricultural \n\n\n\ntechnology. Bangladesh Agricultural Research Council, Dhaka, \nBangladesh. \n\n\n\n \nCopeland, L.O., McDonal, M.B., 2005. Principles of seed science and \n\n\n\ntechnology, Fourth Edition, Springer (India) Pvt. Ltd. New Delhi, India.  \n \nDa Silva, L.J., Dias, D.C.F.D.S., Sekita, M.C., Finger, F.L., 2018. Lipid \n\n\n\nperoxidation and antioxidant enzymes of Jatropha curcas L. seeds \nstored at different maturity stages. Acta Scientiarum Agronomy, 40, \nPp. 34978. \n\n\n\n\n\n\n\nDelouche, J.C., Matthes, R.K., Dougherty, M.C., 1973. Storage of seed in \nsubtropical and tropical regions. Seed Science & Technology, 1, Pp. \n671-700. \n\n\n\n \nGallagher, M.K., Wagenius, S., 2015. Seed source impacts germination and \n\n\n\nearly establishment of dominant grasses in prairie restorations. \nJournal of Applied Ecology, 53, Pp. 251-263. \n\n\n\n \nGupta, A., Aneja, K.R., 2004. Seed deterioration in soybean varieties during \n\n\n\nstorage-physiological attributes. Seed Research, 32, Pp. 26-32. \n \nHaque, S.M.A., Hossain, I., Rahman, M.A., 2014. Effect of Different Storage \n\n\n\nContainers Used for Storing Seeds and Management Practices on Seed \nQuality and Seed Health in CVL-1 Variety. International Journal of \nPlant Pathology, 5, Pp. 28-53. \n\n\n\n \nHossain, M.A., Mannan, S.A., Sultana, K., Khandakar, A.L., 1994. Survey on \n\n\n\nthe constraints of quality jute seed at farm level. Agril. Support Service \nProject (GOB/WORLD BANK/ODA). Dhaka, Bangladesh. \n\n\n\n \nIslam, M.M., 2007. In: Jute seed technology. 1st edition. Pub. by Md. \n\n\n\nMahmudul Islam, 397, Middle Monipur, Mirpur, Dhaka-1216. College \nGate Book Binding and Printing, Mohammadpur, Dhaka. Pp. 160. \n\n\n\n \nIslam, M.M., 2016. Seed quality of two Corchorus spp. Collected from \n\n\n\ndifferent sources in Bangladesh. Journal of Agriculture and \nTechnology Management, 19 (1), Pp. 1-11. \n\n\n\n \nIslam, M.M., 2019. Research and development advances of jute in \n\n\n\nBangladesh: a review. Haya: The Saudi Journal of Life Sciences, 4 (2), \nPp. 52-68. \n\n\n\n \nIslam, M.M., Ali. M.S., 2017. Economic importance of jute in Bangladesh: \n\n\n\nproduction, research achievements and diversification. International \nJournal of Economic Theory and Application, 4 (6), Pp. 45-57. \n\n\n\n \nIslam, M.M., Akter, N., Ahmed, M., Sarkar, M.A.R., Sultana, N., 2002. Effect of \n\n\n\ndrying period, storage container and storage condition of jute \n(Corchorus olitorius L.) seed. Journal of Agricultural Science and \nTechnology, 3 (2), Pp. 94-99. \n\n\n\n \nKhaldun, A.B.M., Haque, M.E., 2009. Seed quality deterioration due to the \n\n\n\nthermal variation of biotic and abiotic factors in cucumber. \nBangladesh Journal of Agricultural Research, 34 (3), Pp. 457-463. \n\n\n\n \nMasum, S.M., Ali, M.H., Amin, A.K.M.R., Asaduzzaman, M., Roy, T.S., 2010. \n\n\n\nEffect of abiotic factors on quality of jute seed. Bangladesh Research \nPublication Journal, 4 (1), Pp. 47-52. \n\n\n\n \nMcDonald, M.B., 2007. Seed moisture and the eqilibrium seed moisture \n\n\n\ncontent curve. Seed Technology, 29 (1), Pp. 7-18. \n \nMollah, M.A.F., 2014. Yield and quality of kenaf seed as influenced by \n\n\n\nproduction and storage environment. PhD dissertation. Dept. of Seed \nSci. & Tech. Bangladesh Agricultural University, Mymensingh. \n\n\n\n \nPradhan, B.K., Badola, H.K., 2012. Effect of storage conditions and storage \n\n\n\nperiods on seed germination in eleven populations of Swertia \nchirayita: a critically endangered medicinal herb in Himalaya. The \nScientific World Journal, doi:10.1100/2012/128105.  \n\n\n\n \nRahman, S., Kazal, M.M.H., Begum, I.A., Alam, M. J., 2017. Exploring the \n\n\n\nfuture potential of jute in Bangladesh. Agriculture, 7 (12), Pp. 96. \n \nRashid, M.M., Khan, M.M.R., Hossain, M.A., Hossain, M.M., 2007. \n\n\n\nManagement of seed borne fungi of jute in Mymensingh region. \nBangladesh. Journal of Crop Science, 18 (1), Pp. 209-214. \n\n\n\n \nRoy, K.K., Khan, M.R., Hossain, M.M., Khokon, A.R., 2011. Feasibility of \n\n\n\nquality improvement of jute seed by plant extracts. Progressive \nAgriculture, 22 (1 and 2), Pp. 1-10. \n\n\n\n \nSikder, F.S., Saha, C.K., Rahman, M., Alam, A.K.M.M., Haque, S., 2008. Jute \n\n\n\nproduction in Bangladesh- an overview. Abstracts of papers. \nInternational Symposium on Jute and Allied Fibres Production, \nUtilization and Marketing. National Library. Kolkata. India. \n\n\n\n \nSilveira, F.A.O., Negreiros, D., Ranieri, B.D., Silva, C.A., Araujo, L.M., \n\n\n\nFernandes, W., 2014. Effect of seed storage on germination, seedling \ngrowth and survival of Mimosa foliolosa (Fabaceae): implications for \n\n\n\n\nhttp://dx.doi.org/10.5772/intechopen.70653\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 24-28 \n \n\n\n\n \nCite The Article: Md. Zablul Tareq, Arif Mohammad Mojakkir, Mir Mehedi Hasan, Md. Jewel Alam, Md. Abu Sadat(2021). Moisture Content And Variety Of Jute Seed Is \n\n\n\nAffected By Long Term Seed Storage. Malaysian Journal Of Sustainable Agriculture, 5(1): 24-28. \n \n\n\n\nseed banks and restoration ecology. Tropical Ecology, 55 (3), Pp. 385-\n392. \n\n\n\n \nStanwood, P.C., McDonald, M.B., 1989. Seed moisture. Crop Science Society \n\n\n\nof America Special Publication, Madison, WI.  \n \nSuma, A., Sreenivasan, K., Singh, A.K., Radhamani, J., 2013. Role of relative \n\n\n\nhumidity in processing and storage of seeds and assessment of \nvariability in storage behaviour in Brassica ssp. And Eruca sativa. The \nScientific World Journal, https://doi.org/10.1155/2013/504141.  \n\n\n\n \nSung, J.M., 1996. Lipid peroxidation and peroxide- scavenging in soybean \n\n\n\nseeds during ageing. Physiologia plantarum, 97, Pp. 85-89.  \n\n\n\n \nTareq, M.Z., Khan, M.A., Mollah, M.A.F., Hasan, M.M., Alam, M.J., 2015. Effect \n\n\n\nof storage environment on jute seed qualities. Bangladesh Journal of \nEnvironmental Science, 29, Pp. 45-48. \n\n\n\n \nWang, W., He, A., Peng, S., Huang, J., Cui, K., Nie, L., 2018. The effect of \n\n\n\nstorage condition and duration on the deterioration of primed rice \nseeds. Frontiers in Plant Science, 9, Pp. 172. \n\n\n\n \nZakaria, A., Sayed, A.N., 2008. Jute microbiological and biochemical \n\n\n\nresearch. Plant Tissue Culture Biotechnology, 18 (2), Pp. 197-220. \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1155/2013/504141\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 61-66 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.61.66 \n\n\n\nCite the Article: Kolapo Adetomiwa, Ojo Christianah Funmilayo, Lawal Adebayo Morenikeji, Abayomi Tajudeen Sarumi, Muhammed, Opeyemi Abdulmumin (2021). \nSensitivity Analysis And Future Farm Size Projection Of Bio -Fortified Cassava Production In Oyo State, Nigeria.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 61-66.\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2021.61.66 \n\n\n\nSENSITIVITY ANALYSIS AND FUTURE FARM SIZE PROJECTION OF BIO-FORTIFIED \nCASSAVA PRODUCTION IN OYO STATE, NIGERIA \n\n\n\nKolapo Adetomiwaa*, Ojo Christianah Funmilayoa, Lawal Adebayo Morenikejia, Abayomi Tajudeen Sarumib, Muhammed, Opeyemi \nAbdulmuminb \n\n\n\na Department of Agricultural Economics, Faculty of Agriculture, Obafemi Awolowo University, Ile Ife, Osun State, Nigeria. \nb Department of Agricultural Economics and Extension, Ekiti State University, Ado-Ekiti, Nigeria. \n\n\n\n*Corresponding Authors Email: kolapoadetomiwa@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 19 November 2020 \nAccepted 23 December 2020 \nAvailable online 31 December 2020\n\n\n\nThe study examined the costs and returns to bio-fortified cassava production and forecast the future farm \nsize of bio-fortified cassava production in Oyo State, Nigeria. A multistage sampling technique was used to \nselect our respondents. Primary data were used for the study which were collected through a well-structured \nquestionnaire. Data collected were analyzed using descriptive, Markov chain, and budgetary analysis. The \nresult of the study showed that TMS 01/0593, TMS 01/0539 and TMS 01/0220 were the mostly grown varies \nof bio-fortified cassava varieties in Oyo State, Nigeria. The result of the budgetary analysis showed that the \naverage net return (net farm income) from the production of bio-fortified cassava was \u20a6196710.95 with RORI \nof 224.95%. The result revealed that at 35% increase in cost of production, the rate of return on investment \ndropped to 140.70% in which the investment will not be viable. The bio-fortified cassava farmers have a great \npotential to boost production through increases in farm sizes of the bio-fortified cassava famers until the year \n2026 when equilibrium would be attained at about 2.85ha. In order to adequately achieve these goals, more \nimproved varieties of bio-fortified cassava should be provided. Consequently, infrastructures should be put \nin place to help boost farmers moral in their cause of production.  \n\n\n\nKEYWORDS \n\n\n\nSensitivity Analysis, bio-fortified cassava, Markov chain, Farm size, Oyo State.\n\n\n\n1. INTRODUCTION \n\n\n\nCassava is the world's fourth most important staple crop after rice, wheat \n\n\n\nand maize, and plays an essential role in food security. Due to cassava\u2019s \n\n\n\ngrowth characteristics and ability to grow in poor soils and regions prone \n\n\n\nto drought, it is preferred by resource poor farmers in many tropical \n\n\n\ncountries (Mtunguja et al., 2019). Cassava (Manihot esculenta Crantz), a \n\n\n\nstarchy root crop, is a major source of food security in Africa because of its \n\n\n\nability to grow in low-quality soil, its resistance to drought and disease, \n\n\n\nand its flexible cultivation cycle (Meridian Institute 2013; Sanni et al., \n\n\n\n2009). Cassava\u2019s harvestable portion, the tubers, can be stored \n\n\n\nunderground until needed, making it an ideal food security crop (Nweke \n\n\n\n2003). Cassava is the most widely consumed food staple in Nigeria (Sanni \n\n\n\net al., 2009). \n\n\n\nCassava is an important staple food in Nigeria (Kolapo et al., 2020). \n\n\n\nAccording to a study about 177,948 million tonnes of cassava were \n\n\n\nproduced in Africa (Otekunrin and Sawicka, 2019). Nigeria is regarded as \n\n\n\nthe world\u2019s largest producer of cassava with a total of about 20.4 percent \n\n\n\nof the world export in year 2017 (Otekunrin and Sawicka, 2019). Cassava \n\n\n\nis a major staple food crop in Nigeria. As defined, a staple crop is the one \n\n\n\nthat is been eaten regularly and which also provides larger proportions of \n\n\n\nthe population\u2019s nutrients (Otekunrin and Sawicka, 2019). Cassava fulfil \n\n\n\nthis purpose as it can be eaten raw or in a processed form. Cassava is an \n\n\n\nessential component of the diet of about 70 million Nigerians (FAO, 2013). \n\n\n\nNigeria, being the largest producer of cassava in the world is producing an \n\n\n\naverage annual estimate of 45 million metric tons which had been \n\n\n\ntranslated into a major global market share of about 19 percent (Hillocks, \n\n\n\n(2002); Phillips et al., 2004). A small fraction of cassava output in the \n\n\n\ncountry is produced for commercial use in the livestock feed, ethanol, \n\n\n\ntextile, confectionery, and food industries, while the majority is produced \n\n\n\nby smallholder farmers for subsistence or small-scale processing \n\n\n\n(Knipscheer et al., 2007). \n\n\n\nCassava is adapted to growing on poor degraded soils and can tolerate low \n\n\n\npH, high levels of exchangeable aluminum and low concentrations of \n\n\n\nphosphorus, conditions that typically limit crop growth (Howeler, 2002). \n\n\n\nSandy soils have been also found to be suitable for cassava production \n\n\n\nbecause of easy root penetration and expansion of the growing root during \n\n\n\ncarbohydrates partitioning. Sandy clay loam soils are also appropriate due \n\n\n\nto the high-water retention capacity which provides a good distribution of \n\n\n\nsoil water for long periods even after the onset of dry season (Mtunguja et \n\n\n\nal., 2016b). Nevertheless, adequate soil nutrient availability important for \n\n\n\nincreasing cassava production and dramatic differences in cassava yield \n\n\n\nhas been reported, with changes in soil nutrient supply (Mtunguja et al., \n\n\n\n2016b). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 61-66 \n\n\n\nCite the Article: Kolapo Adetomiwa, Ojo Christianah Funmilayo, Lawal Adebayo Morenikeji, Abayomi Tajudeen Sarumi, Muhammed, Opeyemi Abdulmumin (2021). \nSensitivity Analysis And Future Farm Size Projection Of Bio -Fortified Cassava Production In Oyo State, Nigeria.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 61-66.\n\n\n\nCassava is the main staple crop in Nigeria. While it is inexpensive and a \n\n\n\ngood source of carbohydrates, it lacks nutritional value, as it is a poor \n\n\n\nsource of protein, vitamins, and minerals (McNulty and Oparinde, 2015). \n\n\n\nNigerians who are restricted to the consumption of a cassava-based diet \n\n\n\nare at risk of micronutrient malnutrition, which can cause blindness, \n\n\n\nstunting, and increased susceptibility to disease. To combat hidden \n\n\n\nhunger, HarvestPlus and its partners have bred provitamin A-rich yellow \n\n\n\ncassava varieties (McNulty and Oparinde, 2015). The production of \n\n\n\nbiofortified vitamin-A cassava started in 2011 with the intervention of the \n\n\n\nInternational Center for Tropical Agriculture (CIAT) and the International \n\n\n\nInstitute of Tropical Agriculture (IITA) which were funded by Harvest Plus \n\n\n\nprogram (Kolapo and Fakokunde, 2020; Kolapo et al., 2020). Five years \n\n\n\nafter the intervention program, statistics revealed that over 1million of \n\n\n\nNigerian farming households grows yellow cassava varieties that contains \n\n\n\nsubstantial quantities of vitamin-A even after processing (Kolapo et al., \n\n\n\n2020). In Nigeria diets today, yellow bio-fortified cassava represents \n\n\n\nadditional source of vitamin A (Saltzman et al., 2014).  \n\n\n\nHowever, majority of the bio-fortified cassava farmers were still \n\n\n\nproducing on a small scale due to a myriad of factors including lack of \n\n\n\ngeneral acceptance of the product despites its nutritional benefits to \n\n\n\nhuman population. According to a study, production of bio-fortified \n\n\n\ncassava is still on a relatively small farm size despites the release of the \n\n\n\nfirst wave of vitamin A cassava in 2011 (Ilona et al., 2017). Hence, to a large \n\n\n\nextent accounts for the low supply of bio-fortified cassava products like \n\n\n\nvitamin A yellow garri, fufu and high-quality cassava flour relative to the \n\n\n\ndemand for these products thus necessitating the need to project the \n\n\n\nfuture farm size with a view to determining what the future holds for bio-\n\n\n\nfortified cassava production. \n\n\n\nIn commercial enterprises, profit is a major motivating factor. The profit \n\n\n\nand profitability levels of farm enterprises may be influenced by the farm \n\n\n\nsize.  This is because it is assumed that with larger farm size the cost of \n\n\n\nproduction is spread across the number of hectares and as such \n\n\n\nprofitability is increased. Thus, it is necessary to examine the trend in size \n\n\n\nof farms to determine their intertemporal performance. The specific \n\n\n\nobjective of the study were to described the socio-economic \n\n\n\ncharacteristics of the bio-fortified cassava farmers; forecast the future \n\n\n\nfarm size of bio-fortified cassava production and estimate the cost and \n\n\n\nreturn to bio-fortified cassava production. \n\n\n\n2. THEORETICAL FRAMEWORK \n\n\n\nIn other to project the future farm size of bio-fortified cassava production, \n\n\n\nMarkov chain model was utilized. Markov chain are one of the conceptual \n\n\n\ndevices used in analyzing the types of changes obtainable when there is \n\n\n\nmovement from one state to another (Anders, 2016). It was first used in \n\n\n\nthe study of Markov A.A in 1907 and has been used in various sectors \n\n\n\nranging from agriculture, health and migration studies to forecast and \n\n\n\npredict future trend. In Agriculture it is useful in predicting and \n\n\n\nforecasting the behavior of farmers as they move from one categories of \n\n\n\nfarm size to another. It is one of stochastic process in which the probability \n\n\n\nor likelihood associated with a set of possible future outcome is stated. A \n\n\n\nstochastic process refers to mathematical model with a sequence of \n\n\n\nrandom variables which assumes that any population of individuals or \n\n\n\nfirms can be classified into various groups. As such, movements between \n\n\n\nstates over time is regarded as a stochastic process (Olatidoye et al., 2018).  \n\n\n\nA finite Markov process is one in which the outcome of a given trial \n\n\n\n(experiment) in the time (t + 1) essentially depends on the outcome of the \n\n\n\ntrial in the preceding time period (t) and this dependence holds at all the \n\n\n\nvarious stages of the trial.  Markov chains are often characterized by the \n\n\n\ndynamic property, such that as the present condition is known, prediction \n\n\n\nabout the future outlook or behavior of the process remain the same, even \n\n\n\nif additional information about past history of the process is known \n\n\n\n(Anders, 2016). Finite Markov chain process often determined by \n\n\n\nspecification of a given set of states (S1, S2\u2026Sn). Only one state is achievable \n\n\n\nat a given time and it moves progressively from one state to another. The \n\n\n\nprobability of moving from Si to Sj is given for every pair category can be \n\n\n\nrepresented in the form of transition matrix P. Pij refers to the probability \n\n\n\nof moving from Si to Sj in the next step. The element of the matrix must be \n\n\n\nnon-negative, and the row sum of the elements is one. when all the initial \n\n\n\nprobability is known, outcome of the nth step, can be gotten \n\n\n\n  (1)  \n\n\n\n\ud835\udc43 =  [\ud835\udc43\ud835\udc56\ud835\udc57] =  [\n\ud835\udc5b\ud835\udc56\ud835\udc57\n\n\n\n\u2211 \ud835\udc5b\ud835\udc56\ud835\udc57\n\n\n\n\ud835\udc5b\n\n\n\n\ud835\udc57=1\n\n\n\n]  \u2265 0 (j = 1, 2, 3, - - -, m)    (2) \n\n\n\nHence, the future path of the stochastic process is given by; \n\n\n\nP (0) P = P(1) state vector in time, t + 1 \n\n\n\nP(1) P = P(2) state vector in time   t + 2 \n\n\n\nP(m-1) p = p(m) state vector in time t+m         (3) \n\n\n\nTherefore, P(0) P(e) gives the fixed probability vector, or equilibrium \n\n\n\nprobability vector of the stochastic process. \n\n\n\nHence: P(m)\u2192 P(e) as m\u2192 \u221e \n\n\n\nP(o) P(e) = P(e) \n\n\n\nP(e) P = P(e)          (4) \n\n\n\nThe equilibrium farm size indicates that the number of people entry a \n\n\n\nparticular category of farm size is equal to the number of farmers leaving \n\n\n\nthe group. The underlying assumptions on which Markov chain includes \n\n\n\nthe following; The structure of the population when the transition \n\n\n\nprobability is made remain constant, the underlying determinant of a \n\n\n\nchange in one category of farm size is represented by a probability of \n\n\n\nindividual movement from one category of farm size to another depends \n\n\n\non the result proceeding of the period. There are several application of \n\n\n\nMarkov model in Agricultural economics such as market structure and \n\n\n\neconomic development. Empirical studies that employed the use of \n\n\n\nMarkov chain include those of (Alimi et al., 2007; Baruwa et al., 2011; \n\n\n\nOlatidoye et al., 2018).  \n\n\n\n3. METHODOLOGY\n\n\n\n3.1 Area of Study \n\n\n\nThe study was conducted in Oyo States, Nigeria. Oyo State is an inland state \n\n\n\nin South-Western Nigeria, with its capital at Ibadan. It is bounded in the \n\n\n\nnorth by Kwara State, in the East by Osun State, in the South by Ogun State \n\n\n\nand in the West partly Ogun State and partly by the Republic of Benin with \n\n\n\na population of 5,591,589 people (NPC, 2006). Oyo State is homogeneous, \n\n\n\nmainly inhabited by the Yoruba ethnic group who are primarily agrarian \n\n\n\nbut have a predilection for living in high-density urban centers. Oyo State \n\n\n\ncovers approximately an area of 28,454 square kilometers. Oyo State is \n\n\n\nlocated in the rainforest vegetation belt of Nigeria on longitude of \n\n\n\n2038.661N and 4038.251N and latitude 908.741E and 701.681E. Agricultural \n\n\n\nactivities in Oyo State include the production of different varieties of \n\n\n\narable food crops since the climatic conditions support the production of \n\n\n\nvarious food crops including cassava, maize, groundnut etc. A large \n\n\n\nproportion of the bio-fortified cassava were being produced in the State as \n\n\n\nthe distribution of bio-fortified cassava stem started in Oyo State in 2011, \n\n\n\nhence the choice of the study area. \n\n\n\n3.2 Sampling procedures and sample size \n\n\n\nMultistage sampling procedures were employed for the study. The first \n\n\n\nstage involved purposive selection of two Local Government Areas (LGAs) \n\n\n\nbecause of the concentration of bio-fortified cassava producers in the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 61-66 \n\n\n\nCite the Article: Kolapo Adetomiwa, Ojo Christianah Funmilayo, Lawal Adebayo Morenikeji, Abayomi Tajudeen Sarumi, Muhammed, Opeyemi Abdulmumin (2021). \nSensitivity Analysis And Future Farm Size Projection Of Bio -Fortified Cassava Production In Oyo State, Nigeria.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 61-66.\n\n\n\nareas. The second stage involved random selection of three communities \n\n\n\nfrom each of the selected LGAs. At the third stage, twenty-five bio-fortified \n\n\n\ncassava farmers were purposively selected from each community to make \n\n\n\na total of 150 (One hundred and fifty) respondents. Primary data were \n\n\n\nused for the study. The primary data were sourced from cross-sectional \n\n\n\nsurvey of bio-fortified cassava farmers in the study area with the aid of \n\n\n\nwell-structured questionnaire to cover information about the \n\n\n\nsocioeconomic characteristics of respondent and inputs and outputs of \n\n\n\nbio-fortified cassava production. Data were collected in December 2018. \n\n\n\n3.3 Analytical techniques \n\n\n\nThe data were analyzed using descriptive statistics, Markov Chain \n\n\n\nAnalysis, Farm budgeting analysis (Gross margin sensitivity). \n\n\n\n3.4 Markov Chain analysis \n\n\n\nMarkov Chain analysis was employed in this study to predict and forecast \n\n\n\nthe future farm size of bio-fortified cassava farmers production. Markov \n\n\n\nChain process is a stochastic model used in the analysis of economic \n\n\n\nvariable with the availability of a time-ordered data. (Amina and Akhigbe, \n\n\n\n2017). Bio-fortified cassava farmers were grouped according to some \n\n\n\ncriteria of farm sizes (states). Secondly the evolution of bio-fortified \n\n\n\ncassava farmers through these states can be regarded as a stochastic \n\n\n\nprocess. The probability of moving from one state (t) to another (t+1) is a \n\n\n\nfunction only of the two states (t, t+1) involved. The movement of the bio-\n\n\n\nfortified cassava farmers within the farm size depends on the initial farm \n\n\n\nsize attained and the number of years involved which is independent of \n\n\n\nthe previous history (Ander, 2016). Within this framework, farm sizes \n\n\n\ncultivated are the variable whose movement over time is to be analyzed, \n\n\n\nand the following class intervals will be used in defining the admissible \n\n\n\nstates.  \n\n\n\nTable 1: Distribution by size \n\n\n\nClass Farm size in (ha) \n\n\n\nS1 1-2 \n\n\n\nS2 2.1-3 \n\n\n\nS3 3.1-4 \n\n\n\nS4 4.1-5 \n\n\n\nTherefore, in a year it is possible for a bio-fortified cassava farmer to be in \n\n\n\nany one of the four specified positions. Having defined the data and the \n\n\n\nranges for each class, the year-to-year history of each bio-fortified cassava \n\n\n\nfarmer in terms of his movement among the various classes was used in \n\n\n\ndeveloping the transition matrix, which reflects the behaviour of the \n\n\n\nsample of bio-fortified cassava farmers.  Let uij represent the number of \n\n\n\nfarmers moving from class i to class j through the years under \n\n\n\nconsideration. The transition probabilities (Pij) can be represented in the \n\n\n\nform of transition matrix P. Pij is the probability of bio-fortified cassava \n\n\n\nfarmers transitioning from state i to j (one farm size category to the other).  \n\n\n\nPij =  \n\ud835\udc461\n\n\n\n\ud835\udc462\n\n\n\n\ud835\udc46\ud835\udc5b\n\n\n\n[\n\n\n\n\ud835\udc461 \ud835\udc462 \u2026 \ud835\udc46\ud835\udc5b\n\n\n\n\ud835\udc4311 \ud835\udc4312 \ud835\udc431\ud835\udc5b\n\n\n\n\ud835\udc4321 \ud835\udc4322 \ud835\udc432\ud835\udc5b\n\n\n\n\ud835\udc43\ud835\udc5b1 \ud835\udc43\ud835\udc5b2 \ud835\udc43\ud835\udc5b\ud835\udc5b\n\n\n\n]  \n\n\n\n \ud835\udc43 =  [\ud835\udc43\ud835\udc56\ud835\udc57] =  [\n\ud835\udc5b\ud835\udc56\ud835\udc57\n\n\n\n\u2211 \ud835\udc5b\ud835\udc56\ud835\udc57\n\n\n\n\ud835\udc5b\n\n\n\n\ud835\udc57=1\n\n\n\n]  \u2265 0 \n\n\n\ni. \u2211 \ud835\udc43\ud835\udc56\ud835\udc57 = 1\n\ud835\udc5a\n\n\n\n\ud835\udc57=1\n (row summation of probability should equal to one) \n\n\n\nii. \ud835\udc43\ud835\udc56\ud835\udc57  \u2265 0   (for all i and j)\n\n\n\niii. Pj = \n\ud835\udc5b\ud835\udc57\n\n\n\n\ud835\udc41\n(1,j = 1,2,3\u2026,.m)\n\n\n\nThe long run equilibrium is attained when the total number of bio-fortified \n\n\n\ncassava farmers entering a given farm category equals the number of \n\n\n\nfarmers exiting. This is expressed as follows: eP = e.  \n\n\n\n(\ud835\udc521, \ud835\udc522, \ud835\udc523,) [\n\ud835\udc4311 \ud835\udc4312 \ud835\udc4313\n\n\n\n\ud835\udc4321 \ud835\udc4322 \ud835\udc4323\n\n\n\n\ud835\udc4331 \ud835\udc4332 \ud835\udc4333\n\n\n\n] = (\ud835\udc521, \ud835\udc522, \ud835\udc523,) \n\n\n\nTable 2: First-order Markov model for farm size transitions \n\n\n\nPeriod 1 (t) \nPeriod 2 (t +1) \n\n\n\nTotal \nS1 S2 S3 \u2026\u2026Sn \n\n\n\nS1 n11 n12 n13 \u2026n1m n1 \nS2 n21 n22 n23 n2m n2 \nS3 n31 n32 n33 n3m n3 \n. . . . . . \n. . . . . . \n. . . . . . \n\n\n\nSm nm1 nm2 nm3 nmm nm \nTotal (Period t +1) n1 n2 n3 n.m N \n\n\n\nSource: Author\u2019s, 2019 \n\n\n\n3.5 Budgetary technique (Gross margin sensitivity) \n\n\n\nThe gross margin of the farm is a measure of output and farm profitability, \n\n\n\nwhich is a useful indicator in planning. Gross Margin (GM) is the difference \n\n\n\nbetween total revenue and total variable cost while Gross Margin \n\n\n\nSensitivity (GMS) is the difference between total revenue and changes in \n\n\n\ntotal variable cost. Since parameters and the output have different \n\n\n\nmeasurement units, they are not directly comparable. This problem can be \n\n\n\novercome by calculating the \u201celasticity\u201d or the percentage change in output \n\n\n\nto a percentage change in other parameters (Pannell, 1997). The \n\n\n\nsensitivity is calculated to explore the impact of assumptions regarding \n\n\n\nthe changes in farm sizes on the gross margin, by using the principle \u201cwhat \n\n\n\nif\u201d (Dachin et al., 2016). The sensitivity is interpreted as the elasticity of \n\n\n\ngross margin to changes in farm sizes by +/- 5%, 10%, 15 and 20%). \n\n\n\nGM = TR-TVC          (5) \n\n\n\nNI=GM -TFC          (6)  \n\n\n\nROI = NFI/TC          (7) \n\n\n\nBCR = TR/TC          (8) \n\n\n\nTVC = Summation of all the variable cost which includes;  \ni. Land preparation \n\n\n\nii. Planting materials \n\n\n\niii. Chemical used \n\n\n\niv. Labour used (planting, weeding, fertilizer and pesticide \n\n\n\napplication and harvesting) \n\n\n\nv. Transportation \n\n\n\nWhere: \nGM = Gross margin \n\n\n\nNFI = Net farm income \n\n\n\nTC = Total cost incurred \n\n\n\nROI = Return on investment \n\n\n\nBCR = Benefit cost ratio \n\n\n\nTVC= Total variable cost incurred \n\n\n\nTFC= Total fixed cost incurred \n\n\n\nTR= Total revenue generated from production \n\n\n\n4. RESULTS AND DISCUSSION\n\n\n\n4.1 Socio-economic characteristics of respondents \n\n\n\nPresented in Table 2 were the socio-economic characteristics of the bio-\n\n\n\nfortified cassava farmers. The mean age of the respondents were \n\n\n\n47(\u00b113.77) which implies that bio-fortified cassava farmers were young \n\n\n\nand active thus expected to be productive. They are also expected to be \n\n\n\nopen to adoption of new innovation in agricultural practices. Majority \n\n\n\n(53%) of the respondents were women. This agree that cassava \n\n\n\nproduction in Nigeria were mostly common among the women gender \n\n\n\n(Oparinde et al., 2014). Majority (79%) of the respondents were married \n\n\n\nimplying that they were responsible. It might be due to the fact that \n\n\n\nmarriage is cherished among the producers. The mean years of formal \n\n\n\neducation was 14.39 (\u00b16.83) which implies that respondents were literate \n\n\n\nand thus, can read and write. The mean household size were 5.31 (\u00b12.26). \n\n\n\nThis implies that the respondents invariably had a medium to large family \n\n\n\nsize and the use of family labor is possible. About 58% of the respondents \n\n\n\nhad access to credit to facilitate their production of bio-fortified cassava. \n\n\n\nThese might be due to the fact that the respondents belong to association. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 61-66 \n\n\n\nCite the Article: Kolapo Adetomiwa, Ojo Christianah Funmilayo, Lawal Adebayo Morenikeji, Abayomi Tajudeen Sarumi, Muhammed, Opeyemi Abdulmumin (2021). \nSensitivity Analysis And Future Farm Size Projection Of Bio -Fortified Cassava Production In Oyo State, Nigeria.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 61-66.\n\n\n\nThe mean years of experience was 14.62(\u00b16.92). This implies that \n\n\n\nrespondents had been into cassava production for a long time even before \n\n\n\nthe introduction of new improved bio-fortified cassava in 2011. Majority \n\n\n\n(86%) of the respondents belong to one association or the other. They can \n\n\n\nthus experience group benefits such as credit facility, inputs etc. \n\n\n\nTable 2: Socio-economic Characteristics of Bio-fortified Vit-A Cassava \n\n\n\nFarmers \n\n\n\nVariables Bio-fortified cassava farmers \n\n\n\nAge (years) 47(\u00b113.77) \n\n\n\nFemale (%) 53.00 \n\n\n\nMarried (%) 79.00 \n\n\n\nFormal education (years) 14.39 (\u00b16.83) \n\n\n\nHousehold size (#) 5.31 (\u00b12.26) \n\n\n\nAccess to credit (%) 58.00 \n\n\n\nYears of experience (years) 14.62(\u00b16.92) \n\n\n\nMembership of association (%) 86.00 \n\n\n\nFigures in parentheses are standard deviation \n\n\n\n4.2 Farm specific characteristics \n\n\n\nPresented in Table 2 is the farm size of bio-fortified cassava farms. The \n\n\n\nresult shows that majority (41.33% and 38% for 2017 and 2018, \n\n\n\nrespectively) of the respondents cultivated between 1-2ha in the two \n\n\n\nyears respectively. The mean farm size in 2017 and 2018 was 2.1ha and \n\n\n\n2.19ha. The results imply that bio-fortified cassava farms were small scale. \n\n\n\nFrom Table 4, about 39.33% of the respondents inherited their farmland, \n\n\n\n22% purchased their farmland, 24% rented their farmland while 14.67% \n\n\n\nof the respondents gotten their farmland through communal/gift. About \n\n\n\n21.33%, 25.33% and 20.66% of the respondents grown TMS 01/0593, \n\n\n\nTMS 01/0539 and TMS 01/0220 respectively.  These varieties of bio-\n\n\n\nfortified cassava were the second wave of the bio-fortified cassava \n\n\n\ndistributed in 2016 and was observed to be an improved variety over the \n\n\n\nones that were first released in 2011 as listed in Table 4, hence the reason \n\n\n\nbehind cassava farmers adopting this varieties. Majority (55.33%) of the \n\n\n\nrespondents practiced intercropping. This implies that they tend to \n\n\n\nmaximize land resources for the production of bio-fortified cassava as \n\n\n\ncrops like maize were observed to be intercropped with Bio-fortified \n\n\n\ncassava. \n\n\n\nTable 3: Farm size of bio-fortified cassava farms \nFarm size (ha) \n\n\n\ncategories \n2017 \n\n\n\nFrequency \n(%) \n\n\n\n2018 \nFrequency \n\n\n\n(%) \n1-2 62 41.33 57 38.00 \n\n\n\n2.1-3 49 32.67 52 34.67 \n3.1-4 36 24.00 37 24.67 \n4.1-5 3 2.00 4 2.66 \nMean 2.14 2.19 \n\n\n\nTable 4: Farm specific characteristics \nVariables Frequency Percentage \nMode of land acquisition \nInherited  59 39.33 \nPurchase 33 22.00 \nRent 36 24.00 \nCommunal/Gift 22 14.67 \nVarieties grown \nTMS 01/1371 16 10.67 \nTMS 01/1412 19 12.67 \nTMS 01/1368 14 9.33 \nTMS 01/0593 32 21.33 \nTMS 01/0539 38 25.33 \nTMS 01/0220 31 20.67 \nAgricultural system practiced \nSole cropping 67 44.67 \nInter cropping 83 55.33 \n\n\n\n4.3 Markov chain analysis for bio-fortified cassava farm size \n\n\n\nThe movement of bio-fortified cassava farmers from one farm size \n\n\n\ncategory to another between the two periods (2017 and 2018) were \n\n\n\npresented in Table 5. The farm size was categorized into four groups; 1-\n\n\n\n2ha, 2.1-3ha, 3.1-4ha and 4.1-5ha. From Table 5, the first cell on the first \n\n\n\nrow (S1S1) contains the number of bio-fortified cassava farmers (53) that \n\n\n\ncultivated between 1-2ha in the first period (2017) and still remained in \n\n\n\nthe same category in the second period (2018). The figure in the second \n\n\n\ncell of first row (S1S2) represents the number of bio-fortified cassava \n\n\n\nfarmers (6) in the farm size category 1-2ha in the first period but had \n\n\n\nmoved to 2.1-3ha farm size category in the second period. The figure (0) \n\n\n\nin the third cell of the first row (S1S3) implies that no farmer in the 1-2ha \n\n\n\nfarm size category in 2017 had moved to 3.1-4ha in the second period \n\n\n\n(2018). This is applicable to fourth cell and for other rows of the transition \n\n\n\nmatrix (Table 5). \n\n\n\nThe transition probability matrix corresponding to the transition matrix \n\n\n\nof Table 5 is shown in Table 6. The entries in the cells on the principal \n\n\n\ndiagonal of Table 6 indicate the tendency for the farmers to remain within \n\n\n\na given category of farm size. These entries show that there was a strong \n\n\n\ntendency (0.90, 0.85. 0.89 and 0.67) for those farmers cultivating farm size \n\n\n\n(1-2ha, 2.1-3ha, 3.1-4ha and 4.1-5ha) respectively to remain there. This \n\n\n\nimplies that for a proportion of 0.90 in the first cell of the principal \n\n\n\ndiagonal (S1S1) for example, as many as 90% of the farmers remained in \n\n\n\nthat category in the second period (2018). The proportion in the second \n\n\n\ncell of the principal diagonal (S1S2) corresponding to farmers cultivating \n\n\n\n2.1-3ha is 0.85 which implies that 85% of the farmers that cultivated 2.1-\n\n\n\n3ha stands in 2017 remained in this category in 2018.  \n\n\n\nHowever, the proportions in the cells to the right of each of the cells in the \n\n\n\nprincipal diagonal indicate the chances of moving to higher categories \n\n\n\nthan that of the principal diagonal cell. Similarly, the proportions in the cell \n\n\n\nto the left of each of the cells on the principal diagonal indicate the chances \n\n\n\nof moving to lower categories than that of the principal diagonal cell. For \n\n\n\nexample, the cells to the right of the first cell on the principal diagonal \n\n\n\n(S1S2, S1S3 and S1S4) contain 0.10 ,0.00 and 0.00 for 2.1-3ha, 3.1-4ha and \n\n\n\n4.1-5ha respectively. This implies that the probability of farmers who \n\n\n\ncultivate 1-2ha category in period one to move to 2.1-3ha category and \n\n\n\nhigher ones in period two is low.  \n\n\n\nTable 5: Transition matrix for farm size categories \n\n\n\nFarm size \ncategories 2017 \n\n\n\n2018 \n\n\n\nS1 1-2 S2 2.1-3 S3 3.1-4 S4 4.1-5 \nTotal \n2018 \n\n\n\nS1 1-2 53 6 0 0 59 \nS2 2.1-3 4 44 4 0 52 \nS3 3.1-4 0 2 32 2 36 \nS4 4.1-5 0 0 1 2 3 \n\n\n\nTotal 2018 57 52 37 4 150 \n\n\n\nTable 6: Transition probability matrix for farm size \nFarm size \n\n\n\ncategories 2017 \n2018 \n\n\n\nS1 1-2 S2 2.1-3 S3 3.1-4 S4 4.1-5 \nS1 1-2 0.90 0.10 0.00 0.00 \n\n\n\nS2 2.1-3 0.07 0.85 0.07 0.00 \nS3 3.1-4 0.00 0.06 0.89 0.06 \nS4 4.1-5 0.00 0.00 0.33 0.67 \n\n\n\n4.4 Equilibrium values, actual and projected pattern of changes in \n\n\n\nfarm size of bio-fortified cassava farmers  \n\n\n\nThe result of the actual and projected farm size for bio-fortified cassava \n\n\n\nfarmers were presented in Table 7. The projection of the structure in \n\n\n\nwhich the farm size of the bio-fortified cassava farmers would attain \n\n\n\nassuming the trend observed on the field during 2017 and 2018 continues \n\n\n\nover time, implies that equilibrium will be attained in year 2026. In \n\n\n\ncomparing the proportion of bio-fortified cassava farmers in different \n\n\n\nfarm size in initial year with equilibrium year, the proportion of farmers \n\n\n\nin farm size 1-2ha and 2.1-3ha will decline from 0.09 and 0.70 to 0.06 and \n\n\n\n0.30 respectively. Considering the proportion of bio-fortified cassava \n\n\n\nfarmers in the farm size group of 3.1-4ha and 4.1-5ha, it would grow from \n\n\n\n0.14 and 0.07 to 0.38 and 0.26 respectively. Furthermore, the mean farm \n\n\n\nsize of bio-fortified cassava farmers on Table 7 shows an upward trend \n\n\n\nover time. At equilibrium, the mean farm size was 2.85 compared to 2.20 \n\n\n\nat the initial year in 2017. The result of this study implied that bio-fortified \n\n\n\ncassava farmers were small scale farmers but they could increase their \n\n\n\nproduction in the future if certain measures were put in place\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 61-66 \n\n\n\nCite the Article: Kolapo Adetomiwa, Ojo Christianah Funmilayo, Lawal Adebayo Morenikeji, Abayomi Tajudeen Sarumi, Muhammed, Opeyemi Abdulmumin (2021). \nSensitivity Analysis And Future Farm Size Projection Of Bio -Fortified Cassava Production In Oyo State, Nigeria.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 61-66.\n\n\n\nTable 7: Actual and projected structure of farm size among bio-fortified cassava farmers \nFarm Size 2017 \n\n\n\n* \n2018 \n\n\n\n** \n2019 2020 2021 2022 2023 2024 2025 2026 \n\n\n\n*** \n2027 \n\n\n\n1-2 0.09 0.08 0.08 0.07 0.07 0.07 0.07 0.06 0.06 0.06 0.06 \n2.1-3 0.70 0.65 0.59 0.51 0.48 0.45 0.42 0.33 0.30 0.30 0.30 \n3.1-4 0.14 0.19 0.24 0.33 0.35 0.36 0.37 0.37 0.38 0.38 0.38 \n4.1-5 0.07  0.08  0.09  0.09  0.10 0.12  0.14  0.24  0.26  0.26  0.26  \nTotal 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 \nMean 2.20 2.25 2.30 2.40 2.45 2.55 2.65 2.70 2.75 2.85 2.85 \n\n\n\n*Actual year \n\n\n\n**Starting (initial) state probability vector \n\n\n\n***Equilibrium probability vector \n\n\n\nTable 8: Average costs andff return to bio-fortified cassava \nproduction per season \n\n\n\nVariables Amount (\u20a6) % of total costs \n\n\n\nA. Total revenue 284159.27 \n\n\n\nVariable cost \n\n\n\nLand preparation 42786.32 48.93 \n\n\n\nPlanting material 8620 9.85 \n\n\n\nFertilizer 1650 \n\n\n\nHerbicides 3400 \n\n\n\nLabor cost 14370 16.43 \n\n\n\nB. Total Variable Costs (TVC) 70826.32 81.00 \n\n\n\nFixed cost \n\n\n\nRent on land 14850 16.97 \n\n\n\nDepreciation on sprayer 843 \n\n\n\nDepreciation on wheelbarrow 929 \n\n\n\nC. Total fixed costs (TFC) 16622 19.00 \n\n\n\nD. Total costs (B+C) 87448.32 \n\n\n\nE. Gross margin (A-B) 213332.95 \n\n\n\nF. Net Farm Income (A-D) 196710.95 \n\n\n\nReturn on Investment (ROI) 2.25 \n\n\n\nBenefit Cost Ratio 3.25 \n\n\n\n4.5 Costs and returns to bio-fortified cassava production \n\n\n\nIn other to ascertain the profitability of bio-fortified cassava production, \n\n\n\nthe average gross margin, net returns, rate of returns and benefit cost ratio \n\n\n\nof the bio-fortified cassava farmers were calculated. The input used, costs, \n\n\n\noutput data generated from the bio-fortified cassava farmers were used to \n\n\n\ncompute the gross margin and net returns to bio-fortified cassava \n\n\n\nproduction. The average costs and returns for the bio-fortified cassava \n\n\n\nproduction were presented in Table 8. The result revealed the revenue \n\n\n\ngenerated for one production season was \u20a6284159.27. From Table 8, the \n\n\n\ncost of land preparation (\u20a642786.32) on individual cost accounted for a \n\n\n\nlarge proportion (48.93%) of the total costs with the total variable costs \n\n\n\n(\u20a670826.32) accounting for the largest proportion (81%) of the total costs. \n\n\n\nRent on land (\u20a614850) accounted for a significant proportion 16.97% of \n\n\n\nthe fixed cost with the total fixed costs accounting for just 19%. \n\n\n\nThe negligible small proportion of the fixed costs shows the crude method \n\n\n\nof agricultural small-scale practices. The average net return (net farm \n\n\n\nincome) from the production of bio-fortified cassava in Table 8 was \n\n\n\n\u20a6196710.95. This implies that the production of bio-fortified cassava is a \n\n\n\nprofitable enterprise. The return on investment, indicated that for every \n\n\n\none naira invested in bio-fortified cassava production, the farmer gains \n\n\n\n\u20a62.25. The implication is that bio-fortified cassava production is profitable. \n\n\n\nThe result agrees with Ogunleye et al. (2019) in the Profitability of \n\n\n\ninvestment and farm level efficiency among groups of Vitamin A cassava \n\n\n\nfarmers in Oyo State Nigeria who found out that bio-fortified cassava \n\n\n\nproduction is a profitable business enterprise. The benefit cost ratio of \n\n\n\n3.25 shows that for every \u20a63.00 return to bio-fortified cassava production, \n\n\n\n25k is been spent on the cost of producing the bio-fortified cassava. \n\n\n\n4.6 Rate of Return on Investment  \n\n\n\nRORI = 100TR TCx\n\n\n\nTC\n\n\n\n\u2212  \n\n\n\n       = 284159.27- 87448.32/87448.32 x 100 \n\n\n\nRORI= 224.95% \n\n\n\n4.7 Sensitivity Analysis and Rate of Return on Investment  \n\n\n\nThe rate of returns on investment of bio-fortified cassava production \n\n\n\nshowed a high return in the enterprise (224.95%). The rate of returns on \n\n\n\ninvestment of bio-fortified cassava production was subjected to a \n\n\n\nsensitivity analysis to establish the point at which profitability might not \n\n\n\nbe certain. With respect to input, increasing the costs from +10 to +30% \n\n\n\ndid not significantly impact the rate of return on investment (Table 9). \n\n\n\nFurthermore, the result revealed that at 35% increase in cost of \n\n\n\nproduction, the rate of return on investment dropped to 140.70%. This \n\n\n\nimplies that with outmost concern, bio-fortified farmers should try as \n\n\n\nmuch as possible to ensure that they cut the cost of production to a \n\n\n\nmaximum of 30% hence, the investment will not be viable and might not \n\n\n\nbe recommended for investment especially if the investment will be \n\n\n\nfinance by bank loan. At the calculated revenue, the rate of return was \n\n\n\n224.95% but when the revenue was reduced by 10%, rate of return \n\n\n\ndropped to 192.45%, at 30% drop in revenue, the rate of return dropped \n\n\n\nto 127.46%. Therefore, for the enterprise to remain profitable, the \n\n\n\ndecrease in revenue should not go beyond 30%. \n\n\n\nTable 9: Sensitivity analysis of Rate of Return on Investment of bio-\nfortified cassava (Increasing Cost) \n\n\n\nVariable \nRORI \n\n\n\nCost Return RORI Remark \n\n\n\nActual \ncost \n\n\n\n87448.32 284159.27 224.95% Actual \nestimate \n\n\n\n+10% cost 96193.15 284159.27 195.40% Recommended \n+15% cost 100565.56 284159.27 182.56% Recommended \n+20% cost 104937.98 284159.27 170.78% Recommended \n+25% cost 109310.40 284159.27 159.95% Recommended \n+30% cost 113682.81 284159.27 149.95% Recommended \n+35% cost 118055.23 284159.27 140.70% Not \n\n\n\nRecommended \n\n\n\nTable 10: Sensitivity analysis of Rate of Return on Investment of bio-\nfortified cassava (Decreasing revenue) \n\n\n\nVariable \nRORI \n\n\n\nCost Return RORI Remark \n\n\n\nActual \ncost \n\n\n\n87448.32 284159.27 224.95% Actual \nestimate \n\n\n\n-10% cost 87448.32 255743.34 192.45% Recommended \n-15% cost 87448.32 241535.37 176.20% Recommended \n-20% cost 87448.32 227327.41 159.95% Recommended \n-25% cost 87448.32 213119.45 143.70% Recommended \n-30% cost 87448.32 198911.48 127.46% Not \n\n\n\nRecommended \n-35% cost 87448.32 184703.52 111.21% Not \n\n\n\nRecommended \n\n\n\n5. CONCLUSIONS \n\n\n\nThe study examined the profitability of bio-fortified cassava production \n\n\n\nand projected the future farm size of the production of bio-fortified \n\n\n\ncassava in Oyo State, Nigeria. The study concluded that production of bio-\n\n\n\nfortified cassava is a profitable enterprise in Oyo State Nigeria. Bio-\n\n\n\nfortified cassava production can adjust positively to incidentals such as \n\n\n\ngeneral price inflation and price changes for inputs and outputs that may \n\n\n\noccur in time. Our study concluded that bio-fortified cassava farmers were \n\n\n\nsmall scale farmers but they could increase their production in the future \n\n\n\nif certain measures were put in place. In order to adequately achieve these \n\n\n\ngoals, more improved varieties of bio-fortified cassava should be provided, \n\n\n\nand also, infrastructures should be put in place to help boost farmers \n\n\n\nmoral in their cause of production.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 61-66 \n\n\n\nCite the Article: Kolapo Adetomiwa, Ojo Christianah Funmilayo, Lawal Adebayo Morenikeji, Abayomi Tajudeen Sarumi, Muhammed, Opeyemi Abdulmumin (2021). \nSensitivity Analysis And Future Farm Size Projection Of Bio -Fortified Cassava Production In Oyo State, Nigeria.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 61-66.\n\n\n\nREFERENCES \n\n\n\nAlimi, T., Baruwa, O.I., Bifarin, J.O., Abogan, P.O., Ajewole, O.C., 2007. \nApplication of Markov Chain in Forecasting Plantain Farm Size in the \nRain Forest Zone of Osun State, Nigeria. Journal of Agricultural and \nRural Development, 2 (1), Pp. 69-83. \n\n\n\nAmina, F.O. Akhigbe-Ahonkai, C.E., 2017. Profitability and Technical \nEfficiency of Grasscutter production in Nigeria: The case of Ekiti State. \nAgricultural Tropica et Subtropica, 50 (1), Pp. 27\u201335. \n\n\n\nAnders, H., 2016.  Economies of Size and Scale in Agriculture: an \ninterpretive review of empirical measurement. Review of agricultural \neconomics, 3 (1), Pp. 227-238. \n\n\n\nBaruwa, I.O., Masuku, M.B., Alimi, T., 2011. Economic Analysis of Plantain \nProduction in Derived Savannah Zone of Osun State. Nigeria. Asian \nJournal of Agricultural Sciences, 3 (5), Pp. 401-407. \n\n\n\nDachin, Anca, Ursu and Ana, 2016. Sensitivity of Gross Margin for Field \nCrops, in Agrarian Economy and Rural Development - Realities and \nPerspectives for Romania. The Research Institute for Agricultural \nEconomy and Rural Development (ICEADR). Bucharest, Pp. 142. \nAvailable at: http://hdl.handle.net/10419/163365 \n\n\n\nFAO, 2003. Food and Agriculture Organization (FAO) \nwebsite.www.fao.org \n\n\n\nHillocks, R., 2002. \u201cCassava in Africa\u201d. CABI Publishing. \n\n\n\nHoweler, R.H., 2002. Cassava mineral nutrition and fertilization. In: \nCassava: Biology, Production and Utilization. (Edited by Hillocks R.J, \nThresh J.M, and Belloti A.), CABI Publishing, UK. Pp. 115-147. \n\n\n\nIITA, 2007. A strategy for industrialization of cassava in Africa: \nProceedings of a small Group meeting, 14-18 November 2005, Ibadan, \nNigeria. \n\n\n\nIlona, P., Bouis, H.E., Palenberg, M., Moursi, M., Oparinde, A., 2017. Vitamin \nA Cassava in Nigeria: Crop Development and Delivery. Afr. J. Food Agric. \nNutr. Dev., 17 (2), Pp. 12000\u201312025. \n\n\n\nKnipscheer, H., Ezedinma, C., Kormawa, P., Asumugha, G., Makinde, K., \nOkechukwu, R., and Dixon, A., 2007. Opportunities in the Industrial \nCassava Market in Nigeria. Ibadan, Nigeria: International Institute of \nTropical Agriculture. \n\n\n\nKolapo, A., Abimbola, E.I., 2020. Consumers\u2019 Preferences and Willingness \nto Pay for BioFortified Vitamin-A Garri in South Western, Nigeria: A \nConjoint Analysis and Double-Hurdle Model Estimation. World \nResearch Journal of Agricultural Sciences, 7 (2), Pp. 221-229. \n\n\n\nKolapo, A., Fakokunde, A.O., 2020. Economic Efficiency of Bio-fortified \n\n\n\ncassava processing in South Western, Nigeria. International journal of \n\n\n\nagriculture, environment and bioresearch, 5 (3), Pp. 191-203. \n\n\n\nKolapo, A., Olayinka, J.Y., Muhammed, O.A., 2020. Market Participation \n\n\n\nand Food Security Status of Bio-Fortified Cassava Processors in South \n\n\n\nWestern Nigeria. International Journal of Sustainable Agricultural \n\n\n\nResearch, 7 (3), Pp. 174-184. \n\n\n\nKolapo, A., Ologundudu, O.M., Adekunle, I.A., 2020. Gender, Membership \nin Farmers\u2019 Association and Adoption of Biofortification In Nigeria: The \nCase of Bio-Fortified Cassava. SSRG International Journal of Agriculture \n& Environmental Science (SSRG-IJAES), 7 (3), Pp. 38-45. \n\n\n\nMeridian Institute. 2013. Cassava Value Chain Overview. Innovations for \nAgricultural Value Chains in Africa: Applying Science and Technology to \n\n\n\nEnhance Cassava, Dairy, and Maize Value Chains. Accessed on \n10/02/2013. http://www.merid.org/en/value-chain-\ninnovations/Examples.aspx \n\n\n\nMcNulty, E., Oparinde, A., 2015. Cassava Value Chain in Nigeria: A Review \nof the Literature to Inform the Integration of Vitamin A Cassava. \nHarvestPlus research for action. \n\n\n\nMtunguja, M.K., Laswai, H.S., Kanju, E., Ndunguru, J., Muzanila, Y.C., 2016b. \nEffect ofgenotype and genotype by environment interaction on total \ncyanide content, fresh root, and starch yield in farmer-preferred \ncassava landraces in Tanzania. Food Science and Nutrition. DOI: \n10.1002/fsn3.345.  \n\n\n\nMtunguja, M.K., Beckles, D.M., Laswai, H.S., Ndunguru, J.C., Sinha, N.J., \n2019. Opportunities to Commercialize Cassava Production for Poverty \nAlleviation and Improved Food Security in Tanzania. African Journal of \nFood Agriculture, Nutrition and Development, 19 (1), Pp. 13928-13946. \nDOI: 10.18697/ajfand.84.BLFB1037 \n\n\n\nNPC (National Population Commission). 200. Population and Housing \nCensus. Population Local Government Area and Sex. Retrieved from: \nhttp://population.gov.ng/ coreactivities/surveys/dataset/2006-phc-\npriority-tables/  \n\n\n\nNweke, F.I., 2003. New Challenges in the Cassava Transformation in \nNigeria and Ghana. Conference Paper No. 8. Paper presented at the \nINVENT, IFPRI, NEPAD, CTA conference. Successes in African \nAgriculture, December 1 and 3, Pretoria. \n\n\n\nOgunleye, A.S., Bamire, A.S., Awolola, O., 2019. Profitability of Investment \nand Farm Level Efficiency Among Groups of Vitamin A Cassava Farmers \nin Oyo State Nigeria. American Journal of Environmental and Resource \nEconomics, 8(1),14-19. doi: 10.11648/j.eco.20190801.13 \n\n\n\nOlatidoye, M.S., Kehinde, A.D., Alimi, T., 2018. Forecasting the Future Farm \nSize in Grass Cutter Production in Osun State, Nigeria: A Markov Chain \nApproach. Asian Journal of Research in Agriculture and Forestry, 2 (3), \nPp. 1-10. \n\n\n\nOparinde, A., Abdoulaye, T., Mignouna, D.B., Bamire, A.S., 2017. Will \nfarmers intend to cultivate Provitamin A genetically modified (GM) \ncassava in Nigeria? Evidence from a k-means segmentation analysis of \nbeliefs and attitudes. PLoS ONE, 12 (7), Pp. e0179427.  \n\n\n\nOtekunrin, O.A., Sawicka, B., 2019. Cassava, a 21st century staple crop: \nHow can Nigeria harness its Enormous Trade potentials?. Acta \nScientific Agriculture, 3 (8), Pp. 194-202. \n\n\n\nPannell, D.J., 1997. Sensitivity analysis of normative economic models: \nTheoretical framework and Practical Strategies. Journal of Agricultural \nEconomics, 16, Pp. 139-152. \n\n\n\nPhillips, T.P., Taylor, D.S., Sanni, L., Akoroda, M.O., 2004. A cassava \nindustrial revolution in Nigeria: The Potential for a new industrial \ncrop\u201d. International Institute of Tropical Agriculture, Ibadan, Nigeria\u201d. \nInternational Fund for Agricultural Development, Food and Agriculture \nOrganization of the United Nations, Rome, Italy. \n\n\n\nSaltzman, A., Birol, E., Bouis, H.E., Boy, E., De Moura, F.F., Islam, Y., Pfeiffer, \nW.H., 2014. Biofortification: Progress towards a more nourishing \nfuture. Bread and Brain, Education and Poverty. Pontifical Academy of \nSciences, Vatican City. Scripta varia 125. Retrieved from: www.pas. \nva/content/dam/accademia/pdf/sv125/sv125-bouis.pdf  \n\n\n\nSanni, L.O., Onadipe, O.O., Ilona, P., Mussagy, M.D., Abass, A., Dixon, A.G.O., \n2009. Successes and Challenges of Cassava Enterprises in West Africa: \nA Case Study of Nigeria, Benin and Sierra Leone. Ibadan, Nigeria: \nInternational Institute of Tropical Agriculture.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 36-39 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.36.39 \n\n\n\nCite the Article: Asma Khatun, S. Sikder and J.C. Joardar (2020). Effect Of Co-Compost Made From Cattle Manure And Sawdust On The Growth And Yield Of Okra \n(Abelmoschus Esculentus L.). Malaysian Journal of Sustainable Agriculture, 4(1): 36-39. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2020.36.39\n\n\n\nEFFECT OF CO-COMPOST MADE FROM CATTLE MANURE AND SAWDUST ON THE \nGROWTH AND YIELD OF OKRA (ABELMOSCHUS ESCULENTUS L.) \n\n\n\nAsma Khatun, S. Sikder and J.C. Joardar* \n\n\n\nSoil, Water and Environment Discipline, Khulna University, Khulna-9208, Bangladesh \n\n\n\n*Corresponding Author E-mail: jcjoardar@yahoo.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 18 December 2019 \nAccepted 23 January 2020 \nAvailable online 12 February 2020\n\n\n\nCo-composting is an effective and environment friendly method of solid waste management to make valuable \n\n\n\norganic soil amendment which helps to maintain soil fertility in a sustainable way. An experiment was \n\n\n\nconducted to make co-compost using cattle manure (CM) and sawdust (SD) in different ratios (w/w) for the \n\n\n\ncorrect mixing proportion of raw materials to investigate the nutrient status of co-compost, and to evaluate \n\n\n\nthe potential value of co-compost after incorporation into soil to form a nutrient rich growth media for \n\n\n\nAbelmoschus esculentus L. The experiment was laid out in a Completely Randomized Design (CRD) with five \n\n\n\ntreatments and three replications comprising of only SD, only CM, sawdust-cattle manure mixture in the ratio \n\n\n\nof 1:1 (SD: CM=1:1), 1:2 (SD: CM= 1:2) and 2:1 (SD: CM= 2:1) by weight. Compost samples were collected for \n\n\n\nnutrient analysis after 75 days of composting. The highest value of total N, P and S were obtained in CM \n\n\n\ncompost. Organic carbon (OC) and C:N ratio were found higher in SD compost. Higher growth and yield of \n\n\n\nokra were recorded under SD:CM= 1:2 treatment. On the basis of the experimental results, combined \n\n\n\napplication of SD and CM at 1:2 ratio was the right mixing proportion. So, the organic fertilizer mixed with \n\n\n\nSD and CM at 1:2 ratio would be an efficient soil amendment that would improve soil quality, promote plant \n\n\n\ngrowth and increase yield. \n\n\n\nKEYWORDS \n\n\n\nCo-composting, cattle manure, sawdust, nutrient, yield, sustainable agriculture. \n\n\n\n1. INTRODUCTION \n\n\n\nThe promotion of sustainable agricultural practices has become the \nprerequisite to control rapid deterioration of soil fertility and productivity \nto meet the growing food demand for increasing population. The \nincreasing use of inorganic fertilizer is the major obstacle that decreases \nsoil quality day by day. To restore soil condition and productivity, the use \nof organic fertilizers is one of the best management practices (Gruhn et al., \n2000; Usman et al., 2015; Ali et al., 2017; Joardar et al., 2018). In that case, \norganic wastes could be considered as perfect sources to produce organic \nfertilizer or soil amendment which helps to convert the infertile land to \nnutrient rich growth medium. Side by side, this management strategy of \norganic waste helps to reduce the environmental problems caused by \ntransportation and unscientific disposal of huge amount of waste \nproducing every day (Burton et al., 2003; Zhang et al., 2003). \n\n\n\nAmong different types of management approaches of organic waste, \ncomposting of organic waste- has recently dragged attention because it is \nan economically feasible and environmentally sound technology to \nproduce humus like organic product through microbial decomposition of \norganic waste under aerobic condition and produce a good quality organic \nsoil conditioner (Kshmanian et al., 2000; Fernandez et al., 2014). When \nmore than one feedstock is used in the same vessel to produce particular \ncompost is referred to as a special method called co-composting. The most \n\n\n\npopular combination of waste for co-composting is mixing animal waste \n(cow dung, poultry litter, pig manure, fecal sludge etc.) with agro based \nwastes (rice straw, sawdust, wheat straw, water hyacinth, food waste etc.) \n(Ogunwande et al., 2008; Anwar et al., 2015; Neves et al., 2009). \n\n\n\nCattle manure and sawdust are very common and mostly available organic \nwastes. Every day a huge amount of CM is producing worldwide. Because \nof its high nutritional value it has been used as organic fertilizer as one of \nthe best management approaches (Abou El-Magd et al., 2006; Ogundare et \nal., 2015). On the other hand, SD is a waste or byproduct of sawmill. Due \nto increasing use of wood and wooden product, a huge amount of SD is also \nproducing daily. Recently, scientists have given attention to use this waste \nas soil amendment in agriculture as it contains very high carbon content \n(more or less 50%) (Garner, 2014; Parry, 2007; Dumitraescu et al., 2009). \nThough the other nutrient contents (especially N) of SD are low but when \nit is added with nitrogen rich organic waste with appropriate proportion, \nit becomes an effective compost fertilizer (Okalebo et al., 2002). Carbon-\nnitrogen (C:N) ratio of wastes is one of the important factors that affects \ncompost characteristics (Ali et al., 2015; Igbokwe et al., 2015; Oluchukwu \net al., 2018). In case of co-composting, mixing CM of low C:N ratio with SD \nof high C:N ratio can be a perfect combination to produce a good quality \ncompost for improving soil structure, texture, C:N ratio, porosity, \naggregate stability, biochemical properties as well as plant productivity \n(Bernal et al., 2009; Dimambro et al., 2016; Ashiono et al., 2017). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 36-39 \n\n\n\nCite the Article: Asma Khatun, S. Sikder and J.C. Joardar (2020). Effect Of Co-Compost Made From Cattle Manure And Sawdust On The Growth And Yield Of Okra \n(Abelmoschus Esculentus L.). Malaysian Journal of Sustainable Agriculture, 4(1): 36-39. \n\n\n\nMoreover, SD being a stable organic product, it decompose slowly which \nhelps to reduce greenhouse gas emission and release nutrient into soil for \nlong time. \n\n\n\nOkra (Abelmoschus esculentus L.) is a widely known and economically \nimportant vegetable that is grown commercially in different parts of the \nworld (Arapitas, 2008; Saifullah et al., 2009). Almost 42 thousand metric \ntons of okra was produced in 2009-10 from 10 thousand hectares of land \nin Bangladesh (BBS, 2011). Okra is a rich source of different types of \nnutrients like- fiber, carbohydrate, vitamin A, B6, C, proteins, folic acid and \nminerals like Ca, Mg and Fe (Yadav et al., 2001; Dilruba et al., 2009). It was \nhypothesized that, co-compost will play an active role in the growth and \nproduction of okra than the sole application of CM and SD compost. The \neffective and perfect proportion of CM and SD for co-composting will also \nbe identified. Co-compost would be considered as a good source of organic \nfertilizer which will play a crucial role to reduce the use of inorganic \nfertilizer and will help to promote sustainable agriculture. Side by side, co-\ncomposting will be the best management approach of different types of \norganic waste. The objective of the present study was to prepare co-\ncompost from CM and SD, and to find out the best mixing combination that \nwould be a good soil organic fertilizer for sustainable agriculture. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Study area \n\n\n\nPot experiment was carried out in the agricultural field laboratory of Soil, \nWater and Environment Discipline, Khulna University, Bangladesh and all \nthe analysis were conducted in the laboratory of the discipline. \n\n\n\n2.2 Sample collection \n\n\n\nSoil sample for this experiment was collected from the agricultural field \ninside the Khulna University campus. Cattle manure (CM) and sawdust \n(SD) were collected from local dairy farm and sawmill, respectively, \nsituated nearby Khulna University campus. \n\n\n\n2.3 Sample preparation \n\n\n\n2.3.1 Soil \n\n\n\nThe collected soil sample was processed in the field laboratory. At first, \nsoil sample was air dried by spreading on the floor then the larger \naggregates were broken down by using wooden hammer and the soil \nsample was passed through a 2-mm sieve for plant growth. \n\n\n\n2.3.2 Co-compost \n\n\n\nFor preparing co-compost, CM and SD were weighed and mixed in three \ndifferent ratios as follows: SD = Sawdust (3.0 kg); CM= Cattle manure (3.0 \nkg); SD:CM (1:1) = Sawdust (1.5 kg) + Cattle manure (1.5 kg); SD:CM (1:2) \n= Sawdust (1.0 kg) + Cattle manure  (2.0 kg); SD:CM (2:1) = Saw dust (2.0 \nkg) + Cattle manure (1.0 kg). Co-composts were prepared by following the \nmethod described in Deepasundari and Mariappan (Deepasundari et al., \n2015). The feed stocks were mixed properly by adding proper amount of \nwater and kept into pots covered by plastic papers for decomposition. The \npots were opened for heat release and aeration after 48 hours later. After \nthat, the mixtures were covered and left for two months for \ndecomposition. Occasional opening was done for heat release and proper \naeration. Next, after 75 days matured composts were collected, processed \nand stored for further chemical analysis and experiment. \n\n\n\n2.4 Experiment set-up \n\n\n\n2.4.1 Design and layout \n\n\n\nIn the experiment, there were five treatments along with control (no \ncompost) and three replications for each treatment, total eighteen pots of \nequal volume (area=232cm2, height=25.4cm) were used. The pots were \nfilled with 2.5 kg of preprocessed soil. The experiment was laid out in \nComplete Randomized Design (CRD). Compost was applied in each pot at \nthe rate of 10 t ha-1 except control. \n\n\n\n2.4.2 Experimental plant and seed sowing \n\n\n\nOkra, a very popular vegetable plant, was taken as experimental plant \nbecause of its high yield and shorter fruiting time (Mondal et al., 2014). \nOnly the healthy, plump and large sized seeds were collected for sowing. \nFour seeds were sown in each pot. \n\n\n\n2.4.3 Intercultural activities \n\n\n\nTap water was applied for the growth of okra when needed. After seed \nsowing, proper care was taken to control pest infestation, other damages \nand to raise healthy and strong seedling.  Fungicide was applied as \npesticide. 10 days later of seed sowing, thinning of seedling was done by \nleaving one well developed seedling in each pot. \n\n\n\n2.4.4 Harvesting and vegetative growth parameters \n\n\n\nAfter 40 days from seed germination, matured okra was harvested and leaf \nnumber of each plant, plant height (cm), number of fruits per plant, fresh \nweight per fruit (gm) were recorded after harvest. Plant height (cm) was \nmeasured with the help of a meter scale from the ground level to the tip of \nthe upper most leaf. Number of fruit was counted and recorded. Weight \n(g) of fruit was weighed by using electric balance. \n\n\n\n2.5 Chemical analysis of compost and soil \n\n\n\nThe collected compost was sun dried, grind in a grinder and mixed \nthoroughly to make homogeneous sample prior to chemical analysis in the \nlaboratory. The pH, EC, OC, Total N, P, K, and S of both compost and soil \nsamples were determined by using the methods mentioned in Imamul Huq \nand Alam (Imamul Huq et al., 2005). \n\n\n\n2.6 Statistical analysis \n\n\n\nCollected data from the experiment were statistically analyzed by using \nANOVA technique (Minitab 16.0). Comparison and significant variation \namong the data were also calculated by using it. Graphs were prepared, \nand other data calculation was done by using Microsoft Excel 2010. \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1 Properties of soil \n\n\n\nSome basic soil properties are presented in the Table 1. The soil was clay \nloam in texture, slightly alkaline and non-saline in nature (Soil Survey, \n1993). \n\n\n\nTable 1: Some basic properties of soil \nProperties Results \n\n\n\nSoil Organic carbon (SOC) 1.71% \n\n\n\npH 8.17 \nElectric conductivity (EC) 0.074 dSm-1 \n\n\n\nNitrogen (N) 0.34% \nPhosphorous (P) 0.13% \n\n\n\nPotassium (K) 0.43% \nSulfur (S) 0.72% \n\n\n\nSodium (Na) 3.64% \n\n\n\n3.2 Properties of prepared co-compost \n\n\n\nThe analytical results of different properties of co-composts are presented \nin Table 2. The pH and EC values were significantly higher in SD:CM(1:1) \nand SD:CM(1:2) combinations, respectively. The maximum OC (15.40%) \nwas obtained in SD, since SD is a C rich material [14\u201315] and minimum \n(13.06%) in CM. The OC content differed significantly (p<0.05) among \ndifferent treatment combinations and followed the order: SD > SD:CM(2:1) \n> SD:CM(1:1) > SD:CM(1:2) > CM. Nitrogen content (2.91%) was found \nsignificantly higher in CM since CM is considered as a good source of N \n[12\u201313] and the lowest obtained in SD (1.26%) by following the order: CM \n> SD:CM(1:2) > SD:CM(1:1) > SD:CM(2:1) > SD. Significantly higher \namount of P and S were found in CM and K (0.49%) was found highest in \nSD:CM(2:1). In case of C:N ratio, as SD and CM are considered as great \nsource of C and N, respectively, so, significantly higher C:N ratio (13.33:1) \nwas obtained in SD and the lowest value (5.19:1) was in CM. Similar results \nwere also revealed by Oluchukwu et al., [20]. The C:N ratio of other \ntreatments were in the order of SD:CM(2:1) > SD:CM(1:1) > SD:CM(1:2) \nwhich perfectly followed the treatment combination ratio. In this \nexperiment, OC, N, P, K values of composts are in the range of constituents \nof matured compost given by Gotaas (Gotaas, 1956). \n\n\n\n3.3 Effects of co-compost on plant growth \n\n\n\n3.3.1 Visual observation of okra plant \n\n\n\nSeed germination was observed four days after seed sowing. First \ngermination was observed in the pot treated with CM compost. After thirty \nthree days, the plants treated with SD:CM(1:2) and only CM compost \nshowed first flowering and fruiting also. After 40 days from seed sowing, \nthe highest vegetative growth and yield were observed under SD:CM(1:2) \ncompost treated plants.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 36-39 \n\n\n\nCite the Article: Asma Khatun, S. Sikder and J.C. Joardar (2020). Effect Of Co-Compost Made From Cattle Manure And Sawdust On The Growth And Yield Of Okra \n(Abelmoschus Esculentus L.). Malaysian Journal of Sustainable Agriculture, 4(1): 36-39. \n\n\n\nTable 2: Some basic properties of co-compost \n\n\n\nProperties \nTreatment \n\n\n\nSD CM SD:CM(1:1) SD:CM(1:2) SD:CM(2:1) \npH 6.75b 6.49c 7.13a 6.85b 6.80b \nEC 1.006c 1.173b 1.004c 1.426a 1.258b \nMoisture content (%) 33.5\u00b10.03 11.6\u00b10.02 13.5\u00b10.03 12.6\u00b10.02 16.1\u00b10.05 \n\n\n\nOrganic carbon (%) 15.40\u00b10.05a 13.06\u00b10.08d 15.13\u00b10.22b 14.94\u00b10.10c 15.21\u00b10.08a \n\n\n\nTotal Nitrogen (%) 1.26\u00b10.05c 2.91\u00b10.18a 1.71\u00b10.13b 1.88\u00b10.10b 1.66\u00b10.20b \nPhosphorous (%) 0.41\u00b10.013c 1.1\u00b1 0.017a 0.86\u00b10.013b 0.97\u00b10.034a 0.81\u00b10.112b \nPotassium (%) 0.45\u00b10.02bc 0.34\u00b10.02d 0.47\u00b10.04ab 0.42\u00b10.02c 0.49\u00b10.03a \nSulfur (%) 1.28\u00b10.02c 2.72\u00b10.25a 2.05\u00b10.18b 2.37\u00b10.25bc 2.29\u00b10.27b \nSodium (%) 2.4\u00b10.28a 2.5\u00b10.00a 2.6\u00b10.14a 2.76\u00b10.42a 2.84\u00b10.14a \nC:N ratio 13.33\u00b10.62a 5.19\u00b10.99c 8.87\u00b10.71b 7.96\u00b10.44b 9.24\u00b11.10b \n\n\n\nValues are the average \u00b1 standard deviation. Different letters above the values are significantly difference among the means \n\n\n\n3.3 Effects of co-compost on plant growth \n\n\n\n3.3.1 Visual observation of okra plant \n\n\n\nSeed germination was observed four days after seed sowing. First \ngermination was observed in the pot treated with CM compost. After thirty \nthree days, the plants treated with SD:CM(1:2) and only CM compost \nshowed first flowering and fruiting also. After 40 days from seed sowing, \nthe highest vegetative growth and yield were observed under SD:CM(1:2) \ncompost treated plants. \n\n\n\n3.3.2 Plant height \n\n\n\nThe average plant heights under different treatments at 40 days are shown \nin Table 3. Plant height differs with types of treatment and treatment \ncombinations. The maximum plant height (44\u00b11.53 cm) was found in \nSD:CM(1:2) where lowest (33.3\u00b11.53cm) was observed in control and \nfollowed the order as SD:CM(1:2) > CM> SD:CM(1:1)> SD:CM(2:1) > SD > \ncontrol. Statistical analysis revealed that, CM alone and combination \nSD:CM(1:2) showed significantly higher (p< 0.01) plant height. \n\n\n\nTable 3: Effect of composts on different plant attributes \n\n\n\nPlant attributes \n\n\n\nTreatment Plant height \n(cm) \n\n\n\nNo of \nleaves/plant \n\n\n\nNo of \nfruits/plant \n\n\n\nFresh weight \n(g/fruit) \n\n\n\nControl 33.3\u00b11.53d 6.3\u00b10.58c 2.33\u00b10.58d 5.6\u00b10.35c \n\n\n\nSD 33.4\u00b10.80d 7.3\u00b10.58c 3.33\u00b10.58cd 5.4\u00b10.77c \n\n\n\nCM 42.0\u00b1 1.0a 11.0\u00b11.0a 5.0\u00b10.0a 9.0\u00b10.90a \n\n\n\nSD:CM(1:1) 39.2\u00b12.43b 10.7\u00b10.58a 3.7\u00b10.58b 7.0\u00b10.55b \n\n\n\nSD:CM(1:2) 44.0\u00b11.53a 12.0\u00b11.0a 5.67\u00b10.58a 10.0\u00b11.0a \n\n\n\nSD:CM(2:1) 36.3\u00b10.58c 9.3\u00b11.15b 3.67\u00b10.58b 6.61\u00b10.98b \n\n\n\nValues are the average \u00b1 standard deviation. Different letters indicate the \nsignificant (p=0.05) differences \n\n\n\n3.3.3 Number of leaves per plant \n\n\n\nThe application of compost at different ratios produced different number \nof leaves per plant. The number of leaves at 40 days are presented in Table \n3 and followed the order: SD:CM(1:2) > CM > SD:CM(1:1) > SD:CM(2:1) > \nSD. The maximum number of leaves (12.0\u00b11 plant-1) was found in 1:2 \nratios of SD and CM mixture where minimum (6.3\u00b10.58 plant-1) was \nobserved in control. Analysis of variance (ANOVA) showed that there was \na significant difference (p<0.001) in total number of leaves per plant under \ndifferent treatments. CM alone and treatment combinations SD:CM(1:1) \nand SD:CM(1:2) were statistically similar but significantly higher than the \nrest of the treatments though SD:CM(1:2) treatment produced the \nmaximum number of leaves per plant than that of others. \n\n\n\n3.3.4 Number of fruits per plant \n\n\n\nThe application of compost had positive effects on the yield of okra as \ncompared to control (Table 3). The highest number of fruits per plant was \nobserved in SD:CM(1:2) treatment (5.67 plant-1), followed by CM (5.0 \nplant-1), SD:CM(1:1) (3.7 plant-1), SD:CM(2:1) (3.67 plant-1) and SD (3.33 \nplant-1). Statistical analysis showed that there was a significant difference \n(p<0.001) in total number of fruits under different treatments. In spite of \nhighest yield under SD:CM(1:2), statistically, CM and SD:CM(1:2) \ntreatments were similar and highly significant and differed with others. \n\n\n\n3.3.5 Fresh weight (FW) per fruit (g) \n\n\n\nThe average FW per fruit varied with different rate and composition of co-\n\n\n\ncompost. SD:CM(1:2) treatment (10.2\u00b11 fruit-1) produced the highest \nfresh weight per fruit and lowest in both control and SD (Table 3). FW per \nfruit was significantly higher and statistically similar (p>0.001) for plant \ntreated with SD:CM(1:2) and CM compost though SD:CM(1:2) produced \nhigher fresh weight (g) per fruit. \n\n\n\nFrom the experimental results, it was clear that, co-compost or combined \napplication of SD and CM showed better growth performance than the sole \napplication of SD and CM compost. The highest plant height, leaves per \nplant, fruits per plant and fruit FW were obtained from SD:CM(1:2) (Table \n3). These results are similar with the findings of Wang et al.,; Deepasundari \nand Mariappan (Wanh et al., 2004; Deepasundari et al., 2015). Another \nresearcher Ashiono et al., stated that, highest seedling growth of Blue gum \nwas found in the soil treated with co-compost of CM and SD at a ratio of \n1:1 (Ashiono et al., 2017). In spite of numerous positive results, Adekunle \ndid not find out any effective result after co-compost application, where he \nused poultry litter and sawdust on okra plant (Adekunle, 2013). Since, the \nhighest results obtained from SD:CM(1:2) combination than that of other \ntwo SD:CM(1:1) and SD:CM(2:1), that means, a right proportion of \nfeedstock is prerequisite and a very important factor for preparing perfect \nco-compost enriched with plant nutrients which is also observed by \nOluchukwu et al. (Oluchukwu et al., 2018). In case of sole application of SD \nand CM, the growth performance of okra under SD treated soil was very \nlow than under CM. The probable reason might be the nutrient content of \nSD is generally low (except C) and due to high C:N ratio it is slow to \ndecompose but CM is a very potential source of plant nutrients (especially \nN) that helps to increase plant growth and production (Okalebo et al.,\n2002; Ashiono et al., 2017). Moreover, CM is a very good source of \nnutrients for okra (Haque et al., 2015). \n\n\n\nSome other experimental results by  Ali et al., and Igbokwe et al., revealed \nthat, if a carbon rich material (SD) was mixed with nitrogen rich source \n(CM), then the produced compost is considered as more efficient for plant \ngrowth and the reason of effectiveness of co-compost lies on a very \nimportant factor called the C:N ratio (Ali et al., 2015; Igbokwe et al., 2015). \nIn our experiment, the C:N ratio of SD was found high and C:N ratio of CM \nwas comparatively low (Table 2). Both the conditions were unfavorable \nfor plant growth. But when SD and CM were mixed together at a particular \ncombination SD:CM(1:2), the optimum C:N ratio was obtained and highest \nplant growth was observed in that case. This is because, SD with high C:N \nratio act as bulking agent and promote decomposition process by \nsupplying sufficient C to microbes and on the other hand, N was delivered \nfrom low C:N ratio product (CM) to carbon rich product for increasing its \ndecomposition rate which help to maintain soil fertility and supply \nadequate amount of nutrients for plant growth and yield (Bernal et al., \n2019; Dimambro et al., 2016; Ashiono et al., 2017). \n\n\n\n4. CONCLUSION \n\n\n\nCo-compost was produced by mixing SD and CM at different rate to \nobserve the nutritional value, changes of nutrient characteristics among \ndifferent treatments and the effects of co-composts on the growth \nperformance of okra. The results showed that the quality of compost \nincreased when SD and CM was mixed at 1:2 ratio than the sole SD and CM \ncompost. Total N, P and S were high in CM compost and the highest values \nof OC, C:N ratio were observed in SD compost. The growth characteristics \nof okra plant revealed that the application of co-compost (SD:CM, 1:2) was \nbetter than any other combinations. So, co-composting of SD and CM at 1:2 \nratio might be the optimum combination for making an efficient organic \nfertilizer from organic waste products. This co-compost would help to \ndecrease the indiscriminate use of inorganic fertilizer which is responsible \nfor soil quality deterioration and will play a promising role to boost up and \nmaintain soil fertility and productivity for sustainable agriculture. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 36-39 \n\n\n\nCite the Article: Asma Khatun, S. Sikder and J.C. Joardar (2020). Effect Of Co-Compost Made From Cattle Manure And Sawdust On The Growth And Yield Of Okra \n(Abelmoschus Esculentus L.). Malaysian Journal of Sustainable Agriculture, 4(1): 36-39. \n\n\n\nREFERENCES  \n\n\n\n1993. Soil survey manual, Soil Survey Division Staff, Chapter 3 Soil \nconservation service. US Department of Agriculture, Washington, DC. \n\n\n\nAbou El-Magd, M.M., El-Bassiony, A.M., Fawzy, Z.F. 2006. Effect of organic \nmanure with or without chemical fertilizers on growth, yield and \nquality of some varieties of broccoli plants, Journal of Applied Sciences \nResearch, 2, 791\u2013798.   \n\n\n\nAdekunle, O. 2013. The effect of combined application of poultry manure \nand sawdust on the growth and yield of okra, Journal of Agricultural \nScience, 5, 169-175. DOI:10.5539/jas.v5n10p169 \n\n\n\nAli, K., Arif, M., Shah, F., Shehzad, A., Munsif, F., Mian, I.A., Mian, A.A. 2017. \nImprovement in maize (Zea mays L.) growth and quality through \nintegrated use of biochar, Pakistan Journal of Botany, 49, 85\u201394. \n\n\n\nAli, M.M., Khan, F.A., Hossain, M.M., Rahman, A.N.M.I. 2015. Preparation of \ncattle manure compost using sawdust and rice straw, Bangladesh \nJournal of Animal Science, 44, 64\u201368. DOI: 10.3329/bjas.v44i1.23145 \n\n\n\nAnwar, Z., Irshad, M., Fareed, I., Saleem, A. 2015. Characterization and \nrecycling of organic waste after co-composting-a review, Journal of \nAgricultural Science, 7, 68\u201379. DOI: 10.5539/jas.v7n4p68 \n\n\n\nArapitsas, P. 2008. Identification and quantification of polyphenolic \ncompounds from okra seeds and skins, Food Chemistry, 110, 1041\u2013\n1045. doi: 10.1016/j.foodchem.2008.03.014 \n\n\n\nAshiono, F.A., Wangechi, H.K., Kinyanjui, M.J. 2017. Effects of sawdust, \nforest soil and cow dung mixtures on growth characteristics of blue gum \n(Eucalyptus saligna) seedlings in South Kinangop forest, Nyandarua, \nKenya, Open Journal of Forestry, 7, 373\u2013387. DOI: \n10.4236/ojf.2017.74022. \n\n\n\nBBS (Bangladesh Bureau of Statistics). 2011. The yearbook of agricultural \nstatistics of Bangladesh. Statistics Division, Ministry of Planning, \nGovernment of People Republic of Bangladesh, Dhaka. \n\n\n\nBernal, M.P., Alburquerque, J.A., Moral, R. 2009. Composting of animal \nmanures and chemical criteria for compost maturity assessment-a \nreview, Bioresource Technology, 100, 5444\u20135453. \ndoi:10.1016/j.biortech.2008.11.027  \n\n\n\nBurton, H., Turner, C. 2003. Treatment strategies for sustainable \nagriculture. Manure Management, second ed., Silsoe Institute, Lister and \nDurling printers. Flitwick Bedford, UK. \n\n\n\nDeepasundari, R., Mariappan, V. 2015. The combined application of cow \n\n\n\ndung and sawdust on the growth and biochemical characteristics of \n\n\n\nVigna radiata (L.) Wilczek, International Journal of Environmental \n\n\n\nBiology, 5, 66\u201371. \n\n\n\nDilruba, S., Hasanuzzaman, M., Karim, R., Nahar, K. 2009. Yield response of \nokra to different sowing time and application of growth hormones, \nJournal of Horticultural Science and Ornamental Plants, 1, 10\u201314. \n\n\n\nDimambro, M.E., Steiner, J., Rayns, F., Wallace, P. 2016. Literature review: \ncompost stability \u2013 impact and assessment, final report, Environment \nAgency. DOI: 10.13140/RG.2.2.15076.58248 \n\n\n\nDumitraescu, C., Saucivc, I., Manciulea, I., Zaha, C. 2009. Obtaining \n\n\n\nbiofertilizer by composting vegetable waste, sewage sludge and saw \n\n\n\ndust, Bulletin of the Transilavania University of Brasor, 2, 117\u2013122.  \n\n\n\nFern\u00e1ndez, J.M., L\u00f3pez-de-S\u00e1, E.G., Polo, A., Plaza, C. 2014.  Short term \nstabilization of organic matter in physically, chemically, and \nbiochemically protected pools in soils amended with municipal wastes, \nClean-Soil Air Water, 42, 487\u2013493. DOI: 10.1002/ clen.20130 0127 \n\n\n\nGarner, E. 2014. Sawdust as a mulch and soil amendment for \nrhododendrons and azaleas, In: A. N. Roberts, A. R. S. Bulletin (Eds.), \nJournal American Rhododendron Society, 5, 58.  \n\n\n\nGotaas, H.B. 1956. Composting \u2013 sanitary disposal and reclamation of \norganic wastes, WHO Geneva. \n\n\n\nGruhn, P., Golett, F., Yudelman, M. 2000. Integrated nutrient management, \nsoil fertility and sustainable agriculture: current issues and future \n\n\n\nchallenges. Food, agriculture and environment discussion, Paper 32. \nInternational Food Policy Research Institute, Washington DC, USA, 1\u20133, \nISBN 0-89629-638-5. \n\n\n\nHaque, M., Rashid, H.O., Pervin, E. 2015. Effect of the combination of \ncowdung and poultry litter on the yield of okra (Abelmoschus esculentus \nL.). Basic Research Journal of Agricultural Science and Review, 4, 193\u2013\n198. ISSN 2315\u20136880 \n\n\n\nIgbokwe, P.K., Asadu, C.O., Okpe, E.C., Okoro, S.E. 2015. Manufacture of bio-\nfertilizer by composting sawdust and other organic waste, International \nJournal of Novel Research in Physics, Chemistry and Mathematics, 2, 6\u2013\n15. ISSN 2394-9651 \n\n\n\nImamul Huq, S.M., Alam, M.D. 2005. (Eds.), A handbook on analysis of soil, \nplant and water. BACER-DU, University of Dhaka, Banglaesh, ISBN-984-\n32-1770-5 \n\n\n\nJoardar, J.C., Rahman, M.M. 2018. Poultry feather waste management and \neffects on plant growth, International Journal of Recycling of Organic \nWaste in Agriculture, 7, 183\u2013188. DOI: 10.1007/s4009 3-018-0204-z \n\n\n\nKashmanian, R.M., Kluchinski, D., Richard, T.L., Walker, J.M. 2000. \nQuantities, characteristics, barriers and incentives for use of organic \nmunicipal by products, land application of agricultural, industrial and \nmunicipal by-products, Science Society of America Books Series, 6, 128\u2013\n167. \n\n\n\nMondal, M.R.I., Sultan, M.K., Nur, S., Sarkar, M.J.U., Alam, M.S., Rahman, \n\n\n\nM.H.H., Krishi Proyukti Hatboi (Handbook of agro-technology). 2014. \n\n\n\n6th ed. Bangladesh agricultural Research institute, Gazipur, 1701. \n\n\n\nNeves, L., Ferreira, V., Oliveira, R. 2009. Co-composting cow manure with \nfood waste: the influence of lipid content, International Journal of \nBiological, Biomolecular, Agricultural, Food and Biotechnological \nEngineering, 3, 529\u2013534. \n\n\n\nOgundare, S.K., Owa, F.D., Etukudo, O.O., Ibitoye-Ayeni, N.K. 2015. \nInfluence of different nitrogen sources on the growth and yield of three \nvarieties of okra (Abelmoschus esculentus) in Kabba, Kogi State, \nNigeria, Agricultural Sciences, 6, 1141\u20131147. DOI: \n10.4236/as.2015.610109 \n\n\n\nOgunwande, G.A., Osunade, J.A., Ogunjimi, L.A.O. 2008. Effects of carbon to \nnitrogen ratio and turning frequency on composting chicken litter in \nturned windrow piles, CIGR E\u2013journal, 10, 1\u201316. \n\n\n\nOkalebo, J.R., Gathua, K.W., Woomer, P.L. 2002. Laboratory methods of soil \nand plant analysis: a working manual second ed., Sacred Africa, Nairobi. \n\n\n\nOluchukwu, A.C., Nebechukwu, A.G., Egbuna, S.O. 2018. Enrichment of \nnutritional contents of sawdust by composting with other nitrogen rich \nagro-wastes for bio-fertilizer synthesis, Journal of Chemical Technology \nand Metallurgy, 53, 430\u2013436. \n\n\n\nParry, M.L. 2007. Climate change 2007\u2013Impacts, adaptation and \nvulnerability: working group II contribution to the fourth assessment \nreport of the IPCC. Cambridge: Cambridge University Press, 4. \n\n\n\nSaifullah, M., Rabbani, M.G. 2009. Evaluation and characterization of okra \n(Abelmoschus esculentus.moench.) genotypes, SAARC Journal of \nAgriculture, 7, 91\u201398. \n\n\n\nUsman, M., Madu, V.U., Alkali, G. 2015. The combined use of organic and \ninorganic fertilizers for improving maize crop productivity in Nigeria, \nInternational Journal of Scientific and Research Publications, 5, 1\u20137. \nISSN 2250\u20133153. \n\n\n\nWang, P., Changa, C.M., Watson, M.E., Dick, W.A., Chen, Y., Hoitink, H.A.J. \n2004. Maturity indices for composted dairy and pig manures, Soil \nBiology and Biochemistry, 36, 767\u2013776. DOI: \n10.1016/j.soilbio.2003.12.012 \n\n\n\nYadav, S.K., Dhankhar, B.S. 2001. Correlation studies between various field \nparameters and seed quality traits in okra cv. Varsha Uphar, Seed \nResearch, 29, 84\u201388. \n\n\n\nZhang, Y., He, Y. 2006. Co-composting solid swine manure with pine \nsawdust as organic substrate, Bioresource Technology, 97, 2024\u20132031. \nDOI: 10.1016/j.biortech.2005.10.004.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 01-05 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.01.05 \n\n\n\nCite the Article: Md. Humaun Kabir, Md. Delwar Hossain, Md. Harun Or Rashid, Md. Shahriar Kobir (2021). Effect Of Varieties And Different Sources Of Nitrogen \nFertilizer On Yield And Yield Contributing Characters Of Baby Corn. Malaysian Journal of Sustainable Agriculture, 5(1): 01-05. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.01.2021.01.05 \n\n\n\nEFFECT OF VARIETIES AND DIFFERENT SOURCES OF NITROGEN FERTILIZER ON \nYIELD AND YIELD CONTRIBUTING CHARACTERS OF BABY CORN\n\n\n\nMd. Humaun Kabira, Md. Delwar Hossaina, Md. Harun Or Rashida, Md. Shahriar Kobirb* \n\n\n\na Department of Agronomy, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh. \nb Bangladesh Agricultural Research Institute, Gazipur, Dhaka, Bangladesh. \n\n\n\n*Corresponding Author Email: shahriar1302027@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 September 2020 \nAccepted 28 October 2020 \nAvailable online 18 November 2020\n\n\n\nMaize as well as baby corn is an exhaustive crop in terms of nutrient and water uptake from soil hence soil \nhealth become deteriorate easily and as different amounts and forms of nutrient supply in baby corn affect \nthe productivity of baby corn so combination of organic and inorganic sources of nutrient is beneficial for soil \nheath and to maximize the productivity of baby corn thus an experiment was conducted at the Agronomy \nField Laboratory, Bangladesh Agricultural University, Mymensingh during November 2017 to February 2018 \nto investigate the effect of varieties and sources of nitrogen fertilizer on yield and yield contributing \ncharacters of baby corn. The experiment was laid out in randomized complete block design (RCBD) with three \nreplications. The experiment consisted of two varieties viz., BARI Sweet corn-1(V1), Baby star (V2) and five \nsources of nitrogen fertilizer viz.,100% recommended N from urea(N1),75% N from urea + 25% N from \ncowdung (N2), 50% N from urea + 50% N from cowdung (N3), 75% N from urea + 25% N from poultry \nmanure(N4),50% N from urea + 50% N from poultry manure(N5).Yield and yield contributing characters of \nbaby corn were significantly influenced by variety, sources of nitrogen fertilizer and their interactions. The \nhighest number of cob plant-1 (1.67), cob length (13.50 cm), cob girth(3.84 cm), cob yield with husk (14.66 t \nha-1), cob yield without  husk (3.52 t ha-1), and fresh fodder yield (42.50 t ha-1) were recorded when Baby star \nwas fertilized with N2 (75% N from urea + 25% N from cowdung) treatment. Therefore, it may be concluded \nthat Baby star is the promising baby corn variety when coupled with (75% N from urea + 25% N from \ncowdung) for maximizing baby corn production and improving soil health.  \n\n\n\nKEYWORDS \n\n\n\nbaby corn, organic fertilizer, nitrogen source, mymensingh.\n\n\n\n1. INTRODUCTION \n\n\n\nMaize (Zea mays) is considered as the most important grain crops in \ndeveloping countries like Bangladesh and in Bangladesh it is ranked as \nsecond grain crops for its capability of higher productivity (Kobir et al., \n2019). After wheat and rice it is ranked as third cereal crops in the world \nfor its acceptability and productivity (Kobir et al., 2020). In the last year \nabout 4.7 million metric ton maize was produced from 0.507 million ha of \nland in Bangladesh. Moreover, the average yield of maize in Bangladesh is \n9.27 ton per hectare (AIS, 2020). \n\n\n\nBaby corn is a converted new commercial economic product of maize and \npeople little known to it. It is because there is lack of availability of high \nyielding variety and improved production technology, besides there is a \ngap in knowledge about use and economic benefits of this promising crop. \nFresh young maize cob which is harvested within 2-3 days of silk \nemergence prior to fertilization is generally known as Baby corn. Maize is \nan important potential crop of Bangladesh  and it is grown almost year-\nround, in addition to increase of the economic return of maize production, \nits production as Baby corn can be the best tool provided that the \navailability of modern agro-production technique (Neupane et al., 2011). \nIn terms of area and production it comprises about 8 and 25 percent in the \nworld, respectively (Subedi et al., 2018). Countries like Sri Lanka, \n\n\n\nMyanmar, Thailand and Taiwan was proved successful in cultivating Baby \ncorn (Dangwal et al., 2010). But cultivation of this crop in Bangladesh is \nvery negligible. For intensive sustainable commercial farming in \nBangladesh production of Baby corn has not yet started for lacks of \nsuitable production techniques. There is lack of information in cultivation \ntechnology, moreover adequate management practices like water \nmanagement; quantity, time and form of fertilizer application had not \nbeen discovered yet.  \n\n\n\nYield and quality of Baby corn is enormously affected by application of \ndifferent levels and forms of nutrition (Kunushi et al., 1986). Food \ninsecurity which is the result of insufficient production so far in term of \ndemand of that crop and this may be overcome by production of more food \nwithin the same area. To increase productivity of Baby corn nutrient like \nnitrogen is essential tool. Nitrogen is actively involved in each living cells \nof plants body and it is act as an important element of chlorophyll and \nprotoplast. A group researcher noticed that there is a remarkable increase \nin response in case of applying nitrogen in Baby corn (Pandey et al., 2002). \nIn addition, Maize, being an exhaustive crop, much attention is required in \nits nutrient management (Naveen and Saikia, 2020). Amount of applied \norganic manure like cowdung, vermi-compost and inorganic fertilizer like \nUrea, TSP, MoP greatly affect the growth performance and yield of Baby \ncorn (Singh et al., 2010; Mahmood et al., 2017). More over organic sources \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 01-05 \n\n\n\n\n\n\n\n \nCite the Article: Md. Humaun Kabir, Md. Delwar Hossain, Md. Harun Or Rashid, Md. Shahriar Kobir (2021). Effect Of Varieties And Different Sources Of Nitrogen \n\n\n\nFertilizer On Yield And Yield Contributing Characters Of Baby Corn. Malaysian Journal of Sustainable Agriculture, 5(1): 01-05. \n \n\n\n\n\n\n\n\nof nutrient can improve the soil physical and chemical properties. \nIntegration of organic (FYM) and chemical fertilizers is a cost-effective tool \nof maintaining soil quality along with increasing productivity of frequently \npracticed cropping system (Neupane et al., 2011). \n \nAs judicious fertilizer application comprising of organic and inorganic \nsource of nutrient can improve the productivity of Baby corn so during \ncultivation of Baby corn all issues of cultivation procedure especially \njudicious and balanced fertilization like combination of organic and \nchemical fertilizer should have taken into consideration. In order to put \nforward toward the commercial Baby corn farming improvement in \nfarming techniques with suitable cultivars is needed. Hence this research \nwas undertaken as Baby corn cultivation allows farmers to convert from \nsubsistence farming to commercial farming in Bangladesh. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Experimental Site \n\n\n\nThe experiment was conducted at the Agronomy Field Laboratory, \n\n\n\nBangladesh Agricultural University, Mymensingh-2202 in the year of \n\n\n\n2017-2018. The experimental site was associated with a medium high land \n\n\n\nwith silt-loam soil texture and the soil was neutral or slightly acidic in \n\n\n\nreaction. The physico-chemical properties of the soil have been presented \n\n\n\nin (Table 1). \n\n\n\n\n\n\n\nTable 1: Physical and chemical properties of soil \nPhysical characteristics of the \nsoil \n\n\n\nChemical characteristics of the \nsoil \n\n\n\n1. Sand (%) (0.2-0.02 min)    : 32 1. pH      : 6.50 \n2. Silt (%) (0.02-0.002 min)  : 60 2. Organic matter (%)    : 1.19 \n3. Clay (%) (<0.002 min)    : 08 3. Total nitrogen (%)      : 0.10 \n4. Soil textural class    : silt loam 4. Available sulphur (ppm)      : \n\n\n\n14.2 \n5. Particle density (g/cc)   : 2.60 5.Available phosphorus (ppm)  : \n\n\n\n16.72 \n6. Bulk density (g/cc)    : 1.35 6. Exchangeable potassium \n\n\n\n(meq %) : 0.12 \n7. Porosity (%)        : 46.67  \n\n\n\nSource: Department of Soil Science, Bangladesh Agricultural University, \n\n\n\nMymensingh. \n\n\n\n2.2 Climate \n\n\n\nThe experimental area was located under the sub-tropical climate which \n\n\n\nis specialized by moderately low rainfall with moderately low temperature \n\n\n\nduring rabi season (October- March) and high temperature with heavy \n\n\n\nrainfall during the kharif season (April-September). Weather information \n\n\n\nat the experimental site during the period of study regarding max \n\n\n\ntemperature, min temperature, rainfall and relative humidity has been \n\n\n\npresented in (Figure 1). \n\n\n\nSource: Department of Irrigation and Water Management, Bangladesh \nAgricultural University, Mymensingh \n\n\n\nFigure 1: Weather data during cropping season (Nov-2017 to Feb-2018) \n\n\n\n2.3 Planting material \n\n\n\nFor conducting the experiment two cultivars of baby corn crops were \n\n\n\nselected namely BARI Sweet corn-1 and Baby star. BARI Sweet corn-1 was \n\n\n\ncollected from Bangladesh agricultural research Institute and Baby star \n\n\n\nwas collected from local seed marketer. \n\n\n\n2.4 Experimental Treatment, design and layout \n\n\n\nThe experiment was conducted considering the following factor \n\n\n\ntreatments: \n\n\n\nFactor A: Sources of nitrogen fertilizer Factor B: Variety \n\n\n\nN1= 100% recommended Nfrom Urea V1= BARI Sweet \n\n\n\ncorn-1 \n\n\n\nN2=75% recommended N from Urea+ \n\n\n\n25%Nfromcowdung (CD) \n\n\n\nV2= Baby star \n\n\n\nN3=50% recommended Nfrom Urea+ \n\n\n\n50%NfromCD \n\n\n\n\n\n\n\nN4=75% recommended N from Urea+ 25% N \n\n\n\nfrom poultry manure (PM) \n\n\n\n\n\n\n\nN5=50% recommended nitrogen from Urea+ \n50% N from PM \n\n\n\n\n\n\n\nHere, recommended fertilizer dose (ha-1) = Urea: 300 kg, TSP: 150 kg, MoP: \n100 kg (Azad et al., 2019). The experiment was laid out in randomized \ncomplete block design (RCBD) with three replications. The treatments \ncomprised two varieties and five sources of nitrogen fertilizers which have \nmentioned above. Each block was divided into ten-unit plots of size 5 m2 \n(2.5 m x 2 m). Thus, the total numbers of unit plots were 30 (2\u00d7 5\u00d7 3). The \ndistance maintained between two-unit plots was 0.5 m and between \nblocks was 1.0 m. \n\n\n\n2.5 Crop husbandry \n\n\n\nThe land was prepared thoroughly by tilling once with a power tiller and \nsubsequently ploughing three times with country plough followed by \nladdering. Well decomposed cowdung and poultry manure were applied \nto plots as per treatment and incorporated and mixed thoroughly with the \nsoil before sowing and full doses of TSP and MoP were applied as per \nrecommended dose as basal before sowing in the plots. One third of the \nurea was applied as a basal dose just before sowing and remaining 2/3 of \nurea was applied in two equal splits each 15 days after sowing (DAS) and \n35 DAS. Seeds of selected corn cultivars were used for sowing with \nrecommended seed rate of 20 kg/ha. Time to time different intercultural \noperations like thinning and gap filling, irrigation, weeding, plant \nprotection measures, detasseling was followed when needed. The green \ncobs were harvested just after two- or three-days past of silk emergence. \nBaby star was harvested on 15 February (90 DAS), BARI Sweet corn-1 was \nharvested on 20 February (94 DAS). \n\n\n\n2.6 Collection of experimental data and statistical analysis \n\n\n\nFive plants from each plot were selected randomly and number of cobs \nplant-1, cob length (cm), cob girth (cm), cob yield with husk (t ha-1), cob \nyield without husk (t ha-1), fresh fodder yield (t ha-1) were collected from \nthese sample plants. Data were then tabulated to make analysis of variance \nand the mean difference was calculated by Duncan\u2019s Multiple Range Test \n(DMRT) according to Gomez and Gomez (1984) with the help of (M-\nSTATC) a software program. \n\n\n\n3. RESULTS \n\n\n\n3.1 Number of cobs plant-1 \n\n\n\nNumber of cob plant-1 was significantly affected by the variety. The \nmaximum number of cob plant-1 was observed in Baby star (1.27) and the \nminimum number of cob plant-1 observed in BARI sweet corn-1 (1.20) \n(Table 2). Number of cob plant-1 of baby corn was significantly influenced \nby different sources of nitrogen fertilizer.  The highest number of cob \nplant-1 (1.502) was found in N2 (75% N from urea + 25% N from cowdung) \nfollowed by (1.335) N4 (75% N from urea + 25% N from poultry manure) \nand the lowest number of cob plant-1 (1.002) was recorded in N1(100% \nrecommended N from urea) (Table 3). The maximum number of cob plant-\n\n\n\n1 (1.67) was observed in Baby star with N2 (75% N from urea + 25% N \nfrom cowdung) and the lowest value (1.00) was obtained when BARI \nsweet corn-1 fertilized with N1(100% recommended N from urea) (Table \n4).  \n \n\n\n\nTable 2: Effect of variety on yield and yield contributing characters of \nbaby corn \n\n\n\nVariety Cob \nplant-1 \n\n\n\nCob length \n(cm) \n\n\n\nCob girth \n(cm) \n\n\n\nYield with husk \n(t ha-1) \n\n\n\nV1 1.20b 10.63 b 2.88 b 9.95b \n\n\n\nV2 1.27 a 11.87 a 3.05 a 12.05a \n\n\n\nLevel of sig. * ** ** ** \n\n\n\nIn a column, figures with same letter (s) or without letter do not differ \nsignificantly whereas figures with dissimilar letter differ significantly (as \nper DMRT). * =Significant at 5% level of probability, ** =Significant at 1% \nlevel of probability, V1 = BARI sweet corn-1, V2 = Baby star \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\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\n2017-Nov 2017-Dec 2018-Jan 2018-Feb\n\n\n\nR\nai\n\n\n\nn\nfa\n\n\n\nll \n(m\n\n\n\nm\n)\n\n\n\nT\nem\n\n\n\n (\n\u25e6c\n\n\n\n) \na\nn\n\n\n\nd\n H\n\n\n\nu\nm\n\n\n\nid\nit\n\n\n\ny\n \n\n\n\n(%\n)\n\n\n\nMonths\n\n\n\nRainfall (mm) Max tem (\u25e6c) Min tem  (\u25e6c) Humidity (%)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 01-05 \n\n\n\n\n\n\n\n \nCite the Article: Md. Humaun Kabir, Md. Delwar Hossain, Md. Harun Or Rashid, Md. Shahriar Kobir (2021). Effect Of Varieties And Different Sources Of Nitrogen \n\n\n\nFertilizer On Yield And Yield Contributing Characters Of Baby Corn. Malaysian Journal of Sustainable Agriculture, 5(1): 01-05. \n \n\n\n\n\n\n\n\nTable 3: Effect of nitrogen sources on yield and yield contributing \n\n\n\ncharacters of baby corn \n\n\n\nNitrogen \n\n\n\nsources \n\n\n\nCob \n\n\n\nplant-1 \n\n\n\nCob length \n\n\n\n(cm) \n\n\n\nCob girth \n\n\n\n(cm) \n\n\n\nYield with husk \n\n\n\n(t ha-1) \n\n\n\nN1 1.002  c 9.750 d 2.690 c 9.580 c \n\n\n\nN2 1.502  a 12.50  a 3.510  a 12.55a \n\n\n\nN3 1.330  b 11.75b 2.915  b 10.95 b \n\n\n\nN4 1.335  b 11.66  b 2.880  b 11.04 b \n\n\n\nN5 1.002  c 10.58  c 2.810  bc 10.90b \n\n\n\nLevel of \n\n\n\nsig. \n** ** ** ** \n\n\n\nIn a column, figures with the same letter (s) or without letter do not differ \nsignificantly whereas figures with dissimilar letter differ significantly (as \nper DMRT).** =Significant at 1% level of probability, N1= (100% urea \u2013 N), \nN2= (75% urea \u2013 N + 25% CD \u2013 N), N3= (50% urea \u2013 N + 50% CD \u2013 N), N4= \n(75% urea \u2013 N + 25% PM \u2013 N), N5= (50% urea \u2013 N + 50% PM \u2013 N). \n\n\n\n3.2 Cob length \n\n\n\nA significant variation was observed due to variety at the cob length of the \nbaby corn. The maximum cob length was observed in Baby star (11.87 cm) \nand minimum in BARI sweet corn-1 (10.63 cm) (Table 2). Sources of \nnitrogen fertilizer had significant influence on cob length of baby corn. The \nhighest number of cob length (12.50 cm) was observed in N2 (75% N from \nurea + 25% N from cowdung) and the lowest number of cob length (9.75 \ncm) was recorded in N1(100% recommended N from urea) (Table 3). The \nlongest cob (13.50 cm) was obtained in Baby star fertilized with N2 (75% \nN from urea + 25% N from cowdung) (Table 4). \n\n\n\n3.3 Cob girth \n\n\n\nThe highest cob girth was found in Baby star (3.05 cm) and the lowest cob \ngirth was found in BARI sweet corn-1 (2.88 cm)(Table 02).The highest cob \nbreadth (3.51 cm) was found in N2 (75% N from urea + 25% N from \ncowdung) and the lowest cob girth (2.69 cm) was recorded in N1(100% \nrecommended N from urea) (Table 3). The maximum cob girth (3.84 cm) \nwas observed in Baby star fertilized with N2 (75% N from urea + 25% N \nfrom cowdung) and the lowest (2.63 cm) was recorded in BARI sweet \ncorn-1fertilized with N1(100% recommended N from urea) (Table 4). \n \n\n\n\nTable 4: Interaction effects of varieties and nitrogen sources on yield \n\n\n\nand yield contributing characters of baby corn \n\n\n\nInteraction \n\n\n\n(Variety x N \n\n\n\nsources) \n\n\n\nCob \n\n\n\nplant-1 \n\n\n\nCob length \n\n\n\n(cm) \n\n\n\nCob girth \n\n\n\n(cm) \n\n\n\nYield with \n\n\n\nhusk (t ha-1) \n\n\n\nV1xN1 1.000    c 9.330   f 2.630   d 7.67  f \n\n\n\nV1xN2 1.334   b 11.50   c 3.180   b 10.44 de \n\n\n\nV1xN3 1.330   b 11.00   cd 2.900   cd 11.13 bcd \n\n\n\nV1xN4 1.333   b 11.00   cd 2.860   cd 10.59 de \n\n\n\nV1xN5 1.000   c 10.33 de 2.800   cd 9.94  e \n\n\n\nV2xN1 1.003   c 10.17   e 2.750   cd 11.49 bc \n\n\n\nV2xN2 1.670  a 13.50 a 3.840  a 14.66 a \n\n\n\nV2xN3 1.330   b 12.50   b 2.930   c 10.77 cd \n\n\n\nV2xN4 1.337   b 12.33   b 2.900   cd 11.49 bc \n\n\n\nV2xN5 1.003   c 10.83   cd 2.820   cd 11.87 b \n\n\n\nLevel of sig. ** * ** ** \n\n\n\nIn a column, figures with the same letter (s) or without letter do not differ \nsignificantly whereas figures with dissimilar letter differ significantly (as \nper DMRT). ** =Significant at 1% level of probability, NS = Not significant, \nV1 = BARI sweet corn-1,V2 = Baby star, N1= (100% urea \u2013 N), N2= (75% \nurea \u2013 N + 25% CD \u2013 N), N3= (50% urea \u2013 N + 50% CD \u2013 N), N4= (75% urea \n\u2013 N + 25% PM \u2013 N), N5= (50% urea \u2013 N + 50% PM \u2013 N). \n\n\n\n3.4 Cob yield with husk \n\n\n\nThe highest cob yield with husk (12.05 t ha-1) was found in Baby star. The \nlowest cob yield with husk (9.95 t ha-1) was observed in BARI sweet corn-\n1 (Table 2).The highest cob yield with husk (12.55 t ha-1) was observed in \nN2 (75% N from urea + 25% N from cowdung) and the lowest cob yield \nwith husk (9.58 t ha-1) was recorded in N1(100% recommended N from \nurea) (Table 3). The highest (14.66 t ha-1) was observed in Baby star \nfertilized with N2 (75% N from urea + 25% N from cowdung) and the \nminimum cob yield with husk (7.67 t ha-1) was obtained in BARI sweet \ncorn-1with N1(100% recommended N from urea) (Table 4). \n \n\n\n\n3.5 Cob yield without husk \n\n\n\nThe maximum cob yield without husk was observed in Baby star (2.91 t \nha-1) and minimum in BARI sweet corn-1 (2.44 t ha-1) (Figure 2). The \nhighest cob yield without husk (3.18 t ha-1) was observed in N2 (75% N \nfrom urea + 25% N from cowdung) and the lowest cob yield without husk \n(2.34 t ha-1) was recorded in N1(100% recommended N from urea) (Figure \n3). The highest (3.52 t ha-1) was observed in Baby star fertilized with N2 \n(75% N from urea + 25% N from cowdung) and the minimum cob weight \nwith husk (1.89 t ha-1) was obtained in BARI sweet corn-1 with N1(100% \nrecommended N from urea) (Figure 4). \n\n\n\n\n\n\n\nFigure 2: Effect of varieties on yield without husk (t ha-1) and fresh \n\n\n\nfodder yield (t ha-1) \n\n\n\n\n\n\n\nFigure 3: Effect of different sources of nitrogen fertilizers on yield \nwithout husk (t ha-1) and fresh fodder yield (t ha-1) \n\n\n\nN1= (100% urea \u2013 N), N2= (75% urea \u2013 N + 25% CD \u2013 N), N3= (50% urea \u2013 \nN + 50% CD \u2013 N), N4= (75% urea \u2013 N + 25% PM \u2013 N), N5= (50% urea \u2013 N + \n50% PM \u2013 N). \n\n\n\n3.6 Fresh fodder yield \n\n\n\nThe maximum fodder yield was observed in Baby star (35.10 t ha-1) and \nminimum in BARI sweet corn-1 (29.18 t ha-1) (Figure 2). The highest \nfodder yield (36.53 t ha-1) was observed in N2 (75% N from urea + 25% N \nfrom cowdung) and the lowest fodder yield (28.31 t ha-1) was recorded in \nN1(100% recommended N from urea) (Figure 3). Due to interaction effect \nthe highest (42.58 t ha-1) was observed in Baby star fertilized with N2 (75% \nN from urea + 25% N from cowdung) while the minimum fodder yield \n(23.15 t ha-1) was obtained in BARI sweet corn-1 with N1(100% \nrecommended N from urea) (Figure 4). \n\n\n\n\n\n\n\nFigure 4: Interaction effect (varieties X different sources of nitrogen) on \ntotal dry matter production \n\n\n\n(V1 = BARI sweet corn-1, V2 = Baby star, N1= (100% urea \u2013 N), N2= (75% \nurea \u2013 N + 25% CD \u2013 N), N3= (50% urea \u2013 N + 50% CD \u2013 N), N4= (75% urea \n\u2013 N + 25% PM \u2013 N), N5= (50% urea \u2013 N + 50% PM \u2013 N). \n\n\n\n3.7 Correlation-co-efficient and regression equation \n\n\n\nCobs plant-1 (r = 0.67) (Figure 5) showed positive correlation with cob \nyield without husk (t ha-1).  \n\n\n\n0\n\n\n\n50\n\n\n\nYield without\nhusk (ton per\n\n\n\nha)\n\n\n\nFresh fodder\nyield (ton per\n\n\n\nha)\n\n\n\n2.44\n\n\n\n29.182.91\n\n\n\n35.1\n\n\n\nBARI sweet corn-1\n\n\n\nBaby star\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\nN1 N2 N3 N4 N5\n\n\n\n2.35 3.18 2.63 2.64 2.59\n\n\n\n28.31\n\n\n\n36.53\n31.94 32.16 31.77\n\n\n\nyi\nel\n\n\n\nd\n (\n\n\n\nt \nh\n\n\n\na\n-1\n\n\n\n)\n\n\n\nDifferent sources of nitrogen\n\n\n\nYield without husk (ton per ha) Fresh fodder yield (ton per ha)\n\n\n\n0\n\n\n\n50\n\n\n\nV1xN1 V1xN2 V1xN3 V1xN4 V1xN5 V2xN1 V2xN2 V2xN3 V2xN4 V2xN5\n\n\n\n1.89 2.84 2.66 2.49 2.33 2.8 3.52 2.6 2.8 2.86\n\n\n\n23.15\n30.49 32.46 30.85 28.98\n\n\n\n33.47\n42.5\n\n\n\n31.43 33.47 34.56\n\n\n\nyi\nel\n\n\n\nd\n (\n\n\n\nth\na\n\n\n\n-1\n)\n\n\n\nInteraction between variety and different sources of nitrogen\n\n\n\nYield without husk (ton per ha) Fresh fodder yield (ton per ha)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 01-05 \n\n\n\n\n\n\n\n \nCite the Article: Md. Humaun Kabir, Md. Delwar Hossain, Md. Harun Or Rashid, Md. Shahriar Kobir (2021). Effect Of Varieties And Different Sources Of Nitrogen \n\n\n\nFertilizer On Yield And Yield Contributing Characters Of Baby Corn. Malaysian Journal of Sustainable Agriculture, 5(1): 01-05. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Correlation -co- efficient and regression equation for cobs \nplant-1 Vs yield without husk (t ha-1) \n\n\n\nCob yield with husk (t ha-1) (r = 0.97), cob length (r = 0.78), fresh fodder \nyield (r = 0.97) showed very strong positive correlation with cob yield \nwithout husk (t ha-1) (Table 5) \n \n\n\n\nTable 5: Correlation -co- efficient and regression equation for yield \ncontributing characters with yield of baby corn \n\n\n\n\n\n\n\nCorrelation-co-\ncoefficient  \n\n\n\nParameters (t ha-1) R\u00b2 value R value \nRegression \n\n\n\nequation \nYield with husk  Vs yield \nwithout husk 0.931 0.97 \n\n\n\ny = 0.263x - \n0.281 \n\n\n\nFresh fodder yield Vs yield \nwithout husk 0.931 0.97 \n\n\n\ny = 0.083x + \n0.003 \n\n\n\nCob length Vs yield without \nhusk 0.606 0.78 \n\n\n\ny = 0.263x - \n0.281 \n\n\n\nCob girth (r = 0.678) (Figure 6) showed positive correlation with cob yield \nwithout husk (t ha-1). \n\n\n\n\n\n\n\nFigure 6: Correlation -co- efficient and regression equation for cob girth \nVs yield without husk (t ha-1) \n\n\n\n4. DISCUSSIONS \n\n\n\nThe number of cob plant-1 in Baby star variety was about 6% greater than \nthe BARI sweet corn-1. Cob length of Baby star was around 12% higher \nthan BARI sweet corn-1. Cob breadth of Baby star was 6% greater than \nBARI sweet corn-1. Cob yield without husk of Baby star was around 19% \ngreater than BARI sweet corn-1. Fodder yield of Baby star was around \n20% greater than BARI sweet corn-1. From the above-mentioned points, \nit is clear that there exist strong differences between two varieties like \nBARI Sweet corn-1 and Baby star for all the yield contributing characters. \nA studied five genotypes of baby corn and found one variety performed \nbetter physical characteristics like ear girth, ear length, ear weight than \nother four varieties (Sharma et al., 2009). This finding was declared that \ndifferent varieties perform differently due to genetic variation among the \ncultivars. Present research findings also corroborate with that previous \nstudy. Similar result was also found by (Surender and Jitendra, 2010; \nSahoo, 2011; Kumar et al., 2015). The number of cob plant-1 in N2 (75% N \nfrom urea + 25% N from cowdung) was about 50% greater than N1 (100% \nrecommended N from urea). Cob length of in N2 (75% N from urea + 25% \nN from cowdung) was around 28% higher than N1 (100% recommended \nN from urea). \n \nFodder yield of N2 (75% N from urea + 25% N from cowdung) was around \n28% greater than N1 (100% recommended N from urea). Cob yield with \nhusk in N2 (75% N from urea + 25% N from cowdung) was 31% higher \nthan N1(100% recommended N from urea). Cob yield without husk of N2 \n(75% N from urea + 25% N from cowdung) was around 35% greater than \nN1(100% recommended N from urea). When variety Baby star was \nfertilized with N2 (75% N from urea + 25% N from cowdung) treatment \nthen all the parameters showed better result in contrast with the \nrecommended mineral fertilization treatment. Maybe it is attained as \ncombined application of organic and inorganic fertilization can increase \nthe micro-organism and enzyme activity and can make available the \nnutrient in the soil (He and Li, 2004; Saha et al., 2008; Sharma and Banik, \n2014). A group researcher revealed that integrated fertilizer application \nin Baby corn can maximize number of cob, corn yield, fresh fodder yield, \ncorn length, corn girth and green cob weight (Singh et al., 2016). In other \nstudy, they found that (\u00be NPK + \u00bd organic fertilizer) resulted in the \nhighest N, P and K uptake and the heaviest weight of sweet corn (Sofyan \n\n\n\nand Sara, 2018).  Our present study also showed more or less similar \nresults. Our result is also at par with (Lone et al., 2013). A study showed \nthat application of recommended N through inorganic sources maximize \nthe yield contributing characters which is statistically similar when N \nfertilizer application combined with 75% from chemical sources and 25% \nfrom FYM or poultry manure or from sheep manure and our present \nexperiment revealed that yield contributing characters of Baby corn \nshowed better result with the application of N fertilizer as 75% from \ninorganic sources and 25% from cowdung (Kumar et al., 2009).   \n\n\n\n5. CONCLUSION \n\n\n\nAccording to the results obtained from the experiment, Baby star \nperformed well considering all the yield and yield contributing characters. \nAmong the different nitrogen fertilizer sources, 75% N from urea + 25% N \nfrom cowdung gave better performance. Baby star fertilized with 75% N \nfrom urea + 25% N from cowdung gave better performance inrespect of \nyield and yield contributing characters. Finally, it can be concluded \nthatBaby star coupled with 75% N from urea + 25% N from cowdung \nappeared as the promising practice in baby corn cultivation in terms of \nbetter yield. \n\n\n\nACKNOWLEDGMENT \n\n\n\nMinistry of science and technology of Bangladesh government has \nfinancially supported to conduct the research work smoothly. Authors are \nexpressing their gratitude to the teachers and staffs of Bangladesh \nAgricultural University who have assisted in the experiment by their \nexpertise and hard work. \n\n\n\nREFERENCES \n\n\n\nAIS. 2020. Agricultural Information Services. Department of Agricultural \nExtension, Bangladesh. Production and area of field crops, Pp.13. \n\n\n\nAzad, A.K., Wahab, A., Saha, M.G., Nesa, Z., Rahman, M.L., Rahman, H.H., \nAmin, L., 2019. Krishi Projukti Hatboi (Handbook on Agro-technology), \n\n\n\n8th edition. Bangladesh Agricultural Research Institute, Gazipur-1701, \nBangladesh, Pp.380.http://www.bari.gov.bd \n\n\n\nDangwal, R.L., Singh, A., Singh, T., Sharma, C.H., 2010. Effect of weeds on \n\n\n\nthe yield of wheat crop in Tehsil Nowshera. Journal of American Science, \n6 (10), Pp. 405-407. \n\n\n\nGomez, K.A., Gomez, A.A., 1984. Statistical procedure for agricultural \nresearch. John Wiley and Sons, New York, second edition, Pp. 680. \n\n\n\nHe, Y., Li, R., 2004.Effect of the organo\u2013inorgano-mixed fertilizer \n\n\n\napplication on sugarcane yield and soil enzymatic activity. Sugar Crops \nChina, 4, Pp. 36-38. \n\n\n\nKobir, M.S., Rahman, M.R., Islam, A.K.M.M., Paul, S., Islam, M.M., Farid, M.N., \n\n\n\nHajong, P., 2019. Yield performance of some maize varieties as \ninfluenced by irrigation management at different growth stages. Res. \nAgric. Livest. Fish., 6 (1), Pp. 57-67. \n\n\n\nKobir, M.S., Rahman, M.R., Islam, A.K.M.M., 2020. Dry matter partitioning \n\n\n\nof maize plant as affected by water management at different growth \nstages. Tropical Agrobiodiversity, 1 (1), Pp. 31-36. \n\n\n\nKumar, A.K., KarunaSagar, G., Chandraka, V., Reddy, P.M., 2009. Influence \nof integrated nitrogen management on yield, nitrogen uptake, soil \n\n\n\nfertility status and economics of baby corn. Indian Journal of \nAgricultural Research, 43 (3), Pp. 227-229. \n\n\n\nKumar, S., Singh, G., Singh, R., Singh, S.S., 2015. Growth and yield of baby \n\n\n\ncorn (Zea Mays L.) as influenced by varieties, spacings and dates of \nsowing. Indian Journal of Agricultural Research, 49 (4), Pp. 353-357. \n\n\n\nKunushi, H.M., Bandel, V.A., Nulford, F.R. 1986. Seasonal P uptake of corn \nunder No till and conventional Till. Management Press, Pp. 293-296. \n\n\n\nLone, A.A., Allai, B.A., Nehvi, F.A., 2013. Growth, yield and economics of \n\n\n\nbaby corn (Zea mays L.) as influenced by integrated nutrient \nmanagement (INM) practices. African Journal of Agricultural Research, \n8 (37), Pp. 4537-4540. \n\n\n\nMahmood, F., Khan, I., Ashraf, U., Shahjad, T., Hussain, S., Shahid, M., Abid, \nM., Ullah, S., 2017. Effect of organic and inorganic manures on maize and \n\n\n\ny = 1.2383x + 1.1509\n\n\n\nR\u00b2 = 0.4412\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8\n\n\n\ny\nie\n\n\n\nld\n w\n\n\n\nit\nh\n\n\n\no\nu\n\n\n\nt \n\n\n\nh\nu\n\n\n\nsk\n (\n\n\n\nt \nh\n\n\n\na\n-1\n\n\n\n)\n\n\n\nCobs plant-1\n\n\n\ny = 1.0164x - 0.3305\n\n\n\nR\u00b2 = 0.6786\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n0 0.5 1 1.5 2 2.5 3 3.5 4 4.5\n\n\n\ny\nie\n\n\n\nld\n w\n\n\n\nth\no\nu\n\n\n\nt \n\n\n\nh\nu\n\n\n\nsk\n (\n\n\n\nt \nh\n\n\n\na\n-1\n\n\n\n)\n\n\n\ncob girth (cm)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 01-05 \n\n\n\n\n\n\n\n \nCite the Article: Md. Humaun Kabir, Md. Delwar Hossain, Md. Harun Or Rashid, Md. Shahriar Kobir (2021). Effect Of Varieties And Different Sources Of Nitrogen \n\n\n\nFertilizer On Yield And Yield Contributing Characters Of Baby Corn. Malaysian Journal of Sustainable Agriculture, 5(1): 01-05. \n \n\n\n\n\n\n\n\ntheir residual impact on soil physico- chemical properties. Journal of \n\n\n\nsoil science and plant nutrition, 17 (1), Pp. 22-32. \n\n\n\nNaveen, J., Saikia, M., 2020. Nutrient Management in Organic Baby Corn \nProduction: A Review. Agricultural Reviews, 41 (1), Pp. 66-72. \n\n\n\nNeupane, M.P., Singh, R.K., Kumar, R., Kumari, A., 2011. Response of Baby \n\n\n\nCorn (Zea mays L.) to Nitrogen Sources and Row Spacing. Environment \n& Ecology, 29 (3), Pp. 1176\u20141179. \n\n\n\nPandey, A.K., Mani, V.P., Prakash, V., Singh, R.D., Gupta, H.S., 2002. Effect of \nvarieties and plant densities on yield, yield attributes and economics of \n\n\n\nbabycorn (Zea mays). Indian Journal of Agronomy, 47 (2), Pp. 221-226. \n\n\n\nSaha, S., Prakash, V., Kundu, S., Kumar, N., Mina, B.L., 2008. Soil enzymatic \nactivity as affected by long-term application of farmyard manure and \nmineral fertilizer under a rainfed soybean\u2013wheat system in N-W \n\n\n\nHimalaya. Eur. J. Soil Biol., 44, Pp. 309-315. \n\n\n\nSahoo, S.C., 2011. Yield and economics of baby corn (Zea mays L.) as \naffected by varieties and levels of nitrogen. Range Management and \n\n\n\nAgroforestry, 32 (2), Pp. 135-137. \n\n\n\nSharma, R.C., Banik, P., 2014. Vermicompost and fertilizer application: \neffect on productivity and profitability of Baby corn (Zea mays) and soil \nhealth, Compost science and utilization, 22 (2), Pp. 83-92. \n\n\n\nSharma, R.K., Saxena, V.K., Malhi, N.S., Grewal, M.S., 2009. Identification of \n\n\n\nsuitable genotypes for baby corn. Journal of Research Punjab \nAgricultural University, 39 (4), Pp. 479-481. \n\n\n\nSingh, G., Singh, N., Kaur, R., 2016. Effect of integrated Fertilizer levels on \n\n\n\nyield and quality parameters of baby corn (Zea mays L.). International \njournal of applied and pure science and agriculture, 2, Pp. 161-166. \n\n\n\nSingh, M.K., Singh, R.N., Singh, S.P., Yadav, M.K., Singh, V.K., 2010.Integrated \nnutrient management for higher yield, quality and profitability of baby \n\n\n\ncorn (Zea mays). Indian Journal of Agronomy, 55 (2), Pp. 100-104. \n\n\n\nSofyan, E.T., Sara, D.S., 2018. The effect of organic and inorganic fertilizer \napplication N, P and K uptake and yield of sweet corn (Zea mays). \nJournal of tropical soil, 23 (3), Pp. 111-116. \n\n\n\nSubedi, S., KC, B., Regmi, D., Bhattarai, A., Chhetri, K., Gnawali, A., 2018. \n\n\n\nStudy of Performance of Baby Corn at Different Combination Organic \nand Inorganic Fertilizers in Mid Hills of Nepal.Agri Res & Tech: Open \n\n\n\nAccess J,17 (3). DOI: 10.19080/ARTOAJ.2018.17.556027 \n\n\n\nSurender, K.C., Jitendra, M., 2010. Estimates of variability, heritability and \ngenetic advance in baby corn. Indian Journal of Horticulture, 67, Pp. \n238-241.\n\n\n\n \n\n\n\n\n\n"
"\n\n Malaysian Journal of Sustainable Agriculture (MJSA)1(2) (2017) 01\n\n\n\nELECTRICAL STIMULATION FOR EGGS AND SEMEN ON TACHYPLEUS\nTRIDENTATUS COLLECTED FROM LOCAL RESTAURANTS IN HONG KONG\nYh Cheung, Oy Lam, Sc Lun, Ch Pang, Kw Wu, Wk Leung\n\n\n\nPo Leung Kuk Laws Foundation College, Hong Kong\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 September 2017 \n\n\n\nAccepted 19 October 2017 \n\n\n\nAvailable online 30 October 2017 \n\n\n\nCite this article as: Yh Cheung, Oy Lam, Sc Lun, Ch Pang, Kw Wu, Wk Leung  (2017).  Electrical Stimulation For Eggs And Semen On \nTavhypleus Tridentatus Collected From Local Restaurants In Hong Kong.   Malaysian Journal of Sustainable Agriculture,  1(2):01.\n\n\n\nThe major threats of horseshoe crabs in Hong Kong include (1) coastal \ndevelopment, whichdestroys the sprawling grounds of horseshoe crabs; and \n(2) fisheries, which reduces its populationin the wild directly. Artificial \nbreeding of juvenile horseshoe crabs for wild release is therefore \nconsidered as one of the means to raise the population of this species.\n\n\n\nHowever, due to a very low population of wild mature and reproductive \nhorseshoe crabs in Hong Kong, a stable supply of eggs and semen is often an \nobstacle in the commencing artificial breeding. In the present study, with \nthe support of a local restaurant, six Tachypleus tridentatus were borrowed \nfor electrical stimulation for eggs and semen. These horseshoe crabs were \n\n\n\nContents List available at RAZI Publishing \nMalaysian Journal of Sustainable Agriculture (MJSA)\n\n\n\nJournal Homepage:  : http://www.razipublishing.com/journals/malaysian- journal- \n\n\n\nISSN: 2521-2931 (Print)\nISSN: 2521-294X (Online)\n\n\n\ncaught in sea by fishermen and sold together with other seafood to  \nrestaurants. After electrical stimulation, the horseshoe crabs were returned \nto the restaurant to continue their display as an attraction to customers \n(consumption of horseshoe crabs is not common).\n\n\n\nUndoubtedly, this is not an ideal conservation method. However, with no \nregulation of law in preventing fishing, trading and consumption of \nhorseshoe crabs, collaboration with restaurants for a relatively stable supply \nof mature and reproductive horseshoe crabs is a possible solution satisfying \nboth needs: Restaurants have no real loss, whereas more juvenile horseshoe \ncrabs can be bred artificially and eventually released to the wild. \n\n\n\nofsustainable-agriculture-mjsa/\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.01\n\n\n\nSHORT COMMUNICATION\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.01\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 51-56 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2022.51.56 \n\n\n\n \nCite The Article: Siriluk Sintupachee, Puttisan Rattanachoo, Suppawan Promproa (2022). Alpha-Mangostin Quality and Quantity Analysis in Nakhon Si Thammarat \n\n\n\nMangosteen Pericarp Using Thin-Layer Chromatography. Malaysian Journal of Sustainable Agricultures, 6(1): 51-56. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2022.51.56 \n\n\n\n\n\n\n\nALPHA-MANGOSTIN QUALITY AND QUANTITY ANALYSIS IN NAKHON SI \nTHAMMARAT MANGOSTEEN PERICARP USING THIN-LAYER CHROMATOGRAPHY \n \nSiriluk Sintupacheea*, Puttisan Rattanachooa, Suppawan Promproab \n \na Program in Creative Innovation Science and Technology, Faculty of Science and Technology, Nakhon Si Thammarat Rajabhat University, Nakhon \nSi Thammarat 80280, Thailand. \nb Program in Mathematics and Statistics, Faculty of Science and Technology, Nakorn Si Thammarat Rajabhat University, Nakhon Si Thammarat \n80280, Thailand \n*Corresponding Author Email:  siriluk_sint@nstru.ac.th \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 08 November 2021 \nAccepted 11 December 2021 \nAvailable online 16 December 2021 \n\n\n\n\n\n\n\nTLC (thin-layer chromatography) is a standard technique for simultaneously screening and monitoring \nchemical character in a large number of samples. The goal of this study was to explore if alpha-mangostin (a \ncommon secondary metabolite) could be detected in mangosteen pericarp phytochemical fingerprints and \nhow much of it could be represented using a standard calibration curve. The antioxidant activity has been \ntouted as a primary active ingredient in many commercial goods, including cosmetics and food supplements. \nTo test for the antioxidant reaction to the DPPH onto the TLC plate, mangosteen pericarps were obtained \nfrom 12 orchards that were grown without pesticides in Cha-Uat (CU), Lan Saka (LS), and Phrom Khiri (PK) \ndistricts of Nakhon Si Thammarat. The samples were dried and powdered before being extracted with \nmethanol using the reflux technique. After that, the TLC was utilized to determine the phytochemical \nfingerprint. The quality of phytochemicals from the LS orchards was found to differ from CU and PK samples, \nwith different bands of compounds at RF 0.2, 0.25, and 0.57. The average amount of alpha-mangostin in the \n12 samples was not statistically different, according to a one-way analysis of variance with a p-value of 0.05. \nThe average range of interest (ROI) intensity area of the antioxidant was investigated using a one-way \nanalysis of variance with a p-value of 0.05 and repeated comparisons across the sample groups by Tukey\uf0a2s \nmultiple comparison test. The average antioxidant reaction between the CU and PS group and the CU and PK \ngroup was significantly different. \n\n\n\nKEYWORDS \n\n\n\nalpha-mangostin, antioxidant, Khiri Wong, phytochemistry, DPPH \n\n\n\n1. INTRODUCTION \n\n\n\nMangosteen brings money to growers every year during harvest season \n(July-November in Nakhon Si Thammarat) when it is exported to both \ndomestic and international markets. According to data from the \nDepartment of Agricultural Extension, mangosteens accounted for two out \nof every four fruits (approximately 25,000 tons) exported from the \nprovince in the year 2020 (Ounlert et al., 2017; Pibul and Jawjit, 2021). In \nterms of taste and fruit characteristics, the mangosteen grown in the three \ndistricts of Cha-Uat (CU), Lan Saka (LS), and Prom Kiri (PK) is unique, \nespecially in Kiri Wong (the sub-district in Lan Saka), which has Thailand's \nbest climate (Ounlert et al., 2017; Suttirak and Manurakchinakorn, 2014). \nIt has a distinct feature that portrays four various colors of mangosteen \nfruit depending on the stage of growth (Figure 1). \n\n\n\nThis is because the terrain of the province is in the highlands, with good \nweather all year, resulting in a diverse range of flora and the greatest \nconditions for mangosteen growth development (Ounlert and Sdoodee, \n2015). Mangosteen is a fruit with a sweet white pulp covered in a thick \npurple outer shell that weighs three times as much as the inside flesh when \nharvested (Aizat et al., 2019). The processing of agricultural waste \nproducts, such as mangosteen pericarp, to produce active ingredients can \n\n\n\nbe advantageous. As a result, it has a direct impact on the income of \nmangosteen producers. It is consequently vital to act as a source of raw \nmaterials for production and transformation into diverse goods. When \nalpha-mangostin is utilized as an ingredient in a variety of goods, it is \ncommonly claimed that its bioactivity varies (Wezeman et al., 2015). \n\n\n\n \nFigure 1: Color of the mangosteen development stage \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 51-56 \n\n\n\n\n\n\n\n \nCite The Article: Siriluk Sintupachee, Puttisan Rattanachoo, Suppawan Promproa (2022). Alpha-Mangostin Quality and Quantity Analysis in Nakhon Si Thammarat \n\n\n\nMangosteen Pericarp Using Thin-Layer Chromatography. Malaysian Journal of Sustainable Agricultures, 6(1): 51-56. \n\n\n\n\n\n\n\nOO\n\n\n\nO O\n\n\n\nO\n\n\n\nO\n\n\n\nH\n\n\n\nH\n\n\n\nH  \nFigure 2: Chemical structure of alpha-mangostin \n\n\n\nFarmers and government organizations are becoming more interested in \nprocessing mangosteen peels into a variety of goods and claiming the \nactive element alpha-mangostin (Figure 2) in pharmacy, nutrition, and \ndietary supplement products (Ng et al., 2018; Nittayananta et al., 2018; \nSiriwattanasatorn et al., 2020; Sukatta et al., 2013; Xie et al., 2015). Alpha-\nmangostin is a plant secondary metabolite, by transferring the synthesis \nof key growth metabolic pathways into the secondary metabolic pathway, \nwere produced during varied conditions to defend themselves from \npathogens, wounds, or improper settings for growth and classed as a \nxanthone with the following qualities according to ancient folklore: It is \nmostly used to treat intestinal disorders, diarrhea, and chronic diarrhea \n(Pedraza-Chaverri et al., 2008). Antibacterial, antioxidant, and antifungal \nproperties of medicinal antibiotics (Abdallah et al., 2016; Al-Massarani et \nal., 2013; Cheok et al., 2012; Febrina and Milanda, 2018a, 2018b; \nMachmudah, 2015; Tjahjani et al., 2014; Xie et al., 2015).  \n\n\n\nIn the alpha-mangostin bioactivity assay as described using chemical \ntechniques for plant isolating material to examine the influence of various \ncomponents, it was established that isoprenoid phytochemicals are a \nsubstance in the essential oil group, and the phenolic group is a colorant \n(Gondokesumo et al., 2019; Herrera-Aco et al., 2019; Suttirak and \nManurakchinakorn, 2014). Scientific methods, on the other hand, were \nutilized to determine the existence of alpha-mangostin in the raw material \nand explore its implications. It will increase community trust among \nconsumers and producers.  \n\n\n\nTLC (thin-layer chromatography) is a well-known chromatographic \ntechnique for screening and separation of non-volatile mixtures. TLC can \nbe used to monitor the progression of a reaction into a phytochemical \nprofile similar to a fingerprint on a sheet, to identify chemical components \nin a mixture and determine the purity of a product. TLC has proven to be a \nuseful technique in a range of applications as pesticide and insecticide \nresidues in food, water, and soil are studied. The color composition of \nfibers is determined. Amino acid analysis, cosmetic contamination \ndetection, active component concentration analysis, purity testing, and \npharmaceutical and prescription identification are just a few of the \nservices available (Chewchinda and Vongsak, 2019; Kumar et al., n.d.; \nMigas et al., 2020; Misra et al., 2009; Pratiwi et al., 2017). TLC is a speedier \napproach than column chromatography for screening plant compounds, \nidentifying plant material, and determining marker content.  \n\n\n\nOn TLC plates, the migration of a compound to a specific spot in the \nchromatogram is utilized to separate compounds, and the pictures are \nemployed as a central feature to demonstrate similarities and differences. \nThe capacity to analyze many samples grown in parallel on a plate and \nthen re-evaluate in different ways with or without chemical derivatization \nis a significant advantage. Even biological activity testing can be \nperformed on the same plate. The purpose of this study was to determine \nthe phytochemical fingerprint and amount of the major active ingredient, \nalpha-mangostin, in mangosteen pericarp collected from the Cha-Uat (CU), \nLan Saka (LS), and Phrom Khiri (PK) districts, as well as screening test for \nthe antioxidant reactivity to DPPH on a TLC plate.   \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Mangosteen sample site \n\n\n\nMangosteens were obtained from 12 non-chemically cultivated \n\n\n\nmangosteen orchards from Cha-Uat (CU) (3 orchards), Lan Saka (LS) (5 \n\n\n\norchards), and Phrom Khiri (PK) (4 orchards) between July and November \n\n\n\n2020, when the mangosteen fruiting season was in full swing (Figure 3). \n\n\n\nThe 12 mangosteen plantations from three districts were chosen because \n\n\n\nthere have been reports of mangosteen output for both domestic and \n\n\n\nexport. They produce almost a third of the province's products (Ounlert et \n\n\n\nal., 2017; Pibul and Jawjit, 2021). Farmers and entrepreneurs have \n\n\n\nexploited mangosteen peels from all three locations as raw materials for a \n\n\n\nvariety of products. The sampling sites' latitude and longitude are \n\n\n\nrepresented in Figure 3. \n\n\n\n\n\n\n\nFigure 3: Nakhon Si Thammarat province and the sampling sites, A: \n\n\n\npresented the location of Nakhon Si Thammarat (in red) in the southern \n\n\n\npart of Thailand, B: showed where the sampling locations were in the \n\n\n\ndistricts of Cha-Uat, Lan Saka, and Prom Khiri. \n\n\n\n2.2 Preparation and extraction of samples \n\n\n\nAll samples were transferred to the Nakhon Si Thammarat Rajaphat \nUniversity's (NSTRU) Specialized Research Unit on Insects and Herbs for \nprocessing. Mangosteen fruit has been peeled and cuts into small pieces, \nthen completely dried in an incubator at 50\u00b0C for two nights before being \nmilled into powder. Three hundred milligrams of mangosteen powder \nwere extracted in an extraction tube containing 10 ml methanol using the \nreflux method at 70\u00b0C for two hours, then vacuum dried the solution at \n70\u00b0C and dissolved the extracts with 2 ml methanol (SyncorePlus, BUCHI), \nthen centrifuged to remove the pellet. Only the clear supernatant was \ntransferred to a new 2 ml microtube and TLC examination was performed. \nThe quality and quantity analyses, as well as the antioxidant activity test \n(the experiment 2.3, 2.4, and 2.5), were conducted at Chulalongkorn \nUniversity's Faculty of Pharmaceutical Sciences. \n\n\n\n2.3 Thin-layer chromatography \n\n\n\n2.3.1  Chemicals \n\n\n\nEuropean Pharmacopoeia reference standard for alpha-mangostin (Figure \n1) Sigma-Aldrich provided (1,3,6-trihydroxy-7-methoxy-2,8-bis(3-\nmethylbut-2-en-1-yl)-9H-xanthen-9-one, CAS number 6147-11-1). (St. \nLouist, MO, USA). The mobile phase's chemical components, toluene, \nacetonitrile, ethyl acetate, and glacial acetic acid, were acquired as the \nanalytical grade from Sigma. The antioxidant activity test utilized a \nreagent solution of 0.5 percent 2,2-diphenyl-1-picrylhydrazyl (DPPH) in \nmethanol obtained from Sigma-Aldrich. \n\n\n\n2.3.2  Chromatographic state and apparatus \n\n\n\nUsing the LINOMAT5 (CAMAG), ten microliters of the extracts were \nspotted onto a 10 cm x 20 cm aluminum silica gel 60F254 TLC plate (Merck) \nversus five dilutions of the alpha-mangostin standard (Sigma) at 125, 250, \n375, 500, and 625 \uf06dg per spot. The plate was then developed in a saturated \nTLC chamber with the mobile phase ratios of 35:5:15:0.15 for toluene, \nacetonitrile, ethyl acetate, and glacial acetic acid, respectively. The plate \nwas allowed to develop up to an 80 mm distance before being withdrawn \nand blown to dry to terminate the chromatographic capillary force \nreaction. The experiment was done in triplicate.  \n\n\n\n2.4 Quality analysis \n\n\n\nTo examine the quality of the mangosteen pericarp by looking at the \nfingerprint of the phytochemical profile on TLC and separating those \nchemicals using a mobile solvent. \n\n\n\n2.4.1  Detection and scanning \n\n\n\nThe CAMAG TLC scanning 3 and VisionCats 1.2 software (CAMAG) were \nused to densitometry the alpha-mangostin. The densitometric scanning \nwas carried out in absorbance mode at a scanning speed of 40 mm/s, with \na light source of deuterium and tungsten at 317 nm. Under 254 and 366 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 51-56 \n\n\n\n\n\n\n\n \nCite The Article: Siriluk Sintupachee, Puttisan Rattanachoo, Suppawan Promproa (2022). Alpha-Mangostin Quality and Quantity Analysis in Nakhon Si Thammarat \n\n\n\nMangosteen Pericarp Using Thin-Layer Chromatography. Malaysian Journal of Sustainable Agricultures, 6(1): 51-56. \n\n\n\n\n\n\n\nnm UV-transluminescent and documentation, the compound fingerprint of \nthe mangosteen pericarp was observed. \n\n\n\n2.5 Quantity analysis  \n\n\n\nA genuine alpha-mangostin standard was utilized as an indication on the \nTLC sheet, and a standard curve at various concentrations was used to \ncalculate the amount of the medication we were interested in. \n\n\n\n2.5.1   Alpha-mangostin standard preparation \n\n\n\nStandard alpha-mangostin stock solution was produced in methanol at 1 \nmg.ml-1 and diluted separately for working standard solution at 62.5, 125, \n187.5, 250, and 312.5 mg.ml-1, all of which were held at 4\u00b0C until use. The \ncalibration curve was applied three times, then developed and scanned as \ndescribed previously. The regression equation for the alpha-mangostin \nwas calculated after the calibration curve was produced by plotting \naverage peak areas vs the corresponding amounts. \n\n\n\n2.6 DPPH activity reaction test   \n\n\n\nTo examine the reaction of the alpha-mangostin found on the TLC plate to \nthe DPPH was done for the antioxidant reaction, the separated fingerprint \nTLC plate was sprayed with 0.5 percent DPPH in methanol, which causes \nthe material to turn yellow after 5 minutes at room temperature in the \ndark. \n\n\n\n2.7 Statistical analysis \n\n\n\nThe standard calibration curve was used to estimate the amount of alpha-\nmangostin. The range of interest intensity (ROI) area of antioxidant \nactivity on TLC plates was calculated using ImageJ software and integrated \ndensity sums of all pixels (Rueden et al., 2017). The statistical analysis was \ncarried out and graphs were generated using the statistical software Prism \n9\u00ae (Graphpad, CA, USA), which included a one-way analysis of variance \n(ANOVA) followed by Tukey\u2019s multiple comparison test. A probability \nlevel of less than 0.05 was used to define statistical significance. \n\n\n\n3. RESULTS \n\n\n\n3.1 Quality analysis \n\n\n\n3.1.1 Fingerprint optimization of chromatographic conditions \n\n\n\nThe mangosteen pericarp methanol extracts could be separated as \ntoluene: acetonitrile: ethyl acetate: glacial acetic acid in the ratio \n35:5:15:0.15, the fingerprint of the extract revealed the alpha-mangostin \nat Rf 0.67, compared to the authentic standard, which could be identified \nunder the 254 and 366 nm UV wavelengths (Figures 4A and 4B). The \nphytochemical fingerprint has the same pattern characteristics as the \nothers, however, there are variances in intensity under 254 nm UV light \nfor all the methanol extracts (Figure 4A). The noteworthy characteristics \nof the mangosteen from Khiri Wong (LS1) and adjacent (LS2, LS3, and LS4 \nfrom Lansaka) reflected the district intensity at Rf 0.2, 0.25, and 0.57 \n(Figure 4B) differ from the sample from PK and CU at 366 nm and could \nnot be seen at 245 nm. Spraying the NP-PEG reagent over all of the \nchemical constituent fingerprints on the TLC plate, which appeared yellow \nto light orange, was used to evaluate the flavonoid group (data not shown). \n\n\n\n \nFigure 4: Phytochemical fingerprint of the mangosteen pericarp \n\n\n\nmethanol extracts. A: the fingerprint under 254 nm UV-transluminescent, \nB: the fingerprint under 366 nm UV-transluminescent. CU1, CU2, and CU3 \n\n\n\nare the samples collected from Cha-Uat district, LS1, LS2, LS3, LS4, and \nLS5 are the samples collected from LanSaka district, PK1, PK2, PK3, and \n\n\n\nPK4 are the samples collected from Prom Kiri district. Std. is alpha-\nmangostin authentic standard and arrow indicated for the position of the \n\n\n\nalpha-mangostin. \n\n\n\n3.1.2 Linear and range \n\n\n\nThe calibration curve was displayed in Figure 5A as a linear model, with a \ncorrelation coefficient (R2) of 0.9931. The alpha-mangostin standard had \na concentration range that was pure, free of impurities and related \ncompounds (Figure 5B). To show the characterization, the alpha-\nmangostin chromatogram was observed at a max wavelength of 317 nm \n(Figure 5C). The extracts' sample chromatogram was shown at Rf 0.67 \u00b1 \n0.05. To fit in a region of the calibration curve, the extract was diluted by \nten times, removing the low intensity of another element in the extract. \nThe extracts' characteristic chromatogram appeared to be identical to the \nactual standard (Figure 5C). \n\n\n\n \nFigure 5: Alpa-mangostin standard calibration curve (A) and \n\n\n\ndensitogram of alpha-mangostin scanned at \uf06c = 317 nm (B) and \nspectrodensitogram of alpha-mangosteen scanned at \uf06c range form 200-\n\n\n\n700 nm (C). \n\n\n\n3.2 Quantity analysis \n\n\n\nFor the alpha-mangostin, the area peck of the scanning chromatogram \nversus the corresponding amounts was determined, as well as the \nregression equation. The average amount of alpha-mangostin content in \npericarp extract was statistically examined using a one-way analysis of \nvariance with a p-value of 0.05, and there was no significant difference \nbetween the sample and the location (Figure 6A). The average quantity of \nalpha-mangostin in pericarp extract was highest in LS4 (0.2482\u00b10.2539 \nmg/100g) and lowest in PK1 (0.1908\u00b10.1773 mg/100g) (Figure 7B). In \nthe CU, LS, and PK districts, the average alpha-mangostin content was \n0.2174\u00b10.1857 mg/100g, 0.2310\u00b10.1994 mg/100g, and 0.1997\u00b10.1681 \nmg/100g, respectively (Figure 6B). \n\n\n\n \nFigure 6: The amount of alpha-mangostin in mangosteen pericarp \n\n\n\nextract. (A) bar graph represents the mean amount of alpha-mangostin of \n12 orchards. (B) bar graph represents the mean amount of alpha-\n\n\n\nmangostin of the orchards from the three districts. \n\n\n\n3.3 Antioxidant activity \n\n\n\nImageJ was used to compute the area of the DPPH reaction on the TLC \nplate for the ROI intensity region of antioxidant activity relative to alpha-\nmangostin (Figure 7). The ROI intensity of alpha-mangostin in the pericarp \nof mangosteens from all 12 plantations was highest in LS4 orchard and \nlowest in CU1 orchard. Utilizing one-way ANOVA statistical analysis, the \naverage intensity of the ROI of the DPPH response was investigated, and \nthere was found to be a significant difference, as well as additional \nmultiple-way ANOVA using Tukey\uf0a2s multiple comparison test and found to \nbe significantly different (Figure 8A). The average intensity of CU, LS, and \nPK, which revealed significant differences between CU and LS, CU and PK, \nbut not between LS and PK (Figure 8B). The average ROI intensities of CU, \nLS, and PK extracts are 26,512\u00b16.04, 35,275.2\u00b13.67, and 32,328\u00b10.21, \nrespectively (Figure 8B).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 51-56 \n\n\n\n\n\n\n\n \nCite The Article: Siriluk Sintupachee, Puttisan Rattanachoo, Suppawan Promproa (2022). Alpha-Mangostin Quality and Quantity Analysis in Nakhon Si Thammarat \n\n\n\nMangosteen Pericarp Using Thin-Layer Chromatography. Malaysian Journal of Sustainable Agricultures, 6(1): 51-56. \n\n\n\n\n\n\n\n \nFigure 7: Phytochemical fingerprint of the mangosteen pericarp \n\n\n\nmethanol extracts and the reaction to DPPH on TLC plate. CU1, CU2, and \nCU3 are mangosteen pericarp samples collected from Cha-Uat, LS1, LS2, \nLS3, LS4, and LS5 are the mangosteen pericarp from Lanska, PK1, PK2, \nPK3, and PK4 are the mangosteen pericarp from Prom Kiri. The Std. is \n\n\n\nalpha-mangostin authentic standard and arrow indicated for the position \nof the alpha-mangostin. \n\n\n\n \nFigure 8: The alpha-mangostin labeling region of interest (ROI) intensity \n\n\n\narea of the mangosteen pericarp extract. (A) bar graph represents the \nmean content of the ROI of 12 mangosteen orchards. (B) bar graph \n\n\n\nrepresents the mean content of ROI of the orchards from three districts. \nThe ns = p \uf03e 0.05, *= p \uf03c 0.05, ** = p \uf03c 0.025, *** p \uf03c 0.001, **** p \uf03c 0.0001. \n\n\n\n4. DISCUSSION  \n\n\n\nMangosteen pericarps are used as raw materials for a variety of goods and \n\n\n\nclaims that it contains vital chemicals, alpha-mangostin has become \n\n\n\nextremely popular.  It's the product's principal ingredient. The invention \n\n\n\nof alpha-mangostin has simplified the detection of materials on a TLC \n\n\n\nsheet, which is a technique for comparing the quality and quantity of a \n\n\n\nsubstance of interest to a reference substance on a sheet containing many \n\n\n\nsamples at the same time. TLC was utilized to explore active chemicals in \n\n\n\nthe mangosteen pericarp, which were evaluated using standard curves \n\n\n\nthat could be analyzed in several sample volumes at the same time. As a \n\n\n\nresult, we have both quality in terms of the presence or absence of critical \n\n\n\ncomponents, as well as a comparable standard that can immediately \n\n\n\ninform us if the samples we evaluated contain the compounds we need.  \n\n\n\nWe performed alpha-mangostin on a TLC plate to test for preparation and \n\n\n\nto demonstrate its essential compounds. The solvent-mobile system can \n\n\n\nseparate the alpha-mangostin from other phytochemicals at the RF \n\n\n\n0.67\u00b10.05 compared to the previous study which indicated the alpha-\n\n\n\nmangostin at 0.37-0.46 using the toluene, ethyl acetate, and formic acid in \n\n\n\nvarious ratios (Kusmayadi et al., 2019; Kusmayadi and Adriani, 2018). \n\n\n\nAccording to the experiment, other molecules will be detectable beneath \n\n\n\nalpha-mangostin. This could be a valuable solvent system for screening \n\n\n\nadditional compounds discovered in the pericarp of the mangosteen. In \n\n\n\nthis investigation, there was no statistically significant difference in alpha-\n\n\n\nmangostin content between the 12 orchards and locations. The alpha-\n\n\n\nmangostin content in this experiment ranged from 0.1908 to 0.2482 \n\n\n\nmg/100g dry weight, according to the methanol extract and similar \n\n\n\nextraction technique, and other before was 4.67-31.50 ng/mg (w/w) \n\n\n\n(Abdallah et al., 2016; Andayani and Ismed, 2017; Chewchinda and \n\n\n\nVongsak, 2019).  \n\n\n\nMicrowave-prepared methanol extracts, on the other hand, were found to \n\n\n\nhave a level that was two times higher than this study. However, the \n\n\n\ncontent of prior articles was determined using the standard's calibration \n\n\n\ncurve from HPLC procedures (Kongkiatpaiboon et al., 2016; Rivero & \n\n\n\nGaribay, 2019; Yodhnu et al., 2009). For antioxidant activity, the \n\n\n\nantioxidant activity was calculated using the reaction area. The reactive \n\n\n\nROI to DPPH expressed on the TLC plate varies greatly between the 12 \n\n\n\norchards and locations. The intensity could indicate the strength of the \n\n\n\nantioxidant activity or the high concentration of the chemical that reacted \n\n\n\nwith DPPH. According to the standard method for investigating the DPPH \n\n\n\nradical scavenging assay, the crude extract was used to calculate the \n\n\n\npercentage of scavenging, while the reaction on the TLC plate was used to \n\n\n\nexpress the intensity of the reaction, which was correlated to the position \n\n\n\nof the interested compound and could be seen in separate fingerprints \n\n\n\n(Ben Mansour et al., 2016; Ghosh et al., 2013).  \n\n\n\nThe method for detecting such qualities on the TLC sheet, on the other \n\n\n\nhand, is a quick and straightforward way to observe a clear comparison. \n\n\n\nTLC procedures are simple, quick, and cost-effective, and the findings may \n\n\n\nbe read promptly. However, there may be limitations on where alpha-\n\n\n\nmangostin can be found. There could be other compounds present, causing \n\n\n\nthe DPPH reaction to being erroneous. In this experiment, screening for \n\n\n\nreactivity to DPPH versus standard produced preliminary data on samples \n\n\n\nreacting to DPPH compared to numerous samples at once and maybe a \n\n\n\nmethod to help solve this problem. The influence of screening alpha-\n\n\n\nmangostin quality and quantity on farmers and entrepreneurs selling their \n\n\n\ncommodities may be that scientific procedures are more reliable.  \n\n\n\n5. CONCLUSIONS  \n\n\n\nIn order to determine the quality and amount of alpha-mangostin in \n\n\n\nmangosteen pericarp, the TLC method was used. It was only able to \n\n\n\ncompare fingerprints and the intensity of compounds identified in \n\n\n\nmangosteen pericarp in one of the 12 samples. In addition to being able to \n\n\n\nanalyze the essential components of interest, multiple samples can be \n\n\n\ngathered at simultaneously. The substance content in the TLC research \n\n\n\nwas 2 - 100 \uf06dL, which is a very little amount of the active component we \n\n\n\nwere looking for, in the nanograms per 100 milligrams dry extract range. \n\n\n\nAntioxidant screening tests on the TLC sheet of the component of interest \n\n\n\nare instantly recognizable and may be compared directly. At the absolute \n\n\n\nleast, it matches the location of the alpha-mangostin we're looking for, and \n\n\n\nit could lead to the discovery of more DPPH-reactive sites, allowing for \n\n\n\nmore investigation. It is the foundational information used to make \n\n\n\nconclusions about subsequent research, whether it is in-depth research or \n\n\n\na product review. The presence of active chemicals in mangosteen \n\n\n\npericarp was confirmed using the TLC method. Continue to support \n\n\n\nproducers in the sector, community, or community enterprise, as well as \n\n\n\nboost customer confidence in the sector, community, or community \n\n\n\nenterprise.  \n\n\n\nAUTHORS CONTRIBUTION \n\n\n\nSS assisted with this study's research, data analysis, locating a publication \n\n\n\nto publish in, and finally writing and formatting the manuscript. PR was in \n\n\n\ncharge of the research, mapping, and GIS gathering for the location. SP \n\n\n\naided with the study and statistical analysis of the data. All of the authors \n\n\n\nwere concerned about the research, reporting, article writing, editing, and, \n\n\n\nfinally, permission for publishing. \n\n\n\nACKNOWLEDGMENT \n\n\n\nThe author (SS) was granted permission to use the central laboratory's \n\n\n\nfacilities by Prof. Dr. Wanchai De-Eknamkul, Faculty of Pharmaceutical \n\n\n\nSciences, Chulalongkorn University. Thank you to the 12 mangosteen \n\n\n\norchard owners who generously donated mangosteen pericarp for use in \n\n\n\nthe experiment, notably the Phetkiri guesthouse community companies, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 51-56 \n\n\n\n\n\n\n\n \nCite The Article: Siriluk Sintupachee, Puttisan Rattanachoo, Suppawan Promproa (2022). Alpha-Mangostin Quality and Quantity Analysis in Nakhon Si Thammarat \n\n\n\nMangosteen Pericarp Using Thin-Layer Chromatography. Malaysian Journal of Sustainable Agricultures, 6(1): 51-56. \n\n\n\n\n\n\n\nKhiri Wong, Lan Saka. This research had no such engagement from any \n\n\n\nfunding source(s). \n\n\n\nREFERENCES \n\n\n\nAbdallah, H.M., El-Bassossy, H.M., Mohamed, G.A., El-halawany, A.M., \nAlshali, K.Z., and Banjar, Z.M., 2016. Phenolics from Garcinia mangostana \nalleviate exaggerated vasoconstriction in metabolic syndrome through \ndirect vasodilatation and nitric oxide generation. BMC Complementary \nand Alternative Medicine, 16 (1), Pp. 359. \nhttps://doi.org/10.1186/s12906-016-1340-5 \n\n\n\nAizat, W.M., Ahmad-Hashim, F.H., Syed Jaafar, S.N., 2019. Valorization of \nmangosteen, \u201cThe Queen of Fruits,\u201d and new advances in postharvest \nand in food and engineering applications: A review. Journal of Advanced \nResearch, 20, Pp. 61\u201370. https://doi.org/10.1016/j.jare.2019.05.005 \n\n\n\nAl-Massarani, S., El Gamal, A., Al-Musayeib, N., Mothana, R., Basudan, O., Al-\nRehaily, A., Farag, M., Assaf, M., El Tahir, K., Maes, L., 2013. \nPhytochemical, Antimicrobial and Antiprotozoal Evaluation of Garcinia \nMangostana Pericarp and \u03b1-Mangostin, Its Major Xanthone Derivative. \nMolecules, 18 (9), Pp. 10599\u201310608. \nhttps://doi.org/10.3390/molecules180910599 \n\n\n\nAndayani, R., Ismed, F., 2017. Analisis \u03b1-Mangostin dalam Minuman Herbal \nKulit Buah Manggis (Garcinia mangostana L.) dengan Metode \nKromatografi Lapis Tipis-Densitometri. Jurnal Sains Farmasi & Klinis, 4 \n(1), Pp. 61. https://doi.org/10.29208/jsfk.2017.4.1.195 \n\n\n\nBen, M.R., Ksouri, W.M., Cluzet, S., Krisa, S., Richard, T., Ksouri, R., 2016. \nAssessment of Antioxidant Activity and Neuroprotective Capacity on \nPC12 Cell Line of Frankenia thymifolia and Related Phenolic LC-MS/MS \nIdentification. Evidence-Based Complementary and Alternative \nMedicine, Pp. 1\u20138. https://doi.org/10.1155/2016/2843463 \n\n\n\nCheok, C.Y., Chin, N.L., Yusof, Y.A., Law, C.L., 2012. Extraction of Total \nPhenolic Content from Garcinia mangostana Linn. Hull. I. Effects of \nSolvents and UV\u2013Vis Spectrophotometer Absorbance Method. Food and \nBioprocess Technology, 5 (7), Pp. 2928\u20132933. \nhttps://doi.org/10.1007/s11947-011-0627-2 \n\n\n\nChewchinda, S., Vongsak, B., 2019. Development and validation of a high-\nperformance thin layer chromatography method for the simultaneous \nquantitation of \u03b1- and \u03b3-mangostins in Thai stingless bee propolis. \nRevista Brasileira de Farmacognosia, 29 (3), Pp. 333\u2013338. \nhttps://doi.org/10.1016/j.bjp.2018.12.004 \n\n\n\nFDA/CDER/\"Beers, D., 2015. Analytical Procedures and Methods \nValidation for Drugs and Biologics. Pp. 18. \n\n\n\nFebrina, D., Milanda, T., 2018. Pharmacological Activity Garcinia \nmangostana Linn: A Review, 8, Pp. 4. \n\n\n\nGhosh, S., Derle, A., Ahire, M., More, P., Jagtap, S., Phadatare, S.D., Patil, A.B., \nJabgunde, A.M., Sharma, G.K., Shinde, V.S., Pardesi, K., Dhavale, D.D., \nChopade, B.A., 2013. Phytochemical Analysis and Free Radical \nScavenging Activity of Medicinal Plants Gnidia glauca and Dioscorea \nbulbifera. PLoS ONE, 8 (12), Pp. e82529. \nhttps://doi.org/10.1371/journal.pone.0082529 \n\n\n\nGondokesumo, M.E., Pardjianto, B., Sumitro, S.B., Widowati, W., Handono, \nK., 2019. Xanthones Analysis and Antioxidant Activity Analysis \n(Applying ESR) of Six Different Maturity Levels of Mangosteen Rind \nExtract (Garcinia mangostana Linn.). Pharmacognosy Journal, 11 (2), Pp. \n369\u2013373. https://doi.org/10.5530/pj.2019.11.56 \n\n\n\nHerrera-Aco, D.R., Medina-Campos, O.N., Pedraza-Chaverri, J., Sciutto-\nConde, E., Rosas-Salgado, G., Fragoso-Gonz\u00e1lez, G., 2019. Alpha-\nmangostin: Anti-inflammatory and antioxidant effects on established \ncollagen-induced arthritis in DBA/1J mice. Food and Chemical \nToxicology, 124, Pp. 300\u2013315. \nhttps://doi.org/10.1016/j.fct.2018.12.018 \n\n\n\nKongkiatpaiboon, S., Vongsak, B., Machana, S., Weerakul, T., Pattarapanich, \nC., 2016. Simultaneous HPLC quantitative analysis of mangostin \nderivatives in Tetragonula pagdeni propolis extracts. Journal of King \nSaud University - Science, 28 (2), Pp. 131\u2013135. \nhttps://doi.org/10.1016/j.jksus.2015.06.007 \n\n\n\nKumar, S., Jyotirmayee, K., Sarangi, M. (n.d.). Thin Layer Chromatography: \nA Tool of Biotechnology for Isolation of Bioactive Compounds from \nMedicinal Plants. 7. \n\n\n\nKusmayadi, A., Adriani, L., 2018. The effect of solvents and extraction time \non total xanthone and antioxidant yields of mangosteen peel (Garcinia \nmangostana L.) extract. Drug Invention Today, 10 (12), Pp. 5. \n\n\n\nKusmayadi, A., Bachtiar, K.R., Prayitno, C.H., 2019. The effects of \nmangosteen peel (Garcinia mangostana L.) and Turmeric (Curcuma \ndomestica Val) flour dietary supplementation on the growth \nperformance, lipid profile, and abdominal fat content in Cihateup ducks. \nVeterinary World, 12 (3), Pp. 402\u2013408. \nhttps://doi.org/10.14202/vetworld.2019.402-408 \n\n\n\nMachmudah, S., 2015. Subcritical Water Extraction of Xanthone from \nMangosteen (Garcinia Mangostana Linn) Pericarp, 5 (1), Pp. 6. \n\n\n\nMigas, P., Stempka, N., Krauze-Baranowska, M., 2020. The use of thin-layer \nchromatography in the assessment of the quality of lutein-containing \ndietary supplements. JPC \u2013 Journal of Planar Chromatography \u2013 Modern \nTLC, 33 (1), Pp. 11\u201318. https://doi.org/10.1007/s00764-019-00001-3 \n\n\n\nMisra, H., Dwivedi, B.K., Mehta, D., Mehta, B.K., Jain, D.C., 2009. \nDevelopment and Validation of High Performance Thin-Layer \nChromatographic Method for Determination of \u03b1-Mangostin in Fruit \nPericarp of Mangosteen Plant (Garcinia mangostana L.) using Ultraviolet \n\u2013 Visible Detection. Rec. Nat. Prod., Pp. 9. \n\n\n\nNg, H.S., Tan, G.Y.T., Lee, K.H., Zimmermann, W., Yim, H.S., Lan, J.C.W., 2018. \nDirect recovery of mangostins from Garcinia mangostana pericarps \nusing cellulase-assisted aqueous micellar biphasic system with \nrecyclable surfactant. Journal of Bioscience and Bioengineering, 126 (4), \nPp. 507\u2013513. https://doi.org/10.1016/j.jbiosc.2018.04.008 \n\n\n\nNittayananta, W., Limsuwan, S., Srichana, T., Sae-Wong, C., Amnuaikit, T., \n2018. Oral spray containing plant-derived compounds is effective \nagainst common oral pathogens. Archives of Oral Biology, 90, Pp. 80\u201385. \nhttps://doi.org/10.1016/j.archoralbio.2018.03.002 \n\n\n\nOunlert, P., Sdoodee, S., 2015. The Effects of Climatic Variability on \nMangosteen Flowering Date in Southern and Eastern of Thailand. \nResearch Journal of Applied Sciences, Engineering and Technology, 11 \n(6), Pp. 617\u2013622. https://doi.org/10.19026/rjaset.11.2021 \n\n\n\nOunlert, P., Sdoodee, S., Tongkhow, P., 2017. The mangosteen flowering \ndate model in Nakhon Si Thammarat province, southern Thailand. \nJournal of Central European Agriculture, 18 (1), Pp. 176\u2013184. \nhttps://doi.org/10.5513/JCEA01/18.1.1876 \n\n\n\nPedraza-Chaverri, J., C\u00e1rdenas-Rodr\u00edguez, N., Orozco-Ibarra, M., P\u00e9rez-\nRojas, J.M., 2008. Medicinal properties of mangosteen (Garcinia \nmangostana). Food and Chemical Toxicology, 46 (10), Pp. 3227\u20133239. \nhttps://doi.org/10.1016/j.fct.2008.07.024 \n\n\n\nPibul, P., Jawjit, S., 2021. Agrochemical Usage Inventory and Measured \nResidues in Streams Within an Off-Season Durian Production Region of \nThailand. Frontiers in Sustainable Food Systems, 5, Pp. 731973. \nhttps://doi.org/10.3389/fsufs.2021.731973 \n\n\n\nPratiwi, L., Fudholi, A., Martien, R., Pramono, S., 2017. Development of TLC \nand HPTLC Method for Determination \u03b1- Mangostin in Mangosteen \nPeels (Garcinia Mangostana L.,). 9 (3), Pp. 6. \n\n\n\nRivero, B., Garibay, I., 2019. Development and Validation of a Stability-\nIndicating HPLC Method for the Quantification of \u03b1-Mangostin in Dietary \nSupplements. Natural Product Communications, 14 (7), Pp. \n1934578X1986394. https://doi.org/10.1177/1934578X19863948 \n\n\n\nRueden, C.T., Schindelin, J., Hiner, M.C., DeZonia, B.E., Walter, A.E., Arena, \nE.T., and Eliceiri, K.W., 2017. ImageJ2: ImageJ for the next generation of \nscientific image data. BMC Bioinformatics, 18 (1), Pp. 529. \nhttps://doi.org/10.1186/s12859-017-1934-z \n\n\n\nSiriwattanasatorn, M., Itharat, A., Thongdeeying, P., Ooraikul, B., 2020. In \nVitro Wound Healing Activities of Three Most Commonly Used Thai \nMedicinal Plants and Their Three Markers. Evidence-Based \nComplementary and Alternative Medicine, Pp. 1\u201311. \nhttps://doi.org/10.1155/2020/6795383 \n\n\n\nSukatta, U., Takenaka, M., Ono, H., Okadome, H., Sotome, I., Nanayama, K., \nThanapase, W., Isobe, S., 2013. Distribution of Major Xanthones in the \nPericarp, Aril, and Yellow Gum of Mangosteen (Garcinia Mangostana \nLinn.) Fruit and Their Contribution to Antioxidative Activity. Bioscience, \nBiotechnology, and Biochemistry, 77 (5), Pp. 984\u2013987. \nhttps://doi.org/10.1271/bbb.120931 \n\n\n\n\nhttps://doi.org/10.1186/s12906-016-1340-5\n\n\nhttps://doi.org/10.1016/j.jare.2019.05.005\n\n\nhttps://doi.org/10.3390/molecules180910599\n\n\nhttps://doi.org/10.29208/jsfk.2017.4.1.195\n\n\nhttps://doi.org/10.1155/2016/2843463\n\n\nhttps://doi.org/10.1007/s11947-011-0627-2\n\n\nhttps://doi.org/10.1016/j.bjp.2018.12.004\n\n\nhttps://doi.org/10.1371/journal.pone.0082529\n\n\nhttps://doi.org/10.5530/pj.2019.11.56\n\n\nhttps://doi.org/10.1016/j.fct.2018.12.018\n\n\nhttps://doi.org/10.1016/j.jksus.2015.06.007\n\n\nhttps://doi.org/10.14202/vetworld.2019.402-408\n\n\nhttps://doi.org/10.1007/s00764-019-00001-3\n\n\nhttps://doi.org/10.1016/j.jbiosc.2018.04.008\n\n\nhttps://doi.org/10.1016/j.archoralbio.2018.03.002\n\n\nhttps://doi.org/10.19026/rjaset.11.2021\n\n\nhttps://doi.org/10.5513/JCEA01/18.1.1876\n\n\nhttps://doi.org/10.1016/j.fct.2008.07.024\n\n\nhttps://doi.org/10.3389/fsufs.2021.731973\n\n\nhttps://doi.org/10.1177/1934578X19863948\n\n\nhttps://doi.org/10.1186/s12859-017-1934-z\n\n\nhttps://doi.org/10.1155/2020/6795383\n\n\nhttps://doi.org/10.1271/bbb.120931\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 51-56 \n\n\n\n\n\n\n\n \nCite The Article: Siriluk Sintupachee, Puttisan Rattanachoo, Suppawan Promproa (2022). Alpha-Mangostin Quality and Quantity Analysis in Nakhon Si Thammarat \n\n\n\nMangosteen Pericarp Using Thin-Layer Chromatography. Malaysian Journal of Sustainable Agricultures, 6(1): 51-56. \n\n\n\n\n\n\n\nSuttirak, W., Manurakchinakorn, S., 2014. In vitro antioxidant properties \nof mangosteen peel extract. Journal of Food Science and Technology, 51 \n(12), Pp. 3546\u20133558. https://doi.org/10.1007/s13197-012-0887-5 \n\n\n\nTjahjani, S., Widowati, W., Khiong, K., Suhendra, A., Tjokropranoto, R., \n2014. Antioxidant Properties of Garcinia Mangostana L (Mangosteen) \nRind. Procedia Chemistry, 13, Pp. 198\u2013203. \nhttps://doi.org/10.1016/j.proche.2014.12.027 \n\n\n\nWezeman, T., Br\u00e4se, S., Masters, K.S., 2015. Xanthone dimers: A compound \nfamily which is both common and privileged. Natural Product Reports, \n32 (1), Pp. 6\u201328. https://doi.org/10.1039/C4NP00050A \n\n\n\nXie, Z., Sintara, M., Chang, T., Ou, B., 2015. Daily consumption of a \nmangosteen\u2010based drink improves in vivo antioxidant and anti\u2010\ninflammatory biomarkers in healthy adults: A randomized, double\u2010\nblind, placebo\u2010controlled clinical trial. Food Science & Nutrition, 3 (4), \nPp. 342\u2013348. https://doi.org/10.1002/fsn3.225 \n\n\n\nYodhnu, S., Sirikatitham, A., Wattanapiromsakul, C., 2009. \u03b1-Mangostin in \nMangosteen Peel Extract: A Tool. Journal of Chromatographic Science, \n47, Pp. 5. \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1007/s13197-012-0887-5\n\n\nhttps://doi.org/10.1016/j.proche.2014.12.027\n\n\nhttps://doi.org/10.1039/C4NP00050A\n\n\nhttps://doi.org/10.1002/fsn3.225\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.82.89 \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 .\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2021.82.89\n\n\n\nMEDICINAL PROPERTIES OF BIOACTIVE COMPOUNDS AND ANTIOXIDANT \nACTIVITY IN Durio zibethinus \n\n\n\nSarah Yew Yen Yee \n\n\n\nOei Family Clinic, Elias Mall #02-316 Singapore 510625 \n*Corresponding Author Email: sarahyyy123@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 26 November 2020 \nAccepted 27 December 2020 \nAvailable online 08 January 2020\n\n\n\nDurio zibethinus, commonly known as Durian, is an exotic Southeast Asian tropical fruit. More than the \npungent aroma the fruit is well-known for, it is said to be beneficial to health as it contains many antioxidants \nand bioactive compounds that have different potentials for positive impacts on health. However, there is \nscant existing literature which gives an overview on the amounts of bioactive compounds in several varieties \nof durian in Southeast Asia, and the relevant health benefits. This review article therefore seeks to consolidate \nthe literature which have identified bioactive compounds and investigated antioxidant activities in durian \ncultivars from Malaysia, Indonesia, Thailand and China, and studies that have given insight on potential \nmedicinal properties of durians. A literature review was conducted using databases Scopus and ScienceDirect \nand a total of 30 articles were reviewed. Total polyphenols and flavonoids were highest in the Mon Thong \ncultivar compared to other Thailand varieties, and ripe or overripe durians were found to contain the highest \namounts of polyphenols and flavonoids. Durians were also found to contain medicinal properties, such as \nanti-inflammatory and antidiabetic potential, and protective effects on cardiac health. Further research on \nthese bioactive compounds in the nutritious fruit with potential medicinal properties can contribute to the \nmedicinal value of durians, as well as benefit the pharmaceutical industries.  \n\n\n\nKEYWORDS \n\n\n\nDurian, Bioactive, Antioxidant, Medicinal. \n\n\n\n1. INTRODUCTION\n\n\n\nDurio zibethinus, more commonly known as Durian, is an exotic fruit from \nthe tropical Durian fruit tree that falls under the hibiscus family, \nMalvaceae (Bombacaceae). There are at least 9 known edible species of \ndurio, with Durio zibethinus being the only available species on the \ninternational market. Often dubbed as the \u2018King of Fruits\u2019, durio zibethinus \nis well-known for its pungent odour which turns many consumers away \n(Ketsa, 2020; Leontowicz et al., 2011). More than its distinctive aroma, \ndurio zibethinus is said to be extremely nutritious, and to contain many \nantioxidants and bioactive compounds that are beneficial to health \n(Charoenkiatkul et al., 2016; Pasko et al., 2019).  \n\n\n\nThe portion of durian that is usually eaten by consumers is the soft, pulpy \npart called the flesh, while the seeds, rinds and hull are usually considered \nas waste. The flesh is however not the only part containing compounds \nthat could be potentially beneficial to health -- compounds in the seeds, \nrinds and hulls, which are usually considered as waste, have also been \nstudied and found to contain bioactive compounds (Wang and Li, 2011). \nFurther research on these compounds can contribute to the medicinal \nvalue of durians, as well as benefit the pharmaceutical industries. To date, \nstudies have been done on how number of bioactive compounds found in \ndurians varies with degree of ripening or different cultivars of durians, \npotential medicinal properties of durians, nutritional properties and \npotential as diet enhancers, as well as on anti-inflammatory, anti-cancer \nor antidiabetic potential (Leontowicz et al., 2007; Huang et al., 2020; \nGorinstein et al., 2011; Evary and Muhammad, 2018).  \n\n\n\nThere are many studies documenting the identities and amounts of \nbioactive compounds in durians and the corresponding antioxidant \nactivity and antioxidant potential levels (Leontowicz et al., 2011; \nCharoenkiatkul et al., 2016; Pasko et al., 2019; Wang and Li, 2011; \nLeontowicz et al., 2007; Huang et al., 2020; Gorinstein et al., 2011; \nArancibia-Avila et al., 2008; Haruenkit et al., 2010; Harunenkit et al., 2007; \nToledo et al., 2009; Isabelle et al., 2010; Evary and Muhammad, 2018; \nAshraf et al., 2011; Leontowicz et al., 2008). This literature review was \nconducted to compile information on the types of bioactive compounds \nfound in durians and antioxidant activities from all studies that have \ndiscussed it. This review will help to provide a summary on which \ncultivars and compounds could be explored further for the properties its \nbioactive components exhibit, and can also provide information on which \ncultivars could be more valuable to the pharmaceutical industry and \nhealth-conscious consumers. \n\n\n\n2. MATERIALS AND METHODS\n\n\n\nRelevant studies were sourced on databases Scopus, PubMed and \nScienceDirect for this literature review. This review focuses on the \nbioactive compounds and antioxidant activities in durians. Key terms \n\u201cDurian\u201d, \u201cDurio\u201d, \u201cBioactive\u201d, \u201cBioactivity\u201d, \u201cPharmaceutical\u201d, \u201cHealth\u201d, \n\u201cAntioxidant\u201d and \u201cMedicinal\u201d were included in the search strategy. \nRelevance of the results from the searches was first assessed through \nreviewing each title and its abstract.  A total of 17, 10 and 15 relevant \narticles were found on Scopus, ScienceDirect and PubMed respectively. \nRelevant articles were also hand-searched from the bibliographies of \n\n\n\n\nmailto:sarahyyy123@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89  \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 \n\n\n\narticles for discussion. Only English articles in the two databases were \nshortlisted. With the English language filter applied, there was no change \nto the number of articles. A total of 8 results, which were duplicates on \nmore than one of the above databases, were removed. \n\n\n\n2.1 Inclusion Criteria \n\n\n\nArticles discussing bioactive compounds and antioxidant activity or \nproperties in durians were shortlisted for review. A total of 30 articles  \nwere considered for final review (Abbas et al., 2016; Khorshidi et al., 2018; \nLoke et al., 2008; Javadi et al., 2016; Lu et al., 2015; Egert et al., 2009; \nSerban et al., 2016; Bondonno et al., 2016; Yang et al., 2017; Kent et al., \n2015; Zhang et al., 2015; Mezzomo and Ferreira, 2016; Power et al., 2018; \nRenzi-Hammond et al., 2017; Bumrungpert et al., 2018; Ferk et al., 2018; \nLeontowicz et al., 2006; Shui and Leong, 2005; Fitrianingsih et al., 2019; \nFeng et al., 2018).  \n\n\n\n2.2 Exclusion Criteria \n\n\n\nArticles discussing bioactivity in other fruits, volatile compounds in \ndurians, durian fruit preservation, were not considered for this literature \nreview.  \n\n\n\n3. RESULTS\n\n\n\n3.1 Bioactive compounds and their amounts found in ripe durians \n\n\n\n11 papers were sourced and consolidated in Table 1 to summarise the \nbioactive compounds that were found in ripe durians from places around \nthe world such as Malaysia and Thailand.  \n\n\n\n3.2 Durians with The Highest Amount of Bioactive Compounds \n\n\n\nIn a study, the Mon Thong cultivar was found to contain higher total \nphenolics than other cultivars (Cha-ni, Kra-dum and Kob-ta-kam), while \nKob-ta-kam had higher carotenoid levels than other varieties \n(Charoenkiatkul et al., 2016). Similarly, in other study, total polyphenols \nand flavonoids were highest in the Mon Thong in comparison with the \nother cultivars, Cha-ni and Kan Yao (Leontowicz et al., 2008). Again, in a \nstudy, the highest amount of bioactive substances was found in the Mon \nThong and Cha-ni cultivars, while the lowest amount was in Kan Yao and \nKra-dum (Toledo et al., 2009). Anthocyanins and flavanols were \nsignificantly higher in Mon Thong than in Kradum and Kan Yao. Hence, \ngenerally across all studies the Mon Thong variety had the highest number \nof bioactive compounds compared to other varieties.  \n\n\n\nMany studies had also compared durian bioactivity at different stages of \nripening (i.e immature, young, mature, ripe, overripe) (Haruenkit et al., \n2010; Arancibia-Avila et al., 2008; Pasko et al., 2019; Leontowicz et al., \n2011). A study found that overripe durians showed notably higher levels \nof polyphenols, flavonoids, flavanols, tannins and ascorbic acid than \nimmature durians (Pasko et al., 2019).  \n\n\n\nThis was also seen in other study which found that overripe and ripe \ndurian fruits, respectively, had the highest level of polyphenols and \nflavonoids concentrations (Haruenkit et al., 2010). A study however found \nthat in ripe durians, sum polyphenols and flavonoids were much higher \ncompared to mature and overripe durians (Arancibia et al., 2008). \nAnthocyanins and flavanols were found to be in significantly higher \namounts in ripe durian than in mature and overripe durians, respectively.  \n\n\n\nPolyphenols and flavonoids were remarkably higher in overripe durians, \nwhile quercetin, ascorbic acid and anthocyanins were higher in ripe \ndurians, and tannins more abundant in mature durians, compared to \nyoung durians in study (Leontowicz et al., 2011). The general trend \nobserved across these studies is that ripe durians contain higher levels of \nbioactive compounds than immature or young durians. \n\n\n\n3.3 Antioxidant Activity Levels in Ripe Durians \n\n\n\n9 papers were sourced and found to contain information about antioxidant \nactivity levels in different cultivars of ripe durians using a range of \nantioxidant assays. The antioxidant activity levels were collated in Table \n2. \n\n\n\n3.3.1 Sources of antioxidant activity in durians \n\n\n\nGenerally, across the studies that discussed antioxidant activity in durians, \nphenolic content was found to be the main contributor to high antioxidant \nactivity in durians (Charoenkiatkul et al., 2016; Arancibia-Avila et al., \n\n\n\n2008; Toledo et al., 2009). A study found that the main contributor to \nantioxidant capacity in durians was the total phenolic content \n(Charoenkiatkul et al., 2015). In a study, the antioxidant capacity was \nmainly derived from the antioxidants that were soluble in alcohol, and the \nantioxidant capacity also had a high correlation coefficient with \npolyphenols, suggesting that the polyphenols had contributed most to \nantioxidant activity (Arancibia-Avila et al., 2008).  \n\n\n\nIn a study, caffeic acid and quercetin constituted the main bioactive \nsubstances in Mon Thong cultivar, with total polyphenols contributing the \nmost to the total antioxidant capacity of the durians (Toledo et al., 2009). \nPhenols were found to display a strong antioxidant activity in one study, \nand flavonoids and flavanols were likely contributors to high antioxidant \nactivity in the fruits investigated in another study (Leontowicz et al., 2006; \nShui and Leong, 2005).  \n\n\n\n3.3.2 Durians with the highest antioxidant activity \n\n\n\nAmong the studies discussed ripe and overripe durians generally had \nhigher antioxidant activity than immature durians (Leontowicz et al., \n2011; Leontowicz et al., 2007; Haruenkit et al., 2007; Toledo et al., 2009). \nMon Thong durian cultivar was found to have the highest antioxidant \nactivity compared to other cultivars like Cha-ni, Kan Yao and Kra-dum in \nthe studies discussed (Toledo et al., 2009; Leontowicz et al., 2008). Highest \nantioxidant capacity and bioactive compounds were found in ripe durians \nstudy, and highest antioxidant capacity found in other study, as compared \nto overripe and mature durians in both studies (Arancibia-Avila et al., \n2008; Leontowicz et al., 2007).  \n\n\n\nA study found that methanol extract of overripe durians showed the \nhighest antioxidant activity, compared to immature, mature and ripe \ndurians (Haruenkit et al., 2010). Similarly, in other study, it was found that \noverripe durians generally had the highest antioxidant potential \n(Leontowicz et al., 2011)9.  \n\n\n\nIn comparing antioxidant capacity in different cultivars of durians, some \nresearchers study found that the antioxidant activity of Mon Thong \ncultivar was significantly higher than in Kradum and in Kan Yao (Toledo \net al., 2009). The DPPH, \u03b2-carotene and Folin\u2013Ciocalteu assays showed a \nsignificant increase in the antioxidant capacities and in the content of total \npolyphenols in Mon Thong and Chani samples in Leontowicz et al\u2019s 2008 \nstudy (Leontowicz et al., 2008). \n\n\n\n4. DISCUSSION\n\n\n\n4.1 Other relevant literature \n\n\n\nSeveral other literature reviews have been done on the nutritional value, \nbioactivity, and potential health benefits of durian in general on human \nhealth. A group researcher had summarised briefly the bioactive contents \nof a few durian varieties, and found that the Kob-ta-kam variety had the \nhighest carotenoids and beta-carotene levels (Mohd et al., 2020).  \n\n\n\nIn a study, the durian pulp across various varieties was found to contain \nlinoleic acid, myristic acid, oleic acid, 10-octadecenoic acid, palmitoleic \nacid, palmitic acid, and stearic acid, with alpha-carotene and beta-carotene \nin durian pulp of, in particular, the Chani and Monthong varieties (Mohd \net al., 2020).  \n\n\n\nThe total carotenoid content was found to be higher in the Chani variety \ncompared to the Monthong variety, which aligns with what our paper has \nfound as well.  \n\n\n\nSome of researchers found three main flavonoids: flavanones, which \ninclude hesperetin and hesperidin; flavonols, which include morin, \nquercetin, rutin, kaempferol, myricetin; and flavones, which include \nluteolin and apigenin (Aziz and Jalil, 2019).  \n\n\n\nThe main flavonol in the Monthong variety was found to be quercetin \nwhile the main flavones in durians in general were found to be luteolin and \napigenin. The phenolic acids found in durians were hydroxycinnamic acid \nderivatives, including caffeic, p-coumaric, ferulic, p-anisic acid; and \nhydroxybenzoic acid, including gallic and vanillic acid, with the main \nhydroxybenzoic acid in Monthong, Chani and Pung Manee varieties being \ngallic acid.  \n\n\n\nTotal carotenoid content was higher in Thailand varieties of durian, \ncompared with Malaysian varieties. All in all, these findings largely \ncorroborate with what our paper has found. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89  \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 .\n\n\n\nTable 1: Bioactive Compounds Found In Durians from Various Studies \n\n\n\nBioactive compounds found in ripe durians and their amounts if available Authors, year, species of durians and country \n\n\n\nCarotenoids (\u03bcg/100 g dry matter) \nLutein \nMon Thong: 136; Cha-ni: 129; Kra-dum:130; Kob-ta-kam: 225 \nAlpha-carotene \nMon Thong: 13; Cha-ni: 279; Kra-dum:126; Kob-ta-kam: 821 \nBeta-carotene \nMon Thong: 117; Cha-ni: 421; Kra-dum: 600; Kob-ta-kam: 1202 \nFatty acids  \nLauric acid C12:0, Myristic acid C14:0, Palmitic acid C16:0, Palmitoleic acid C16:1, Stearic acid C18:0, Oleic acid C18:1 n9, Linoleic acid C18:2 n6, \u03b3-\nLinolenic acid C18:3 n6 \n\n\n\nCharoenkiatkul et al., 2016 \n\n\n\nMon-thong, Chani, Kradum and Kob-ta-kam \n\n\n\nThailand \n\n\n\nPolyphenols \n4 mg GAE/g DW \nFlavanols \n113.9 \u03bcg CE/g DW \nFlavonoids \n2.06 mg CE/g DW \nTannins \n0.88 mg CE/g DW \nAscorbic acid \n1.46 mgAsc/g DW \n\n\n\nPa\u015bko et al., 2019 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nPolyphenols \n2.58 mg GAE \nFlavonoids \n1.523 CE \nFlavanols \n67.05\u03bcg CE \nAnthocyanins \n17.12 mg C3GE \nVitamin C (ascorbic acid) \n5.65mg \nTannin \n1.37mg CE \nBeta-carotenoids \n4.94\u00b5g/g \n\n\n\nGorinstein et al., 2011 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nTotal polyphenols \n374.4mg GAE \nTotal flavonoids \n97.9mg CE \nAnthocyanins \n442.7\u03bcg C3GE \nFlavanols \n177.1\u03bcg CE \nPhenolic acids/Flavonoids \nVanillic acid, Caffeic acid, p-Coumaric acid, Cinnamic acid, Morin, Quercetin, Myricetin, Apigenin, Campherol \n\n\n\nArancibia-Avila et al., 2008 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nPolyphenols \n3.3mg GAE/g \nFlavonoids \n2.2mg CE /g \nFlavanols \n101.0\u03bcg CE/g \nTannins \n0.8mg CE/g \nFatty acids  \nCapric acid (C10:0), Palmitic acid (C16:0), Stearic acid (C18:0), Arachidic acid (C20:0), Oleic acid (C18:1), Linoleic acid (C18:2) \n\n\n\nHaruenkit et al., 2010 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nPolyphenols \n2.14 mg GAE \nFlavonoids \n311.2\u03bcg CE \nQuercetin \n68.9mg \n\n\n\nLeontowicz et al., 2011 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89  \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 .\n\n\n\nFlavanols \n16.61\u03bcg CE \nTannins \n1.52mg CE \nAscorbic acid \n2.33mg  \nAnthocyanins \n1.43mg \n\n\n\nTotal polyphenols \n309.7mg GAE/100g FW \nFree polyphenols \n37.1 mg GAE/100g of FW \nTotal flavonoids \n85.1mg CE/100g FW \nFree flavonoids \n21.2mg CE/100g FW \nPhenolic acids \nCaffeic, p-coumaric, cinnamic \nvanilic \nFlavonoids \nQuercetin, Morin, Myricetin, Apigenin, Campherol \n\n\n\nHaruenkit et al., 2011 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nTotal polyphenols (mg GAE/100g FW) \nMon Thong: 361.4; Cha-ni: 321.1; Pung Manee: 310.5; Kra-dum: 271.5; Kan Yao: 283.2 \nTotal flavonoids (mg CE/100g FW) \nMon Thong: 93.9; Cha-ni: 81.6; Pung Manee: 78.8; Kra-dum: 69.2; Kan Yao: 72.1 \nAnthocyanins (\u00b5g C3GE/100 g FW) \nMon Thong: 427.3; Cha-ni: 379.1; Pung Manee:367.3; Kra-dum:320.2; Kan Yao: 335.3 \nFlavanols(\u00b5g CE/100 g FW) \nMon Thong: 177.4; Cha-ni: 152.2; Pung Manee:147.1; Kra-dum:128.6; Kan Yao: 134.4 \n\n\n\nToledo et al., 2009 \n\n\n\nMon Thong, Cha-ni, Pung Manee, Kra-dum, Kan Yao \n\n\n\nThailand \n\n\n\nLipophilic antioxidants \nLutein, Zeaxanthin, beta-cryptoxanthin, Lycopene, Carotene, Tocopherol, Tocotrienol \n\n\n\nIsabelle et al., 2010 \n\n\n\nUnknown durian cultivar from Malaysia \n\n\n\nTotal polyphenols \nHexane extract: 19.55 mg/g GAE \nEthyl acetate extract: 90.62 mg/g GAE \nEthanol extract: 102.92mg/g GAE \nTotal flavonoid content \nEthyl acetate extract: 1.004 mg/g QE, ethanol extracts: 1.88mg/g QE \n\n\n\nEvary and Nur, 2018 \n\n\n\nUnknown durian cultivar from Indonesia \n\n\n\nTotal phenolic contents (mg/L GAE) \nChaer Phoy: 690.62 \nAng Jin: 998.29  \nD11: 730.46  \nYah Kang:825.37 \nTotal flavonoid contents (mg/L CE) \nChaer Phoy:219.27 \nAng Jin: 220.34  \nD11: 211.36 \nYah Kang: 216.61 \nTotal carotenoids contents (mg/L beta carotene equivalent) \nChaer Phoy: 0.07 \nAng Jin: 0.06 \nD11: 0.08 \nYah Kang: 0.05 \nVitamin C contents (mg/L) \nChaer Phoy: ~24 \nAng Jin: 18.87 \nD11: 25.13 \nYah Kang: ~22 \n\n\n\nM. A Ashraf et al., 2011 \n\n\n\nChaer Phoy, D11, Yah Kang, Ang Jin \n\n\n\nMalaysia \n\n\n\nAbbreviations: GAE: gallic acid equivalent; TE: Trolox equivalent; QE: quercetin equivalent; C3GE: cyanidin-3-glucoside equivalent; FW: fresh weight; DW: dry weight\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89 \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 .\n\n\n\nTable 2: Antioxidant Activity In Durians \n\n\n\nAuthors, year, species of durian, country Assays Antioxidant activity in ripe durian fruits \n\n\n\nCharoenkiatkul et al., 2016 \n\n\n\nMon-thong, Chani, Kradum and Kob-ta-kam \n\n\n\nThailand \n\n\n\nDPPH  Mon Thong: 8\u03bcmole TE/g dry matter  \n\n\n\nCha-ni: 4 \u03bcmol TE/g dry matter \n\n\n\nKra-dum: 6 \u03bcmol TE/g dry matter \n\n\n\nKob-ta-kam: 6 \u03bcmol TE/g dry matter \n\n\n\nFRAP  Mon Thong: 16\u03bcmol TE/g dry matter  \n\n\n\nCha-ni: 11\u03bcmole TE/g dry matter \n\n\n\nKra-dum: 16\u03bcmol TE/g dry matter \n\n\n\nKob-ta-kam: 16\u03bcmol TE/g dry matter \n\n\n\nORAC  Mon Thong: 62\u03bcmol TE/g dry matter  \n\n\n\nCha-ni: 72\u03bcmol TE/g dry matter \n\n\n\nKra-dum: 67\u03bcmol TE/g dry matter \n\n\n\nKob-ta-kam: 73\u03bcmol TE/g dry matter \n\n\n\nPa\u015bko et al., 2019 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nDPPH  7.48\u03bcmol TE/g dry matter \n\n\n\nCUPRAC  25.8\u03bcmol TE/g dry matter \n\n\n\nFRAP  15.8 \u03bcmol TE/g dry matter \n\n\n\nABTS\u00b7+ 32.7\u03bcmol TE/g dry matter \n\n\n\nArancibia-Avila et al., 2008 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nFRAP 270.4\u03bcmol TE \n\n\n\nCUPRAC 1112.7\u03bcmol TE \n\n\n\nBeta-carotene inhibition 76.8% inhibition \n\n\n\nHaruenkit et al., 2010 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nCUPRAC  26.1\u03bcmol TE/g \n\n\n\nDPPH  6.1\u03bcmol TE/g \n\n\n\nABTS\u00b7+ 31.3\u03bcmol TE/g \n\n\n\nFRAP 14.9\u03bcmol TE/g \n\n\n\nLeontowicz et al., 2011 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nABTS\u00b7+ 11.06\u03bcmol TE/g \n\n\n\nFRAP  5.87\u03bcmol TE/g \n\n\n\nCUPRAC  2.21\u03bcmol TE/g \n\n\n\nDPPH  1.12\u03bcmol TE/g \n\n\n\nHaruenkit et al., 2010 \n\n\n\nMon Thong \n\n\n\nThailand \n\n\n\nTDPPH  228.2 mmol TE/100g FW  \n\n\n\nFDPPH  35.3 mmol TE/100g FW \n\n\n\nTABTS  2016.3 mmol TE/100g FW \n\n\n\nFABTS  321.2 mmol TE/100g FW \n\n\n\nToledo et al., 2009 \n\n\n\nMon Thong, Cha-ni, Pung Manee, Kra-dum, \nKan Yao \n\n\n\nThailand \n\n\n\nFRAP Mon Thong: \n260.8\u00b5mol TE/100 g FW \n\n\n\nCha-ni: 232.1\u00b5mol TE/100 g FW \n\n\n\nPung Manee:224.9\u00b5mol TE/100 g FW \n\n\n\nKra-dum:197.4\u00b5mol TE/100 g FW \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89  \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 .\n\n\n\nKan Yao:204.7\u00b5mol TE/100 g FW \n\n\n\nCUPRAC Mon Thong:1075.6\u00b5mol TE/100 g FW \n\n\n\nCha-ni: 955.4\u00b5mol TE/100 g FW \n\n\n\nPung Manee: 924.9\u00b5mol TE/100 g FW \n\n\n\nKra-dum: 806.5\u00b5mol TE/100 g FW \n\n\n\nKan Yao:845.5\u00b5mol TE/100 g FW \n\n\n\nABTS Mon Thong: 2352.7\u00b5mol TE/100 g FW \n\n\n\nCha-ni: 2091.4\u00b5mol TE/100 g FW \n\n\n\nPung Manee:2020.4\u00b5mol TE/100 g FW \n\n\n\nKra-dum:1773.2\u00b5mol TE/100 g FW \n\n\n\nKan Yao:1843.6\u00b5mol TE/100 g FW \n\n\n\nEvary and Nur, 2018 \n\n\n\nUnknown durian cultivar from Indonesia \n\n\n\nDPPH Hexane extracts: IC50 of 541.28\u00b5g/ml \n\n\n\nEthyl acetate extracts: IC50 of 83.95 \u00b5g/ml \n\n\n\nEthanol extracts: IC50 of 11.24 \u00b5g/ml \n\n\n\n\u03b2-carotene-linoleic acid Hexane extracts: IC50 of 273.58\u00b5g/ml \n\n\n\nEthyl acetate extracts: IC50 of 139.53 \u00b5g/ml \n\n\n\nEthanol extracts: IC50 of 166.83 \u00b5g/ml \n\n\n\n\u03b1-Glucosidase Hexane extracts: IC50 of 3.346\u00b5g/ml \n\n\n\nEthyl acetate extracts: IC50 of 23.693 \u00b5g/ml \n\n\n\nEthanol extracts: IC50 of 119.84 \u00b5g/ml \n\n\n\nWang and Li, 2011 \n\n\n\nUnknown durian cultivar from Guangdong, \nChina \n\n\n\nReducing power (Fe3+) MEDS IC50: 280.79 \u00b5g/mL \n\n\n\nReducing power (Cu2+) MEDS IC50: 154.67\u00b5g/mL \n\n\n\n\u2022OH MEDS IC50: 324.63\u00b5g/mL \n\n\n\nO2\u2022- MEDS IC50:770.52\u00b5g/mL \n\n\n\nAnti-lipid peroxidation MEDS IC50: 4.45\u00b5g/mL \n\n\n\nDPPH MEDS IC50: 102.37\u00b5g/mL \n\n\n\nABTS MEDS IC50: 19.50\u00b5g/mL \n\n\n\nChelating power MEDS IC50: 63.95\u00b5g/mL \n\n\n\nAbbreviations: \n\n\n\nDPPH: 1,1-Diphenyl-2-picrylhydrazyl; CUPRAC: Cupric reducing antioxidant \ncapacity; FRAP: Ferric-reducing/antioxidant power; ABTS\u00b7+: 2,2-Azino-\nbis(3-ethyl-benzothiazoline-6-sulphonic acid) diammonium salt; ORAC: \nOxygen radical absorbance capacity; MEDS: Methanol extract of durian \nshell; TDPPH: 1,1-diphenyl-2-picrylhydrazyl determined in the fruit extracts \nof total polyphenols; FDPPH: 1,1-diphenyl-2-picrylhydrazyl determined in \nthe extracts of free polyphenols; TABTS: 2,2\u2018-azinobis (3-\nethylbenzothiazoline-6-sulfonic acid) in the fruit extracts of total \npolyphenols; FABTS: 2,2\u2018-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) \nin the fruit extracts of free polyphenols \n\n\n\n4.2 Potential Benefits of Bioactive Compounds in Durians on Human \nHealth \n\n\n\nPolyphenols are a bioactive class of compounds abundant in durians as \nseen from the studies in Table 1. Examples of classes of polyphenols \ninclude flavonoids and phenolic acids, while an example of a class of \nflavonoids includes flavanols and anthocyanins (Abbas et al., 2016). \n\n\n\nTaking quercetin supplements had decreased resistin levels in plasma of \nwomen with polycystic ovary syndrome, as well as its gene expression, \nwhich suggested a correlation with antidiabetic potential of quercetin, in \nthese women (Khorshidi et al., 2018). Quercetin has also been shown to \nimprove endothelial functioning and hence contribute to cardiovascular \nhealth by regulating concentrations of vasoactive nitric oxide products \nand endothelin-1 in the blood circulation (Loke et al., 2008). In another \nstudy, supplementation of five hundred milligrams a day of quercetin for \n8 weeks resulted in notable improvements in clinical symptoms and \ndisease activity in women with rheumatoid arthritis (Javadi et al., 2016).  \n\n\n\nQuercetin was also found to exhibit potential for antiviral activity in some \nhepatitis C patients, and supplementation of quercetin also significantly \nreduced systolic blood pressure and low-density lipoprotein cholesterol \nlevels in overweight test subjects, thus suggesting that quercetin could \nprovide protection against cardiovascular disease (Lu et al., 2015; Egert et \nal., 2009). In another similar trial carried out using quercetin supplements, \nit was observed that systolic and diastolic blood pressure was notably \nlowered, especially at doses greater than 500mg/day (Serban et al., 2016). \nAnother study however found that using lower concentrations of isolated \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89  \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 .\n\n\n\nquercetin resulted in no notable effect on general cardiometabolic health, \nblood pressure or endothelial function. Purified anthocyanins were found \nto favorably affect glycemic control and lipid profile of Chinese adults with \nprediabetes or early untreated diabetes (Bondonno et al., 2016; Yang et al., \n2017). Daily consumption of anthocyanin-rich cherry juice was found to \nhave improved short- and long-term memory and verbal fluency in older \nadults with dementia, and also lowered blood pressure in general (Kent et \nal., 2015).  \n\n\n\nIn another study [27], a 12-week supplement of purified anthocyanin was \nfound to benefit the health of non-alcoholic fatty liver disease patients by \nimproving insulin resistance and indicators of liver injury (Zhang et al., \n2015).  Carotenoids are also found to have beneficial anti-inflammatory \nproperties. Examples of carotenoids are lutein and zeaxanthin (Mezzomo \nand Ferreira, 2016). One study demonstrated a memory-enhancing effect \nof daily supplementation with lutein, zeaxanthin, and meso-zeaxanthin in \nhealthy subjects with low macular pigment at baseline via their \nantioxidant and anti-inflammatory properties (Power et al., 2018). \nSupplementation with lutein and zeaxanthin was also found to improve \ncentral nervous system xanthophyll levels and cognitive function among \nhealthy young adults (Renzi-Hammond et al., 2017). Phenolic acids are \nanother beneficial group of bioactive compounds in durians. Ferulic acid \nsupplementation was found to have potential to improve lipid profiles and \noxidative stress, as well as inflammation in hyperlipidemic subjects, and \nhence, ferulic acid has potential to reduce risk factors associated with \ncardiovascular disease (Bumrungpert et al., 2018). Small amounts of gallic \nacid were found to prevent oxidative DNA damage and reduce indicators \nthat reflect inflammation and heightened risks of cancer and \ncardiovascular diseases (Ferk et al., 2018). \n\n\n\n4.3 Potential Medicinal Properties of Durians on Human Health \n\n\n\nSeveral studies have been done, discussing the potential medicinal \nproperties of Durio zibethinus.  \n\n\n\n4.3.1 Durian rinds and anti-inflammatory or antibacterial effects \n\n\n\nRinds of durian were shown to have potential as an antibacterial agent \nagainst Propionibacterium acne bacterias, which cause skin acne. One \nstudy also found that durian shells could serve as anti-inflammatory \nagents for medicinal use. Propacin isolated from durian peels had an anti-\ninflammatory effect on lipopolysaccharide-induced RAW264.7 cells, \nsuggesting that propacin may have the potential to be developed as a \ntherapeutic agent for inflammatory-related diseases.  \n\n\n\n4.3.2 Durians and anti-diabetic or heart-protective effects \n\n\n\nRoot extracts of durians in ethanol were shown to have high ability to \ninhibit the \u03b1-glucosidase enzyme, and hence have high potential as \nantidiabetic agents. Ripe Mon Thong durian had a positive effect on plasma \nlipid profile and plasma antioxidant activity in rats fed cholesterol-\ncontaining diets and did not raise the plasma glucose level, suggesting that \nripe Mon Thong durian could be beneficial to patients suffering from \nhypercholesterolemia and diabetes mellitus. Ripe durians were also found \nto have liver and heart-protective effects in cholesterol-fed rats. In general, \nripe durians can be said to have good antioxidant capacities and health-\nprotective activity. \n\n\n\n5. CONCLUSION\n\n\n\nIn conclusion, durians of different cultivars contain many bioactive \ncompounds and exhibit high levels of antioxidant activity. They have been \nfound to contain medicinal properties, such as anti-inflammatory and \nantidiabetic potential. This review has consolidated the durian varieties \nand bioactive compounds or antioxidants that have been found in them. \nAs current literature on the potential health benefits of durians is based \non experiments on animals, the effect on humans might not be exactly as \npostulated. Further future research on the medicinal properties of \ndifferent cultivars of durians could be carried out in greater detail to \ninvestigate and find more links between durians and their direct health \nbenefits in humans. \n\n\n\nREFERENCES \n\n\n\nAbbas, M., Saeed, F., Anjum, F.M., Natural polyphenols: An overview. \nInternational Journal of Food Properties, 20 (8), Pp. 1689-1699. \n\n\n\nArancibia-Avila, P., Toledo, F., Park, Y.S., 2008. Antioxidant properties of \ndurian fruit as influenced by ripening. LWT - Food Science and \nTechnology, 41 (10), Pp. 2118-2125.  \n\n\n\nAshraf, M.A., Maah, M.J., Yusoff, I., Wajid, A., 2011. Study of Antioxidant \nPotential of Tropical Fruit. International Journal of Bioscience \nBiochemistry and Bioinformatics, 1 (1), \ndoi:10.7763/IJBBB.2011.V1.10 \n\n\n\nAziz, N., Mhd, J.A., 2019. Bioactive Compounds, Nutritional Value, and \nPotential Health Benefits of Indigenous Durian (Durio Zibethinus \nMurr.): A Review. Foods., 8 (3), Pp. 96.  \n\n\n\nBondonno, N.P., Bondonno, C.P., Rich, L., 2016. Acute effects of quercetin-\n3-O-glucoside on endothelial function and blood pressure: a \nrandomized dose-response study. The American Journal of Clinical \nNutrition, 104 (1), Pp. 97-103.  \n\n\n\nBumrungpert, A., Lilitchan, S., Tuntipopipat, S., Tirawanchai, N., Komindr, \nS., 2018. Ferulic Acid Supplementation Improves Lipid Profiles, \nOxidative Stress, and Inflammatory Status in Hyperlipidemic Subjects: \nA Randomized, Double-Blind, Placebo-Controlled Clinical Trial. \nNutrients, 10 (6), Pp. 713.  \n\n\n\nCharoenkiatkul, S., Thiyajai, P., Judprasong, K., 2016. Nutrients and \nbioactive compounds in popular and indigenous durian (Durio \nzibethinus murr.). Food Chemistry, 193, Pp. 181-186.  \n\n\n\nEgert, S., Bosy-Westphal, A., Seiberl, J., 2009. Quercetin reduces systolic \nblood pressure and plasma oxidised low-density lipoprotein \nconcentrations in overweight subjects with a high-cardiovascular \ndisease risk phenotype: a double-blinded, placebo-controlled cross-\nover study. British Journal of Nutrition, 102 (7), Pp. 1065-1074.  \n\n\n\nEvary, Y.M., Muhammad, Nur, A., 2018. Antioxidant and Antidiabetic \nCapacity of Hexane, Ethylacetate and Ethanol Extracts of Durio \nzibethinus Murr. Root. Pharmacognosy Journal, 10 (5), Pp. 937-940.  \n\n\n\nFeng, J., Yi, X., Huang, W., Wang, Y., He, X., 2018. Novel triterpenoids and \nglycosides from durian exert pronounced anti-inflammatory \nactivities. Food Chemistry, 241, Pp. 215-221.  \n\n\n\nFerk, F., Kundi, M., Brath, H., 2018. Gallic Acid Improves Health-Associated \nBiochemical Parameters and Prevents Oxidative Damage of DNA in \nType 2 Diabetes Patients: Results of a Placebo-Controlled Pilot Study. \nMolecular Nutrition & Food Research, 62 (4), Pp. 1700482.  \n\n\n\nFitrianingsih, F., Soyata, A., Wigati, S., 2019. The Antibacterial Activities of \nDurian Rinds Extract (Durio Zibethinus) Against Propionibacterium \nacne. IOP Conference Series: Earth and Environmental Science, 391, \nPp. 012013.  \n\n\n\nGorinstein, S., Poovarodom, S., Leontowicz, H., 2011. Antioxidant \nproperties and bioactive constituents of some rare exotic Thai fruits \nand comparison with conventional fruits. Food Research \nInternational, 44 (7), Pp. 2222-2232.  \n\n\n\nHaruenkit, R., Poovarodom, S., Leontowicz, H., 2007. Comparative Study of \nHealth Properties and Nutritional Value of Durian, Mangosteen, and \nSnake Fruit:  Experiments In vitro and In vivo. Journal of Agricultural \nand Food Chemistry, 55 (14), Pp. 5842-5849.  \n\n\n\nHaruenkit, R., Poovarodom, S., Vearasilp, S., 2010. Comparison of bioactive \ncompounds, antioxidant and antiproliferative activities of Mon Thong \ndurian during ripening. Food Chemistry, 118 (3), Pp. 540-547.  \n\n\n\nHuang, Y., Wang, Y., Xu, J., Feng, J., He, X., 2020. Propacin, a \ncoumarinolignoid isolated from durian, inhibits the \nlipopolysaccharide-induced inflammatory response in macrophages \nthrough the MAPK and NF-\u03baB pathways. Food & Function, 11 (1), Pp. \n596-605.  \n\n\n\nIsabelle, M., Lee, B.L., Lim, M.T., Koh, W.P., Huang, D., Ong, C.N., 2010. \nAntioxidant activity and profiles of common fruits in Singapore. Food \nChemistry, 123 (1), Pp. 77-84.  \n\n\n\nJavadi, F., Ahmadzadeh, A., Eghtesadi, S., 2016. The Effect of Quercetin on \nInflammatory Factors and Clinical Symptoms in Women with \nRheumatoid Arthritis: A Double-Blind, Randomized Controlled Trial. \nJournal of the American College of Nutrition, 36 (1), 9-15.  \n\n\n\nKent, K., Charlton, K., Roodenrys, S., 2015. Consumption of anthocyanin-\nrich cherry juice for 12 weeks improves memory and cognition in \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 82-89  \n\n\n\nCite The Article: Sarah Yew Yen Yee(2021). Medicinal Properties Of Bioactive Compounds And Antioxidant Activity In Durio Zibethinus. \nMalaysian Journal Of Sustainable Agriculture, 5(2): 82-89 .\n\n\n\nolder adults with mild-to-moderate dementia. European Journal of \nNutrition, 56 (1), Pp. 333-341.  \n\n\n\nKetsa, S., 2018. Durian\u2014Durio Zibethinus (Rodrigues S, de Oliveira Silva \nE, de Brito ES, eds.). Academic Press; 2018:169\u2013180. \nhttps://www.sciencedirect.com/science/article/pii/B97801280313\n84000228. Accessed September 11, 2020. \n\n\n\nKhorshidi, M., Moini, A., Alipoor, E., 2018. The effects of quercetin \nsupplementation on metabolic and hormonal parameters as well as \nplasma concentration and gene expression of resistin in overweight or \nobese women with polycystic ovary syndrome. Phytotherapy \nResearch, 32 (11), Pp. 2282-2289.  \n\n\n\nLeontowicz, H., Leontowicz, M., Drzewiecki, J., 2006. Bioactive properties \nof Snake fruit (Salacca edulis Reinw) and Mangosteen (Garcinia \nmangostana) and their influence on plasma lipid profile and \nantioxidant activity in rats fed cholesterol. European Food Research \nand Technology, 223 (5), Pp. 697-703.  \n\n\n\nLeontowicz, H., Leontowicz, M., Haruenkit, R., 2008. Durian (Durio \nzibethinus Murr.) cultivars as nutritional supplementation to rat\u2019s \ndiets. Food and Chemical Toxicology, 46 (2), Pp. 581-589.  \n\n\n\nLeontowicz, H., Leontowicz, M., Jesion, I., 2011. Positive effects of durian \nfruit at different stages of ripening on the hearts and livers of rats fed \ndiets high in cholesterol. European Journal of Integrative Medicine, 3 \n(3), Pp. e169-e181.  \n\n\n\nLeontowicz, M., Leontowicz, H., Jastrzebski, Z., 2007. The nutritional and \nmetabolic indices in rats fed cholesterol-containing diets \nsupplemented with durian at different stages of ripening. BioFactors., \n29 (2-3), Pp. 123-136.  \n\n\n\nLoke, W.M., Hodgson, J.M., Proudfoot, J.M., McKinley, A.J., Puddey, I.B., \nCroft, K.D., 2008. Pure dietary flavonoids quercetin and (\u2212)-\nepicatechin augment nitric oxide products and reduce endothelin-1 \nacutely in healthy men. The American Journal of Clinical Nutrition, 88 \n(4), Pp. 1018-1025.  \n\n\n\nLu, N.T., Crespi, C.M., Liu, N.M., 2015. A Phase I Dose Escalation Study \nDemonstrates Quercetin Safety and Explores Potential for \nBioflavonoid Antivirals in Patients with Chronic Hepatitis C. \nPhytotherapy Research, 30 (1), Pp. 160-168.  \n\n\n\nMezzomo, N., Ferreira, S.R.S., 2016. Carotenoids Functionality, Sources, \nand Processing by Supercritical Technology: A Review. Journal of \nChemistry, Pp. 1-16.  \n\n\n\nMohd, A.M., Hashim, N., Aziz, S., Lasekan, O., 2020. Exploring the chemical \ncomposition, emerging applications, potential uses, and health \nbenefits of durian: A review. Food Control, 113, Pp. 107189.  \n\n\n\nPa\u015bko, P., Tyszka-Czochara, M., Trojan, S., 2019. Glycolytic genes \nexpression, proapoptotic potential in relation to the total content of \nbioactive compounds in durian fruits. Food Research International, \n125, Pp. 108563.  \n\n\n\nPower, R., Coen, R.F., Beatty, S., 2018. Supplemental Retinal Carotenoids \nEnhance Memory in Healthy Individuals with Low Levels of Macular \nPigment in A Randomized, Double-Blind, Placebo-Controlled Clinical \nTrial. Journal of Alzheimer\u2019s Disease, 61 (3), Pp. 947-961.  \n\n\n\nRenzi-Hammond, L., Bovier, E., Fletcher, L., 2017. Effects of a Lutein and \nZeaxanthin Intervention on Cognitive Function: A Randomized, \nDouble-Masked, Placebo-Controlled Trial of Younger Healthy Adults. \nNutrients, 9 (11), 1246.  \n\n\n\nSerban, M., Sahebkar, A., Zanchetti, A., 2016. Effects of Quercetin on Blood \nPressure: A Systematic Review and Meta\u2010Analysis of Randomized \nControlled Trials. Journal of the American Heart Association, 5 (7). \ndoi:10.1161/jaha.115.002713 \n\n\n\nShui, G., Leong, L.P., 2005. Screening and Identification of Antioxidants in \nBiological Samples Using High-Performance Liquid \nChromatography\u2212Mass Spectrometry and Its Application onSalacca \nedulisReinw. Journal of Agricultural and Food Chemistry, 53 (4), Pp. \n880-886. doi:10.1021/jf049112q \n\n\n\nToledo, F., Arancibia-Avila, P., Park, Y.S., 2009. Screening of the antioxidant \nand nutritional properties, phenolic contents and proteins of five \ndurian cultivars. International Journal of Food Sciences and Nutrition, \n59 (5), Pp. 415-427. doi:10.1080/09637480701603082 \n\n\n\nWang, L., Li, X., 2011. Antioxidant Activity of Durian (Durio zibethinus \nMurr.) Shell in vitro. Asian Journal of Pharmaceutical and Biological \nResearch, 1 (4). \n\n\n\nYang, L., Ling, W., Yang, Y., 2017. Role of Purified Anthocyanins in \nImproving Cardiometabolic Risk Factors in Chinese Men and Women \nwith Prediabetes or Early Untreated Diabetes\u2014A Randomized \nControlled Trial. Nutrients, 9 (10), Pp. 1104. doi:10.3390/nu9101104 \n\n\n\nZhang, P.W., Chen, F.X., Li, D., Ling, W.H., Guo, H.H., 2015. A CONSORT-\nCompliant, Randomized, Double-Blind, Placebo-Controlled Pilot Trial \nof Purified Anthocyanin in Patients with Nonalcoholic Fatty Liver \nDisease. Medicine, 94 (20), Pp. e758. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 44-50 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2022.44.50 \n\n\n\n \nCite the Article: Ndlovu Wiseman, Sabine Moebs, Marizvikuru Mwale, Jethro Zuwarimwe (2022). The Role of Support Organisations in Promoting Organic Farming \n\n\n\nInnovations and Sustainability. Malaysian Journal of Sustainable Agriculture, 6(1): 44-50. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2022.44.50 \n\n\n\n\n\n\n\n\n\n\n\nTHE ROLE OF SUPPORT ORGANISATIONS IN PROMOTING ORGANIC FARMING \nINNOVATIONS AND SUSTAINABILITY \n \nNdlovu Wisemana*, Sabine Moebsb, Marizvikuru Mwalea, Jethro Zuwarimwea \n\n\n\n \naInstitute for Rural Development, P. Bag X5050, University of Venda, Thohoyandou, South Africa \nbDigital Business Management & International Business, Duale Hochschule Baden-Wuerttemberg Heidenheim, Germany \n\n\n\n*Corresponding Author E-mail: wiseman.ndlovu@outlook.com \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 02 August 2021 \nAccepted 15 September 2021 \nAvailable online 16 October 2021 \n\n\n\n Innovation is a product of collaborative effort and processes that make use of the expertise of and involves \nmultiple stakeholders in its engineering. Most current studies focus on challenges, opportunities, and \nstrategies for innovation. However, the role of support organisations and their specific innovative practices \nthat foster sustainability in organic farming remain scantly researched and unknown. This study investigated \ninnovative practices emanating from collaboration between support organisations/groups and farmers. Also, \nthe question on how these practices influence the sustainability of organic farming was answered.  A cross-\nsectional explorative research design was applied to collect data through semi-structured questions using \ninterviews and focus groups in Bavaria and Baden-Wuerttemberg federal states, Germany. Innovative \npractices were performed at three key organisational levels namely; compliance with organic farming \nstandards; production processes as well as marketing and consumer engagement. The findings revealed that \ncollaborative innovative practices by support organisations/groups at the market and consumer engagement \nlevel was greater compared to other levels. The importance of innovative practices varied across the four \ndimensions (environmental, social, political, and economic) of organic farming sustainability. Organic \nfarming innovations must be enhanced to improve the organic farming situation like improving area \nproductivity, balancing for environmental friendly and safer agricultural practices as well as food security.  \n\n\n\nKEYWORDS \n\n\n\nFarmer organisations, innovation, organic farming innovation, support groups/organisations, sustainable \nagriculture. \n\n\n\n1. INTRODUCTION \n\n\n\n \nOrganic farming is nature friendly and classified as a sustainable \nagricultural practice. It has the potential to reduce the environmental \ndamage by modern conventional farming practices (Smith et al., 2019). \nHowever, current trends and challenges, like increasing consumer \ndemand, organic seed production and weed management challenges, \nfragmented supply chain development, land area productivity limitations, \nand regional adaptation constraints threatens the future success of the \norganic sector (Jouzi et al., 2017). These challenges hinder the realisation \nof organic farming innovations (OFIs) and sustainability (Nedumaran, \n2020). This requires that different stakeholders support continuous \nadaptation and adoption of innovative farming practices and processes. \nOFIs are a critical component for the survival of organic farming, ensuring \nsupply of healthy food, as well as reducing carbon footprint and \nenvironmental degradation. Although studies support that innovation \nrequires the involvement of all stakeholders, fewer studies have \ninvestigated the role of different support groups/organisations in \npromoting OFIs. Current studies focus on challenges, importance, \nopportunities, promotion, the need for and strategies of OFIs (Canali, et al., \n2020; Clark, 2020; Zagata et al., 2020).  \n\n\n\nPadel et al. (2015) for example researched the roles of different \n\n\n\nstakeholders in supporting innovation, but only focused on farmers, \n\n\n\nresearchers, and knowledge exchange for innovation. Some focused \n\n\n\nmainly on product, process, or technological innovation (Niggli et al., \n\n\n\n2017). In the reviewed literature, social aspects, as well as the role of these \n\n\n\norganisations in the diffusion and adaptation of OFIs, is scanty \n\n\n\n(Bokelmann et al., 2012). Studies show that OFIs are a product of a \n\n\n\nfunctioning entire support system (H\u00e4ring et al., 2012). Hence, the \n\n\n\ninnovative practices that emanate from collaborative activities between \n\n\n\nsupport groups/organisations and organic farmers were investigated in \n\n\n\nthe present study. The study demonstrates how different support \n\n\n\norganisations at different levels collaborate with farmers to achieve OFIs. \n\n\n\nIn addition, the study illustrates how innovative practices promote social, \n\n\n\neconomic, political, and environmental organic sustainability. \n\n\n\n2. BACKGROUND TO THE STUDY \n\n\n\n \nIt is expensive due to increased labour expenses for monitoring and \nweeding, to produce organic products unlike in conventional farming \n(Jouzi et al., 2017). Organic farming requires a greater deal of managerial \neffort (Asadollahpour et al., 2014; Tiraieyari et al., 2017). Moreover, low \nyields per hectare are reported and stringent regulations present a hurdle \nfor farmers intending to convert to organic farming. For instance, a farmer \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 44-50 \n\n\n\n\n\n\n\n \nCite the Article: Ndlovu Wiseman, Sabine Moebs, Marizvikuru Mwale, Jethro Zuwarimwe (2022). The Role of Support Organisations in Promoting Organic Farming \n\n\n\nInnovations and Sustainability. Malaysian Journal of Sustainable Agriculture, 6(1): 44-50. \n \n\n\n\n\n\n\n\nneeds three years of yearly certification to qualify. Marsh et al. (2017) add \nthat limited organic farming knowledge is another major setback for most \nfarmers. The consensus that adopting innovation and its diffusion is key to \nimproving the attraction and sustainability of organic farming, requires \nthat stakeholder-specific innovative practices are known and understood. \nThis necessitates that support organisations\u2019 practices that promote OFIs \nare identified for improved and precise decision-making by different \nstakeholders in the entire organic supply chain (Food and Agriculture \nOrganisation, 2018).   \n \nInnovation is an outcome of collaborative efforts rather than the outcome \nof a single entity (Bouncken, 2011; Soosay et al., 2008). OFI results in new \nproduction systems or processes technologies, structures, plans, and \nprogrammes about organic farming (Damanpour, 1991: 694). \nCollaboration dismantles barriers to learning, as well as allows better \nunderstanding and communication between different stakeholders. As a \nresult, farmers access reliable information that fosters process efficiency \nand collectively promotes innovativeness across the organic farming \nsupply chain (Bouncken, 2011). Identifying the roles of each player and \nthe level at which they contribute the most adds to the toolkit for \nmanaging and diffusion of OFIs. Like other forms of farming, in different \nregions and countries, organic farming in Germany is supported by \ndifferent organisations, operating at different levels. They also have \ndifferent goals, functions, and objectives.  \n \nGerman is committed to increasing the number of organic farmers by 2030 \nto meet consumer and environmental goals. This study was conducted in \nGermany to identify and assess the innovative practices that emanate from \ncollaborative partnerships between organic farmers and support \norganisations. Generating knowledge contributes to enhancing creativity \nin, and support for OFIs. This easies organic farming adaptation through \nknowledge transfer among key stakeholders (farmers, researchers, \npractitioners support organisations, and policymakers) and ensures \nsustainability in the sector. Brzezina et al. (2017) recently in Germany \nfound that organic farming challenges among small-scale farmers are a \nthreat its sustainability. The authors further revealed that the \nsustainability of the sector cannot solely lie on quick fix growth-oriented \noptions, like subsidies. Rather, anticipating and managing inevitable \norganic faring limits like low area productivity and market dynamics for \nexample is crucial for sustained OFIs and sustainability. \n \nMost organic farmers in Germany are members of different associations, \ngroups, and farmers\u2019 organisations that serve their interests. Apart from \nBioland and Demeter (the largest and oldest organic associations), \nNaturland, Biokreis, Bundesverband \u00d6kologischer Weinbau (Federation \nfor Organic Viticulture, ECOVIN; G\u00e4a; Ecoland; Biopark; and Verbund \n\u00d6koh\u00f6fe are some of the old organic organisations. There are also other \nindependent farmer organisations while some are funded by the \ngovernment. The federal government's support to these organisations is \nin line with the EU resolutions of absorbing higher costs associated with \nthe transition from conventional to organic farming by conversion \nincentives (European Union, 2017). It is expected that they reduce \ntransactionally; information; bargaining and decision making; and \nmonitoring and enforcement costs, thus creating a conducive environment \nfor the diffusion of OFIs (Coase, 1992; Dahlman, 1979). In the last decade, \nBrenes Mu\u00f1oz, Lakner, & Br\u00fcmmer (2011) found that a high level of \naffiliation to support organisations or producer associations was a key \nfactor for the success of organic farming in Germany. \n \n\n\n\nThe organic farming sector continues to grow with an estimated growth of \n\n\n\nover 50% in the last decade (Hamm et al., 2017; Federal Ministry of Food \n\n\n\nand Agriculture, 2021). Currently, this represents half of the total targeted \n\n\n\narable land of 20% to be converted to organic by 2030 (Federal Ministry \n\n\n\nof Food and Agriculture, 2021). The growth in the sector is also influenced \n\n\n\nby consumer demand and increasing awareness about environmental \n\n\n\nprotection. For example, amid Corona global pandemic, the organic \n\n\n\nmarket still grew by 22.3 % representing 14.99 billion EUR and this is \n\n\n\napproximately 6.4 % of the organic share on the food market (Ami, 2021). \n\n\n\nMoreover, a recent Nutrition Report by the Federal Ministry of Food and \n\n\n\nAgriculture (2021) shows that every second person looks out for the Bio-\n\n\n\nSiegel (organic certified products) when shopping. To meet these targets \n\n\n\nand growing demand, strengthening support structures and enhancing \n\n\n\ninnovation is essential (de Paula et al., 2019). For this reason, this study \n\n\n\nevaluated the collaborative role played by support organisations and \n\n\n\ngroups with farmers in inculcating OFIs and sustainability. Specifically, the \n\n\n\nfollowing questions are answered. \n\n\n\n\u2022 Which support organisations are involved with farmers that \n\n\n\npromote OFIs?  \n\n\n\n\u2022 What are the innovative practices distributed within the \n\n\n\norganic farming sector that come from the process of \n\n\n\ncollaboration?  \n\n\n\n\u2022 What is the impact of such innovative practices in improving the \n\n\n\nsustainability of organic farming? \n\n\n\n3. METHODOLOGY AND MATERIALS \n\n\n\n3.1 Study area and population \n\n\n\nThis study was conducted in the federal states of Bavaria and Baden-\n\n\n\nWuerttemberg, Germany. These states have the highest number of organic \n\n\n\nfarms and produce (Federal Statistical Office, 2017) in Germany, \n\n\n\ncomparatively. For example, Baden-Wuerttemberg has a total of 39 820 \n\n\n\nfarms while Bavaria has 88 150. In terms of organic area and the total \n\n\n\nnumber of producing farms, Bavaria has 314 182 ha and 9,093 (30.4%) \n\n\n\nwhile Baden-Wuerttemberg has 165 640 ha and 8,649 (29.4%), \n\n\n\nrespectively (Federal Statistical Office, 2017). The study population \n\n\n\nincluded snowballed organic farmers, regional government\u2019s department \n\n\n\nof agriculture representatives, organic farming consultants as well as \n\n\n\nsupport organisations. Firstly, a farmer was identified and referral of a \n\n\n\nfarmer, support organisations representative or consultant readily \n\n\n\navailable was sought thereafter. Also, experts were consulted as key \n\n\n\ninformants. Combining different set of population categories helped \n\n\n\ncomplete the triangulation of results ensuring their validity and reliability. \n\n\n\n3.2 Data collection \n\n\n\nData was collected from the 11th June to the 28th July 2019. The OFIs were \n\n\n\nassessed using semi-structured questions through face-to-face (n =11) \n\n\n\nand telephonic (n = 3) interviews as well as two focus group discussions. \n\n\n\nEach group had 4 and 5 members randomly constituted. Focus groups \n\n\n\nwere conveniently composed and were based on the coincidence of \n\n\n\nfarmers\u2019 meetings during data collection visitations. All interviews were \n\n\n\ncarried out directly on the farms in the Fall, except for 3 that were \n\n\n\nconducted telephonically. A translator to and from English to the native \n\n\n\nGerman language was utilized for a better understanding of the issues \n\n\n\ndiscussed. The purpose of the study and the rights of the respondents such \n\n\n\nas voluntary participation was explained to the respondents. Thereafter, \n\n\n\nrespondents were asked to give their consent to participate before the \n\n\n\ninterview session. \n\n\n\n3.3 Data analysis \n\n\n\nData collected were analysed into two phases. Firstly, the data was \n\n\n\nthematically analysed with the aid of Atlas Ti version 8.1.4. This was done \n\n\n\nto identify organisations or groups involved in supporting farmers and \n\n\n\ntheir associated activities perceived to promote innovation. Also, the \n\n\n\nsoftware was utilised to link a family of codes and establish the \n\n\n\nrelationship between issues using the network diagram (McKether & \n\n\n\nFriese, 2016). Secondly, a framework of classification of innovative \n\n\n\npractices by Krishnan et al. (2021) was adapted to unpack and explain the \n\n\n\nroles of support organisations or groups and how they collaborate with \n\n\n\nfarmers to support and promote innovation in the organic sector. \n\n\n\n4. RESULTS AND DISCUSSIONS \n\n\n\n4.1 Demographic profile of respondents \n\n\n\nTwenty-three (23) agricultural support experts, farmers, and support \n\n\n\norganisation representatives participated in the study.. Males (69.6%) \n\n\n\nwere the most represented similar with the age group of between 41 and \n\n\n\n60 years (65.2%) as illustrated in Table 1. Moreover, most information \n\n\n\nwas provided by farmers (39.1%) compared to other groups to \n\n\n\nunderstand the impact of the studied organisations on the farm level. \n\n\n\nParticipating farmers were involved in crop and animal production. \n\n\n\nSpecifically, those in animal production practiced poultry, cattle, goat, and \n\n\n\npig rearing. On the other hand, crop producers focused on vines, wheat, \n\n\n\nand horticultural produce such as potatoes, tomatoes, and cucumber. As \n\n\n\nobserved during the study, farmers who practiced animal production were \n\n\n\nalso involved in crop production, however, on a limited scale. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 44-50 \n\n\n\n\n\n\n\n \nCite the Article: Ndlovu Wiseman, Sabine Moebs, Marizvikuru Mwale, Jethro Zuwarimwe (2022). The Role of Support Organisations in Promoting Organic Farming \n\n\n\nInnovations and Sustainability. Malaysian Journal of Sustainable Agriculture, 6(1): 44-50. \n \n\n\n\n\n\n\n\nTable 1: Cross-sectional study demographic information of the \nparticipants (N = 23). \n\n\n\nItem Category Frequencies (%) \nGender \n \n \nAge \n \n \n \n \n \nRegion  \n \n \nRespondents \ntype \n\n\n\nFemale \nMale \n \n21 and 30 years \n31 to 40 years \n41 to 50 years \n51 to 60 years \n61 years and above \n \nBavaria \nBaden-Wuerttemberg \nAgricultural support experts \nFarmers \nOrganic farming consultancy \nFarmer organisations  \nLobby organisation \nRegional Government: \nDepartment of Agriculture  \n\n\n\n7 (30.4%) \n16 (69.6%) \n\n\n\n \n2 (9.0%) \n\n\n\n3 (13.0%) \n7 (30.4%) \n8 (34.8%) \n3 (13.0%) \n\n\n\n \n14 (60.9%) \n9 (39.1%) \n\n\n\n \n2 (9.0%) \n\n\n\n9 (39.1%) \n2 (9.0%) \n\n\n\n3 (13.0%) \n2 (9.0%) \n1 (4.0%) \n\n\n\n4.2 Collaborative Organic Farming Innovations (Support \n\n\n\nOrganisations and Farmers) \n\n\n\nThe analysis of literature and collected data shows that there are three \n\n\n\ntypes of support organisation among organic farmers. These are farmer \n\n\n\norganisations, lobby groups, and organic farming consultancies. Different \n\n\n\ninnovative practices and contributions by each organisation were \n\n\n\ntherefore categorised accordingly (Table 2). These innovative practices \n\n\n\nare further categorised per the level at which they are performed. A model \n\n\n\nfor the classification of innovative practices for sustainability by Krishnan \n\n\n\net al. (2021) is used to illustrate how the collaborative practices between \n\n\n\nsupport organisations and farmers ensured organic sector sustainability.   \n\n\n\nFigure 1 illustrates the different innovative practices arising out of the \n\n\n\nsupport organisations\u2019 collaboration under each level of contribution as \n\n\n\nillustrated in Table 2. The contribution levels are: production processes; \n\n\n\nmarketing and consumer engagement; as well as compliance and organic \n\n\n\nfarming standards were data-driven as opposed to the original model \n\n\n\nwhere levels were based on the supply chain processes that are plan, \n\n\n\nmake, source, deliver and return. Codes such as PC2 represent innovative \n\n\n\npractices at the level of production processes by private consultancy \n\n\n\norganisations whereas LG3 represents innovative practices at the level of \n\n\n\ncompliance and organic farming standards. Similarly, marketing and \n\n\n\nconsumer engagement are considered. The model has three hierarchical \n\n\n\nphases used to classify OFIs emanating from collaborative practices. The \n\n\n\nfirst phase of the business model shows the foundation for sustainable \n\n\n\nOFIs and links for organisations, farmers, and consumers. This phase \n\n\n\nillustrates that there must be an uninterrupted and swift exchange of \n\n\n\ninformation among organisations, farmers, and consumers. This forms the \n\n\n\nbasis for achieving OFIs. In the second innovative phase, different support \n\n\n\ngroups and organisations synthesize, gather, use and adapt the existing \n\n\n\npractices and available information. It is at this stage where organisations \n\n\n\nand support groups demonstrate their innovative practices. \n\n\n\nDifferent innovative practices by each organisation are classified \n\n\n\naccording to the different levels at which they are performed. It is worth \n\n\n\nnoting that, unlike the original model, political sustainability was added \n\n\n\nbased on the present analysis. The next section discusses the different \n\n\n\ninnovative practices according  to types of support organisations based on \n\n\n\nthe empirical evidence and supported by the literature. Thereafter, the \n\n\n\nsustainability of OFIs is outlined, and conclusions are given. \n\n\n\n\n\n\n\nTable 2: Innovative practices from the collaboration between organic farmers and support organisations in Baden-Wuerttemberg and the Bavarian States, \n\n\n\nGermany \n\n\n\nCONTRIBUTION LEVEL  PRIVATE CONSULTANCIES (PC) FARMER ORGANISATIONS (FO) LOBBY GROUPS (LG) \n\n\n\nCompliance and Setting \n\n\n\nOrganic Farming \n\n\n\nStandards \n\n\n\n\n\n\n\n\n\n\n\n\u2022 Continuous flow and transparency of \n\n\n\ninformation (PC1) \n\n\n\n\u2022 Crop and animal breeds variety selection \n\n\n\nstrategies (PC2) \n\n\n\n\n\n\n\n\u2022 Information exchange and sharing \n\n\n\n(FO1) \n\n\n\n\u2022 Crop and animal breeds variety \n\n\n\nselection strategies (FO2) \n\n\n\n\u2022 Information sharing (Specifying \n\n\n\nEU regulations) (LG1) \n\n\n\n\u2022 Political representation (LG2) \n\n\n\n\u2022 Policy alignment at EU and \n\n\n\nnational contributions (LG3) \n\n\n\n\u2022 Environmental protection \n\n\n\nawareness (LG4) \n\n\n\nProduction processes  \n\n\n\n\n\n\n\n\n\n\n\n\u2022 Crop and animal breeds variety selection \n\n\n\nstrategies (PC3) \n\n\n\n\u2022 Distribute and share information on best \n\n\n\norganic farming practices (PC4) \n\n\n\n\u2022  Training farmers on best organic farming \n\n\n\npractices (PC5) \n\n\n\n\u2022 Adaptation of varieties and animal breeds \n\n\n\nthrough on and off-farm research activities \n\n\n\n(PC6) \n\n\n\n\u2022 Special events meeting for knowledge \n\n\n\nexchange among farmers and between \n\n\n\nfarmers with experts (PC7) \n\n\n\n\u2022 Promote site-adapted land management \n\n\n\n(manure production and preparation, \n\n\n\nsustainable tillage practices) (PC8) \n\n\n\n\u2022 Knowledge sharing (consumer \n\n\n\ninformation, retailer expectations, \n\n\n\nresearch output, general information) \n\n\n\n(FO3) \n\n\n\n\u2022 On-farm research activities (FO4) \n\n\n\n\u2022 Crops and animal breeds variety \n\n\n\nselection strategies (FO5) \n\n\n\n\u2022 Promote site-adapted land \n\n\n\nmanagement (manure production and \n\n\n\npreparation, sustainable tillage \n\n\n\npractices) (FO6) \n\n\n\n\u2022 Special events meeting for \n\n\n\nknowledge exchange among farmers \n\n\n\nand between farmers with experts \n\n\n\n(FO7) \n\n\n\n\u2022 No Evidence \n\n\n\n\n\n\n\n\n\n\n\nMarketing and Consumer \n\n\n\nEngagement \n\n\n\n\u2022 Consumer-farmer meetings (PC9) \n\n\n\n\u2022 Farmer-schools\u2019 engagement programmes \n\n\n\n(PC10) \n\n\n\n\u2022 Retail space advocacy (PC11) \n\n\n\n\u2022 Advocacy for regional organics produce \n\n\n\n(PC12) \n\n\n\n\u2022 Warehousing and bulk buying from farmers \n\n\n\n(PC13) \n\n\n\n\u2022 Labelling and branding on behalf of farmers \n\n\n\n(PC14) \n\n\n\n\u2022 Market search and research (PC15) \n\n\n\n\u2022 Providing a warehouse and ready market \n\n\n\nfor farmer produce (PC16) \n\n\n\n\u2022 Networking opportunities for collaborative \n\n\n\nmarketing (farmer-farmer; farmer-expert \n\n\n\nand retailer-farmer meetings) (PC17) \n\n\n\n\u2022 Developing marketing strategies \n\n\n\n(FO8) \n\n\n\n\u2022 Group and collaborative marketing \n\n\n\n(FO9) \n\n\n\n\u2022 Market research (FO10) \n\n\n\n\u2022  Encourage processing and \n\n\n\nmarketing of organic produce (FO11) \n\n\n\n\u2022 Retail space advocacy (FO12) \n\n\n\n\u2022 Networking opportunities for \n\n\n\ncollaborative marketing (farmer-\n\n\n\nfarmer; farmer-expert and retailer-\n\n\n\nfarmer meetings) (FO13) \n\n\n\n\u2022 No evidence \n\n\n\nKey:  PC = private consultancy organisations; LG = lobby groups; FO = famer organisations\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 44-50 \n\n\n\n\n\n\n\n \nCite the Article: Ndlovu Wiseman, Sabine Moebs, Marizvikuru Mwale, Jethro Zuwarimwe (2022). The Role of Support Organisations in Promoting Organic Farming \n\n\n\nInnovations and Sustainability. Malaysian Journal of Sustainable Agriculture, 6(1): 44-50. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 1:  A framework of classification of innovative practices. Source: Krishnan et al. (2021) \n\n\n\n4.2.1 Lobby organisations \n\n\n\n \nLobby organisations\u2019 innovative practices include information sharing, \npolitical representation, and policy alignment. These practices were \nperformed at the level of compliance and setting standards for organic \nfarming by representing farmer\u2019s unique needs and adapting policies at \nthe regional, national, and EU levels. Direct contact with farmers, \ninteractive meetings, and research initiatives on farmers\u2019 challenges \nhelped these organizations capture, communicate, and represent farmers\u2019 \nneeds in policy adaptation. These organisations shared farmer\u2019s \nexpectations in organic farming through publications and representation \nin local organic farming policy adaptation and implementation. To \nillustrate this, one crop producer in Bavaria said, \u201c\u2026 both organizations \nand farmers try to influence decisions (organic) in the community. In \nBavaria, there is a separate officer who does lobby work for us. Who collects \nfarmer\u2019s challenges and bring them to parliament, the Bavarian \nparliament\u201d. While being policy brokers was also noted as critical, Kingiri, \nAnn & Andy (2012) explain that this role presents varying opportunities \nand challenges. For instance, their legitimacy is often challenged mainly \nbecause of potential conflicts with governments, and market actors. \nFurthermore, their local position may provide insufficient clout for \ndeveloping long-lasting relationships with relevant actors. This fuelled the \ngenerally held view by farmers that these organizations \u201cdo nothing\u201d when \nit comes to representing their needs. Despite, these sentiments, the role of \nlocal lobby organic organizations has gained recognition in the European \nCommission policy level for dedicated innovation platforms and for \nadvocating for farmer-first models for participatory research (Delate et al., \n2017).  \n\n\n\nAlthough farmers, had reservations about the importance of this practice \n\n\n\nby lobby organisations, evidence shows that it stabilised conditions and \n\n\n\nlevelled the playing field for organic farmers including its support systems. \n\n\n\nFor instance, a cattle farmer from Baden-Wuerttemberg, came to learn and \n\n\n\nunderstand the importance of lobby organisations after a visit to Brussels \n\n\n\noffices at the EU parliament. The farmer said, \u201cNow, I have a very good look \n\n\n\nat their lobby role after this first quarter of the year because I was there in \n\n\n\nBrussels. I talked to people, how they work, they showed me what goes on \n\n\n\nthere. In my opinion, after that, I say it is important what they do because if \n\n\n\nthey did not do it, it will be more difficult for farmers than it is now\u201d. \n\n\n\nSimilarly, Yang et al. (2014) in China stated that such organizations act as \n\n\n\nsystemic intermediaries that take the role of the coordinator in the service \n\n\n\nsystems by bridging the gap between the research, policy systems, and \n\n\n\neveryday farming practice. Furthermore, a 30 years\u2019 reflection on organic \n\n\n\nfarming by Youngberg & DeMuth (2013) strongly indicates that lobbying \n\n\n\nand advocacy activities contributed not only to organic agriculture \n\n\n\nevolution but emerged along with this altered favourable policy \n\n\n\nenvironment in the USA. Thus, its advocacy work practices by these \n\n\n\norganizations play an important role in shaping, not only the path of \n\n\n\norganic agriculture but also the overall politics of organic agriculture. \n\n\n\nThere was no evidence of innovative practices from lobby organisations \n\n\n\nthat exist at the level of production processes and marketing and \n\n\n\nconsumer engagement. \n\n\n\n\n\n\n\n4.2.2 Farmer organisations \n\n\n\nFarmer organisations performed varied innovative practices in all three \n\n\n\ndimensions of contributions. Like lobby groups and organisations, these \n\n\n\norganisations ensured a seamless and continuous flow of information \n\n\n\nabout the farming standards and assisted farmers on how to comply with \n\n\n\nEU and regional organic farming standards. Also, at the production \n\n\n\nprocesses level key innovative practice farmer organisations involved the \n\n\n\ncreation of interface meetings among farmers as well as between farmers \n\n\n\nand different categories of experts. Specifically, specialists in climate \n\n\n\nvariability adaptation, extension services, and organic farming specialists \n\n\n\nwere invited by these organisations at different stages of production to \n\n\n\nshare information and discuss farmer challenges. Evidence from, \n\n\n\nCameroon (Mbangari & Fonteh, 2020); Tanzania (Aku et al., 2018); \n\n\n\nRwanda (Aboniyo & Mourand, 2017) as well as in United States of America \n\n\n\nand Italy (Delate et al., 2017), support that for innovation to take place, \n\n\n\ncollaborative information production through research and timely sharing \n\n\n\nis critical for farmers\u2019 resilience. \n\n\n\nA farming consultant in Baden-Wuerttemberg said, \u201cWith all the farmers, \n\n\n\nwe have met only one time \u2026 There are also, \u201clet\u2019s meet meetings\u201d for specific \n\n\n\ngroups, for example, meat, so far we have met three times now...\u201d These \n\n\n\nmeetings will normally result in farmers working together or with experts \n\n\n\nto address the immediate production challenges. For example, \n\n\n\ncollaborative marketing was a product of farmer meetings. A beef farmer \n\n\n\nin the Baden-Wuerttemberg who is a member of a newly formed farmer \n\n\n\norganization said, \u201c\u2026 during our meetings we found that farmers want to \n\n\n\nsell organic meat in the area. So, the farmers came together and it \n\n\n\ncontributed to the formation of this organization\u201d. Furthermore, farmer \n\n\n\norganisations and consultancies were also actively involved in promoting \n\n\n\norganic farming using funding obtained at the government level. In \n\n\n\nsupport of this, a representative from a local organization in the state of \n\n\n\nBaden-Wuerttemberg said, \u201cThere are funds available which you can apply \n\n\n\nfor as a community, as the region. \u2026. You write a concept or project of what \n\n\n\nyou want to do to strengthen organic agriculture in your region. \u2026 the \n\n\n\nmoney covers the administrative costs of the organization\u201d. \n\n\n\n4.2.3 Consultancy organisations \n\n\n\nThe innovative practices of consultancies organisations were varied and \n\n\n\nvast compared to other categories. These organisations contributed the \n\n\n\nmost to the production processes and marketing of organic farm produce. \n\n\n\nAt the production level, organisations fostered innovativeness among \n\n\n\nfarmers through crop and seed variety selection strategies; sharing \n\n\n\nproduction-related information and best organic farming practices. A key \n\n\n\nfeature that fostered innovation was sharing of relevant information at \n\n\n\ndifferent stages of the production process. This was highlighted by a \n\n\n\nvineyard consultant in Bavaria who stated that. \u201c\u2026 from April to August we \n\n\n\nhave at first, the growers\u2019 information, what is to spray, about the plant, soil \n\n\n\ninformation, and what seeds to plant. August to September, we \u2026look for the \n\n\n\nquality of the grapes. \u2026 acid, moisture, \u2026. Collected information [from \n\n\n\ndifferent farmers] is also distributed in the community of farmers\u201d. \u2026 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 44-50 \n\n\n\n\n\n\n\n \nCite the Article: Ndlovu Wiseman, Sabine Moebs, Marizvikuru Mwale, Jethro Zuwarimwe (2022). The Role of Support Organisations in Promoting Organic Farming \n\n\n\nInnovations and Sustainability. Malaysian Journal of Sustainable Agriculture, 6(1): 44-50. \n \n\n\n\n\n\n\n\nfarmers make their measurements\u201d. In support of this, a horticultural \n\n\n\nfarmer in Bavaria also hinted at the importance of organisational practices \n\n\n\nin promoting innovation and survival of their farming operations. \u201c\u2026 since \n\n\n\n1999, we are glad we joined this organization [name given]. They are \u2026 good \n\n\n\nfor information transfer. They send \u2026, a specialist come to the farm to \n\n\n\nobserve my potato and tomato. He identifies problems I would not normally \n\n\n\nsee\u201d. Training farmers on best organic farming practices and promoting \n\n\n\nsite-adapted land management (manure production and preparation, \n\n\n\nsustainable tillage practices) were major innovative practices by \n\n\n\nconsultancies that fostered sustainability in the organic sector. \n\n\n\nAdaptation of crop and animal breed varieties through on and off-farm \n\n\n\nresearch activities also emerged as a key theme that promoted innovation \n\n\n\nand sustainability in the sector. Like the findings of this study, Hellin & \n\n\n\nCamacho (2017) pointed out that to successfully innovate in organic \n\n\n\nfarming, research activities must be site-based and involve a degree of task \n\n\n\nsharing between researchers and farmers. \u201cWe work with farmers a lot \u2026 \n\n\n\nwe have a group of farmers; we meet once every month to learn. We visit one \n\n\n\nfarm and have different themes, or subjects [to discuss]. For example, today \n\n\n\nin the evening we will go to an organic farm and we have organically \n\n\n\nproduced seeds for oats. We look at how these organically produced seeds \n\n\n\nare growing\u201d a consultant in the state of Bavaria said. Also, brainstorming \n\n\n\nsections facilitated by these organisations was a crucial feature for \n\n\n\ninnovation that facilitated the adaptation and survival of organic farming. \n\n\n\n\u201cWith the farmers, we do round-table discussions where farmers are invited \n\n\n\nbased on the subject matter. For example, lately, our focus is on the matter \n\n\n\nof baby goat meat in the effort to make a special organic dish from the baby. \n\n\n\nBecause in Germany, most of the organic goat from milk production is sold \n\n\n\nabroad. There is uncertainty whether this will be possible in the future, if \n\n\n\nthere is no market for goats in German itself producers will get into problems \n\n\n\nbecause it will be difficult to sell their goats\u201d a farmer in Nuremberg, state \n\n\n\nof Bavaria stated. \n\n\n\nLike the farmer organisations, consultancy organisations\u2019 innovative \n\n\n\npractices were also visible in marketing and consumer engagement. \n\n\n\nAwareness campaigns, interface meetings between farmers and \n\n\n\nconsumers, shelve space advocacy, and publications about the importance \n\n\n\nof organic products in different platforms were the main contributions to \n\n\n\nOFIs. The organisations brought consumers and farmers together and \n\n\n\nexplained to the consumer why they should buy organically produced \n\n\n\nregional products. These findings are also supported by Delate et al. \n\n\n\n(2017) and Ihnatenko & Novak (2018) who argued that linking farmers \n\n\n\nand the public was an innovative practice that enhances sustainability \n\n\n\namong farmers. \n\n\n\nFurthermore, to ensure the sustainability of the organic sector, the \n\n\n\norganisations invested in the future markets. For example, a consultant \n\n\n\npracticing since the year 1990 and serving as a board member in one of \n\n\n\nthe organizations in the Bavarian state, said they had established an \n\n\n\nongoing schools program where learners interact with organic farmers to \n\n\n\nlearn about the importance of organic products and environmental \n\n\n\nprotection. Also, farmer meetings were other forms of marketing \n\n\n\ninnovation. A cattle farmer in Baden-Wuerttemberg echoed these \n\n\n\nsentiments and said \u201cIn these farmers\u2019 meetings, it is not only just farmers, \n\n\n\nthere are people from the restaurants, and others, they are also there. You \n\n\n\nget connected with butchers, so you are not just in this one specific group of \n\n\n\nfarmers\u201d. Local retailers and shops are approached and encouraged to \n\n\n\nstock and sell regional produced organic products. Through tagging and \n\n\n\nbranding locally produced organic products with the organisation\u2019s logo \n\n\n\nfrom member farmers is an example of how innovation in marketing \n\n\n\nmanifested. Using the organisation\u2019s logo on organic products, enabled the \n\n\n\norganisations to know which supermarkets stocked their organic products \n\n\n\nfrom the region and which ones did not. Armed with this information, local \n\n\n\nsupermarkets are approached and an attempt is made to market farmers\u2019 \n\n\n\nproducts and has them included on their shelves. The results of the study \n\n\n\nconcur with Hao et al. (2018) in China among apple farmers as well as \n\n\n\nstudies by Jitmun & Kuwornu (2019) in Thailand and Forney & Haberli \n\n\n\n(2017) in Switzerland among the dairy farmers that support organisations \n\n\n\nplay a critical role in marketing innovation. \n\n\n\n4.3 Innovative Practices and Sustainability of Organic Farming \n\n\n\nFigure 2 is an extract from Figure 1 that shows the analysis of how \n\n\n\ndifferent innovative practices contribute to the sustainability of OFIs. The \n\n\n\nresults suggest that innovative practices from the marketing and \n\n\n\nconsumer engagement influence the social, economic, and political \n\n\n\nsustainability while practices in production processes were more relevant \n\n\n\nto economic and environmental sustainability. For example, farmer-school \n\n\n\nengagement programmes facilitated by farmer organisations create \n\n\n\nawareness to children about the importance of environmental protection \n\n\n\nand organic farming. In this way, the future organic markets are secured \n\n\n\nfor example. Innovative practices identified in this study, illustrate how \n\n\n\nsustainability can be achieved at different levels of OFIs contribution by \n\n\n\ndifferent stakeholders. The framework of the study is adapted based on \n\n\n\nthe empirical findings from the participants. Earlier, in Bavaria and Baden-\n\n\n\nWuerttemberg (Brenes Mu\u00f1oz et al., 2011) found that direct marketing, \n\n\n\nsignificantly influenced farm growth suggesting that marketing \n\n\n\ncontributes to the economic sustainability of organic farming. Moreover, \n\n\n\nthe study revealed that less efficient farms grew faster than more efficient \n\n\n\nones. This might indicate that while increasing farm productivity is part of \n\n\n\nthe organic farming sustainability cocktail, natural processes and \n\n\n\nenvironmental concerns should be factored in the process. These findings \n\n\n\nemphasize the revelation by Brzezina et al. (2017) that growth-driven \n\n\n\nsupport approaches to organic farming have unintended consequences. \n\n\n\nHence,  OFI processes aimed at increasing productivity in the sector should \n\n\n\nbe anticipated and managed within the limits of organic production. From \n\n\n\nFigures 1 and 2 it is possible to identify the organisations and know at \n\n\n\nwhich level of the OFIs they are more relevant. Also, the innovation gap \n\n\n\ncan easily be identified through a process of innovation. It is also evident \n\n\n\nfrom the figure that the number of innovative practices occurring at the \n\n\n\nmarket and consumer engagement is greater compared to all the other \n\n\n\nlevels. The framework highlights the roles of farmer organisations in the \n\n\n\ncollaborative promotion of OFIs. \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Organic Farming Innovation Sustainability. Source: Atlas Ti \n\n\n\nsoftware \n\n\n\n5. CONCLUSIONS \n\n\n\nThe study investigated and identified the roles of support groups and \n\n\n\norganizations towards achieving OFIs and sustainability. There are three \n\n\n\ncategories of support organisations namely, lobby groups, farmer \n\n\n\norganisations, and private consultancies. The role of these organisations \n\n\n\noccurs at different levels namely: compliant and setting organic farming \n\n\n\nstandards; production processes; and marketing and consumer \n\n\n\nengagement. The analysis suggested that farmer organisations and \n\n\n\nconsultancies contribute the most in promoting OFIs. These innovations \n\n\n\ninclude facilitating access to resources such as knowledge, finance, \n\n\n\nemotional support, and capacity building. Joint activities of farmers and \n\n\n\norganisations such as on-farm research activities emerged as critical in \n\n\n\nfostering OFIs. Moreover, while farmer organisations and consultancies \n\n\n\ncontributed significantly to the economic, environmental, and social \n\n\n\naspects of organic farming sustainability, lobby organisations are more \n\n\n\neffective in environmental and political aspects. For example, innovative \n\n\n\npractices from collaboration between farmers and farmer organisations \n\n\n\nresulted in site-adapted land management (manure production and \n\n\n\npreparation, sustainable tillage practices). While collaboration between \n\n\n\nfarmers and consultancies led to improved marketing innovations such as \n\n\n\ncollective labelling and branding on behalf of farmers as well as retail shelf \n\n\n\nspace advocacy. The identified OFIs contribute to problem solving and \n\n\n\noffer tools that could be used to increase effectiveness, efficiency, and \n\n\n\nproductivity in organic farming. Consequently, viability for the \n\n\n\nenvironment, plants, animals, and human beings could be improved or \n\n\n\nachieved. Hence, the OFIs emanating from collaboration between farmers \n\n\n\nand support organisations at different levels must be enhanced to attain \n\n\n\nlocal and scalable solutions for sustainable agricultural practices and \n\n\n\nregenerative farming. The study recommends that the nature of support \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 44-50 \n\n\n\n\n\n\n\n \nCite the Article: Ndlovu Wiseman, Sabine Moebs, Marizvikuru Mwale, Jethro Zuwarimwe (2022). The Role of Support Organisations in Promoting Organic Farming \n\n\n\nInnovations and Sustainability. Malaysian Journal of Sustainable Agriculture, 6(1): 44-50. \n \n\n\n\n\n\n\n\nby each group of organisations be further studied to unpack the complex \n\n\n\nnature OFIs and facilitate their diffusion for organic farming sustainability. \n\n\n\nACKNOWLEDGMENT \n\n\n\nThe study was funded by Baden-Wuerttemberg-STIPENDIUM a program \n\n\n\nof the Baden-Wuerttemberg Stiftung. The study is a collaboration between \n\n\n\nthe University of Venda, South Africa, and Baden-Wuerttemberg \n\n\n\nCooperative State University (DHBW), Germany. \n\n\n\nREFERENCES \n\n\n\n \nAboniyo, J., & Mourad, K. A. (2017). Role of community-based \n\n\n\norganizations in transferring climate change information in Rwanda. \n \nAku, A., Mshenga, P., Afari-Sefa, V., & Ochieng, J. (2018). Effect of market \n\n\n\naccess provided by farmer organizations on smallholder vegetable \nfarmer\u2019s income in Tanzania. Cogent Food & Agriculture, 4(1), 1560596. \n\n\n\n \nAmi, D. S. (2021) The organic market in Germany - highlights 2020. Biofach \n\n\n\nCongress \n \nAsadollahpour, A., Omidinajafabadi, & Jamalhosseini, S. (2014). Factors \n\n\n\naffecting the conversion to organic farming in Iran: A case study of \nMazandaran rice producers. Science International (Lahore), 26(4), 1844-\n1860. \n\n\n\n \nBokelmann, W., Dornberg, A., Schwerdtner, W., Kuntosch, A., Busse, M., \n\n\n\nK\u00f6nig, B., Siebert, R., Koschatzky, K., Stahlecker, T. (2012). Sektorstudie \nzur Untersuchung des Innovationssystems der deutschen \nLandwirtschaft. \n\n\n\n \nBouncken, R. B. (2011). Supply chain contingencies: The effects of \n\n\n\nupstream directives on supplier's innovation performance. Engineering \nManagement Journal, 23(4), 36-46. \n\n\n\n \nBrenes Mu\u00f1oz, T., Lakner, S., & Br\u00fcmmer, B. (2011). Determinants of \n\n\n\neconomic growth in organic farming: the case of Bavaria and Baden-\nWuerttemberg (No. 726-2016-49922). \n\n\n\n \nBrzezina, N., Biely, K., Helfgott, A., Kopainsky, B., Vervoort, J., & Mathijs, E. \n\n\n\n(2017). Development of organic farming in Europe at the crossroads: \nLooking for the way forward through system archetypes \nlenses. Sustainability, 9(5), 821. \n\n\n\n \nClark, S. (2020). Organic Farming and Climate Change: The Need for \n\n\n\nInnovation. Sustainability, 12(17), 7012. \n\n\n\nCanali, S., Antichi, D., Cristiano, S., Diacono, M., Ferrante, V., Migliorini, P., ... \n\n\n\n& Colombo, L. (2020). Levers and Obstacles of Effective Research and \n\n\n\nInnovation for Organic Food and Farming in Italy. Agronomy, 10(8), \n\n\n\n1181. \n\n\n\nCoase, RH (1992). The problem of social cost. Public studies, (45). \n\n\n\n \nDahlman, C. J. (1979). The problem of externality. The journal of law and \n\n\n\neconomics, 22(1), 141-162. \n \nDamanpour, F. (1991). Organizational innovation: A meta-analysis of \n\n\n\neffects of determinants and moderators. Academy of management \njournal, 34(3), 555-590. \n\n\n\n \nDelate, K., Canali, S., Turnbull, R., Tan, R., & Colombo, L. (2017). \n\n\n\nParticipatory organic research in the USA and Italy: Across a continuum \nof farmer\u2013researcher partnerships. Renewable Agriculture and Food \nSystems, 32(4), 331-348. \n\n\n\n \nde Paula, I. C., de Campos, E. A. R., Pagani, R. N., Guarnieri, P., & Kaviani, M. \n\n\n\nA. (2019). Are collaboration and trust sources for innovation in the \nreverse logistics? Insights from a systematic literature review. Supply \nChain Management: An International Journal. \n\n\n\n \nEuropean Union. (2017). Precision farming: sowing the seeds of a new \n\n\n\nagricultural revolution. \n\n\n\nFederal Ministry of Food and Agriculture, Germany (2021). Organic \n\n\n\nFarming in Germany. Available at, \n\n\n\nhttps://www.bmel.de/EN/topics/farming/organic-farming/organic-\n\n\n\nfarming_node.html. [accessed on 1 July 2021]. \n\n\n\nFederal Statistical Office, Germany (2017). Organic Agriculture. Available \n\n\n\nat https://www.bfs.admin.ch/bfs/en/home/statistics/catalogues-\n\n\n\ndatabases/maps.assetdetail.5266714.html. Accessed on 1 July 2017. \n\n\n\n \nFood and Agriculture Organisation. (2018). Transforming food and \n\n\n\nagriculture to achieve the SDGs: 20 interconnected actions to guide \ndecision-makers. Technical Reference Document. Rome: Licence: CC BY-\nNC-SA 3.0 IGO. \n\n\n\n \nForney, J., & H\u00e4berli, I. (2017). Co-operative values beyond hybridity: The \n\n\n\ncase of farmers\u2019 organizations in the Swiss dairy sector. Journal of Rural \nStudies, 53, 236-246. \n\n\n\n \nHamm, U., H\u00e4ring, A. M., H\u00fclsbergen, K. J., Isermeyer, F., Lange, S., Niggli, \n\n\n\nU., ... & Horn, S. (2017). The research strategy of the German Agricultural \nResearch Alliance (DAFA) for the development of the organic farming \nand food sector in Germany. Organic Agriculture, 7(3), 225-242. \n\n\n\nHao, J., Bijman, J., Gardebroek, C., Heerink, N., Heijman, W., & Huo, X. \n\n\n\n(2018). Cooperative membership and farmers\u2019 choice of marketing \n\n\n\nchannels\u2013Evidence from apple farmer \n\n\n\nH\u00e4ring, AM, Blodau, S., Braun, C., Meyerhoff, C., & Winkler, C. (2012). \n\n\n\nResearch on organic agriculture and food management in Shaanxi and \n\n\n\nShandong Provinces, China. Food Policy, 74, 53-64. \n\n\n\n \nHellin, J., & Camacho, C. (2017). Agricultural research organisations\u2019 role \n\n\n\nin the emergence of agricultural innovation systems. Development in \nPractice, 27(1), 111-115. \n\n\n\n \nIhnatenko, M., & Novak, N. (2018). Development of regional programs for \n\n\n\nthe development of agrarian enterprises with organic production on the \nbasis of the European and international experience. Baltic Journal of \nEconomic Studies, 4(4), 126-133.  \n\n\n\n \nJitmun, T., & Kuwornu, J. K. (2019). Factors influencing the choice of \n\n\n\nmarketing channels: evidence from dairy farmers in \nThailand. International Journal of Value Chain Management, 10(2), 123-\n140. \n\n\n\n \nJouzi, Z., Azadi, H., Taheri, F., Zarafshan, K., Gebrehiwot, K., Van Passel, S., \n\n\n\n& Lebailly, P. (2017). Organic farming and small-scale farmers: Main \nopportunities and challenges. Ecological Economics, 132, 144-154. \n\n\n\n \nKingiri, A. N., and Andy H. (2012 \"The role of policy brokers: The case of \n\n\n\nbiotechnology in Kenya.\" Review of Policy Research 29(4), 492-522. \n \nKrishnan, R., Yen, P., Agarwal, R., Arshinder, K., & Bajada, C. (2021). \n\n\n\nCollaborative innovation and sustainability in the food supply chain-\nevidence from farmer producer organisations. Resources, Conservation \nand Recycling, 168, 105253. \n\n\n\n \nLastra-Bravo, X., Hubbard, C., Garrod, G., & Tol\u00f3n-Becerra, A. (2015). What \n\n\n\ndrives farmers\u2019 participation in EU agri-environmental schemes?: \nResults from a qualitative meta-analysis. Environmental Science & Policy, \n54, 1-9. \n\n\n\n \nMarsh, L., Zoumenou, V., Cotton, C., & Hashem, F. (2017). Organic farming: \n\n\n\nknowledge, practices, and views of limited resource farmers and non-\nfarmers on the Delmarva Peninsula. Organic Agriculture, 7(2), 125-132. \n\n\n\n \nMbangari, K. F., Fonteh, M. F., & Fouepe, G. H. F. (2020). Role of Farmer \n\n\n\nOrganizations in the Reinforcement of the Economic Capacities of \nFarmers in Mezam Division, Cameroon. Asian Journal of Agricultural \nExtension, Economics & Sociology, 80-91. \n\n\n\n \nMcKether, W. L., & Friese, S. (2016). Qualitative social network analysis \n\n\n\nwith ATLAS. ti. 10.14279/depositonce-5079. \n \nMwamalaji, E. M. M. A. N. U. E. L. (2019). Determinants and effectiveness of \n\n\n\ncollective action in Cocoa marketing among small scale producers in \nKyela District, Tanzania (Doctoral dissertation, Sokoine University of \nAgriculture). \n\n\n\n \nNiggli, U., Andres, C., Willer, H., & Baker, B. P. (2017). Building a global \n\n\n\nplatform for organic farming research, innovation and technology \ntransfer. Organic Agriculture, 7(3), 209-224. \n\n\n\n \nNedumaran, D. G. (2020). Sustainable Development and Challenges of \n\n\n\nOrganic Farming Practices. Sustainable Development and Challenges of \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 44-50 \n\n\n\n\n\n\n\n \nCite the Article: Ndlovu Wiseman, Sabine Moebs, Marizvikuru Mwale, Jethro Zuwarimwe (2022). The Role of Support Organisations in Promoting Organic Farming \n\n\n\nInnovations and Sustainability. Malaysian Journal of Sustainable Agriculture, 6(1): 44-50. \n \n\n\n\n\n\n\n\nOrganic Farming Practices (March 10, 2020). \n \nPadel, S., Vaarst, M., & Zaralis, K. (2015). Supporting innovation in organic \n\n\n\nagriculture: A European perspective using experience from the SOLID \nproject. Sustainable Agriculture Research, 4(3), 32-41.  \n\n\n\n \nSmith, L. G., Kirk, G. J., Jones, P. J., & Williams, A. G. (2019). The greenhouse \n\n\n\ngas impacts of converting food production in England and Wales to \norganic methods. Nature Communications, 10(1), 1-10. \n\n\n\n \nSoosay, C. A., Hyland, P. W., & Ferrer, M. (2008). Supply chain \n\n\n\ncollaboration: capabilities for continuous innovation. Supply chain \nmanagement: An international journal. \n\n\n\n \nTiraieyari, N., Hamzah, A., & Samah, B. A. (2017). Organic farming and \n\n\n\nsustainable agriculture in Malaysia: organic farmers\u2019 challenges towards \nadoption. Sustainable Development of Organic Agriculture: Historical \nPerspectives, 135. \n\n\n\n \nYang, H., Klerkx, L., & Leeuwis, C. (2014). Functions and limitations of \n\n\n\nfarmer cooperatives as innovation intermediaries: Findings from \nChina. Agricultural Systems, 127, 115-125. \n\n\n\n \nYoungbeurg, G., & DeMuth, S. P. (2013). Organic agriculture in the United \n\n\n\nStates: A 30-year retrospective. Renewable Agriculture and Food \nSystems, 28(4), 294-328. \n\n\n\n \nZagata, L., Sutherland, L. A., Hrab\u00e1k, J., & Lostak, M. (2020). Mobilising the \n\n\n\nPast: Towards a Conceptualisation of Retro\u2010Innovation. Sociologia \nRuralis, 60(3), 639-660. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA)1(2) (2017) 15-17\n\n\n\nWATER MANAGEMENT OF THE MEKONG RIVER\nGao Yun1*, StewartWilliams2, Dai Wenbin2\n\n\n\n1Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang \n050061, China\n2School of Land and Food, Department of Environmental Study and Geography, Tasmania University, \nTasmania 7005, Australia.\n*Corresponding Author email: gaoyun0526@163.com\n\n\n\nARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 September 2017 \nAccepted 19 October 2017 \nAvailable online 30 October 2017 \n\n\n\nKeywords: \n\n\n\nTransboundary river, Mekong \nregion, Riparian countries, \nChallenges of water management\n\n\n\nABSTRACT\n\n\n\nIn the coming decades, river pollution is a serious problem not only in Asia but around the world. As the river flows \nthrough many countries, the development and water quality of the Mekong River is related directly to stability and \npeace of the region. This study analysed regulatory authority and legislation in Mekong River and discussed the \ninadequacy of Mekong River current management. Through collecting data and documents, it analysed and concluded \ncurrent river management situation of Mekong River. At last, this paper advised several recommendations and tried \nto find out which approaches will make Mekong Basin under a good environment of sustainable development.\n\n\n\nCite this article as: Gao Yun, StewartWilliams, Dai Wenbin  (2017).  Water Management Of \nThe Mekong River.  Malaysian Journal of Sustainable Agriculture,  1(2):15-17.\n\n\n\n1. INTRODUCTION\n\n\n\n1.1 Geography of the Mekong River Basin\n\n\n\nIn mainland Southeast Asia, the Mekong River Basin is the significant source \nof water, flowing through or forming the international border of six \ncountries: China (in particular, Yunnan Province), Myanmar, Laos, Thailand, \nCambodia, and Vietnam [1]. The headwater of the river is in China\u2019s Qinghai \nProvince and the watershed is over 810000 km2 [2]. So, the Mekong River is \nthe eighth largest river in the world and the largest international river in \nSoutheast Asia which has 4880 km distance from the source to entrance. \nMore than 80 million people use the Mekong River for irrigation \nwater, drinking water, fishing and transportation [3]. Upper Mekong \nRiver (China section) is named Lancang River in China. 20% of volume \nof water comes from watersheds in Yunnan Province and flows into \nMekong\u2019s mainstream [4].\n\n\n\n1.2 The challenges for water management in the Mekong River\n\n\n\nAs a transboundary river, the Mekong River has the same issues on conflicts \nof water resources uneven distribution and water pollution. Uneven \ndistribution of water quantity could be the greatest potential issue. Combing \nwith growing pressure on water resources, competing needs for \ndevelopment are also a potential conflict. International agreements or \ninstitutional arrangements lack could also be a hidden trouble [5]. Due to \nthe fast-economic growth and high resource potential, the pressures of \nMekong\u2019s resources may specifically high in current and the future [6].\n\n\n\nPollution and water quality issues are also serious now in this region. In the \nupper Mekong River basin (Yunnan Province, China), there are many \nindustrial enterprises were surveyed in 2000, and some of them were shut \ndown because of water pollution. Besides these industrial factories, a mass \nof hydropower stations has been built on the upper Mekong River. Mekong \nRiver Commission pointed that it should have further investigation for some \ntoxicity of water on Lao section of Mekong River [7].\n\n\n\n2. RESEARCH AIMS AND METHODS\n\n\n\n2.1 Problems\n\n\n\nDuring fast social economic development, pollution of the Mekong River has \nbecome more serious. Agriculture is a main pollution source. Because of the \nbackward agricultural technology, farmers use a great deal of pesticides and \nfertilizers which brings serious pollution to the surface water. With the \nincrease in the population, more people live close to the river. Thus, a large \nnumber of domestic wastewater discharged into river. There are many \nmining factories built close to Mekong River, some of them are small size \nmining enterprises which do not have advanced technology and financial \nsupport. So, a great amount of industrial wastewater is discharged into the \n\n\n\nContents List available at RAZI Publishing \nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nJournal Homepage:  : http://www.razipublishing.com/journals/malaysian- journal- \n\n\n\nISSN: 2521-2931 (Print)\nISSN: 2521-294X (Online)\n\n\n\nriver also. These problems present significant challenges in the planning for \nwater quality management in the Mekong River.\n\n\n\nThe aims of this research are as follows:\n\n\n\n(1) Gain a better understanding of the achievements, gaps and challenges for \nplanning in transboundary water resources management;\n\n\n\n(2) Identify the principal challenges of planning for water quality \nmanagement in the Mekong River, and review the planning practices and \nstrategies currently in place for mitigating those challenges;\n\n\n\n(3) Evaluate planning for water quality management in the Mekong River in \na comparison with transboundary water resources management practices \nelsewhere;\n\n\n\n(4) Develop recommendations to improve planning for water quality \nmanagement in the Mekong River.\n\n\n\nThe 6 riparian countries of Mekong River are all developing countries. Most \nof them are agricultural nations and have less-developed industries. In \naddition, according to a study, environmental problems such as water \nresource pollution and ecosystem destruction are not yet too severe in that \narea [8]. For the Mekong River\u2019s better development, it cannot repeat the \nmodel of \"pollution first, treatment later\" as it was in the history of \ndeveloped countries. \n\n\n\n3. INADEQUACY OF MEKONG RIVER CURRENT MANAGEMENT\n\n\n\n3.1 Lack of unified coordination authority\n\n\n\nMekong River\u2019s future is intertwined with the lives of citizens of six riparian \nstates, meaning it requires unified, coordinated administration. This \nauthority could govern the water resources in accordance with actual \nconditions of the basin and principles of equity and justice. Meanwhile, it \nmay provide a comprehensive guide for development and utilization of the \nwhole river basin [9]. There are three main organizations in the Mekong \nRiver Basin, each with different rationales, members, goals and weaknesses \n(Table 1 and Table 2).\n\n\n\nofsustainable-agriculture-mjsa/\nhttps://doi.org/10.26480/mjsa.02.2017.15.17\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.15.17\n\n\n\n\n\n\n16  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 15-17  \n\n\n\nthrough strengthening cooperation on improving environmental \nconditions, China in the GMS can promote forest and fish protection in the \nMekong Basin, allowing the GMS to play a more prominent role in the \nfuture [11]. \n\n\n\n4.2 Improvement of supervision \n\n\n\nA supervision system should be established for the entire Mekong River. \nThe supervision system needs to formulate as soon as possible and be \nadapted for a local context, such as a unified environmental standard, joint \ndetection, public participation, information sharing and pollution control. \nThe management of the Mekong River Basin does not adhere to current \nsupervision measures. Strengthen supervision should be based upon the \nestablishment of regulatory agencies. Besides the function of supervision, \nthe regulatory agencies should have supporting roles, such as penalties \nand evaluation schemes [12]. \n\n\n\n4.3 Public participation and multi-participation \n\n\n\nPublic participation and multi-participation are necessary in dealing with \nvarious conflicts of interest. Public participation such as policy making \nand planning from engineers, politicians and landowners is significant in \nthe decision-making process of resource management [13]. Protection of \nthe Mekong environment is not only the duty of the government, but also \nthat of factories, enterprises and residents. For water quality management \nof the Mekong River, public participation and multi-participation will play \na vital role [14-16]. NGOs promote the environmental governance in the \nMekong River, working in fields such as providing environmental \ninformation, supervision of environmental cooperation, environmental \nresearch and promoting basin countries cooperation [11]. As NGOs, if \nmore water protection organizations and industry associations participate \nin important decision-making process, the decisions and policies will as a \nresult be more transparent and practicable [14].\n\n\n\n5. CONCLUSION\n\n\n\nThe water resources of the Mekong River should be developed as a whole. \nThe best way to develop the Mekong river should include mutual \ncoordination and shared interests. Although there are many problems in \nthe Mekong region, it still has opportunity for the whole Mekong River \nBasin countries. In addition, riparian countries are gradually increasing \ncooperation concerning water resource development. \n\n\n\nThe Mekong region is one of the areas with the most substantial natural \naccumulations of water resources. The protection of the ecological \nenvironment is also a key focus. Drawing experience from the successful \nmanagement of other country, the Mekong River Basin can have \nsustainable ecological development. However, the riparian countries must \nwork closely with each other to reap the benefits. The governments of the \nlower Mekong Basin have promised to increase data collection efforts and \nto share data, improving decision making for trans-boundary water \nsharing in the basin. Although China and Burma are still not members of \nthe MRC, there is increased cooperation among them. The Greater Mekong \nSub-region, which includes all six riparian countries, also focuses on basin \ndevelopment and environmental protection. There will be more \ncooperation of the whole  riparian state. \n\n\n\nREFERENCES \n\n\n\n[1] Buxton, M., Kelly, M., and Martin, J.  2003. Environmental conflicts in \nthe Mekong River Basin, Report to the Mekong River Commission, School \nof Social Science and Planning, the Royal Melbourne Institute of \nTechnology.  RMIT University.\n\n\n\n[2] Chen, L. H., Zeng, Z. G., and He, D. M. 2003. Coordinating the \nrelationships between interest parties in development of the international \nriver-A case study of Lancang-Mekong. World Regional Studies, 12 (1), \n71-78.\n\n\n\n[3] Chen, S. R. 2008. A review on environmental cooperation in Great \nMekong Sub- region-lessons from Rhine River Regulation.  Journal of \nYunnan Normal University, 28 (5), 69-74.\n\n\n\n[4] Deng, H. 2011. Game-playing of Lancang-Mekong River water \nresources (master\u2019s thesis). Jinan University, China.\n\n\n\n[5] Du, J. 2010. Water pollution prevention and control mechanism of \nLancang-Mekong River- European river pollution management experience \n(master\u2019s thesis), Kunming University of Science and Technology, China.\n\n\n\n[6] Goh, E. 2004. China in the Mekong River basin: the regional security \n\n\n\nTable 1: MRC, GMS and QEC [10]\n\n\n\nTable 2: Member States [10]\n\n\n\n3.2 Imperfect water pollution legislation \n\n\n\nThe Mekong River is a transboundary river, flowing through six riparian \nstates with different, imperfect and impertinent water legislation and \npolicies. If there were no unified laws and regulations regarding the trans-\nboundary river basin level, current agreements would easily collapse. \n\n\n\nExisting conventions and agreements include the Joint Declaration of \nPrinciples for Utilization of the Waters of the Mekong Basin, Agreement on \nthe Cooperation for the Sustainable Development of the Mekong River \nBasin, The Greater Mekong Sub-Region Economic Cooperation Program \nStrategic Framework and Kunming Statement. These conventions and \nagreements were established for basin management and international \ncooperation with no legal standing. In addition, these conventions and \nagreements are not signed by all six riparian countries, so they cannot \naccomplish comprehensive water pollution management [9].  \n\n\n\n3.3 Lack of information and data \n\n\n\nIt has communication barriers. At present, an instrumental database has \nbeen established for the Mekong River basin, headed by the secretariat of \nthe MRC. According to the majority of studies in the last 40 years of the \nMekong, there are appropriate condition for hydrological research and \nreviews of hydrological trends. However, on the whole, the information is \nout of date and incomplete, especially regarding the field of deforestation, \nsoil erosion and sediment deposition.\n\n\n\n4. RECOMMENDATIONS\n\n\n\n4.1 Cooperation in the Mekong River Basin\n\n\n\nThe current management organizations of the Mekong Basin do not have \nspecific departments dedicated to controlling water pollution; China \nmanages the upper Mekong River and the MRC manages downstream [9].  \n\n\n\nDue to disagreements between upstream and downstream countries, \nChina should ideally adopt targeted cooperation in selected fields with the \nMRC (something downstream countries are encouraging) to promote \nintegrative management, cooperation and ensuring safety of water \nresources of the Mekong Basin. The cooperation topic should deal with \nissues of concern such as ecological environment protection, hydropower \ndevelopment and climate change. Water resource development involves \nthe interests of multiple parties, meaning international cooperation must \nbe conducted gradually. Besides the MRC, there are many organizations in \nthe Mekong River with similar functions and issues. For example, the GMS \nalso focuses on environmental governance in the Mekong. As a result, \n\n\n\nCite this article as: Gao Yun, StewartWilliams, Dai Wenbin  (2017).  Water Management Of \nThe Mekong River.  Malaysian Journal of Sustainable Agriculture,  1(2):15-17.\n\n\n\n\n\n\n\n\n17  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 15-17  \n\n\n\nimplications of resource development on the Lancang Jiang (RSIS \nWorking Paper, No. 69). Singapore: Nanyang Technological University.\n\n\n\n[7] Gupta, A. D. 2005. Challenges and opportunities for integrated water \nresources management in Mekong River Basin. In International \nSymposium on the Role of Water Sciences in Transboundary River Basin \nManagement, 10-12 March 2005. Thailand: Asian Institute of Technology.\n\n\n\n[8] He, D., Feng, Y., Gan, S., and You, W.H. 2006. Transboundary \nhydrological effects of hydropower dam construction on the Lancang \nRiver. Chinese Science Bulletin, 51 (22), 16-24.\n\n\n\n[9] Hensengerth, O. 2009. Transboundary river cooperation and the \nregional public good: The case of the Mekong River. Contemporary \nSoutheast Asia: A Journal of International and Strategic Affairs, 31 (2), \n326-349.\n\n\n\n[10] Hirsch, P., and Cheong, G. 1996. Natural resource management in the \nMekong River Basin: Perspectives for Australian development \ncooperation.\n\n\n\n[11] Hinton, P. 1998. Resource management in the Yunnan Mekong basin \n(No. 72). Asia Research Centre, Murdoch University.\n\n\n\n[12] MRC. 2008. An assessment of water quality in the Lower Mekong \nBasin. In Ongley, E. & Burnhill, T. (Eds.), MRC Technical Paper No.19. Lao \nPDR: Mekong River Commission.\n\n\n\n[13] Myint, T. 2003. Democracy in global environmental governance: \nissues, interests, and actors in the Mekong and the Rhine. Indiana Journal \nof Global Legal Studies, 10 (1), 287-314.\n\n\n\n[14] Ringler, C., von Braun, J., and Rosegrant, M. W. 2004. Water policy \nanalysis for the Mekong River Basin. Water International, 29 (1), 30-42.\n\n\n\n[15] Tang, H. X. 1999. Water resources in the Lancang-Mekong River Basin \nand analysis on the present situation of its utilization. Yunnan Geographic \nEnvironment Research, 11 (1), 16-25.\n\n\n\n[16] Wang, X. 2012. Research on the Cooperation Mechanism of Lancang-\nMekong River Water Resource. Around Southeast Asia, 10, 73-76.\n\n\n\nCite this article as: Gao Yun, StewartWilliams, Dai Wenbin  (2017).  Water Management Of \nThe Mekong River.  Malaysian Journal of Sustainable Agriculture,  1(2):15-17.\n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 44-48 \n\n\n\nCite The Article: : Pervin Akter, Barat Sultana (2019). Allelopathic Effects, Yields And Qualitative Phytochemical Screening Of Root Exudates Of Five Weeds Species. \nMalaysian Journal of Sustainable Agriculture, 3(1): 44-48 \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\n    ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 April 2019 \nAccepted 29 May 2019 \nAvailable online 14 June 2019 \n\n\n\nABSTRACT \n\n\n\nThis research investigated the allelopathic effects, the yields and qualitative phytochemical screening of the \nwater extract of root exudates of five weed species i.e. Cyperus rotundus L. (T1), Marselia quadrifolia L. (T2), \nLudwigia hyssopifolia (G. Don) Exell, (T3) Pistia stratiotes L. (T4) and Colocasia esculenta L. (T5). The allelopathic \ntests of root exudates on five weed species showed that all the extracts had the pronounced inhibitory effect on \ncowpea and mungbean (tested crops). The yields of root exudates of the selected weed species varied. Root \nexudate of T3 showed the highest yield whereby T1 contained the lowest one. A preliminary phytochemical test \nshowed the positive result of alkaloids, flavonoids, phenols and carbohydrates whereas proteins, amino acids, \ntannins, saponins, have been found to be absent in the root exudates of tested weeds. The results evidenced \nthat these mentioned weeds contain compounds in their root exudates which may cause allelopathic effects on \nboth tested crops. \n\n\n\nKEYWORDS \n\n\n\nAllelopathy, Colocassia esculenta. Cyperus rotundus, Ludwigia hyssopifolia, Marselia quadrifolia, Pistia \nstratiotes and Root exudates\n\n\n\n1. INTRODUCTION \n\n\n\nAllelopathy, a term was first coined by a researcher that concerned a \nbiological and chemical interaction within the communities by the \naddition of certain compounds to the rhizosphere environment and thus \ninfluence the agricultural systems [1]. In cultivated crops, weeds are \nhighly successful organisms in nature and established as an integral part \nof our agroecosystem [2]. Weeds always compete for the light, moisture, \nmacro, and micronutrients with the neighboring crop plants for their \ngrowth and development and influence to the crop productivity by \nliberating a number of chemicals in the soil through roots [3].  The \nbiologically active chemicals that released to the soil are so-called \nallelochemicals [4]. They are present in varieties of plant tissues including \nleaves, flowers, fruits, stems, roots, rhizomes and seeds [5]. \nAllelochemicals are released by means of volatilization, decomposition of \nplant residues and triggered either synergistic or antagonistic phenomena \nor displays allelopathic stress [6]. \n\n\n\nRoot exudation is another source of allelochemicals released by plant \nroots, most of which are organic and normal plant components are formed \nduring photosynthesis and accompanying plant process [7]. Thus, it \nsignifies the carbon cost to the plant during their lifetime altogether up \nto17 % of the photosynthetically fixed carbon [8,9].  The reported amount, \nhowever, can vary depending on the author; maximum values range \naround 30 %. The deposition of root exudates in the rhizosphere is \ndetermined by the various biotic and abiotic factors [10]. High root \ndensities, the cultivars, plant age and the degree of environmental stress \ngreatly influence the amount of root exudation [11]. In addition, the \ntransportation mechanism of metabolites is also one of the major causes \nin releasing of exudate compounds [12]. Based on the number of \nliteratures, it is conceived that the exude compounds are separated by \nlow- and high- molecular- weight compounds that are present in the \nintercellular space of root tip tissues and root hairs. They may leak either \n\n\n\nfrom root cells or be transported via the phloem from other tissues \n[13,14]. The low-molecular-weight compounds belong to primary and \nsecondary metabolites of which carbohydrates, amino acids, phenolic with \na variety of other metabolites are allegedly thought to be the mainstream \ncomponents of root exudates [15]. Compounds such as Isoflavone, \nphytoanticipin-antimicrobal components, allelopathic chemicals are \nassociated with various functions that nourish the rhizosphere \nenvironment [16]. \n\n\n\nSeveral investigators have been reported the implications of root exudates \nin interactions among plants and soil microorganisms and plant-plant \ninteraction and their crucial role in nutrient mobilization through direct \ncoordination complex formation of micronutrients [17]. Studies showed \nthat it acts as an inhibitor to plant growth. For example, sorgoleone a \nhydrophobic allelopathic compound was identified as root exudates from \nthe roots of Sorghum bicolor L. Moench) and showed a potent inhibitory \neffect on the growth of several broadleaf plants [18]. However, literature \nregarding the allelopathic effect of root exudate on seed germination and \nseedling growth under laboratory conditions is insufficient [19]. Hence, \nthe objective of these study is to evaluate the yields, phytochemical \nanalysis and allopathic effects of water extracts of root exudates of Cyperus \nrotundus L., Marselia quadrifolia L., Ludwigia hyssopifolia (G. Don) Exell, \nPistia stratiotes L. and Colocasia esculenta L. on seed germination and \nseedling growth of mungbean and cowpea under laboratory conditions. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Plant materials \n\n\n\nFive common weed species namely Cyperus rotundus L. (T1), Marselia \nquadrifolia L. (T2), Ludwigia hyssopifolia (G. Don) Exell. (T3), Pistia \nstratiotes L. (T4) and Colocasia esculenta L. (T5) were selected on the \ncampus of Chittagong University (Hathazari. Chittagong, Bangladesh) in \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.01.2019.44.48\n\n\n\n  RESEARCH ARTICLE \n\n\n\nALLELOPATHIC EFFECTS, YIELDS AND QUALITATIVE PHYTOCHEMICAL \nSCREENING OF ROOT EXUDATES OF FIVE WEED SPECIES \n\n\n\nPervin Akter* and Barat Sultana \n\n\n\nDepartment of Botany, University of Chittagong, Chittagong-4331, Bangladesh \n*Corresponding Author Email: pervinakter730@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:pervinakter730@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 44-48 \n\n\n\nCite The Article: : Pervin Akter, Barat Sultana (2019). Allelopathic Effects, Yields And Qualitative Phytochemical Screening Of Root Exudates Of Five Weeds Species. \nMalaysian Journal of Sustainable Agriculture, 3(1): 44-48 \n\n\n\nSeptember 2017 and identified by a botanist at Chittagong university. The \nseeds of mungbean (Vigna radiata L. R.Wilczek), and cowpea (Vigna \nunguiculata L. Walp.) were collected from Hathazari Local bazar, \nHathazari, Chittagong. \n\n\n\n2.2 Preparation of Root Exudates \n\n\n\nApproximately 50 plants of each weed species were uprooted around the \ncrop fields without hampering its roots and washed with running tap \nwater (three times) followed by distilled water. The plant roots were \nimmediately transferred to the conical flask containing 300 ml of distilled \nwater and kept for 5 h under sunlight for collecting root exudation. The \nwater extract of root exudates was collected by filtering through Whatman \nfilter paper (No. 1). The exudates were dried using the water bath at 60 \u00b0C \nand measured. About half of the dried exudates were redissolved in 50 ml \nsterile distilled water used for allelopathy experiment on mungbean and \ncowpea. The rest of the dried material was preserved at 4 \u00b0C refrigerator \nfor phytochemical analysis. \n\n\n\n2.3 Bioassay technique \n\n\n\nSeeds of mungbean and cowpea were surface sterilized with 70% ethanol \nfor 3 minutes, subsequently, they were washed gently with sterile distilled \nwater up to 5 times to remove chemicals. Fifteen seeds of tested crops \nwere spread on 9 cm glass Petri dishes containing a two-fold filter paper \nmoistened with 5 ml of root exudates. Seeds were soaked in sterile \ndistilled water used as control (T0). All the treatments including control \ngroup were kept in the dark chamber for 2 days. The plates were then \n\n\n\ntransferred at room temperature for the next 8 days. Throughout the \nexperiment period, care was taken to add an equal volume of root \nexudates in each Petri dish periodically. The treated seeds were observed \nevery day. The seeds were considered germinated when radicle length \nwas over 2 mm. After 10 days, seed germinability, radicle and hypocotyl \nlength, fresh and dry weight of seedlings of the tested and control crops \n\n\n\nwere recorded. \n\n\n\n2.4 Phytochemical analysis of root exudates \n\n\n\nThe dry root exudate sample had suspended with 10 ml of distilled water \n\n\n\nand incubated for 72 hours at room temperature. The supernatant was used to \ndetect the various bioactive compounds according to the method \ndescribed by a researcher [20]. \n\n\n\n2.5 Statistical analysis \n\n\n\nThe experiment was conducted for thrice and the data were expressed as \nmean \u00b1 standard deviation (SD) using Microsoft excel 2010. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Effect of root exudate on seed germination of mungbean and \ncowpea \n\n\n\nThe data presented in Figure 1 revealed that the root exudate of five weed \nspecies had an inhibitory effect on seed germination of mungbean and \ncowpea and the degree of the inhibition varied depending on the species \ntested. In case of mungbean and cowpea, the germination was ranged 60% \nto 80% and 40% to 80% irrespective of all the treatments respectively, \nwhile control on both crops set 90% of germinated seeds. This occurrence \nrepresenting as an assurance of seed quality under laboratory conditions \nbefore field trial. The inhibitory effect was found to be in the proportion of \nthe germination percentage where the maximum germination having \nrecorded in T1 and T3 followed in mungbean. In contrary, the highest and \nthe lowest seed germination were recorded in T5 and T4 respectively in \ncowpea. Our results indicate that root exudate from weed species affects \ngreatly on mungbean than cowpea in terms of germination ability. \n\n\n\nFigure 1: Effect of water extracts of root exudate of five weeds on the percentage of seed germination of mungbean and cowpea after 10 days incubation \n\n\n\n3.2 Effect of root exudate on radicle and hypocotyl growth of \nmungbean and cowpea \n\n\n\nAmong two crops on-test, the length of the radicle and hypocotyl in control \nplants was found to be maximum whereas root exudate of weed species \nclearly showed inhibition of both tested crops (Figure 2 and 3). In \nmungbean, the length of the radicle ranged from 0.82 \u00b1 0.25 to 2.95 \u00b1 0.57 \ncm and the hypocotyl length varied from 2.18 \u00b1 0.58 to 5.50 \u00b1 1.28 cm \ntreated with T1 toT5 root exudates (Table 1). However, it was not the \nsame in cowpea. Cowpea, comparatively showed a higher range of the \nradicle (3.70 \u00b1 1.89 cm. to 5.40 \u00b1 1.75 cm.) and hypocotyl length (3.84 \u00b1 \n0.78 cm. to 4.95 \u00b1 1.45 cm.) compared to control (Table 1). In case of \n\n\n\nhypocotyl length, Root exudate of T3 (71.6 %) and T4 (67.3 %) showed \nhigher inhibition effect followed by T2 (31.5%), T5 (37.3 %) and T1 (18.2 \n%) in mungbean while T4 (9.3%) was found to be the lowest inhibitory \neffect followed by T2 (22.2 %) in cowpea (Figure 2 and 3). It was noticed \nthat the hypocotyl length was increased twice than radicle length in \nmungbean. The maximum inhibitory effect on radicle growth was found in \nT4 (79.4 %) followed by T3 (71.3%) in mungbean (Figure 3). Both radicle \nand hypocotyl affected by root exudates of the tested weeds in mungbean \nin comparison to cowpea. Consequently, the ratio of radicle and hypocotyl \nwas found to be lower in mungbean (0.33 to 0.54) than cowpea (0.76 to \n1.28) for all the treatments (Table 1). The control plants revealed good \nhealth of radicle and hypocotyl in both the tested crops. \n\n\n\nTable 1: Effects of root exudates of five weeds on the length (cm) and the ratio of radicle and hypocotyl of mungbean and cowpea (mean \u00b1 SD) \n\n\n\nTreatments \nTest crops \n\n\n\nMungbean Cowpea \nRL  \n\n\n\n(cm) \nHL \n\n\n\n(cm) \nR : H RL \n\n\n\n(cm) \nHL \n\n\n\n(cm) \nR : H \n\n\n\nT0 4.64 \u00b1 1.14 7.68 \u00b1 1.30 0.61 6.97 \u00b1 1.15 5.46 \u00b1 1.25 1.28 \n\n\n\nT1 2.95 \u00b1 0.57 5.50 \u00b1 1.28 0.54 3.70 \u00b1 1.89 3.84 \u00b1 0.78 0.96 \n\n\n\nT2 2.56 \u00b1 0.50 5.26 \u00b1 1.49 0.49 5.40 \u00b1 1.75 4.25 \u00b1 1.23 1.27 \n\n\n\nT3 1.16 \u00b1 0.37 2.18 \u00b1 0.58 0.53 4.58 \u00b1 1.03 3.85 \u00b1 1.20 1.19 \n\n\n\nT4 0.82 \u00b1 0.25 2.51 \u00b1 1.20 0.33 3.75 \u00b1 1.20 4.95 \u00b1 1.45 0.76 \n\n\n\nT5 1.86 \u00b1 0.68 4.82 \u00b1 1.47 0.39 5.00 \u00b1 1.24 3.95 \u00b1 1.15 1.27 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 44-48 \n\n\n\nCite The Article: : Pervin Akter, Barat Sultana (2019). Allelopathic Effects, Yields And Qualitative Phytochemical Screening Of Root Exudates Of Five Weeds Species. \nMalaysian Journal of Sustainable Agriculture, 3(1): 44-48 \n\n\n\nFigure 2: Inhibition of hypocotyl elongation of mungbean and cowpea as affected by root exudates of selected weeds, after 10 days incubation. \n\n\n\nFigure 3: Inhibition of radicle elongation of mungbean and cowpea as affected by root exudates of selected weeds, after 10 days incubation \n\n\n\nDuring physiological activity in case of growth and development of plants, \nsome known allelopathic compounds are synthesized and showed the \ninhibitory effect on seed germination [21]. For example, Inhibitors such as \nphenols and terpenoids found to be involved in the allelopathic activity on \ncrop plants [22]. Besides, the presence of p-coumaric acid, gallic acid, \nferulic acid, p-hydroxybenzoic acid, and anisic acid can be secreted as a \npotent exudates and induced as an inhibitors on seed germination [23]. In \naddition, some regulatory polyphenols bind with other hormones and \ncaused reduction of seedling growth. For example, ferulic acid, t-cinnamic \nacid, chlorogenic acid, p-coumaric acid, coumarin interact with ABA and \nshowed additive inhibitory effects, both on seed germination and seedling \nin mung bean. A researcher who have concluded the root exudates of \nbarnyardgrass suppressed the growth of rice, lettuce (Lactuca sativa L.) \nand monochoria (Monochoria vaginalis) during the early growth stages \n[24]. Similarly, the water-soluble root exudate (WRE) of Tithonia \ndiversifoliaon effect on the the germination, growth of pepper (Capsicum \nannum L.) and tomato (Lycopersicon esculentum Mill.) were recorded [25]. \nHowever, in our result, radish exhibited a lower germination percentage \ncompared to cucumber (Figure 1). \n\n\n\nFrom the present work, it was observed that the metabolites present in \nroot exudate effect seedling growth (radicle and hypocotyl length) \ncorroborated with a researcher who observed that water-soluble root \nexudate of Tithonia diversifolia suppressed the seedling growth of pepper \nand tomato. The Similar results were also found on lettuce growth [26]. \nFurther, root exudates of Burmuda buttermut showed 34-42 % inhibition \nof the tested crops which agree with our findings [27]. The inhibitory \neffect was also observed by a researcher by aqueous extracts of four native \nMexcan desert plants in Zea mays, Phaseolus vulgaris, Cucurbita pepo and \nLycopersicon esculentum [28]. Another researcher reported that p- \nhydroxyl madelic acid as an root exudate substance which cause inhibition \n\n\n\nof root elongation in rice released by the young barnyard grass [29]. As \nphenolic compounds released from the weed roots or residues to the soil \nand effect on the seed germination and seedling growth. The action of such \nallelopathic compounds influences on specific plant processes such as- cell \ndivision and elongation, the action of inherent growth regulators, mineral \nuptake, photosynthesis, respiration, stomata opening, protein synthesis, \nmembrane permeability and specific action [30]. \n\n\n\n3.3 Root exudate yields \n\n\n\nThe extraction yield was expressed as gm from 50 plants from five weed \nspecies and illustrated in Figure 4. The yields ranged from 1.34 to 2.50 gm \namong the weed species. Ludwigia hyssopifolia (T3) and Colocasia \nesculenta L. (T5), comparatively showed the better yield (2.50 gm. And 2 \ngm.) than in Cyperus rotunus L. (T1)), Marselia quadrifolia (T2) and Pistia \nstratiotes L. (T4) which produced 1.34 gm., 1.65 gm., 1.63 gm. respectively. \nNearly 30-40% carbon-based compound released to the soil as root \nexudate but it depends on the plant species, maturity and environmental \ncondition and the sampling method for root exudate collection [31]. The \nsite of the plant is also important. a previous researcher demonstrated that \nthe root apex is the predominant site of exudation in healthy young plants \nwhich is clearly separated from older tissues and concluded the main site \nof exudation belonging to the immediately behind the root tips named as \nlongitudinal cell junctions [32]. Considering the fact we selected the \nmentioned weed species randomly from different crop fields at a young \nstage and immediately collected root exudates by distilled water to give a \nnatural environment for root exudate collection and measured. Although \nthe exudate yields varied to different solvent extraction methods, \nhowever, water is rather more suitable for exudate collection and gives \nmore yields than that of other solvents [33]. \n\n\n\nFigure 4: Yields of root exudate (water extract) of five weed species \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 44-48 \n\n\n\nCite The Article: : Pervin Akter, Barat Sultana (2019). Allelopathic Effects, Yields And Qualitative Phytochemical Screening Of Root Exudates Of Five Weeds Species. \nMalaysian Journal of Sustainable Agriculture, 3(1): 44-48 \n\n\n\n3.4 Phytochemical analysis \n\n\n\nPhytochemical analysis plays a major source of information on the \nanalytical and instrumental methodology in plant sciences. As shown in \nTable 2, it is clear that all the root exudates in different reagent showed a \npositive response for alkaloids. Root exudates of T1 to T5 showed \u2018+++\u2019 \n(high concentration) in Dragendroff\u2019s reagent while Hager\u2019s reagent, \nMayer\u2019s reagent, and wagner\u2019s reagent gave \u2018++\u2019 (low concentration). In \nthe tannic acid test, T3, T4, and T5 exhibited \u2018++\u2019 while very low \nconcentration, \u2018+\u2019 was found in T1 and T2 root exudates as FeCl3 test. In \naddition to alkaloids, qualitative assessment for four other secondary \n\n\n\nmetabolites, viz. carbohydrate, flavonoids, phenols, amino acid, and \nprotein, tannins, and saponins test were also done. Carbohydrate, \nflavonoid and phenols were present in all the exudates. Tannins, saponins, \namino acids, and proteins were not found to be present. Reported from the \nliterature, the above-mentioned allelochemicals were present in leaves, \nflowers, stem, and roots in the weed species [34]. The concentration was \nvaried with the extraction procedures [35]. Since the root exudate yields \nlower than other plant parts, thus it is difficult to detect all of the \nsecondary metabolites investigated in the present study by the traditional \nmethod. Therefore, high-throughput screening method would the best \nchoice to detect the metabolites in the root exudates.\n\n\n\nTable 2: Qualitative phytochemical screening test of root exudates of five weed species \n\n\n\nTreatments \n\n\n\nName of the test Type of test T1 T2 T3 T4 T5 \n\n\n\nAlkaloids \n\n\n\nDragendroff \nWagner \nMayer \nHager \nTannic \nFeCl3 \n\n\n\n+++ \n++ \n++ \n++ \n+ \n+ \n\n\n\n+++ \n++ \n++ \n++ \n+ \n+ \n\n\n\n+++ \n++ \n++ \n++ \n++ \n+ \n\n\n\n+++ \n++ \n++ \n++ \n++ \n+ \n\n\n\n+++ \n++ \n++ \n++ \n++ \n+ \n\n\n\nCarbohydrates \nFlavonoids \n\n\n\nPhenols \nTannins \n\n\n\nSaponins \nProteins \n\n\n\nAmino acid \n\n\n\n+ \n+ \n+ \n- \n- \n- \n- \n\n\n\n+ \n+ \n+ \n- \n- \n- \n- \n\n\n\n+ \n+ \n+ \n- \n- \n- \n- \n\n\n\n+ \n+ \n+ \n- \n- \n- \n- \n\n\n\n+ \n+ \n+ \n- \n- \n- \n- \n\n\n\n* (-: Not detected, +: Low concentration, ++: Moderate concentration.\n+++: High concentration \n\n\n\n4. CONCLUSION\n\n\n\nOn the basis of observation and experimental output, the study indicates \nthat the root exudates of five weeds species have the negative effect on the \ngermination of seeds as well as the growth of seedlings of tested crops \n(mungbean and cowpea). This interrelationship between weed and crops \nproved that the resulted effect was due to the presence of allelochemicals \nin the root exudates of the weeds. In the ecological niche, the crops and the \nweeds compete for the nutrition and in some cases has an antagonistic \neffect. So, the presence of these weeds beside the crop field should be \nchecked and uprooted in the early stage of seedlings before cultivation. \nAnd in the meantime, the field should be checked frequently after sowing \nthe seed to evade the computational effect of the weeds. Furthermore, the \nallelochemicals present in the root exudates of these weed species should \nbe identified by high throughput technique for the deeper understanding \nof weed and crop interactions. \n\n\n\nREFERENCES \n\n\n\n[1] Molish, H.  1937. Der Einflusseiner Pflanze auf die Andere.  \n\n\n\nAllelopathie.  Gustav Fisher Verlag, Jena, Germany. \n\n\n\n[2] Qasem, J.R., Foy, C.L. 2001. Weed allelopathy, It\u2019s ecological implants \n\n\n\n& future prospects. A review. Journal crop Procedure, 4, 43-119. \n\n\n\n[3] Batish, D.R., Singh, H.P., Kaurs and Kohli P.K. 2007. Root modified \n\n\n\ninterference of nettle leaved goosefoot (Chenopodium murale) on wheat \n\n\n\n(Triticum aestivum). Journal of Agronomy and Crop Science, 193, 37-44. \n\n\n\n[4] Salgude, P., Pol, M., Kanade, M. 2015. Allelopathic effect of Cuscuta \n\n\n\nreflexa Roxb. on some physiological aspects in wheat. Bionano frontier, 8, \n\n\n\n179-181. \n\n\n\n[5] Putnam, A.R. 1987. Preface to special Allelopathy Edition. Plant and \n\n\n\nSoil, 98 (3). \n\n\n\n[6] Singh, N.B., Sunauna, Allelopathic stress produced by bitter gourd \n\n\n\n(Momordica Charantia L.) Journal of Stress Physiology & Biochemistry, 10 \n\n\n\n(2), 5-14. \n\n\n\n[7] Broeckling, CD-ROM., Broz, AK., Bergelson, J., Manter, D.K., Vivanco,\n\n\n\nJM. 2008.  Root exudates regulate soil fungal community composition and \n\n\n\ndiversity. Applied and Environmental Microbiology, 74, 738\u2013744. \n\n\n\n[8] van Dam, N.M., Bouwmeester, H.J. 2016. Metabolomics in the \n\n\n\nrhizosphere. Tapping into belowground chemical communication. Trends \n\n\n\nin Plant Science 21, 256\u2013265. \n\n\n\n[9] Nguyen, C.  2003.  Rhizodeposition of organic C by plants. Mechanisms \n\n\n\nand controls. Agronomie, 23, 375\u2013396. \n\n\n\n[10] Cavin, L., Mountford, E.P., Peterken, G.F., Jump, A.S. 2013. Extreme \n\n\n\ndrought alters competitive dominance within and between tree species in \n\n\n\na mixed forest stand. Functional Ecology, 27, 1424-1435. \n\n\n\n[11] Neumann, G., R\u00f6mheld, V. 2007. The release of root exudates as \n\n\n\naffected by the plant physiological status.  In:  Pinton, R, Varanini, Z, \n\n\n\nNannipieri, P, eds.  The Rhizosphere. Biochemistry and Organic Substances \n\n\n\nat the Soil\u2013Plant Interface. Boca Raton, London, New York: CRC Press, 41\u2013\n\n\n\n93. \n\n\n\n[12] Loyola-Vargas, V.M., Broeckling, C.D., Dayakar, B.V., Vivanco, J.M. \n\n\n\n2007. Effect of transporters on the secretion of phytochemicals by the \n\n\n\nroots of Arabidopsis thaliana. Planta 225, 301\u2013310. \n\n\n\n[13] Bakker, M.G., Manter, D.K., Sheflin, A.M., Weir, T.L., Vivanco, J.M. \n\n\n\n2012 Harnessing the rhizosphere microbiome through plant breeding and \n\n\n\nagricultural management. Plant Soil 360, 1 \u2013 13. \n\n\n\n[14] Chaparro, J.M., Badri, D.V., Bakker, M.G., Sugiyama, A., Manter, D.K., \n\n\n\nVivanco, J.M. 2013. Root exudation of phytochemicals in Arabidopsis \n\n\n\nfollows specific patterns that are developmentally programmed and \n\n\n\ncorrelate with soil microbial functions. PLoS One. 8, e55731. \n\n\n\n[15] Nardi, S., Concheri, G., Pizzeghello, D., Sturaro, A., Rella, R., Parvoli, G. \n\n\n\n2000. Soil organic matter mobilization by root exudates. Chemosphere. 5, \n\n\n\n653\u2013658. \n\n\n\n[16] Mimmo, T., Del Buono, D., Terzano, R., Tomasi, N., Vigani, G., \n\n\n\nCrecchio, C., Pinton, R., Zocchi, G., Cesco, S. 2014.  Rhizospheric organic \n\n\n\ncompounds in the soil\u2013microorganism\u2013plant system: their role in iron \n\n\n\navailability. European Journal of Soil Science. 65 (5), 629-642. \n\n\n\n[17] Einhellig, F.A., Souza, I.F. 1992.  Phototoxicity of sorgoleone found in \n\n\n\ngrain sorghum root exudates.  Journal of chemical Ecology, 18, 1-11. \n\n\n\n[18] Guodong, Y., Baoll, Z., Xinyu, Z., Zijun Z., Yuanyuan, W., Yiming, Z., \n\n\n\nShuwen, L., Qingdao Z., Yuan, G. and Long, T. 2016. Effects of Tomato Root\n\n\n\nexudates on Meloidogyne incognita. Plos one, 11(4), 1-16. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 44-48 \n\n\n\nCite The Article: : Pervin Akter, Barat Sultana (2019). Allelopathic Effects, Yields And Qualitative Phytochemical Screening Of Root Exudates Of Five Weeds Species. \n\n\n\nMalaysian Journal of Sustainable Agriculture, 3(1): 44-48 \n\n\n\n[19] Ebrahmi, L., Hassaanejad, S. 2015. Allelopathic effects of syrian bean \n\n\n\ncaper (Zygophyllum fabago L.) on seed germination and seedling growth \n\n\n\nof eastern dodder (Cuscuta monogyna Vahl.). Journal of Biodiversity and \n\n\n\nEnvironental Sciences, 7 (2), 253-260. \n\n\n\n[20] Rajinikanth, M., Samatha, T., Srinivas, P., Shyamsundarachary, R., \n\n\n\nRama, S.N. 2013. Preliminary phytochemical analysis of leaf, stem, root \n\n\n\nand seed extracts of Arachis hypogaea. International Journl of\n\n\n\nPharmaceutical Sciebces Review and Research, 201(1), 134-139. \n\n\n\n[21] Zhang, E-P, Zhang, S-H, Zhang, W-B, Li, L-L., Li, T-L. 2010. Effects of \n\n\n\nexogenic benzoic acid and cinnamic acid on the root oxidative damage of \n\n\n\ntomato seedlings. Journal of Horticultural and Foresty, 2(2), 022-029. \n\n\n\n[22] Khan, D., Xuan, T.D., Chung, I.M. 2007. Rice allelopathy and the \n\n\n\npossibility for weed management. Annals of applied biology, 151(3), 325-\n\n\n\n339. \n\n\n\n[23] Batish, D.R., Kaur, S., Singh, H.P., Kohli, R.K. 2008. Role of root \n\n\n\nmediated interactions in phytotoxic interference of Ageratum conyzoides \n\n\n\nwith rice (Oryza sativa). Flora, 204, 388-395. \n\n\n\n[24] Xuan, T.D., Shinkichi, T., Khanh,T.D., Chung, M. I 2005. Biological \n\n\n\ncontrol of weeds and plant pathogens in paddy rice by exploiting plant \n\n\n\nallelopathy: an overview. Crop Protection, 24(3), 97-206. \n\n\n\n[25] Otusanya, O.O., Ikonoh, O.W. Ilori, O.J. 2008. Allelopathic\n\n\n\nPotentials of Tithonia diversifolia  (Hemsl) A. Gray: Effect on the\n\n\n\nGermination,  Growth and Chlorophyll Accumulation of Capsicum\n\n\n\nannum  L. and Lycopersicon esculentum  Mill.   Internationl Journal of \n\n\n\nBotany, 4, 471-475.\n\n\n\n[26] Shiraishi, S., Watanabe, I., Kuno, K., Fujii, Y. 2005. Evaluation of the \n\n\n\nallelopathic activity of five oxalidaceae cove plants and the demonstration \n\n\n\nof potent weed suppression by Oxalis species. Weed Biology and \n\n\n\nManagement, 5, 128-136 \n\n\n\n[27] Travols, I.S., Paspatis, E., Psomadeli, E. 2008. Allelopathic potential \n\n\n\nof Oxalis pes-caprae Tissue and Root Exudates as a Tool for Integrated \n\n\n\nWeed Management. Journal of Agronomy, 7(2), 202-205. \n\n\n\n[28] Romero-Romero, T., Anya, A.L., Cruz-Ortega, R.M. 2002. Screening \n\n\n\nfor the effects of phytochemical variability on cytoplasmic protein \n\n\n\nsynthesis pattern of crop plants. Journal of Chemical Ecology, 28, 601-613. \n\n\n\n[29] Yammata, T., Yokotani-Tomita, K., Kosemura, S., Yamada, K., \n\n\n\nHasegava, K. 1999. Allelopathic substnces exuded from the roots of \n\n\n\ngerminating barnyard grass (Echinocloa Crusgalli L.). Journal of Plant \n\n\n\nGrowth Regulation, 18 (2) 65-67. \n\n\n\n[30] Reigosa, M.J., Sanchez-Moreiras, A., Gonzales, L. 1999. \n\n\n\nEcophysiological approach in allelopathy. Critical. Reviews. In \n\n\n\nPlant. Sciences. 18, 577-608. \n\n\n\n[31] Oburger, E., Dell'mour, M., Hann, S., Wieshammer, G., Puschenreiter, \n\n\n\nM., Wenzel, W.W. 2013. Evaluation of a novel tool for sampling root \n\n\n\nexudates from soil-grown plants compared to conventional techniques. \n\n\n\nEnvironmental and Experimental Botany, 87, 235\u2013247. \n\n\n\n[32] Bowen, G.D. 1979. Integrated and experimental approaches to study \n\n\n\nthe growth of organisms around root and seeds. In Schippers B, Grams W, \n\n\n\neds. Soil-Borne Pathogens. Academic Press, London, pp. 209-277. \n\n\n\n[33] Popovici, J. 2010. Differential effects of rare specific flavonoids on \n\n\n\ncompatible and incompatible strains in the Myrica gale-\n\n\n\nFrankiaactinorhizal symbiosis. Applied amd Environmental Microbiology, \n\n\n\n76, 2451\u20132460. \n\n\n\n[34] Wangila, T.P. 2017. Phytochemical Analysis and Antimicrobial \n\n\n\nActivities of Cyperus rotundus and Typha latifolia Reeds Plants from Lugari \n\n\n\nRegion of Western Kenya. Pharm. Analytical Chemidtry, 3(3), 2471-2698. \n\n\n\n[35] Mbabe, B.O., Edeoga, H.O., Afolayan, A.J. 2012. Phytochemical \n\n\n\nanalysis and antioxidants activities of aqueous stem bark extract of Schotia \n\n\n\nlatifolia Jacq. Asian Pacific Jourmal of Tropical Biomedicime, 2(2), 118-\n\n\n\n124. \n\n\n\n\nhttp://ascidatabase.com/author.php?author=O.O.&last=Otusanya\n\n\nhttp://ascidatabase.com/author.php?author=O.W.&last=Ikonoh\n\n\nhttp://ascidatabase.com/author.php?author=O.J.&last=Ilori\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 17-21 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2022.17.21 \n\n\n\n \nCite The Article: Nur Atiqah Abdul Rahim, Mohammad Tariqur Rahman, Intan Azura Shahdan  (2022). Blood Meal Supplement Improves Exploration Behaviour But \n\n\n\nIncreases Escape Attempt. Malaysian Journal of Sustainable Agricultures, 6(1): 17-21. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2022.17.21 \n\n\n\n\n\n\n\nBLOOD MEAL SUPPLEMENT IMPROVES EXPLORATION BEHAVIOUR BUT \nINCREASES ESCAPE ATTEMPT \n \nNur Atiqah Abdul Rahima, Mohammad Tariqur Rahmanb, Intan Azura Shahdana* \n \na Biomedical Sciences Department, Faculty of Allied Health Sciences, International Islamic University Malaysia, Jalan Sultan Ahmad Shah, 25200 \nKuantan, Pahang, Malaysia \nb Faculty of Dentistry, University Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia \n*Corresponding author E-mail: intan_azura@iium.edu.my, azura.iium@gmail.com \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 18 July 2021 \nAccepted 20 August 2021 \nAvailable online 24 August 2021 \n\n\n\n\n\n\n\nBlood meal as an animal feed supplement promotes agricultural sustainability. Blood meal which is high in \nproteins, lacks certain nutrients hence is expected to give impact on the chicken behaviour and welfare. This \nstudy was carried out to determine the impact of blood meal supplement on chicken behaviour. The study \ninvolved 100 chickens which were bred in semi-opened poultry house for 6 weeks. At 6th week, chickens \nprovided with fish meal (FM) only had a higher body weight compared to that of the group provided with FM \nand blood meal supplement (FBM). Normal behaviour such as walking, standing, feeding, drinking, dust \nbathing and lying down were not significantly affected by the changes in the meal (p>0.05). However, based \non a single assessor assessment, FBM group displayed higher score in explore and escape characteristics, \nthan the FM group. On the other hand, FM group displayed a slightly higher score for fear behaviour than the \nFBM.  Findings in this study leads to the conclusion that blood meal supplement has influence on the welfare \nin chickens farming in terms of their exploration, fear, and escape behaviours.  Therefore, amount of blood \nmeal as animal feed supplement in poultry production should be determined carefully to avoid any potential \ndetrimental effect on poultry welfare.  \n\n\n\nKEYWORDS \n\n\n\nanimal behaviour, Gallus domesticus L., poultry nutrition, protein meal, welfare \n\n\n\n1. INTRODUCTION \n\n\n\nBlood meal is favoured by animal nutritionists and environmentalists \nbecause of its high protein content and benefit as a sound disposal that \npromotes agricultural sustainability.  Post-slaughtered blood of various \npoultry and fish are dried after coagulation and separation of the water \ncontent to prepare powdered blood meal (Beski et al., 2015). Benefits of \nblood meal as protein sources for poultry are attributed to the economic \nimpact and productivity. Blood meal is rich in lysine and trace minerals \n(Ravindran et al., 2005; Seifdavati et al., 2008). Blood meal in balanced \nbroiler diets resulted in better growth performance (Donkoh et al., 1999; \nKhawaja et al., 2007; Seifdavati et al., 2008; Makinde and Sonaiya, 2011; \nAdeyemi et al., 2012). \n\n\n\nHowever, outcome of the blood meal varies, depending on the amount of \nblood meal added to the feed. The usage of less than 5% of blood meal in \nfeed improved the performance of poultry (Anoh and Akpet, 2013). \nChickens were found to have improved body weight (BW) gain with no \nadverse effect on their growth rate when 3% of blood meal was included \nin the feed (Khawaja et al., 2007).  To the contrary, 3% or higher blood \nmeal was shown to have no influence in the feed intake as well as the BW \ngain in chickens (Seifdavati et al., 2008). Despite the commercial benefit of \nblood meal in poultry production, the efficiency of meat production should \nnot compromise health and wellbeing of the chickens.   \n\n\n\nIn many instances, poultry nutrition is formulated without advocating the \n\n\n\nanimal welfare (Oso et al., 2011; Kalmar et al., 2013; Goldberg, 2016; Wu \net al., 2017). It can be noted that balanced diet formula promotes calm \nanimals and consequently aids in the ease of handling, improved \nproductivity and enhanced husbandry system.  Blood meal supplements \ncan cause imbalance of certain amino acids in poultry feed, which \nindirectly may affect chicken behaviour. Thus, adding blood meal into \nchicken\u2019s diet should be given careful consideration, given its unknown \nimpact on chicken wellbeing.  \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Animals, farm facilities and dietary treatments \n \n\n\n\nThis study was approved by the Institutional Animal Care and Ethics \nCommittee (IACUC) at IIUM [Reference#/2017(3)]. The study was \nconducted for 6 weeks between end of February until early April 2017, at \na farm in Kuantan, Malaysia. Cross-bred chickens were reared in a semi-\nopened, 32.4 m2 poultry house, with netted walls for ventilation and wood \nshaven, saw dust and sand as beddings. For this study, a wall was placed \nto divide the area of the house into two compartments, for two study \ngroups. Each compartment was provided with a feeder and a drinker to \nensure ad libitum feed and water.  Brown-coloured feed pellets (Cargill, \nMalaysia) containing fish meal (FM) as the source of protein feed were fed \nto all chicks. One hundred 3-day-old crossbred chicks, with initial BW of \n70 \u00b1 7 g, were distributed randomly into two groups (50 for each group): \none was fed with FM only and the other group was fed with FM \n\n\n\n\nmailto:intan_azura@iium.edu.my\n\n\nmailto:azura.iium@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 17-21 \n\n\n\n\n\n\n\n \nCite The Article: Nur Atiqah Abdul Rahim, Mohammad Tariqur Rahman, Intan Azura Shahdan (2022). Blood Meal Supplement Improves Exploration Behaviour But \n\n\n\nIncreases Escape Attempt. Malaysian Journal of Sustainable Agricultures, 6(1): 17-21. \n\n\n\n\n\n\n\nsupplemented with 3% blood meal protein (FBM, Justlong Import & \nExport Co. Ltd., Dalian; source of blood, chickens). Both FM and FBM chicks \nwere provided with the same base feed (FM) on the first week.  On 2nd \nweek onwards, FM chicks were given FM only feed and FBM chickens were \nfed with FM with blood meal supplement. \n \n2.2 Growth \n\n\n\n \nWeight gained was recorded on weekly basis using portable weighing \nscale (HB Series, New York).  After adding blood meal powder to the feed \npellets, the colour of the pellet changed slightly to red.   To ensure that \ncolours do not affect the amount of feed uptake, a preference test on colour \nwas conducted (see S1) and weight gain were illustrated in Figure S2.  \n  \n2.3 Experimental procedure \n\n\n\n \nBirds were weekly monitored in the poultry house at the age of 2 weeks \nold and onwards, between 0900-1300, using a wide-angled video camera \n(Sony HD Handycam, San Diego) and a smartphone camera (OPPO, \nDongguan).  Every week, each episode of video recording involved 10 min \nof normal behaviour recording, followed by 1 min of each novel object test. \nWithin the 10 min recording, normal behaviour was recorded for three \nrepeats of 2 min cycles. \n \n2.3.1 Normal behaviour assessment \n\n\n\n \nAnimals were subjected to visual observation using scan sampling \n(Newberry et al., 1987), in which the behaviour in each group was \nobserved at 20 s intervals throughout 2 min recording. At the end of each \n2 min cycle, the cumulative frequency of behaviour was recorded, and the \ncycle was repeated thrice. The mean for the frequency of normal \nbehaviour were recorded and the proportion of chickens walking or \nstanding (WS), feeding (FEED), drinking (DRNK) and dust bathing or lying \ndown (DBL) over all behaviours were determined. \n  \n2.3.2 Novel object test \n\n\n\n \nFor testing, 2 types of stressors were introduced to the chickens: (i) a \ndistraction by throwing an object; and (ii) an interference whereby a \nhandler stood still or walked for 1 min in a consistent pattern inside the \nchicken compartment (Uzunova, 2007; Forkman et al., 2007). A rating \nsystem was designed to assess three characteristics of bird behaviour in \nnovel situations: (i) fear; (ii) exploratory; and (iii) escape attempt.  \nInduced behaviour was recorded by a single assessor according to the \nethogram provided in Table 1.  \n \n\n\n\nTable 1: Ethogram used for novel object test. \n\n\n\nStressor Behaviour Definition \n\n\n\nA thrown object \n(i.e. glove) \n\n\n\nExplore Approach and peck at the object, \ncircle around it and look at it, with \nneck fully straightened forward \ntowards the object \n\n\n\nFear Startle and quickly move away \nfrom the object. \n\n\n\nEscape Attempt to jump over the \npartition/wall. \n\n\n\nA handler \n(standing still or \nwalking in the \ncompartment) \n\n\n\nExplore Look at the (standing/non-\nmoving) handler and walk \ntowards him/her. \n\n\n\nFear Run away from the (walking) \nhandler, with their wings flapping \nvigorously.  \n\n\n\nStep on other chickens in their \neffort to avoid the (walking) \nhandler. \n\n\n\nEscape Attempt to jump over the \npartition/wall.  \n\n\n\n \n2.4 Statistical analysis \n\n\n\n \nData was analysed by GraphPad Prism (California). Mann-Whitney U test \nwas performed to determine the colour preference for the feed.  \nIndependent t-test was used to analyse BW gained by weeks between \nexperimental diets that are normally distributed. Descriptive statistics and \ntwo-way ANOVA was conducted to illustrate and analyse the normal \nbehaviour of the chickens (which include normal activity such as WS, \n\n\n\nFEED, DRNK and DBL) in both treatment groups (FM and FBM). \nHomogeneity of variance was assessed, and appropriate corrections were \nmade if necessary. All experiments were performed in a randomised \nfashion.  \n\n\n\n3. RESULTS AND DISCUSSION  \n\n\n\n3.1 Effects of blood meal supplement on growth performance \n\n\n\nThis study examined the impact of blood meal supplement on the \nchickens\u2019 growth performance.  Weekly BW gained for both FM and FBM \ngroups were almost similar (\u03c1>0.05; Figure 1).  The ranges of weekly \nweight gain were 67-319 g for FM group and 71-290 g for FBM group. Both \ngroups saw the highest weight gain at week 5. Despite similar weight gain \nin both groups, at 6 weeks old, BW of chicks in FM group were greater than \nthe FBM group (\u03c1=0.029). Colour of feed did not influence the feed uptake \nby the birds.  It was noted earlier that the colour for FBM was reddish, and \nthis may have some effects on the feeding, but our results found that there \nwas no preference for colours of the feed among the birds (\u03c1=0.218; \nSupplementary Figure S1). Hence, we are satisfied that the significant low \nBW in FBM group was not due to the feed colour (nor low feed uptake). \nEarlier studies showed that 3% blood meal supplement improved faecal \ndigestibility, as well as reducing relative cost per unit weight gain \n(Khawaja et al., 2007). Anoh and Akpet (2013) reported no significant \ndifferences when broilers were fed with FM only and FM with 5% \nbloodmeal.  In contrast, weight gain was found to be compromised when \nbroilers were fed with 5% bloodmeal (Caires et al., 2010). It could be \nsuggested that these chickens lack the enzymes to break down the \nbloodmeal and the imbalance amino acids level hijacked the metabolism \n(Caires et al., 2010; Anoh and Akpet, 2013).  The cumulative effect could \nbe apparent at week 6, which would explain the low BW detected in FBM \ngroup. \n\n\n\nFigure 1: Distribution of the mean of chicken\u2019s BW over 6 weeks growth \nin both chicken groups. BW, body weight; FBM, fish-blood meal; FM, fish \n\n\n\nmeal. *\u03c1-value < 0.05 \n\n\n\n3.2     Impacts of blood meal supplement on chicken behaviour \n\n\n\n3.2.1   Normal behaviour \n\n\n\nIn this study, the impact of FM and FBM was studied in normal and induced \nbehaviours.  No significance difference was observed in FM and FBM \ngroups, in terms of feeding (p=0.25), drinking (p=0.98), and dust bathing \nor lying down activities (p=0.81; Table 2). Only walking or standing \nactivity was different significantly between the treatment groups (p=0.02; \nFigure 2).  Increased walking and standing activity maybe associated with \nincreased heat stress or by high body temperatures (Costa et al., 2012).  \nHowever, there was no increase in drinking activity for the FBM group to \nsupport heat stress as the cause of the increased WS activity in FBM group \n(Table 2). Young chicks in the FBM group are found to walk and stand up \nmore than the ones in FM group although the reason for this is unclear. In \nfact, walking activity was varied between week 1 and 3 for chickens in FBM \ngroup, and between weeks 1, 2 and 4 in the FM group. However, when \ncomparing the proportions of normal behaviour over total behaviour, all \nactivities were statistically similar in both groups, suggesting that the \nbloodmeal supplementation does not affect the normal behaviour of the \nchickens, including the WS activity. Our study also agreed with previous \nstudy that all activities including FEED, DRNK and WS had declined \nsignificantly with increasing age of chickens (Newberry et al., 1987). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 17-21 \n\n\n\n\n\n\n\n \nCite The Article: Nur Atiqah Abdul Rahim, Mohammad Tariqur Rahman, Intan Azura Shahdan (2022). Blood Meal Supplement Improves Exploration Behaviour But \n\n\n\nIncreases Escape Attempt. Malaysian Journal of Sustainable Agricultures, 6(1): 17-21. \n\n\n\n\n\n\n\nTable 2: Comparison between feed type and behaviour (T-test, n=30) \n\n\n\nBehaviour \ncategory \n\n\n\nProportion of behaviour\u00a5 \n\n\n\nFish meal (%) Fish-blood \nmeal (%) \n\n\n\nSignificance \n\n\n\nWS 36.8\u00b111.9 44.2\u00b118.9 NS \n\n\n\nFEED 10.1\u00b19.0 11.6\u00b15.7 NS \n\n\n\nDRNK 2.9\u00b12.0 2.7\u00b12.5 NS \n\n\n\nDBL 48.9\u00b116.0 46.6\u00b114.8 NS \n\u00a5  Percentage of number of chickens over total of chickens observed in the \ngroup. \n\n\n\nNS, not significant. FEED, feeding; DBL, dust bathing or lying; DRNK, \ndrinking; WS, walking & standing. \n\n\n\nFigure 2: Box plots showing the range of data and standard deviations \nfor the frequency of chicken\u2019s behaviour in each group: (A) WS, walking \nand standing; (B) DBL, dust bathing or lying; (C) FEED, feeding; and (D) \nDRINK, drinking.  Both groups were similar in terms of the frequency of \nnormal behaviours, except for in (A) where group provided with blood \n\n\n\nmeal supplement showed higher WS activity than the fish meal only \ngroup. \n\n\n\nInjury due to feather pecking was observed in two birds from the FBM \ngroup, and 1 bird from the FM group between week 3 and 4. It is likely that \nthe injury involves not only pecking, but also a possible removal of the \nfeathers of one bird by another (Costa et al., 2012), and this behaviour is \nconsidered by Bracke and Hopster (2006) as a symptom of negative \nwelfare status.  In fact, authors noted that these injuries were severe, due \nto the excessive number of feathers being pulled out, and the presence of \nblood from the injuries.  Various parts that were affected by feather \npecking include the neck, wing, and at the back. Severe feather pecking is \nconsidered detrimental to bird welfare because it causes pain (Gentle and \nHunter, 1991), and the blood from the injuries may lead to cannibalism \n(Duncan and Hughes, 1972).  Authors also noted that injured chickens \nwere less sociable and demonstrated a tendency to isolate themselves \nfrom the group. However, since birds from both groups were injured, no \nconclusive findings were available to link the feather pecking behaviour \nwith the blood meal supplement.   \n\n\n\n3.2.2     Induced behaviour \n\n\n\nInduced behaviour was assessed by novel object tests.  Behavioural scores \nfor FBM group are higher for explore and escape characteristics (Table 3).  \nFM chickens scored higher for fear characteristic than the FBM chickens. \nExploration behaviour is said to be an essential for animal survival, thus a \nquality trait in animal welfare. This alone may support the use of blood \nmeal in feed.  However, escape attempt is associated with animal agitation \nin response to stress, as well as a sign of fear (EFSA et al., 2019). Hence the \ntwo characteristics might be contradicting observation.  Fear, on the other \nhand, can be seen as the animals\u2019 reaction to a perceived danger which can \nbe harmful to health and affect productivity in husbandry systems (Zulkifli \nand Siti Nor Azah, 2004; Agnvall et al., 2014; Meuser et al., 2021). In this \nstudy, the novel object and handler were perceived as a threat and the \nchickens responded by avoiding and withdrawing from the novel object.  \nThis behaviour could also be attributed to active/pro-active fight and \n\n\n\nflight response (Armstrong et al., 2020).   \n\n\n\nOn the contrary, the opposite to active response is the reactive/passive \nresponse with individuals displaying a freezing-type fear response \n(Armstrong et al., 2020). A delayed reaction, where chickens were seen \nslowly reacting to the novel objects is also an indication of a fear response. \nThe fear characteristic appears to be diminished as the chickens became \nfamiliar with the stressors. The behavioural score for week 5 was lower \nthan week 3 for both groups (Table 3). A decreased reaction of fear \ntowards the stressor can be seen as an indicator for a good adaptation \n(Agnvall et al., 2014; Meuser et al., 2021). However, FBM group displayed \nincreased escape behaviour in week 5 whereby more than 6 chickens \nsuccessfully flew over the partition wall, suggesting an increased agitation \nin that group. However, escape propensity was described as a more \nproactive behavioral type instead of fearfulness in red junglefowl (Zidar et \nal., 2017; Rubene and L\u00f8vlie, 2021).  \n\n\n\nTable 3: Rating scores for induced behaviour (Score: 0, no such \nbehaviour; 1, least likely towards the behaviour; 2, likely towards the \n\n\n\nbehaviour; 3, very likely towards the behaviour) \n\n\n\nBehaviour Stressor Behavioural score\u2206 \n\n\n\nFM FBM \n\n\n\nExplore \n\n\n\n\n\n\n\nObject 2 3 \n\n\n\nStanding/non-moving \nhandler  \n\n\n\n1 3 \n\n\n\nScore  3 6* \n\n\n\nFear Object  3 3 \n\n\n\nWalking handler  \n\n\n\n(Week3)                  \n\n\n\n(Week 5) \n\n\n\n\n\n\n\n3 \n\n\n\n1 \n\n\n\n\n\n\n\n2 \n\n\n\n1 \n\n\n\nScore  7* 6 \n\n\n\nEscape  Object 0 0 \n\n\n\nHandler \n\n\n\n(Week 3)                   \n(Week 5) \n\n\n\n\n\n\n\n2 \n\n\n\n1 \n\n\n\n\n\n\n\n2 \n\n\n\n3 \n\n\n\nScore  3 5* \n\n\n\n\u2206 For each behaviour, higher scores between the two groups are indicated \nin asterisk (*) and bold. \n\n\n\nFM, fish meal group; FBM, fish meal with blood meal supplement group. \n\n\n\nFeed composition and unavailability of macro- or micro-nutrients could \ncontribute to different animal behaviours towards certain.  Metallic taste \nand odour from the iron of haemoglobin from the blood meal supplement \nare repugnant in chicken diet.  Aggressive behaviour could also be due to \nspecific restrictions in animal proteins.   For example, experiments have \nshown that protein-deficient diets can result in growing pigs mutilating \nthe bodies of other individuals by biting off their tails (Fraser, 1987; Fraser \net al., 1991). Jensen et al. (1993) found that protein-deprived growing pigs \nwill spend more time directing foraging behaviour at straw than \nindividuals who are able to meet their protein requirements. Malnutrition \ncan lead to aggressive behaviour and boldness as seen in many studies on \ninsects.  Aggressive behaviour is particularly predicted to vary with level \nof protein intake (Wilder and Rypstra, 2008). In cannibalistic species such \nas Mormon crickets, protein deficiency is also likely to increase the \nexpression of aggressive behaviour because the deficit of proteins leads to \nincreased frequency of cannibalism (Simpson et al., 2006). Blood meal \nsupplement in poultry feed results in the imbalance of certain amino acids \nin chickens.  \n\n\n\nThe importance of the current study is not limited to chicken welfare.  \nCertain cultures and religious adherents have strict diet, which include \nforbidden in the consumption of blood. The halal (permissibility) in Islam \nis often mentioned with tayyibat which means good, pleasant, delightful, \ndelicious, sweet, pure and clean.  According to the Islamic jurisprudence, \nany form of blood that flows out of the body, such as during slaughtering, \nis deemed as impure or filth (Nurdeng, 2009).  It is perhaps because of this \nreason, the use of blood meal in animal feed could be opposed in halal diet \n(Ofori and Hsieh, 2011; Shahdan et al., 2016). Certain religions advocate \neating food which is not only good for the body, but also the mind and \nspirit. Hence, the use of blood meal in chicken feed may need to be \nappraised if the industry is considering to promote this as an alternative \nfor, say, halal meat production which tenet includes prohibition to \nconsume meat and meat products that could be injurious to one\u2019s physical \nhealth and detrimental to the character and spiritual faculties of man \n(Nurdeng, 2009). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 17-21 \n\n\n\n\n\n\n\n \nCite The Article: Nur Atiqah Abdul Rahim, Mohammad Tariqur Rahman, Intan Azura Shahdan (2022). Blood Meal Supplement Improves Exploration Behaviour But \n\n\n\nIncreases Escape Attempt. Malaysian Journal of Sustainable Agricultures, 6(1): 17-21. \n\n\n\n\n\n\n\n4. CONCLUSION  \n\n\n\nThis study provides an observation on the impact of blood meal \nsupplement in chicken\u2019s diet on fear, exploration and escape attempts.  \nThese three traits of behavior are important from husbandry point of view \nin ensuring efficient production and animal welfare.  Blood meal \nsupplement did not influence the normal behavior of the chickens.  \nAlthough chickens from FBM group had higher frequency of walking and \nstanding than the FM group, when looking at individual activity over the \nentire normal behavior, both FM and FBM groups had similar normal \nbehaviour.  We adapted scoring system as a tool for novel object tests to \nassess free range chickens. The tests demonstrated that FM group had a \nslightly higher fear behaviour than the FBM.  However, FBM group \ndisplayed more escape attempts than the FM.   \n\n\n\nThis could suggest that both groups experienced fear in novel situations, \nbut FBM group had an overwhelming flight response to cope with the fear.  \nIn addition, FBM group demonstrated higher exploration behaviour than \nthe FM, indicating a positive behavior.  In husbandry setting, it is \nimportant to reduce fear and escape behaviours to improve the animal \nwelfare.  Exploration behaviour indicates positive adaptation of the \nchickens to novel situation and this would enhance productivity.  \nFormulating animal diet must take into account the aspect of animal \nwelfare, and behavioural tests is one of the tools for such assessments.  We \nconcluded that induced behaviour is a better approach to examine the \nimpact of diet, and future studies may consider other behaviours such as \nwing flapping and tonic immobility as the test parameters. Indeed, blood \nmeal is a good source of protein and environmentally a sustainable option \nfor poultry producers. However, poultry producers may consider other \nfactors including welfare of the chickens, as well as the halal market, when \nmaking a choice for an affordable and quality protein feed.  \n\n\n\nDECLARATION \n\n\n\nWe declared that this manuscript is an original work, and at present, has \nnot been sent, in part or in whole, for publication to another scientific \njournal. \n\n\n\nCONFLICT OF INTERESTS \n\n\n\nThe authors declare that the research was conducted in the absence of any \ncommercial or financial relationships that could be construed as a \npotential conflict of interest. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWe would like to thank Ms. Rosmita Talib and Ms. Norhasimah Ahmat, the \nowners of Shiroz Farm, who allowed us to conduct our study at their \npremises, and Mr. Muhammad Ali who provided technical assistance at the \nfarm. This study was financially supported by the International Islamic \nUniversity Malaysia\u2019s internal grant (RIGS 15-026-0026). \n\n\n\nREFERENCES \n\n\n\nAdeyemi, O.A., Adekoya, J.A., Sobayo, R.A., 2012. Performance Of Broiler \nChickens Fed Diets Containing Cassava Leaf: Blood Meal Mix As \nReplacement For Soybean Meal. Revista Cientifica Udo Agricola 12, Pp. \n212\u2013219. \n\n\n\nAgnvall, B., Ali, A., Olby, S., Jensen, P., 2014. Red Junglefowl (Gallus Gallus) \nSelected For Low Fear Of Humans Are Larger, More Dominant And \nProduce Larger Offspring. Animal 8, Pp. 1498\u20131505.  \n\n\n\nAnoh, K.U., Akpet, S.O., 2013. Growth Response Of Broiler Chickens Fed \nDiets Containing Blood Meal With Enzyme Supplementation As A \nReplacement For Fish Meal. Iosr Journal Of Agriculture And Veterinary \nScience 4, Pp. 31\u201334.  \n\n\n\nArmstrong, E.A., Voelkl, B., Voegeli, S., Gebhardt-Henrich, S.G., Guy, J.H., \nSandilands, V., Boswell, T., Toscano, M.J., Smulders, T. V, 2020. Cell \nProliferation In The Adult Chicken Hippocampus Correlates With \nIndividual Differences In Time Spent In Outdoor Areas And Tonic \nImmobility. Frontiers In Veterinary Science 7, 587.  \n\n\n\nBeski, S.S.M., Swick, R.A., Iji, P.A., 2015. Specialized Protein Products In \nBroiler Chicken Nutrition\u202f: A Review. Journal Of Animal Nutrition 1, \nPp. 47\u201353.  \n\n\n\nBracke, M.B.M., Hopster, H., 2006. Assessing The Importance Of Natural \nBehavior For Animal Welfare. Journal Of Agricultural And \nEnvironmental Ethics 19, Pp. 77\u201389.  \n\n\n\nCaires, C.M., Fernandes, E.A., Fagundes, N.S., Carvalho, A.P., Maciel, M.P., \nOliveira, B.R., 2010. The Use Of Animal Byproducts In Broiler \nFeeds.Use Of Animal Co-Products In Broilers Diets. Revista Brasileira \nDe Ciencia Avicola 12, Pp. 41\u201346. \n\n\n\nCosta, L.S., Pereira, D.F., Bueno, L.G.F., Pandorfi, H., 2012. Some Aspects Of \nChicken Behavior And Welfare. Brazilian Journal Of Poultry Science \n14, Pp. 159\u2013164. \n\n\n\nDonkoh, A., Atuahene, C.C., Anang, D.M., Ofori, S.K., 1999. Chemical \nComposition Of Solar-Dried Blood Meal And Its Effect On Performance \nOf Broiler Chickens. Animal Feed Science And Technology 81, Pp. 299\u2013\n307.  \n\n\n\nDuncan, I.J.H., Hughes, B.O., 1972. The Influence Of Strain And \nEnvironmental Factors Upon Feather Pecking And Cannibalism In \nFowls. British Poultry Science 13, Pp. 525\u2013547.  \n\n\n\nEfsa Ahaw Panel (Efsa Panel On Animal Health And Animal Welfare), \nNielsen, S.S., Alvarez, J., Bicout, D.J., Calistri, P., Depner, K., Drewe, J.A., \nGarin-Bastuji, B., Gonzales Rojas, J.L., Gort\u00e1zar Schmidt, C., Miranda \nChueca, M.\u00c1., Roberts, H.C., Sihvonen, L.H., Spoolder, H., Stahl, K., \nVelarde Calvo, A., Viltrop, A., Winckler, C., Candiani, D., Fabris, C., Van \nDer Stede, Y., Michel, V., 2019. Scientific Opinion On Slaughter Of \nAnimals: Poultry. Efsa Journal. European Food Safety Authority \n17(11), E05849\u2013E05849. \n\n\n\nForkman, B., Boissy, A., Meunier-Sala\u00fcn, M.C., Canali, E., Jones, R.B., 2007. \nA Critical Review Of Fear Tests Used On Cattle, Pigs, Sheep, Poultry \nAnd Horses. Physiology And Behavior 92, Pp. 340\u2013374.  \n\n\n\nFraser, D., 1987. Mineral-Deficient Diets And The Pig\u2019s Attraction To \nBlood: Implications For Tail-Biting. Canadian Journal Of Animal \nScience 67, Pp. 909\u2013918.  \n\n\n\nFraser, D., Bernon, D.E., Ball, R.O., 1991. Enhanced Attraction To Blood By \nPigs With Inadequate Dietary Protein Supplementation. Canadian \nJournal Of Animal Science 71, Pp. 611\u2013619.  \n\n\n\nGentle, M.J., Hunter, L.N., 1991. Physiological And Behavioural Responses \nAssociated With Feather Removal In Gallus Gallus Var Domesticus. \nResearch In Veterinary Science 50, Pp. 95\u2013101.  \n\n\n\nGoldberg, A.M., 2016. Farm Animal Welfare And Human Health. Current \nEnvironmental Health Reports 3, Pp. 313\u2013321.  \n\n\n\nJensen, M.B., Kyriazakis, I., Lawrence, A.B., 1993. The Activity And Straw \nDirected Behaviour Of Pigs Offered Foods With Different Crude \nProtein Content. Applied Animal Behaviour Science 37, Pp. 211\u2013221.  \n\n\n\nKalmar, I.D., Vanrompay, D., Janssens, G.P.J., 2013. Broiler Ascites \nSyndrome: Collateral Damage From Efficient Feed To Meat \nConversion. Veterinary Journal 197, Pp. 169\u2013174.  \n\n\n\nKhawaja, T., Khan, S.H., Ansari, N.N., 2007. Effect Of Different Levels Of \nBlood Meal On Broiler Performance During Two Phases Of Growth. \nInternational Journal Of Poultry Science 6, Pp. 880\u2013885. \n\n\n\nKhosravinia, H., 2007. Preference Of Broiler Chicks For Color Of Lighting \nAnd Feed. Journal Of Poultry Science 44, Pp. 213\u2013219.  \n\n\n\nMakinde, O.A., Sonaiya, E.B., 2011. Utilization Of Sun-Dried Maize Offal \nWith Blood Meal In Diets For Broiler Chickens. Open Journal Of Animal \nSciences 1, Pp. 106\u2013111.  \n\n\n\nMeuser, V., Weinhold, L., Hillemacher, S., Tiemann, I., 2021. Welfare-\nRelated Behaviors In Chickens: Characterization Of Fear And \nExploration In Local And Commercial Chicken Strains. Animals\u202f: An \nOpen Access Journal From Mdpi 11, 679.  \n\n\n\nNewberry, R.C., Hunt, J.R., Gardiner, E.E., Canada, A., Columbia, B., 1987. \nInfluence Of Light Intensity On Behavior And Performance Of Broiler \nChickens. Poultry Science 67, Pp. 1020\u20131025. \n\n\n\nNurdeng, D., 2009. Lawful And Unlawful Foods In Islamic Law Focus On \nIslamic Medical And Ethical Aspects. International Food Research \nJournal 16, Pp. 469\u2013478. \n\n\n\nOfori, J.A., Hsieh, Y.-H.P., 2011. Blood-Derived Products For Human \nConsumption. Revelation And Science 1, Pp. 14\u201321. \n\n\n\nOso, A.O., Idowu, A.A., Niameh, O.T., 2011. Growth Response, Nutrient And \nMineral Retention, Bone Mineralisation And Walking Ability Of Broiler \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 17-21 \n\n\n\n\n\n\n\n \nCite The Article: Nur Atiqah Abdul Rahim, Mohammad Tariqur Rahman, Intan Azura Shahdan (2022). Blood Meal Supplement Improves Exploration Behaviour But \n\n\n\nIncreases Escape Attempt. Malaysian Journal of Sustainable Agricultures, 6(1): 17-21. \n\n\n\n\n\n\n\nChickens Fed With Dietary Inclusion Of Various Unconventional \nMineral Sources. Journal Of Animal Physiology And Animal Nutrition \n95, Pp. 461\u2013467.  \n\n\n\nRavindran, V., Hew, L.I., Ravindran, G., Bryden, W.L., 2005. Apparent Ileal \nDigestibility Of Amino Acids In Dietary Ingredients For Broiler \nChickens. Animal Science 81, 85\u201397. \nHttps://Doi.Org/10.1079/Asc42240085 \n\n\n\nRubene, D., L\u00f8vlie, H., 2021. Red Junglefowl Chicks Seek Contact With \nHumans During Foraging Task. Frontiers In Psychology 12, 675526.  \n\n\n\nSeifdavati, J., Navidshad, B., Seyedsharifi, R., Sobhani, A., 2008. Effects Of A \nLocally Produced Blood Meal On Performance, Carcass Traits And \nNitrogen Retention Of Broiler Chickens. Pakistan Journal Of Biological \nSciences 11, Pp. 1625\u20131629.  \n\n\n\nShahdan, I.A., Regenstein, J.M., Shahabuddin, A.S., Rahman, M.T., 2016. \nDeveloping Control Points For Halal Slaughtering Of Poultry. Poultry \nScience 95, Pp. 1680\u20131692.  \n\n\n\nSimpson, S.J., Sword, G.A., Lorch, P.D., Couzin, I.D., 2006. Cannibal Crickets \nOn A Forced March For Protein And Salt. Proceedings Of The National \nAcademy Of Sciences Of The United States Of America 103, Pp. 4152\u2013\n4156.  \n\n\n\nUzunova, K., 2007. Study Of Behaviour Of Broiler Chickens Subjected To \nBiotic Stressors. Trakia Journal Of Sciences 5, Pp. 16\u201318. \n\n\n\nWilder, S.M., Rypstra, A.L., 2008. Diet Quality Affects Mating Behaviour \nAnd Egg Production In A Wolf Spider. Animal Behaviour 76, Pp. 439\u2013\n445.  \n\n\n\nWu, D., Wu, S.B., Choct, M., Swick, R.A., 2017. Performance, Intestinal \nMicroflora, And Amino Acid Digestibility Altered By Exogenous \nEnzymes In Broilers Fed Wheat-Or Sorghum-Based Diets. Journal Of \nAnimal Science 95, Pp. 740\u2013751. \n\n\n\nZidar, J., Sorato, E., Malmqvist, A.-M., Jansson, E., Rosher, C., Jensen, P., \nFavati, A., L\u00f8vlie, H., 2017. Early Experience Affects Adult Personality \nIn The Red Junglefowl: A Role For Cognitive Stimulation? Behavioural \nProcesses 134, Pp. 78\u201386. \nHttps://Doi.Org/Https://Doi.Org/10.1016/J.Beproc.2016.06.003 \n\n\n\nZulkifli, I., Siti Nor Azah, A., 2004. Fear And Stress Reactions, And The \nPerformance Of Commercial Broiler Chickens Subjected To Regular \nPleasant And Unpleasant Contacts With Human Being. Applied Animal \nBehaviour Science 88, Pp. 77\u201387.  \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 28-32 \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 April 2019 \nAccepted 10 May 2019 \nAvailable online 14 May 2019 \n\n\n\nABSTRACT\n\n\n\nThe study was conducted to evaluate the effect of photoperiod on the growth performance and behavioral \npattern of Achatina achatina. Ninety snails of uniform weights were used for the study which lasted for 56 days. \nThe snails were randomly assigned to three treatments and each treatment was replicated three times with 10 \nsnails per replicate. The snails were exposed to different light duration. Treatment 1 had 12 hours light and 12 \nhours darkness, Treatment 2 was subjected to 18 hours light and 6 hours darkness, Treatment 3 was subjected \nto 24 hours light. The data collected was analysed using one-way analysis of variance and Duncan multiple \nrange test for significant mean separation. Data were collected on feed intake, weight gain, time of feeding and \nreproductive behaviors. The results of the experiment showed that there were no significant differences \n(P>0.05) in final weight gain, average daily weight gain, total feed intake, average daily feed intake, feed \nconversion ratio, duration of courtship, duration of feeding and cost of feed per kg weight gain between \ntreatments. The results also showed that there were significant differences in number of eggs laid and mating \nduration between treatments. From the result, it was concluded that the best photoperiod for Achatina achatina \nis 24 hours light as it produced the lowest feed conversion ratio and cost of feed per kg weight gain and that \nwas recommended for effective growth of Achatina achatina. \n\n\n\nKEYWORDS \n\n\n\nAchatina achatina, photoperiod, behavioral, growth, performance  \n\n\n\n1. INTRODUCTION \n\n\n\nAchatina achatina are the largest land snails in the world. They are \n\n\n\nsometimes more difficult to breed than other African snails. Tigers are \n\n\n\nfound within the dense forest floors in the forest zone of Ghana and also in \n\n\n\nthe humid riparian forest floors. They are believed to have three-year \n\n\n\nbreeding cycle which is longer than other snails. Average adult shell length \n\n\n\nis 18cm with an average diameter of 9cm. In exceptional cases the shell \n\n\n\ncan grow to be 30cm long, but this is very unlikely, especially in captivity. \n\n\n\nLarge ones may achieve a shell length of 22cm. \n\n\n\nThe Giant African Land snail is known to eat more than 500 different types \n\n\n\nof plant. Snails are strong and can lift up to 10 times their body weight in \n\n\n\nvertical position. When moving, snails leave behind a trail of mucus. This \n\n\n\nmucus acts as a lubricant to reduce friction against the surface where they \n\n\n\npass. The life expectancy of snails depends on their habitat and the species. \n\n\n\nSome of them only live for about five years. However, others in captivity \n\n\n\ncan live up to 25 years. Some species of snails hibernate during the dry \n\n\n\nmonths of the year. They cover their bodies with a thin layer of mucus \n\n\n\nwhich prevents them from drying out. \n\n\n\nSome land snails feed on other terrestrial snails. The snails that just hatch \n\n\n\nthe egg can eat their shells and even other eggs of snails. Land snails hatch \n\n\n\nfrom eggs. Some predators of terrestrial snails are beetles, rats, mice, \n\n\n\nturtles, salamanders and some birds. \n\n\n\nThe importance of snail meat cannot be over emphasized; it is a good \n\n\n\nsource of animal protein containing about 18% crude protein of high  \n\n\n\nbiological value and can help sustain the average global per capita meat \n\n\n\nconsumption at over 40kg, which has, doubled from the consumption in \n\n\n\n1960 [1]. The meat contains all essential amino acids such as lysine, \n\n\n\nmethionine, etc. The meat is highly prized, contains low fat content and \n\n\n\nlow cholesterol levels which make it a good antidote for fat related \n\n\n\ndiseases like hypertension, etc. The meat is rich in calcium, iron, \n\n\n\nphosphorus and potassium which are essential or macro- minerals needed \n\n\n\nfor strong bones, osmo-regulation and metabolic activities in the body of \n\n\n\nman. Snail meat is a good source of vitamin A, B6, E and K which are \n\n\n\nrequired for proper utilization of primary nutrients such as carbohydrate, \n\n\n\nprotein, fat and oil [2]. \n\n\n\nThe study of animal behavior begins with understanding how an animal's \n\n\n\nphysiology and anatomy are integrated with its behavior. Both external \n\n\n\nand internal stimuli prompt behaviors such as external information like \n\n\n\nthreat from other animals, sounds and smells or weather conditions and \n\n\n\ninternal information like hunger and fear [3]. Animals behave in certain \n\n\n\nways for four basic reasons; to find food and water, to interact in social \n\n\n\ngroups, to avoid predators and to reproduce. Behavior is anything an \n\n\n\nanimal does involving action and a response to a stimulus. Animals are as \n\n\n\nintelligent as they need to be to survive in their environment. They often \n\n\n\nare thought of as intelligent if they can be trained to do certain behaviors. \n\n\n\nBut animals do amazing things in their own habitats. \n\n\n\nLights duration and intensity play an important role in the regulation and \n\n\n\ncontrol of production, reproduction, behavior and welfare of animals [4,5]. \n\n\n\nFor individuals of many species, the annual cycle of changing photoperiod \n\n\n\nprovides the environmental switch between seasonal phenotypes. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.01.2019.28.32\n\n\n\n  RESEARCH ARTICLE \n\n\n\nEFFECT OF PHOTOPERIOD ON THE GROWTH PERFORMANCE AND \nBEHAVIORAL PATTERN OF ACHATINA ACHATINA SNAIL \n\n\n\nLC Ugwuowo1*, CI Ebenebe,1 CI Ezeano,2 CC Nnadi1 \n\n\n\n1Department of Animal Science, Nnamdi Azikiwe University Awka, Anambra State, Nigeria \n2Department of Agricultural Economics and Extension, Nnamdi Azikiwe University Awka, Anambra State, Nigeria \n\n\n\n*Corresponding Author Email:  chidilu2002@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\nCite The Article: LC Ugwuowo, CI Ebenebe, CI Ezeano, CC Nnadi (2019). Effect Of Photoperiod On The Growth Performance And Behavioral Pattern Of \nAchatina Achatina Snail. Malaysian Journal of Sustainable Agriculture, 3(1): 28-32. \n\n\n\n\nmailto:chidilu2002@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 28-32 \n\n\n\nAnimals will adapt to a change in day length. Learning ability of an animal \n\n\n\ndepends upon the inheritance, but learning is important for developing a \n\n\n\nparticular act in the organism. An organism possesses genes where in any \n\n\n\nchange in a single unit of gene will bring about change in the behavioral \n\n\n\npattern of that organism. Behavior of an organism or animal is achieved by \n\n\n\nthe interaction of genes with environments and on the learning abilities of \n\n\n\nthe organism. Memory is an essential part of learning in animal. Animal \n\n\n\ncollects information about their surroundings in the learning process. \n\n\n\nMany behavioral patterns are controlled by the nervous system and are \n\n\n\nmodified through learning. The present study was designed to know and \n\n\n\ndetermine the effect of photoperiod on the growth performance and \n\n\n\nbehavioral pattern of Achatina achatina Snail. \n\n\n\n1.1 Statement of problem \n\n\n\nThe feeds of these animals are very cheap to procure. Their faeces do not \n\n\n\nsmell like other animals whose droppings make people uncomfortable and \n\n\n\ncause environmental pollution. Farmers complain that it is difficult to raise \n\n\n\nsnails because they do not know their behavioral pattern. \n\n\n\n1.2 Objectives of the study \n\n\n\nThe objective of this study is to determine the effects of different \n\n\n\nphotoperiods on the growth performance and behavioral pattern of \n\n\n\nAchatina achatina. \n\n\n\n1.3 Specific objectives of study  \n\n\n\n1. To evaluate the effect of different photoperiods on the feed intake of \nAchatina achatina snails \n\n\n\n2. To evaluate the effect of different photoperiods on the weight gain\nof Achatina achatina snails \n\n\n\n3. To evaluate the effect of different photoperiods on the reproductive\nbehaviors of Achatina achatina snails \n\n\n\n4. To evaluate the effect of different photoperiods on the cost of \nproducing Achatina achatina snail \n\n\n\n2. MATERIALS AND METHOD \n\n\n\n2.1 Location and duration of study \n\n\n\nThe experiment was carried out at Umuriam village Nawfia in Njikoka \n\n\n\nLocal government area. It is located near the capital of Anambra state, \n\n\n\nAwka. Awka lies within the coordinates 6\u00b012N and 7\u00b00E in the tropical \n\n\n\nzone of Nigeria. It experiences two seasons brought about by the two \n\n\n\npredominant winds, the south western monsoon wind from the Atlantic \n\n\n\nOcean and the North Eastern dry wind from across the Sahara Desert. The \n\n\n\ntemperature is generally hot and humid within the range of 27-28\u00b0c during \n\n\n\nJuly through December but rises to 35\u00b0c between February and April [6]. \n\n\n\nThe experiment lasted for 9 weeks (63 days) including one week of \n\n\n\nacclimatization. \n\n\n\n2.2 Procurement of experimental animals \n\n\n\nNinety grower snails (Achatina achatina) of average weight were got from \n\n\n\na local snail trader at Ochanja market, Onitsha, Anambra state. They were \n\n\n\ntransported in well aerated jute bag from the place they were procured to \n\n\n\nthe experimental site. The jute bag used in the transportation contained \n\n\n\nlittle garden (loamy) soil to reduce the stress of transportation. They were \n\n\n\nprotected from direct rays of sunlight during transportation. The jute bag \n\n\n\nwas placed in a bowl to reduce heat from the vehicle. \n\n\n\n2.3 Acclimatization of the experimental animals \n\n\n\nOn arrival, the snails were placed in their improvised housing which is a \n\n\n\nbasket. The snails were covered very well to prevent them from escaping. \n\n\n\nAfter two days, they were placed in the plastic bowl which was covered \n\n\n\nwith a mosquito net and tied with a twine rope to prevent the snails from \n\n\n\nescaping and also prevent pests and predators from attacking the snails. \n\n\n\nThe bowls were filled with one third of sterilized loamy soil got from the \n\n\n\nfarm. The snails were fed with paw-paw leaves and plantain leaves during \n\n\n\nthe one week of acclimatization. \n\n\n\n2.4 Formulation of diets \n\n\n\nFeed was compounded for the snails using maize, sorghum, wheat offal, \n\n\n\ngroundnut cake, soya bean meal, palm kernel cake, fish meal, bone meal, \n\n\n\nlimestone, premix (vitamin and trace minerals), methionine and lysine. \n\n\n\nThe three treatments were fed with the same feed. The ingredients used \n\n\n\nin compounding the feed were measured using a sensitive scale of model \n\n\n\nSF 400 and capacity 5000\u00d71g/17702\u00d7o.102. The place where the feed was \n\n\n\ncompounded was well swept to avoid contamination of the feed. The \n\n\n\nfeedstuffs were mixed thoroughly after measuring out the required \n\n\n\nquantity to be used. The gross composition of the diet is given in table \n\n\n\n3.1below: \n\n\n\nTable 1: Feed Formula \n\n\n\nFeedstuff Quantity in kg \n\n\n\nMaize 24 \n\n\n\nSorghum 12.25 \n\n\n\nWheat Offal 17 \n\n\n\nGroundnut Cake 15 \n\n\n\nSoya bean Meal 15 \n\n\n\nPalm Kernel Cake 6 \n\n\n\nFish Meal 5 \n\n\n\nBone Meal 3 \n\n\n\nLimestone 2 \n\n\n\nVit/Min Premix 0.25 \n\n\n\nLysine 0.25 \n\n\n\nMethionine 0.25 \n\n\n\nTotal 100 \n\n\n\n% Crude Protein 24.1375 \n\n\n\n% Energy (k/cal) 2220.54 \n\n\n\n2.5 Experimental design \n\n\n\nThe experimental design was completely randomized design (CRD). The \n\n\n\ntreatments were formed based on the period of light the snails were \n\n\n\nsubjected to. The three treatments were labeled treatment 1(T1), \n\n\n\ntreatment 2 (T2) and treatment 3 (T3). The treatment 1, was subjected to \n\n\n\n12 hours of light and 12 hours of darkness which equally served as the \n\n\n\ncontrol, treatment 2 (T2), was subjected to 18 hours of light and 6 hours \n\n\n\nof darkness while treatment 3 (T3) was subjected to 24 hours of light. Each \n\n\n\nof the treatments consists of three replicates R1, R2 and R3 and 10 snails \n\n\n\nwere placed in each replicate. \n\n\n\n2.6 Identification of the experimental animals \n\n\n\nThe experimental animals were numbered on their shells using an \n\n\n\nindelible marker (igle) having a fine needle type metal nip of 0.8mm. They \n\n\n\nwere placed in the bowl according to their numbers. \n\n\n\nTreatment one: T1R1; 1-10, T1R2; 11-20 and T1R3; 21-30 \n\n\n\nTreatment two: T2R1; 31-40, T2R2; 41-50 and T2R3; 51-60 \n\n\n\nTreatment three: T3R1; 61-70, T3R2; 71-80 and T3R3; 81-90 \n\n\n\n2.7 Management of the experimental unit and animals \n\n\n\nThe management practices that were carried out during the experiment \n\n\n\ninclude; sweeping of the experimental unit, removing the snails from the \n\n\n\nCite The Article: LC Ugwuowo, CI Ebenebe, CI Ezeano, CC Nnadi (2019). Effect Of Photoperiod On The Growth Performance And Behavioral Pattern Of \nAchatina Achatina Snail. Malaysian Journal of Sustainable Agriculture, 3(1): 28-32. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 28-32 \n\n\n\nbowl in order to remove their feaces, weighing the remaining feed, \n\n\n\nchecking for Mortality, feeding the animals, discarding the remaining feed, \n\n\n\nsprinkling lukewarm water on the snails in a very cold weather, sprinkling \n\n\n\nwater on the soil, turning the soil, checking for eggs, weighing of the snails \n\n\n\nand placing of the weighed feed for the snails. \n\n\n\n2.7.1 Cleaning of the experimental materials \n\n\n\nThe feeding trough was cleaned and washed with clean water. The \n\n\n\nmosquito nets were washed without any detergent. The plastic bowl \n\n\n\n(housing pen) was kept clean by removing the feacal materials and some \n\n\n\ndrop of feed inside the plastic bowl. The washed feeding trough was dried \n\n\n\nwith a clean towel before placing feed for the snails to prevent the feed \n\n\n\nfrom caking. \n\n\n\n2.8 Data collection \n\n\n\nFeed intake and weight gain were measured using sensitive weighing \n\n\n\nbalance (electronic kitchen scale). Data on the behavioral pattern was \n\n\n\ncollected everyday by closely observing the snails 2 hours in the morning, \n\n\n\nafternoon and night. \n\n\n\n2.9 Light Duration for the experimental treatments \n\n\n\nThe three treatments were subjected to different light period. \n\n\n\nT1; 12 hours light and 12 hours darkness \n\n\n\nT2; 18 hours light and 6 hours darkness; the light source was white bulb \n\n\n\nand electric rechargeable lantern from 06.00p.m to 12.00a.m every day. \n\n\n\nT3; 24 hours light; the light source was white bulb and electric \n\n\n\nrechargeable lantern from 06.00p.m to 07.00a.m. \n\n\n\n2.10 Statistical analysis \n\n\n\nThe data collected were subjected to Analysis of variance (ANOVA) using \n\n\n\nSPSS analytical package and significant means were separated using \n\n\n\nDuncan\u2019s New Multiple Range test, at 5% probability level [7]. \n\n\n\n3. RESULTS \n\n\n\nOne experimental diet was used in the study. The proximate analysis \n\n\n\nshows that it contained crude protein value of 19.50 % which is high \n\n\n\nenough to support proper growth and development of Achatina achatina. \n\n\n\nThe ash, fat, crude fiber and nitrogen free extract of the diet were high and \n\n\n\nenough to support snail growth. \n\n\n\nTable 2: Proximate Analysis of Feed fed to the Experimental Animals \n\n\n\nDry matter 90.64 \nMoisture 9.36 \n\n\n\nAsh 8.96 \nCrude Protein 19.50 \n\n\n\nFat 6.70 \nCrude Fiber 7.34 \n\n\n\nNitrogen Free Extract 48.14 \n\n\n\nTable 3: Effect of Photoperiod on the Growth Parameters \n\n\n\nParameters T1 T2 T3 \nInitial body weight(g) 49.03 2.82 38.06 2.22 46.23 1.35 \n\n\n\nFinal body weight(g) 67.53 7.71 55.66 7.95 64.20 3.70 \n\n\n\nTotal body weight gain (g) 18.50 10.31 17.60 5.74 17.96 \n\n\n\nAverage daily weight gain(g) 0.33 0.18 0.31 0.10 0.32 0.90 \n\n\n\nTotal feed intake (g) 52.5 4.00 54.23 3.13 53.23 2.23 \n\n\n\nAverage daily feed intake (g) 0.93 0.06 0.97 0.05 0.95 0.04 \n\n\n\nFeed conversion ratio 3.94 3.08 3.30 1.00 3.10 0.76 \n\n\n\nMeans with different superscript are statistically different (P<0.05). \n\n\n\nThe initial weights of the animals that were used for the experiment were \n\n\n\nalmost the same. There were also no significance differences (p> 0.05) in \n\n\n\nother growth parameters that were measured during the experiment. \n\n\n\nTable 4: Effect of Photoperiod on reproductive behaviors \n\n\n\nParameters T1 T2 T3 \nDuration of courtship(minutes) 52.00 44.53 67.66 22.07 88.00 23.06 \n\n\n\nDuration of mating (minutes) \n\n\n\n125.66 4.04b \n176.00 28.21a 192.33 24.17a \n\n\n\nDuration of feeding (minutes) 93.33 30.23 57.00 17.69 83.00 22.64 \n\n\n\nNo of eggs laid 89.00 32.74b 102.00 36.59b 233.00 73.91a \n\n\n\nMeans with different superscript are statistically different (P<0.05). \n\n\n\nThere were differences in reproductive behaviour of snails exposed to the \n\n\n\nvarious photoperiods. The snails mated longer when exposed to 24 hours \n\n\n\nlight than in the other treatments. Duration of courtship did not show \n\n\n\nsignificance difference, but courtship was equally longer under 24 hours \n\n\n\nlight. Snails under 24 hours light also laid more eggs than snails in other \n\n\n\ntreatments. \n\n\n\nThere were no significance differences (p> 0.05) in the cost of feed and \n\n\n\ncost of feed per kg weight gain of the snails raised under the various \n\n\n\nphotoperiods. Feed cost was almost the same among treatments but cost \n\n\n\nof feed per kg weight gain was lowest under 24 hours of exposure to light. \n\n\n\nCite The Article: LC Ugwuowo, CI Ebenebe, CI Ezeano, CC Nnadi (2019). Effect Of Photoperiod On The Growth Performance And Behavioral Pattern Of \nAchatina Achatina Snail. Malaysian Journal of Sustainable Agriculture, 3(1): 28-32. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 28-32 \n\n\n\n\n\n\n\nTable 5: Effect of Photoperiod on the Cost of Production \n\n\n\nParameters T1 T2 T3 \nCost of feed intake (#) 7.15 0.54 7.38 0.42 7.25 0.03 \n\n\n\nCost of feed per kg weight gain (#) 28.51 23.09 24.39 7.26 22.38 4.55 \n\n\n\nTable 6: Effect of Photoperiod on the time of feeding \n\n\n\nTime T1 T2 T3 \n12.00a.m Feeding Feeding Feeding \n02.00a.m Feeding Feeding Feeding \n06.00a.m Few active Feeding Feeding \n07.00a.m Few active No feeding No feeding \n\n\n\nCloudy weather \n09.00a.m \n\n\n\nFeeding Feeding Feeding \n\n\n\nCloudy weather \n11.00a.m \n\n\n\nFew active Few active Few active \n\n\n\n12.00p.m No activity No activity No activity \n01.00p.m No activity Few active No activity \n\n\n\nCloudy weather \n06.00p.m \n\n\n\nFeeding Feeding Feeding \n\n\n\n11.00p.m Feeding Feeding Feeding \n\n\n\nThe table shows that the snails were more active feeding during the \n\n\n\nmorning and in the evening. The snails were less active during the \n\n\n\nafternoon. The table also shows that snails like feeding under cloudy \n\n\n\nweather especially in the morning and afternoon.  \n\n\n\n4. DISCUSSION\n\n\n\n4.1 Proximate Analysis of Feed fed to the Experimental Animals \n\n\n\nTable 2 shows the result of the proximate analysis of feed fed to \n\n\n\nexperimental animals. The result on the proximate analysis of feed fed to \n\n\n\nthe experimental animals shows that the experimental diet had a crude \n\n\n\nprotein of 19.50; fat content of 6.70; ash content of 8.96; dry matter \n\n\n\ncontent of 90.64; moisture content of 9.36 and nitrogen free extract of \n\n\n\n48.14. The crude protein was higher than 15-25% crude protein that was \n\n\n\nreported by a scholar for snails especially on intensive commercial \n\n\n\nsnailery [8]. \n\n\n\n4.2 Effect of Photoperiod on growth parameters of Achatina achatina \n\n\n\nsnail \n\n\n\nTable 3 above presents the result on the effect of photoperiod on growth \n\n\n\nparameters of Achatina achatina snail. The table shows that there were no \n\n\n\nsignificant differences (P> 0.05) in initial weight, final body weight, total \n\n\n\nbody weight, average daily weight gain, total feed intake, average daily \n\n\n\nfeed intake and feed conversion ratio. The result obtained may be \n\n\n\nattributable to the fact that one diet was used for the experiment, but the \n\n\n\ndifferent photoperiods did not affect their weight gain and feed intake \n\n\n\nwhich also depends on the palatability of the diet [9]. \n\n\n\nIt was noted that treatment 1 compares favourably with treatment 3 in \n\n\n\ntotal feed intake.  This also agrees to what was reported by a researcher \n\n\n\nthat exposure of snails to continuous light at night increases their activity \n\n\n\nand rate of feed consumption and promote rapid growth [10]. \n\n\n\nThere was no significant difference (P> 0.05) in average daily feed intake \nof the treatments. There was also no significant difference between \ntreatments in feed conversion ratio. Treatment 1 had the highest feed \n\n\n\nconversion ratio of 3.94 3.08 while treatment 3 had the lowest feed \n\n\n\nconversion ratio of 3.10 0.76. \n\n\n\n4.3 Effect of Photoperiod on the reproductive behaviors \n\n\n\nThe table 4 above shows no significant difference (P>0.05) in duration of \n\n\n\ncourtship among treatments. Treatment 3 had 88.00 23.06 minutes as \n\n\n\nthe highest duration of courtship while treatment 1 had the lowest \n\n\n\nduration of 52.00 44.53 minutes. They used their tentacles in finding a \n\n\n\nmate. If the tentacle is withdrawn during this process, it might be that that \n\n\n\nparticular one already had a mate. The ones courting seem to feed or eat \n\n\n\nfrom the same side of the feeding trough. It was also observed that their \n\n\n\ntentacles seem to stand still during courtship if they like each other but \n\n\n\nsometimes it is crossed. It was also observed that Achatina achatina \n\n\n\nexpands their posterior and exterior invagination during courtship display \n\n\n\nand also when they are excited. When one of the courting mates is on heat, \n\n\n\nit will start bringing out it's love dart to notify the other one that it's on \n\n\n\nheat (sex drive). \n\n\n\nThere was significant difference (P>0.05) in duration of mating among \n\n\n\ntreatments. Treatment 3 had the highest duration of 192.33+_ 24.17 \n\n\n\nminutes while treatment 1 had the lowest duration of 125.66+_4.04 \n\n\n\nminutes. This may be due to the regular exposure to light. \n\n\n\nIt was observed that the one on top during mating produces or releases \n\n\n\nmucus first which lubricates the love dart and keeps their body moist \n\n\n\nwhen it is becoming dry. \n\n\n\nDuring mating, the one beneath crosses the love dart first having its head \n\n\n\ninside the shell while the other twists its head to the position that will be \n\n\n\ncomfortable for the mating. The love dart is situated on the right-hand side \n\n\n\nof the snails. \n\n\n\nAfter mating, the one that have the love dart under separates first followed \n\n\n\nby the one on top. There was significant difference (p<0.05) in the number \n\n\n\nof eggs laid from the different treatments during the experiment. \n\n\n\nTreatment 3 had the highest number of eggs with 233.00 73.91 while \n\n\n\ntreatment 1 had the lowest number with 89.00 32.74. This rhymes with \n\n\n\nwhat was reported in the work of a researcher which said that natural \n\n\n\nphotoperiod favours maximum egg production output in giant snails. \n\n\n\n4.4 Effect of Photoperiod on the cost of production \n\n\n\nThe table 5 shows no significant difference (P>0.05) in the cost of feed \n\n\n\nintake among treatments with treatment 2 having the highest cost of #7.38\n\n\n\n0.42 while treatment 1 had the lowest cost of #7.15 0.54. It equally \n\n\n\nshows no significant difference (P>0.05) in cost of feed per kg weight gain \n\n\n\namong treatments with treatment1 having the highest cost of feed per kg \n\n\n\nweight gain of #28.51 23.09 while treatment 3 had the lowest cost of \n\n\n\nfeed per kg weight gain of #22.38 4.55. The economic implication of this \n\n\n\nis that it is cheaper to make snail add 1kg of flesh under treatment 3 than \n\n\n\nunder treatment 1. \n\n\n\nCite The Article: LC Ugwuowo, CI Ebenebe, CI Ezeano, CC Nnadi (2019). Effect Of Photoperiod On The Growth Performance And Behavioral Pattern Of \nAchatina Achatina Snail. Malaysian Journal of Sustainable Agriculture, 3(1): 28-32. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 28-32 \n\n\n\nCite The Article: LC Ugwuowo, CI Ebenebe, CI Ezeano, CC Nnadi (2019). Effect Of Photoperiod On The Growth Performance And Behavioral Pattern Of \nAchatina Achatina Snail. Malaysian Journal of Sustainable Agriculture, 3(1): 28-32. \n\n\n\n4.5 Effect of Photoperiod on the time of feeding \n\n\n\nThe table 6 above shows that snails eat more when the environment is cool \n\n\n\nand when the weather is cloudy. They burrow into the soil when the \n\n\n\nweather is hot. They freely move their tentacles during feeding. They do \n\n\n\nnot chew pawpaw leaves rather they bit and swallow immediately but in \n\n\n\nformulated feed, they chew it a little. When water is sprinkled on them, \n\n\n\nthey move their body right and left and then bring out their headfirst \n\n\n\nfollowed by their full body. It was observed that snails are not distracted \n\n\n\nby slow and melodious music when eating. \n\n\n\n5. CONCLUSION\n\n\n\nThe results had shown that exposing snails to 24 hours of light seems to \n\n\n\nhave favoured their growth performance and reproduction in terms of \n\n\n\nduration of mating and number of eggs produced during the experiment. \n\n\n\nFurthermore, the result also showed that snails feed very well when the \n\n\n\nweather is cool and cloudy and also enjoys a slow and melodious music \n\n\n\nwhen feeding.     \n\n\n\nREFERENCES \n\n\n\n[1] Gill, M., Gibson, J.P., Lee, M.R.F. 2018. Livestock production evolving to \n\n\n\ncontribute to sustainable societies. Animal, 12(8):1696\u20131698 \u00a9 The \n\n\n\nAnimal Consortium 2018. \n\n\n\n[2] Omole, A.J., Osunkeye, O.J, Odijide, J.O, Sodamola, M.O., Popoola, Y.A \n\n\n\n2006. The African Giant Land Snails published by greenhouse choice \n\n\n\nAgriculture. \n\n\n\n[3] Sara, T. 2014. Animal Behavior Introduction. The nature education, \n\n\n\nknowledge project. \n\n\n\n[4] Deep, A., Schwean-Lardner, K., Crowe, T.G., Francher, B.I., Classen, H.L. \n\n\n\n2010. Effect of light density on broiler production processing \n\n\n\ncharacteristics and welfare. Poultry Science, 89.2326-2333. \n\n\n\n[5] Schwean-Lardner, K., Francher, B.I., Classen, H.L. 2013. Minimum light\n\n\n\nintensity threshold to prevent negative effects on broiler production and \n\n\n\nwelfare. British Poultry Science 54:686-694. \n\n\n\n[6] www.latlong.net.nigeria. \n\n\n\n[7] Duncan, D.B. 1955. Multiple range and multiple F. test biometrics, \n\n\n\n11:1-42. \n\n\n\n[8] Fasokin, A.O., Isaac, I.J., Nkanga, I., Usoro, O.O. 2007. Effect of various \n\n\n\nfeeding regime on the performance of snails (A. marginata) Proc., 25th \n\n\n\nAnn. Conf. Of the Nigerian society For Animal Production. Micheal Okpara \n\n\n\nUniversity of Agriculture, Umudike, Nigeria, March 19-23, 2000, 314-315. \n\n\n\n[9] Tchowan, M.G., Ngoula, F., Kenfack, A., Tchoumboue, J., 2017. Growth \n\n\n\nperformance of giant African land snail (Achatinaachatina) fed by \n\n\n\nformulated diet with different energy level. Scientific Journal of Veterinary \n\n\n\nAdvances, 6(11):195-20 \n\n\n\n[10] Hodasi, J.K. 1984. Some observation on edible land snail of West \nAfrican. world Animal review, 52:198-217\n\n\n\n\nhttp://www.latlong.net.nigeria/\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 05-07 \n\n\n\nArticle: Md. Reaz Mahamud (2019). Large Scale Production And Increased Shelf Life Of Trichoderma Harzianum Inoculums In Semi Solid Medium. Malaysian Journal of \nSustainable Agriculture, 3(1): 05-07. \n\n\n\n\n\n\n\nISSN: 2222-7059 (Print) \nEISSN: 2222-7067 (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 15 November 2018  \nAccepted 17 December 2018 \nAvailable online 2 January 2019 \n\n\n\nABSTRACT\n\n\n\nTrichoderma harzianum is a well-known bio control agent that is commercially produced to prevent development \nof several soil borne plant pathogen. In addition to control of plant diseases, T. harzianum also promotes plant \ngrowth. Solid and liquid state fermentation methods are commonly used for mass production of T. harzianum \ninoculums. Solid fermentation is expensive as it requires substrate for fermentation. Liquid fermentation is also \nundesirable due to increase of pH, chemical degradation and lower shelf life over the time of storage. Therefore, an \nalternative semi solid medium has been developed for large scale fermentation of T. harizanum inoculums. The \nnewly developed medium showed more cell count of T. harizanum and less chemical changes (most probably \noxidation) over time compared to solid or liquid media. Two to three days after fermentation, the newly developed \nsemi solid medium showed less production of gas compared to other media. Therefore, the newly developed semi \nsolid medium could be used to increase the quality and quantity of T. harizanum inoculums for large scale \ncommercial production. \n\n\n\nKEYWORDS \n\n\n\nTrichoderma harzianum, semi solid medium, large scale production, biopesticide \n\n\n\n1. INTRODUCTION \n\n\n\n Trichoderma was first classified as fungi in 1794 and observed as \n\n\n\nproblematic and embraced alternative of several fungi like Puccinia, \n\n\n\nMucor, Ascobolus and a few slime molds. This concept leaded Trichoderma \n\n\n\nwhich is identified as T. viride before 1969. Therefore, most of the \n\n\n\nidentified taxa before 1969 are in all probability of misidentified since T. \n\n\n\nviride could be a comparatively rare species. \n\n\n\nBased on a study, trichoderma harzianum is the bio-control agent \n\n\n\nespecially used in foliar application, seed treatment, as well as soil \n\n\n\ntreatment to protect from various plant pathogens that cause diseases [1-\n\n\n\n3]. It is also used for manufacturing of enzymes. Currently, the plant \n\n\n\ndiseases caused by Botrytis, Fusarium and Penicillium sp. are treated by \n\n\n\nthe 3Tac which commercial biotechnological products of Trichoderma. \n\n\n\nAccording to research, to meet the increased demands of food and fiber, \n\n\n\ngreen revolution is essential in intensified agriculture of near future [4]. \n\n\n\nIn our modern food crop production system, the use of chemical pesticides \n\n\n\nand fertilizers has adverse effect on natural ecosystem resulted \n\n\n\ndestruction of beneficial organism like honey bees, effect on non-target \n\n\n\npests, chemical residues in food, feed and fodder [5]. According to a \n\n\n\nscholar, T. harzianum is mainly used and produced to prevent the \n\n\n\ndevelopment of several soil borne plant pathogens that not only cause \n\n\n\ndiseases but also compete for nutrients and space, do mycoparasitism, \n\n\n\nproduce inhibitory compounds as well as secretes chitinolytic enzymes \n\n\n\n[6,7]. \n\n\n\nTrichoderma species are principally green-spore forming ascomycetes \n\n\n\npresent in nearly every type of temperate and tropical soils. Based on a \n\n\n\nstudy, most of the Trichoderma species are found in decaying plants and  \n\n\n\nwithin the rhizosphere of plants [8-11]. Their diverse metabolic alteration \n\n\n\nand aggressive competitive nature help to colonize in their habits. \n\n\n\nThe antagonistic fungi T. harzianum has shown as a promising bio-control  \n\n\n\nagent against soil borne plant pathogens. T. harzianum could be \n\n\n\nrecommended not only for control the plant diseases but also as a growth \n\n\n\npromoter. According to several scholars, bacteria and fungi are multiplied \n\n\n\nby different fermentation techniques and sold in the different markets as \n\n\n\nto use for control product growth [12-18]. Study showed the beneficial \n\n\n\naction of Trichoderma spp. is not only fighting for pathogens but also \n\n\n\nplaying role for opportunistic plant symbionts [19-22]. According to \n\n\n\nprevious studies, this interaction with plants initially starts in the \n\n\n\nrhizosphere and continues to root proliferation, plant growth, and plant \n\n\n\nprotection [23-27]. Hence, these fungi may be applied for redress of \n\n\n\nimpure soil and water by the treatment to become acceptable for plants \n\n\n\n[28-31]. Based on a study, the disease in Chili could be controlled \n\n\n\neffectively by T. harzianum. T. harzianum are soil borne within the field, \n\n\n\ngreen-spore ascomycetes which will be found everywhere in the planet \n\n\n\n[32-36]. \n\n\n\nThis study is conducting the large-scale production of \n\n\n\nTrichoderma harzianum into the gel/semi gel media and increasing the \n\n\n\nshelf life, before this study we found only the production of T. harzianumis \n\n\n\nspp. in small amount and short time shelf life. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 The used chemical composition of semi/gel T. harzinum for 20L \n\n\n\nT. harzinum culture (solid) = 200g, citric acid = 45g, ascorbic acid = 45g, \n\n\n\nsodium benzoate = 100g, potassium sorbet = 100g, xanthan gum = 200g, \n\n\n\nfood color = 2g to 3g.\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\n\n\n\nDOI : http://doi.org/10.26480/mjsa.01.2019.05.07\n\n\n\n  REVIEW ARTICLE \n\n\n\nLARGE SCALE PRODUCTION AND INCREASED SHELF LIFE OF TRICHODERMA \nHARZIANUM INOCULUMS IN SEMI SOLID MEDIUM \n\n\n\nMd. Reaz Mahamud* \n\n\n\nDaffodil International University, Dhaka, Bangladesh \n*Corresponding Author Email: reaz.nfe@daffodilvarsity.edu.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:reaz.nfe@daffodilvarsity.edu.bd\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 05-07 \n\n\n\nArticle: Md. Reaz Mahamud (2019). Large Scale Production And Increased Shelf Life Of Trichoderma Harzianum Inoculums In Semi Solid Medium. Malaysian Journal of \nSustainable Agriculture, 3(1): 05-07. \n\n\n\n2.2 T. harzinum culture preparation (solid state fermentation) \n\n\n\nThe solid-state fermentation of T. harzinum is applying for the processes during which insoluble elements or fungi in water are used for growth of \n\n\n\nmicroorganism. Within the fermentative process, rice was not exceeding the capability of saturation stage for growth of T. harzinum. \n\n\n\nFigure 1: Solid & Semi/Gel T. harzinum \n\n\n\n2.3 Measuring the all ingredients \n\n\n\nAll ingredients were taken at right amount. The citric acid and ascorbic \n\n\n\nacid were used to lower pH (4.5 to 4.8) of medium to avoid production of \n\n\n\nexcessive CO2 through fermentation; xanthan gum was added for gel \n\n\n\nformation and stabilization of the culture and other ingredients. \n\n\n\n2.4 Mixing all the ingredients \n\n\n\nFirst the water and xanthan gum were mixed by mixing machine and then \n\n\n\nthe T. harzianum culture (solid fermented form) was added and mixed for \n\n\n\n30 minutes. Then, all the rest of the ingredients were added and mixed \n\n\n\nproperly for 5 hours for getting homogenized gel, with a shelf life of about \n\n\n\n06 (Six) months. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nT. harzinum was successfully grown in the proposed semisolid medium \n\n\n\nand showed better result than the other media, (about 5.6 x 105 cfu, where \n\n\n\n3.56 x 103 was in previous media). Semi solid medium has been developed \n\n\n\nfor large scale fermentation of T. harizanum inoculums. The newly \n\n\n\ndeveloped medium showed more cell count of T. harizanum and less \n\n\n\nchemical changes (most probably oxidation) over time compared to solid \n\n\n\nor liquid media. Two to three days after fermentation, the newly \n\n\n\ndeveloped semi solid medium showed less production of gas compared to \n\n\n\nother media. \n\n\n\nTable 1: Microbiological result of T. harzinum \n\n\n\nMicrobiological result of T. harzinum \n\n\n\nPrevious cfu \ncount of T. \nharzinum \n\n\n\nPresent \ncfu count \n\n\n\nof T. \nharzinum \n\n\n\n(In this \nresearch) \n\n\n\nAmount of \nsample \n\n\n\nSample \ncolour \n\n\n\nRemarks \n\n\n\n3.56 x 103 5.6 x 105 1ml Green & \nYellow \n\n\n\nIncreased \nthe cfu \ncount \n\n\n\nFigure 2: After 12 days fermentation at 35oC to 37oC \n\n\n\n3.1 Observation and Morphology of Trichoderma \n\n\n\nThis fungus is easy to reproduce at a low-cost cooked rice medium and its \n\n\n\npotential makes it deserve of the attention of people. The gel formation of \n\n\n\nT. harzinum are developed for the increasing shelf life and prevent the \n\n\n\noxidation of liquid suspension of T. harzinum, liquid suspension form of T. \n\n\n\nharzinum are oxidized into two or three days and produce CO2 gas and \n\n\n\nchanges the color of liquid suspension of T. harzinum. Before this study,the \n\n\n\ngrowth range of T. harzinum into a media is only 3.8 x 103 cfu, now in this \n\n\n\nmedia the growth of T. harzinum is more increased and found 5.6 x 105 cfu \n\n\n\ninto a normal culture media (Plate Count Agar or Nutrient Agar media). \n\n\n\n4. CONCLUSION \n\n\n\nThis semi/gel solid medium has been developed for large scale \n\n\n\nfermentation of T. harizanum inoculums. The newly developed medium \n\n\n\nshowed more cell count of T. harizanum (about 5.6 x 105 cfu) and less \n\n\n\nchemical changes (most probably oxidation) over time compared to solid \n\n\n\nor liquid media. Two to three days after fermentation, the newly \n\n\n\ndeveloped semi solid medium showed less production of gas compared to \n\n\n\nother media. Before this study the growth rate of T. harzinum into a media \n\n\n\nis only 3.8 x 103 cfu, now in this media the growth of T. harzinum is more \n\n\n\nincrease and found 5.6 x 105 cfu into a normal culture media (Plate Count \n\n\n\nAgar or Nutrient Agar media). \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThis research was the part of the B. Sc project of the first author as a partial \n\n\n\nfulfillment of his degree B.Sc in Nutrition and Food Engineering, Daffodil \n\n\n\nInternational University, Dhaka-1207, Bangladesh. The authors used \n\n\n\npersonal fund to conduct the research. \n\n\n\nREFERENCES \n\n\n\n[1] Kumar, S., Thakur, M., Rani, A. 2014. Trichoderma: mass production, \n\n\n\nformulation, quality control, delivery and its scope in commercialization \n\n\n\nin India for the management of plant diseases. African Journal of \n\n\n\nAgricultural Research, 9(53), 3838-3852. \n\n\n\n[2] Junaid, J.M., Dar, N.A., Bhat, T.A., Bhat, A.H., Bhat, M.A. 2013. \n\n\n\nCommercial bio-control agents and their mechanism of action in the \n\n\n\nmanagement of plant pathogens. International Journal of Modern Plant \n\n\n\nand Animal Sciences, 1(2), 39-57. \n\n\n\n[3] Singh, H.B. 2014. Management of plant pathogens with \n\n\n\nmicroorganisms. Proceedings of the Indian National Science Academy, \n\n\n\n80(2), 443-454. \n\n\n\n[4] Pandey, A.,   Selvakumar, P.,  Soccol, C., Nigam, P. 1999. Solid State \n\n\n\nFermentation for the Production of Industrial Enzymes. Current Science, \n\n\n\n77(1),149-162. \n\n\n\n[5] Bae, H., Roberts, D.P., Lim, H.S., Strem, M.D., Park, S.C., Ryu, C.M., \n\n\n\nMelnick, R.L., Bailey, B.A. 2011. Endophytic Trichoderma isolates from \n\n\n\ntropical environments delay disease onset and induce resistance against \n\n\n\nPhytophthoracapsici in hot pepper using multiple mechanisms. Molwcular \n\n\n\nPlant Microbes Interaction, 24, 336\u201351. \n\n\n\n[6] Druzhinina, I., Kubicek, C.P. 2005. Species concepts and biodiversity in \n\n\n\nTrichoderma and Hypocrea: from aggregate species to species clusters. \n\n\n\nJournal of Zhejiang University Science, 6(2), 100\u2013112. \n\n\n\n\nhttps://www.researchgate.net/profile/Ashok_Pandey5\n\n\nhttps://www.researchgate.net/scientific-contributions/2126905768_P_Selvakumar\n\n\nhttps://www.researchgate.net/profile/Carlos_Soccol\n\n\nhttps://www.researchgate.net/profile/Poonam_Nigam\n\n\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1389624\n\n\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1389624\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)3(1) (2019) 05-07 \n\n\n\nArticle: Md. Reaz Mahamud (2019). Large Scale Production And Increased Shelf Life Of Trichoderma Harzianum Inoculums In Semi Solid Medium. Malaysian Journal of \nSustainable Agriculture, 3(1): 05-07. \n\n\n\n[7] Bissett, J. 1991. A revision of the genus Trichoderma. III. Section \n\n\n\nPachybasium\". Canadian Journal of Botany, 69(11), 2373\u20132417. \n\n\n\n[8] Samuels, G.J. 2006. Trichoderma: Systematics, the Sexual State, and \n\n\n\nEcology. Phytopathology, 96(2), 195\u2013206. \n\n\n\n[9] Harman, G.E. 2006. Overview of mechanisms and uses of Trichoderma \n\n\n\nspp.\". Phytopathology, 96(2), 190\u2013194. \n\n\n\n[10] Beyer, W.M, Wuest, P.J., Anderson, M.G. 2007. Green mold of \n\n\n\nMushrooms. Pennsylvania State University, Pennsylvania State University \n\n\n\nextension bulletin, Retrieved 2007-08-02. \n\n\n\n[11] Samuels, G.J., Dodd, S.L., Gams, W., Castlebury, L.A., Petrini, O. 2002. \n\n\n\nTrichoderma species associated with the green mold epidemic of \n\n\n\ncommercially grown Agaricus bisporus. Mycologia, 94(1), 146\u2013170. \n\n\n\n[12] Li, D., Nicosia, M.G., Mosca, S., Mercurio, R., Schena, L. 2015. Dieback \n\n\n\nof pinusnigra seedlings caused by a strain of Trichoderma viride. Plant \n\n\n\nDisease, 99(1), 44\u201349. \n\n\n\n[13] Reason Discovered for the Toxicity of Indoor Mould. 2012. Science \n\n\n\nDaily, (Oct. 12, 2012): \n\n\n\nhttp://www.sciencedaily.com/releases/2012/10/121012074655.htm \n\n\n\n[14] Mikkola, R., Andersson, M.A., Kredics, L.,  Grigoriev, P.A., Sundell, N., \n\n\n\nSalkinoja\u2010Salonen, M.S. 2012. 20-Residue and 11-residue peptaibols from \n\n\n\nthe fungus Trichoderma longibrachiatum are synergistic in forming Na+/K+ \n\n\n\n-permeable channels and adverse action towards mammalian cells. FEBS \n\n\n\nJournal, 279, 4172\u20134190. doi:10.1111/febs.12010 \n\n\n\n[15] Dreyfuss, M., H\u00e4rri, E., Hofmann, H., Kobel, H., Pache, W., Tscherter, \n\n\n\nH. 1976. Cyclosporin A and C: new metabolites from Trichoderma \n\n\n\npolysporum (Link ex Pers.) Rifai. European Journal of Applied \n\n\n\nMicrobiology, 3, 125\u2013133. doi:10.1007/bf00928431 \n\n\n\n[16] Chong, F.W., Chakravarthi, S., Nagaraja, H.S., Thanikachalam, P.M., \n\n\n\nLee, N. 2009. Expression of transforming growth factor-beta and \n\n\n\ndetermination of apoptotic index in histopathological sections for \n\n\n\nassessment of the effects of Apigenin (4',5',7'-Trihydroxyflavone) on \n\n\n\nCyclosporine A induced renal damage. Malaysian Journal of Pathology, \n\n\n\n31(1), 35\u201343. \n\n\n\n[17] Azin, M., Moravej, R., Zareh, D. 2007. Self-directing optimization of \n\n\n\nparameters for extracellular chitinase production by Trichoderma \n\n\n\nharzianum in batch mode. Process Biochemistry, 34(6\u20137), 563\u2013566. \n\n\n\ndoi:10.1016/S0032-9592(98)00128-9 \n\n\n\n[18] Felse, P.A., Panda, T. 1999. Production of xylanase by Trichoderma \n\n\n\nlongibrachiatum on a mixture of wheat bran and wheat straw: \n\n\n\nOptimization of culture condition by Taguchi method. Enzyme and \n\n\n\nMicrobial Technology, 40(4), 801\u2013805. \n\n\n\ndoi:10.1016/j.enzmictec.2006.06.013 \n\n\n\n[19] Tan, S.H. 2013. Morphological characterization and sequence \n\n\n\nanalysis of 5.8s-its region of Trichoderma species. Bachelor Thesis, Faculty \n\n\n\nof Science, Universiti Tunku Abdul Rahman. Available online: \n\n\n\nhttp://eprints.utar.edu.my/844/ \n\n\n\n[20] Olabiyi, T.I., Ojo, O.J., Adewuyi, B.O. 2016. Impact assessment of \n\n\n\nneem compost and Trichoderma harzianum solution in the control of root \n\n\n\nknot nematode disease on cowpea. International Journal of \n\n\n\nPhytopathology, 5(2), 67-71. \n\n\n\n[21] Pratibha, S., Amr, N.P., Mahesh, K.S., Swati, D. 2012. Field \n\n\n\ndemonstration of Trichoderma harzianum as a plant growth promoter in \n\n\n\nwheat (Triticum aestivum L). Journal of Agricultural Science, 4(8), 2012. \n\n\n\n[22] Jaime, M., Luis, V., Rodrigo, H. Ximena, B. Luz, M.P. 2009. Biocontrol \n\n\n\ncapacity of wild and mutant Trichoderma harzianum (Rifai) strains on \n\n\n\nRhizoctonia solani 618: effect of temperature and soil type during storage. \n\n\n\nElectronic Journal of Biotechnology, 12(4), 2314-2321. \n\n\n\n[23] Maria, H.S.G., Gustavo, H.G. 1998. Trichoderma \n\n\n\nharzianum transformant has high extracellular alkaline proteinase \n\n\n\nexpression during specific mycoparasitic interactions. Genetics and \n\n\n\nMolecular Biology, 21(3). DOI: 10.1590/S1415-4757199800030000. \n\n\n\n[24] Maria, H.S.G., Gustavo, H.G. 1999. Trichoderma \n\n\n\nharzianum transformant has high extracellular alkaline proteinase \n\n\n\nexpression during specific mycoparasitic interactions. Genetics and \n\n\n\nMolecular Biology, 23(6), 1641-1653. \n\n\n\n[25] Ashraf, A.K., Sinha, A.P., Rathi, Y.P.S. 2005. Plant growth promoting \n\n\n\nactivity of Trichoderma harzianum on rice seed germination and seedling \n\n\n\nvigor. Indian Journal of Agricultural Research, 39(4), 256-262. \n\n\n\n[26] Nihad,H.M., Jamal, H.K.  2014. Effect of two isolates of Trichoderma \n\n\n\nharzianum on total nitrogen, chlorophyll a & b contents and yield of wheat \n\n\n\n(Triticum aestivum L) class Eba'a- 95. International Journal of Science and \n\n\n\nResearch, 3(8), 2319-7064. \n\n\n\n[27] Laila, N., Seri, I.B.M., Noorhazira, B.S. 2015. Trichoderma harzianum \n\n\n\nT32 growth and antagonistic performance against Ganoderma boninense \n\n\n\non different culture media. 3rd International Conference on Biological, \n\n\n\nChemical & Environmental Sciences (BCES-2015) Sept. 21-22, 2015 Kuala \n\n\n\nLumpur (Malaysia), Available online: \n\n\n\numkeprints.umk.edu.my/5546/1/2428C0915047.pdf. \n\n\n\n[28] Naeimi, S., Okhovvat, S.M., Javan-Nikkhah, M., V\u00e1gv\u00f6lgyi, C., \n\n\n\nKhosravi, V., Kredics, L. 2010. Biological control of Rhizoctonia solani AG1-\n\n\n\n1A, the causal agent of rice sheath blight with Trichoderma strains. \n\n\n\nPhytopathologia Mediterranea, 49(3), 287-300. \n\n\n\n[29] Benitez, T., Rincin, A.M., Limin, M.C. Codin, A.C. 2004. Biocontrol \n\n\n\nmechanisms of Trichoderma strains. International Microbiology, 7, 249-\n\n\n\n260. \n\n\n\n[30] Elad, Y. 1994. Biological control of grape grey mould by Trichoderma \n\n\n\nharzianum. Crop Protection, 13, 35-38. \n\n\n\n[31] Fourie, P.H., Halleen, F., van der Vyver, J., and Schreuder, W. 2001. \n\n\n\nEffect of Trichoderma treatments on the occurrence of decline pathogens \n\n\n\nin the roots and rootstocks of nursery grapevines. Phytopathologia \n\n\n\nMediterranea, 40, 473-478. \n\n\n\n[32] Fourie, P.H., Halleen, F. 2002. Investigation on the occurrence of \n\n\n\nPhaeomoniella chlamydospora in canes of rootstock mother vines. \n\n\n\nAustralian Plant Pathology, 31, 425-426. \n\n\n\n[33] Fourie, P.H., Halleen, F. 2004. Occurrence of grapevine trunk disease \n\n\n\npathogens in rootstock mother plants in South Africa. Australasian Plant \n\n\n\nPathology, 33, 313-315. \n\n\n\n[34] Fourie, P.H., Halleen, F. 2004. Proactive control of Petri disease of \n\n\n\ngrapevines through treatment of propagation material. Plant Disease, 88, \n\n\n\n1241-1245. \n\n\n\n[35] Fourie, P.H., Halleen, F. 2006. Chemical and biological protection of \n\n\n\ngrapevine propagation material from trunk disease pathogens. European \n\n\n\nJournal of Plant Pathology, 116, 255-265. \n\n\n\n[36] Halleen, F., Crous, P.W., Petrini, O. 2003. Fungi associated with \nhealthy grapevine cuttings in nurseries, with special reference to \npathogens involved in the decline of young vines. Australasian Plant \nPathology, 32, 47-52. \n\n\n\n\nhttp://www.nrcresearchpress.com/doi/abs/10.1139/b91-298#.VIPU-ntkkXw\n\n\nhttp://www.nrcresearchpress.com/doi/abs/10.1139/b91-298#.VIPU-ntkkXw\n\n\nhttp://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-96-0195\n\n\nhttp://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-96-0195\n\n\nhttp://www.ppath.cas.psu.edu/MushGrowInfo/Trichoderma%20Green%20Mold.html\n\n\nhttp://www.ppath.cas.psu.edu/MushGrowInfo/Trichoderma%20Green%20Mold.html\n\n\nhttp://www.mycologia.org/cgi/content/full/94/1/146\n\n\nhttp://www.mycologia.org/cgi/content/full/94/1/146\n\n\nhttp://apsjournals.apsnet.org/doi/abs/10.1094/PDIS-04-14-0433-RE\n\n\nhttp://apsjournals.apsnet.org/doi/abs/10.1094/PDIS-04-14-0433-RE\n\n\nhttp://www.sciencedaily.com/releases/2012/10/121012074655.htm\n\n\nhttps://febs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Mikkola%2C+Raimo\n\n\nhttps://febs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Andersson%2C+Maria+A\n\n\nhttps://febs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Kredics%2C+L%C3%A1szl%C3%B3\n\n\nhttps://febs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Grigoriev%2C+Pavel+A\n\n\nhttps://febs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Sundell%2C+Nina\n\n\nhttps://febs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Salkinoja-Salonen%2C+Mirja+S\n\n\nhttps://en.wikipedia.org/wiki/Digital_object_identifier\n\n\nhttps://doi.org/10.1111%2Ffebs.12010\n\n\nhttps://en.wikipedia.org/wiki/Digital_object_identifier\n\n\nhttps://doi.org/10.1007%2Fbf00928431\n\n\nhttps://en.wikipedia.org/wiki/Digital_object_identifier\n\n\nhttps://doi.org/10.1016%2FS0032-9592%2898%2900128-9\n\n\nhttps://en.wikipedia.org/wiki/Taguchi_methods\n\n\nhttps://en.wikipedia.org/wiki/Digital_object_identifier\n\n\nhttps://doi.org/10.1016%2Fj.enzmictec.2006.06.013\n\n\nhttp://eprints.utar.edu.my/844/\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 27-30 \n\n\n\nCite The Article: Md. Saikat Hossain Bhuiyan, Dr. M. A. Malek, Md. Mohsin Ali Sarkar, Majharul Islam, Md. Wasim Akram (2019). Genetic Variance And Performance Of \nSesame Mutants For Yield Contributing Characters. Malaysian Journal of Sustainable Agriculture, 3(2): 27-30. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 4 January 2019 \nAccepted 19 February 2019 \nAvailable online 27 March 2019 \n\n\n\nABSTRACT\n\n\n\nIn Bangladesh average sesame production is lower than other sesame producing country of the world, Therefore \nan experiment was conducted using five sesame M5 mutants along with the mother variety to observe their \nperformances regarding seed yield and other yield attributes. Analysis of variance showed highly significant \nvariations among the mutants and check for most of the characters. The mutant SM-07 required the shortest \nmaturity period and produced the tallest plant and highest number of capsules plant-1 in each location and \ncombined over locations, where as SM-01 and the mother variety Binatil-1 required the longest maturity period. \nResults over different locations also showed that the three mutants SM-06, SM-04 and SM-07 produced significantly \nhigher seed yield (1477, 1449 and 1438 kg ha-1, respectively) which was 7.3, 5.2 and 4.4% higher than the mother \nvariety Binatil-1 with seed yield of 1377 kg ha-1. This suggests that mutation techniques can be fruitfully applied to \ndevelop variety with higher seed yield and other improved agronomic traits of sesame. \n\n\n\nKEYWORDS \n\n\n\nSesame, genetic component, yield and yield component, mutant \n\n\n\n1. INTRODUCTION \n\n\n\nSesamu indicum L. (Family Pedaliaceae) commonly known as sesame is an \n\n\n\nimportant oilseed crop. It is referred as \u2018queen of oilseeds\u2019 due to its regard \n\n\n\nby the users and owing to its oil quality [1]. It is one of the most ancient \n\n\n\ncrops in the world known to mankind, with archeological evidences dating \n\n\n\nback to 2250 and 1750 BC at Harappa in the Indus valley [2]. Ironically, it \n\n\n\nis considered as an \u2018orphan crop\u2019 due to meager research efforts attributed \n\n\n\nto the fact that it is not a mandate crop for any international crop research \n\n\n\ninstitute [3]. Sesame is mainly a crop of warmer areas including Asia and \n\n\n\nAfrica, In Bangladesh the total production is 2970 metric tons [4,5]. \n\n\n\nAverage productivity of sesame has lowered ranging from 144 to 234 kg \n\n\n\nha-1 compared to past 20 years which has led to a gap in the demand and \n\n\n\nthe supply [6]. It is an excellent rotation crop of cotton, maize, groundnut, \n\n\n\nwheat, and sorghum. It reduces nematode populations that attack cotton \n\n\n\nand groundnut [7]. Its deep and extensive root system makes it an \n\n\n\nexcellent soil builder. It also improves soil texture, retains moisture and \n\n\n\nreduces soil erosion. The left over composted sesame leaves also help in \n\n\n\nmoisture retention of the soil making favorable conditions for planting the \n\n\n\nnext crop. Considering the importance of sesame, development of higher \n\n\n\nyielding sesame variety is persistent demand.  \n\n\n\nFor any plant breeding programme, creation of genetic variation followed \n\n\n\nby selection plays an important role in developing improved crop \n\n\n\nvarieties. Therefore, genetic variations in useful traits are prerequisites for \n\n\n\nany crop improvement programme. Like other breeding proramme in \n\n\n\nsesame creation of variability transpires to be primary step to get  \n\n\n\ndesirable types. Mutation breeding has long been known as a potential \n\n\n\ntechnique to unlock additional genetic variability for supplementing \n\n\n\nconventional crop breeding methodology. Mutagenesis offers a unique \n\n\n\nscope for creating variation, as it may alter even those genes that are \n\n\n\ncommon to all the varieties of a species. Induced mutation has been \n\n\n\nextensively and successfully used for the improvement of many crops \n\n\n\nincluding oilseed crop like sesame. Henceforth an attempt was made to \n\n\n\nselect desirable sesame mutant line with high yield potential. \n\n\n\n2. MATERIALS AND METHOD \n\n\n\nSeeds of sesame variety Binatil-1 were irradiated with 500, 600, 700 and \n800 Gy doses of gamma rays using Co60 gamma cell to create genetic \nvariations. Irradiated seeds then sown to grow M1 generation at BINA, \nMymensingh in 2011 for selecting desirable mutants in subsequent \ngenerations. Selection was made in each of M2, M3 and M4 generation based \non desired agronomic traits. From M4 populations, five homozygous and \ntrue breed mutants namely SM-01, SM-04, SM-05, SM-06 and SM-07 were \nselected for further evaluation. These five true breeding mutants along \nwith the mother variety Binatil-1 and check variety Binatil-2 were \nevaluated at three sesame growing areas of Bangladesh during 2017 \nfollowing randomized complete block design with three replicates. Seeds \nwere sown within first week of March 2017 maintaining unit plot size of \n20m2 (5.0m \u00d7 4.0m) with a line spacing of 25cm and 6-8cm for plant to \nplant within rows. Recommended production packages like weeding, \nthinning and application of fertilizers, irrigation, pesticide etc. were done \nuniformly to ensure normal growth and development of the plants in each \nplot as and when necessitated. \n\n\n\nData were taken on different morphological traits and yield attributes like \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.02.2019.27.30 \n\n\n\n  RESEARCH ARTICLE \n\n\n\nGENETIC VARIANCE AND PERFORMANCE OF SESAME MUTANTS FOR YIELD \nCONTRIBUTING CHARACTERS \n\n\n\nMd. Saikat Hossain Bhuiyan 1*, Dr. M. A. Malek2, Md. Mohsin Ali Sarkar3 Majharul Islam4, Md. Wasim Akram5 \n\n\n\n1Scientific officer, Plant Breeding division, Bangladesh Institute of Nuclear Agriculture, Mymenshing, Bangladesh. \n2Chief Scientific officer, Plant Breeding division, Bangladesh Institute of Nuclear Agriculture, Mymenshing, Bangladesh. \n3Senior Scientific Officer Agriculture Economics Division, Bangladesh Institute of Nuclear Agriculture, Mymenshing, Bangladesh. \n4Scientific officer, Bangladesh Institute of Nuclear Agriculture. Substation Sunamganj, Bangladesh \n5Researcher Plant Breeding Division, Bangladesh Institute of Nuclear Agriculture, Mymenshing, Bangladesh. \n*Corresponding Author Email: saikat.ag88@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:saikat.ag88@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 27-30 \n\n\n\nCite The Article: Md. Saikat Hossain Bhuiyan, Dr. M. A. Malek, Md. Mohsin Ali Sarkar, Majharul Islam, Md. Wasim Ak ram (2019). Genetic Variance And Performance  Of \nSesame Mutants For Yield Contributing Characters. Malaysian Journal of Sustainable Agriculture, 3(2): 27-30. \n\n\n\nplant height, number of branches plant-1, number of capsules plant-1 and \nnumber of seedscapsule-1 from 10 randomly selected representative \nplants from each plot at maturity. List of all the traits under study and their \ndescription of measurement have been presented in Table 1. The collected \ndata were analyzed statistically according to the design followed using the \nanalysis of variance (ANOVA) technique following Gomez & Gomez (1984). \nThe mean values were compared by DMRT at 5% level of significance. \nStandard heterosis for each character was expressed as per cent increase \n\n\n\nor decrease of mutant over the standard variety (SV). Using formula \nsuggested by a group researchers [8].  The t\u2019 test was applied to determine \nsignificant difference of mutant means from respective standard parent \nvalues using formulae as reported in a study by a group researchers [9]. \nThe mean square of genotypic and phenotypic variances were estimated \naccording to a study [10]. \n\n\n\nTable 1: List of seven different traits and their description of measurement \n\n\n\nSerial \nNo. \n\n\n\nTraits Methods of measurement \n\n\n\n1 \n\n\n\n2 \n3 \n4 \n5 \n6 \n\n\n\nDays to maturity \n\n\n\nPlant height (cm) \nBranches plant-1 (no.) \ncapsule plant-1 (no.) \nSeeds siliqua-1 (no.) \nSeed yield (kg/ha)      \n\n\n\nThe number of days from sowing to 70% siliquae turned into brownis color \nThe height from the base to the tip of the plant \n\n\n\nTotal number of primary branches plant-1 \nTotal number of capsule with seeds in a plant \nTotal number of seeds in a capsule \nWeighting the seeds produced in a plot and then converted into kg ha-1 \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nIn M5 generation, the analysis of variance for different quantitative \ncharacters revealed that mean squares were highly significant for all the \ntraits indicating the existence of high genetic variability among the \nmutants for yield and yield components (data was not presented here). In \n\n\n\nother words, mutation induced substantial genetic variability among the \nlines. Significant variations for different quantitative characters have also \nbeen reported in sesame earlier by Begum and Dasgupta 2015 and in other \noilseed crops that\u2019s findings confirmed the present observation [11-13]. \n\n\n\nTable 2: Estimation of genetic component of variation for yield and yield contributing \n\n\n\nVG VP PCV GCV h2b GA (%) GA \n\n\n\nPlant height 135.2 216.5 11.48 9.07 62.43 14.74 18.0 \n\n\n\nDays to maturity 13.09 20.94 5.95 4.18 62.51 6.81 5.89 \n\n\n\nBranches plant-1 5.56 8.89 185.1 146.45 60.54 2.38 3.98 \n\n\n\nCapsule  plant-1 345.68 553.09 34.94 27.56 78.78 56.20 37.78 \n\n\n\nSeeds Capsule-1 49.54 62.33 10.60 9.48 79.48 19.11 12.85 \n\n\n\nCapsule length 1.78 2.84 56.93 45.07 62.67 73.52 2.17 \n\n\n\nSeed yield 6881.6 11010.6 7.35 5.81 61.5 69.5 9.4 \n\n\n\nHere, VG = Genotypic component of variance, GCV = Genotypic coefficient \nof variance, VP = Phenotypic component of variance, PCV = Phenotypic \ncoefficient of variance, h2b = Broad sense heritability and GA = Genetic \nadvance \n\n\n\nThe component of variant along with coefficient of variability and genetic \nparameter of some yield component of the studied genotype are present \nat Table-1. Seed yield showed the higher genotypic and phenotypic \nvariability followed by capsule plant-1. Capsule length showed minimum \ndifference considering genotypic and phenotypic components of variance \nfollowed by branch plant-1. The narrow differences between genotypic and \nphenotypic components of variance indicate those major portions of this \nphenotypic variance are genetic in nature. In this study capsule length \nshowed higher broad sense heritability followed by capsule plant-1. The \nlowest broad sense heritability was found in case of branch plant-1. Higher \ngenetic advanced was found in case of capsule length followed by seed \nyield. The lowest genetic advance was found in case of branch plant-1 \nfollowed by capsule length. Higher broad sense heritability along with high \ngenetic advance is usually more helpful in predicting the resultant effect \nfor selection of the best individual than heritability only. Here higher \nheritability with higher genetic advance was found for the characters seed \ncapsules-1, capsule plant-1 plant height and days to maturity. So this type of \ncharacters needs to be consider for sesame improvement.   \n\n\n\nMean values of three individual locations for understand the \nperformances of the studied genotype have been consider and presented \nin Table 2. Maturity period is the most important and frequently observed \ncharacter which can be modified in oilseed using induced mutation. \nSignificant differences were observed for days to maturity in different \nlocations. At Magura, except SM-07 other mutants matured earlier then \nmother variety Binatil-1. Both Ishurdi and Magura, SM-07 required the \nshortest period of 85 and 84 days respectively to mature. At Ishurdi \nmutant SM-01 and check Binatil-2 took the highest maturity period of 89 \n\n\n\ndays. Like other two location at Chapainawabganj, mutant SM-07 also \nrequired the shortest maturity period of 83 days having non-significant \ndifference with other mutants while Binatil-1, Binatil-2 and mutant SM-01 \nrequired the longest duration 87 days. It is observed that, most of the \nmutants matured earlier than the mother variety. This result revealed that \nthrough induced mutation maturity period can be reduced. Induction of \nearly maturity in the mutants of oilseed has been reported in some study \non sesame which confirm the present result [14,15].  \n\n\n\nPlant height differed significantly when comparing the mean of locations. \nAt Magura, mutant SM-07 produced the highest plant height (136cm) \nclosely followed by SM-06 (134cm) while SM-05 produced the shortest \nplant of 116cm. At Ishurdi, SM-07 also produced the tallest plant (122cm) \nwhich was closely followed by mother variety Binatil-1(119cm) and \nmutants SM-04 and SM-05. Statistically non-significant plant height was \nobtained from SM-01 and SM-06 whereas SM-06 produced the shortest \nplant of 115cm. similar result was also obtained at Chapainawabganj. \nSesame demonstrates indeterminate growth habit, which causes non-\nsynchronous maturity and very high plant height; these prevent \nmechanized harvesting. Shorter plant height is therefore an essential part \nof adaptation in modern agricultural systems with combine harvesting. \nReducing plant height also improves lodging resistance, which is another \nproblem in sesame cultivation. However, low plant height seems to be a \ndisadvantage with respect to higher seed yield, because plants of greater \nheight tend to bear more capsules and thus yield more seed. Development \nof shorter mutants in oilseed Brassica has been reported in studies [16,17]. \nThese results also conform that using induced mutation plant stature can \nbe altered in oil seed crops.  \n\n\n\nThe number of branches is one of the important selection criteria in \nsesame improvement programs because a higher number of branches \nenable bearing more capsules per plant and result in higher seed yield \n[18].  In this study, all the mutant except SM-07 are uniculam type.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 27-30 \n\n\n\nCite The Article: Md. Saikat Hossain Bhuiyan, Dr. M. A. Malek, Md. Mohsin Ali Sarkar, Majharul Islam, Md. Wasim Ak ram (2019). Genetic Variance And Performance  Of \nSesame Mutants For Yield Contributing Characters. Malaysian Journal of Sustainable Agriculture, 3(2): 27-30. \n\n\n\nTable 3: Mean performance of sesame mutant lines along with check varieties for different quantitative characters \n\n\n\nMutants/ Check Plant height (cm) Branches \n\n\n\nplant-1 (no.) \n\n\n\nCapsule plant-1 \n\n\n\n(no.) \n\n\n\nSeeds capsule-1 \n\n\n\n(no.) \n\n\n\nCapsule \n\n\n\nlength \n\n\n\nSeed yield (kg ha-1) Days to \n\n\n\nmaturity \n\n\n\nMagura \n\n\n\nSM-01 132a 1c 71c 75bc 2.67b 1386b 88a \n\n\n\nSM-06 134a 1c 78b 79b 2.71b 1431a 86b \n\n\n\nSM-07 136a 3.2a 87a 77bc 2.91c 1433a 84c \n\n\n\nSM-04 133a 1c 78b 73d 2.61d 1456a 85bc \n\n\n\nBinatil-1 124b 1c 63d 99a 4.53a 1366b 88a \n\n\n\nSM-05 116b 1c 61d 78ab 2.90a 1395b 86b \n\n\n\nBinatil-2 122b 2.7b 64b 77bc 2.54b 1400ab 88a \n\n\n\nIshurdi \n\n\n\nSM-01 116c 1c 53c 54c 3.45a 1417c 89a \n\n\n\nSM-06 115c 1c 51c 58c 2.43c 1500a 87b \n\n\n\nSM-07 122a 2.73b 75a 62bc 2.79c 1433bc 85b \n\n\n\nSM-04 119b 1c 65b 62bc 2.50bc 1492a 86b \n\n\n\nBinatil-1 119b 1c 52c 75a 3.93a 1408c 89a \n\n\n\nSM-05 119b 1c 65b 63bc 2.45b 1417c 87b \n\n\n\nBinatil-2 116c 3.73a 65b 71b 3.09a 1458b 89a \n\n\n\nChapainawabganj \n\n\n\nSM-01 125ab 1b 57d 72c 2.55b 1417d 87a \n\n\n\nSM-06 134a 1b 73b 82b 2.65b 1500a 85ab \n\n\n\nSM-07 136a 3.4a 85a 78bc 2.97b 1450c 83c \n\n\n\nSM-04 131a 1b 77ab 76bc 2.67b 1458b 84bc \n\n\n\nBinatil-1 119b 1b 59d 96a 4.43a 1358e 87a \n\n\n\nSM-05 116b 1b 64c 79bc 3.07ab 1375e 85ab \n\n\n\nBinatil-2 120b 3.2a 69b 78bc 2.55b 1408de 87a \n\n\n\nNote: Same letter(s) in a column for individual location/combined \nmeans/location means do not differ significantly at 5% level of \nsignificance. \n\n\n\nAt Magura, SM-07 produced the highest number of capsules plant-1 (87) \nclosely followed by mutants SM-06 and SM-04 (74) and mother variety \nproduced the lowest number (63) which was statistically similar with \nmutant SM-05 (61). Like Magura, both of Ishurdi and Chapainawabganj, \nSM-07 produced the highest number capsules plant-1 (75 and 85 \nrespectively). In oilseed Brassica, as a consequence of mutagenesis \nreported higher siliqua number in developed mutants over their mothers \n[19-21]. Significant differences were also observed for capsule length in \ndifferent locations. On an average, mother variety Binatil-1 produced the \nhighest capsule length which was significantly different with all other \nmutants but at Ishurdi, mutant SM-06 produced statistically similar \ncapsule length that was 3.45cm whereas in Binatil-1 it was 3.93cm. A \ngroup researchers also find this type of result in his research work on \nsesame [22].  \n\n\n\nLike other character number of seeds capsule-1 differ significantly both in \nindividual locations and combined over locations. At Magur, maximum \nnumber of seeds capsule-1 was obtained from mother variety Binatil-1(99) \nfollowed by mutant SM-05 (78) while mutant SM-04 produce minimum \nseeds capsules-1. At Ishurdi, maximum number of seeds capsules-1 was \nobtained from mother variety Binatil-1(75) followed by check variety \nBinatil-2 (71) while mutant SM-01 produce minimum seeds capsules-1 \n\n\n\n(54) number. Similarly, at Chapainawabganj, Binatil-1 produced \nmaximum seeds capsule-1 (96) followed by SM-06 (82) and mutant SM-01 \nproduced lowest seeds capsules-1 that was 72 number. Baydar stated that \ncapsule production is one of most important traits defining the ideal type \nof sesame plant; it has also been identified [18,23].  Although produced the \nhighest number of capsules, the seed yield of this genotypes was lower \nthan the others or control because sesame\u2019s indeterminate character can \ncause non-mature capsule production at harvest time. \n\n\n\nThe mutants SM-07 produced the highest seed yield of 1456 kg ha-1 \nfollowed by SM-04 (1433 kg ha-1) and SM-06 (1431 kg ha-1) which was \nstatistically similar to each other. Mother variety Binatil-1 produced the \nlowest yield of 1366kg ha-1 which was statistically identical with two other \nmutants SM-01 and SM-04, on the other hand check variety Binatil-2 \nproduced seed yield of 1400 kg ha-1 having statistically significant \ndifferent from all other mutants and mother check variety at Magtura. At \nIshurdi mutants were also performed higher seed yields, the mutant SM-\n07 produced the higher seed yield of 1500kg ha-1 closely followed by the \nmutant SM-04 which produced 1492kg ha-1 of seed yield, like this at \nChapainawabganj mutant SM-07 produced the higher seed yield of 1500kg \nha-1 closely followed by mother variety Binatil-1 which produced 1458kg \nha-1 of seed yield. Positive shift in mean values due to the enhancing effect \nof gamma-rays was also reported earlier by many research workers likes \nin sesame and rapeseed [21, 24]. \n\n\n\nTable 4: Standard heterosis for different yield contributing component of sesame mutant \n\n\n\nMutants/ Check Plant height (cm) Days to maturity Branches \n\n\n\nplant-1 (no.) \n\n\n\nCapsules plant-1 \n\n\n\n(no.) \n\n\n\nSeeds Capsules -1 \n\n\n\n(no.) \n\n\n\nCapsules length Seed yield (kg \n\n\n\nha-1) \n\n\n\nSM-01 1.70* - -50** -9.09** -10.66** -6.25** -1.12** \n\n\n\nSM-06 4.20* -2.27** -50** -1.50** -2.66** -4.77** -3.80** \n\n\n\nSM-07 10.08* -4.5** 55.5* 24.24* 10.24* 6.25* 1.80* \n\n\n\nSM-04 4.20* -3.4** -50** 10.24* 6.60* -4.77** 1.14* \n\n\n\nSM-05 -5.04** -2.27** -40** -4.54** -2.40** 2.94* -1.80** \n\n\n\n*indicates significant at 5% level \n** indicates significant at 1% level  \n\n\n\nCompared with Check variety Binatil-2 at seed yield was decreased 1.12% \nin SM-01, 3.80% in SM-06 and 1.18% in SM-05. At the mutant SM-07 it \n\n\n\nincreased 1.8% and 1.14% in SM-04 over check variety Binatil-2. In plant \nbreeding, generation of genotypes having improved yield contributing \ncharacters is the main objective for achieving higher yield [25]. In oilseed, \nthe most important yield attributes responsible for the increased seed \nyield are the capsule number and seed number in capsule. Seed yield is a \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 27-30 \n\n\n\nCite The Article: Md. Saikat Hossain Bhuiyan, Dr. M. A. Malek, Md. Mohsin Ali Sarkar, Majharul Islam, Md. Wasim Ak ram (2019). Genetic Variance And Performance  Of \nSesame Mutants For Yield Contributing Characters. Malaysian Journal of Sustainable Agriculture, 3(2): 27-30. \n\n\n\ncomplex quantitative character governed by a large number of genes and \nis greatly affected by environmental fluctuations [26, 27]. In seed yield and \nits attributes and other morphological traits variations were observed \namong the locations which are due to the variations in environments \namong the locations. \n\n\n\n4. CONCLUSION\n\n\n\nIt was observed that among the mutants and mother variety three mutants \nSM-04, SM-06 and SM-07 performed better for seed yield and yield \ncontributing characters which can be selected for further trials to be \nregistered as varieties. Moreover, this study suggests that for Sesame \nimprovement breeding program researcher need to consider the \ncharacters like seed capsules-1, capsule plant-1 plant height and days.  It \nalso concludes that gamma rays irradiation can be fruitfully applied to \ninduce mutants in Sesame with higher seed yield and other improved \nagronomic traits. \n\n\n\nREFERENCE \n\n\n\n[1] Bedigian, D., Harlan, J.R. 1986. Evidence for cultivation of sesame in the \n\n\n\nancient world. Economic Botany, 40 (2), 137\u2013154. \n\n\n\n[2] Najeeb, U., Mirza, M.Y., Jilani, G., Mubashir, A.K., Zhou, W.J. 2012. \n\n\n\nSesame. In: Gupta SK (ed), Technological innovations in major world oil \n\n\n\ncrops, vol 1. Springer, New York, pp 131\u2013145. \n\n\n\n[3] Bhat, K.V., Babrekar, P.P., Lakhanpaul, S. 1999. Study of genetic \n\n\n\ndiversity in Indian and exotic sesame (Sesamum indicum L.) germplasm \n\n\n\nusing random amplified polymorphic DNA (RAPD) markers. Euphytica, \n\n\n\n110, 21\u201333. \n\n\n\n[4] Ashri, A. 1988. Sesame breeding-objectives and approaches. In: Oil \n\n\n\ncrops-sunflower, linseed and sesame. In: Proceedings of 4th oil crop \n\n\n\nnetwork workshop, Njoro. \n\n\n\n[5] BBS. 2016. Statistical Pocket Book of Bangladesh. Bangladesh Bureau \n\n\n\nof Statistics, Ministry of Planning, Government of People\u2019s Republic of \n\n\n\nBangladesh. pp:117. \n\n\n\n[6] Raikwar, R.S., Srivastva, P. 2013. Productivity enhancement of sesame \n\n\n\n(Sesamumindicum L.) through improved production technologies. African \n\n\n\nJournal of Agricultural Research, 8 (47), 6073\u20136078. \n\n\n\n[7] Elbadri, G.A., Yassin, A.M. 2010. Sesame\u2019s protective role in crop \n\n\n\nnematode control. In Bedigian D(ed) Sesame: the genus Sesamum, CRC \n\n\n\nPress, Boca Raton, pp 211\u2013217. \n\n\n\n[8] Virmani, S.S., Viraktamath, B.C., Casal, C.L., Toledo, R.S., Lopez, M.T., \n\n\n\nManalo, J.O. 1997. Hybrid Rice Breeding Manual, International Rice \n\n\n\nResearch Institute, Philippines. \n\n\n\n[9] Wynne, J.C., Emmer, D.A., Rice, P.W. 1970. Combining ability estimates \n\n\n\nin Arachis hypogaea L. II. Field perfomramne of F1 hybrids. Crop Science, \n\n\n\n10, 713\u2013 715. \n\n\n\n[10] Johnson, H.W., Robinson, H., Comstock, R.F. 1955. Estimates of genetic \n\n\n\nand   environmental variability in soybean. Agronomy Journal, 47, 314 \u2013 \n\n\n\n318. \n\n\n\n[11] Ali, N., Javidfar, F., Attary, A.A. 2002. Genetic variability, correlation \n\n\n\nand path analysis of yield and its components in winter rapeeseed \n\n\n\n(Brassica napus L.). Pakistan Journal of Botany, 34 (2), 145\u2013150. \n\n\n\n[12] Hasan, M., Seyis, F., Badani, A.G., Pons-K\u00fchnemann, J., Friedt, W., L\u00fchs, \n\n\n\nW., Snowdon, R.J. 2006. Analysis of genetic diversity in the Brassica napus \n\n\n\nL. gene pool using SSR markers. Genetic Resources and Crop Evolution, 53, \n\n\n\n793\u2013802. \n\n\n\n[13] Mahmud, F., Rasul, M.G., Rahim, M.A. 2008. Genetic \n\n\n\ndiversity analysis in some advanced lines of Brassica napus. Science \n\n\n\nAsia, 34, 432\u2013434. \n\n\n\n[14] Shah, S.A., Rahman, K. 2009. Yield and growth response of rapeseed \n\n\n\n(Brassica napus L.) mutants to different seeding rates and sowing dates. \n\n\n\nPakistan Journal of Botany, 41 (6), 2711\u20132716. \n\n\n\n[15] Begum, T., Dasgupta. 2014. Induced genetic variability, heritability \n\n\n\nand genetic advance in sesame (SesamumindicumL.). SABRAO Journal of \n\n\n\nBreeding and Genetics, 46, 21-33. \n\n\n\n[16] Shah, S.A., Ali, I., Rahman, S. 1990. Induction and selection of superior \n\n\n\ngenetic variables of oilseed rape, Brassica napus L. The Nucleus, 7, 37\u201340. \n\n\n\n[17] Malek, M.A., Begum, H.A., Begum, M., Sattar, M.A., Ismail, M.R., Rafii, \n\n\n\nM.Y. 2012a. Development of two high yielding mutant varieties of mustard \n\n\n\n(Brassica juncea (L.) Czern.) through gamma rays irradiation. Australian \n\n\n\nJournal of Crop Science, 6 (5), 922\u2013927. \n\n\n\n[18] Baydar, H. 2005. Breeding for the improvement of the ideal plant type \n\n\n\nof sesame. Plant Breeding, 124, 263-267. \n\n\n\n[19] Javed, M.A., Siddiqui, M.A., Khan, M.K.R., Khatri, A., Khan, I.A., Dehar, \n\n\n\nN.A., Khanzada, M.H., Khan, R. 2003. Development of high yielding mutants \n\n\n\nof Brassica campestris L. cv. Toria selection through gamma rays \n\n\n\nirradiation. Asian Journal of Plant Sciences, 2 (2), 192\u2013195. \n\n\n\n[20] Khatri, A., Khan, I.A., Siddiqui, M.A., Raza, S., Nizamani, G.S. 2005. \n\n\n\nEvaluation of high yielding mutants of Brassica juncea cv. S-9 developed \n\n\n\nthrough gamma rays and EMS. Pakistan Journal of Botany, 37 (2), 279\u2013\n\n\n\n284. \n\n\n\n[21] Malek, M.A., Ismail, M.R., Monshi, F.I., Mondal, M.M.A., Alam, M.N. \n\n\n\n2012b. Selection of promising rapeseed mutants through multi-location \n\n\n\ntrials. Bangladesh Journal of Botany, 41 (1), 111\u2013114. \n\n\n\n[22] Sarwar, G., Haq, M.A., Chaudhry, M.B., Rabbani, I. 2007. Evaluation of \n\n\n\nearly and high yielding mutants of sesame (Sesamumindicum L.) for \n\n\n\ndifferent genetic parameters. Journal of Agricultural Research, 45, 125-\n\n\n\n133. \n\n\n\n[23] Osman H.E. 1989. Heterosis and path coefficient analysis in sesame. \n\n\n\nActa agronomic Hungarica, 38, 105-112. \n\n\n\n[24] Begum, T., Dasgupta. 2015. Amelioration of Seed Yield, Oil Content \n\n\n\nand Oil Quality Through Induced Mutagenesis in Sesame \n\n\n\n(SesamumIndicumL.) Bangladesh Journal of Botany, 44 (1), 15-22. \n\n\n\n[25] Ahmad, M., Khan, M.A., Zafar, M., Sultana, S. 2010. Environment \n\n\n\nfriendly renewable energy from sesame biodiesel energy sources. Energy \n\n\n\nSources Part A, 32, 189\u2013196. \n\n\n\n[26] Gomez, K.A., Gomez, A.A. 1984. Statistical Procedures for Agricultural \n\n\n\nResearch. John Wiley and Sons, USA. \n\n\n\n[27] Morris, J.B. 2002. Food, industrial, nutraceutical and pharmaceutical \n\n\n\nuses of sesame genetic resources. In: Janick J, Whipkey A (eds) Trends in \n\n\n\nnew crops and new uses. ASHS Press, Alexandria, pp 153\u2013156. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 75-80 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.75.80 \n\n\n\nCite the Article: Emanuel Joel Lao, Uffe J\u00f8rgensen (2020). The Mineral Nitrogen Distribution From The Combined Free-Range Pig Farming And Energy Crop Production \nSystem. Malaysian Journal of Sustainable Agriculture, 4(2): 75-80. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.75.80 \n\n\n\nTHE MINERAL NITROGEN DISTRIBUTION FROM THE COMBINED FREE-RANGE \nPIG FARMING AND ENERGY CROP PRODUCTION SYSTEM \n\n\n\nEmanuel Joel Laoa *, Uffe J\u00f8rgensenb\n\n\n\naDepartment of Sustainable Agriculture, Biodiversity and Ecosystem Management, The Nelson Mandela African Institution of Science and \nTechnology (NM-AIST). P. O. Box 447, Tengeru, Arusha - Tanzania. \nbDepartment of Agroecology, Faculty of Science and Technology, Aarhus University, Blichers Alle\u00b4 20, P.O. Box 50, 8830 Tjele, Denmark \n*Corresponding Author\u2019s E-mail: laoemanueljoel@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 04 March 2020\n\n\n\nKeeping pigs in the outdoor pasture convey a high risk of environmental pollution through nitrate leaching. \nIntegrating grassland based free-range pigs with energy crops has been proposed as an alternative approach \nto reduce pollution. This study investigated the Nmin distribution and potential NO3-N leaching at various soil \ndepths (0-25, 25-50, 50-100 cm) and distances (0.5, 2.5, 4.5, 6.5 and 9.5) from willow trees in the lactating \nsows' paddocks. The results observed the highest levels of Nmin close to the huts and adjacent to feed troughs \nand the lowest Nmin levels close to the willow trees with a 1 m soil depth. For soil water analysis, the nitrate \nleaching as expected was the highest near the huts with an average of 37 mg NO3-N/liter followed by 28 Mg \nNO3-N/liter at 6.5 m with the lowest levels close to willow trees. The lowest NO3-N leaching around willow \nzone could be subjected to high water and nutrients uptake by trees. The 9.5 m close to feeders had the low \nleaching which could be due to low NO3-N as NH4-N dominated with 90% of the total Nmin with about 79% of \nthis being in topsoil. Therefore, with a long growing season and deep root system of energy crops, the \npaddock design should maximize the trees uptake potential near this zone as pigs are known to have high \nexcretion activities near shelter zones. As a result, a substantial N loss through nitrate leaching could be \nminimized.  \n\n\n\nKEYWORDS \n\n\n\nNitrate leaching, Free-range farming, Energy crops, Mineral nitrogen, Animal welfare.\n\n\n\n1. INTRODUCTION \n\n\n\nFree-range pig production is typically comprised of pregnant and lactating \nsows with piglets being grazing outside, roaming and resting around the \npasture by day and sleep in small huts during night time (Horsted et al, \n2012; Webb et al, 2014; Williams et al, 2005). The EU regulation requires \nfree-range pigs to have permanent access to pasture during summer for at \nleast 150 days a year even though some farmers tend to keep the pigs even \nlonger. Weaning for piglets according to the regulation is done at 40 days \nsince farrowing even though there are country-specific conditions that \nelongate the weaning age (Directive, 2008). With the indoors systems for \nweaners, which include a small outdoor running space, they will be fed \nuntil reaching the slaughter weight (Kongsted & Hermansen, 2005). In \nsome farming systems, depending on national standards, farm-specific \nobjectives, and local environment, different combinations of both outdoor \nand indoor settings can be practiced (Vieuille et al, 2003). Like in other EU \ncountries, the presence of grazing areas in Danish free-range pig \nproduction has raised concerns about possible environmental impacts \nincluding increased ammonia volatilization (Sommer & Hutchings, 2001), \ndenitrification (Petersen et al, 2001) and high nitrate leaching (Eriksen, \n2001). The high N and P surplus from the urine and defecations have the \nenvironmental implication of increased leaching rate which may lead to \ncontamination of groundwater. This has negative health impacts on \nhumans (Nie et al, 2019; Williams et al, 2000) but also affects the aquatic \necosystems through eutrophication (Honeyman, 2005; Quintern & \n\n\n\nSundrum, 2006). The N loss in the free-range system is not distributed \nequally over the grassland as high N loss rates are more pronounced in \nhotspot areas such as near the huts, shelters and feeders compared to the \nrest of the field (Andresen, 2000). Apart from excess N and P loading \nproblems to the environment, the free-range organic pigs have been \nassociated with higher piglet mortality rate compared to indoors \nconventional pigs (Bilkei, 1995), management challenges due to seasonal \nweather fluctuations (Honeyman, 2005) and maintenance of the grassland \ncover (Vieuille et al, 2003). The combination of high nutrient loss plus \nhigher piglet mortality rates in organic pig farming has led to lower N and \nP efficiencies (Nielsen & Kristensen, 2005). \n\n\n\nIntegrating free-range pig farming with selected energy crops particularly \nwillow (Salix spp.), poplar (Populus spp.) and miscanthus has been a \nproposed approach to improve animal welfare by providing shelter and \nprotection in adverse weather conditions (Horsted et al, 2012). Also, the \ntrees with their high water and N uptake may reduce nutrients losses to \nthe environment as well as improving agricultural diversity important for \nthe ecosystem services. With the average nitrate leaching in Danish \nagricultural land being used to be 70 kg N/ha, perennial energy crops such \nas willow and miscanthus have shown promising results of reducing \nbetween 40 - 65 kg N/ha (J\u00f8rgensen et al, 2005). With deep and the \npermanent root system, the root zone for willow, for instance, can be as \ndeep as 1.3 m when well established even though the root depth could vary \nwith soil type, willow clone type, nitrogen source, and management \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 75-80 \n\n\n\nCite the Article: Emanuel Joel Lao, Uffe J\u00f8rgensen (2020). The Mineral Nitrogen Distribution From The Combined Free-Range Pig Farming And Energy Crop Production \nSystem. Malaysian Journal of Sustainable Agriculture, 4(2): 75-80. \n\n\n\n(Mortensen et al, 1998; Smith et al, 2013). Additionally, willow is known to \nbe tolerant to high plant density and waterlogging conditions as well as the \ncoppicing ability (Mortensen et al, 1998; Sevel et al, 2014; Volk et al, 2006).  \nTherefore, with the established energy crops grown along paddocks' \nboundaries in the study site, the investigation was done to quantify the \ndistribution of mineral nitrogen (Nmin) in the soil for both nitrate (NO3-N) \nand ammonium (NH4-N) at various soil depths (0-25, 25-50, 50-100 cm) \nand distances (0.5, 2.5, 4.5, 6.5 and 9.5) from trees. Additionally, the \nquantification of NO3-N leaching at each distance point was done using \nceramic suction cups to understand the potential leaching of NO3-N in this \npig farming system.  \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Site description  \n\n\n\nThe experiment was carried out in a free-range organic pig farm at \nHovborgvej Brorup, Region of Southern Denmark (Ulveh\u00f8jvej 1, 6650 \nBr\u00f8rup) located at 55\u00b0 34' 35'' N, 8\u00b0 59' 30'' E. This farm is among the two \nbiggest organic pig farms in Denmark with established energy crops in the \npig paddocks (Kongsted, 2015). The willow together with poplar trees \nhave been established back in 2009 in a 1-hectare area of grassland that is \nused for lactating sows with the willow not been harvested at least by the \ntime this study was carried out (Kongsted, 2015). \n\n\n\n2.2 Experiment layout \n\n\n\nIn order to investigate nitrate leaching which in Denmark normally occurs \nbetween September and March (Blicher-Mathiesen et al, 2014), the \nceramic suction cups were installed in autumn of 2014 (late October) in \nthe paddocks where the pigs had been removed out 4 weeks before the \ninstallation started. The electric fence that marks the end of the paddock \nwas between the two willows rows located on each side of the paddock. \nFour measurement rows of 10 meters long from the willow were \nestablished with five suction cups in each row at increasing distances of \n0.5, 2.5, 4.5, 6.5 and 9.5 meters from the willow as seen in Figure 1. Due to \nthe nature of willow roots which can go as deep as 1.3 m from the surface, \nthe drilling machine made a hole of 1.5 m depth from the surface. \nAdditionally, a 30 ml solution of Silica Flour Millisil M 6.1 was applied at the \nbottom before inserting the cup so as ensure good contact between the \nsuction cup and soil. The sampling and vacuum control tubes from the five \nsuction cups in each row were connected in one vacuum control chamber \nor also called a sampling chamber. \n\n\n\nFigure 1: An experimental layout showing four measurement rows, each \n\n\n\nwith five suction cups established against the rows of willow trees on each \n\n\n\nside of the paddock. The grazing area in the middle of the paddock each \n\n\n\nside of the willow rows was not covered with grass and was mainly \n\n\n\ndominated by mud during installation rather than grass. \n\n\n\n2.3 Soil Sampling \n\n\n\nAt a distance of 1.5 m on each side of each suction cup installed, the soil \ndrill was used to take soil samples at three depth levels of 0 - 25 cm, 25 - \n50 cm and 50 - 100 cm from the soil surface. The samples were used to \ncharacterize both physical and chemical soil properties including soil \ntexture, total carbon, phosphorus, mineral-N (NO3- and NH4+) and soil pH. \nTherefore for all the soil sampling units and at all depth levels, there were \na total of 120 samples.  \n\n\n\nFigure 2: A sketch illustrating a typical suction cup and its components \n\n\n\nFigure 3: The suction cups used in the experiment \n\n\n\nFigure 4: A vacuum control chamber as seen after sampling and vacuum \n\n\n\ncontrol tubes of five suction cups in one measurement row are already \n\n\n\nconnected \n\n\n\n2.4 Soil mineral N (Nitrate-N and Ammonium-N) analysis \n\n\n\nFor mineral N analysis (NO3\n- and NH4\n\n\n\n+), samples collected were stored at \ntemperature -20 C to avoid the volatilization of ammonium which is \ntriggered as the temperature increases. One day before the analysis, the \nsamples were taken off the freezer, followed by weighing them and then \nmix with Potassium Chloride (1 M KCl) before being taken to a shaker for \nthe solution and soil particles to thoroughly mix. The soil samples were \nthen centrifuged for 5 minutes at 3000 rpm (rounds per minute). Using \nSpectrophotometry method, the samples were analyzed for Ammonium-N \nand Nitrate-N using the procedures outlined by Best, (1976). In the \nAutoanalyser machine, there were separate tubes for both nitrate and \nammonium that pass through the pre-mounted analyzer membrane. \nHydrazine was used to convert nitrite into nitrate under Copper catalyst \n(CuSO42-). The nitrate was then reacted with sulphur amide and \nethylenediamine (C2H4(NH2)2) to an azo dye (which can be range from \nbrown-orange-pink colour depending on the NO3- concentration) while \nthe actual concentration was determined using a spectrophotometer at \n520 nm. For analysis of ammonium-N concentration, the ammonium \nreacts with salicylate (C7H6O3) and sodium dichloroisocyanurate under \nthe catalyst Sodium nitroprusside Na2[Fe(CN)5NO] to form a pale green or \nemerald solution before being determined by the spectrophotometer at \n660 nm. The separate concentration readings for nitrate and ammonium \nfrom the spectrophotometer were eventually being displayed in the \nconnected computer.  \n\n\n\n2.5 Soil Water Sampling and Analysis  \n\n\n\nNitrate leaching was determined using the NO3-N concentration in the soil \nwater that was sampled once every 2 weeks since the installation of cups \nwith the first measurement being on 7th November 2014 until 5th March \n2015. A suction of 70 kPa was applied 2 days before taking samples which \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 75-80 \n\n\n\nCite the Article: Emanuel Joel Lao, Uffe J\u00f8rgensen (2020). The Mineral Nitrogen Distribution From The Combined Free-Range Pig Farming And Energy Crop Production \nSystem. Malaysian Journal of Sustainable Agriculture, 4(2): 75-80. \n\n\n\nmade soil water slowly penetrate the suction cup. After collection of \nsamples for 9 times, water samples were sent to a private and independent \nenvironmental laboratory called \u201cAnalyTech Milj\u00f8laboratorium A/S\u201d for \nanalysis of NO3-N concentrations in soil water. Using the UV \nspectrophotometer method as applied by Navone (1964), the wavelength \nof 220 nm was applied to obtain the nitrate levels in the sample. However, \nwith the presence of dissolved organic matter in the sample which could \nalso be absorbed in 220, the UV of 275 was used where only nitrate cannot \nbe absorbed and the difference between the two wavelengths gives the \napproximate nitrate levels in the soil water. To avoid or minimize the \ninterference effect of some suspended materials such as hydroxide and \ncarbonates, simple filtration together with additional hydrochloric acid is \nnormally used. Other reagents used with this method include distilled \nwater that is nitrate-free.  \n\n\n\n3. STATISTICAL ANALYSIS \n\n\n\nFor Nmin analysis, the two way factorial ANOVA design with the main \neffects of distance and depths was used. The distance consisted of five \nlevels (0.5, 2.5, 4.5, 6.5 and 9.5 meters) while the depth had three levels \n(0-25, 25-50 and 50-100 cm). The four replications were represented as \nfour measurement rows. Whenever there was a significant difference \n(when the F-test (P\u22640.05), the Tukey test was used to point out which pair \nof concentration means differed significantly. For soil water NO3-N \nanalysis, the two way factorial ANOVA design was also used with the main \neffect with this analysis being distance from willow and time of sampling. \nANOVA was used to test whether there was an interaction of time of \nsampling and distance from willow on the nitrate leaching potential or \nwhether the nitrate concentrations were affected by the time of sampling \nor the distance from willow trees.  \n\n\n\n4. RESULTS \n\n\n\n4.1 Nitrate-N distribution at various soils depths and distances \n\n\n\nfrom willow \n\n\n\nWith the NO3-N distribution in the lactating sow paddocks, statistical \nanalysis found a significant interaction effect of the depth and distance \n(df=8, p<0.05). There was also a strong significant difference in NO3-N \ndistribution due to distance at various points from the willow trees (df = \n4, p<0.001) while analysis found no effect due to the depth variations \n(df=2, p>0.05). The NO3-N distribution tended to increase from 0.5 m \nbefore reached peak levels at 4.5 m and then sharply decreased to the last \ndistance point (Figure 5). At the distance 4.5 m from the willow, NO3-N \nconcentration was significantly higher at all the soil depths compared to \nother distance points. \n\n\n\n4.2 Ammonium-N distribution with soil depths and distances \n\n\n\nvariations \n\n\n\nThe statistical analysis also showed the interaction effect of depth and \ndistance on the distribution of NH4-N in the pigs' paddocks (df = 8, p< \n0.05). There was also a strong significant influence of both distances (df = \n4, p<0.001) and depth (df = 2, p<0.001). The NH4-N from willow to 4.5 m \nwas statistically not different at all the three soil depths even though a \nlarge proportion of NH4-N was found in topsoil. NH4-N was however \nsignificantly higher at 6.5 and 9.5 m distance with 70.5 and 90.6 Kg N/ha \nrespectively which were 3 times higher compared to the NH4-N in the first \n4.5 m from the willow trees (Figure 6). In all the distance points from \nwillow, the topsoil (0 - 25 cm) contributed between 73 to 87% of the total \nNH4-N in the whole soil profile. The distances 6.5 and 9.5 m are where the \nfeeders were located in the summer and early autumn before the \nexperiment commence as seen in Figure 6.  \n\n\n\n4.3 Total mineral-N distribution at different soil depths and \n\n\n\ndistance from the willow   \n\n\n\nWhen considering the total average Nmin for the four measurement rows, \nthe statistical analysis did not find the interactive effect of the two main \nfactors on Nmin distribution (df=8, p>0.05). There was however strong \nstatistical significance due to the main factors depth variations (df=2, \np<0.001) and distance (df=4, p<0.001). From the willow up to 9.5 m \ndistance, the Nmin showed a significant increasing trend for the topsoil (0- \n25 cm) with the lowest levels of 25.4 Kg N/ha at 0.5 m and the highest of \n72.6 Kg N/ha at 9.5m. The constituents of this Nmin differed with distances. \nFor instance, the highest contribution of NO3-N in the topsoil (0-25 cm) \nwas found at 4.5 m with about 64% of Nmin while 90 and 98% of NH4-N \naccounted for total mineral N at 6.5 and 9.5 m respectively. For the other \nlower soil profiles, there was an increasing trend for mineral N up to 4.5 \nm while at 6.5 and 9.5 m the mineral N seemed to decrease.  \n\n\n\nFigure 5: Nitrate-N distribution at different soil depths and distance \n\n\n\nfrom the willow \n\n\n\nFigure 6: Ammonium-N distribution at different soil depths and distance \n\n\n\nfrom the willow \n\n\n\nFigure 7: Mineral -N distribution at different soil depths and distances \n\n\n\nfrom willow \n\n\n\nTable 1: ANOVA output summarizing the significance levels existed \n\n\n\non the soil samples (NH4-N,  NO3-N and Mineral N) and in the soil \n\n\n\nwater (NO3-N) \n\n\n\nVariables \nMain Factors \n\n\n\n& Interaction \n\n\n\nDegrees of \n\n\n\nFreedom \nP-value Significance \n\n\n\nNO3-N \n\n\n\nconcentration \n\n\n\nin soil \n\n\n\nDistance: \n\n\n\nDepth \n8 0.0206 * \n\n\n\nDistance 4 8.952e-09 *** \n\n\n\nDepth 2 0.1709 NS \n\n\n\nNH4-N \n\n\n\nconcentration \n\n\n\nin soil \n\n\n\nDistance: \n\n\n\nDepth \n8 0.01832 * \n\n\n\nDistance 4 1.798e-08 *** \n\n\n\nDepth 2 < 2.2e-16 *** \n\n\n\nNmin \n\n\n\nconcentration \n\n\n\nin soil \n\n\n\nDistance: \n\n\n\nDepth \n8 0.06761 NS \n\n\n\nDistance 4 0.0002116 *** \n\n\n\nDepth 2 < 2e-16 *** \n\n\n\nNO3-N in Soil \n\n\n\nwater \n\n\n\nDistance: \n\n\n\nSampling \n\n\n\nTime \n\n\n\n32 0.9012 NS \n\n\n\nDistance 4 0.001523 ** \n\n\n\nSampling \n\n\n\nTime \n8 0.939754 NS \n\n\n\nLegend: NS - Not significant, * - Significance level  \nSignificance codes:   \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\n0.5 2.5 4.5 6.5\n\n\n\nN\nO\n\n\n\n3\n-N\n\n\n\n d\nis\n\n\n\ntr\nib\n\n\n\nu\nti\n\n\n\no\nn\n\n\n\n (\nK\n\n\n\ng\n N\n\n\n\n/h\na\n\n\n\n)\n\n\n\nDistances from willow (m)\n\n\n\n0-25 cm\n\n\n\n25-50 cm\n\n\n\n50-100\n\n\n\ncm\n\n\n\nSoil Depths\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\n0.5 2.5 4.5 6.5 9.5\n\n\n\nA\nm\n\n\n\nm\no\n\n\n\nn\niu\n\n\n\nm\n-N\n\n\n\n  \nd\n\n\n\nis\ntr\n\n\n\nib\nu\n\n\n\nti\no\n\n\n\nn\n  \n\n\n\n(k\ng\n\n\n\nN\n/h\n\n\n\na\n)\n\n\n\nDistance from the willow  (m)\n\n\n\n0-25 cm\n\n\n\n25-50 cm\n\n\n\n50-100 cm\n\n\n\nSoil Depths\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\n0.5 2.5 4.5 6.5 9.5\n\n\n\nM\nin\n\n\n\ne\nr\na\nl \nN\n\n\n\n (\nK\n\n\n\ng\n N\n\n\n\n/h\na\n)\n\n\n\nDistance from the willow (m)\n\n\n\n0-25 cm\n\n\n\n25-50 cm\n\n\n\n50-100 cm\n\n\n\nSoil Depths\n\n\n\nNS P > 0.05 \n\n\n\n* P < 0.05 \n\n\n\n** P < 0.01 \n\n\n\n*** P <0.001 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 75-80 \n\n\n\nCite the Article: Emanuel Joel Lao, Uffe J\u00f8rgensen (2020). The Mineral Nitrogen Distribution From The Combined Free-Range Pig Farming And Energy Crop Production \nSystem. Malaysian Journal of Sustainable Agriculture, 4(2): 75-80. \n\n\n\n4.4 Nitrate-N concentration in the soil water  \n\n\n\nEven though there was an interaction effect of distance from the willow \nand sampling time on the NO3-N concentration in the soil water, the \nstatistical analysis didn\u2019t find them significant (df=32, p>0.05). The NO3-N \nconcentration level was only found to be significantly affected by the \ndistance variations (df=4, p<0.01) while NO3-N variation at each distance \ndue to time measurement was not statistically significant (df=4, p>0.05) as \nsummarized in Table 1. With variations due to distances, the Tukey test \nfound only significant difference of NO3-N concentration at 4.5 and 6.5 \ndistances when they were compared to the closest measurement point to \nthe willow (0.5 m) and at 9.5 m. The other variations with distances were \nnot statistically different. The average NO3-N in soil water didn\u2019t show high \nvariations with sampling dates throughout the experiment where the \nhighest and the lowest average levels were 24 and 18 mg NO3-N/liter, \nrespectively. Unlike sampling dates, distance variations revealed high NO3-\nN differences during the sampling period. For example, 4.5 m recorded the \nhighest average level of 37 mg NO3-N/liter for all sampling dates even \nthough the highest concentration was 57 mg NO3-N/liter on 18th Dec 2014 \n(fourth sampling). Unlike other distances, the NO3-N at 9.5 had the lowest \naverage difference between the highest and lowest levels with 6 mg NO3-\nN/liter while 4.5 m had the biggest average difference with 45 mg NO3-\nN/liter. \n\n\n\nFigure 8: The average NO3-N concentrations in the soil water at different \n\n\n\nsampling periods and different distance points \n\n\n\n5. DISCUSSION\n\n\n\n5.1 Spatial Mineral N distribution in the paddocks  \n\n\n\nSeveral studies have documented the heterogeneity in the spatial \ndistribution of Nmin in the outdoor pig paddocks (Horsted et al., 2012; \nKongsted & Hermansen, 2005). The defecation behavior and hence \nnutrients distribution has most been influenced by the spatial allocation of \nfeatures such as feeding troughs, huts (Webb et al., 2014) and perennial \ntrees within paddocks (Horsted et al., 2012). In the semi-natural \nenvironment, where there are diverse environmental and social stimuli, \nStolba & Wood-Gush (1989) studied that pigs use mostly wooded and \nbushes areas for their shelter compared to other areas in the paddock. If \navailable, pigs tend to find the shelter especially for temperature \namelioration and where there is less stress from wind velocity (Stolba & \nWood-Gush, 1989). This would, however, results in increased excretory \nbehavior and hence nutrients loading close to these shelter zones as \nreported by an earlier study by Horsted et al., (2012) in free-range pigs \nwith willow and miscanthus that reported high excretory behavior near \nthe willow zones. From this study (Horsted et al., 2012), the pigs spent \n54% of their activities for resting near the trees and about 49% of the \nexcretion behavior close to the willow zone.  \n\n\n\nThe current study observed an increasing trend for mineral-N from willow \nup to 4.5 m, which was where the huts located. Compared to the closest \nmeasurement to the willow with 42 Kg N/ha, the Nmin at 4.5 m was three-\nfold with 149 Kg N/ha (with about 84% being NO3-N). At further distances \nof 6.5 and 9.5 m, even though the Nmin wasn\u2019t as higher as at 4.5 distance, \nthere were about two-fold Nmin as compared to the willow zone (0.5 m). \nThe significantly low Nmin in the willow zone as found in our experiment \ncould be explained by two main reasons. Firstly, may be due to higher N \ndemand by the willow as Nmin was increasing as moving away from the \ntrees from 42 Kg N/ha at 0.5 m and reached peak levels of 149 Kg N/ha at \n4.5 m before having a nearly uniform Nmin at 6.5 and 9.5 m with 100 Kg \nN/ha. Willow with their fast biomass growth, long growing season and \ndeep root system carry a potential for a significant Nmin uptake (Dimitriou \net al, 2012; Uffe J\u00f8rgensen et al., 2005; Sevel et al., 2014). Previous studies \nhave reported the energy crops to have a  potential of reducing 40 -  65 Kg \n\n\n\nN/ha of NO3-N that could be easily lost by leaching in sandy soil. With the \nwillow in a study area being about 6 years old (Kongsted, 2015), the root \nsystem would be expected to be well established up to few metres \nhorizontally away from the trees and this could reduce the Nmin \n\n\n\nconcentration from around the trees up to 4.5 m distance in the study site.  \n\n\n\nAlso, the second possible explanation for low Nmin close to the willow than \nwhat we expected was due to spatial allocation of trees, huts, and feeding \nand water troughs. The distance between huts and feeders was only about \n4 metres apart and which was nearly the same distance from the huts to \nthe trees. The experiment by Horsted et al., (2012) reported high excretion \nactivities close to the willow, with the willow zone located between the \nhuts and feeders while in our current study the hut was located between \nthe willow trees and feed & water troughs. Unlike our experimental \npaddocks, the study by Horsted et al., (2012) was in particular complex \nwith different zones within a single paddock which include zones of grass, \nwillow, miscanthus plus willow + poplar for both small and large \npaddocks. The grazing area for pigs in our study area had little grass cover \nfor the sows and this have made pigs to depend most of their daily diet \nfrom the imported feed. This might be the reason for higher levels of Nmin \nbetween the hut and feeding troughs where pigs could spend most of their \ntime. High NH4-N concentration at 6.5 and 9.5 which were about 71 and 91 \n% of total Nmin respectively, reflect the high urination hotspots as the two \ndistance being close to the feed& water troughs. The pigs' urine that is \nmainly in the form of urea could rapidly change into NH4-N before being \noxidized overtime when favourable conditions of nitrifications are \navailable (Salomon et al, 2007). This has hence resulted in higher NH4-N \nlevels that were about 3 and 4 times more at 6.5 and 9.5 m respectively as \ncompared to NH4-N close to the willow. Eriksen, (2001) in the outdoor pig \nfarming found the highest Nmin in the feeding area particularly at the 0 - 40 \ncm soil depth which was similar to our experiment.  \n\n\n\n5.2 Nitrate leaching from the paddocks\n\n\n\nNitrate leaching from the outdoor paddocks is associated with excess NO3-\nN that could result in eutrophication. While most NH4-N would be attached \nto soil particles, the NO3-N is highly soluble to water and the high \nconcentration in the soil with an increased percolation especially from \nautumn and winter could increase its loss into groundwater. NH4-N with \ntime is mineralized into NO3-N which could either be denitrified, taken by \ncrops or being added to the NO3-N pool which is prone to leaching. \n\n\n\nWhen the soil sampling was done back in the autumn of 2014, the nitrate-\nN in the soil was on average the highest at 4.5 m for all the soil depths \ncompared to other distance points (Figure 5). This has prevailed in the first \nfour soil water samplings between 7th November and 18th with 4.5 m \nhaving the highest levels than other distances that range between 40 to 57 \nmg NO3-N/liter. Also, the high leaching rates from late December to late \nJanuary for all distance points (with exception of 4.5 m) were a result of \nprecipitation and melting snow since the evapotranspiration during this \nperiod was insignificant. The higher early percolation in autumn and early \nwinter resulted in lower NO3-N from mid-January. The existed nitrate-N \nduring the soil sampling, however, could have been accumulated from \nprevious production season through the mineralized organic-N and \nammonium-N. This means the distribution might possible not accounted \nonly for NO3-N from the sows kept in the paddock during spring and \nsummer of 2014. \n\n\n\nThe 2.5 m distance from willow which was somehow close to the sow\u2019s \nhuts also had secondly highest NO3-N from soil samples apart from 4.5 m. \nUnlike for 4.5 m, the 2.5 m didn\u2019t reveal higher leaching as recorded from \nsoil water sampling. This could due to NO3-N uptake by extended roots of \nthese perennial crops close to 2.5 m. The 0.5 m distance which had the \nsecond-lowest NO3-N from the soil samples prevailed the lowest leaching. \nThe lowest nitrate leaching at 0.5 m and unexpectedly highly reduced \nnitrate-N in soil water at 2.5 m favor our hypothesis which expected low \nleaching near willow zones. This could, however, be the result of both low \nexcretion activities and the high N uptake by willow roots even though the \nclear-cut influence of two could be difficult to be established as an analysis \nfor sow\u2019s excretion behavior wasn\u2019t conducted. On the distances which \nwere close to the feeders (i.e. 6.5 and 9.5 m), most of Nmin was dominated \nby NH4-N particularly at 9.5 m which made us anticipate low leaching rates. \nWith mineralization rate being insignificant during periods of low soil \ntemperatures, most of the NH4- N was assumed to be adsorbed by soil \nparticles and this has resulted in low leaching rates at 9.5 m. However, \ncontrasting a 9.5 m distance which had only a NO3-N total of 9.6 Kg N/ha \nthrough a 1 m soil column, the 6.5 m had 30.7 kg NO3-N/ha with the latter \nhaving about two-thirds of the amount (20.4 Kg N/ha) in the 50 \u2013 100 cm \nsoil layer. The high NO3-N in the lower soil at 6.5 could be the reason for \nhigher leaching more than at 9.5 m. This has made the 6.5 m to have the \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\n7\nth\n\n\n\n N\no\nv\n\n\n\n2\n1\nst\n\n\n\n N\no\nv\n\n\n\n4\nth\n\n\n\n D\nec\n\n\n\n1\n8\nth\n\n\n\n D\nec\n\n\n\n9\nth\n\n\n\n J\nan\n\n\n\n1\n9\nth\n\n\n\n J\nan\n\n\n\n2\n8\nth\n\n\n\n J\nan\n\n\n\n1\n9\nth\n\n\n\n F\neb\n\n\n\n5\nth\n\n\n\n M\nar\n\n\n\nN\nO\n\n\n\n3\n-N\n\n\n\n c\no\nn\n\n\n\nce\nn\n\n\n\ntr\nat\n\n\n\nio\nn\n\n\n\n i\nn\n\n\n\n s\no\nil\n\n\n\n w\nat\n\n\n\ner\n (\n\n\n\nM\ng\n\n\n\n N\nO\n\n\n\n3\n-\n\n\n\nN\n/l\n\n\n\nir\ne)\n\n\n\nSampling dates between November 2014 and March 2015\n\n\n\n0.5 m\n\n\n\n2.5 m\n\n\n\n4.5 m\n\n\n\n6.5 m\n\n\n\n9.5 m\n\n\n\nDistance from \n\n\n\nWillow\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 75-80 \n\n\n\nCite the Article: Emanuel Joel Lao, Uffe J\u00f8rgensen (2020). The Mineral Nitrogen Distribution From The Combined Free-Range Pig Farming And Energy Crop Production \nSystem. Malaysian Journal of Sustainable Agriculture, 4(2): 75-80 \n\n\n\nsecond-highest leaching next to 4.5 m with an average of 28 mg/liter \nthroughout the soil water sampling where the highest leaching at this \ndistance was at 44 mg NO3-N/liter in mid-January and lowest levels came \na month after. The mineralization rate of nitrification depends mainly on \nsoil temperature and moisture.  \n\n\n\nThe rate of nitrifier activities (of genera Nitrosomonas and Nitrobacter) \nresponsible for mineralization decreases with low temperatures. From \nour experiment\u2019s soil temperature which has progressively been \ndecreasing from an average of 130 C in October to the lowest levels of 10 C \nin February suggests some nitrification might have still been taking place \nup to autumn. The activity rate of nitrifiers is insignificant at temperatures \nbelow 50 C while the rate increases with temperature and the significant N \nmineralization can be achieved from 150 C. The soil moisture content may \nhowever influence the nitrifiers\u2019 activity rate and so is the N \nmineralization. Therefore, these high NH4-N levels remained in the \ngrassland especially near feeders will be mineralized and being available \nfrom spring and summer. \n\n\n\n6. CONCLUSION \n\n\n\nThe spatial allocation of features such as feeding and water troughs, huts \nand trees along boundaries within sows' paddocks has been reported in \nearlier studies to influence on the activities and defecation behavior of \npigs. In the semi-natural environment, pigs tend to spend most of their \ntime close to the bushy and woody zone especially for regulating body \ntemperature and wind velocity which in turn can create an uneven \ndistribution of Nmin. In the current study, however, Nmin levels were instead \nlower at closer distances to the trees with the high levels found near the \nhuts and feeders. Apart from the known high water and Nmin uptake due to \nthe fast biomass growth, long growing season and deep root system by \nwillow, the other possible explanation for this finding was the influence \nspatial allocation of feeders, willow and huts. Pigs were assumed to spend \nmost of their activities between the huts and the feeders since the grass \ncover was the insignificant feed source during autumn and they almost \nentirely depended their feed intake from the feeders. In addition to \nimproved paddock design, other management options that could help to \nreduce nutrient loss and ensure even distribution of Nmin include frequent \nreallocation of feeders and huts as this reduces the enormous loss from \nhotspot areas as well as improving grassland cover. \n\n\n\nCONFLICT OF INTEREST \n\n\n\nThere is no conflict of interest from the authors \n\n\n\nACKNOWLEDGMENT \n\n\n\nThis study was financially supported by the project \u201cpEcosystem - Pig \nproduction in eco-efficient organic systems\u201d led by Agroecology \nDepartment, Aarhus University in cooperation with the Centre of \ndevelopment for outdoor livestock production, Knowledge Centre for \nAgriculture, Danish Pig Research Centre and Organic Denmark. \n\n\n\nREFERENCES \n\n\n\nAndresen, N. 2000. The foraging pig. Resource utilisation, interaction, \nperformance and behaviour of pigs in cropping systems. Acta \nUniversitatis Agriculturae Sueciae-Agraria, (227). \n\n\n\nBest, E. K. 1976. An automated method for determining nitrate nitrogen in \nsoil extracts. Queensland Journal of Agricultural and Animal Sciences. \n\n\n\nBilkei, G. 1995. Herd health strategy for improving the reproductive-\nperformance of pigs. Magyar Allatorvosok Lapja, 50(10), 766\u2013768. \n\n\n\nBlicher-Mathiesen, G., Andersen, H. E., & Larsen, S. E. 2014. Nitrogen field \nbalances and suction cup-measured N leaching in Danish catchments. \nAgriculture, Ecosystems & Environment, 196, 69\u201375. \n\n\n\nDimitriou, I., Mola-Yudego, B., & Aronsson, P. 2012. Impact of willow short \nrotation coppice on water quality. Bioenergy Research, 5(3), 537\u2013545. \n\n\n\nDirective, C. 2008. Council Directive 2008/120/EC of 18 December 2008 \nlaying down minimum standards for the protection of pigs. Official \nJournal of the European Union, 316, 36\u201338. \n\n\n\nEriksen, J. 2001. Implications of grazing by sows for nitrate leaching from \ngrassland and the succeeding cereal crop. Grass and Forage Science, \n56(4), 317\u2013322. \n\n\n\nHoneyman, M. S. 2005. Extensive bedded indoor and outdoor pig \nproduction systems in USA: current trends and effects on animal care \nand product quality. Livestock Production Science, 94(1\u20132), 15\u201324. \n\n\n\nHorsted, K., Kongsted, A. G., J\u00f8rgensen, U., & S\u00f8rensen, J. 2012. Combined \nproduction of free-range pigs and energy crops\u2014animal behaviour and \ncrop damages. Livestock Science, 150(1\u20133), 200\u2013208. \n\n\n\nJ\u00f8rgensen, U, & Mortensen, J. 2000. Combined energy crop production and \ngroundwater protection. Do Energy Crops Have a Future in Denmark, \n(29), 97\u2013104. \n\n\n\nJ\u00f8rgensen, Uffe, Dalgaard, T., & Kristensen, E. S. 2005. Biomass energy in \norganic farming\u2014the potential role of short rotation coppice. Biomass \nand Bioenergy, 28(2), 237\u2013248. \n\n\n\nJ\u00f8rgensen, Uffe, & Hansen, E. M. 1998. Nitrate leaching from Miscanthus, \nwillow, grain crops and rape. In Nitrate leaching from Miscanthus, \nwillow, grain crops and rape (pp. 208\u2013218). \n\n\n\nKongsted, A. G. 2015. Initial Stakeholder Meeting Report Agroforestry for \norganic poultry and pig production in Denmark. Troldgaarden, \nSkanderborg, Denmark. Retrieved from \nhttps://www.agforward.eu/index.php/en/free-range-pigs-integrated-\nwith-energy-crops.html \n\n\n\nKongsted, A. G., & Hermansen, J. E. 2005. Organic pig production\u2013With \nparticular reference to Danish production conditions. \n\n\n\nMortensen, J\u00f8., Nielsen, K. H., & J\u00d8rgensen, U. 1998. Nitrate leaching during \nestablishment of willow (Salix viminalis) on two soil types and at two \nfertilization levels. Biomass and Bioenergy, 15(6), 457\u2013466. \n\n\n\nNavone, R. 1964. Proposed method for nitrate in potable waters. Journal\u2010\nAmerican Water Works Association, 56(6), 781\u2013783. \n\n\n\nNie, E., Zheng, G., & Ma, C. 2019. Characterization of odorous pollution and \nhealth risk  assessment of volatile organic compound emissions in \nswine facilities. Atmospheric Environment, 117233. \n\n\n\nNielsen, A. H., & Kristensen, I. S. 2005. Nitrogen and phosphorus surpluses \non Danish dairy and pig farms in relation to farm characteristics. \nLivestock Production Science, 96(1), 97\u2013107. \n\n\n\nPetersen, S. O., Kristensen, K., & Eriksen, J. 2001. Denitrification losses from \noutdoor piglet production. Journal of Environmental Quality, 30(3), \n1051\u20131058. \n\n\n\nPugesgaard, S., Schelde, K., Larsen, S. U., L\u00e6rke, P. E., & J\u00f8rgensen, U. 2015. \nComparing annual and perennial crops for bioenergy production\u2013\ninfluence on nitrate leaching and energy balance. Gcb Bioenergy, 7(5), \n1136\u20131149. \n\n\n\nQuintern, M., & Sundrum, A. 2006. Ecological risks of outdoor pig fattening \nin organic farming and strategies for their reduction\u2014Results of a field \nexperiment in the centre of Germany. Agriculture, Ecosystems & \nEnvironment, 117(4), 238\u2013250. \n\n\n\nSalomon, E., \u00c5kerhielm, H., Lindahl, C., & Lindgren, K. 2007. Outdoor pig \nfattening at two Swedish organic farms\u2014Spatial and temporal load of \nnutrients and potential environmental impact. Agriculture, Ecosystems \n& Environment, 121(4), 407\u2013418. \n\n\n\nSevel, L., Ingerslev, M., Nord-Larsen, T., J\u00f8rgensen, U., Holm, P. E., Schelde, \nK., & Raulund-Rasmussen, K. 2014. Fertilization of SRC willow, II: \nleaching and element balances. BioEnergy Research, 7(1), 338\u2013352. \n\n\n\nSmith, J., Pearce, B. D., & Wolfe, M. S. 2013. Reconciling productivity with \nprotection of the environment: Is temperate agroforestry the answer? \nRenewable Agriculture and Food Systems, 28(1), 80\u201392. \n\n\n\nSommer, S. G., & Hutchings, N. J. 2001. Ammonia emission from field \napplied manure and its reduction. European Journal of Agronomy, 15(1), \n1\u201315. \n\n\n\nStolba, A., & Wood-Gush, D. G. M. 1989. The behaviour of pigs in a semi-\nnatural environment. Animal Science, 48(2), 419\u2013425. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 75-80 \n\n\n\nCite the Article: Emanuel Joel Lao, Uffe J\u00f8rgensen (2020). The Mineral Nitrogen Distribution From The Combined Free-Range Pig Farming And Energy Crop Production \nSystem. Malaysian Journal of Sustainable Agriculture, 4(2): 75-80. \n\n\n\nVieuille, C., Berger, F., Le Pape, G., & Bellanger, D. 2003. Sow behaviour \ninvolved in the crushing of piglets in outdoor farrowing huts\u2014a brief \nreport. Applied Animal Behaviour Science, 80(2), 109\u2013115. \n\n\n\nVolk, T. A., Abrahamson, L. P., Nowak, C. A., Smart, L. B., Tharakan, P. J., & \nWhite, E. H. 2006. The development of short-rotation willow in the \nnortheastern United States for bioenergy and bioproducts, agroforestry \nand phytoremediation. Biomass and Bioenergy, 30(8\u20139), 715\u2013727. \n\n\n\nWebb, J., Broomfield, M., Jones, S., & Donovan, B. 2014. Ammonia and odour \nemissions from UK pig farms and nitrogen leaching from outdoor pig \n\n\n\nproduction. A review. Science of the Total Environment, 470, 865\u2013875. \n\n\n\nWilliams, J. R., Chambers, B. J., Hartley, A. R., & Chalmers, A. G. 2005. Nitrate \nleaching and residual soil nitrogen supply following outdoor pig \nfarming. Soil Use and Management, 21(2), 245\u2013252. \n\n\n\nWilliams, J. R., Chambers, B. J., Hartley, A. R., Ellis, S., & Guise, H. J. 2000. \n\n\n\nNitrogen losses from outdoor pig farming systems. Soil Use and \n\n\n\nManagement, 16(4), 237\u2013243. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 111-114 \n \n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \nwww.myjsustainagri.com \n\n\n\nDOI: \n10.26480/mjsa.02.2021.111.114 \n\n\n\n \nCite the Article: Anuj Lamichhane, Mamata K.C., Manisha Shrestha and Binaya Baral (2021). Effect of Seed Priming on Germination of Okra \n\n\n\n(Abelmoschus esculentus var. Arka Anamika). Malaysian Journal of Sustainable Agriculture, 5(2): 111-114. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.02.2021.111.114 \n\n\n\n\n\n\n\n \nEFFECT OF SEED PRIMING ON GERMINATION OF OKRA (Abelmoschus esculentus \nvar. Arka Anamika) \n\n\n\n \nAnuj Lamichhane*, Mamata K.C., Manisha Shrestha and Binaya Baral \n\n\n\n \nAgriculture and Forestry University, Rampur, Chitwan, Nepal \n*Corresponding Author e-mail: anuj.lamichhane23@gmail.com      \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 05 February 2021 \nAccepted 01 March 2021 \nAvailable online 29 March 2021 \n\n\n\n Seed priming is an effective, eco-friendly method to promote seed germination and seedling vigor of okra to \novercome the reduced and delayed germination in fresh or stored okra seeds caused by seed hardness. An \nexperiment was carried to evaluate the effects of different priming on okra seeds germination and seedling \nvigor using Arka Anamika variety at Horticulture lab of Agriculture and Forestry University, Rampur, \nChitwan, Nepal. Investigation was carried out with 6 treatments (T1: seed priming with tap water, T2: seed \npriming with 200ppm NAA solution, T3: seed priming with 10% PEG-200 solution, T4: seed priming with \n200ppm GA3 solution, T5: seed priming with 5% Trichoderma solution and T6 no priming) with 4 replications \nin Complete Randomized Design (CRD). Seeds primed with T1 to T5 were soaked for 24 hours and shade dried \nfor 6 hours before sowing. Priming with T4 was found to be best in terms of maximum seed germination \n(60.12%), seed vigor index (5772.68 cm), mean germination rate (7.53 seeds per day). The highest shoot \nlength (81.40 mm) was observed at T1 whereas enhancement of root length occurred with the priming with \nT3. All treatments had a significant positive effect on all the germination parameters in comparison to control. \nThe study concluded that GA3 priming enhanced germination as well as seed vigor in okra and hydro priming \nand tricho-priming can be used as an alternative to GA3 priming among farmers in Nepal.  \n\n\n\nKEYWORDS \n\n\n\nAbelmoschus esculentus; okra; priming; seed germination; seed vigor. \n\n\n\n1. INTRODUCTION \n\n\n\nOkra [Abelmoschus esculentus (L.) Moench] is a native crop of Tropical \nAfrica that belongs to the family Malvaceae. For its robust nature, dietary \nfibers and distinct seed protein balanced in both lysine and tryptophan \namino acids; it is also called \u201ca perfect villager\u2019s vegetable\u201d (Kumar et al., \n2010). It is an important summer vegetable of Nepal and is mainly \ncultivated for its tender pods and is consumed in many different forms; \nraw, steamed, boiled, or fried ( Farinde et al., 2007; Maurya et al., 2013). \n\n\n\nGermination is considered a critical stage in the life cycle of weed and crop \nplants (Radosevich et al., 1997). Genotype, sowing date, time of pod \nharvest, seed moisture content, and micronutrient applications affect the \ngermination of okra seeds. (Purquerio et al., 2010; El Balla et al., 2011; \nMohammadikenarmereki, 2014). Okra seeds germinate very slowly and \nunevenly although they are viable seeds. Reduced, delayed, and erratic \nemergence is a serious problem in okra cultivation caused by seed \nhardness as it creates problems in rapid germination and uniform field \nstand (Purquerio et al., 2010). The hard seed coat restricts the water \nimbibition and uniform growth and development of the embryo and as a \nresult interferes with seed germination  (Mereddy et al., 2015). \n\n\n\nThe problem of low germination due to the hard seed coat in okra can be \novercome by seed priming. Seed priming is the process of controlled \nhydration of seeds which is potentially able to promote rapid and more \nuniform seed germination and plant growth (Sharma et al., 2014). Priming \nallows some of the metabolic processes necessary for germination to occur \n\n\n\nwithout germination taking place. Seed priming induced synchronized \ngermination, increased seed vigor, and growth of seedlings under stressful \nconditions i.e. increase in germination and emergence rate (Bajehbaj, \n2010). Different seed priming methods has been used to enhance \ngermination and seed vigor of okra. Among them, Hydro-priming i.e. seed \nsoaking in pure water and re-drying to original moisture content before \nsowing; Osmo-priming i.e. soaking the seed in a solution of osmoticum; \nHormonal priming i.e. soaking of seeds in different plant growth \nregulators(GA3, NAA, etc); halo-priming i.e. use of salt solutions for seed \nsoaking, bio-priming i.e. seed imbibition together with biological \ninoculation(bacteria, fungi, etc.) of seed and solid-matrix priming i.e. seed \nsoaking in solid medium(matrix) for controlled water uptake; are \ncommonly used seed priming methods (Lutts et al., 2016). \n\n\n\nThe experiment aimed to study the effect of various priming treatments \non the germination of okra seeds for overcoming the germination \nhindrance of okra seeds. \n\n\n\n2. METHODOLOGY \n\n\n\n2.1 Experiment design and Treatments \n\n\n\nAn experiment was carried out in the Horticulture Lab (27\u00b038' N latitude \nand 84\u00b020' E longitude) at Agriculture and Forestry University in March \n2020 to evaluate the effect of different priming methods on the seed \ngermination and seedling development of okra in Complete Randomized \nDesign (CRD). \n\n\n\n\nmailto:anuj.lamichhane23@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 111-114 \n \n\n\n\n \nCite the Article: Anuj Lamichhane, Mamata K.C., Manisha Shrestha and Binaya Baral (2021). Effect of Seed Priming on Germination of Okra \n\n\n\n(Abelmoschus esculentus var. Arka Anamika). Malaysian Journal of Sustainable Agriculture, 5(2): 111-114. \n \n\n\n\n\n\n\n\nThe seeds of Okra [Abelmoschus esculentus (L.) Moench var. Arka \nAnamika] was used as research material. The experiment consisted of 6 \ntreatments (Table 1) with 4 replications. \n\n\n\nTable 1: Treatments and their concentration used in this research \n Treatment Concentration \nT1 Hydro-Priming  \nT2 NAA-Priming 200ppm NAA solution \nT3 PEG-Priming 10% PEG-200 solution \nT4 GA3-Priming 200 ppm GA3 solution \nT5 Trichoderma-Priming 5% solution of Trichoderma \nT6 Control (No Priming)  \n\n\n\nThe treatment solutions were prepared and seeds were primed with \nrespective treatments for 24 hours followed by shade drying for 6 hours. \nThe primed seeds were then sown in a germination tray and were \nwatered. All the replicate-trays were watered at 5 PM daily until the \nexperiment was completed. \n\n\n\n2.2 Data Collection \n\n\n\nThere were 42 seeds sown on each plot. The data on the number of \ngerminated seeds was taken daily until the number of germinated seeds \nremains the same in two consecutive counts. The root and shoot length of \nseedlings was measured at the end of the germination counting day by \nrandomly selecting 7 seedlings from each plot. SGP, SGR, MGT, SVI and AC \nwere calculated according the Table 2 ( Tompsett & Pritchard, 1998; Ranal \n& De Santana, 2006; Rezaie & Yarnia, 2009; Vashisth & Nagarajan, 2010). \n\n\n\nTable 2: Formulas related to the variables related to seed \ngermination used in this research \n\n\n\n Variable Formula References \n\n\n\nSGP Germination \nPercentage GP = \ud835\udc41\ud835\udc41\ud835\udc41\ud835\udc41\ud835\udc41\ud835\udc41 \u00d7100 (Rezaie & Yarnia, \n\n\n\n2009) \n\n\n\nSGR Germination Rate GR = \u2211   \ud835\udc41\ud835\udc41\ud835\udc41\ud835\udc41\n\ud835\udc37\ud835\udc37\ud835\udc41\ud835\udc41\n\n\n\n\ud835\udc51\ud835\udc51\n\ud835\udc41\ud835\udc41=1  (Ranal & De \n\n\n\nSantana, 2006) \n\n\n\nMGT \nMean \nGermination \nTime \n\n\n\nMGT = \u2211(\ud835\udc5b\ud835\udc5b\ud835\udc37\ud835\udc37)\n\u2211\ud835\udc5b\ud835\udc5b\n\n\n\n (Tompsett & \nPritchard, 1998) \n\n\n\nSVI Seedling Vigor \nIndex \n\n\n\nSVI = \n(SL+RL)\n\n\n\n2\n\u00d7G \n\n\n\n(Vashisth & \nNagarajan, 2010) \n\n\n\nAC Allometric \nCoefficient AC = \ud835\udc45\ud835\udc45\ud835\udc45\ud835\udc45\n\n\n\n\ud835\udc46\ud835\udc46\ud835\udc45\ud835\udc45\n (Ranal & De \n\n\n\nSantana, 2006) \n\n\n\nNote: N (The number of germinated seeds), Nt (Number of seeds used), \nD (The number of days after germination), RL (Root length), SL (Shoot \nlength), G (Ultimate germination), n(nth day), SGP (Seed Germination \nPercentage), SGR (Seed Germination Rate), MGT (Mean Germination \nTime), SVI (Seedling Vigor Index), AC (Allometric Coefficient) \n\n\n\n2.3 Statistical analysis \n\n\n\nObtained data were analyzed by using MS-Excel and RStudio software and \nmean comparisons were done by Duncan multiple range tests (DMRT) at \n0.05 level of significance. \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Effect on the shoot and root length of seedlings \n\n\n\nThe shoot length and root length of seedlings showed significant variation \nwith the application of various priming treatments (Table 3).  The \nmaximum shoot length (81.40 mm) was recorded in T1, and the minimum \nshoot length (56.08 mm) was observed in the control condition (Fig1). \nSimilarly, the maximum (125.03 mm) and minimum (61.43 mm) root \nlengths were observed in T3 and T2 respectively. Increment in root and \nshoot length were also observed by other researchers due to seed priming \ntreatment  (Dubey et al., 2007; Tian et al., 2014). Though auxin (NAA) is \nbelieved to have a certain role in the root initiation (\u0160tefan\u010di\u010d et al., 2005), \nthis study showed it had an adverse effect on the root length of the \nseedling compared to control. Similar observations were also reported by \nother researchers (Sevik & Guney, 2013; Jyoti et al., 2016). \n\n\n\n\n\n\n\nFigure 1: Effect of different priming treatments on root length and shoot \nlength of okra seedlings. \n\n\n\n\n\n\n\nTable 3: Effect of different seed priming on germination and its parameter. \n\n\n\nTreatments \nRoot \nlength \n(mm) \n\n\n\nShoot \nlength \n(mm) \n\n\n\nMean days to \ngermination \n\n\n\nGermination \nPercentage (%) \n\n\n\nGermination \nRate \n\n\n\nSeed Vigor \nIndex (mm) \n\n\n\nAllometric \nCoefficient \n\n\n\nHydro-priming 82.35d 81.40a 7.23c 54.17b 6.54b 4447.26b 1.01d \n\n\n\nNAA-priming \n(200ppm) 61.43e 63.93cd 9.88ab 47.62c 4.70d 2982.65c 0.96d \n\n\n\nPEG priming \n(10%) 125.03a 70.70bc 10.69a 50.60bc 4.09e 4949.29b 1.77a \n\n\n\nGA3-priming \n(200ppm) 113.93b 78.75ab 6.80c 60.12a 7.53a 5772.68a 1.45b \n\n\n\nTricho-priming \n(5%) 96.63c 79.85a 8.89b 52.98b 5.56c 4676.43b 1.21c \n\n\n\nControl (No-\npriming) 76.70d 56.08d 8.96b 34.52d 2.95f 2291.19d 1.37b \n\n\n\nLSD (0.05) 10.41 8.11 1.10 4.20 0.57 521.48 0.15 \n\n\n\nSEM(\u00b1) 1.42 1.11 0.15 0.58 0.076 71.35 0.02 \nF-probability <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 \n\n\n\nCV,% 7.53 7.58 8.44 5.64 7.30 8.34 7.52 \nGrand Mean 92.68 71.78 8.74 50.00 5.23 4186.58 1.30 \n\n\n\nNote: the common letter(s) within the column indicate a non-significant difference based on the Duncan multiple range test (DMRT) at 0.05 level of \nsignificance \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 111-114 \n \n\n\n\n \nCite the Article: Anuj Lamichhane, Mamata K.C., Manisha Shrestha and Binaya Baral (2021). Effect of Seed Priming on Germination of Okra \n\n\n\n(Abelmoschus esculentus var. Arka Anamika). Malaysian Journal of Sustainable Agriculture, 5(2): 111-114. \n \n\n\n\n\n\n\n\n3.2 Effect on Seed Germination Percentage (SGP) \n\n\n\nStatistical analysis of Figure 2 showed significant differences in treatments \nat P \u2264 0.05 levels. Results showed that all seed priming treatments were \nfound effective in enhancing the germination percentage compared to \ncontrol. However, among them, the T4 i.e. priming with 200ppm GA3 \nsolution showed a maximum seed germination percentage of 60.12% \n(Table3). T1, T3, and T5 increased germination percentage but were non-\nsignificant among themselves (LSD=4.09). Generally, seed germination \noccurs in three phases: imbibition, lag phase, and radicle growth and \nemergence. The lag phase is prolonged due to seed priming so that pre-\ngermination physiological and biochemical processes take place which \nkeeps the seed from germination. In agreement with other researchers, \nour result also marked a notable increase in germination percentage on \naccount of seed priming (Muhammad & Shik Rha, 2007; Mohammadi et al., \n2014; Oliveira et al., 2019). \n\n\n\n\n\n\n\nFigure 2: Effect of different priming treatments on germination \npercentage of okra seeds. \n\n\n\n3.3 Effect on Germination Rate (GR) and Mean Germination Time \n(MGT) \n\n\n\nThe germination rate and mean germination time varied with different \ntreatments significantly (p<0.05). The maximum germination rate of 7.53 \nseeds/day was observed in the 4th treatment (T4) and the minimum \ngermination rate was 2.95 seeds/day in control. Similarly, mean \ngermination time was found lowest for the 4th treatment (T4) i.e. 6.80 \ndays, and highest for the 3rd treatment (T3) i.e. 10.69 days. The endosperm \nis rapidly utilized for the synthesis of various amino acids and amides in \nGA3 treated seeds (Gupta & Mukherjee, 1982), which could be the cause \nfor the elevated germination rate and reduced mean germination time in \nT4. A highly significant (P\u22640.01) negative correlation (r = -0.675**) was \nfound between GR and MGT representing a trend of increase in Mean \nGermination Time (MGT) with a decrease in Germination Rate (GR) \n(Figure3). \n\n\n\n\n\n\n\nFigure 3: Effect of different priming treatments on germination rate and \nmean germination time of okra seeds. \n\n\n\n3.4 Effect on Seed Vigor Index (SVI) \n\n\n\nSignificant variations were observed due to various seed priming \ntreatments as compared to the control group on seed vigor index (Table \n3). T4 (200ppm GA3 priming) showed a greater influence on seed vigor \nindex, and the germination rate was slightly elevated in T1, T3, and T5 when \ncompared with other treatments. The maximum seed vigor index was \nfound 5772.68 in the 4th treatment (T4) and minimum SVI was found \n2291.19 in control (Figure 4). Correlation between SVI and GP was found \npositive (r = 0.885**) and highly significant (P \u2264 0.01). The enhancement in \nseed vigor in primed seeds might be due to low membrane injury coupled \n\n\n\nwith high enzyme activities (dehydrogenase and amylase) (Pandey et al., \n2017). The enhancement in seed vigor index due to seed priming holds \nclose conformity to other researchers ( Maiti et al., 2011; Tania & Rhaman, \n2020). \n\n\n\n\n\n\n\nFigure 4: Effect of different priming treatments on the Seed Vigor Index \nof okra seeds \n\n\n\n3.5 Effect on Allotropic Coefficient (AC) \n\n\n\nA significant effect was reported in the AC value at a 5% level of \nsignificance due to the seed priming treatments according to our research. \nThe lower AC value suggests that root growth was lower than the shoot \ngrowth; also, it means that plumule/shoot is more receptive to seed \npriming than the radicle/root. Similarly, a higher AC value suggests that \nseed priming has a productive impact on radicle or root growth in \ncomparison to plumule or shoot because AC is resulting from root \nlength/shoot length. The maximum AC value observed was 1.77 in the 3rd \ntreatment(T3) and the minimum AC value observed was 0.97 in the 2nd \ntreatment (T2) (Figure 5). \n\n\n\n\n\n\n\nFigure 5: Effect of different priming treatments on Allotropic Coefficient \nof okra seedlings. \n\n\n\n4. CONCLUSION \n\n\n\nPriming of okra seeds might be the best option to overcome the reduced \nand delayed germination in fresh or stored okra seeds caused by seed \nhardness. Priming of seed before sowing facilitates the plant growth and \ndevelopment and its yield. Okra seed priming with different treatments on \nseed germination and seedling vigor revealed that the GA3 priming was \nbetter than any other treatment whereas hydro priming and tricho-\npriming can be used as an alternative to GA3 priming. So, seed priming is a \nuseful technique for improving the germination percentage, germination \nrate, seedling growth, mean germination time and tolerant to different \nabiotic and biotic factors. However, further research needs to be done to \nknow the impact of seed priming on the morphological characters and \nyield of okra. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors would like to express their special thanks to the Department \nof Horticulture, Agriculture and Forestry University, Rampur Chitwan \nNepal, for providing the resources and for all assistance and guidance \nduring the research. \n\n\n\nREFERENCES \n\n\n\nBajehbaj, A. A. (2010). The effects of NaCl priming on salt tolerance in \nsunflower germination and seedling grown under salinity conditions. \nAfrican Journal of Biotechnology, 9(12), 1764\u20131770. \nhttps://doi.org/10.5897/ajb10.1019 \n\n\n\n\nhttps://doi.org/10.5897/ajb10.1019\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 111-114 \n \n\n\n\n \nCite the Article: Anuj Lamichhane, Mamata K.C., Manisha Shrestha and Binaya Baral (2021). Effect of Seed Priming on Germination of Okra \n\n\n\n(Abelmoschus esculentus var. Arka Anamika). Malaysian Journal of Sustainable Agriculture, 5(2): 111-114. \n \n\n\n\n\n\n\n\nDubey, S. C., Suresh, M., & Singh, B. (2007). Evaluation of Trichoderma \nspecies against Fusarium oxysporum f. sp. ciceris for integrated \nmanagement of chickpea wilt. Biological Control, 40(1), 118\u2013127. \nhttps://doi.org/10.1016/j.biocontrol.2006.06.006 \n\n\n\nEl Balla, M. M. ., Saidahmed, A., & Makkawi, M. (2011). Effect of moisture \ncontent and maturity on hardseededness and germination in okra \n(Abelmoschus esculentus L. Moench). International Journal of Plant \nPhysiology and Biochemistry, 3(6), 102\u2013107. \nhttp://www.academicjournals.org/ijppb \n\n\n\nFarinde, A. J., Owolarafe, O. K., & Ogungbemi, O. I. (2007). An overview of \nProduction, Processing, Marketing and Utilisation of Okra in Egbedore \nLocal Government Area of Osun State, Nigeria. Agric. Engg. \nInternational: The CIGR Ejournal. Manus. 2(9), 1\u201317. \n\n\n\nGupta, P., & Mukherjee, D. (1982). Influence of GA3 pre-soaking of seeds \non biochemical changes in seedling parts of Pennisetum typhoides \nRich. Proceedings of Indian National Science Academy, 45(5), 642\u2013\n648. \n\n\n\nJyoti, B., Gaurav, S. S., & Pant, U. (2016). Use of growth regulators as \npriming agent for improvement of seed vigour in tomato \n(Lycopersicum esculentum). Journal of Applied and Natural Science, \n8(1), 84\u201387. https://doi.org/10.31018/jans.v8i1.752 \n\n\n\nKumar, S., Dagnoko, S., Haougui, A., Ratnadass, A., Pasternak, D., & Kouame, \nC. (2010). Okra (abelmoschus spp.) in west and central africa: \nPotential and progress on its improvement. African Journal of \nAgricultural Research, 5(25), 3590\u20133598. \nhttps://doi.org/10.5897/AJAR10.839 \n\n\n\nLutts, S., Benincasa, P., Wojtyla, L., Kubala, S., Pace, R., Lechowska, K., \nQuinet, M., & Garnczarska, M. (2016). Seed Priming: New \nComprehensive Approaches for an Old Empirical Technique. New \nChallenges in Seed Biology - Basic and Translational Research Driving \nSeed Technology, 1\u201346. https://doi.org/10.5772/64420 \n\n\n\nMaiti, R. K., Vidyasagar, P., Rajkumar, D., Ramaswamy, A., Gonzalez, & \nRodriguez, H. (2011). Seed Priming Improves Seedling Vigor and Yield \nof few Vegetable Crops. International Journal of Bio-Resource and \nStress Management, 2(1), 125\u2013130. \nhttp://www.indianjournals.com/ijor.aspx?target=ijor:ijbsm&volume\n=2&issue=1&article=022 \n\n\n\nMaurya, R. P., Bailey, J. A. B., & Chandler, J. S. A. C. (2013). Impact of Plant \nSpacing and Picking Interval on the Growth, Fruit Quality and Yield of \nOkra (Abelmoschus esculentus (L.) Moench). American Journal of \nAgriculture and Forestry, 1(4), 48. \nhttps://doi.org/10.11648/j.ajaf.20130104.11 \n\n\n\nMereddy, R., Wu, L., Hallgren, S. W., Wu, Y., & Conway, K. E. (2015). Solid \nMatrix Priming Improves Seedling Vigor of Okra Seeds. Proceedings of \nthe Oklahoma Academy of Science, 80(April 2015), 33\u201337. \n\n\n\nMohammadi, G., Khah, E. M., Petropoulos, S. A., Chachalis, D. B., Akbari, F., \n& Yarsi, G. (2014). Effect of Gibberellic Acid and Harvesting Time on \nthe Seed Quality of Four Okra Cultivars. Journal of Agricultural \nScience, 6(7). https://doi.org/10.5539/jas.v6n7p200 \n\n\n\nMohammadikenarmereki, G. (2014). Studies of factors effecting an \nimprovement of seed production and seed quality in okra , seed \nhardness and methods of overcoming this problem. University of \nThessaly. \n\n\n\nMuhammad, J., & Shik Rha, E. (2007). Gibberellic Acid (GA3) Enhance Seed \nWater Uptake, Germination and Early Seedling Growth in Sugar Beet \nunder Salt Stress. Pakistan Journal of Biological Sciences, 10(4), 654\u2013\n658. https://doi.org/10.3923/pjbs.2007.654.658 \n\n\n\nOliveira, C. E. D. S., Steiner, F., Zuffo, A. M., Zoz, T., Alves, C. Z., & De Aguiar, \nV. C. B. (2019). Seed priming improves the germination and growth \nrate of melon seedlings under saline stress. Ciencia Rural, 49(7), 1\u201311. \nhttps://doi.org/10.1590/0103-8478cr20180588 \n\n\n\nPandey, P., Bhanuprakash, K., & Umesha. (2017). Effect of Seed Priming on \nBiochemical Changes in Fresh and Aged Seeds of Cucumber. Journal of \nAgricultural Studies, 5(2), 62. \nhttps://doi.org/10.5296/jas.v5i3.11637 \n\n\n\nPurquerio, L. F. V, Lago, A. A. do, & Passos, F. A. (2010). Germination and \nhardseedness of seeds in okra elite lines. Horticultura Brasileira, \n28(2), 232\u2013235. https://doi.org/10.1590/s0102-\n05362010000200017 \n\n\n\nRadosevich, S., Holt, J., & Ghersa, C. (1997). Weed ecology: implications for \nmanagement. John Wiley and Sons. \n\n\n\nRanal, M. A., & De Santana, D. G. (2006). How and why to measure the \ngermination process? Revista Brasileira de Botanica, 29(1), 1\u201311. \nhttps://doi.org/10.1590/S0100-84042006000100002 \n\n\n\nRezaie, F., & Yarnia, M. (2009). Allelopathic effects of Chenopodium album, \nAmaranthus retroflexus and Cynodon dactylon on germination and \ngrowth of safflower. Journal of Food, Agriculture and Environment, \n7(2), 516\u2013521. \n\n\n\nRhaman, M. S., Rauf, F., Tania, S. S., & Khatun, M. (2020). Seed Priming \nMethods\u202f: Application in Field Crops and Future Perspectives. 5(2), 8\u2013\n19. https://doi.org/10.9734/AJRCS/2020/v5i230091 \n\n\n\nSevik, H., & Guney, K. (2013). Effects of IAA, IBA, NAA, and GA3 on rooting \nand morphological features of melissa officinalis L. stem cuttings. The \nScientific World Journal, 2013(2001). \nhttps://doi.org/10.1155/2013/909507 \n\n\n\nSharma, A. D., Rathore, S. V. S., Srinivasan, K., & Tyagi, R. K. (2014). \nComparison of various seed priming methods for seed germination, \nseedling vigour and fruit yield in okra (Abelmoschus esculentus L. \nMoench). Scientia Horticulturae, 165, 75\u201381. \nhttps://doi.org/10.1016/j.scienta.2013.10.044 \n\n\n\n\u0160tefan\u010di\u010d, M., \u0160tampar, F., & Osterc, G. (2005). Influence of IAA and IBA on \nroot development and quality of Prunus \u201cGiSelA 5\u201d leafy cuttings. \nHortScience, 40(7), 2052\u20132055. \nhttps://doi.org/10.21273/hortsci.40.7.2052 \n\n\n\nTania, S. S., & Rhaman, M. S. (2020). Hydro-priming and halo-priming \nimprove seed germination, yield and yield contributing characters of \nOkra (Abelmoschus esculentus L.). The Journal of the Society for \nTropical Plant Research, 7(1) (April), 86\u201393. \nhttps://doi.org/10.22271/tpr.2020.v7.i1.012 \n\n\n\nTian, Y., Guan, B., Zhou, D., Yu, J., Li, G., & Lou, Y. (2014). Responses of seed \ngermination, seedling growth, and seed yield traits to seed \npretreatment in maize (Zea mays L.). Scientific World Journal, 2014. \nhttps://doi.org/10.1155/2014/834630 \n\n\n\nTompsett, P. B., & Pritchard, H. W. (1998). The effect of chilling and \nmoisture status on the germination, desiccation tolerance and \nlongevity of Aesculus hippocastanum L. seed. Annals of Botany, 82(2), \n249\u2013261. https://doi.org/10.1006/anbo.1998.0676 \n\n\n\nVashisth, A., & Nagarajan, S. (2010). Effect on germination and early \ngrowth characteristics in sunflower (Helianthus annuus) seeds \nexposed to static magnetic field. Journal of Plant Physiology, 167(2), \n149\u2013156. https://doi.org/10.1016/j.jplph.2009.08.011 \n\n\n\n\n\n\n\n \n\n\n\n\nhttps://doi.org/10.1016/j.biocontrol.2006.06.006\n\n\nhttp://www.academicjournals.org/ijppb\n\n\nhttps://doi.org/10.31018/jans.v8i1.752\n\n\nhttps://doi.org/10.5897/AJAR10.839\n\n\nhttps://doi.org/10.5772/64420\n\n\nhttp://www.indianjournals.com/ijor.aspx?target=ijor:ijbsm&volume=2&issue=1&article=022\n\n\nhttp://www.indianjournals.com/ijor.aspx?target=ijor:ijbsm&volume=2&issue=1&article=022\n\n\nhttps://doi.org/10.11648/j.ajaf.20130104.11\n\n\nhttps://doi.org/10.5539/jas.v6n7p200\n\n\nhttps://doi.org/10.3923/pjbs.2007.654.658\n\n\nhttps://doi.org/10.1590/0103-8478cr20180588\n\n\nhttps://doi.org/10.5296/jas.v5i3.11637\n\n\nhttps://doi.org/10.1590/s0102-05362010000200017\n\n\nhttps://doi.org/10.1590/s0102-05362010000200017\n\n\nhttps://doi.org/10.1590/S0100-84042006000100002\n\n\nhttps://doi.org/10.9734/AJRCS/2020/v5i230091\n\n\nhttps://doi.org/10.1155/2013/909507\n\n\nhttps://doi.org/10.1016/j.scienta.2013.10.044\n\n\nhttps://doi.org/10.21273/hortsci.40.7.2052\n\n\nhttps://doi.org/10.22271/tpr.2020.v7.i1.012\n\n\nhttps://doi.org/10.1155/2014/834630\n\n\nhttps://doi.org/10.1006/anbo.1998.0676\n\n\n\n\n \n2.3 Statistical analysis\n\n\nObtained data were analyzed by using MS-Excel and RStudio software and mean comparisons were done by Duncan multiple range tests (DMRT) at 0.05 level of significance.\n\n\n3. RESULT AND DISCUSSION\n\n\n3.1 Effect on the shoot and root length of seedlings\n\n\n3.2 Effect on Seed Germination Percentage (SGP)\n\n\n3.4 Effect on Seed Vigor Index (SVI)\n\n\n4. CONCLUSION\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2018) 15-18 \n\n\n\nCite the article: Khairunnisa Abdul Halim, Ee Ling Yong (2018). Integrating Two -Stage Up-Flow Anaerobic Sludge Blanket With A Single-Stage Aerobic Packed-Bed \nReactorfor Raw Palm Oil Mill Effluent Treatment . Malaysian Journal of Sustainable Agriculture , 1(1) : 15-18. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nAnnually, enormous amount of palm oil mill effluent (POME) ranging between 56.58 to 70.55 million m3 are \nproducedduring the production of palm oil. Its acidic and high organic loading characteristics can cause severe water \npollution if discharged without proper treatment. In Malaysia, most oil palm production mills adopted ponding \ntreatment system. However, this treatment requires high retention time and large build area. Thus, the treatment \nparadigm has shifted tointegrated high rate bioreactors by coupling anaerobic and aerobic processesdue to the \nincompetency of the conventional treatment in complying the standard effluent discharged outlined by the \nDepartment of Environment.Despite the outstanding treatment performance exhibited by this bioreactor, diluted \nPOME was used in almost all previous studies instead of fresh raw POME. Therefore, the researched bioreactors may \nnot be as efficient in real application. This present study aimed to employ the principle of two-stage anaerobic \nprocess followed by a single stage aerobic process for the treatment of fresh raw POME, in whicha two-stage upflow \nanaerobic sludge blanket digester was integrated with a single-stage anaerobic packed bed reactor. This is to ensure \nthe lignocellulosic components will be broken down into simpler organic compounds in the first stage anaerobic \nbioreactor followed by their reduction in the second stage anaerobic and single stage aerobic bioreactors.With this, \nthe potential mechanical problems and inhibition on the operational interference of the currently available \nintegrated system is rectified. Thus, the overall performance can be enhanced.The treatment efficiency of this system \nwas examined by evaluating the removal of several important parameters, namely chemical oxygen demand (COD) \nand sludge reduction reported in terms of total suspended solids (TSS). Throughout the 150 days of operation, \napproximately 93% and 55% of reduction were observed for COD and TSS, respectively, suggesting this integrated \nsystem was competent in treating high strength wastewater.Nonetheless, further research need to be made to \nensure the stability consistency and feasibility of this integrated system.  \n\n\n\n KEYWORDS \n\n\n\nraw palm oil mill effluent, two-stage anaerobic, aerobic, chemical oxygen demand (COD), total suspended solid \n(TSS).\n\n\n\n1. INTRODUCTION \n\n\n\nPalm oil industry is one of the largest agricultural industries in Malaysia \nwhere it accounts for the annual production output of approximately 17 \nmillion tonnes of crude palm oil (CPO) based on Malaysia Palm Oil Board \n(MPOB) report of 2016 [1]. The huge production of the output has \nindirectly caused the increase in the amount of waste that need to be \nmanaged, especially POME. Owing to the nature of POME that has acidic \npH and contains high concentration of organic contaminants, a proper \nwaste management is required to prevent water pollution in the water \nbodies. \n\n\n\nPOME preference that has high concentrations of organic contaminants \nand biodegradable, best to be treated biologically through anaerobic \nprocess. Generally, the anaerobic ponding system has been employed \nconventionally for decades, where almost 80% of the palm oil millers still \npersist with this method, due to low cost and low manpower needed. \nHowever, long retention time and large treatment area interests the \nmillers to start investing on the anaerobic digestion, and widely explored \nby the scholars. Nevertheless, implementation of anaerobic process alone \nwould hardly produce effluents that comply to the standard discharged \nlimit outlined by the Department of Environmental (DOE).   Due to the \nlimitations of the process, the researchers have shifted the paradigm of the \ntreatment technologies towards integrated high-rate bioreactors, by \nassimilating the anaerobic and aerobic process, to enhance the \nperformance of the treatment as well as produce better quality of effluents \n[2-4].  \n\n\n\nFew studies have been conducted on the employment of an integrated \nanaerobic-aerobic system in treating various types of wastewater, and \nshow an excellent performance [2,3,5,6]. Chan and fellow associates \nproved that combination of the anaerobic and aerobic processes in the \nsame reactor attained high removal of organic loadings with 95% of COD \nremoval at high loading rate of 0.5 g-COD/L/day [2]. However, most of the \nresearchers tend to use diluted POME for their studies, to prevent pumps \nclogging [2,7-9]. This caused the concentrations of organic loadings and \ncellulosic compounds were reduced apparently. Meanwhile, the high ratio \nof lignin to cellulose compounds contains in fresh POME inhibits the \nperformance of the integrated system due to degradation difficulties. \nThus, due to that the real application of the integrated system for POME \ntreatment somehow were doubted. \n\n\n\nIn order to overcome the shortcomings of the currently studied integrated \ntreatment system for fresh POME, researchers started to introduce the \nprinciple of multi-stage anaerobic digestion to enhance the performance \nof the contaminant removal via two-stage anaerobic digestion system [10-\n13]. On top of that, the lignocellulosic compounds are believed would be \nbroken down in the first-stage of anaerobic digester, and degraded to the \nsimpler compound in the following anaerobic digester. Furthermore, the \nprinciple of two-stage anaerobic process would enhance the conversion of \nsubstrate, boost the efficiency of COD removal and energy recovery as well \nas has been proven to be competent in treating various industrial \nwastewater including oil-rich waste such as POME [14-16]. \n\n\n\nThus, in this study the principle of coupled two-stage anaerobic and \naerobic process was introduced to treat the fresh POME. The purpose of \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.01.2018.15-18 \n\n\n\nINTEGRATING TWO-STAGE UP-FLOW ANAEROBIC SLUDGE BLANKET WITH A \nSINGLE-STAGE AEROBIC PACKED-BED REACTORFOR RAW PALM OIL MILL \nEFFLUENT TREATMENT \n\n\n\nKhairunnisa Abdul Halim, Ee Ling Yong* \n\n\n\nDepartment of Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bharu, Johor, Malaysia. \n*Corresponding Author email: eeling@utm.my\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2018) 15-18 \n\n\n\nCite the article: Khairunnisa Abdul Halim, Ee Ling Yong (2018). Integrating Two-Stage Up-Flow Anaerobic Sludge Blanket With A Single-Stage Aerobic Packed-Bed Reactorfor \nRaw Palm Oil Mill Effluent Treatment . Malaysian Journal of Sustainable Agriculture , 1(1) : 15-18. \n\n\n\nthis study was to investigate the performance of the integrated two-stage \nup-flow anaerobic sludge blanket (UASB) with a single-stage packed bed \naerobic bioreactor in treating raw POME treatment, which focusing on \nchemical oxygen demand (COD) and total suspended solid (TSS) \nreduction. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Seed sludge preparation \n\n\n\nSludge collected from the facultative pond treating POME of Felda Bukit \nBesar, Wilayah Bukit Besar, Johor, Malaysia was used as a seed sludge for \nall bioreactors in this study. The acclimatized sludge was operated by \ndischarging half volume of cultured broth and refilled to the original \nvolume of the reactor with the addition of: fresh POME (for the AD1); \nPOME discharged from AD1 and aerobic reactor with ratio of 1:1 (for the \nAD2); and POME discharged from AD2 (for the aerobic reactor). Upon \nfeeding the sample into the AD1 and AD2 reactor, the nitrogen gas was \npurged in order to serve anaerobic condition inside the reactor.  The cycle \nwas repeated for every 48 hours until all the reactors stabilizes, in which \nin this study the acclimatization stage took 90 days.  \n\n\n\nFor each start-up, an initial microbial concentration in terms of mixed \nliquor suspended solid (MLVSS) was 13,000 mg-vSS/L, meanwhile the pH \nand total suspended solid (TSS) of the seed sludge sample was 4.6 and 14.9 \ng-SS/L, respectively.\n\n\n\n2.2 Studied wastewater \n\n\n\nThe palm oil mill effluent POME wastewater was collected from Felda \nBukit Besar Palm Oil Mill in Wilayah Bukit Besar, Johor Malaysia. Upon \ncollection, the samples were preserved at temperature below 4\u00b0C to avoid \nbiodegradation of the sample. The following Table 1 shows the \ncharacteristics of the studied wastewater. \n\n\n\nTable 1: Characteristics of POME wastewater \n\n\n\nParameter Concentration \n\n\n\npH 4.6 \nBOD5 756 mg/L \nCOD 239,000 \u2013 431,500 mg/L \nTSS 5,500 mg/L \nAmmoniacal Nitrogen 30 mg/L \nNitrate 895 mg/L \nNitrite 2,000 mg/L \nSulfate 1,050 mg/L \nSulfide 6.0 mg/L \n\n\n\n2.3 Experiment setup \n\n\n\nThe laboratory scale experimental setup consists of two units anaerobic \ndigester (AD) of up-flow anaerobic sludge blanket (UASB) listed as AD1 \nand AD2; and a single-stage aerobic packed bed reactor. The schematic \nexperimental setup was illustrated as in Figure 1. \n\n\n\nFigure 1: Schematic Diagram of Reactor Setup \n\n\n\nIn this system, AD1 reactor was fabricated with 100 mm and 400 mm in \ndiameter and height, respectively; while both AD2 and aerobic reactor \nhave 100 mm and 300 mm of diameter and height, respectively. Although \nAD1 and AD2 underwent the same type of treatment, the dimension of the \nAD2 was smaller compared to the AD1, however, it does not jeopardize the \nperformance. This is because, the nutrients to the bacteria inside the AD2 \nreactor was adequate. The AD1 and AD2 were fabricated with both ends \ncompletely covered, while the aerobic reactor has appended with an air \ndiffuser at the bottom of the reactor. In this study both AD2 and aerobic \nreactor was packed with polypropylene plastic media and has \napproximately 2.2L of liquid volume; whereasthe AD1 reactor has \napproximately about 3.2L of liquid volume, with a headspace of 0.5L for \neach reactor.  \n\n\n\n2.4 Integrated system operation and monitoring \n\n\n\nThe two-stage UASB started to be integrated with an aerobic packed bed \nreactor once the biological stability was attained, which in this study after \n90 days of operation with 48 hours of hydraulic retention time (HRT). \nDuring the operation of the integrated system, the AD1 reactor was fed \nwith filtered fresh POME by using a peristaltic pump (Cole Parmer, USA), \ncontinuously at the flow rate of 1.6 L/day. The unfiltered POME can \nseverely clog the tubing systems of the pumps. The effluent from the AD1 \nwas fed directly into the AD2 reactor, while the effluent in AD2 reactor was \npumpedinto the aerobic reactor. All the reactors possessed the same \nflowrate and HRT of 1.6L and 48 hours, respectively throughout the \noperation. At the same time, the recirculation process also has been  \n\n\n\nemployed from the aerobic reactor to the AD2 reactor, with a flow rate of \n0.6 L/day. The performance of this integrated system was evaluated  \n\n\n\nperiodically based on COD and TSS until 150 days of operation. \n\n\n\n2.5 Measurements and analytical methods \n\n\n\nIn order to monitor the performance of the entire integrated system, the \nCOD and TSS concentration was monitored periodically. The COD \nconcentration was determined by using the Hach High Range Plus, (200-\n15,000 mg/L COD) Reagents.while the TSS concentration by following the \nMethod 2540B from the APHA Standard Method for the Examination of \nWater and Wastewater [17]. \n\n\n\n3. RESULTS AND DISCUSSIONS \n\n\n\nThe results were discussed in two sections, consists of the performance \nduring the start-up phase and during the integrated phase.  \n\n\n\n3.1 Start-up performance \n\n\n\nFigure 2 indicates the performance of a) COD and b) TSS in terms of \npercentage of removal (%), for AD1, AD2 and aerobic reactor during the \nacclimatization phase. Based on Figure 2 a), the AD2 reactor shows \nexcellent performance in removing COD, followed by aerobic and AD1 \nreactor with 87.3%, 41.8% and 34.3%, respectively. This is happening due \nto the lower concentration of COD fed into both AD2 reactor and aerobic \nreactor compared to the AD1 reactor. Meanwhile, from the Figure 2 b), it \nshows that negative removal of TSS was plotted from each reactor during \nthe start-up. This is because of the acclimatization of substrates that lead \nto the increment of the sludge formation. On the other hand, the TSS \nconcentration inside the aerobic reactor shows a decreasing pattern \nthroughout the end of 90 days, indicates that removal of sludge start to \noccur.   \n\n\n\nV = 8L \n\n\n\nRaw \n\n\n\nPOME \n\n\n\n4\n0\n\n\n\n c\nm\n\n\n\n\n\n\n\n10 cm \n\n\n\nV = 3.2L \n\n\n\n10 cm \n\n\n\n10 cm 3\n0\n\n\n\n c\nm\n\n\n\n\n\n\n\n3\n0\n\n\n\n c\nm\n\n\n\n V = 2.2L V = 2.2L \n\n\n\nEffluent \n\n\n\nR P P \n\n\n\nAD1\n\n\n\nAD2 Aerobic\n\n\n\nP \n\n\n\n16\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2018) 15-18 \n\n\n\nCite the article: Khairunnisa Abdul Halim, Ee Ling Yong (2018). Integrating Two -Stage Up-Flow Anaerobic Sludge Blanket With A Single-Stage Aerobic Packed-Bed \nReactorfor Raw Palm Oil Mill Effluent Treatment . Malaysian Journal of Sustainable Agriculture , 1(1) : 15-18. \n\n\n\nFigure 2: a) COD; and b) TSS Removal during the Start-up Process \n\n\n\n3.2 Integrated system performance \nFigure 3 (a) and (b) depicts the COD and TSS removal efficiency of the entire integrated system, respectively.  \n\n\n\nFigure 3: a) COD; and b) TSS Removal during the Integrated System \n\n\n\nReferred to the Figure 3.a), excellent performance of the system was \nrecorded, which approximately 92% to 94.5% of COD removal was \nattained. At the end of 150 days of operation, the effluent concentration of \nCOD of the integrated system was 16,450 mg-COD/L, indicates about 93% \nof COD reduction were achieved.  On top of that, despite of using fresh \nPOME in this study, which have high influent COD concentration, this \nsystem able to performed efficiently.   \n\n\n\nMeanwhile, from the Figure 3.b), the fluctuating trend was spotted from \nthe profile, shows about 15% to 55% of TSS removal was accomplished. \nAt the end of the experiment operational days,  the effluent TSS \nconcentration was 6,700 mg-SS/L, with 55% of SS reduction. However, \nfurther study on this implemented principle on treating sludge reduction \nis vital in order to improve the performance of removal efficiency. \n\n\n\n4. CONCLUSIONS \n\n\n\nThroughout this study, the following conclusions were enclosed: \n\n\n\n1. The principle of the two-stage anaerobic coupled with single stage \naerobic process in treating fresh raw POME was successfully \napplied in this study. Throughout this study, satisfactory reduction \nof COD and TSS was attained, implying this integrated system was \ncompetent in treating high strength wastewater. However, further \nresearch need to be made to ensure the stability consistency and \nfeasibility of this integrated system. \n\n\n\n2. Through the acclimatization stage, negative removal of TSS was \nspotted from each of the reactor. This is due to the acclimatization \nof the substrates during the start-up period. Meanwhile, the AD2 \nreactor shows a better COD removal performance followed by \naerobic and AD1 reactor, which 87.3%, 41.8% and 34.3%, \nrespectively, was attained during the start-up period. \n\n\n\n3. During the integration phase, COD shows a stable trend of profile \nranged about 92-94% of removal. At the same time, the SS profile \nplotted a fluctuated trend ranged between 15% to 55% of removal. \n\n\n\n4. At the end of the 150 days of operating period, excellent removal \nefficiency of COD and SS was achieved with 93% and 55% of \nremoval, respectively. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors would like to thank Felda Bukit Besar, Wilayah Bukit Besar,  \n\n\n\nJohor, Malaysia for providing wastewater and seed sludge and the \nPotential Academic Staff Grant from Universiti Teknologi Malaysia for  \n\n\n\nproviding financial support forthis research. The staffs and technicians of \nEnvironmental Engineering Laboratory, under Department of \nEnvironmental Engineering, Universiti Teknologi Malaysia Johor Bharu, \nMalaysia are also acknowledged. \n\n\n\nREFERENCES \n\n\n\n[1] MPOB. 2017. Malaysian Oil Palm Statistics 2016, 36th Ed. Economics \nAnd Industry Development Division, Malaysian Palm Oil Board, Malaysia. \n\n\n\n[2] Chan, Y.J., Chong, M.F., Law, C.L. 2012. An Integrated Anaerobic\u2013\nAerobic Bioreactor (Iaab) For The Treatment Of Palm Oil Mill Effluent \n(Pome): Start-Up And Steady State Performance. Process Biochemistry, 47 \n(3), 485-495. \n\n\n\n[3] Chan, Y.J., Chong, M.F., Law, C.L. 2013. Optimization Of Palm Oil Mill \nEffluent Treatment In An Integrated Anaerobic-Aerobic Bioreactor. \nSustainable Environment Research, 23 (3), 153-170. \n\n\n\n[4] Chan, Y.J., Chong, M.F., Law, C.L. 2017. Performance And Kinetic \nEvaluation Of An Integrated Anaerobic\u2013Aerobic Bioreactor In The \nTreatment Of Palm Oil Mill Effluent. Environmental Technology, 38 (8), \n1005-1021. \n\n\n\n[5] Chan, Y.J., Chong, M.F., Law, C.L., Hassell, D. 2009. A Review On \nAnaerobic\u2013Aerobic Treatment Of Industrial And Municipal Wastewater. \nChemical Engineering Journal, 155 (1-2), 1-18. \n\n\n\n[6] Wang, R.M., Wang, Y., Ma, G.P., He, Y.F., Zhao, Y.Q. 2009. Efficiency Of \nPorous Burnt-Coke Carrier On Treatment Of Potato Starch Wastewater \nWith An Anaerobic\u2013Aerobic Bioreactor. Chemical Engineering Journal, \n148 (1), 35-40. \n\n\n\n[7] Baranitharan, E., Khan, M. R., Prasad, D. 2013. Treatment Of Palm Oil \nMill Effluent In Microbial Fuel Cell Using Polyacrylonitrile Carbon Felt As \nElectrode. Journal Of Medical And Bioengineering, 2 (4), 252-256. \n\n\n\n[8] Taha, M.R., Ibrahim, A.H. 2014. Cod Removal From Anaerobically \nTreated Palm Oil Mill Effluent (At-Pome) Via Aerated Heterogeneous \nFenton Process: Optimization Study. Journal Of Water Process \nEngineering, 1, 8-16. \n\n\n\n17\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2018) 15-18 \n\n\n\nCite the article: Khairunnisa Abdul Halim, Ee Ling Yong (2018). Integrating Two-Stage Up-Flow Anaerobic Sludge Blanket With A Single-Stage Aerobic Packed-Bed Reactorfor \nRaw Palm Oil Mill Effluent Treatment . Malaysian Journal of Sustainable Agriculture , 1(1) : 15-18. \n\n\n\n[9] Soleimaninanadegani, M., Manshad, S. 2014. Enhancement Of \nBiodegradation Of Palm Oil Mill Effluents By Local Isolated \nMicroorganisms. International Scholarly Research Notices.\nhttp://dx.doi.org/10.1155/2014/727049 \n\n\n\n[10] Choi, W.H., Shin, C.H., Son, S.M., Ghorpade, P.A., Kim, J.J., Park, J.Y. \n2013. Anaerobic Treatment Of Palm Oil Mill Effluent Using Combined \nHigh-Rate Anaerobic Reactors. Bioresource Technology, 141, 138-144. \n\n\n\n[11] Jeong, J.Y., Son, S.M., Pyon, J.H., Park, J.Y. 2014. Performance \nComparison Between Mesophilic And Thermophilic Anaerobic Reactors \nFor Treatment Of Palm Oil Mill Effluent. Bioresource Technology, 165, \n122-128. \n\n\n\n[12] Mamimin, C., Singkhala, A., Kongjan, P., Suraraksa, B., Prasertsan, P., \nImai, T., O-Thong, S. 2015. Two-Stage Thermophilic Fermentation And \nMesophilic Methanogen Process For Biohythane Production From Palm \nOil Mill Effluent. International Journal Of Hydrogen Energy, 40 (19), 6319-\n6328. \n\n\n\n[13] Krishnan, S., Singh, L., Sakinah, M., Thakur, S., Wahid, Z.A., Alkasrawi, \nM. 2016. Process Enhancement Of Hydrogen And Methane Production \nFrom Palm Oil Mill Effluent Using Two-Stage Thermophilic And Mesophilic \nFermentation. International Journal Of Hydrogen Energy, 41 (30), 12888-\n12898. \n\n\n\n[14] Azbar, N., Speece, R.E. 2001. Two-Phase, Two-Stage, And Single-\nStage Anaerobic Process Comparison. Journal Of Environmental \nEngineering, 127, 240-248. \n\n\n\n[15] Cuetos, M.J., G\u00f3mez, X., Escapa, A., Mor\u00e1n, A. 2007. Evaluation And \nSimultaneous Optimization Of Bio-Hydrogen Production Using 32 \nFactorial Design And The Desirability Function. Journal Of Power Sources, \n169 (1), 131-139. \n\n\n\n[16] Hidalgo, D., Mart\u00edn-Marroqu\u00edn, J.M., Sastre, E. 2014. Single-Phase \nAnd Two-Phase Anaerobic Co-Digestion Of Residues From The Treatment \nProcess Of Waste Vegetable Oil And Pig Manure. Bioenergy Research, 7 (2), \n670-680. \n\n\n\n[17] APHA. 2005. Standard Methods For The Examination Of Water And \nWastewater 21th Ed Washington, Dc.: American Public Health Association.\n\n\n\n18\n\n\n\n\nhttp://dx.doi.org/10.1155/2014/727049\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 01-03 \n\n\n\nCite this article *Muhammad Arshadullah1, Muhammad Suhaib1, RaheelBaber1, Malik Usama2, Badar-uz-Zaman1, Imdad Ali Mahmood1and Syed Ishtiaq Hyder 1 \nGrowth of Chenopodium quiona Wild under Naturally Salt Affected SoilsSpaces Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 01-03 \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 August 2016 \n\n\n\nAccepted 12 December 2016 \n\n\n\nAvailable online 20 January 2017 \n\n\n\nKeywords: \n\n\n\nSalinity/ sodicity, Sodium \nAbsorption Ratio, Electrical \nconductivity, halophyte, Salt \ntolerance  and  Quiona  Growth \n\n\n\n  ABSTRACT \n\n\n\nSalinity and sodicity is today one of the most shocking threat in the irrigated agriculture. Mostly this is an abiotic \nstrain that influences germination and plant growth. Quinoa (Chenopodium quinoa Wild.) has garnered much \nattention in recent years because it is an excellent source of plant-based protein and is highly tolerance of soil \nsalinity and sodicity. Protein content in most quinoa accessions has been reported to range from 12 to 17%, \ndepending on variety, environment, and input sit is traditionally called the mother of grains having the potential \nto habitat under high saline sodic conditions environment. The aim of the present protocol was to investigate the \ngermination and growth of quinoa plant under different naturally salt affected soils. Quiona weeds were sown \nin different salt affected soils comparing with a normal soil. A pot experiment was planned using randomized \ncomplete block design with three replicates. Non- significant results regarding germination among different \nnaturally salt affected and normal soils was determined However germination percentage was reduced to 66.8 \n% by soil5 having (SAR= 37.2). In other words Quinoa seeds were germinated up to (SAR= 37.2). Results of Quinoa \nplant height, fresh weight, and dry weight after two weeks were significantly affected by different naturally salt \naffected and normal soils. This study revealed the quiona growth was inversely proportional to the sodium \nabsorption ratio. Reduction in growth parameters was associated with increasing trend of SAR due to the \npresence of excessive salts in plant tissues. \n\n\n\n1. INTRODUCTION \n\n\n\nSoil salinity and sodicity effect brutal harms in agriculture globally, and \nsalt acceptance in crops is an enormously important attribute and a key \nhub of research. Injurious effects of high salinity and sodicity on crops are \ncomprehensive and affect plants in several ways: alteration of metabolic \nprocesses, ion toxicity, drought stress, oxidative stress, nutritional \ndisorders, membrane disorganization and reduction of cell division and \nexpansion [1, 2. 3, 4, 5and 6]. Consequently development and survival of \ncrop growth are retarded [7and 8]. Two major stresses affecting plants \nunder salinity and sodicity are osmotic and ionic stresses. Osmotic stress, \ngoing on at once in the root medium on salts disclosure, can cause in \ninhibition of water uptake, cell expansion and lateral bud development \nand finally disturbs the plant growth as well as other physiological \nprocesses in plant [9]. \n\n\n\nWorldwide area under salt affected soils has been above 800 million \nhectares [10].1.5 million hectares of soils is salinized due to irrigation \nissue \nand improper drainage in Turkey [11].Soil salinity and sodicity is the \nmajor abiotic stress that retards plant growth as well as losses badly the \nproduction nationally and globally [12,13and14] because most crop \nspecies are salt receptive glycophytes [2]. \n\n\n\nSalinity and sodicity are the mainly common ecological bullying to \nworldwide crop production, especially in arid and semi-arid climates, \nwhere land degradation, water shortage and population growth are \nalready a major concern [9 and 15]. More than 800 million ha of land is salt-\naffected, which is over 6% of the world\u2019s land area and this area lowers \nthe economics of the country [16]. Worldwide, salt-affected area is \nincreasing as more and more land is ultimately claimed and irrigated for  \n\n\n\nagricultural production to meet the exponential population growth  \nand stagnant production can be increased with the best utilization \nof these salt- affected lands [17 and 18]. Due to untenable irrigation \npractices, about 1.6 million ha year-1  of irrigated lands become \nsaline and go out of production due  to secondary salinization and \nthis quinoa crop can also tolerate drought stress [13]. The global \nannual cost of salinity is likely to be well over US$12 billion [19]. \nHence, the future of agricultural production will ever more depend \non our ability to grow plants on salt-affected and marginal lands \nusing low (brackish or even saline) waters [20]. Keeping in view, \nthe present study planned to investigate the best salt affected soil \nfor the largely adaptation of C. quinoa. \n\n\n\n2.Material and Methods \n\n\n\nA pot experiment was carried out at NARC Islamabad to see the \nimpact of salinity and sodicity on quinoa growth under different \nnaturally normal and salt- affected soils. The soil samples were \ncollected from different fields at 30 cm depth for the conductance of \npot experiment. Soil samples were prepared for analysis of pH, ECe, \nNa, K, Ca+Mg, Zn, Cu, Fe, Mn and soil texture. SAR of these soils was \ndetermined to qualify their identification according to salt- affected \ntypes. Randomized complete block design was applied with five \ndifferent soils (Table1) with three replications. 350 grams soil was \nused in each pot. Six quinoa seeds were sown in each pot to see the \ngermination, plant height fresh weight and dry weight Ionic \nconcentration in Quinoa plant tissues under different naturally soil \naffected soils after two weeks were determined for quality. \n\n\n\nTable 1: Physico-chemical properties of different naturally normal \n\n\n\nContents List available at RAZI Publishing \n\n\n\nISSN: 2521-2931  (Print) \nISSN: 2521-294X  (Online) \n\n\n\nGrowth of Chenopodium quiona Wild under Naturally Salt Affected Soils \n\n\n\n*Muhammad Arshadullah1, Muhammad Suhaib1, RaheelBaber1, Malik Usama2, Badar-uz-Zaman1, Imdad Ali Mahmood1and \nSyed Ishtiaq Hyder 1 \n\n\n\n1Land Resources Research Institute, National Agricultural Research Centre, Park Road, Islamabad-45500, Pakistan \n2Departmentof Soil & Environmental Sciences, University of Haripur, KPK, Pakistan Correspondence Author;arshadullah1965@gmail.com \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nJournal Homepage: http://www.razipublishing.com/journals/malaysian- journal- of-\n\n\n\nsustainable-agriculture-mjsa/ \n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.01.03\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nmailto:arshadullah1965@gmail.com\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.01.03\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.01.06\n\n\n\n\n\n\n*Muhammad Arshadullah1, Muhammad Suhaib1, RaheelBaber1, Malik Usama2, Badar-uz-Zaman1, Imdad Ali Mahmood1and Syed Ishtiaq Hyder 1 Growth of\nChenopodium quiona Wild under Naturally Salt Affected SoilsSpaces Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 01-03   \n\n\n\nCite this article *Muhammad Arshadullah1, Muhammad Suhaib1, RaheelBaber1, Malik Usama2, Badar-uz-Zaman1, Imdad Ali Mahmood1and Syed Ishtiaq Hyder 1 \nGrowth of Chenopodium quiona Wild under Naturally Salt Affected SoilsSpaces Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 01-03 \n\n\n\n2 \n\n\n\nand salt- affected soils \n\n\n\nProperties Soil1 Soil2 Soil3 Soil4 Soil5\n\n\n\npH 7.43 8.05 9.29 8.77 9.51 \n\n\n\nECe (dSm-1) 1.2 5.5 1.98 1.5 1.43 \n\n\n\nSAR 2.92 15.78 13.04 25.49 37.26 \n\n\n\nNa (mg Kg-1) 6.52 33.04 39.13 52 72.65 \n\n\n\nK (mg Kg-1) 5.90 7.82 5.51 5.51 5.90 \n\n\n\nNa: K 1.10 4.23 7.10 9.44 12.31 \n\n\n\nCa+Mg   (mgKg- \n\n\n\n1) \n\n\n\n10 8.8 18 8.42 6.64 \n\n\n\nZn (mg Kg-1) 13 1.65 0.8 0.3 .004 \n\n\n\nCu (mg Kg-1) 2.5 2.05 1.5 1.55 .031 \n\n\n\nFe (mg Kg-1) 0.45 5.65 .116 2.4 .052 \n\n\n\nMn (mg Kg-1) 2.15 3 1.4 0.95 0.95 \n\n\n\nTexture Sandy \nloam \n\n\n\nSilty \nloam \n\n\n\nloam Clay \nloam \n\n\n\nSilty \nloam \n\n\n\n3.Results and Discussion \n\n\n\nNon- significant results regarding germination among different naturally \nsalt affected and normal soils were indicated in table-2. However \ngermination percentage was reduced to 66.8 % by soil5 having (SAR= 37.2). \nIn other words Quinoa seeds were germinated up to (SAR= 37.2). Tolerance \nof this plant at this increased SAR showed a remarkable utilization of salt \naffected lands for food security Lodging problem of quinoa seedlings   was \n\n\n\nalso noted. The lodging might be due to very thick and weak stem of quinoa \nseedlings. \nResults of Quinoa plant height, fresh weight, and dry weight after two \nweeks were significantly affected by different naturally salt affected and \nnormal soils (Table2). Growth of a plant is a very limiting factor in salt \naffected soils, So the significant increase in all growth parameters showed \nthe well adaptation of this plant against the cancer of soil \ni.e.salinity/sodicity. The maximum plant height (5.55cm) was attained at \nsoil1 (SAR=2.92) followed by 4.50 and 4.50 cm in soil3 and soil 2 respectively \nhaving SAR= 13.04 and 15.78. These two figures are statistically at par with \neach other. Lowest plant height (2.65cm) was attained by Soil5 i.e. SAR= \n37.26. This was confirmed that plant height was decreased as well as the \nSAR value was increased. Maximum fresh weight (7.99mg plant-1) was \ngained by soil1 (SAR=2.92) and it was statistically at par with (7.52mg plant- \n\n\n\n1) in soil 2 having SAR= 15.78. Similarly the maximum dry weight (3.65mg \nplant-1) was recorded in soil1 (SAR=2.92) followed by (3.30 mg plant-1) in \nsoil 2 having SAR= 15.78. High concentration of salts especially sodium ions \nin the soil solution retards plant growth due to reduction in soil water \nosmotic potential and decreasing the growth rate at the end [21]. Further, \nmore amounts of salt existing in the plant tissues will finally create toxic \nlevels in the older transpiring leaves, causing premature senescence and \nreducing the assimilation, and consequently the growth[2 and 21]. \n\n\n\nTable 2: Growth of quinoa plant after two weeks under different natural \nsoil conditions \n\n\n\nTable 3: Ionic concentration in Quinoa plant tissues under different \nnaturally soil affected soils after two weeks \n\n\n\nResults in table-3showed significant Na, K, Na/K, Zn and Fe \nconcentration while Cu and Mn concentrations indicated non-\nsignificant behaviour in Quinoa plant tissues after two weeks under \nnaturally normal and salt- affected soils.. Maximum Na (67.2 ppm) \nwas recorded at soil 5with SAR=37.2 and lowest (12.9 ppm) in the \nnormal soil1. Na/K was maximum (0.57) at soil 5with SAR=37.2 and \nthe least 0.15bythe normal soil1.Fe was recorded the maximum \n(62.5 ppm) at the normal soil1and lowest (12.9 ppm) in the normal \nsoil1. Na/K was maximum (0.57) at soil 5with SAR=37.2 and the least \n0.15 by soil 5with SAR=37.2 while Cu and Mn showed non- \nsignificant results. Maximum utilization of toxic salt improves soil \nhealth and better utilization of this marginal soil for medium salt \ntolerance crops. \n\n\n\n4.Conclusion \nIn other words Quinoa seeds were germinated up to (SAR= 37.2). \nResults of Quinoa plant height, fresh weight, and dry weight after \ntwo weeks were significantly affected by different naturally salt \naffected and normal soils. This study revealed the quiona growth \nwas inversely proportional to the sodium absorption ratio. \nReduction in growth parameters was associated with increasing \ntrend of SAR due to the presence of excessive salts in plant tissues. \nFinally this plant can provide a great jump for the utilization of \nhighly salt \u2013 affected lands  in environmentally approach. \n\n\n\nReferences \n\n\n\n[1] Hasegawa PM,    RA Bressan ,    JK Zhu , HJ  ,Bohnert,2000. \nPlant cellular and molecular responses to high salinity. Annual \nReview of Plant Physiology and Plant Molecular Biology; 51:463-\n499. \n\n\n\n[2] Munns,  R.,  2002.  Comparative  physiology  of  salt   and \nwater stress. Plant Cell Environ. 25, 239\u2013250. \n\n\n\n[3] Muscolo A, M Sidari, C Santonoceto, U Anastasi, G    Preiti. \n2007. Response of four genotypes of lentil to salt stress conditions. \nSeed Science and Technology 35:497\u2013503. \n\n\n\n[4] Muscolo  A,  MR  Panuccio,A  Heshel.   2013. \nEcophysiology  of Pennisetum clandestinum: a valuable salt tolerant \ngrass. Environmental and Experimental Botany 92:55\u201363. \n\n\n\n[5] Zhu JK. 2007. Plant salt stress. In: O\u2019Daly A, ed. \nEncyclopedia   of \n\n\n\nlife sciences. Chichester: John Wiley & Sons, Ltd, 1\u20133. \n\n\n\n[6] Sidari  M,  C  Santonoceto,  UAnastasi,  GPreiti, AMuscolo. \n2008. Variations in four genotypes of lentil under NaCl-salinity \nstress. American Journal of Agriculture and Biological Science \n3:410\u2013416. \n\n\n\n[7] Muscolo A, M  Sidari, MRPanuccio, C  Santonoceto, F, \nOrsini, S DePascale. 2011. Plant responses in saline and semi arid \nenvironments: an overview. The European Journal of Plant Science \nand Biotechnology 5:1\u2013 11. \n\n\n\n[8] Schleiff Uand AMuscolo. 2011. Fresh look at plant salt \ntolerance as affected by dynamics at the soil/root-interface using \nLeek and Rape as model crops. The European Journal of Plant \nScience and Biotechnology 5:27\u201332. \n\n\n\n[9] Munns  R,  M  Tester.  2008.  Mechanisms  of  salinity \ntolerance. Annual Review of Plant Biology 59:651\u2013681. \n\n\n\n[10] Rengasamy, P. 2010. Soil processes affecting crop \nproduction in salt-affected soils. Functional Plant Biology, 37: 613-\n620 \n\n\n\n[11] FAO,  2014.  Irrigation  in  the  Middle  East  region    in \n\n\n\nfigures- AQUASTAT Survey 2008. \n\n\n\nhttp://www.fao.org/nr/water/aquastat/countries_regions/tur/T\n\n\n\nUR- CP_eng.pdf \n\n\n\n\nhttp://www.fao.org/nr/water/aquastat/countries_regions/tur/TUR-CP_eng.pdf\n\n\nhttp://www.fao.org/nr/water/aquastat/countries_regions/tur/TUR-CP_eng.pdf\n\n\nhttp://www.fao.org/nr/water/aquastat/countries_regions/tur/TUR-CP_eng.pdf\n\n\n\n\n\n\n*Muhammad Arshadullah1, Muhammad Suhaib1, RaheelBaber1, Malik Usama2, Badar-uz-Zaman1, Imdad Ali Mahmood1and Syed Ishtiaq Hyder 1 Growth of\nChenopodium quiona Wild under Naturally Salt Affected SoilsSpaces Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 01-03   \n\n\n\nCite this article *Muhammad Arshadullah1, Muhammad Suhaib1, RaheelBaber1, Malik Usama2, Badar-uz-Zaman1, Imdad Ali Mahmood1and Syed Ishtiaq Hyder 1 \nGrowth of Chenopodium quiona Wild under Naturally Salt Affected SoilsSpaces Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 01-03 \n\n\n\n3 \n\n\n\n[12]Pitman,M.G.,andAL\u00e4uchli,.,2002..\u201cGlobalimpactofsalinityandagri\ncultur alecosystems,\u201dinSalinity:Environment-Plants- \nMolecules,edsA.L\u00e4uchliandU.L\u00fct(Netherlands:Springer),3\u201320. \n\n\n\n[13] Tanji  KK  2002.Salinity  in the  soil  environment.  In: \nL\u00e4uchli A. L\u00fcttge U (ed) Salinity: environment\u2013 plants\u2013 molecules.\nKluwer Academic Publishers, Dordrecht, pp 21\u201325 \n\n\n\n[14] Hu   YC   and   U   Schmidhalter,2005.   Drought   and salinity: \na comparison of their effects on mineral nutrition of plants\u2019 Plant Nutr \nSoil Sci 168:541\u2013549 \n\n\n\n[15] Geissler N, S Hussin, HWKoyro, 2010.Elevated atmospheric \nCO2 concentration enhances salinity tolerance in Aster tripoliumL. \nPlanta 231:583\u2013594 \n\n\n\n[16] Rengasamy   P   2006.   World   salinization   with emphasis \non Australia. J Exp Bot 57:1017\u201323 \n\n\n\n[17] Lambers H 2003. Dry land salinity: A key environmental \nissue in southern AustraliaIntroduction. Plant Soil 257: VVii \n\n\n\n[18] Munns   R.,   2005.   Genes   and   salt   tolerance: bringing \nthem together. New Phytol 167: 645\u201363 \n\n\n\n[19] Qadir  M,  A  Tubeileh,  J  Akhtar,ALarbi,  PS  Minhas,    MA \nKhan 2008.Productivity enhancement of salt-affected environments \nthrough crop diversification. Land Degradation Development. 19:429\u2013\n453 \n\n\n\n[20] Rozema  J  and  TJ  Flowers,  2008.Crops  for  a  salinized \nworld. \n\n\n\nScience 322:1478\u20131480 \n\n\n\n[21] Munns, R., 2009: Strategies for crop improvement in saline \nsoils. In: M. Ashraf, M. Ozturk, and H. R. Athar, eds. Salinity and Water \nStress: Improving Crop Efficiency, pp. 99\u2013110. Springer, The \nNetherlands. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 26-28 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.26.28 \n\n\n\n \nCite the Article: Nur Atikah Azhar, Zarina Zainuddin (2020). Tissue Culture Of Ficus Carica Variety Btm-6.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 26-28. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2020.26.28 \n\n\n\n\n\n\n\n \nTISSUE CULTURE OF Ficus carica VARIETY BTM-6  \n \nNur Atikah Azhara, Zarina Zainuddinb* \n \na Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Jalan Sultan Ahmad Shah, Bandar Indera \n\n\n\nMahkota, 25200 Kuantan, Pahang, Malaysia \nb Department of Plant Science, Kulliyyah of Science, International Islamic University Malaysia, Jalan Sultan Ahmad Shah, Bandar Indera \n\n\n\nMahkota, 25200 Kuantan, Pahang, Malaysia \n\n\n\n*Corresponding Author Email: zzarina@iium.edu.my \n\n\n\n  \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 01 December 2019 \nAccepted 06 January 2020 \nAvailable online 05 February 2020 \n\n\n\n\n\n\n\nFicus carica or commonly known as fig plant is a deciduous plant originated from southwest Asia and eastern \n\n\n\nMediterranean. It has many benefits in medical field especially to treat diseases such as rheumatism and \n\n\n\nhaemorrhoids due to its high laxative activity effect. The main objective of this study is to develop in vitro \n\n\n\nclonal propagation method for rapid production of Ficus carica variety BTM-6 plantlet using different plant \n\n\n\ngrowth regulators (PGRs) through shoot induction and multiplication, rootings and subsequent \n\n\n\nestablishment in soil following acclimatization. Surface sterilisation of the explant was done using sodium \n\n\n\nhypochlorite as the disinfectant. Pre-treatment of the explants with carbendazim successfully reduced the \n\n\n\noccurrence of fungal contamination. To investigate the effect of plant growth regulators on shoot induction, \n\n\n\nexplants were cultured in different concentrations of PGRs either singly or in combination. No shoot and root \n\n\n\ninductions were observed but calli were successfully induced on MS medium containing 2 mg/l BA only, 2 \n\n\n\nmg/l BA in combination with 0.5 mg/l NAA and MS media supplemented with 0.5 mg/l BA in combination \n\n\n\nwith 0.5 mg/l NAA. A further in-depth study using other different types of plant growth regulators at various \n\n\n\nconcentrations is required in order to establish a complete tissue culture protocol of this particular plant \n\n\n\nspecies. \n\n\n\n\n\n\n\nKEYWORDS \n\n\n\nFicus carica, callus, BA, NAA. \n\n\n\n1. INTRODUCTION \n\n\n\nFicus carica or commonly called as fig is a deciduous plant from the family \n\n\n\nof Moraceae and originated from southwest Asia and eastern \n\n\n\nMediterranean. The commercial fig is originally from all around the world \n\n\n\nincluding the Mediterranean region, Australia, China, Hungary, England \n\n\n\nand Turkey (Mawa et al., 2013; Qrunfleh et al., 2013). Various forms of figs \n\n\n\ncan be found, either tall and large trees, small trees, bushes or shrubs with \n\n\n\nextended roots and with simple, alternate, entire or lobate leaves (Soliman \n\n\n\net al., 2010). The matured fruit of fig has tough skin and will crack upon \n\n\n\nripeness. It has mass-bound seeds with outer ring in white and maroon \n\n\n\nred jelly-like flesh. \n\n\n\nFig has many benefits to human especially in medical field. Figs are very \n\n\n\nuseful in treating different diseases including hemorrhoids and \n\n\n\nrheumatism (Paknahad and Sharafi, 2015). Figs are among plant species \n\n\n\nthat have been mentioned in the Holy Quran besides olives, grapes, \n\n\n\npomegranates and dates. Few hadiths narrated about the benefits of figs \n\n\n\nmentioned by Prophet Muhammad (PBUH). Abu ad-Darda\u2019 A.S narrated \n\n\n\nthat the Prophet (PBUH) said: \n\n\n\n\u201cIf I could say that a fruit was sent down from Heaven (to earth), I would say \n\n\n\nit is figs, because the Heaven's fruit has no stones. Eat it, as it cures \n\n\n\nhemorrhoids and it is useful for treating gout\" (Shahih al-Bukhari) \n\n\n\nTherefore, it is obvious that figs are beneficial in Islamic perspectives. \n\n\n\nAbundant phenolic compounds in the leaf of fig plant has made it beneficial \n\n\n\nto treat diabetes, hepatic and renal stones. The extract of fig leaves was \n\n\n\nbelieved to have anti-diabetic hence reducing glucose level (Ibrahim et al., \n\n\n\n2009). \n\n\n\nFicus carica is usually propagated using cuttings, either from softwood \n\n\n\ncuttings or hardwood cuttings (Sousa et al., 2013). Sexual propagation by \n\n\n\nusing seeds is not preferred because seeds of fig are nonviable (Qrunfleh \n\n\n\net al., 2013). Propagation and breeding of woody plants has been widely \n\n\n\ndone using tissue culture technique. Successful case of plantlet \n\n\n\nregeneration from apical/axillary buds and nodal explants either with or \n\n\n\nwithout encapsulation through tissue culture of mulberry (also a member \n\n\n\nof family Moracae) has been reported (Mustafa and Taha, 2012). Due to \n\n\n\nthe problem of nonviable seeds and also the demand in fig is increasing, it \n\n\n\nis necessary to find alternative methods for fig propagation, specifically \n\n\n\nthrough tissue culture technique that can speed up the rate of propagation \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 26-28 \n\n\n\n\n\n\n\n \nCite the Article: Nur Atikah Azhar, Zarina Zainuddin (2020). Tissue Culture Of Ficus Carica Variety Btm-6.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 26-28. \n \n\n\n\n\n\n\n\nby using various parts of the plant as the explants with the aid of plant \n\n\n\ngrowth regulators for shoot and root induction. \n\n\n\n2. MATERIAL AND METHODS \n\n\n\n2.1 Mother plant preparation \n\n\n\nA healthy growing Ficus carica variety BTM-6 was obtained from nursery \n\n\n\nof Kulliyyah of Science as a mother plant. Healthy shoots with at least 3 \n\n\n\nnodes were selected for vegetative propagation to ensure the availability \n\n\n\nof explants for in vitro culture. The stems with shoots were cut in slant, \n\n\n\nmoistened with water and dipped in rooting powder prior to inoculation \n\n\n\ninto peat moss in stem propagation cups. The cups were placed under \n\n\n\ncomplete shade in a misting room and allowed to grow adventitious roots \n\n\n\nfor two weeks. Water was sprayed on peat moss twice daily. \n\n\n\nAfter two weeks, the plantlets were planted in polybags filled with \n\n\n\napproximately 5 kg soil. Then, the plants were grown under fully shade in \n\n\n\nnatural growth conditions at Kulliyyah of Science\u2019s nursery. Each polybag \n\n\n\nwas watered once daily and fertilized weekly. After approximately 3 \n\n\n\nmonths, mother plants were ready for the explants collection for in vitro \n\n\n\nclonal propagation. \n\n\n\n2.2 Surface sterilization of explants and culture initiation \n\n\n\nHealthy and young leaves were collected from 2-3 months old field grown \n\n\n\nfig. For surface sterilization, initially, the explants were washed \n\n\n\nthoroughly under running tap water for overnight. Then, the explants \n\n\n\nwere pre-treated using fungicide (0.2% of Carbendazim) for 30 minutes. \n\n\n\nAfter 30 minutes of incubation in fungicide, the explants were rinsed \n\n\n\nthoroughly using distilled water and finally with sterile distilled. To \n\n\n\ninitiate the surface sterilization steps, the explants were soaked in 70% \n\n\n\n(v/v) ethanol for 15 seconds. Then, the explants were incubated in 2.5% \n\n\n\n(v/v) sodium hypoclorite for 15 minutes and lastly, the explants were \n\n\n\nrinsed using sterile distilled water for three times.  \n\n\n\nAfter surface sterilization, the explants were dried using sterilized filter \n\n\n\npaper. The leaves were cut approximately 1cm x 1cm using sterilized \n\n\n\nscalpel. Then, the explants were introduced in MS basal medium \n\n\n\nsupplemented with 30 g/l sucrose and 8% agar to evaluate the response \n\n\n\nof explants on different treatments.  \n\n\n\n2.3 Shoot induction and multiplication \n\n\n\nAfter surface sterilization, the leave cuttings were cultured in different \n\n\n\nconcentrations of benzyladenine (BA) (0.5, 1.0, 1.5 and 2.0 mg/l) either \n\n\n\nsingly or in combination with 0.5 mg/l kinetin (Kn) or 0.5 mg/l 1-\n\n\n\nnaphthaleneacetic acid (NAA), for shoot induction and multiplication. The \n\n\n\nMS medium without PGR was used as control. All culture boxes were \n\n\n\nincubated in a growth room with 25\u00b12\u00b0C temperature; humidity 60-70%; \n\n\n\n16/8 hours day/light cycle with light intensity of 2500 lx (white \n\n\n\nluminescence bulb). \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\nInitially in this study, high level of contamination occurred where fungal \n\n\n\ncontamination was really severe. To overcome the problem with fungal \n\n\n\ncontamination, explants were pre-treated with fungicide. 0.2% \n\n\n\ncarbendazim was used as a pre-treatment. It was observed that with \n\n\n\ncarbendazim pre-treatment, contamination only occurred after few weeks \n\n\n\nof culturing and the number of contaminated culture boxes were also \n\n\n\nminimized (Figure 1). Carbendazim is a fungicide that has wide spectrum \n\n\n\nand has been used to prohibit fungal growth in the production of edible \n\n\n\nmushroom (Xia et al., 2016). This fungicide is very useful for a wide range \n\n\n\nof plant diseases, but the effectiveness is limited due to lack of aqueous \n\n\n\nactivity (Leng et al., 2014). \n\n\n\n\n\n\n\nFigure 1: Culture: A) Contaminated with fungi without pre-treatment \n\n\n\nwith carbendazim and B) Clear from contamination after pre-treatment \n\n\n\nwith carbendazim \n\n\n\nResults for shoot induction is summarized in Table 1. Instead of producing \n\n\n\nshoots on all media tested for shoot induction, only formation of calli coud \n\n\n\nbe observed. Calli were successfully induced from treatments of MS \n\n\n\nmedium supplemented with 2.0 mg/l BA, 0.5 mg/l BA + 0.5 mg/l NAA and \n\n\n\n2.0 mg/l BA + 0.5 mg/l NAA (Figure 2). Results obtained in this study was \n\n\n\nin contrast with previous work where the best response of shoot induction \n\n\n\nand multiplication was obtained on MS medium supplemented with 2.0 \n\n\n\nmg/l BA + 0.5 mg/l NAA (Howlader et al., 2014). It was also observed that \n\n\n\nthere was no significant difference on the production of shoots among the \n\n\n\ndifferent varieties. The effectiveness of BA to induce callus formation was \n\n\n\nalso reported by other researchers. For example, callus was found to grow \n\n\n\nrapidly in saline conditions supplemented with PGR resulted in different \n\n\n\ngenotypic of calli (Benderradji et al., 2011). Embryonic calli could easily \n\n\n\nformed in medium supplemented with the addition of cytokinin; BAP at \n\n\n\nlow concentration (Bradley et al., 2001). A study on the effect of different \n\n\n\ntypes of media and carryover effect on common fig cultivars was \n\n\n\nconducted. The types of culture media used were Murashige and Skoog \n\n\n\n(MS), Woody Plant Medium (WPM) and Olive Medium (OM). Calli and \n\n\n\nshoots were successfully developed on all types of media supplemented \n\n\n\nwith 1 mg/l BA as the PGR (Al-Shomali et al., 2017). Another study on in \n\n\n\nvitro propagation of fig cultivars found that the best proliferation of shoots \n\n\n\nwas on MS media containing 1.0 mg/l BA compared to kinetin. Aboudi \n\n\n\ncultivar was the best proliferated using cytokinins compared to other \n\n\n\ncultivars which were Sultani and Conadria (Ahmed-Amen et al., 2014). \n\n\n\nTable 1: Response of explants towards different concentrations and \n\n\n\ncombinations of plant growth regulator(s) \n\n\n\nConcentration of Plant Growth \n\n\n\nRegulators \n\n\n\nCallus Induction \n\n\n\n0 mg/l BA No \n\n\n\n0.5 mg/l BA No \n\n\n\n1.0 mg/l BA No \n\n\n\n1.5 mg/l BA No \n\n\n\n2.0 mg/l BA Yes \n\n\n\n0.5 mg/l BA + 0.5 mg/l NAA Yes \n\n\n\n1.0 mg/l BA + 0.5 mg/l NAA No \n\n\n\n1.5 mg/l BA + 0.5 mg/l NAA No \n\n\n\n2.0 mg/l BA + 0.5 mg/l NAA Yes \n\n\n\n0.5 mg/l BA + 0.5 mg/l Kin No \n\n\n\n1.0 mg/l BA + 0.5 mg/l Kin No \n\n\n\n1.5 mg/l BA + 0.5 mg/l Kin No \n\n\n\n2.0 mg/l BA + 0.5 mg/l Kin No \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Compact callus induced from leaf explants after 5 weeks of \n\n\n\nculture on MS medium supplemented with A) 2.0 mg/l BA, B) 0.5 mg/l \n\n\n\nBA + 0.5 mg/l NAA and C) 2.0 mg/l BA + 0.5 mg/l NAA \n\n\n\n4. CONCLUSION \n\n\n\nAn optimum protocol for surface sterilization of F. carica leave explants \n\n\n\nhas been established to reduce contaminations where the explants were \n\n\n\npre-treated with 0.2% carbendazim (fungicide) and further sterilized with \n\n\n\n2.5% sodium hypochlorite as the disinfectant. Overall, calli have been \n\n\n\nsuccessfully induced on MS media supplemented with 2.0 mg/l BA singly \n\n\n\nand also on MS media supplemented with 0.5 mg/l BA in combination with \n\n\n\n0.5 mg/l NAA and 2.0 mg/l BA in combination with 0.5 mg/l NAA. \n\n\n\nREFERENCES \n\n\n\nAhmed-Amen, K.I., Shaaban, M.M., El-Azab, D.S. and Fathy, R.M. 2014. In \n\n\n\nvitro propagation of some fig (Ficus carica L.) cultivars. International \n\n\n\nJournal of Academic Research, Part A, 6(6), 382-389. \n\n\n\nAl-Shomali, I., Sadder, M.T. and Ateyyeha, A. 2017. Culture media \n\n\n\ncomparative assessment of common fig (Ficus carica L.) and carryover \n\n\n\neffect. Jordan Journal of Biological Sciences, 10(1). \n\n\n\nA B\n\n\n\nA B C\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 26-28 \n\n\n\n\n\n\n\n \nCite the Article: Nur Atikah Azhar, Zarina Zainuddin (2020). Tissue Culture Of Ficus Carica Variety Btm-6.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 26-28. \n \n\n\n\n\n\n\n\nBenderradji, L., Brini, F., Kellou, K., Ykhlef, N., Djekoun, A., Masmoudi, K. \n\n\n\nand Bouzerzour, H. 2011. Callus induction, proliferation, and plantlets \n\n\n\nregeneration of two bread wheat (Triticum aestivum L.) genotypes \n\n\n\nunder saline and heat stress conditions. ISRN Agronomy. \n\n\n\nBradley, D.E., Bruneau, A.H. and Qu, R. 2001. Effects of cultivar, explant \n\n\n\ntreatment, and medium supplements on callus induction and plantlet \n\n\n\nregeneration in perennial ryegrass. Int. Turfgrass Soc. Res. J., 9, 152-\n\n\n\n156. \n\n\n\nHowlader, S.M., Fuller, M.P., Hemaid, I.A.S., Al-Zahrani, H.S. and Metwali, \n\n\n\nE.M.R. 2014. Influence of different concentrations of slat stress on in \n\n\n\nvitro multiplication of some fig (Ficus carica L.) cultivars. Life Science \n\n\n\nJournal, 11(10), 386-397. \n\n\n\nIbrahim, K.M., Al-Shawi, N.N. andAl-Juboury, R.A. 2009. A study on the \n\n\n\nhypoglycemic effect of Ficus carica L. leaves aqueous extract against \n\n\n\nalloxan-induced diabetes in rabbits. Medical Journal of Babylon, 6(3-4). \n\n\n\nLeng, P., Zhang, Z., Li, Q., Zhao, M. and Pan, G. 2014. Microemulsion \n\n\n\nformulation of carbendazim and its in vitro antifungal activities \n\n\n\nevaluation. PloS One, 9(10), e109580. \n\n\n\nMawa, S., Hussain, K. and Jantan, I. 2013. Ficus carica L. (Moraceae): \n\n\n\nphytochemistry, traditional uses, and biological activities. Evidence-\n\n\n\nBased Complementary and Alternative Medicine. \n\n\n\nMustafa, N.S. and Taha, R.A. 2012. Influence of plant growth regulators and \n\n\n\nsubculturing on in vitro multiplication of some fig (Ficus carica) \n\n\n\ncultivars. Journal of Applied Sciences Research, 8(8), 4038-4044. \n\n\n\nPaknahad, A. and Sharafi, M. 2015. Benefits of fig as viewed by Islam and \n\n\n\nmodern medicine. International Journal of Agriculture and Crop \n\n\n\nSciences, 8(5), 682-685. \n\n\n\nQrunfleh, I.M., Shatnawi, M.M. and Al-Ajlouni, Z.I. 2013. Effect of different \n\n\n\nconcentrations of carbon source, salinity and gelling agent on in vitro \n\n\n\ngrowth of fig (Ficus carica L.). African Journal of Biotechnology, 12(9). \n\n\n\nSoliman, H.I., Gabr, M. and Abdallah, N.A. 2010. Efficient transformation \n\n\n\nand regeneration of fig (Ficus carica L.) via somatic embryogenesis. GM \n\n\n\nCrops, 1(1), 40-51. \n\n\n\nSousa, C.M., Busquet, R.N., Vasconcellos, M.A.D.S. and Miranda, R.M. 2013. \n\n\n\nEffects of auxin and misting on the rooting of herbaceous and hardwood \n\n\n\ncuttings from the fig tree. Revista Ci\u00eancia Agron\u00f4mica, 44(2), 334-338. \n\n\n\nXia, E., Tao, W., Yao, X., Wang, J. and Tang, F. 2016. Effects of processing on \ncarbendazim residue in Pleurotus ostreatus. Food Science & Nutrition, \n4(4), 645-650.  \n\n\n\n \n\n\n\n\n\n"
"\n\nCite the article: Sanjay Mahato, Susmita Bhuju, Jiban Shrestha (2018). Effect Of Trichoderma Viride As Biofertilizer On Growth And Yield Of Wheat. \nMalaysian Journal of Sustainable Agriculture, 2(2) : 01-05.\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ     \n\n\n\n   ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nThis experiment was conducted to find out the effects of Trichoderma viride on growth and yield of wheat at \nInstitute of Agriculture and Animal Science, Lamjung Campus, Sundarbazar, Lamjung during December 2016 \u2013 \nApril 2017. The experiment consisted of seven treatments; (T1: Control; T2: Soil + NPK; T3: Soil inoculated \nTrichoderma; T4: Trichoderma + FYM; T5: Trichoderma + \u00bd NPK; T6: Trichoderma + NPK and T7 = Trichoderma \n+ NPK + FYM) laid out in completely randomized design (CRD) with three replications. The results showed that \nTrichoderma viride increased the plant height (4.6%), root weight (1.5%), leaf length (0.3%), panicle weight \n(9.1%), number of grains (3.8%), grain yield (36.5%), biological yield (13.7%), and biomass yield (2.7%) over \ncontrol; while root length (-17.4%), number of leaves (-8.4%), tiller number (-10.8%), panicle number (-6.7%), \npanicle length (-8.4%) highlighted the negative impact of T. viride on wheat plant. T. viride displayed antagonism \nwith inorganic fertilizer. When T. viride and NPK were accompanied with farmyard manure, most of the growth \nand yield parameter showed the highest value. Though Trichoderma viride decreases several growth \nparameters, it still can be used as biofertilizer which increases the grain yield. Using T. viride with a full dose of \nNPK during sowing stage may not be efficient and economical in terms of productivity. Introducing farmyard \nmanure to T. viride gives better yield than T. viride alone.\n\n\n\n KEYWORDS \n\n\n\nTrichoderma viride, wheat, NPK, grain yield.\n\n\n\n1. INTRODUCTION\n\n\n\nWheat (Triticum aestivum) is the most extensively grown cereal crop in \nthe world. The optimum temperature for vegetative growth is 16-22 \u00b0C \nand requires about 14-15 \u00b0C optimum average temperature at the time \nof maturity. Temperatures above 25 \u00b0C during this period tend to \ndecrease grain weight. Wheat can be grown successfully in those regions \nwhere annual rainfall varies from 25 to 150 cm [1]. Wheat is an \nimportant non-leguminous crop which requires a high input of chemical \nfertilizers. The nutrients removal principally NPK by the wheat crop is \n227 kg/ha [2]. Nitrogen (N) is the most limiting nutrient for wheat \nproduction that affects the speedy plant growth and improves grain yield \n[3].\n\n\n\nTrichoderma species are the fungi that are present in nearly in all soils \nand other habitats. Trichoderma species include T. harzianum, T. viride, T. \nkoningii, T. hamatum and other species [4, 5]. Trichoderma colonizes the \nroot surface or cortex and proliferate best when there are abundant \nhealthy roots [6]. They have evolved numerous mechanism for both \nattacks of the fungi and for enhancing the root growth [7]. Trichoderma \nhas the capacity to produce antibiotics, parasitize other fungi, and \ncompete with deleterious microorganisms which were considered to be \nthe basis for how Trichoderma exert beneficial effects on plant growth \nand development [8]. The benefits of Trichoderma species in improving \nplant growth can be realized through several mechanisms which include \nmycoparasitism, antibiosis, degradation of toxins, inactivation of \npathogenic enzymes pathways, resistance against pathogens, enhanced \nnutrient uptake, solubilization, sequestration of inorganic nutrients and \nenhanced root hair development [9, 10]. Trichoderma helps to increase \nplant hormone which helps to increase root growth and root hair \nformation that results in the more efficient use of nitrogen, phosphorus, \npotassium and micronutrient andincrease seedling vigor and \ngermination [11].\n\n\n\nA group researcher documented that Trichoderma harzianum and \nTrichoderma asperellum are highly rhizosphere competent and able to \nstimulate the growth and immune defense of plants [8]. Some of \nresearcher reported that Trichoderma harzianum has the ability to \nsolubilize phosphate and micronutrients that could be made available to \n\n\n\nto plant [12]. Trichoderma harzianum and Trichoderma viride enhanced \nrice and tomato root and shoot length [13, 14]. Seed germination, root \nlength, shoot length, fresh weight, dry weight, and vigor index were \nsignificantly increased by T. viride and P. fluorescens [15]. Research has \nfound that the corn plant colonized with Trichoderma strain T22 \nrequires 40% less nitrogen fertilizer than the plants which lack these \nfungi and, hence, helps to minimize the damage to the environment [16]. \nSome group researchers also documented that recommended dose of \nNPK and 50% biofertilizer and compost + 50% NPK showed similar \neffects on growth, dry matter and yield of mustard [17]. The seed yield \nper plant was increased by 5.34% over the recommended dose of NPK \napplied. Trichoderma longibrachiatum has a higher potential of parasitic \nand lethal effects against Heterodera avenae, but its effects on wheat are \nfairly high in promoting plant growth and nematode control [18]. One \nstrain of Trichoderma increases the numbers of deep roots at as much as \na meter below the soil surface [19]. These deep roots cause crops like \ncorn and ornamental plants like turf grass to become more resistant to \ndrought. \n\n\n\nThe application of Trichoderma harzanium T22 increased all measured \nparameters such as growth parameters, chlorophyll content, starch \ncontent, nucleic acids content, total protein and phytohormone of maize \nplant [20]. Another group researchers found that Trichoderma was able \nto enhance rice growth components such as plant height, leaf number, \ntiller number, root length and shoot fresh weight [21]. Other researchers \nreported that elicitors released by Trichoderma are involved in \ntriggering expressions of defense protein within the plant to induce plant \nimmunity against pathogens and, in turn, improve plant growth [22]. \nTrichoderma koningi that colonized the roots of Lotus japonicas was \nfound to produce is flavonoid and phytoalexinvesitol and increase plant \ndry weight [23]. Since a few works is done to understand the impact of \nTrichoderma and wheat, this study on the wheat plant is done to \nunderstand itsrole as fungicides and/or growth promoters.\n\n\n\n2. MATERIALS AND METHODS\n\n\n\n2.1 Experimental Site\n\n\n\nThe experimental study was carried out in the premise of Institute of \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.02.2018.01.05 \n\n\n\nEFFECT OF TRICHODERMA VIRIDE AS BIOFERTILIZER ON GROWTH \nAND YIELD OF WHEAT \nSanjay Mahato1,2, *, Susmita Bhuju2, Jiban Shrestha3\n\n\n\n1Aasra Research and Education Academic Counsel, Biratnagar-7, Nepal\n2Institute of Agriculture and Animal Science, Lamjung Campus, Nepal\n3Nepal Agricultural Research Council, Nepal\n*Corresponding Author Email: Mahato.sanjay@gmail.com\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 01-05 \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 01-05 \n\n\n\nAgriculture and Animal Science, Lamjung Campus, Sundarbazar in the \nwestern mid hills of Nepal during December 2016 \u2013 April 2017 [24-28]. \nThis place has a humid tropical climate with an annual rainfall of 280 cm. \nThe geographical position of the farm is at the latitude of 28\u00b0 8' 41\"N and \nlongitude of 84\u00b0 24' 43\" E and elevation of 610masl.\n\n\n\nThe soil sample was sent to Soil Laboratory of Soil Management \nDirectorate, a Nepal Government establishment, Kathmandu, Nepal.The \npH, nitrogen, organic matter, phosphorus, and potash content of the soil \nsample were evaluated. All the chemical needed for the analysis in soil \nlab were procured from BDH Company (UK or India) and E. Merck \n(Germany or India). For pH, 20 g of soil was mixed with 20 ml of distilled \nwater in 1:1 ratio. After mixing for 1 minute, the solution was allowed to \nstand for 1 hour. pH meter was dipped in the stirred soil solution and the \npH was measured [29,30]. 1 g of soil (particle size 0.2 mm) was scooped \ninto a 500 mL Erlenmeyer flask using standard scooping techniques.  10 \nmL of 1N Na2Cr2O7 solution and 20 mL of concentrated sulfuric acid was \nadded and were allowed to react for 30 minutes. 200 ml distilled water, \n30 drops diphenylamine indicator and 0.2 g NaF were added to the flask. \n0.5 N ferrous ammonium sulphate solution was used to titrate the blank \nand the sample. Finally, the organic matter was calculated [31-33]. \nKjeldahl method was used for the determination of total nitrogen. \nAvailable phosphorus was determined by Bray and Kurtz No. 1 Method. \nFor the determination of potassium, Flame Photometer Method was \nused.\n\n\n\n2.2 Treatments combination and Field trial management\n\n\n\n2.2.1 Design of experiment\n\n\n\nThe experiment was carried out in pot following Completely \nRandomized Design (CRD) with seven treatments three replications. 21 \npots included in the study had four wheat plants each. To maintain \nsuitable moisture condition in the pot, the hole was drilled into the pot. \nFor pot filling, the soil was procured from the horticultural farm of IAAS, \nSundarbazaar, Lamjung, Nepal. The soil was mixed thoroughly and the \npot (15 cm in diameter and 15 cm in height) was filled with 2.5 kg of soil. \nEight wheat seeds of Gautam variety were, then, placed in each pot. \nSeeds were collected from a commercial seed trader of Sundarbazaar, \nLamjung, Nepal [34].\n\n\n\nThe treatments were control (T1), only inorganic fertilizer 120:80:80 \nNPK kg ha-1 (T2), Trichoderma soil inoculated (T3), Trichoderma + 10 \ntha-1 FYM (T4), Trichoderma + 60:40:40 NPK kg ha-1(T5), Trichoderma \n+ 120:80:80 NPK kg ha-1(T6), Trichoderma + 120:80:80 NPK kg ha-1+ \n10 t ha-1 FYM (T7). For control, no manures and fertilizers were applied. \nFertilizer sources were Trichoderma viride, FYM and chemical fertilizer \nNPK (Urea (CO(NH2)2) (HiMedia Laboratories, Mumbai, India) for \nnitrogen (N), Muriate of Potash (KCl) (HiMedia Laboratories, Mumbai, \nIndia) for potassium (K) and Diammonium phosphate ((NH4)2HPO4) \n(HiMedia Laboratories, Mumbai, India) for phosphorous (P)). \nTrichoderma viride was used as biofertilizer which was obtained from \nAASRA Research and Education Academy Counsel, Biratnagar, Nepal.\n\n\n\nFYM was used from the IAAS Campus Farm at the rate of 10 ton/ha. \nFarmyard manure was tested in lab for the presence of Trichoderma and \nit was found to contain nearly 106cfu/ml of conidia. As far as species is \nconcerned most of them was T. viride while a few were T. harzianum. For \nenumerating the viable spores of Trichoderma in a formulation, the serial \ndilution was done with Tween 20 and the dilution was restricted to 10-9. \nThe tips was changed for each dilution without fail. The higher dilution \nof 10-8 was spread on Potato Dextrose Agar Plate (HiMedia Laboratories, \nMumbai, India) in triplicate. Plates with colony count of 8-80 only was \nconsidered for enumeration [35].\n\n\n\nTo estimate the amount of fertilizer and FYM for a single plant, plant \npopulation per hectare (Pp) was calculated using the formula. The total \namount of fertilizer and FYM required for one hectare was divided by \nplant population per hectare. Thus, the needed amount of fertilizer per \nplant was obtained.\n\n\n\n43.2 g of well-decomposed FYM (Farmyard manure) per pot was used as \nfor 4 plants. As inorganic fertilizer 120 kg of urea for nitrogen, 80 kg of \nMOP for potassium and 80 kg of DAP for phosphorous were used as NPK \nsource for a hectare [36]. Urea has 46 % of nitrogen, DAP has 46 % of \nphosphorous and 18 % nitrogen while MOP has 60 % of potassium. So, \nfor a single pot (in full treatments like T2, T6, and T7) which contained 4 \nwheat plant was supplied with 0.715 g of urea, 0.56 g of DAP and 0.28 g \nof MOP. In treatment T5, half of above-mentioned quantity of NPK was \nused. All this fertilizer was applied before sowing the seed.\n\n\n\n2.2.2 Trichoderma soil inoculation\n\n\n\n109cfu/ml conidial suspension of Trichoderma viride was diluted in 5 \nliters of water so as to prepare a solution strength of 2X105cfu/ml. For \neach pot, 100 ml of solution was used which accounted 2X107cfuof \nTrichoderma per pot. 100 ml of the solution was used to drench the soil \nper pot [11, 24].\n\n\n\n2.2.3 Sowing, Irrigation, Weed control and Harvesting\n\n\n\nSowing and light irrigation were done on December 26, 2016. After the \ncomplete germination, the wheat plants were thinned out leaving only \nfour wheat plants in each pot. Plant to plant distance of 6 cm was \nmaintained. Irrigation with 250 ml of water was done on an interval of \ntwo days which subsequently decreased to once a week when plants \nneared to harvest.\n\n\n\nHand weeding was done on 35th and 55thdays of sowing. Aphid \ninfestation was controlled by spraying detergent water (2 teaspoon \ndetergent per liter of water) to wheat plants for two weeks on alternate \ndays. When the aphid infestation was not controlled, Rogohit \n(Dimethoate 30% EC i.e. Emulsifiable concentrate; HPM Chemicals & \nFertilizers Ltd., Delhi, India) were applied. Harvesting was done \nmanually on April 17, 2017 (113 days after sowing (DAS)).\n\n\n\n2.3 Data Collection and analysis\n\n\n\nPlant height (cm), leaf number, leaf length and width (cm), number of \ntillers per plant, panicle number, panicle length (cm) and weight (g), \nnumber of grains per plant, root length (cm), dry root weight (g), dry \nshoot weight (g), total biomass (ton/ha), yield per plant, grain yield \n(ton/ha) and biological yield (ton/ha) were taken. MS-Excel worksheet \nversion 13 was used to record the data and perform simple statistical \nanalysis as well as table, charts, and graph. Further statistical analysis to \ndetermine the significance (at a level of 5%) among various treatments \nwas performed using Genstat version 15.\n\n\n\n3. RESULTS\n\n\n\nThe soil pH was found to be slightly acidic 6.0, organic matter 2.81% \n(medium), nitrogen 0.14% (medium), phosphorus 216.68 kg/ha (high) \nand potash 534.9 kg/ha (high). Taking T1 (only soil) as control, plant \nheight showed the highest increase of 14.5% in T2 (only inorganic \nfertilizer), but when mixed with Trichoderma as in T6, the height \nincrease was only 4.6% which was also seen in T3 (only Trichoderma). \nInterestingly an increase of 11.2% was observed when half of NPK was \nused with Trichoderma (T5). Trichoderma with FYM (T4) showed a 9.5% \nincrease which was severely affected when NPK was introduced to it \n(T7). A slight increase of 1.4% was seen in T7 over control (Table 1).\n\n\n\nTable 1: Effect of treatments on growth performance of wheat in terms \nof measured values and standard errors\n\n\n\n2\n\n\n\n10,000 m2 X number of seeds per stand\nPp =\n\n\n\nProduct of spacing (m2)\n  ------------- (Eq. 1) \n\n\n\nThe product of spacing used was 18 cm X 6 cm while the number of seed \nper stand was 1. This resulted in plant population of 925926 per hectare.\n\n\n\nAmount of fertilizer per hectare\nFertilizer per plant =\n\n\n\nPlant population per hectare\n  ------- (Eq. 2) \n\n\n\nTreatment \n\n\n\nPlant height \n\n\n\n(cm) \n\n\n\nRoot length \n\n\n\n(cm) \n\n\n\nDry root wt \n\n\n\n(g) \n\n\n\nDry Shoot \n\n\n\nwt (g) \n\n\n\nBiomass \n\n\n\n(t/ha) \n\n\n\nT1 0.739c\uf0b10.09\n\n\n\nT2 1.31a\uf0b10.09\n\n\n\nT3 0.75c\uf0b10.25\n\n\n\nT4 0.78bc\uf0b10.1\n\n\n\nT5 1.14ab\uf0b10.04\n\n\n\nT6 0.89bc\uf0b10.05\n\n\n\nT7 \n\n\n\n65.975c\uf0b12.5 15.338ab\uf0b10.68\n\n\n\n75.55a\uf0b11.39 17.05a\uf0b10.35\n\n\n\n69.033abc\uf0b11.85 12.675bc\uf0b11.87\n\n\n\n72.267abc\uf0b10.91 14.991ab\uf0b11.12 \n\n\n\n73.35ab\uf0b13.74 11.35c\uf0b10.56\n\n\n\n69.025abc\uf0b12.76 16.275a\uf0b10.51\n\n\n\n66.875bc\uf0b12.15 17.633a\uf0b11.2 1.35a\uf0b10.12\n\n\n\n2.07b\u00b10.06 2.59b\uf0b10.13 \n\n\n\n3.53a\u00b10.10 4.47a\uf0b10.07\n\n\n\n2.13b\u00b10.36 2.66b\uf0b10.56 \n\n\n\n2.23b\u00b10.36 2.79b\uf0b10.24 \n\n\n\n3.18a\u00b10.28 3.99a\uf0b10.29\n\n\n\n3.3a\u00b10.25 3.88a\uf0b10.27\n\n\n\n3.49a\u00b10.18 4.48a\uf0b10.27\n\n\n\nCV% 5.8 11.8 21.5 15.4 14.6 \n\n\n\nF value 0.100 0.006 0.008 <0.001 <0.001 \n\n\n\nT1: Control; T2: Soil + NPK; T3: Soil inoculated Trichoderma; T4: \nTrichoderma + FYM; T5: Trichoderma + \u00bd NPK; T6: Trichoderma + NPK \nand T7 = Trichoderma + NPK + FYM\n\n\n\nTrichoderma profoundly exhibited the root length inhibitory (17.37%) \nnature as shown by T3 comparative to control (T1) which was in line with \nT4 (Table 1). The efficacy of NPK was reduced (T5 and T6) by \nTrichoderma. Root length was lower than control in T4 (2.26%) and T5 \n\n\n\nCite the article: Sanjay Mahato, Susmita Bhuju, Jiban Shrestha (2018). Effect Of Trichoderma Viride As Biofertilizer On Growth And Yield Of Wheat. \nMalaysian Journal of Sustainable Agriculture, 2(2) : 01-05. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 01-05 \n\n\n\n(26.01%). Inorganic fertilizer increased root length by 11.16% (T2) and \n14.96% (T7) over control (T1). The antagonistic relationship of \nTrichoderma and chemical fertilizer was also observed in root length. \nTrichoderma merely exhibited increase (1.49%) in root weight as shown \nby T3, while the addition of farmyard manure with Trichoderma (T4) \nslightly increased (5.55%) the root weight. Inorganic fertilizer increases \nroot weight by 77.27% (T2) over control (T1). The root weight increased \nby 54.26% with Trichoderma combined to lower NPK (T5) in contrast to \nT6 having an increase of 20.43%. The highest increase of 82.68% was \nobserved in T7. Despite the lower root length than control in T3, T4, and \nT5; root weight was higher which was due to the higher root density T3, \nT4, and T5.\n\n\n\nLeast number of the leaf (Table 2) was seen in Trichoderma treatment \n(T3) which was found 8.4% less than control. Such type of result was also \nobserved in T6 (NPK and Trichoderma) with a 7.7% decrease in leaf \nnumber over control. A rise of 40.1% in leaf number was seen in T7 \n(Trichoderma + Full NPK + FYM) which showed a greater improvement \nover the 5.5% increase observed with Trichoderma and FYM. The highest \nnumber of leaves was found in T7. Trichoderma (T3) didn\u2019t show any \nobservable change in leaf length and width over control (T1) while NPK as \nT2 and T7 showed a high increase of 11.5% and 14.84% in leaf length \nrespectively and 30% and 43% in leaf width respectively. The antagonism \nof NPK and Trichoderma was observed even in leaf length (Table 2).\n\n\n\nTable 2: Effect of treatments on growth performance of wheat in terms of \nmeasured values and standard errors\n\n\n\nT1: Control; T2: Soil + NPK; T3: Soil inoculated Trichoderma; T4: \nTrichoderma + FYM; T5: Trichoderma + \u00bd NPK; T6: Trichoderma + NPK \nand T7 = Trichoderma + NPK + FYM\n\n\n\nFigure 1: Effects of different treatments on grain yield of wheat (T1: \nControl; T2: Soil + NPK; T3: Soil inoculated Trichoderma; T4: Trichoderma \n+ FYM; T5: Trichoderma + \u00bd NPK; T6: Trichoderma + NPK and T7 = \nTrichoderma + NPK + FYM)\n\n\n\nGrain yield per hectare was 1.53 ton/ha in T7 (Trichoderma + FYM + NPK) \nwhich showed a 75.8% increase over control (T1). This increase was \nslightly higher than T2 (69.3%) which valued to 1.47 ton/ha. Considering \nhalf of the NPK used in T5, the yield of 1.44 ton/ha with an increase of \n65.9% was a good output. Trichoderma treatment (T3) could only \nincrease 36.5% of grain yield while Trichoderma with farmyard manure \n(T4) accounted an increase of 41.8% (Table 3). A full dose of NPK and \nTrichoderma mixture (T6) showed a poor yield of 1.33 ton/ha with an \nincrease of 52.4% which was much lesser than T5 where half of the NPK \nwas used with Trichoderma.\n\n\n\nTotal biomass was highly affected in T3 and T4 and illustrated the slight \nincrease in biomass with Trichoderma either used alone or in combination \nwith farmyard manure (Table 1). The highest increase of 73.0% was \nobserved in T7which was slightly over T2 (72.6%). Introducing \nTrichoderma with half of NPK (T5) gave a higher biomass (54.1%) than T6 \n(49.8%). As far as biological yield was considered, Trichoderma in T3 and \nT4 showed only 13.7% and 18.7% increase over control respectively, \nwhile T2 (only chemical fertilizer) and T7 (NPK + FYM + Trichoderma) \nshowed the higher increase of 70.5% and 71.2% respectively. The \nantagonistic relationship of Trichoderma and chemical fertilizer was \nclearly visible in T5 and T6. The increase in dry biomass with Trichoderma \ntreatment was supported by Cuevas [25] in tomato.\n\n\n\n4. DISCUSSION\n\n\n\nThe impact of Trichoderma on plant height was in harmony with the \nfindings of a group researcher where T. viride inoculated cotton plants \nincreased shoot length when compared with the control [15]. The \nstunting of T6 (Trichoderma+ Full NPK) and T7 (Trichoderma+ Full NPK + \nFYM) may be due to enhanced ammonium uptake, resulting in ammonia \ntoxicity. A good increase in height of T5 having half NPK and Trichoderma \nas a treatment approves the logic of ammonium toxicity [26]. In case of \nplant height, it is evident that there is an antagonistic relationship \nbetween chemical fertilizer and Trichoderma possibly because of \nammonium toxicity. As a result, plant height is adversely affected. Less is \nthe chemical fertilizer, lesser adversity observed.\n\n\n\nThe negative impact of Trichoderma on root length is also state in a study \nof Arabidopsis [27]. The antagonistic feature of chemical fertilizer and \nTrichoderma is supported by the findings of Badar and Qureshi on \nVignamungo [28]. Trichoderma showed increased root and shoot growth \nin this pot experiment. The stronger root system leads to an improved \nuptake of water, minerals, and nutrients when the root surface area \nresponds to nutrient limitation circumstances [16].\n\n\n\nThe negative impact of Trichoderma on the number of leaves is also \nreported some researcher on maize leaves [20]. It illustrates the negative \nimpact of Trichoderma on leaf number of the wheat plant. The number of \nleaves displays an antagonistic relationship between inorganic fertilizer \nand Trichoderma. Farmyard manure facilitates NPK and Trichoderma \nmixture which surprisingly increases leaves number.\n\n\n\nThe inhibitory nature of Trichoderma for tiller number, panicle number, \nand panicle length is supported by the observation of rice while \ncontradicted by the studies of another studies in rice [25, 29,30]. \nTrichoderma shows an increase in panicle weight as well as the number of \ngrains. The findings of Trichoderma over control is aligned with one more \nstudy [31]. High chemical fertilizer with Trichoderma is inhibitory for \npanicle weight and the number of grains.\n\n\n\n3\n\n\n\nTreatment \n\n\n\nLeaf number Leaf length \n\n\n\n53 DAS \n\n\n\nLeaf width \n\n\n\non 53 DAS \n\n\n\nTiller \n\n\n\nnumber \n\n\n\nPanicle \n\n\n\nnumber \n\n\n\nT1 1b\uf0b10.06 \n\n\n\nT2 1.3a\uf0b10.03 \n\n\n\nT3 1b\uf0b10.00 \n\n\n\n3.083bc\uf0b10.08 1.25ab\uf0b10.14\n\n\n\n3.167bc\uf0b10.08 1.667a\uf0b10.22\n\n\n\n2.75c\uf0b10.29 1.167b\uf0b10.08 \n\n\n\nT4 1.03b\uf0b10.03 \n\n\n\nT5 1.03b\uf0b10.03 \n\n\n\nT6 1.067b\uf0b10.07 \n\n\n\nT7 \n\n\n\n12.555b\uf0b10.57\n\n\n\n13.667b\uf0b11.54\n\n\n\n11.5b\uf0b11.56 \n\n\n\n13.25b\uf0b11.0 \n\n\n\n13.833b\uf0b10.58\n\n\n\n11.583b\uf0b10.17\n\n\n\n17.583a\uf0b10.22\n\n\n\n26.93c\uf0b10.55 \n\n\n\n30.03ab\uf0b10.41\n\n\n\n27c\uf0b10.3\n\n\n\n28.43abc\uf0b11.37 \n\n\n\n28.67abc\uf0b10.44 \n\n\n\n28.2c\uf0b10.92 \n\n\n\n30.93a\uf0b11.16 1.43a\uf0b10.07 \n\n\n\n3.417b\uf0b10.17 1.333ab\uf0b10.08 \n\n\n\n3.583ab\uf0b10.22 1.333ab\uf0b10.08 \n\n\n\n3.083bc\uf0b10.08 1.333ab\uf0b10.22 \n\n\n\n4.083a\uf0b10.36 1.5ab\uf0b10.14\n\n\n\nCV% 12.5 5 7.2 11.1 19.1 \n\n\n\nF value 0.009 0.036 <0.001 0.013 0.361 \n\n\n\nT1: Control; T2: Soil + NPK; T3: Soil inoculated Trichoderma; T4: \nTrichoderma + FYM; T5: Trichoderma + \u00bd NPK; T6: Trichoderma + NPK \nand T7 = Trichoderma + NPK + FYM\n\n\n\nTrichoderma (T3) decreased the tiller number by 10.81% over control. The \nnegative relation was observed between chemical fertilizer and \nTrichoderma as illustrated by T5 (16.22%) and T6 (0.0%). The assistance \nof FYM either with Trichoderma or combination of Trichoderma with NPK \npromoted the increase in tiller number by 10.8% (T4) and 32.4% (T7). The \nhighest number of panicle was observed in chemical fertilizer treatment \n(T2) with an increase of 33.3% over control (T1). As expected, \nTrichoderma showed the negative impact by decreasing the value to 6.6% \n(T3) comparative to control. Here also, Trichoderma showed negative \nrelation with chemical fertilizer as evident in T4, T5, T6, and T7. Panicle \nlength was shorter than control in case of T3 (8.37%) and T4 (9.67%) \nwhich showed the inhibitory impact of not only Trichoderma but also of its \ncombination with FYM on panicle length. T5 illustrated that lower quantity \nof chemical fertilizer could yield better panicle length, but higher dose \nwould be inhibitory as in T6.\n\n\n\nPanicle weight almost followed the trend of panicle length (Table 3). With \nTrichoderma, only 9.1% increase was observed over control while NPK \nshowed an increase of 23.0% in panicle weight. Like panicle length, T5 \nillustrated that lower quantity of chemical fertilizer could yield better \npanicle weight (42.4%), but higher dose would be inhibitory as in T6 \n(33.0%). The number of grains per plant was the highest (36.9) in \ntreatment T7 and the lowest (29.2) was in T1. T3 (30.3) and T4 (30.6) \nwere nearly equal while T2, T5, and T6 were slightly above of 32 grains. T7 \nshowed an increase in 26.7% over control.\n\n\n\nTable 3: Effect of treatments on growth and yield of wheat in terms of \nmeasured values and standard errors\n\n\n\nTreatment \n\n\n\nPanicle length \n\n\n\n(cm) \n\n\n\nPanicle wt \n\n\n\n(g) \n\n\n\nTotal number \n\n\n\nof grains \n\n\n\nGrain Yield \n\n\n\n(t/ ha) \n\n\n\nBiological yield \n\n\n\n(t/ha) \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\nT7 \n\n\n\n10.95ab\uf0b10.48 1.683c\uf0b10.14 29.166ab\uf0b13.42 0.87d\uf0b10.07 2.78b\uf0b10.11 \n\n\n\n11.775a\uf0b10.36 2.07abc\uf0b10.13 32.276a\uf0b13.19 1.473ab\uf0b10.08 4.74a\uf0b10.11 \n\n\n\n10.033b\uf0b10.53 1.836bc\uf0b10.03 30.276a\uf0b10.98 1.188c\uf0b10.09 3.16b\uf0b10.26 \n\n\n\n9.892b\uf0b10.95 1.875bc\uf0b10.14 30.583a\uf0b12.96 1.234c\uf0b10.05 3.3b\uf0b10.36 \n\n\n\n12.283a\uf0b10.23 2.397a\uf0b10.26 32.25a\uf0b11.75 1.44ab\uf0b10.04 4.38a\uf0b10.24 \n\n\n\n11.45ab\uf0b10.53 2.239ab\uf0b10.07 32.054a\uf0b13.16 1.326bc\uf0b10.05 4.38a\uf0b10.28 \n\n\n\n11.867a\uf0b10.18 2.433a\uf0b10.15 36.943a\uf0b13.84 1.530a\uf0b10.07 4.76a\uf0b10.23 \n\n\n\nCV% 8.1 12.4 15.8 8.8 10.7 \n\n\n\nF value 0.037 0.018 0.628 <0.001 <0.001 \n\n\n\nCite the article: Sanjay Mahato, Susmita Bhuju, Jiban Shrestha (2018). Effect Of Trichoderma Viride As Biofertilizer On Growth And Yield Of Wheat. \nMalaysian Journal of Sustainable Agriculture, 2(2) : 01-05.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 01-05 \n\n\n\nThe result of grain yield in Trichoderma is supported by the study of a \ngroup researcher which shows a significant increase in the yield of wheat \nof about 29% in Jaipur and 36% in Kota [31]. Trichoderma hazarium has \nthe ability to solubilize phosphate and micronutrients that could be made \navailable to plant [12]. Though the total combination of Trichoderma, \nfarmyard manure, and full NPK yield was 75.8%, the yield increases of \n65.9% with half of NPK and Trichoderma could be basic highlight  \nconsidering that only NPK treatment yield was 69.3% higher. In this \nexperiment, the increase in yield can also be attributed to the application \nof Trichoderma bioformulation along with FYM which helped increasing \nthe colonies by providing nutrient to Trichoderma thereby increasing the \nplant growth and yield of wheat [31].\n\n\n\nThough our finding of Trichoderma is as a growth promoter with certain \nlimitations, Trichoderma has been fully supported as a growth promoter \non numerous cultivated plants [16, 32, 33, 36]. This specified the potential \nuse of the biofertilizers as a reasonable alternative for crop production, \nwith a minimization of the ecological impact and improvement of soil \necology.\n\n\n\n5. CONCLUSION\n\n\n\nTrichoderma shows a slight increase in the plant height, panicle weight, \nnumber of grains, grain yield, biological yield, and biomass yield over \ncontrol; while root length, number of leaves, tiller number, panicle \nnumber, panicle length highlight the negative impact of Trichoderma on \nthe wheat plant. Trichoderma shows antagonism with inorganic fertilizer. \nIn most of the parameters, more is the inorganic fertilizer with \nTrichoderma, higher is the antagonism. When Trichoderma and NPK are \naccompanied with farmyard manure, most of the growth and yield \nparameter shows the highest value, but the yield was slightly higher than \nNPK alone treatment. This finding indicates that while sowing seed, the \nuse of Trichoderma with FYM and NPK may not improve the yield over \nNPK to a greater extent. Hence it is indicated that Trichoderma viride can \nbe a growth promoter and be used as a biofertilizer.\n\n\n\nCONFLICT OF INTEREST\n\n\n\nThe author(s) declare(s) that there is no conflict of interest regarding the \npublication of this paper.\n\n\n\nACKNOWLEDGEMENTS\n\n\n\nThis research received no specific grant from any funding agency in the \npublic, commercial, or not-for-profit sectors. The authors are thankful to \nAASRA Research and Education Academic Counsel, Biratnagar \u20137 for \nproviding the strains like Trichoderma viride and other technical support.\n\n\n\nREFERENCES\n\n\n\n[1] Mathur, G.M., Meena, G.M., Anuradha, S. 2017. Role of chelated \nmicronutrient and their salts for improving crop production of wheat \n(Triticum aestivum L.). International Journal of Current Microbiology and \nApplied Science, 6 (8), 1042-1048.\n\n\n\n[2] Kharub, A.S., Sharma, V.K. 2002. Effect of nutrient combinations on \nwheat productivity under typicustochrept soils of Karnal. Annals of plant \nand soil research, 4 (1), 124-126.\n\n\n\n[3] Jaga, P.K., Upadhyaya, V.B. 2013. Effect of FYM, biofertilizer and \nchemical fertilizers on wheat. Asian Journal of Plant and Soil Sciences, 8 \n(1), 185-188.\n\n\n\n[4] Rifai, M.A. 1969. A revision of the genus Trichoderma. Mycological \nPapers, 116, 1-56.\n\n\n\n[5] Jaklitsch, W.M., Voglmayr, H. 2015. Biodiversity of Trichoderma \n(Hypocreaceae) in Southern Europe and Macaronesia. Studies in \nMycology, 80, 1\u201387.\n\n\n\n[6] Hermosa, R., Viterbo, A., Chet, I., Monte, E. 2012. Plant-beneficial \neffects of Trichoderma and of its genes. Microbiology, 158, 17\u201325.\n\n\n\n[7] Sathiyaseelan, K., Sivasakthivelan, P., Lenin, G. 2009. Evaluation of \nantagonistic activity and shelf life study of Trichoderma viride. Bot. Res. \nIntl., 2 (3), 195-197.\n\n\n\n[8] Harman, G.E., Howell, C.R., Viterbo, A., Chet, I., Lorito, M. 2004. \nTrichoderma species- opportunistic, avirulent plant symbionts. Nature \nReviews Microbiology, 2, 43-56.\n\n\n\n[9] Harman, G.E. 2006. Overview of mechanisms and uses of Trichoderma\n\n\n\nspp. Phytopathology, 96 (2), 190-4.\n\n\n\n[10] Lorito, M., Woo, S.L., Harman, G.E., Monte, E. 2010. Translational \nresearch on Trichoderma: from 'omics to the field, Annual Review of \nPhytopathology, 48, 395-417.\n\n\n\n[11] Mastouri, F., Bjorkman, T., Harman, G.E. 2010. Seed treatment with \nTrichoderma harzianum alleviates biotic, abiotic, and physiological \nstresses in germinating seeds and seedlings. Phytopathology, 100 (11), \n1213-1221.\n\n\n\n[12] Li, R.X., Cai, F., Pang, G., Shen, Q.R., Li, R., Chen, W. 2015. \nSolubilisation of phosphate and micronutrients by Trichoderma \nharzianum and its relationship with the promotion of tomato plant \ngrowth. PLoS One, 10 (6), e0130081. \n\n\n\n[13] Balasubramanian, N., ThamilPriya, V., Shanmugaiah, V., \nLalithakumari, D. 2014. Effect of improved Trichoderma fusants and their \nparent strains in control of sheath blight of rice and wilt of tomato. \nJournal of Plant Diseases and Protection, 121 (2), 71-78.\n\n\n\n[14] Shukla, N., Awasthi, R.P., Rawat, L., Kumar, J. 2012. Biochemical and \nphysiological responses of rice (Oryza sativa L.) as influenced by \nTrichoderma harzianum under drought stress. Plant Physiology and \nBiochemistry, 54, 78-88.\n\n\n\n[15] Shanmugaiah, V., Balasubramanian, N., Gomathinayagam, S., \nManoharan, P.T., Rajendran, A. 2009. Effect of single application of \nTrichoderma viride and Pseudomonas fluorescens on growth promotion in \ncotton plants. African Journal of Agricultural Research, 4 (11), 1220-1225.\n\n\n\n[16] Harman, G.E. 2000. Myths and dogmas of biocontrol changes in \nperceptions derived from research on Trichoderma harzinum T-22. Plant \nDisease, 84 (4), 377-393.\n\n\n\n[17] Haque, M., Haque, A., Ilias, G.N.M., Molla, A.H. 2011. Trichoderma-\nEnriched Biofertilizer: A prospective substitute of inorganic fertilizer for \nmustard (Brassica campestris) production. The Agriculturists, 8 (2), \n66-73.\n\n\n\n[18] Zhang, S.W., Gan, Y.T., Xue, Y.Y., Xu, B.L. 2014. The parasitic and lethal \neffects of Trichoderma longibrachiatum against Heterodera avenae. \nBiological Control, 72, 1\u20138.\n\n\n\n[19] Harman, G.E., Kubicek, C.P. 1998. Trichoderma and Gliocladium. \nEnzymes, Biological Control and Commercial Applications, Taylor and \nFrancis Ltd, London, 2.\n\n\n\n[20] Akladious, S.A., Abbas, S.M. 2014. Application of Trichoderma \nharzianum T22 as a biofertilizer potential in maize growth. Journal of \nPlant Nutrition, 37 (1), 30-49.\n\n\n\n[21] Doni, F., Isahak, A., CheMohd Zain, C.R., Wan Yusoff, W.M. 2014. \nPhysiological and growth response of rice plants (Oryza sativa L.) to \nTrichoderma spp. Inoculants. AMB Express, 4, 45.\n\n\n\n[22] Thakur, M., Sohal, B.S. 2013. Role of elicitors in inducing resistance in \nplants against pathogen infection: a review. ISRN Biochemistry, 1\u201310.\n\n\n\n[23] Masunaka, A., Hyakumachi, M., Takenaka, S. 2011. Plant growth-\npromoting fungus, Trichoderma koningi suppresses isoflavonoid \nphytoalexin vestitol production for colonization on/in the roots of Lotus \njaponicas. Microbes and Environments, 26, 128\u2013134.\n\n\n\n[25] Cuevas, V.C. 2006. Soil inoculation with Trichoderma pseudokoningii \nRifai enhances yield of rice. Philippine Journal of Science, 135 (1), 31-37.\n\n\n\n[24] Chirino-Valle, I., Kandula, D., Littlejohn, C., Hill, R., Walker, M., Shields, \nM., Wratten, S. 2016. Potential of the beneficial fungus Trichoderma to \nenhance ecosystem-service provision in the biofuel grass Miscanthus x \ngiganteus in agriculture. Scientific Reports, 6, 25109. http://\ndoi.org/10.1038/srep25109\n\n\n\n[26] Neumann, B., Laing, M. 2006. Trichoderma: An ally in the quest for \nsoil system sustainability, in: N. Uphoff, E. Fernandes, H. Herren et al., \n(Eds.), Biological approaches to sustainable soil system, Boca Raton FL: \nTaylor & Francis Ltd., New York, US, 491\u2013500.\n\n\n\n4\n\n\n\nCite the article: Sanjay Mahato, Susmita Bhuju, Jiban Shrestha (2018). Effect Of Trichoderma Viride As Biofertilizer On Growth And Yield Of Wheat. \nMalaysian Journal of Sustainable Agriculture, 2(2) : 01-05. \n\n\n\n[27] Nieto-Jacobo, M.F., Steyaert, J.M., Badillo, F.B.S., Nguyen, D.V., Rost\u00e1s, \nM., Braithwaite, M., De Souza, J.T., Bremont, J.F.J., Ohkura, M., Stewart, A., \nMendoza, A.M. 2017. Environmental growth conditions of Trichoderma \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 01-05 5\n\n\n\n[29] de Fran\u00e7a, S.K.S., Cardoso, A.F., Lustosa, D.C., Ramos, E.M.L.S., de \nFilippi, M.C.C., da Silva, G.B. 2014. Biocontrol of sheath blight by \nTrichoderma asperellum in tropical lowland rice. Agronomy for \nSustainable Development, 35 (1), 317\u2013324. doi 10.1007/\ns13593-014-0244-3\n\n\n\n[30] El-Katatny, M.H., Idres, M.M. 2014. Effects of single and combined \ninoculations with Azospirillum brasilense and Trichoderma harzianum on \nseedling growth or yield parameters of wheat (Triticum vulgaris L., Giza \n168) and corn (Zea mays L., Hybrid 310). Journal of Plant Nutrition, 37 \n(12), 1913-1936.\n\n\n\n[31] Sharma, P., Patel, A.N.M., Saini, K., Deep, S. 2012. Field demonstration \nof Trichoderma harzianum as a plant growth promoter in wheat (Triticum \naestivum L). Journal of Agricultural Science, 4 (8), 65-73.\n\n\n\n[32] Yadav, J., Verma, J.P., Tiwari, K.N. 2011. Plant growth promoting \nactivities of fungi and their effect on chickpea plant growth. Asian Journal \nof Experimental Biological Sciences, 4, 291\u2013299.\n\n\n\n[33] Yedidia, I., Srivastva, A.K., Kapulnik, Y., Chet, I. 2001. Effect of \nTrichoderma harzianum on microelement concentrations and increased \ngrowth of cucumber plants. Plant Soil, 235, 235\u2013242.\n\n\n\n[34] NARC Newsletter, vol. 11, no. 3, 2004. http://narc.gov.np/publicaton/\npdf/newsletter/Vol%2011%20No%203.pdf Accessed on 17 Jan 2018.\n\n\n\n[35] Sriram, S., Savitha, M.J. 2011. Enumeration of colony forming units of \nTrichoderma in formulations \u2013 precautions to be taken to avoid errors \nduring serial dilution. Journal of Biological Control, 25 (1), 64\u201367.\n\n\n\n[36] Mahato, S., Neupane, S. 2017. Comparative study of impact of \nAzotobacter and Trichoderma with other fertilizers on maize growth. \nJMRD, 3 (1), 1-16. doi: http://dx.doi.org/10.3126/jmrd.v3i1.18915..\n\n\n\nCite the article: Sanjay Mahato, Susmita Bhuju, Jiban Shrestha (2018). Effect Of Trichoderma Viride As Biofertilizer On Growth And Yield Of Wheat. \nMalaysian Journal of Sustainable Agriculture, 2(2) : 01-05.\n\n\n\nspp. affect indole acetic acid derivatives, volatile organic compounds, and \nplant growth promotion. Frontiers in Plant Science, 8, 102.\n\n\n\n[28] Badar, R., Qureshi, S.A. 2012. Comparative effect of Trichoderma \nhamatum and host-specific Rhizobium species on growth of Vignamungo. \nJournal of Applied Pharmaceutical Science, 2 (4), 128-132.\n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.115.122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2021.115.122 \n\n\n\nRELATIVE TOXICITY OF SOME CHEMICAL PESTICIDES AGAINST JUTE HAIRY \nCATERPILLAR (SPILOSOMA OBLIQUA W.) IN TOSSA JUTE  \n(CORCHORUS OLITORIUS L.) \n\n\n\nMd. Sohanur Rahmana*, Md. Nazrul Islama, Mohammad Sahin Polana, Fakhar Uddin Talukderb and Md. Mia Mukulc\n\n\n\na Department of Entomology, Bangladesh Jute Research Institute, Dhaka, Bangladesh. \nb Department of Plant Pathology, Bangladesh Jute Research Institute, Dhaka, Bangladesh. \nc Department of Olitorius breeding, Bangladesh Jute Research Institute, Dhaka, Bangladesh. \n\n\n\n*Corresponding author email: sohanbau2010@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 10 January 2021 \nAccepted 16 February 2021 \nAvailable online 05 April 2021 \n\n\n\nPesticides have been a major contributor to the growth of agricultural productivity and food supply. \nPesticides were a key factor in significant agricultural productivity growth during the last century and \ncontinue to be a critical factor in reducing crop damage. Fifteen insecticides were investigated to select their \neffective and economic doses against Hairy caterpillar in a Tossa Jute variety during April-October\u2019 2020 at \nthe Department of Entomology, Manikganj and Narayanganj, Bangladesh Jute Research Institute (BJRI), \nBangladesh following Randomized Completely Block Design with three replications. All new insecticides with \na standard were found effective for controlling jute hairy caterpillar giving 95.38, 94.55, 95.19, 92.85, 93.59, \n94.22, 93.49, 93.62, 89.84, 95.72, 93.56, 93.38, 94.42, 95.39, 91.34 and 95.41 % at Manikganj; 95.78, 93.32, \n93.97, 93.18, 92.09, 92.49, 93.74, 92.93, 92.29, 93.69, 93.95, 93.17, 95.31, 94.99, 92.11 and 94.53 % reduction \nof infestation at Narayanganj at 5th day after spray over control plot respectively. In Manikganj, the highest \nfibre yield (3.66 t/ha) was found in the plot treated with Rock 20 EC and the lowest (2.96t/ha) was found in \nReset 20WDG treated plot. In case of Narayanganj, the highest fibre yield (3.85 t/ha) was found in the plot \ntreated with Proxy 20 EC and the lowest (2.79t/ha) was found in Daman treated plot. These insecticides can \nbe recommended for the farmer\u2019s use to control jute hairy caterpillar.  \n\n\n\nKEYWORDS \n\n\n\neffectiveness, insecticides, jute hairy caterpillar, jute, yield.\n\n\n\n1. INTRODUCTION\n\n\n\nJute is a principal fibre crop in the world. It is the most important cash crop \n\n\n\nand the biggest foreign exchange earner of Bangladesh. It ranks second to \n\n\n\nthe cotton among all the natural fibre production (Talukder et al., 1989). \n\n\n\nJute is attacked by various insect and mite pests. More than 40 species of \n\n\n\ninsects and mites are considered to be the pests of jute in Bangladesh \n\n\n\n(Kabir, 1975). All parts of the plants are subject to attack. Among the jute \n\n\n\npests Spilarctia obliqua commonly known as jute hairy caterpillar, is the \n\n\n\nworst one (Kabir and Khan, 1968; Sharif, 1962). The Bihar hairy \n\n\n\ncaterpillar, Spilarctia obliqua (Walker) (Arctiidae: Lepidoptera), is a \n\n\n\nwidely distributed, serious polyphagous pest (Gupta and Bhattacharya, \n\n\n\n2008). The pests cause loss in yield and quality of fibres (Rahman and \n\n\n\nKhan, 2010). Spilosoma obliqua (walker) belongs to the family Arctiidae \n\n\n\nof Lepidoptera order is a polyphagous insect causing serious damage to a \n\n\n\nvariety of crops (Bhattacharya et al.,1995). Different insects and mite pest \n\n\n\nattack jute during the growing season. Jute hairy caterpillar, Spilarctia \n\n\n\nobliqua (walker) under the family Arctiidae of lepidoptera order is one of \n\n\n\nthese destructive insect pests of jute that can reduce up to 18.5% fibre \n\n\n\nyield depending on their intensity of infestation. There are many synthetic \n\n\n\ninsecticides available in the local market to control jute hairy caterpillar \n\n\n\nbut all are not available in all over the country and all are not equally \n\n\n\neffective. Moreover, indiscriminate and repeated use of same chemical \n\n\n\nmight lead to develop resistance in target pest. Therefore, new chemical \n\n\n\npesticides were needed to include in the recommendation list, which will \n\n\n\nhelp to overcome the resistance problem of pest against insecticides. \n\n\n\nWhen more insecticides will be available in the market, farmers will have \n\n\n\na chance to choose insecticides according to the availability and \n\n\n\naffordability. \n\n\n\nThere are many manmade insecticides existing in the local market to \n\n\n\ncontrol jute hairy caterpillar but all are not available through the country \n\n\n\nand all are not likewise effective. Moreover, haphazard and recurrent use \n\n\n\nof same chemical might lead to development of resistance in target pest. \n\n\n\nTherefore, more number of chemical pesticides should be encompassed in \n\n\n\nthe reference lists, which will help to overcome the resistance problem of \n\n\n\npest against insecticides. Furthermore, farmers will have a chance to \n\n\n\nchoose insecticides according to accessibility and cost. So, an experiment \n\n\n\nwas taken following two objectives (i). to estimate the efficacy of some \n\n\n\n\nmailto:sohanbau2010@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\ninsecticides against jute hairy caterpillar under natural condition at field \n\n\n\nlevel and compare with a standard chemical insecticide, (ii). to select \n\n\n\noperative and cost-effective doses of these chemical insecticides for the \n\n\n\njute cultivator\u2019s use \n\n\n\n2. MATERIALS AND METHODS\n\n\n\n2.1 Location and design of the experiment \n\n\n\nThe investigation was carried out as two parts viz. effective and economic \n\n\n\ndose fixation of 15 pesticides under laboratory condition at BJRI, Dhaka \n\n\n\n(23\u00b045'30.1356\"N, 90\u00b022'45.642\"E) and application of their selected \n\n\n\ndoses on Jute hairy caterpillar in field condition of Jute crop at Manikganj \n\n\n\n(23\u00b053'27\"N, 90\u00b01'0\"E) and Narayanganj (23\u00b037'24\"N, 90\u00b030'4\"E) District \n\n\n\nof Bangladesh during the jute growing season (April-October, 2020). The \n\n\n\nexperiments were conducted in Randomized Complete Block Design with \n\n\n\nthree replications. \n\n\n\n2.2 Test materials and their chemical structure \n\n\n\nFifteen insecticides of different generic groups were collected from \n\n\n\ndifferent pesticide companies and used in this experiment (Table 1).  \n\n\n\nTable 1: Insecticides used as test materials \n\n\n\nSerial No. Common/Trade name Generic name [Active ingredient(s)] \n\n\n\n1 Symazine 80WDG Cyromazine 80% \n\n\n\n2 Reset 20WDG Flufenozuron 10% + Ethiprole 10% \n\n\n\n3 Virtako 40WG Chlorantraniliprole + Thiamethoxam \n\n\n\n4 Laida 2.5EC Lamda-cyhalothrin \n\n\n\n5 Proxy 20 EC Proxifen 5% + Fenpropathrin 15% \n\n\n\n6 Rock 20 EC Pyriproxifen 5% + Fenpropathrin 15% \n\n\n\n7 Daman Beauvaria bassiana \n\n\n\n8 Pulser 20EC Pyriproxifen 5% + Fenpropathrin 15% \n\n\n\n9 Dynamite 60WDG  Dihalo-pyrazole amid 40% + Thiamathoxam 20% \n\n\n\n10 Nishan 20EC Pyriproxiyfen 5%+ Fenpropathrin 15% \n\n\n\n11 Foringout 80WDG Nitenpyrum 20%+ Pymetrozine 60% \n\n\n\n12 Triple 33 WDG Emamectin Benzoate 15% + Lufenuron 3% \n\n\n\n13 Veto 20SL Imidaclopride \n\n\n\n14 Starlux 25 EC Quinalphos 25% \n\n\n\n15 Foni plus 12SC Chlorfenapyr 10% +Emamectin Benzoate 2% \n\n\n\n16 Hayzinon 60 EC (Standard) Diazinon \n\n\n\nIts chemical structure was collected from internet (Fig. 1). \n\n\n\nFlufenozuron    Ethiprole   Chlorantraniliprole \n\n\n\nLamda-cyhalothrin   Proxifen     Fenpropathrin \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\nDihalo-pyrazole amid Nitenpyrum  Cyromazine \n\n\n\nPymetrozine Emamectin Benzoate Pyriproxifen \n\n\n\nLufenuron    Imidaclopride Quinalphos \n\n\n\nChlorfenapyr   Diazinon   Thiamethoxam \n\n\n\nFigure 1: Chemical structure of different generic group insecticides\n\n\n\n2.3 Fixation of chemical dose under artificial condition \n\n\n\nDose fixation experiment was conducted at laboratory of Entomology \n\n\n\nDepartment, BJRI, Dhaka following Randomized Complete Block Design \n\n\n\nwith three replications. 3rd instar of 20 jute hairy caterpillars were kept in \n\n\n\neach plastic pot (Figure 2). Then, 15 leaves of jute plant spraying with \n\n\n\nselective insecticides with their respective doses were kept in each pot \n\n\n\n(Figure 3).  \n\n\n\nFigure 2: Jute hairy caterpillar \n\n\n\n  Figure 3: Laboratory trial of insecticides  \n\n\n\nThen each pot was wrapped with cloths. The untreated pot was used as \n\n\n\ncontrol. Three different doses of each insecticide were applied to fix \n\n\n\neffective and economic dose (Table 2). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\nTable 2: Three different doses of each insecticide tested for dose \n\n\n\nfixation \n\n\n\nSl. \n\n\n\nNo. \nName of insecticides Dose/ha \n\n\n\nDose/ \n\n\n\nLitre \n\n\n\n1 Cyromazine 80% \n\n\n\n600gm/ha 1.2 gm \n\n\n\n500gm/ha 1 gm \n\n\n\n400gm/ha .8 gm \n\n\n\n2 \nFlufenozuron 10% + Ethiprole \n\n\n\n10% \n\n\n\n400gm/ha 0.8 gm \n\n\n\n300gm/ha 0.6 gm \n\n\n\n200gm/ha 0.4 gm \n\n\n\n3 \nChlorantraniliprole + \n\n\n\nThiamethoxam \n\n\n\n125gm/ha 0.25 gm \n\n\n\n100gm/ha 0.2 gm \n\n\n\n75gm/ha 0.15 gm \n\n\n\n4 Lamda-cyhalothrin \n\n\n\n600ml/ha 1.2 ml \n\n\n\n500ml/ha 1 ml \n\n\n\n400ml/ha 0.8 ml \n\n\n\n5 \nProxifen 5% + Fenpropathrin \n\n\n\n15% \n\n\n\n700ml/ha 1.4 ml \n\n\n\n650ml/ha 1.3 ml \n\n\n\n600ml/ha 1.2 ml \n\n\n\n6 \nPyriproxifen 5% + \n\n\n\nFenpropathrin 15% \n\n\n\n700ml/ha 1.4 ml \n\n\n\n650ml/ha 1.3 ml \n\n\n\n600ml/ha 1.2 ml \n\n\n\n7 Beauvaria bassiana \n\n\n\n2.75 kg /ha 0.0055 kg  \n\n\n\n2.5kg /ha 0.005 kg  \n\n\n\n2.25kg /ha 0.0045 kg  \n\n\n\n8 \nPyriproxifen 5% + \n\n\n\nFenpropathrin 15% \n\n\n\n600 gm/ha 1.2 gm \n\n\n\n500 gm/ha 1 gm \n\n\n\n400 gm/ha 0.8 gm \n\n\n\n9 \nDihalo-pyrazole amid 40% + \n\n\n\nThiamathoxam 20% \n\n\n\n100gm/ha 0.2 gm \n\n\n\n75gm/ha 0.15 gm \n\n\n\n50gm/ha 0.1 gm \n\n\n\n10 \nPyriproxiyfen 5%+ \n\n\n\nFenpropathrin 15% \n\n\n\n30gm/ha 0.06 gm \n\n\n\n21gm/ha 0.042 gm \n\n\n\n15 gm/ha 0.03 gm \n\n\n\n11 \nNitenpyrum 20%+ \n\n\n\nPymetrozine 60% \n\n\n\n150 gm/ha 0.3 gm \n\n\n\n100 gm/ha 0.2 gm \n\n\n\n50 gm/ha 0.1 gm \n\n\n\n12 \nEmamectin Benzoate 15% + \n\n\n\nLufenuron 3% \n\n\n\n150gm/ha 0.3 gm \n\n\n\n125gm/ha 0.25 gm \n\n\n\n100gm/ha 0.2 gm \n\n\n\n13 Imidaclopride \n\n\n\n250ml/ha 0.5 ml \n\n\n\n200ml/ha 0.4 ml \n\n\n\n150ml/ha 0.3 ml \n\n\n\n14 Quinalphos 25% \n\n\n\n2.0 lit/ha 0.004 lit \n\n\n\n1.68 lit/ha 0.00336 lit \n\n\n\n1.25 lit/ha 0.0025 lit \n\n\n\n15 \nChlorfenapyr 10% \n\n\n\n+Emamectin Benzoate 2% \n\n\n\n800 ml/ha 1.6 ml \n\n\n\n700 ml/ha 1.4 ml \n\n\n\n600 ml/ha 1.2 ml \n\n\n\nPopulation of caterpillar pot-1 were recorded at 3rd and 5th day after spray.  \n\n\n\nPercent reduction of insect population at 3rd and 5th days after spray were \n\n\n\ncalculated (Equation.i) according to Abbott\u2019s formula (1925). \n\n\n\nCorrected mortality (%) = (1 \u2212\n\ud835\udc41\ud835\udc62\ud835\udc5a\ud835\udc4f\ud835\udc52\ud835\udc5f \ud835\udc5c\ud835\udc53 \ud835\udc56\ud835\udc5b\ud835\udc60\ud835\udc52\ud835\udc50\ud835\udc61\ud835\udc60 \ud835\udc4e\ud835\udc53\ud835\udc61\ud835\udc52\ud835\udc5f \ud835\udc61\ud835\udc5f\ud835\udc52\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc5a\ud835\udc52\ud835\udc5b\ud835\udc61\n\n\n\n\ud835\udc41\ud835\udc62\ud835\udc5a\ud835\udc4f\ud835\udc52\ud835\udc5f \ud835\udc5c\ud835\udc53 \ud835\udc56\ud835\udc5b\ud835\udc60\ud835\udc52\ud835\udc50\ud835\udc61\ud835\udc60 \ud835\udc56\ud835\udc5b \ud835\udc50\ud835\udc5c\ud835\udc5b\ud835\udc61\ud835\udc5f\ud835\udc5c\ud835\udc59\n) \u00d7 100\u2026..(i) \n\n\n\n2.4 Field Experiment \n\n\n\nA standard tossa jute variety O-9897 was grown in the field of Manikganj \n\n\n\nand Narayanganj during the jute-growing season (April-August, 2020). \n\n\n\nUnit plot size was 2x2.1m2 with three replications. Randomized Complete \n\n\n\nBlock Design (RCBD) was followed (Gomez and Gomez,1984). Normal \n\n\n\nagronomical practices were followed. Recommended doses of fertilizers \n\n\n\nwere applied in the experimental plots. Natural infestation of second and \n\n\n\nthird instar of jute hairy caterpillar was allowed to build up in the plot \n\n\n\n(Rahman and Khan, 2010). In July, when maximum infestation was found \n\n\n\nin the field fifteen new insecticides Symazine 80WDG, Reset 20WDG, \n\n\n\nVirtako 40WG, Laida 2.5EC, Proxy 20 EC, Rock 20 EC, Daman, Pulser 20EC,  \n\n\n\nDynamite 60WDG, Nishan 20EC, Foringout 80WDG, Triple 33 WDG, Veto \n\n\n\n20SL,  Starlux 25 EC,  Foni plus 12SC @ 500 gm/ha, 300 gm/ha, 100 gm/ha, \n\n\n\n500 ml/ha, 650 ml/ha, 2.5kg/ha, 500gm/ha, 75 gm/ ha, 21 gm/ ha, 100 \n\n\n\ngm/ ha, 125 gm/ha, 200ml/ha, 1.68 litre/ha, and 700 ml/ha respectively \n\n\n\nwere sprayed along with a standard insecticide Hayzinon 60EC@ \n\n\n\n550ml/ha (Figure 4). \n\n\n\nFigure 4: Field trial of insecticides \n\n\n\nControl plots were remained untreated. Population of caterpillar/plot was \n\n\n\nrecorded before spray and at 5th day after spray. Data of percent reduction \n\n\n\nof insect population at  5th day after spray were calculated (Equation. i) \n\n\n\naccording to Abbott\u2019s formula (Abbott\u2019s, 1925). \n\n\n\n2.5 Data analyses \n\n\n\nThe data were collected sincerely and compiled using Microsoft Excel \n\n\n\nProgram (2010) and were analyzed in Statistical Analysis Software \n\n\n\n(Statistix10). \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Dose fixation \n\n\n\nThree doses of all insecticides were applied at each plastic pot, where the \n\n\n\nsecond one was proposed by respective companies and the other two \n\n\n\ndoses were higher one and lower one from the proposed dose. The results \n\n\n\nrevealed that the higher doses were little more effective than proposed \n\n\n\ndose or similarly effective in respect of mortality but these doses were not \n\n\n\ncost effective and lower doses were less effective than the proposed doses. \n\n\n\nSo, proposed doses were selected for the field trial (Table 3). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\nTable 3: Preliminary dose fixation trial at laboratory in Entomology Department of Bangladesh Jute Research Institute (BJRI), Dhaka, 2020 \n\n\n\nSl. No. \nName of insecticides \n\n\n\nDose/ha Dose/ Litre \n\n\n\nNumber of \n\n\n\ncaterpillar/ plot \n\n\n\nbefore spray \n\n\n\n% Reduction of \n\n\n\ncaterpillar after \n\n\n\nspray \n\n\n\nat 3rd \n\n\n\nday \nat 5th day \n\n\n\n1 Cyromazine 80% \n\n\n\n600gm/ha 1.2 gm/ha 20 70 90 \n\n\n\n500gm/ha 1 gm/ha 20 75 95 \n\n\n\n400gm/ha 0.8 gm/ha 20 60 75 \n\n\n\n2 Flufenozuron 10% + Ethiprole 10% \n\n\n\n400gm/ha 0.8 gm/ha 20 70 90 \n\n\n\n300gm/ha 0.6 gm/ha 20 75 90 \n\n\n\n200gm/ha 0.4 gm/ha 20 65 75 \n\n\n\n3 \nChlorantraniliprole + \n\n\n\nThiamethoxam \n\n\n\n125gm/ha 0.25 gm/ha 20 65 90 \n\n\n\n100gm/ha 0.2 gm/ha 20 75 95 \n\n\n\n75gm/ha 0.15 gm/ha 20 60 70 \n\n\n\n4 Lamda-cyhalothrin \n\n\n\n600ml/ha 1.2 ml/ha 20 65 95 \n\n\n\n500ml/ha 1 ml/ha 20 70 85 \n\n\n\n400ml/ha 0.8 ml/ha 20 55 70 \n\n\n\n5 Proxifen 5% + Fenpropathrin 15% \n\n\n\n700ml/ha 1.4 ml/ha 20 70 90 \n\n\n\n650ml/ha 1.3 ml/ha 20 75 95 \n\n\n\n600ml/ha 1.2 ml/ha 20 60 70 \n\n\n\n6 \nPyriproxifen 5% + Fenpropathrin \n\n\n\n15% \n\n\n\n700ml/ha 1.4 ml/ha 20 75 95 \n\n\n\n650ml/ha 1.3 ml/ha 20 70 100 \n\n\n\n600ml/ha 1.2 ml/ha 20 50 65 \n\n\n\n7 Beauvaria bassiana \n\n\n\n2.75 kg /ha 0.0055 kg /ha 20 70 95 \n\n\n\n2.5kg /ha 0.005 kg /ha 20 75 90 \n\n\n\n2.25kg /ha 0.0045 kg /ha 20 50 70 \n\n\n\n8 \nPyriproxifen 5% + Fenpropathrin \n\n\n\n15% \n\n\n\n600 gm/ha 1.2 gm/ha 20 75 95 \n\n\n\n500 gm/ha 1 gm/ha 20 80 100 \n\n\n\n400 gm/ha 0.8 gm/ha 20 50 75 \n\n\n\n9 \nDihalo-pyrazole amid 40% + \n\n\n\nThiamathoxam 20% \n\n\n\n100gm/ha 0.2 gm/ha 20 65 95 \n\n\n\n75gm/ha 0.15 gm/ha 20 75 90 \n\n\n\n50gm/ha 0.1 gm/ha 20 60 75 \n\n\n\n10 \nPyriproxiyfen 5%+ Fenpropathrin \n\n\n\n15% \n\n\n\n30gm/ha 0.06 gm/ha 20 65 100 \n\n\n\n21gm/ha 0.042gm/ha 20 75 95 \n\n\n\n15 gm/ha 0.03 gm/ha 20 60 70 \n\n\n\n11 \nNitenpyrum 20%+ Pymetrozine \n\n\n\n60% \n\n\n\n150 gm/ha 0.3 gm/ha 20 75 95 \n\n\n\n100 gm/ha 0.2 gm/ha 20 75 90 \n\n\n\n50 gm/ha 0.1 gm/ha 20 55 70 \n\n\n\n12 \nEmamectin Benzoate 15% + \n\n\n\nLufenuron 3% \n\n\n\n150gm/ha 0.3 gm/ha 20 70 95 \n\n\n\n125gm/ha 0.25 gm/ha 20 70 95 \n\n\n\n100gm/ha 0.2 gm/ha 20 55 65 \n\n\n\n13 Imidaclopride \n\n\n\n250ml/ha 0.5 ml/ha 20 60 100 \n\n\n\n200ml/ha 0.4 ml/ha 20 70 95 \n\n\n\n150ml/ha 0.3 ml/ha 20 60 75 \n\n\n\n14 Quinalphos 25% \n\n\n\n2.0 lit/ha 0.004 lit/ha 20 70 100 \n\n\n\n1.68 lit/ha 0.00336 lit/ha 20 70 95 \n\n\n\n1.25 lit/ha 0.0025 lit/ha 20 65 75 \n\n\n\n15 \nChlorfenapyr 10% +Emamectin \n\n\n\nBenzoate 2% \n\n\n\n800 ml/ha 1.6 ml/ha 20 65 90 \n\n\n\n700 ml/ha 1.4 ml/ha 20 70 90 \n\n\n\n600 ml/ha 1.2 ml/ha 20 55 70 \n\n\n\n16 Diazinon \n\n\n\n650ml/ha 20 65 95 \n\n\n\n550ml/ha 20 65 95 \n\n\n\n450ml/ha 20 50 70 \n\n\n\n17 Control (water) \n\n\n\n- 20 20 40 \n\n\n\n- 20 25 45 \n\n\n\n- 20 25 35 \n\n\n\n3.2 Field evaluation of insecticides \n\n\n\nFifteen new insecticides Symazine 80WDG, Reset 20WDG, Virtako 40WG, \n\n\n\nLaida 2.5EC, Proxy 20 EC, Rock 20 EC, Daman, Pulser 20EC,  Dynamite \n\n\n\n60WDG, Nishan 20EC, Foringout 80WDG, Triple 33 WDG, Veto 20SL,  \n\n\n\nStarlux 25 EC,  Foni plus 12SC,   and a standard Hayzinon 60 EC @ 500 \n\n\n\ngm/ha, 300 gm/ha, 100 gm/ha, 500 ml/ha, 650 ml/ha, 2.57 kg /ha, 500 \n\n\n\ngm/ha, 75 gm/ ha, 21 gm/ ha, 100 gm/ ha, 125 gm/ha, 200ml/ha, 1.68 \n\n\n\nlitre/ha, 700 ml/ha and 550ml/ha respectively, were found effective for \n\n\n\ncontrolling jute hairy caterpillar giving more than 89.84% reduction of \n\n\n\ninfestation in at JAES, Manikganj and Tarabo sub-station, Narayanganj at \n\n\n\n5th day after spray (Table 4). In case of Manikganj, fifteen different \n\n\n\ninsecticides group along with a standard group insecticide were found \n\n\n\neffective for controlling jute hairy caterpillar giving 95.38, 94.55, 95.19, \n\n\n\n92.85, 93.59, 94.22, 93.49, 93.62, 89.84, 95.72, 93.56, 93.38, 94.42, 95.39, \n\n\n\n91.34 and 95.41 % reduction of infestation over control plot respectively \n\n\n\n(Table 4). This result is an agreement with that result of sultan et. al. \n\n\n\n(2019) who tested Fusion 20SL (Imidacloprid 20 SL), Pilarmit 10 SL \n\n\n\n(Nitenpyrum), Lama 24 SC (Thiacloprid 24% SC) and Thipro 18%SC \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\n(Thiamethoxam 12% + Fipronil 6%) @ 500 ml/ha, 300 ml/ha, 120 ml/ha \n\n\n\nand 100ml/ha respectively and found effective against jute hairy \n\n\n\ncaterpillar giving 91.09, 89.41, 93.32 and 93.24 % reduction of infestation \n\n\n\nover control plot respectively. The highest reduction 95.72% was obtained \n\n\n\nfrom the plot treated with Nishan 20EC (Pyriproxiyfen 5%+ \n\n\n\nFenpropathrin 15%) which was statistically similar with other \n\n\n\ninsecticides over control plot. The lowest reduction 89.84% was obtained \n\n\n\nfrom the plot treated with Dynamite 60WDG (Dihalo-pyrazole amid 40% \n\n\n\n+ Thiamathoxam 20%). In case of Tarabo, Narayanganj, fifteen different \n\n\n\ninsecticides group along with one standard group insecticide were found \n\n\n\neffective for controlling jute hairy caterpillar giving 95.78, 93.32, 93.97, \n\n\n\n93.18, 92.09, 92.49, 93.74, 92.93, 92.29, 93.69, 93.95, 93.17, 95.31, 94.99, \n\n\n\n92.11 and 94.53 % reduction of infestation over control plot respectively \n\n\n\n(Table 4). A group researchers tested Heping 10WDG (Emamectin \n\n\n\nBenzoate), Lamtech 2.5 EC (Lambda cyhalothrin), Fair Kill 10 EC \n\n\n\n(Cypermethrin), Key 70 WG (Imidacloprid 70 %) and Newril 85 WP @ 750 \n\n\n\ngm/ha, 750 gm/ha, 200 ml/ha, 550 ml/ha, 30 gm/ha and 1.7 kg/ha and \n\n\n\nfound effective against jute hairy caterpillar giving 86.13, 81.51, 74.11, \n\n\n\n76.39 and 76.15 reduction of infestation over control plot respectively \n\n\n\n(Rahman et al., 2020).  Among these insecticides, the highest percent \n\n\n\nreduction of jute hairy caterpillar (95.78%) was obtained from the plot \n\n\n\ntreated with Symazine 80WDG (Cyromazine 80%) which was statistically \n\n\n\nsimilar with other insecticides. The lowest reduction 92.09% was \n\n\n\nobtained from the plot treated with Proxy 20 EC (Proxifen 5% + \n\n\n\nFenpropathrin 15%). All insecticides showed statistically similar result \n\n\n\nexcept control plot.  Similar result was found by who worked with Celeron \n\n\n\n50 EC and Nokon 60 EC against jute hairy caterpillar and found 92.50% \n\n\n\nand 90.70% reduction of infestation respectively (Polan et al., 2009). Some \n\n\n\nresearchers worked with Quinalphos 25 EC and Dianzinon 60 EC against \n\n\n\njute hairy caterpillar and found 96.73 and 95.46% reduction of infestation, \n\n\n\nrespectively (Banu et al., 2007). Zaman worked with Pyriphos 20EC @ \n\n\n\n0.025% a.i. and found 86.67% reduction of jute hairy caterpillar after 72 \n\n\n\nhours of spray (Zaman, 1990). Similar experiment with some \n\n\n\norganophosphorus insecticides was done by (15) found 93.3% mortality \n\n\n\nafter 72 hours of spray when he worked with Biocyp 10EC @ 0.75 cc/lit \n\n\n\nagainst jute hairy caterpillar in the laboratory. They also worked with \n\n\n\nKarate 2.5EC @0.01872% a.i. in the field and found 97.7% and 97.4% \n\n\n\nreduction in two locations. Some researchers reported that emamectin \n\n\n\nbenzoate 5 SG showed most toxicity to the 3rd instar larvae of S. obliqua \n\n\n\n(Nair et al., 2007). Devi and Srivastava reported that imidacloprid showed \n\n\n\nsame percent reduction of hairy caterpillar infestation (Devi and \n\n\n\nSrivastava, 2018).  Salim and Abed conducted an experiment on chemical \n\n\n\ncontrol against Spilosoma obliqua on cabbage where he reported that \n\n\n\nCypermethrin (0.07%) significantly increased population reduction of \n\n\n\nSpilosoma obliqua (Salim and Abed, 2015). \n\n\n\nTable 4: Field efficacy evaluation of new insecticide against jute hairy caterpillar at Manikganj and Narayanganj, 2020 \n\n\n\nSL \n\n\n\nNo \n\n\n\nTrade \n\n\n\nname \n\n\n\nGeneric \n\n\n\nName \nDose/ha \n\n\n\nManikganj,  2020 Tarabo, 2020 \n\n\n\nNo. of \n\n\n\ncaterpillar/ \n\n\n\nplot before \n\n\n\nspray \n\n\n\n(average) \n\n\n\n% Reduction \n\n\n\nof infestation \n\n\n\nafter 5 days \n\n\n\nof spray \n\n\n\nNo. of \n\n\n\ncaterpillar/ \n\n\n\nplot before \n\n\n\nspray \n\n\n\naverage) \n\n\n\n%Reduction of \n\n\n\ninfestation after \n\n\n\n5 days of spray \n\n\n\n1. Symazine 80WDG Cyromazine 80% 500gm/ha 76.33 95.38a 73.00 95.78a \n\n\n\n2. Reset 20WDG \nFlufenozuron 10% + \n\n\n\nEthiprole 10% \n300gm/ha 72.33 94.55a 68.67 93.32a \n\n\n\n3. Virtako 40WG \nChlorantraniliprole + \n\n\n\nThiamethoxam \n100gm/ha 91.67 95.19a 88.33 93.97a \n\n\n\n4. Laida 2.5EC Lamda-cyhalothrin 500ml/ha 85.67 92.85a 88.33 93.18a \n\n\n\n5. Proxy 20 EC \nProxifen 5% + \n\n\n\nFenpropathrin 15% \n650ml/ha 81.67 93.59a 78.33 92.09a \n\n\n\n6. Rock 20 EC \nPyriproxifen 5% + \n\n\n\nFenpropathrin 15% \n650ml/ha 96.67 94.22a 86.00 92.49a \n\n\n\n7. Daman Beauvaria bassiana 2.5kg /ha 78.33 93.49a 70.00 93.74a \n\n\n\n8. Pulser 20EC \nPyriproxifen 5% + \n\n\n\nFenpropathrin 15% \n500 gm/ha 81.33 93.62a 77.67 92.93a \n\n\n\n9. Dynamite 60WDG \n\n\n\nDihalo-pyrazole amid \n\n\n\n40% + Thiamathoxam \n\n\n\n20% \n\n\n\n75gm/ha 64.00 89.84a 70.33 92.29a \n\n\n\n10. Nishan 20EC \nPyriproxiyfen 5%+ \n\n\n\nFenpropathrin 15% \n21gm/ha 95.00 95.72a 79.33 93.69a \n\n\n\n11. Foringout 80WDG \nNitenpyrum 20%+ \n\n\n\nPymetrozine 60% \n100gm/ha 98.67 93.56a 85.00 93.95a \n\n\n\n12. Triple 33 WDG \nEmamectin Benzoate 15% \n\n\n\n+ Lufenuron 3% \n125gm/ha 78.67 93.38a 81.67 93.17a \n\n\n\n13. Veto 20SL Imidaclopride 200ml/ha 70.00 94.42a 71.67 95.31a \n\n\n\n14. Starlux 25 EC Quinalphos 25% 1.68 lit/ha 76.33 95.39a 79.67 94.99a \n\n\n\n15. Foni plus 12SC \nChlorfenapyr 10% \n\n\n\n+Emamectin Benzoate 2% \n700 ml/ha 72.33 91.34a 76.67 92.11a \n\n\n\n16. \nHayzinon 60 EC \n\n\n\n(Standard) \n\n\n\nDiazinon 550ml/ha \n95.00 95.41a 90.00 94.53a \n\n\n\n17. Control (water) 70.00 23.13b 66.67 19.20b \n\n\n\n18. LSD(5%) 6.69 7.22 \n\n\n\n3.3 Effects of insecticides on jute fibre and stick yield production \n\n\n\nPesticides have been a major contributor to the growth of agricultural \n\n\n\nproductivity and food supply. Pesticides were a key factor in significant \n\n\n\nagricultural productivity growth during the last century and continue to \n\n\n\nbe a critical factor in reducing crop damage (Steven et al., 2007). Jute fibre \n\n\n\nyield is damaged up to 15% due to the attack of jute hairy caterpillar. Due \n\n\n\nto the application of fifteen insecticides with their respective dose, jute \n\n\n\nfibre yield was varied from each other. In Manikganj, the highest fibre yield \n\n\n\n(3.66 t/ha) was found in the plot which was treated with Rock 20 EC \n\n\n\nfollowed by Foni plus 12SC (3.65t/ha). The lowest (2.96t/ha) fibre yield \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\nwas found in Reset 20WDG treated plot which was higher over control plot \n\n\n\n(Figure 5). In case of Narayanganj, the highest fibre yield (3.85 t/ha) was \n\n\n\nfound in the plot which was treated with Proxy 20 EC followed by Foni \n\n\n\nplus 12SC (3.84t/ha). The lowest (2.79t/ha) fibre yield was found in \n\n\n\nDaman treated plot which was higher over control plot. This result is \n\n\n\nagreement where they reported that the minimum fibre yield was \n\n\n\nrecorded to be 17.96 q/ha from the control plot that was not spraying with \n\n\n\nchemical (Rahman and Khan, 2010). \n\n\n\nSome researchers in his two separate experiments, found similar fibre \n\n\n\nyield after insecticides and acaricides spraying in Corcorus olitorius  jute \n\n\n\n(Rahman et al., 2020). A group researcher also stated similar fibre yield \n\n\n\nafter insecticides application in his experiment (Sultan et al., 2019). In \n\n\n\nother hand, some study showed same result when they tested eight Tossa \n\n\n\nJute (Corchorus olitorius L.) for the analyses of variability, Euclidean \n\n\n\nclustering and principal components for genetic diversity of some olitorius \n\n\n\nvarieties in his study (Mukul et al., 2020). Some researchers also \n\n\n\nconducted an experiment on black gram for evaluation of insecticides \n\n\n\nwhere he reported that in case of yield, all the treatments showed \n\n\n\nsignificant increase of yield in black gram (Mandal et al., 2013). All plots \n\n\n\ntreated with insecticides showed higher fibre yield than the plot with no \n\n\n\ninsecticide sprayed/control plot. From the result, it was noticed that \n\n\n\ninsecticides have positive impact on jute fibre yield production. \n\n\n\nFigure 5: Effect of insecticides on fibre yield production of Corchorus \n\n\n\nolitorius. \n\n\n\nIn stick yield production at Manikganj, the highest stick yield (13.82 t/ha) \n\n\n\nwas found in the plot which was treated with Hayzinon 60 EC followed by \n\n\n\nProxy 20 EC (12.56t/ha). The lowest (5.90t/ha) fibre yield was found in \n\n\n\nTriple 33 WDG treated plot which was higher over control plot (Figure 6). \n\n\n\nIn case of Narayanganj, the highest stick yield (10.52t/ha) was found in \n\n\n\nthe plot which was treated with Foni plus 12SC and statistically similar to \n\n\n\nProxy 20 EC (9.58t/ha), Hayzinon 60 EC (8.85t/ha) and Starlux 25 EC \n\n\n\n(8.65t/ha). This result is an agreement with that result of (Rahman et al., \n\n\n\n2020; Mukul, 2020). Mukul Also found same result when he examined \n\n\n\ntwelve Tossa Jute (Corchorus olitorius L.) genotypes for elucidation of \n\n\n\nGenotypic Variability, Character Association, and Genetic Diversity for \n\n\n\nStem Anatomy (Mukul, 2020). The lowest (6.04t/ha) stick yield was found \n\n\n\nin Symazine 80WDG treated plot which was higher over control plot. In \n\n\n\nanother study, tested twelve tossa jute (Corchorus olitorius L.) genotypes \n\n\n\nbased on variability, heritability and genetic advances for yield and yield \n\n\n\nattributing morphological traits and found same fibre and stick yield in \n\n\n\ntheir research work (Mukul et al., 2020). \n\n\n\nFigure 6: Effect of insecticides on stick yield production of Corchorus \n\n\n\nolitorius. \n\n\n\n3.4 Correlation and regression study \n\n\n\nThe degree of statistical relationship between fibre weight and stick \n\n\n\nweight in both locations has been found significant relationship at 5% \n\n\n\nlevel of probability (P>0.05). The positive slopes exhibited positive \n\n\n\nrelationship. In Narayanganj, the degree of relationship between fibre \n\n\n\nyield and stick yield was studied (Figure 7). The result revealed that fibre \n\n\n\nyield and stick yield have a direct significant positive relationship at 5% \n\n\n\nlevel of significance which has been confirmed with correlation co-\n\n\n\nefficient r = 0.9673. The relationship was more evident by the equation Y= \n\n\n\nand sowing gradual Y = 3.12x \u2013 2.28 increase in stick yield with the \n\n\n\nincrease of fibre yield. \n\n\n\nFigure 7: Relationship between fibre yield and stick yield of Corchorus \n\n\n\nolitorius in Narayanganj \n\n\n\n4. CONCLUSION\n\n\n\nBased on the findings of present research work, it can be resolved that all \n\n\n\ntested insecticides considering percent reduction of infestation over \n\n\n\ncontrol (> 89.84%), fibre yield (>2.79 t/ha) and stick yield (>5.90 t/ha) \n\n\n\nwere more or less similar to the standard and better than control. All \n\n\n\nchemical insecticides can be suggested for the farmers\u2019 use. In this \n\n\n\nexperiment, there were some new combinations of insecticide, which \n\n\n\nworks with low dose on wide range of insects species. These combinations \n\n\n\nwill be effective to solve resistance problem. As well as farmers will have \n\n\n\na chance to select alternate chemical insecticides according to availability \n\n\n\nand affordability. \n\n\n\nAUTHORS' CONTRIBUTION\n\n\n\nMd. Sohanur Rahman contributed in research conduction, data analysis, \n\n\n\nsearching journal for publication and finally manuscript writing & \n\n\n\nprocessing of this article. Mohammad Sahin Polan and Md. Nazrul Islam \n\n\n\nconducted the research. Fakhar Uddin Talukder and Md. Mia Mukul helped \n\n\n\nin research conduction. All the authors were concerned all about the \n\n\n\nresearch investigation, reporting, article writing, correction and finally \n\n\n\napproval for publication. \n\n\n\nACKNOWLEDGEMENT\n\n\n\nAuthor takes a chance to express his thankfulness to in charge and \n\n\n\nrespective personnel\u2019s of Manikganj and Narayanganj station, Bangladesh \n\n\n\nJute Research Institute, Ministry of agriculture which had the technical \n\n\n\nsupport on the successful completion of this research work. \n\n\n\nREFERENCES\n\n\n\nAbbott, W.S., 1925. A method of computing the effectiveness of an \n\n\n\ninsecticide. J. Econ. Entomol., 18, Pp. 265-267. \n\n\n\n Ahmmed, S., Islam, M.N., Polan, M.S., Rahman, M.S., Rahman, M.T., 2019. \n\n\n\nEffectiveness of Some Chemical Insecticides Against Jute Hairy. Int. J. \n\n\n\nSustain. Agril. Tech., 15 (4), Pp. 01-05. \n\n\n\nBanu, H., Islam, M.N., Haque, S.M.A., Kamruzzaman, A.S.M., and Polan, M.S., \n\n\n\n2007. Effectiveness of some insecticides against jute hairy caterpillar, \n\n\n\n(Spilarctia obliqua Walker). Int. J. Sustain. Agril. Tech., 3 (5), Pp. 30-32. \n\n\n\n0.00\n\n\n\n2.00\n\n\n\n4.00\n\n\n\n6.00\n\n\n\nFi\nb\n\n\n\nre\n y\n\n\n\nie\nld\n\n\n\n (\nt \n\n\n\nh\na-\n\n\n\n1\n)\n\n\n\nInsecticides\n\n\n\nManikganj Narayanganj\n\n\n\n0.00\n\n\n\n2.00\n\n\n\n4.00\n\n\n\n6.00\n\n\n\n8.00\n\n\n\n10.00\n\n\n\n12.00\n\n\n\n14.00\n\n\n\n16.00\n\n\n\nS\nti\n\n\n\nck\n y\n\n\n\nie\nld\n\n\n\n (\nt/\n\n\n\nh\na\n)\n\n\n\nInsecticides\n\n\n\nManikganj Narayanganj\n\n\n\ny = 3.12x - 2.28\nR\u00b2 = 0.9357\n\n\n\n0.00\n\n\n\n2.00\n\n\n\n4.00\n\n\n\n6.00\n\n\n\n8.00\n\n\n\n10.00\n\n\n\n12.00\n\n\n\n0.00 1.00 2.00 3.00 4.00 5.00\n\n\n\nS\nti\n\n\n\nck\n y\n\n\n\nie\nld\n\n\n\n (\nt/\n\n\n\nh\na\n\n\n\n)\n\n\n\nFibre yield (t/ha)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 115-122 \n\n\n\nCite the Article: Md. Sohanur Rahman, Md. Nazrul Islam, Mohammad Sahin Polan, Fakhar Uddin Talukder and Md. Mia Mukul (2021).  \nRelative Toxicity of Some Chemical Pesticides Against Jute Hairy Caterpillar (Spilosoma Obliqua W.) In Tossa Jute (Corchorus Olitorius L.).  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 5(2): 115-122. \n\n\n\nBhattacharya, P.K., Ram, W.H., and Sarker, S., 1995. Spilosoma obliqua \n\n\n\n(Walker): economic importance, biology, host range and breeding for \n\n\n\nresistance in soybean. Agric. Rev. India. 16 (l&2), Pp. 23-40. \n\n\n\nDevi, N.I., Srivastava, R.P., 2018. Bioefficacy of Ethiprole 40 + Imidacloprid \n\n\n\n40 (Glamore 80wg) Against Bihar Hairy Caterpillar, Spilarctia Obliqua \n\n\n\n(Walker). Indian Journal of Entomology, 80 (2), Pp. 197-202. \n\n\n\nhttps://doi.org/10.5958/0974-8172.2018.00035.4 \n\n\n\nGomez, K.A., and Gomez, A.A., 1984. Statistical Procedures for Agriculture \n\n\n\nResearch. John Weley and Sons. Inc., New York, Pp. 67-265. \n\n\n\nGupta, G., Bhattacharya, A.K., 2008. Assessing toxicity of postemergence \n\n\n\nherbicides to the Spilarctia obliqua Walker (Lepidoptera: Arctiidae). \n\n\n\nJournal of Pest Science, 81, Pp.  9- 15. https://doi.org/10.1007/s10340-\n\n\n\n007-0175-8  \n\n\n\nKabir, A.K.M.F., 1975. White mite, Hemitarsonemus latus (Banks) Ewing. In \n\n\n\njute pests of Bangladesh. Bangladesh Jute Research Institute. Dhaka, \n\n\n\nBangladesh. Pp. 28-33. \n\n\n\nKabir, A.K.M.F., Khan, S.A., 1968. Bioassay of some insecticides for the \n\n\n\ncontrol of jute hairy caterpillar, Diacrisia oblique Walk. Indian Journal \n\n\n\nof Agricultural Science, 6 (1-2), Pp.131-138. \n\n\n\nKabir, S.M.H., Maleque, M.U.M.A., 1974. Effectiveness of some, organ \n\n\n\nphosphorus insecticides against jute hairy caterpillar, Diacrisia obliqua \n\n\n\n(Walker). Bangladesh. L Zool. 2 (2), Pp. 23-40. \n\n\n\nMandal, D., Bhowmik, P., Baral, K., 2013. Evaluation of insecticides for the \n\n\n\nmanagement of bihar hairy caterpillar, Spilosoma obliqua walk. \n\n\n\n(Lepidoptera: Arctiidae) in black gram (Vigna Mungo L.). The Bioscan., \n\n\n\n8 (2), Pp. 429-43. https://doi.org/10.20546/ijcmas.2018.706.069 \n\n\n\nMukul, M.M., 2020. Elucidation of Genotypic Variability, Character \n\n\n\nAssociation, and Genetic Diversity for Stem Anatomy of Twelve Tossa \n\n\n\nJute (Corchorus olitorius L.) Genotypes. BioMed Research International, \n\n\n\nVolume 2020, Article ID 9424725, Pp.16, \n\n\n\nhttps://doi.org/10.1155/2020/9424725 \n\n\n\nMukul, M.M., Akter, N., Ahmed, S.S.U., Mostofa, M.G., and Ghosh, R.K., 2020. \n\n\n\nGenetic diversity analyses of twelve tossa jute (Corchorus olitorius L.) \n\n\n\nGenotypes based on variability, heritability and genetic advances for \n\n\n\nyield and yield attributing morphological traits. Int. J. Plant Breed. \n\n\n\nGenet., 14, Pp. 9-16. DOI: 10.3923/ijpbg.2020.9.16 \n\n\n\nMukul, M.M., Akter, N., Mostofa, M.G., Rahman, M.S., Hossain, M.A., Roy, \n\n\n\nD.C., Jui, S. A., Karim, M.M., Ferdush, J., Hoque, M.M., Mollah, M.A.F., 2020. \n\n\n\nAnalyses of variability, euclidean clustering and principal components \n\n\n\nfor genetic diversity of eight Tossa Jute (Corchorus olitorius L.). Plant \n\n\n\nScience Today, 7 (4), Pp. 564\u2013576. \n\n\n\nhttps://doi.org/10.14719/pst.2020.7.4.854 \n\n\n\nNair, N., Sekh, K., Debnath, M., Chakraborty, S., Somchoudhury, A.K., 2007. \n\n\n\nRelative toxicity of some chemicals to bihar hairy caterpillar, Spilarctia \n\n\n\nobliqua Walker (Arctiidae, Lepidoptera). Journal of Crop Weed., 3 (1), \n\n\n\nPp. 1-2. https://doi.org/10.1016/j.micres.2005.04.006 \n\n\n\nPolan, M.S., Banu, H., Islam, M.N., Haque, S.M.A., and Mosaddeque, H.Q.M., \n\n\n\n2009. Field Efficacy and Evaluation of Effective Dose of some \n\n\n\ninsecticides against jute hairy caterpillar Spilosoma obliqua (Walker) \n\n\n\nBangladesh. J. Jute Fib. Res., 29 (1-2), Pp. 69-75. \n\n\n\nRahman, M.S., Islam, M.N., Talukder, F.U., Sultan, M.T., 2020. Evaluation of \n\n\n\ninsecticides for the management of jute hairy caterpillar, spilosoma \n\n\n\nobliqua walk. (lepidoptera: arctiidae) in jute (Corcorus Olitorius), \n\n\n\nInternational Journal of Entomology Research, 5 (4), Pp. 71-77.  \n\n\n\nRahman, M.S., Polan, M.S., Islam, M.N., Rahman, M.A., 2020. Effect of \n\n\n\nacaricides on yellow mite, Polyphagotarsonemus latus infestation in \n\n\n\njute and its response to fibre yield, Journal of Entomology and Zoology \n\n\n\nStudies, 8 (1), Pp. 1083-1088, \n\n\n\nhttp://www.entomoljournal.com/search/?q=8-1-358 \n\n\n\nRahman, S., Khan, M.R., 2010. Integrated management approach for \n\n\n\ncontrol of the pest complex of olitorius jute, Corchorus olitorius L, \n\n\n\nJournal of Plant Protection Research. 50 (3), Pp. 340\u2013346. DOI: \n\n\n\n10.2478/v10045-010-0058-5 \n\n\n\nSalim, H.A., Abed, M.S., 2015. Effect of botanical extracts, biological and \n\n\n\nchemical control against Spilosoma oblique on cabbage (Brassica \n\n\n\noleracea). Journal of Entomology and Zoology Studies, 3 (1), Pp. 43-46. \n\n\n\nSharif, M., 1962. Jute and the possibilities of their adequate control. Jute \n\n\n\nand Jute Fabrics, 2 (3), Pp. 63-70. \n\n\n\nSteven, E., Lei, S.Z., Zilberman, D., 2007. The Economics of Pesticides and \n\n\n\nPest Control.  International Review of Environmental and Resource \n\n\n\nEconomics, 1 (3), Pp. 271-326. \n\n\n\nhttps://doi.org/10.1561/101.00000007 \n\n\n\nTalukder, D., Khan, A.R., Hasan, M., 1989. Growth of Diacrisia obliqua \n\n\n\n[Lepidoptera:Arctiidae] with low doses of Bacillus thringiensis Var. \n\n\n\nKurstaki. Entomophaga, 34 (4), Pp. 587-589. \n\n\n\nhttps://doi.org/10.1007/bf02374397 \n\n\n\nZaman, F., 1990. Evaluation of pesticides. Annual Report, 1990. BJRI, \nDhaka. Pp. 235-238.\n\n\n\n\nhttps://doi.org/10.5958/0974-8172.2018.00035.4\n\n\nhttps://doi.org/10.1007/s10340-007-0175-8\n\n\nhttps://doi.org/10.1007/s10340-007-0175-8\n\n\nhttps://doi.org/10.20546/ijcmas.2018.706.069\n\n\nhttps://doi.org/10.1561/101.00000007\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 60-63 \n\n\n\nCite The Article: Roshan Dhakal, Binod Joshi, Rupak Karn, Sagar Bhusal And Bibek Acharya (2019) A Review On Scenario, Challenges And Prospects Of Poultry Production \nIn Nepal. Malaysian Journal Of Sustainable Agriculture, 3(2) : 60-63. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 29 July 2019 \nAccepted 30 August 2019 \nAvailable online 12 September 2019\n\n\n\nABSTRACT\n\n\n\nPoultry production is a growing industry that accounts about 3.5% of total GDP. The number of commercial farm is \nconcentrated to districts like Chitwan, Kathmandu and Kaski but major of the poultry farming is followed by rural people \nwhich is under the free range system and low input production system. This review was written to summarize and study \nthe present status, challenges and potential of poultry farming. In order to meet the demand of the poultry, the \ncommercial poultry population has increased about more than two times than the last decade. Similarly, there has been \nmarked increase in the number of laying birds, meat production and egg production in these recent years. But it has not \nbeen able to surpass the demand in the market. To meet the demand through commercialization, people have suffered \ndifferent challenges like increase cost of production, lack of maintenance of bio-security, improper maintenance of \nhousing ,lack of  proper knowledge about poultry production ,irregular supply of qualifiable chicks ,religious and cultural \nrestrictions ,outbreak of different diseases , lack of slaughter house and processing plant etc which has threatened the \npoultry business which can be uplifted through certain management strategies along with policies, programs and \nawareness campaign. \n\n\n\n KEYWORDS \n\n\n\nbio-security, commercialization, strategies, demand.\n\n\n\n1. INTRODUCTION \n\n\n\nAgriculture is an important occupation in the context of Nepal. One of the \nfastest ways to supply human protein is through poultry. Agriculture \naccounts for about 33.7% of the national GDP, but the poultry sector \naccounts for about 3.5% of the TGDP [1]. Nepal is one of the best places of \npoultry rearing due to its rich biodiversity. Nepal is one of the highest \npercentages of Asian livestock (livestock and poultry, 5.8 per family), and \nis 70 per cent of the population rearing some types of livestock [2]. FAO \nhas recommended as a daily average protein intake by a person should be \nabout 65 g /day of which more than 50% should be from the animal source \n[2]. \n\n\n\nNepal has complex and various topography of land where livestock \nfarming exists in all the regions including poultry farming however most \nof the farmers raise small numbers of livestock in small land holdings [3]. \nThe major district with higher number of poultry farms in Nepal are \nChitwan, Kathmandu and Kaski [4]. The poultry farming as a business \nseemed to be started since 1980, however the expected achievement has \nbeen achieved yet. The fowl population in Nepal is about 47.96 million \nwith 28.3% laying hen, producing 887.24 million table eggs. Average \nannual growth rate of hen egg production has been 2.43% during last ten \nyears [5]. In the recent years the size of poultry population has \nsignificantly increased and the presently population of laying hens is \n8233616 and the meat production from poultry 42810 metric tons [6-8].  \nIn rural areas of Nepal, background poultry farming is still an important \nsource of cash generation and protein supplement which are well adapted \nto the low input production system, tolerance to the diseases, and poor \nnutrition. Intensive grain-based poultry farming along with extensive free-\nrange system is flourishing day by day with significant contribution on the \nnational GDP. The objective of our review is to study about the present \nstatus, problems and potential of poultry production in Nepal. \n\n\n\n2. METHODOLOGY\n\n\n\nThis review is completely based on the secondary data which are collected \nfrom the study of different journals, research papers, books, articles and \nmagazines. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Scenario of Poultry Business in Nepal \n\n\n\nNepal lies at 112th position for chicken meat production of world, which is \nat the 92nd for egg production in world [9]. Currently, total investment in \nthis sector is around 22billion and total number of broiler farm is around \n1000 and layer farm are 500. The grandparent stock for the poultry was \nestablished by Cobb Nepal with start of production from September 2013 \n[9]. The total production of broiler is 1170573 and layer chicken per week \nis 118208.  The total feed production is 646845 tonnes in 2010/2011 and \ndemand of poultry meat per day is around 150000kg/day [9]. \n\n\n\nIn spite of the fact that growing prices of feed materials, climatic extremes, \nand lack of good conductive government policies, poultry industry is \ngrowing as the emerging profit-motive industry since past decade. \nDifferent components like feed industries, hatcheries, integrated egg \nproducer, meat processor, medicine, packaging and allied agencies, big \nlayer farm, big broiler farm etc are the important components of poultry \nindustry, in use in Nepal .The number of medicine and health institute \nrelated to poultry is relatively few but number of feed industries \n,hatcheries and integrated egg producer has been increased to 111,98 and \n150 respectively which is clearly depicted in the table no 1. \n\n\n\nTable 1: Different structure of poultry industry in Nepal \n\n\n\nS.N. Poultry scenario  Total no \n1. Feed industries 111 \n2. Hatcheries 98 \n3. Integrated egg producer 150 \n4. Integrated broiler producer 65 \n5. Meat Processor 8 \n6. Medicine 6 \n7. Packaging and allied agencies 50 \n8. Big layer farm >500 \n9. Big broiler farm >1000 \n\n\n\n(Source: MOAD 2013 [8], Nepal Feed Industry Association Brochure 2011 \n[10]) \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.02.2019.60.63 \n\n\n\nA REVIEW ON SCENARIO, CHALLENGES AND PROSPECTS OF POULTRY \nPRODUCTION IN NEPAL \n\n\n\nRoshan Dhakal*, Binod Joshi, Rupak karn, Sagar Bhusal and Bibek Acharya \n\n\n\nAgriculture and Forestry University, Chitwan, Nepal \n*Corresponding Author E-mail: wrotsan@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\nREVIEW ARTICLE \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 60-63 \n  \n  \n\n\n\n\n\n\n\nCite The Article: Roshan Dhakal, Binod Joshi, Rupak Karn, Sagar Bhusal And Bibek Acharya (2019) A Review On Scenario, Challenges And Prospects Of Poultry Production In \nNepal. Malaysian Journal Of Sustainable Agriculture, 3(2) : 60-63. \n\n\n\n\n\n\n\n3.2 National poultry flock \n \n\n\n\nFrom the figure 1, it is clearly visible that the population of poultry has \nbeen in increasing trend which has increased from 21.37 million in 2002 \nto 68.6 million in 2016 even though the slight decrease in seen in 2005 and \n2009 than that of previous years. However, population of duck has been \ndecreased from 0.41 million in 2002 to 0.39 million in 2012. Similarly, \naccording to the census 2001/2002, the number of pigeons and other \nbirds were reported to be 1845234 and 57313 respectively [11]. \n \n\n\n\nIn our country, there is the commercial use of exotic breeds such as Cobb \n100, Cobb 500, Venn Cobb, Cobb Avian, Lohman Indian River, Hubbard \nFlex, Ross-308, Kasila, Hyline brown. Lohman brown, H&N Nick brown, \nBovans brown, B.V. 380, Isa brown, Dominant CZ and Hisex brown which \nare the major breed reared under more intensive management systems \nwith adequate housing, nutrition and health control [9]. Similarly, the \nnative breeds of poultry are hardy in nature, suitable for scavenging with \nhigh meat quality such as Sakini, Ghanti Khuile and Puwakh ulte (Dumse) \n[9]. \n \n\n\n\n \n \nFigure 1: Poultry population (in thousands) in different years (Source: \nStatistical information on Nepalese Agriculture 2015/16 [12]) \n \n\n\n\n3.3 Production \n \n\n\n\nThe Figures 2 and 3 is statistics of production of chicken and duck meat \nalong with their egg production. The chicken meat production has \nincreased from 15881 tonnes in 2003/04 to 5504 tonnes in 2015/16 but \nthe duck meat production was almost constant during the period with very \nslight changes over year. \n \n\n\n\n\n\n\n\nFigure 2: Meat production in different years (Source: Statistical \ninformation on Nepalese Agriculture 2015/16, [12]). \n \nExcept in 2008/09, there is also increase in egg production from about 560 \nmillion in 2003/2004 to 1294 million in 2015/2016 which is clearly seen \nin fig no 3. \n \n\n\n\n \n \nFigure 3: Egg production in different years (Source: Statistical \ninformation on Nepalese Agriculture 2015/16, [12]). \n\n\n\nSimilarly, the numbers of laying hens consistently increased from 6.68 \nmillion in 2003/04 to 12.35 million in 2015/16 whereas the population of \nduck layer has been decreased from 0.21 million in 2003/04 to 0.18 \nmillion in 2015/16. \n \n\n\n\n \n \nFigure 4: Egg production in different years (Source: Statistical \ninformation on Nepalese Agriculture 2015/16, [12]). \n \nFigure 5 shows the contribution from buffalo, mutton, pork and chicken \nmeat represented in 2000/01 around 64 percent, 21 percent, 8 percent \nand 7 percent, respectively. These proportions remained similar until \n2005/06. However, poultry meat production has increased significantly \nfrom about 7-8% to 15 % in 2010/2011. This shows the demand of the \npoultry is increasing widely at the present context. \n \n\n\n\n \nFigure 5: Percentage of share of different animals in meat production. \n(Source: Statistical information on Nepalese Agriculture 2010/11, [13]). \n \nFig 6 has clearly showed the distribution of chickens and ducks in different \ngeographical region. The population of chickens is higher in hilly region, \nfollowed in terai and mountain region whereas the population of ducks is \nhigher in terai region about 72% , followed in hilly region and lowest in \nmountain region. Due to unfavorable climate and geographical complexity, \nthe population of poultry is less in mountain region. \n \n\n\n\n \n \nFigure 6: Distribution of poultry by physiographic region. (Source: \nStatistical information on Nepalese Agriculture 2010/11, [13]). \n \nPoultry is considered as the prime source of protein as it has been \nproviding protein content to  lots of household members and also helps in \nboosting  the food security of the nation [14,15,16,17]. Poultry are dual \npurpose breed which help to increase financial turnover. Poultry farming \nacts as an employment opportunity to the women and rural people of the \ncountry and help to increase the GDP of the country [18]. At present the \nnumber of outgoing people has significantly increased and to stop this \nscenario of brain drain from our country poultry farming can be best \nalternative for Nepal. Poultry manure is another important aspect of \npoultry as it contain all 13 essential plant nutrient among which N2 ,p2o5 \n\n\n\nand k2o content is 1-1.8%, 1.4-1.6%, 0.8-0.9% respectively [19]. \nFurthermore, poultry soft feathers can be important source for making \nsleeping bag, winter jacket, blanket, pillow etc.  Nowadays, they are used \nin experimental trial for research and other studies. This clearly shows the \n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n2000/2001 2005/2006 2010/2011\n\n\n\nBuffalo\n\n\n\nMutton (goat/sheep )\n\n\n\nPig\n\n\n\nPoultry\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 60-63 \n  \n  \n\n\n\n\n\n\n\nCite The Article: Roshan Dhakal, Binod Joshi, Rupak Karn, Sagar Bhusal And Bibek Acharya (2019) A Review On Scenario, Challenges And Prospects Of Poultry Production In \nNepal. Malaysian Journal Of Sustainable Agriculture, 3(2) : 60-63. \n\n\n\n\n\n\n\nimportance and increasing demand of poultry production. The demand of \npoultry meat and eggs is increasing at the annual rate of 25% and 10% \nrespectively in urban areas but the annual growth rate of poultry birds is \nonly 2.38% [20]. This clearly shows huge scope of poultry with increasing \ndemand in the sector. \n \n\n\n\n3.4 Challenges \n \n\n\n\nThe poultry business is being challenging day by day. Some of the key \npoints regarding this are explained below: \n \n\n\n\n3.4.1 Increasing cost of production \n \n\n\n\nThe cost of feed ingredients are in the scenario of increasing day by day \nwhich has increased the per unit cost of production of poultry, which is the \nmajor challenges for the poor Nepalese farmers. In more than 75 percent \nof total cost of production, the high cost of feed and treatment has its share \nsince production of raw ingredients especially the maize sustains only 40-\n50% of demand for feed production [18]. \n \n\n\n\n3.4.2 Irregular supply of qualifiable chicks at competitive price in the \ncontext of Nepalese market \n \n\n\n\nAs chicks are the key components for poultry enterprise and the overall \nproduction is totally dependent upon the quality of chicks bought. As in \nthe context of Nepal, there is no any such appropriate legal standard for \nthe quality assurance of chicks, which is being hot issue in this sector. \n \n\n\n\n3.4.3 Lack of proper knowledge about poultry production \n \n\n\n\nAs poultry is an enterprise requiring intensive care and management for \nits success. But still we are lagging behind in terms of perfect knowledge \nfor successful poultry production. \n \n\n\n\n3.4.4 Improper maintenance of housing \n \n\n\n\nScientific housing is the basic requirement for getting profit in rearing any \nanimals. In terms of poultry production, Nepalese are still following the \nconvectional housing. Most of the poultry based farmers are from rural \nareas where still people follow conventional system of poultry production \n[2]. \n \n\n\n\n3.4.5 Lack of maintenance of bio-security throughout the production \n \n\n\n\nBio-security is the issue should be kept in mind during any enterprise \nadoption. Issues like bacteria, virus, theft, etc. have also been troublesome \nin the context of poultry farming in Nepal. Poor bio-security, inefficient \ndisease diagnosis, treatment as well as prevention are other major \nproblems for poultry raising farmers. About 95% of small-scale poultry \nentrepreneurs do not have any formal training on farm management [20]. \n \n3.4.6 Poor association of poultry farmers across the value or supply \nchain \n \nFor any enterprise to be success, there should be strong association among \nthe different related sectors. Poor association among these sectors have \ncrated problem in value and supply chain with is itself a major problem. \n \n3.4.7 Price fluctuation in poultry\u2019s meat \n \nInstability in price meat among different times of year has also resulted in \nhindrance for the success of poultry enterprise in Nepal. Several ups and \ndown in the unit price of poultry has been a challenging problem.  \n \n3.4.8 Lack of enforcement of rules, regulations and guidelines \n \nRules, regulations and guidelines for poultry farming has not been made. \nThe already made rules and regulations are also not being formulated \nproperly. \n \n3.4.9 Lack of grandparent stock farm in Nepal \n \nThere is the lack of grandparent stock farms in Nepal which hinders the \nchicken production thus increasing the cost of chickens. Similarly, \ngrandparents stock farm and hatchery farms are not still flourished in our \ncountry [6]. Limited hatchery has also aided in the increase in price of \nchicks. \n \n 3.4.10 Religious and cultural restrictions \n \nDue to the religious and cultural factors, poultry enterprise is not being \nable to flourish in every community. As the poultry enterprise is still \nbanned in some of the Brahmins communities. \n \n\n\n\n3.4.11 Outbreak of different diseases \n \n\n\n\nDifferent new diseases are outbreak every year as like Bird Flu, \nInfluenza,H1N1 ,etc. But among them Bird Flu is the major disease \nresulting in heavy loss every year. Infectious Bursal Disease (IBD) is one \nof the major disease problems in Nepal followed by New Castle disease, \ncoccidiosis and pullorum amongst the major infectious diseases. \n \n\n\n\n3.4.12 Inability to maintain the temperature inside the housing \n \n\n\n\nTemperature is a key factor need to be maintained during housing. Proper \ntemperature management is much needed for the success of poultry. Due \nto improper management of temperature many chickens are dead due to \nlow temperature (in winter) and high temperature (in summer) \n \n\n\n\n3.4.13 Issues on health status due to heavy use of antibiotics \n \n\n\n\nThere is heavy use of antibiotics in poultry farming for obtaining fast \ngrowth. The antibiotics have created some sort of side effects in human \nhealth resulting in health hazards. About 50% of antibiotics are prescribed \ninappropriately [21]. About 71% of veterinary drugs sale are sold by self-\nprescription rather than qualified registered veterinarian [22]. \n \n3.4.14 Lack of slaughterhouse and processing plant \n \nSlaughterhouse and processing plant are not evenly distributed in all parts \nof the country. Proper management of available slaughterhouse is also an \nissue. \n \n3.4.15 Lack of quality feed ingredients \n \nQuality feed ingredients are still lacking. We are not having quality and \nbalanced feed from reliable source containing the standard for quality \nassurance. Most of the feed are imported from India, low in quality.  \n \n3.4.16 Possibility of immediate disease transmission threatening \npublic health \n \n Due to open borders, a large quantity of chickens is being imported from \nneighboring countries like India and China. Due to the lack of good \nquarantine control, several zoonotic diseases are also imported in our \ncountry. \n \n3.5 Prospects \n \nNepal is in a way to become a poultry hub and currently Nepal has been \nable to produce sufficient product but unable to meet all demand of \ncountry. Commercial sectors are involving in this sector. Poultry has \nbecome a major occupation of most of the people in Nepal. It has largely \nassisted the GDP of Nepal. People are being aware of their health and most \nof them have stop to consume meat containing high cholesterol level like \ngoat, buffalo, pig etc. and are heading towards poultry. Being a cheaper \nsource of Protein and nutrient as well as healthy, people are preferring \nwhite meat rather than red. It has been emerging as a leading sector in \nNepal and has provided job to many youths. Competition has been \nincreased in this sector that has make benefit to consumer but the price of \ncost of production is not able to reduce. Endorsement of rules and \nregulation regarding quality of products in market, butcher house etc. will \nensure hygienic and healthy product. This will create to avoid price \ndiscrimination while selecting quality products. \n \n\n\n\nIt has become utmost important to supervise regarding quality, \nvaccination, feed ingredient, and related product. Technical training and \nassistant given to farmer must be increased to and ensure bio-security. \nThe unofficial entry of poultry and related product has become threat to \npoultry industry and will continue in future. The threat of Bird Flu and \npossible outbreak of other disease will challenge to this industry. The \ngovernment and related agencies have made some remarkable effort to \ndecreased possible outbreaks in future. Poultry feed industries has able to \nproduced required feed ingredient needed for country. Some unofficial \nnews regarding the export of feed to India and chicks to Bhutan is coming. \nIf it is true, then it would be a great achievement in Poultry sector. We can \nconclude that, if authority concerned to this sector can minimize the price \nand diseases regarding poultry as well as ensure healthy product, there \nwill substantial increased in the demand. There is no doubt that poultry \nsector will be the backbone of national economy in near future. \n \n4. CONCLUSION AND RECOMMENDATIONS \n\n\n\n \nPoultry industry is a growing business in these recent years. The demand \nof its egg, meat, and chicken is increasing at the increasing rate but the \nproduction has not been able to surpass the requirement. There are \nvarious hindrances in trade, marketing, health, quality sanitation, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 60-63 \n  \n  \n\n\n\n\n\n\n\nCite The Article: Roshan Dhakal, Binod Joshi, Rupak Karn, Sagar Bhusal And Bibek Acharya (2019) A Review On Scenario, Challenges And Prospects Of Poultry Production In \nNepal. Malaysian Journal Of Sustainable Agriculture, 3(2) : 60-63. \n\n\n\n\n\n\n\nmanagement causing the production inefficient. Such challenges are \nthreatening day by day, causing the poultry business at risk. If certain \nmanagement strategies like policies, programs and awareness campaign, \nbetter quarantine check and quality control, extension of private and \npublic industries and various poultry regarding accessories can be done at \ntime, no doubt poultry business will cover more than today's coverage of \nTGDP. \n \nREFERENCES \n \n[1] CBS. 2012. Statistical Yearbook of Nepal, National Planning \nCommission, Nepal, 76. \n \n[2] FAO. 2009. Food and Agriculture Organization article on egg. Food and \nAgriculture Organization of United Nations Archived from the original on \n2004- 03-07. \n \n[3] Pradhanang, U.B., Pradhanang, S.M., Sthapit, A., Krakauer, N.Y.,  Jha., \nALakhankar, T. (2015) National Livestock Policy of Nepal: Needs and \nOpportunities, 5, 103-131.  \n \n[4] MOAC. 2014. Economic Survey for fiscal year 2013/2014. Economic \nSurvey, Minsitry of Agriculture and Cooperatives, Government of Nepal, \nSingha Durbar, Kathmandu, Nepal. \n \n[5] Osti, R., Zhou, D., Singh, V., Bhattarai, D., Chaudhary, H. 2016. An \nEconomic Analysis of Poultry Egg Production in Nepal.  Pakistan Journal of \nNutrition, 15(8), 715-724. DOI: 10.3923/pjn.2016.715.724. \n \n[6] MOAD. 2013. Statistical information on Nepalese Agriculture. \nAgribusiness Promotion and Statistics Division, Singh Durbar, Kathmandu, \nNepal, 41-42. \n \n[7] MOAC. 2012. Economic Survey for fiscal year 2012/2013. Economic \nSurvey, Minsitry of Agriculture and Cooperatives, Government of Nepal, \nSingha Durbar, Kathmandu, Nepal. \n \n[8] MOAC. 2013. Economic Survey for fiscal year 2013/2014. Economic \nSurvey, Minsitry of Agriculture and Cooperatives, Government of Nepal, \nSingha Durbar, Kathmandu, Nepal. \n \n[9] FAO. 2014.  Livestock Country Reviews. \n \n[10] NFIAB. 2011. Nepal Feed Association Bronchure (NFIAB), 21-24. \n \n[11] CBS. 2002. National Sample Census of Agriculture 2001/2002, \nNational Planning Commission Secretariat, Government of Nepal. \nhttp://cbs.gov.np/Agriculture/Agriculture%20Census2001/Nepal. \n \n\n\n\n[12] Government of Nepal. 2015/2016. Statistical Information on \nNepalese Agriculture. Ministry of Agriculture and Cooperatives, Agri-\nBusiness Promotion and Statistical Division, Singha Darbar, Kathmandu, \nNepal. \n \n[13] Government of Nepal. 2010/2011. Statistical Information on \nNepalese Agriculture. Ministry of Agriculture and Cooperatives, Agri-\nBusiness Promotion and Statistical Division, Singha Darbar, Kathmandu, \nNepal. \n \n[14] Mallia, J.G. 1999. Observations on family poultry units in parts of \nCentral America and sustainable development opportunities, Livestock \nResearch for Rural Development. \n11(3).http://www.cipav.org.co/lrrd/lrrd11/3/m al113.htm. \n \n[15] Permin, A., Pedersen, G., Riise, J.C. 2001. Poultry as a tool for poverty \nalleviation: opportunities and problems related to poultry production at \nvillage levels, In: R. G. Alders and P. B. Spradbrow (Eds.), Proceedings of \nthe workshop on Newcastle disease control in village chickens held from \n6-9 March 2000 at Maputo, Mozambique. \n \n[16] Walker, P., P. Rhubart-Berg, S. McKenzie, K. Kelling, and R.S. \nLawrence. 2005. Public health implications of meat production and \nconsumption, Public Health Nutrition, 8(4), 348-356. \n \n[17] Thomsen, K.A., Chrysostome, C., Houndonougbo, F.M. 2005. \nStrategies for income generation and marketing within the local context \u2013 \nthe case of smallholder poultry production and micro  credits in Benin, \nPaper presented at the workshop Does poultry reduce poverty and assure \nfood security? \u2013 a need for rethinking the approaches held on 30-31 \nAugust, 2005, Copenhagen, Denmark.  \n \n[18] Sharma, B. 2010. Review paper: Poultry production, management \nand bio-security measures, Journal of Agriculture and Environment, 11. \n \n[19] Chastain, John P., James J. Camberato, and Peter Skewes. (1999) \nPoultry Manure Production and Nutrient Content. 1\u201317. \n \n[20] Acharya, Prasad, K., Kaphle, K. 2015. Major Issues for Sustainable \nPoultry Sector in Nepal, Global Journal of Animal Scientific Research, 3(1), \n227\u201339. \n \n[21] DDA. 2012. Drug bulletin of Nepal: Department of Drug \nadministration, Ministry of Health and Population, Government of Nepal, \n23(3), 2829. \n \n[22] Khatiwada, S., Acharya, K.R. 2013. Trends in Antimicrobial Use in \nFood Animals of Nepal 2008-2012, B.V.Sc and A.H research thesis, Institute \nof Agriculture and animal science (IAAS), 44. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 77-81 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.77.81 \n\n\n\nCite The Article: Amrit Shrestha, Narayan Raj Joshi, Bhishma Raj Dahal, Subash Bhandari, Shree Ram Acharya, Bandana Osti(2021 ).Determinants Of Productivity And \nMajor Produciton Constraints Of Mango Farming In Saptari District Of Nepal. Malaysian Journal Of Sustainable Agriculture, 5(2): 77-81\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.02.2021.77.81 \n\n\n\nDETERMINANTS OF PRODUCTIVITY AND MAJOR PRODUCITON CONSTRAINTS OF \nMANGO FARMING IN SAPTARI DISTRICT OF NEPAL \n\n\n\nAmrit Shresthaa*, Narayan Raj Joshib, Bhishma Raj Dahala, Subash Bhandaria, Shree Ram Acharyaa, Bandana Ostia\n\n\n\na Agriculture and Forestry University, Chitwan, Nepal \nb Department of Agricultural Extension and Rural Sociology \n*Corresponding author: akasher9320@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 27 November 2020 \nAccepted 28 December 2020 \nAvailable online 07 January 2020\n\n\n\nMango is one of the major fruit crops of Terai region of Nepal; however, farmers are experiencing poor \nproductivity. Therefore, a study was conducted to determine the factors affecting the productivity and major \nconstraints of the mango production in Saptari district of Nepal. Pre-tested semi-structured questionnaire \nwas administered among randomly selected 106 farmers from the district of Nepal. Face to face interview \nwas scheduled to obtain the data from sampled farmers from March 26 to May 25, 2020. Multiple regressions \nwere used to access the various factors affecting the productivity of the mango. The regression model \ndepicted that the total number of productive trees and training on commercial mango production was found \nstatistically significant at 1% level of significance. A unit change in the total number of productive trees was \nfound to change the productivity by 0.94 units. Additionally, one-unit change in the trainings regarding \ncommercial mango farming caused the change in productivity by 0.53 units.  Further, incidence of diseases \nand pests, poor access to market, lack of irrigation facility, incidences of natural hazards and modicum of \nfertilizers on orchard were the major production constraints of mango in Spatari district of Nepal. Therefore, \nthe study has suggested indispensable need training on commercial mango cultivation practices in Saptari \ndistrict of Nepal.  \n\n\n\nKEYWORDS \n\n\n\nLoranthus, Mango productivity and multiple regression. \n\n\n\n1. INTRODUCTION\n\n\n\nNepal is an agricultural country with 65.6 % of population involved in \nagriculture (MoALD, 2019). In Nepal, agriculture and forestry sector \ncontributes 28.89% share in the national GDP (MoALD, 2017). Nepal is \ndivided into mountainous, hilly and terai region. Nepal is a country with \nhuge diversity that provides the opportunity for cultivation of various \nagricultural commodity. Terai region of this country is considered as the \nfood basket of the country due to its plain and fertile land. Terai region is \nhighly cultivable for cereals, fruits and vegetables. The soil and climate of \nthe terai region of Nepal is considers good for the cultivation of various \nfruits and vegetables.  Fruit cultivation is one of the major commodities \ncontributing to the AGDP. The total area under fruit cultivation in Nepal is \n1, 62,660 ha with production of 9.22 Mt/ha (MoALD, 2017). Out of the total \nfruit cultivation area grown in Nepal mango occupies an area of 48,204 ha \n(MoALD, 2017). Mango is one of the major tropical fruit crops grown in the \nterai region of Nepal. 39,664 ha of land is under mango cultivation in terai \nregion (MoALD, 2017). Further, Saptari district of eastern terai is the \nmajor mango producing district with 7,165 ha of area under cultivation \n(MoALD, 2017).  \n\n\n\nMango, king of fruits, belongs to the family Anacardiaceae (Bose and Mitra, \n1996). Hot and humid climate of province 2 of Nepal is magnificently \nsuitable for the cultivation of the Mango. The major cultivars of mango \ngrown in Nepal are Amarpali, Mallika, Neelam, Maldah, Calcuttia, Dasheri, \nBombay green, Krishnabhog, Chausa, Cipia, Fazil, Alfanso, Pakistani, Gualb \nkhash, Zardalu and Sukul (RARS, 2015). The major fruits grown in Nepal \n\n\n\nincludes citrus, mango, apple and banana. Mango is mainly grown in the \nfrost-free areas which mainly include the terai region of Nepal. Mango is \ngrown in the altitude above 600 m with very few rainfalls during the time \nof flowering. The favorable temperature for proper growth of the mango \nis 240 to 270 C. Mango has been found to grown on a wide scale of soils. The \nsoil favorable for the cultivation of mango is deep and well-drained loamy \nto sandy loam soils. pH range for flourishing of mango cultivation is 5.5 to \n7.5. These all conditions for the growth of the mango are suitable for the \nterai region.  \n\n\n\nThe major diseases incident in mango are powdery mildew, mango \nmalformation, bacterial black spot diseases of mango, tip die-back, \nanthracnose, black spot diseases, sooty mold and fruit decay (Akhtar and \nAlam, 2002). Besides diseases the major insects and pests responsible for \nthe low mango productivity are mango hopper, mealy bug, inflorescence \nmidge, fruit fly, scale, shoot borer, leaf weber and stone weevil but the loss \nin yield is irrespective to the genotype of the mango rather to the organ \nspecific pests (Chowdhury, 2015). Inappropriate orchard management \nbeing major reason for the decline in productivity (Saeed et al., 2012). \nDecline in the productivity is known to occur due to the incidence of \ndiseases and pests as well as conventional technique of the mango farming \nunable to adapt to the modern technology (Mango, 2020). Climatic change \nhas also played an important role in the yield of the mango.  \n\n\n\nIncrease in the temperature during cold season has resulted in increase in \nthe productivity however, climate related changes have adversely affected \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 77-81 \n \n\n\n\n \nCite The Article: Amrit Shrestha, Narayan Raj Joshi, Bhishma Raj Dahal, Subash Bhandari, Shree Ram Acharya, Bandana Osti(2021 ).Determinants Of Productivity And \n\n\n\nMajor Produciton Constraints Of Mango Farming In Saptari District Of Nepal. Malaysian Journal Of Sustainable Agriculture, 5(2): 77-81. \n \n\n\n\nthe fruiting and flowering pattern (Makhmale et al., 2016). Additionally, \nthe productivity is influenced by the application of both the irrigation and \nfertilizer use in the orchard, without there scientific use the productivity \nis decreased (Panwar et al., 2005).  \n \nThe productivity of the mango is sum of the effort of the both socio-\ndemographic character of the farmer and biological character of mango \nitself. Socio-demographic character is found to significantly affect the \nproductivity as found by the study on productivity of paddy in Sri Lanka \n(Siriwardana and Jayawardena, 2014).  The study of the socio-\ndemographic character and its impact to the productivity is also \nmandatory as studied by this survey. The study is aimed to determine the \nvarious factors affecting the productivity of the mango in Saptari district \nof Nepal. \n \n\n\n\n2. STATEMENT OF PROBLEM \n \nThe production of the mango has been decreased for five years before \n2018 in Mango capital of Nepal i.e. saptari district. The productivity of the \nmango in the Saptari district is 7 Mt/ha which below the average yield of \nthe mango of Nepal which is 8 Mt/ha (MoALD, 2017). The production of \nmango is decreasing. In addition, the productivity of the mango is \nfollowing retardation due to infestation of pests, disease, and faulty mango \norchard management practices (Acema et al., 2016).  \n \nMango sudden decline, has been one of the most important determinants \nfor the low productivity of the mango. The low productivity has resulted \nin the low income of the farmers, mango cultivation is the major \noccupation of the people living in the Saptari district. Unavailability of the \nproper post-harvest storage facility is also the major problem in this \nreason however the decrease in the productivity directly hampers the \nliving standard and the day-to-day activity of the farmers.  \n \nDue to climate change various outbreaks of pests and diseases are seen as \nwell as due to the perennial nature of the mango it is difficult to apply the \nmitigation approaches. The productivity of the mango for the year 2014, \n2015, 2016, 2018 and 2019 are 3.5Mt/ha, 4Mt/ha, 3Mt/ha, 4.86Mt/ha and \n2.8Mt/ha respectively. \n \n\n\n\n3. OBJECTIVES \n \n\n\n\nGeneral objective: \n\uf0b7 The general objective of the study was to identify the determinants & \nmajor constraints of mango farming in Saptari district.  \nSpecific objectives: \n1. To determine the factors affecting the production of mango farming.  \n2. To identify the production potential and major constraints of mango \nproduction.   \n\n\n\n\n\n\n\n4. METHODOLOGY \n \n4.1 Study area and Sample size \n \n\n\n\nThe study was conducted in Saptari, a tropical eastern terai of Province-2, \nNepal. The district encompasses within coordinates of 26\u2070 34' 59.99\u201d N \nlongitudes to 86\u2070 44' 59.99\"E latitude (LATITUDE, 2020). The vast fertile \nplain of the district is highly suitable for growing variety of crops, \nparticularly suitable for growing mango. Since, the area is highly occupied \nby commercial mango farming with total area greater than one thousand \nhectares; the government of Nepal has declared it as Mango Super Zone \nunder Prime Minister Agriculture Modernization Project (PMAMP).  \n \nThe district is laden with large number of farmers; therefore, the area was \nselected purposively. Ward number 7, 9 and 12 of Kanchanrup \nmunicipality, Saptari was selected for the study. Out of the farmers listed \nin the Super Zone of the Mango in Saptari district, 106 farmers were \nrandomly selected for the study. Pre-tested semi-structured questionnaire \nand predetermined interview schedule was used to obtained data. \nFurther, prescheduled Face to face interview technique was adopted to \nobtain data from March 26 to May 25, 2020. \n \n4.2 Data Analysis \n \nThe data collected from the respondents were coded and tabulated in the \nMicrosoft excel. Then, the file was imported into the software Python 3.7.4 \nfor further analysis.  After the entry of the data, descriptive statistics \n(mean, standard deviation), inferential statistics (chi-square test and One-\nWay ANOVA), and analytical statistics (multiple linear regression) were \nperformed. For the comparison of socio-demographic characteristics the \nsampled farmers were categorized into the three different groups small, \n\n\n\nmedium and large respectively. The categorization was done on the basis \nof the mean \u00b1 standard deviation.  \n \n4.3 Econometrics  \n \nFactor affecting the mango productivity was accessed using the multiple \nregression model. Additionally, for the determination of the productivity \nof the Coffee in Gulmi District of Nepal similar multiple regression model \nwas used (Bhattarai et al., 2020). Hence, the model used for the estimation \nof the productivity of the mango was also used as multiple regression and \nthe model is given as: \n \nln (Yproductivity) = \u03b1+\u03b21 lnX1+ \u03b22 lnX2+ \u03b23 lnX3+ \u03b24 lnX4+ \u03b25X5+ \u03b26X6 \n\n\n\nWhere ln Y= natural log of the productivity which is the dependent \nvariable \n \nHere,  \nX1= Total economically active member  \nX2= Cost of FYM (farm yard manure) \nX3= Cost of pesticide \nX4= Total number of Productive trees \nX5= Dummy for intercropping if respondents says yes=1 and if not=0 \nX6= Dummy for training on mango farming if respondents says yes=1 and \nif not= 0 \n\u03b1 = intercept made on the regression line \n\u03b21 to \u03b26 are the coefficients of the farmers category.  \n \nIn this model 4 independent variable with 2 dummy variables was \nselected, to avoid the multicollinearity in the model the intercorrelated \nindependent variable was removed selecting only one that affected the \nmost. \n \n4.4 Ranking of problems \n \nProblem for the production of the mango were ranked with the help of \nforced ranking technique. The formula given below was used to find the \nindex for intensity of production problem faced by producers.  \n \n\n\n\nIimp   =    \u2211\nN\n\n\n\nSifi\nWhere, \n\n\n\nIimp   = index of importance \n\u2211      = summation \nSi      = Ith scale value \nFi      = frequency of ith importance given by the respondents \nN      = total number of respondents \nSimilar technique was also used by Subedi et.al for the ranking of the \nproblems in potato (Subedi et al., 2019). \n \n\n\n\n5. RESULTS AND DISCUSSION \n \n5.1 Socio demographic characteristics of the sampled respondents of \nsaptari district \n \nThe important socio-demographic characteristic of the sampled \nhousehold is mentioned in table No. 1. The average age of the household \nhead was found to be 54.44 years, 54.27 years and 58.89 years \nrespectively for small, medium and large farmers respectively. The \ndependency ratio was found to be higher in medium farmers (0.88) as \ncompared to the large farmers (0.78) and small farmers (0.75).  \n \nTable 2 represents the socio-demographic characteristics of important \ncategorical variable of the respondents. The tyrant education status was \nfound to be illiterate for small farmer (70%) whereas SLC for the medium \nfarmers (35.30%) and bachelors and above (46.43%) for the large \nfarmers. The data was found to be significant at 1% level of significance \nfor the education status of the different farmer category.  \n \nRegarding the religion of the farmers category was found very dominating \nwith 100% in both small farmer and large farmer whilst 98.53% in \nmedium farmer and the remaining small amount being the Muslim truly \nrepresenting Hinduism in Nepal.  \n \nMajor ethnic status was found to be Janjati in small farmers (70%) \nwhereas Brahmin/Chettri being impervious in medium farmer (58.82%) \nand large farmer (67.86%). The society seems more or less patriarchal as \nmajor percentage of the household decision was taken by male in all the \ndifferent farmers category respectively 60%, 72.06% and 89.29% for \nsmall farmers, medium farmers and large farmers. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 77-81 \n \n\n\n\n \nCite The Article: Amrit Shrestha, Narayan Raj Joshi, Bhishma Raj Dahal, Subash Bhandari, Shree Ram Acharya, Bandana Osti(2021 ).Determinants Of Productivity And \n\n\n\nMajor Produciton Constraints Of Mango Farming In Saptari District Of Nepal. Malaysian Journal Of Sustainable Agriculture, 5(2): 77-81. \n \n\n\n\nTable 1: Socio-demographic characteristics (continuous variable) of sampled respondents  \n\n\n\nVariables Small Farmers \n(Mean\u00b1Standard deviation) \n\n\n\nMedium farmers \n(Mean\u00b1Standard deviation) \n\n\n\nLarge farmers \n(Mean\u00b1Standard deviation) \n\n\n\nAge of the HHH1 54.44\u00b115.66 54.17\u00b114.05 58.89\u00b114.10 \nHH2 size 5.33\u00b12.12 6.26\u00b12.78 6.79\u00b12.91 \n\n\n\nMale members of HH 2.78\u00b11.30 3.41\u00b11.47 3.43\u00b11.50 \nFemale members of HH 2.56\u00b11.24 2.82\u00b11.72 3.50\u00b11.62 \nEconomically active members 3.56\u00b11.81 3.69\u00b11.76 4.21\u00b11.62 \nDependent population 1.78\u00b11.30 2.66\u00b12.11 3.00\u00b12.09 \nTotal mango cultivated land(ha) 0.19\u00b10.06 0.64\u00b10.24 2.97\u00b12.52 \nDependency ratio3 0.75\u00b10.90 0.88\u00b10.84 0.78\u00b10.55 \n\n\n\nTotal number of productive trees 8.80\u00b14.49 36.12\u00b124.54 172.93\u00b1164.64. \n\n\n\nTable 2: Socio demographic characteristics of the sampled respondents of saptari district of Nepal (2020)  \n\n\n\nVariables \nFarmers Category \n\n\n\nChi-square value P-value \nSmall farmers Medium farmers Large farmers \n\n\n\nGender of HHH (male) 6(60) 51(75) 2(85.71) 2.9141ns 0.233 \n\n\n\nEducation status      \n\n\n\nIlliterate 7(70) 20(29.42) 6(21.43) 23.908*** 0.008 \nLiterate 0(0) 2(2.94) 1(3.57)   \n\n\n\nPrimary up to 5 0(0) 6(8.82) 1(3.57)   \n\n\n\nLower secondary up to 8 0(0) 8(11.76) 1(3.57)   \n\n\n\nSLC 2(20) 24(35.30) 6(21.43)   \n\n\n\n+2/Certificate 0(0) 0(0) 0(0)   \n\n\n\nBachelors and above 1(10) 8(11.76) 12(46.43)   \n\n\n\nReligion      \n\n\n\nHindu 10(100) 67(98.53) 28(100) 0.56ns 0.754 \nMuslim 0(0) 1(1.47) 0(0)   \n\n\n\nEthnic group      \n\n\n\nBrahmin/Chettri 3(30) 40(58.82) 19(67.86) 5.68ns 0.22 \nJanjati 7(70) 26(38.25) 9(32.14)   \n\n\n\nOthers 0(0) 2(2.94) 0(0)   \n\n\n\nFamily type      \n\n\n\nNuclear 7(70) 52(76.47) 19(67.86) 0.830ns 0.66 \nMain occupation (mango farming) 5(50) 42(61.77) 21(75) 9.144ns 0.521 \nHousehold decision (Male) 6(60) 49(72.06) 25(89.29) 4.922ns 0.29 \n\n\n\n Training received (Yes) 1(10) 13(19.12) 9(32.14) 2.869ns 0.238 \n Intercropping (Yes) 3(30) 13(19.12) 2(7.12) 3.344ns 0.188 \n\n\n\nexplains the approach of the different category of farmer\u2019s to the training \nregarding commercial farming of the mango. Very less farmer\u2019s had \nreceived the training on mango farming. In small farmers 10% followed by \n19.12% in medium farmer\u2019s and 32.14% in large farmers were found to \nreceive the training on mango farming. The difference among the different \ncategory however was not found significant at any level of significance. \nAdoption of the intercropping is found to be adopted by skimpy farmers \n\n\n\nof different category. Mainly intercropping in the orchard was done by the \nfarmers whose orchard is young with wheat. 70%, 80.88% and 92.86% of \nthe farmers were found not to adopt intercropping respectively in small, \nmedium and large farmers category.  \n \n5.2 Factors affecting mango productivity \n \n\n\n\n \nTable 3: Factors affecting the mango productivity \n\n\n\n  Coefficient  Standard error t-stat P-value Lower 95% Upper 95% \nIntercept 1.953 0.374 5.216 0.000 1.209 0.975 \nActive member of family -0.5828* 0.339 -1.721 0.089 -1.256 0.091 \n\n\n\nCost of FYM  0.0324 0.035 0.924 0.358 -0.037 0.102 \nCost of Pesticide  -1.106e-05** 5.04e-06 -2.195 0.031 -2.11e-05 -1.04e-06 \nTotal number of \nproductive trees \n\n\n\n0.9410*** 0.18 5.229 0.000 0.583 1.299 \n\n\n\nIntercropping -0.085 0.11 -0.772 0.442 -0.305 0.134 \n\n\n\nTraining on commercial \nmango farming \n\n\n\n0.5270*** 0.171 3.083 0.003 0.187 0.867 \n\n\n\nAdjusted R- squared=0.381    F-static= 10.86*** \n\n\n\n*, ** and *** represents significance at 10%, 5% and 1% level of significance. \n \nThe model was found to be significant at 1% level of significance with F-\nstatic value of 10. 86. The adjusted R-squared was found to be 0.381 which \nimplies that 38.1% of the productivity is explained by the independent \nvariable incorporated in the model. Economically active population of the \nfamily was found to be significant but negatively at 10% level of \nsignificance. It is found to affect negatively because the active population \nare not involved in the agricultural occupation rather involved in other \njobs except agriculture. So only old aged farmers were found to be \ninvolved in the farm activities and according to the study in Pakistan it was \nfound that the old farmers were not able to adopt to the modern practices \nas compared to the young farmers (Hassan et al., 2002). Similarly, the cost \nof the FYM was found to affect the productivity positively but not \nsignificantly.  \n \nThe FYM plays a very important role in the productivity of mango. As \nmango is perennial plant its nutrient requirement can be fulfilled by FYM \nfinally impacting in the increase of the yield (Iyer, 2004). Additionally, use \n\n\n\nof the pesticide was also found to be significant and negative at 5% level \nof the significance. However, the impact of the change in the productivity \ndue to pesticide is very low. As the study on time series analysis of the \norchard fruit in Great Britain from 1992-2008 the use of the pesticide was \nfound to have both negative and positive impact on the production, the \nstudy revealed that during the time series analysis the productivity of the \nsix of the seven crops increased whereas for custard apple and plum it was \nfound to have decreased (Cross, 2013). Unscientific use of the pesticide in \nthe orchard results in the productivity rather than increase. Another \nindependent variable total number of the productive tress was found to \nsignificantly and positively influence the productivity and was found \nsignificant at 1% level of significance.  \n \n\n\n\nIn a study conducted on the production of guava the number of productive \nguava trees were also found to positively affect the production and was \nfound significant as well (Khushk et al., 2009). One-unit change in the total \nnumber of the productive trees resulted in 0.94 units change in the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 77-81 \n \n\n\n\n \nCite The Article: Amrit Shrestha, Narayan Raj Joshi, Bhishma Raj Dahal, Subash Bhandari, Shree Ram Acharya, Bandana Osti(2021 ).Determinants Of Productivity And \n\n\n\nMajor Produciton Constraints Of Mango Farming In Saptari District Of Nepal. Malaysian Journal Of Sustainable Agriculture, 5(2): 77-81. \n \n\n\n\nproductivity. Training on commercial mango farming was also found to be \nsignificant positively at 1% level of significance. Unit change in the training \nand support resulted in the 0.53 units change in the productivity. Study on \nthe productivity of the mango in Pakistan also found that the farmers in \ntouch with the extension agents were found to have positive effect on the \nproductivity (Shahbaz et al., 2017). More the farmers are in touch with the \nextension agents more will be the number of trainings received by them.  \nFarmers taking trainings are well versed with the modern techniques and \nare plausible to create better orchard management practices. \nIntercropping in the orchard of the mango was found to negatively affect \nthe productivity of the mango. Lachungpa when intercropped maize with \nthe citrus, the yield of the citrus was found to decrease (Lachungpa, 2004). \nThe effect of the intercrops to the productivity of the crop depend upon \nthe rooting behavior and the strata of the soil from which absorption of \nthe nutrient takes place (Awasthi and Saroj, 2004). Due to the lack of the \nuse of the fertilizer on the field and any other management practice \nresulting in the nutrition depletion and competition for the nutrient \nabsorption intercropping mango with wheat was found to affect \nnegatively. \n \n\n\n\n5.3 Constraints of the mango production in Saptari district of Nepal \n \n\n\n\nThe forced ranking method with five-point scale was used for the ranking \nof the prevalent constraints in mango farming. Incidence of diseases and \npest was found to be the major problem constraining mango farming with \nthe index value of 0.945.  Every orchard was found to be infected by pests \nand diseases mainly pest Mango stem borer (Batocera rufomaculata \nDejan). A study conducted in the eastern terai region of the Nepal recorded \nmango stem borer to be the major insect pest of this region (Upadhyay et \nal., 2013). None of the orchards under survey were free from pests. Lack \nof proper access to the market and market price was ranked second with \nindex value of 0.692. Due to higher amount of the mangoes being imported \nfrom the Indian border at a cheaper rate the Nepalese mango were unable \nto get the proper price and market. As well from the survey very large \namount of farmers of every farmers category were found to adopt contract \nfarming as a result the contract persons acted as the arbitrator resulting \nin decrease in farmers\u2019 profit.  \n \n\n\n\nUnavailability of the irrigation facility ranked third with index value of \n0.602. Although being the district of the largest river of Nepal Saptakoshi \nthe irrigation facility was found to be the problem it is because no \nirrigation channel is made in the area of mango orchards as well due to the \npoor economic condition of the farmers, they were not able to pay for the \nboring of underground water. Natural hazards mainly hailstorm and heavy \nrainfall during the initial fruit development stage has resulted in heavy \ndecrease in the total production of mango. Natural hazard is ranked 4th \nwith index value of 0.464. The unavailability of the fertilizer is ranked last \nwith index value of 0.296. It is ranked last, because of the more illiterate \nfarmers in the area they were still unaware about the impact of the \nfertilizers on the yield of the mango. They believed only in the FYM but not \nchemical fertilizers.  In a study conducted for assessment of the \nconstraints of the mango in Ethiopia scarcity of the irrigation, pest and \ndiseases and limited technology were found to be the major constraints \n(Hussen and Yimer, 2013). \n \n\n\n\nTable 4: Ranking of major problem of mango production \nProblems  Index value Rank \nHigh incidence of pest and diseases 0.945 I \n\n\n\nLack of proper access of market and \nmarket price \n\n\n\n0.692 II \n\n\n\nUnavailability of irrigation  0.602 III \nNatural Hazards 0.464 IV \nUnavailability of fertilizers 0.296 V \n\n\n\n\n\n\n\n6. CONCLUSIONS \n \n\n\n\nThe study was made to overview the determinants of mango productivity \nand problems of mango production. The average age of different farmers \ncategory small, medium and large was found to be 54.44 years, 54.17 years \nand 58.89 years respectively. The average land holding used for the mango \ncultivation were found to be 0.19 ha, 0.64 ha and 2.97 ha respectively. To \ndetermine the factors affecting productivity multiple regression was used. \nActive population and cost of pesticide was found negatively significant \nand significant at 10% and 5% level of the significance respectively. \nTraining on commercial mango farming and total number of the \nproductive trees were found to positively alter productivity and was found \nsignificant at 1% level of significance. The major constraints in mango \nfarming were found to be incidence of diseases and pests, unavailability of \nmarket and market price and unavailability of the irrigation was \ndetermined as most hindering factors of production. Although use of \npesticide is used the incidence of diseases and pests is not reduced hence \n\n\n\nit is recommended for better and scientific use of pesticide rather in \nlumpsum. It is recommended to take care of the orchard and practice \norchard management practice rather leave it is as it is as seen on the \nsurvey. As well intercropping shall be discouraged as it is resulting in the \ndecrease of the productivity. \n \n\n\n\nACKNOWLEDGEMENTS \n \n\n\n\nWe would like to thank with utmost sincerity and gratitude to Prime \nMinister Agriculture Modernization Project (PMAMP) for providing \nresearch funding. We would like to thank Agriculture and Forestry \nUniversity for their support and all the farmers. \n \n\n\n\nCONFLICT OF INTREST \n \nThe authors declare that there are no conflicts of interest regarding the \npublication of this manuscript. \n \n\n\n\nREFERENCES \n \n\n\n\nAcema, D., Odama, E., Egama, D., Asiku, B., 2016. Assessment of Mango \nPests, Diseases and Orchard Management Practices in West Nile Zone \nof Uganda. Agriculture, Forestry and Fisheries, 5 (3), Pp. 57-63. doi: \n10.11648/j.aff.20160503.15  \n\n\n\n \nAkhtar, K.P., Alam, S.S., 2002. Assesment keys for Some Important Diseases \n\n\n\nof Mango. Pakistan Journal of Biological Sciences, 5 (2), Pp. 246-250. \nAwasthi, O.P., Saroj, P.L., 2004. Economic Analysis of Mango Multistrata \n\n\n\nIntercropping. Tropical Science, 44 (1), Pp. 43-47. \ndoi:https://doi.org/10.1002/ts.131 \n\n\n\n \nBhattarai, C., Bhandari, D., Bhandari, S., Bhusal, K., Khatri, B., Srivastava, A., \n\n\n\nSapkota, B.K., 2020. Factors affecting the productivity of coffee in \nGulmi and Arghakhanchi districts of Nepal. Journal of Agriculture and \nNatural Resources, Pp. 51-60. \ndoi:https://doi.org/10.3126/janr.v3i1.27017 \n\n\n\n \nBose, T.K., Mitra, S.K., 1996. Fruits: Tropical and Sub-tropical. Social \n\n\n\nSciences, Agriculture and Horticulture, Pp. 206. \n \nCBS. 2014. Population Monograph of Nepal. Kathmandu: Government of \n\n\n\nNepal, National Planning Commision Secretariat, Central Bureau of \nStatistics. \n\n\n\n \nChowdhury, S.K., 2015. Diversity and nature of damage of mango insect \n\n\n\npests at Kaliachak-II Block of Malda, West Bengal, India. Journal of \nEntomology and Zoology Studies, 3 (4), Pp. 307-311. \n\n\n\n \nCross, P., 2013. Pesticide hazard trends in orchard fruitproduction in Great \n\n\n\nBritain from 1992 to 2008: A time-series analysis. Pest Management \nScience, 69 (6), Pp. 768-774. \n\n\n\n \nHassan, M.Z., Irshad, N.M., Muhammad, B.N., 2002. Effect of Socio-\n\n\n\nEconomic Aspects of Mango Growers on the Adoption of \nRecommended Horticultural Practices. Pak. J. Agri. Sci, 39 (1), Pp. 20-\n21. \n\n\n\n \nHussen, S., Yimer, Z., 2013. Assessment of Produciton Potentials and \n\n\n\nConstraints of Mango (Mangifera indica) at Bati, Oromiya Zone, \nEthiopia. International Journal of Sciences: Basic and Applied \nResearch, 11 (1), Pp. 1-9. \n\n\n\n \nIyer, C.P., 2004. Growing Mango Under Organic System. Acta Horticulturae, \n\n\n\n645, Pp. 71-84. doi:10.17660/actahortic.2004.645.4 \n \nKhushk, A.M., Memon, A., Lashari, M.I., 2009. Factors Affecting Guava \n\n\n\nProduction in Pakistan. Journal of Agriculutral Research (03681157), \n47 (2). \n\n\n\nLachungpa, K., 2004. Intercropping of Agri/Horti Crops with Special \nReference to Mandarin (Citrus Reticulate Blanco) in Sikkim (India). \nScientific Research an Academic Publisher. Retrieved from \nhttp://www.regional.org.au/au/asa/2004/poster/2/3/1954_lachu\nngpak.htm \n\n\n\n \nLatitude. 2020. Retrieved September 30, from Latitude.to, maps, \n\n\n\ngeolocated articles, latitude longitude coordinate conversion.: \nhttps://latitude.to/articles-by-country/np/nepal/61818/saptari-\ndistrict \n\n\n\n \nMakhmale, S., Bhutada, P., Yadav, L., Yadav, B.K., 2016. Impact of climate \n\n\n\nchange on phenology of Mango-The case study. Ecology, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(2) (2021) 77-81 \n \n\n\n\n \nCite The Article: Amrit Shrestha, Narayan Raj Joshi, Bhishma Raj Dahal, Subash Bhandari, Shree Ram Acharya, Bandana Osti(2021 ).Determinants Of Productivity And \n\n\n\nMajor Produciton Constraints Of Mango Farming In Saptari District Of Nepal. Malaysian Journal Of Sustainable Agriculture, 5(2): 77-81. \n \n\n\n\nEnvironment and Conservation, 22, Pp. 119-124. \n \nMango, S.Z., 2020. Annual Report. Mango block implementation unit, \n\n\n\nPMAMP, MoALD, Government of Nepal. \n \nMoALD. 2017. Statistical Information on Nepalese Agriculture \n\n\n\n2073/74(2017). Singhadurbar, Kathmandu: Agri-Business \nPromotion and Statistics Division. Agri Statistics Section, \nGovernment of Nepal. \n\n\n\n \nMoALD. 2019. Krishi Diary. Agriculture Information and Training Centre. \n\n\n\nSingha durbar, Kathmandu: Agri-Business Promotion and Statistics \nDivision, Agri Statics Section, Government of Nepal. \n\n\n\n \nPanwar, R., Singh, S.K., Singh, C.P., Singh, P.K., 2005. Mango fruit yield and \n\n\n\nquality improvement through fertigation along with mulch. Indian \nJournal of Agricultural Sciences, 77 (10). \n\n\n\n \nRARS. 2015. Annual Report. Tarahara, Sunsari: Government of Nepal.  \n \nSaeed, S., Massod, A., Khan, S.M., 2012. Diseased Plant as a Source of \n\n\n\nDissemination of Mango Sudden Death Disease in Healthy Mango \nPlant. Pakistan Journal of Phytopathology, 24 (I), Pp. 21-25. \n\n\n\n \nShahbaz, P., Haq, S., Boz, I., Murtaza, R., 2017. An Assessment of \n\n\n\nDeterminants Responsible for Low Mango Productivity in District \nMuzzafargarh, Pakistan. Journal of Food Science and Engineeering, \nPp. 135-140. doi: 10.17265/2159-5828/2017.08.004 \n\n\n\n \nSiriwardana, A.N., Jayawardena, L.A., 2014. Socio-Demographic Factors \n\n\n\nContributing to the Productivity in Paddy. Tropical Agricultural \nResearch, 25 (3), Pp. 437-444. \n\n\n\n \nSubedi, S., Ghimire, Y.N., Gautam, S., Poudel, H.K., Shrestha, J., 2019. \n\n\n\nEconomics of potato (Solanum tuberosum L.) production in terai \nregion of Nepal. Archives of Agriculture and Environmental Science, \n4 (1), Pp. 57-62. https://doi.org/10.26832/24566632.2019.040109  \n\n\n\n \nUpadhyay, S.K., Chaudhary, B., Sapkota, B., 2013. Integrated Management \n\n\n\nof Mango Stem Borer (Batocera rufomaculata Dejan) in Nepal. Glob J. \n\n\n\nBiol Agric Health Sci, 2 (4), Pp. 132-135. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 56-59 \n\n\n\nCite The Article: Barali Sunaina, Jha Ritesh Kumar, Karn Rupak, Regmi Mahesh (2019). A Case Study on Soil Fertility Status and Maize Productivity in Dang \nDistrict, Nepal. Malaysian Journal of Sustainable Agriculture, 3(2): 56-59. \n\n\n\n\n\n\n\n ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 July 2019 \nAccepted 26 August2019 \nAvailable online 11 September 2019\n\n\n\nABSTRACT\n\n\n\nThe study was conducted to know the soil fertility status and maize productivity along with soil management \npractices being adopted in Lamahi municipality and Rapti rural municipality of Dang district to have a basis for the \nunderstanding of possible management options for better soil fertility and productivity. 333 soil samples from \ndifferent maize farmers\u2019 field were tested for soil fertility parameters during the program in which 60 soil samples \nwere also collected from maize fields. Next, crop and soil management survey was carried out through a household \ninterview in the sampled field. These data were used to identify the range of critical soil-test concentrations of \nnutrients and to assess the production status of maize and the soil management practices in farmers filed and \nevaluate the current fertilizer practices of farmers. The result showed that there was a dominance of neutral-alkaline \nsoils with low organic matter & nitrogen levels with high P and medium K. Similarly, maize productivity of the \ndistrict was found to be 3.3 ton per hectare. It is found that most farmers were adopting traditional crop \nmanagement practices for maize cultivation with a high dependency on chemical fertilizers for fertilization.  \n\n\n\n KEYWORDS \n\n\n\nMaize, Soil, Productivity. \n\n\n\n1. INTRODUCTION \n\n\n\nMaize (Zea mays L.) is the second important crop after rice of the district \nand staple crop of hills of Nepal in terms of area (District Agriculture \nDevelopment Office, Dang, 2072) and third important crop in worldwide \nafter rice and wheat. In Nepal, maize was introduced probably at the \nbeginning of the 17th century and it is growing throughout the year in \nDang district.  In the Dang district, the productivity of maize in the \nirrigated field was found 2.01 MT/ha while in the non-irrigated field, it is \n2.5 MT/ha (District Agriculture Development Office, Dang, 2072). In the \nDang district, the area under maize crop was 23,200 ha in the FY 2015, \nproducing 46,168 ton of grains  (which represents just 2.15% of total \nannual maize production) with mean yield of 1.99 MT/ha, which is low \ncompared to national productivity 2.5 MT/ha [7]. Furthermore, in Dang \ndistrict, most of the maize area is occupied by hybrid during the various \ngrowing season especially for spring and winter with adequate irrigation \nsystem among commercial farmers. To increase the yield, farmers need to \napply a high level of N, P, K and adequate amount of organic fertilizer in \ncombination in hybrid maize so that soil fertility status can be maintained. \nHowever, soil analysis is not done at all by maize commercial farmers in \nthe district to know soil inherent nutrient supply capacity and to \ndetermine N, P, K fertilizer doses. \n\n\n\nIn Dang, there is a lack of information about the nutrient status of soil to \nfacilitate the implementation of better soil fertility practices. Despite the \ngreat potential of maize farming, production is a low and substantial \namount of maize is imported every year. The farm level yield of maize \n(2.45 t/ha) is not satisfactory as compared to attainable yield (5.7 t/ha) in \nNepal [5-6]. Maize is a heavy feeder crop, soil fertility status and nutrient \nmanagement practices directly affect its production. Soil-test based \nfertility management is important for sustainable soil management and \nsustained productivity.  This study aims to identify the status of soil \nfertility and maize productivity existed in the district and know the soil \nmanagement practices being adopted. To reach this focal mission, the \nfollowing specific objectives were considered. \n\n\n\n\u2022 To study soil fertility parameters (pH, organic matter, N, P, K) of\nDang district. \n\n\n\n\u2022 To find out maize productivity in the above-mentioned municipality.\n\u2022 To know about soil management practices adopted in these areas\n\n\n\nthrough the survey. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Case Study Site and Sub-sector \n\n\n\nMaize is a popular cereal crop ranking second in terms of area and \nproduction in Nepal [8]. The Lamahi municipality and Rapti Municipality \nof Deukhuri valley of Dang district were the study site. In the above-\nmentioned municipalities, almost all farmers household were growing \nmaize in different scales.  Here, Maize is sown in summer, winter and \nspring i.e. throughout the year. Usually, commercial hybrid maize adapted \nto this area sowed behind the plough through the use of rope or use of rope \nand spade only.  During the growing season, the maize was irrigated using \na furrow system with low water use efficiency. All fields included in this \nstudy have a climate described as semi-arid with hot summer and \nrelatively cold winter, a mean annual air temperature of cold winter, mean \nannual precipitation around mostly falling between June to September. \n\n\n\n2.2 Case Study Unit of Analysis \n\n\n\nAs mentioned before, this study was carried out in one of the Midwestern \nTerai district of Nepal. The District as a whole and two municipalities \n(Lamahi municipality & Rapti rural municipality) have been taken as a unit \nfor the analysis for the case study. \n\n\n\n2.3 Sample and Sampling Techniques \n\n\n\nThe sample population is the representative. The population of the case \nstudy was farmers from the municipality mentioned before. At first, the \nsampling frame was prepared by using the various source of information \nsuch as discussion with Maize Superzone inn-keepers committee and \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.02.2019.56.59\n\n\n\nA CASE STUDY ON SOIL FERTILITY STATUS AND MAIZE PRODUCTIVITY IN DANG \nDISTRICT, NEPAL \nBarali Sunaina1, Jha Ritesh Kumar2*, Karn Rupak2 and Regmi Mahesh3 \n\n\n\n1Horticulture Development Officer, Prime Minister Agriculture Modernization Project \n2Faculty of Agriculture, Agriculture and Forestry University, Chitwan, Nepal  \n3Senior Agriculture Officer, Prime Minister Agriculture Modernization Project \n*Corresponding Author Email: ritesh.lord.of.truth@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\n\nmailto:ritesh.lord.of.truth@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustai nable Agriculture (MJSA) 3(2) (2019) 56-59 \n\n\n\nCite The Article: Barali Sunaina, Jha Ritesh Kumar, Karn Rupak, Regmi Mahesh (2019). A Case Study on Soil Fertility Status and Maize Productivity in Dang \nDistrict, Nepal. Malaysian Journal of Sustainable Agriculture, 3(2) : 56-59. \n\n\n\ncooperatives heads. 60 households/farmers were selected from maize \ngrowing farmers under Superzone PM-AMP. For the household survey, \nprobability-based simple random sampling was used. 30 from Lamahi \nmunicipality and 30 from Rapti rural municipality, 60 in total were \ninterviewed with the pre-tested semi-structured questionnaire for data \ncollection related to crop/soil management practices being adopted. \n\n\n\n2.4 Research Design \n\n\n\nA household survey was carried out to collect data from responding to \nfarmers. A standardized pre-tested interview schedule was administered \nto the farmers. Information related to maize cultivating household, \nhousehold characteristics, constraint of maize production, level of \nproduction, and crop and soil management practices during maize farming \nhad been collected from the farmers. Observation, informal group \ndiscussion, and key informant survey were also carried based on snowball \nsampling method. \n\n\n\n2.5 Observation and Observation Methods \n\n\n\nMostly observation is seen as either a participant or non-participant \nmethod of observation. In this study, the observation method of \ncomplementing was used and contextualize the issue. Since maize is \ngrown throughout the year in the study area, it was possible to observe \nthe maize standing crop in the field and farmer working in the maize field \nand other agricultural activities performed in the farm. \n\n\n\n2.6 Data Collection and Analysis \n\n\n\nData collected include maize yield for the household, quantity and type of \nfertilizer used, quantity and type of seed used, labour used (both family \nand hired). Additionally, data was also collected for the method of \nploughing \u2013 hand, Bullock or tractor, and method of sowing and crop/soil \nmanagement practices cropping method \u2013 mono-cropping or \nintercropping, use of organic manure, method of fertilizer application.  For \nsoil characteristics, soil samples were collected from randomly selected \nfarmer\u2019s field and tested in mobile soil testing van by soil scientist from \nRegional soil testing laboratory, khajura, Banke. A total of 333 soil samples \nwere tested with funding from Superzone Implementation Unit, PM-AMP, \nDeukhuri, Dang. \n\n\n\nThe quantitative information obtained from the household was entered \nsystematically in the computer system. Proper coding was done to feed the \ndata in Microsoft Excel. Soil test report data were entered in the MS-Excel \nand analyzed in it. Data collected from the survey were entered and \nanalyzed in SPSS.16.0. The table and graphs were generated to present the \nresult. \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Soil Nutrient Status \n\n\n\nSoil pH is an important chemical parameter of soil that affects nutrient \navailability [2]. Soil pH analyses showed that soils were classified as \nslightly acidic (3%), neutral (56.67%) and slightly alkaline (33.33%). It \ncan be clearly seen that there was a dominance of neutral-alkaline soils \nthat is mainly due to low-rainfall and high level of reference \nevapotranspiration. \n\n\n\nFigure 1: pH Status of Soil \n\n\n\nSoil samples were also tested for soil organic matter contents and results \nshowed that very low (1-3.33%), low (24-80%) and medium (5 to \n16.67%) organic matter in the soil. It is important to note that in the soils \nthe highest OM levels were found where farmers use different organic \nfarmers such as FYM, poultry manure, goat manure, or mixture once or \ntwice in the crop cycle and also incorporated the residues back to the soil \n\n\n\nwhile field preparation.OM lows due to the application of chemical \nfertilizers only; as there is easy access to chemical fertilizer. And the poor \nsupply of organic manure application practices. \n\n\n\nFigure 2: Organic Matter Status of Soil \n\n\n\nNitrogen content in the soil was mainly low (60% of total) with a few very \nlow (3.33%) and medium (36.67%). Available P values were mostly very \nhigh(53.33% of total), and high(20%) and medium (6.67%) whereas \nfewer soil samples had low (10 %) and very low (10 %), which suggested \nthat previous practices of burning of the residue have increased P content \nin soil . Also, with the increased use of DAP has been building up a high P \nstatus in these soils. \n\n\n\nSimilarly, soil available K were very high (13 %), medium (50 %), low (6 \n%), high (30 %). This is majorly due to low K fertilization practices and \nhigh K source which is inherent from parental material is decreasing in \nNepalese soil, but the risk of K leaching in the soil is low so much of the \nsoils are high in K. \n\n\n\nFigure 3: Nitrogen, Phosphorous and Potash status of the soil \n\n\n\n3.2 Maize productivity Situation \n\n\n\nThe yield of maize in Superzone (maize), Deukhuri Dang under which \nLamahi municipality and Rapti municipality occurs was found to be 3.3 ton \nper hectare. The yield was found to be 2.40 ton/ha in spring season while \nin winter, the yield was higher i.e. 4.27 ton/ha. This huge gap between \nspring and winter yield is due to the fact that almost all farmers grow \nhybrid maize in winter which has the capacity of double production than \nimproved maize. The yield of maize is lower compared to attainable yield \ni.e. 5.7 MT/ha due to lower plant population maintenance & poor nutrient \nmanagement practices i.e. most apply single dose of urea and no/poor \ncombination of organic &inorganic fertilizer [10].  Integration of organic \nand inorganic nutrient sources generated the highest yields in maize-\nbased cropping system [9]. \n\n\n\nFigure 4: Variability of maize yield in Lamahi Municipality and Rapti \nrural Municipality under Superzone (maize) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustai nable Agriculture (MJSA) 3(2) (2019) 56-59 \n\n\n\nCite The Article: Barali Sunaina, Jha Ritesh Kumar, Karn Rupak, Regmi Mahesh (2019). A Case Study on Soil Fertility Status and Maize Productivity in Dang \nDistrict, Nepal. Malaysian Journal of Sustainable Agriculture, 3(2) : 56-59. \n\n\n\n3.3 Soil Fertility Management Practices \n\n\n\nIn the study area, the farmers were not found much aware of soil fertility \ndegradation and were not adopting any suitable soil management \npractices as just two out of sixty respondents ever tested their soil from \ntheir field. However, the application of FYM and chemical fertilizers were \nnoted principle practices for maintaining soil fertility in which a \ncombination of both was not so common. \n\n\n\n3.3.1 Application of Organic Manure \n\n\n\nFarmers in the study were adopting the integration of crop and livestock \nin which livestock provides FYM to crops. Quality and quantity of manure \nare important to enhance soil fertility and increase the productivity of \nmaize. Integrated farming is the main source of organic manure in Nepal. \nThe major livestock and birds reared in the study area were a cow, buffalo, \ncalves, poultry, goats, sheep and pig. \n\n\n\nTable 1: Status of Integrated farming in Superzone (2017) \n\n\n\nIntegrated Non-Integrated\n\n\n\nLamahi municipality 76.5% 23.5% \nRapti rural municipality 81.5% 18.5% \n\n\n\nThe application of organic fertilizer appears to improve soil fertility [9]. \nThough only 55 percent of the respondents use organic manure in the \nmaize and 59 percent of the respondent use organic manure once in a year, \nmainly for vegetables during spring. The main source of organic manure is \nFYM followed by poultry manure and compost manure. Only two out of \ntotal respondent has the practice of using green manure. Although, the \nquantity used is very low ranging from 0.4 ton to 15 ton. \n\n\n\nFigure 5: Organic manure use situation in Superzone Dang \n\n\n\nFarm Yard Manure Preservation & Application time in the field: \n\n\n\nMost of the farm shed (61%) in the study area is the traditional one (Figure \nno. 6). The quality of FYM in improved cowshed is better supporting the \ngrowth of the crops. Proper use of FYM needs a great knowledge to \npreserve its nutrient. \n\n\n\nFigure 6: Farmer Having Improved and Traditional Type of Farm Shed \n\n\n\nMost of the respondents in the study area showed good practices of FYM \napplication i.e. most mixed the manure into the soil within the same day to \n10 days after application in the field. \n\n\n\n3.3.2 Application of Chemical Fertilizer \n\n\n\nThe farmers in the study area were found heavily dependent on chemical \nfertilizers, especially urea, for fertilization in the maize field. Most of the \nfarmers used chemical fertilizer solely for maize cultivation. The \ncommonly used fertilizers were urea, Diammonium phosphate (DAP) and \nMuriate of Potash. Most of the farmers in Lamahi municipality were using \na combination of three chemical fertilizers while in case of Rapti rural \nmunicipality, most were using urea only as a source of fertilizer for maize \nproduction. \n\n\n\nFigure 7: Use of Chemical Fertilizers Singly or In Combination in \nSuperzone \n\n\n\nFigure 8:  Average dose of urea, DAP and Potash used in the Superzone \n\n\n\nMost of the farmers did not apply chemical fertilizers at a recommended \ndose as they used their own judgment in the study area as shown in Figure \nno. 9. The study revealed that most of the farmers have increased the \napplication rate of chemical fertilizers overall due to subsidies provided \nby the government whereas the use of FYM has decreased because of less \nlivestock rearing in practice and fewer family members in the family. \nHowever, the farmers\u2019 practices of chemical fertilizer application in the \nmaize field differ vastly among Lamahi municipality and Rapti rural \nmunicipality with more quantity being used in the former one. This is \nmainly due to the fact the Lamahi is one of the main city of the Dang district \nwhere Farmers from Lamahi municipality has easier access than farmers \nfrom the rural municipality and are more trained. The reasons for the low \nuse of chemical fertilizer included high cost, non-availability at key times \nand a lack of knowledge of their use [5]. \n\n\n\nTable 2: Ranges of doses of various fertilizers being adopted for maize \n\n\n\nUrea \n(kg/kattha) \n\n\n\nDAP \n(kg/kattha) \n\n\n\nPotash \n(kg/kattha) \n\n\n\nMean Max. Min. Mean Max. Min. Mean Max. Min. \nLamahi 4.6824 16 0 2.66 5 0 0.931 2.5 0 \n\n\n\nRapti 2.41 6 0 1.42 6.5 0 0.74 3.3 0 \n\n\n\nBasically, they used urea on different splits on maize i.e. once (after first \nweeding); twice (at field preparation and at first weeding / after first \nweeding and at the tasselling stage) and thrice (at field preparation, after \nfirst weeding and at the tasselling stage) as shown in figure no. 7. \n\n\n\nFigure 9: Situation of Split Application of Urea in Maize \n\n\n\n3.3.3 Inclusion of Legumes & Intercropping Practices \n\n\n\nLeguminous crops can play an important role to maintain soil fertility and \nsustain crop production. Legume also adds nutrient, provide ground cover \nreducing soil erosion & increase organic matter. Legumes grown in less \nfertile soil improves the soil health by fixing atmospheric N and may \npartially supplement the use of inorganic fertilizers [4].  In the study area, \nthe inclusion of legumes was not common in practices. Few farmers (13 \n\n\n\n39%\n\n\n\n61%\n\n\n\nImproved\n\n\n\nTraditional\n\n\n\n45%\n\n\n\n15%\n\n\n\n40%\n\n\n\nSplit Application of Urea\n\n\n\nonce\n\n\n\ntwice\n\n\n\nThrice\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustai nable Agriculture (MJSA) 3(2) (2019) 56-59 \n\n\n\nCite The Article: Barali Sunaina, Jha Ritesh Kumar, Karn Rupak, Regmi Mahesh (2019). A Case Study on Soil Fertility Status and Maize Productivity in Dang \nDistrict, Nepal. Malaysian Journal of Sustainable Agriculture, 3(2) : 56-59. \n\n\n\npercent out of total) were practising intercropping with maize. \nMaintaining any form of plant cover reduces nutrient losses in the eroded \nsoil materials. Intercropping in maize helps to protect the soil as it covers \nthe ground and reduces the weed infestation [1]. \n\n\n\nTable 3: Situation of intercropping in maize \n\n\n\nIntercropping in \nmaize \n\n\n\nNo intercropping in \nmaize \n\n\n\nLamahi municipality 2.9% 97.1% \n\n\n\nRapti rural \nmunicipality 25.9% 74.1% \n\n\n\n4. CONCLUSION \n\n\n\nFrom this study, it can be concluded that in overall, the soil fertility status \nof the study area is poor and approaching towards alkalinity losing its \nproductivity. Moreover, the maize yield of the area was found low \ncompared to attainable yield. Use of minor quantity of organic manure \n(FYM, poultry manure, and green manure), use of chemical fertilizers, \ninclusion of legume crops in cropping system and use of nutrients carried \ndown from the forest and villages in the first spring flood were some soil \nnutrient management activities adopted in the study area with little \nknowledge on sustainable soil management practices. So, for enhancing \nthe efficacy of the maize production and soil fertility knowledge, future \nresearch strategy should be built based on the soil fertility status of the \nfarm and some interventions is necessary to develop appropriate relation \nbetween soil nutrient status and maize production. This shows that the \nprovision of training related to sustainable soil management practices and \nscientific use of both organic and inorganic fertilizers based on soil testing \nresult is the prime need of the farmers for the sustainability of the system. \n\n\n\nREFERENCES \n\n\n\n[1] Atreya, K., Sharma. S., Bajracharya, R.M. 2005. Minimization of Soil and \nNutrient Losses in Maize-Based Cropping Systems In The Mid-Hills Of \nCentral Nepal, Journal of Science, Engineering and Technology, (1). \n\n\n\n[2] Brady, N.C., Weil, R.R. 2002. The nature and properties of soils (13th \nedition), Pearson Education, New Jersey. \n\n\n\n[3] Nepal, D. 2015. District Agriculture Development Office, Annual \nAgriculture Development Program and Progress: A glance, 2072/073. \n\n\n\n[4] Ghosh, P.K., Bandyopadhyay, K.K., Wanjari, R.H., Manna, M.C., Misra, \nA.K., Mohanty, M., Rao, A.S. 2007. Legume effect for enhancing productivity \nand nutrient use-efficiency in major cropping systems - an Indian \nperspective: a review, Journal of Sustainable Agriculture, 30(1), 59-86. \n\n\n\n[5] KC, G., Karki, T.B., Shrestha, J., Acchami, B.B. 2015. Status and prospects \nof maize research in Nepal, Journal of Maize Research and Development, \n1(1), 1-9. \n\n\n\n[6] MOAD. 2014. Statistical Information on Nepalese Agriculture, Ministry \nof Agricultural Development, 2014. \n\n\n\n[7] MOAD. 2015. Statistical Information on Nepalese Agriculture, Ministry \nof Agricultural Development. \n\n\n\n[8] MOAD. 2016. Statistical Information on Nepalese Agriculture, Ministry \nof Agricultural Development. \n\n\n\n[9] Muyayabantu, G.M., Kadiata B.D., Nkongolo, K.K. 2012. The response of \nmaize to different organic and inorganic fertilization regimes in monocrop \nand intercrop systems in a Sub-Saharan Africa Region. Journal of Soil \nScience and Environmental Management, 3(2), 42-48.   DOI: \n10.5897/JSSEM11.079 ISSN 2141-2391. \n\n\n\n[10] NMRP. 2016. Maize Production in mid hills of Nepal: from food to feed \nsecurity.\n\n\n\n\n\n\n \n1. INTRODUCTION\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 92-96 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.92.96 \n\n\n\nCite The Article: Rabar Fatah Salih, Ekhlass Mamand Hamad, Tara Namiq Ismail (2022). Commercial and Field Factors of Selecting Kenaf Fibers As Alternative \nMaterials in Industrial Applications. Journal of Sustainable Agricultures, 6(2): 92-96. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.92.96 \n\n\n\nCOMMERCIAL AND FIELD FACTORS OF SELECTING KENAF FIBERS AS \nALTERNATIVE MATERIALS IN INDUSTRIAL APPLICATIONS \n\n\n\nRabar Fatah Saliha,c*, Ekhlass Mamand Hamadb, Tara Namiq Ismailb \n\n\n\naDepartment of Field Crops, College of Agricultural Engineering Sciences, Salahaddin University-Erbil \nbDepartment of Forestry, College of Agricultural Engineering Sciences, Salahaddin University-Erbil \ncDepartment of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, (UPM) Serdang, Selangor \n*Corresponding Author E-mail: rabar.salih@su.edu.krd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 06 February 2021 \nAccepted 10 March 2022 \nAvailable online 18 March 2022\n\n\n\nThis work was carried out from 15 July 2021 in Grdarasha Field, College of Agricultural Engineering Sciences, \nSalahaddin University-Erbil. It aims to show the impact of using kenaf fibers as alternative materials in \nmanufacturing. Global climate change and environment pollution cause to do this kind of researching. Kenaf \n(Hibiscus cannabinus L.) is a fast growing natural crops, belongs to the Malvaceae family. It is an industrial \ncrop has high potential for cultivation in a tropical climate and also which resistance to various soil types and \nclimate. Selecting the raw materials for industrial applications is more important. Actually, kenaf fibers have \nmany advantages to use in wade range of applications, also it's fibers not just a part of plant useful as raw \nmaterial but also leaves and seeds have many other advantages and uses. The results show that there is a \nsignificant between varieties on growth and fiber yield properties. The highest plant high was of FH952 by \n(368.33 cm), while the best values of total fresh and dry stem yields were found of HC2 and V36, by almost \n(219.33 and 60.93 t/ha), respectively. Providing these results through kenaf plant could be considered as \nsubstitute materials for timber and other biocomposite manufactures, and also it causes to safe environment \nby absorption optimal value of carbon dioxide (CO2), then cutting of woodland trees will be decreased. Finally \nrecommended to cultivation fiber crops (kenaf) globally to conserve environment. \n\n\n\nKEYWORDS \n\n\n\nkenaf, growth, yield, economy, ecology, manufacturing. \n\n\n\n1. INTRODUCTION \n\n\n\nProtect environment can be done by producing goods that can minimize \ndamage to the environment. Biocomposite is produced using natural or \nsemi-natural materials, therefore it can easily be disposed, thus, minimize \nharm to the environment. Kenaf is selected as an extra substitute material \nfor making biocomposite since of its fast growing characteristics which \ncreates it capable to deliver a great volume of raw material in a short \nperiod of time. People should encourage to respond to the government\u2019s \ncall on green technology in order to preserve the environment (Kamal, \n2014). \n\n\n\nA researcher stated that kenaf stem can replace rubber wood particles up \nto 50% but the resin level must be kept at 10% or more because lower \nresin level (\u2a7d8%) significantly decrease strength of the particleboard \n(Paridah et al., 2014). Particleboard produced of kenaf stems is better than \nproduced of bast fiber or core fiber alone. (Juliana et al., 2012). \nAdditionally, kenaf fibers were used of manufacturing medium density \nfiberboard (MDF), (Aisyah et al., 2013). \n\n\n\nA previous researcher stated that, market for kenaf is still uncertain. It is \nbecause this crop is very new in Malaysia, but luckily it has potentials to \nbe commercialized as biocomposite which can be used for many industrial \npurposes (Kamal, 2014). Previous published studies have established that \nkenaf biocomposite is appropriate to be used as automotive components \n(Chen et al., 2005; Qatu, 2011). With that impacts of woven kenaf \ncomposites for applying in automotive structural studies in Malaysia, but \nunfortunately they are still far from commercialization (Lee et al., 2021). \n\n\n\nThus, the outstanding mechanical properties that come with an untreated \nkenaf composite, suggest a useful alternative material for automotive \nparts generation such as dashboard and door panel (Radzuan et al., 2020). \n\n\n\nSubstitution for industrial wood chips by kenaf core chips or bast fibers in \nexperimental particleboards showed that in some cases the new products \nmay be comparable to those made from pure industrial wood chips and \ntherefore they may satisfy the applied standards and be accepted in the \nmarket (Grigoriou et al., 2000a,b). \n\n\n\nA group of researchers concluded that, kenaf can be planted as a major \ncrop since their fibers have potential impact to be used as feedstock and \nas forage crops (Salih and Qader, 2020). Studies showed that the kenaf \nplant had the optimal CO2 absorption among the other crops. It can absorb \n1.5 times the carbon dioxide by its weight (Mohanty et al., 2005).  \nMoreover, the quantity of CO2 would be reduced in the atmosphere by \nusing kenaf fibers in concrete. However, using natural fibers as an \nalternative for concrete reinforcement is of interest not only due to \nincreasing ductility and versatility of the material but also from an \nenvironmental perspective (Baghban and Mahjoub, 2020). \n\n\n\n2. MATERIAL AND METHODS \n\n\n\n2.1 Materials  \n\n\n\nThree kenaf varieties were selected as plant materials in this current \nstudy; FH952, V36, and HC2. Seeds for all varieties were provided by the \nInstitute of Tropical Forestry and Forest Production (INTROP) at the \n\n\n\n\nmailto:rabar.salih@su.edu.krd\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 92-96 \n\n\n\nCite The Article: Rabar Fatah Salih, Ekhlass Mamand Hamad, Tara Namiq Ismail (2022). Commercial and Field Factors of Selecting Kenaf Fibers As Alternative \nMaterials in Industrial Applications. Journal of Sustainable Agricultures, 6(2): 92-96. \n\n\n\nUniversiti Putra Malaysia (UPM). Additionally, chemical fertilizer as NPK \nwas added as basal fertilizer. \n\n\n\n2.2 Methods \n\n\n\n2.2.1 Location \n\n\n\nThis study was conducted at Grdarasha Field, College of Agricultural \nEngineering Sciences, Salahaddin University-Erbil, which is located at \n(Latitude 36. 10116 N and Longitude 44.00925 E), and elevation of 415 \nmeters above sea level. Figure 1 shows the geographical location of the \nstudy site. \n\n\n\nFigure 1: Geographical location of the study \n\n\n\n2.2.2 Experimental design \n\n\n\nRandomized Complete Block Design (RCBD) was applied as experimental \ndesign with three replications. Seeds of three kenaf varieties were selected \nas mentioned earlier and then sowed on 15 Jun 2021, in the depth of (2-3 \ncm). The plot size was 1m2, distance between plants was about 10 cm, \nwhile between row to row just 30 cm, which was plant density 400000 \nplants/ha. 15g/m2 of NPK fertilizer was added to each plot as basal \nfertilizer on 2 August 2021. \n\n\n\n2.2.3 Sampling method \n\n\n\nFive plants were randomly selected from each treatment plot, which were \nmanually harvested on 20 November 2021. Next, growth and fiber yield \nwere determined. Field stick measuring devices was used to measure \n\n\n\nplant height at the end stage of plant growth, stem diameter was measured \nby using digital caliper from 10 cm above of the ground surface. Stem, bast \nand core fibers were sun dried, which was indirectly as can be seen in \n(Figure 2). \n\n\n\nFigure 2: Stem, bast and core fibers were sun dried \n\n\n\n2.3  Data analysis \n\n\n\nData on plant growth (plant height and stem diameter), and also yield \nparameters such as; total fresh and dry stem yield, fresh and dry core fiber \nyield, and fresh and dry bast fiber yield were subjected to Analysis of \nVariance (ANOVA) by using SPSS Statistics (IBM SPSS Statistics 21). Least \nSignificant Difference (LSD) at P \u2264 0.05 was used to perform the mean \ncomparison. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Growth parameters \n\n\n\nPlant height and stem diameter were significantly changed between \nvarieties (Table 1). The highest plant high was recorded by 368.33 cm of \nFH952 variety, followed by V36 and HC2 (351.67 and 340.67 cm), \nrespectively (Figure 3). Results of this current study strongly supported \nby (Salih et al., 2014a). This was also in accordance with the study of a \nprevious researcher which stated that different kenaf varieties have \ndifferent height level (Agbaje et al., 2008). Conversely, the biggest stem \ndiameter was recorded by HC2 (31.64 mm), while smallest stem diameter \nwas found of FH952 by (26.95 mm), (Figure 3). \n\n\n\nTable 1: The analysis of variance (ANOVA) for the effect of varieties on the growth and fiber yield parameters of kenaf plant \n\n\n\nGrowth and yield parameters \n\n\n\nPH    SD    TFY    TDY \n\n\n\nV DF MS F.V P.V MS F.V P.V MS F.V P.V MS F.V P.V* \n\n\n\n2 0.06 5.69 0.041 19.32 5.77 0.04 101.67 0.19 0.83 58.72 1.10 0.39 \n\n\n\n2 20.25 2.05 0.17 0.44 0.46 0.51 0.03 0.12 0.73 13.44 1.23 0.28 \n\n\n\n2 56.25 5.69 0.03 5.76 5.94 0.02 0.00 0.01 0.95 75.11 6.85 0.02 \n\n\n\nFCY    DCY    FBY    DBY \n\n\n\nDF MS F.V P.V MS F.V P.V MS F.V P.V MS F.V  P.V \n\n\n\nV 2 439.27 2.78 0.14 31.22 1.00 0.42 92.39 1.50 0.30 5.14 1.24 0.35 \n\n\n\n*Significant at 5%, when p-value less than 0.05 (typically \u2264 0.05).\n\n\n\n**V= Variety (FH952, V36, HC2), PH= Plant height, SD= Stem diameter, TFY= Total fresh yield, TDY= Total dry yield, FCY= Fresh core yield, DCY= Dry core \nyield, FBY= Fresh bast yield, DBY= Dry bast yield, DF= Degrees of freedom, MS= Mean square, F.V= F. value and P.V= P value.  \n\n\n\nGrowth and field characteristics of fiber crops especially bast fiber; kenaf, \njute, and hemp are mostly important when using their fibers of industrial \napplications, since its affect to amount of bast and core fibers are collecting \nfrom them. Additionally, healthy growth could cause to quality augment \nand also end uses (Salih, 2016). Both growth characteristics plant high and \nstem diameter directly affected to yield parameters. Here could ask, which \n\n\n\none is more effective? Generally, together may better, but from these \nresults other answer may found (Figures 4-6). Additionally, increasing \neach other may causes to choose kenaf plant as alternative material in \nindustrial applications. \n\n\n\nBeside of these characteristics, rapidly growing of kenaf plant and its \nresistance to various soil types and climate which is encourage farmers to \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 92-96 \n\n\n\nCite The Article: Rabar Fatah Salih, Ekhlass Mamand Hamad, Tara Namiq Ismail (2022). Commercial and Field Factors of Selecting Kenaf Fibers As Alternative \nMaterials in Industrial Applications. Journal of Sustainable Agricultures, 6(2): 92-96. \n\n\n\ngrowing kenaf. Several months only are enough to decide of harvesting \nkenaf plants, while the lowest time of cutting any trees to convert to timber \nmay need to several years. All of these field factors enhanced kenaf \npositions' and then could become the best source of raw material in wide \nrange of manufacture processing. Results of a study were also helpful for \nthe application of kenaf residues in the wood composites industry (Xu et \nal., 2013). Additionally, they concluded that a potential alternative \nmaterial for wood in manufacturing composite boards is kenaf core fiber. \n\n\n\nFigure 3: Effect of variety on plant height and stem diameter \n\n\n\n3.2 Fiber yield parameters \n\n\n\nHigh amount of total stem, core and bast fiber yield and their physical and \nmechanical characteristics of kenaf are helpful to convention it's fibers to \nbiocomposite. Figure 4 shows influence of variety on total fresh and dry \nstem yield (bast and core), the highest amount of these characteristics \nwere recorded of HC2 was almost (219.33 t/ha) of TFY, while the highest \namount of TDY was found by V36 variety (57.33 t/ha). These results are \nin agreement with a study that determined HC2 variety had the maximum \nvalue of fiber yields as compared to the other varieties were studied \n(Hossain et al., 2011). These results of fiber yield showed that all varieties \napplicable as fundamental stuff compared to forest trees. Which refers to \na broad set of realization about the valuable of kenaf plant economically \nand ecologically for production of fibers differentiation to forest tree. \n\n\n\nAdditionally, the best results of core and bast fiber yield were recorded by \nV36 variety which were (109.07, 43.07, 63.73 and 17.60 t/ha), for fresh \nand dry core and bast fiber yields respectively (Figures 5 and 6). Based on \nthese results, could say that V36 was better to obtain of high fiber yield \nthan both varieties in this current study. \n\n\n\nHigh quantity of production is not enough but also how to get of it is \nimportant. Which mean that, how to reduce manufacturing/production \ncosts of proper time environmentally. As known that, world climate \nchange is the biggest issues requested to find appropriate solving way to \nsafe and renew environment globally. Forest protection is necessary since \ndecrease and lost each tree meaning that rise carbon dioxide (CO2), and \nthen causes to climate pollution as can be seen globally. So, the best way \nfor that purpose is encouraging farmers to growing natural fibers \nespecially kenaf, jute and hemp. Next, the most obviously point is a create \nrule globally for factors and companies to use these natural fibers as \nalternative materials. Results from this current study should be focused by \nresearchers and scientists to develop this kind of natural fibers because of \nits important which known as ecofriendly fibers. For example, beside of \nrainforest protection, kenaf plant absorbs CO2 from the atmosphere more \nthan any other crop, about 1.5 tons of CO2 seems sufficient in order to \nproduce 1 ton of dry matter of kenaf. It means that each hectare of kenaf \nconsumes 30 to 40 t of CO2 per growing cycle (Kimball and Idso, 1983). \n\n\n\n17.60 t/ha just dry bast fiber, which is not low value (Figure 6). \nFurthermore, dry core fiber around (43.07 t/ha) figure 5, and total dry \nstem yield was (60.93 t/ha), while total fresh stem yield for each variety \nwas above (200 t/ha), (Figure 4). That is on the time, kenaf plant is a faster \ngrowing crop as previously mentioned, so in some countries can produce \nit twice in the year. \n\n\n\nFigure 4: Effect of variety on total fresh and dry stem yield \n\n\n\nFigure 5: Effect of variety on fresh and dry core fiber yield \n\n\n\nFigure 6: Effect of variety on fresh and dry bast fiber yield \n\n\n\nOn the other side, as known Paulownia spp. is a fast growing woody crops \nis a very important source for the generation of the bioenergetics biomass, \nand which multiple values and high adaptability with climate conditions \n(Icka et al., 2016). Each paulownia tree after (5 to 7) years old can generate \n1 m3 timber in a surface with density of (2000 plants/ha), offering a total \nproduction of (330 t/ha), (Ates et al., 2008). However, in 1974 the trees \nhad grown with average dbh of 30.1 cm, 13.5 m height, 0.3927 m3 \nindividual timber volume with 400 trees per hectare totaling 153.2 \nm3/timber volume/ha (Rao, 1986). Alongside of paulownia tree, populus \nand also eucalyptus trees were taken to comparison with kenaf plant. \n\n\n\n368.33\n\n\n\n351.67\n\n\n\n340.67\n\n\n\n26.95\n\n\n\n27.62\n\n\n\n31.64\n\n\n\n0.00 100.00 200.00 300.00 400.00\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nP\nH\n\n\n\n (\ncm\n\n\n\n)\nSD\n\n\n\n (\nm\n\n\n\nm\n)\n\n\n\n207.73\n\n\n\n212.67\n\n\n\n219.33\n\n\n\n52.13\n\n\n\n60.93\n\n\n\n57.33\n\n\n\n0.00 50.00 100.00 150.00 200.00 250.00\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nTF\nY \n\n\n\n(t\n/h\n\n\n\na)\nTD\n\n\n\nY \n(t\n\n\n\n/h\na)\n\n\n\nChart Title\n\n\n\n87.07\n\n\n\n109.07\n\n\n\n106.80\n\n\n\n36.93\n\n\n\n43.07\n\n\n\n41.73\n\n\n\n0.00 20.00 40.00 60.00 80.00 100.00 120.00\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nFC\nY\n\n\n\n (\nt/\n\n\n\nh\na)\n\n\n\nD\nC\n\n\n\nY\n (\n\n\n\nt/\nh\n\n\n\na)\n\n\n\n52.67\n\n\n\n63.73\n\n\n\n57.47\n\n\n\n15.33\n\n\n\n17.60\n\n\n\n15.33\n\n\n\n0.00 20.00 40.00 60.00 80.00\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nFH952\n\n\n\nV36\n\n\n\nHC2\n\n\n\nFB\nY\n\n\n\n (\nt/\n\n\n\nh\na)\n\n\n\nD\nB\n\n\n\nY\n (\n\n\n\nt/\nh\n\n\n\na)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 92-96 \n\n\n\nCite The Article: Rabar Fatah Salih, Ekhlass Mamand Hamad, Tara Namiq Ismail (2022). Commercial and Field Factors of Selecting Kenaf Fibers As Alternative \nMaterials in Industrial Applications. Journal of Sustainable Agricultures, 6(2): 92-96. \n\n\n\nTable 2 displays the natural fibers are consumption globally to product \ntimber and biocomposite. Based on the data from this table could say that \nkenaf plant was prevailing on all trees about the water requirements, time \nof harvesting and then dry matter production. \n\n\n\nIn other point, cutting forest trees adds carbon dioxide, and then back into \nthe atmosphere which is the main cause of climate change and rise \ntemperature. As a researcher reported that, high concentration of carbon \ndioxide (CO2) leads to increase the atmospheric temperature which will \nhave many impacts on plant (Banwart, 2011). Furthermore, this stated \nfrom this current study strongly confirmed by a previous researcher \n\n\n\nwhose reported that, the fiber yield increased 52 to 56%, when the plant \nwas treated with doubling CO2 in open field plot and growth chamber \n(Campbell et al., 2010). Also, atmospheric temperature effects on the fiber \nyield. So, it means that if cultivation of natural fibers increased the CO2 \n\n\n\nabsorption will be increased directly. \n\n\n\nMany individual actions considerate worldwide to decrease global \nwarming, but the great effort here is how to collective these actions to stop \nthis issue. Believed that, cultivation natural fibers as kenaf plant is a key to \nbright the way ahead of not only researchers and scientists, but also of \nfactors and companies due to many advantages as previously mentioned.\n\n\n\nTable 2: Comparison between plant density, water requirements, time of harvesting and dry matter/biomass production of Kenaf, Paulownia, Populus \nand Eucalyptus \n\n\n\nCrops/Tree \nPopulation \n\n\n\nPlant/ha \n\n\n\nWater required \n\n\n\nmm or liters \nHarvesting time \n\n\n\nProduction \n\n\n\nt/ha \nReferences \n\n\n\nKenaf 400000-600000 780\u20131200mm 3-5 months 17-60 DM* \n\n\n\nCurrent study; \n\n\n\nBa\u00f1uelos et al. (2002); \nDanalatos and Archontoulis \n\n\n\n(2010); Basri et al. (2014); Salih \net al. (2014b); Salih (2016); \n\n\n\nSalih and Qader (2020) \n\n\n\nPaulownia 2000 2000 L/tree 5-7 years \n330** Ates et al. (2008); Garc\u00eda-\n\n\n\nMorote et al. (2014) \n\n\n\nPopulus 1111-5425 440,000 L/ha 10 years 146*** \nFang et al. (2007); Ba\u00f1uelos et \n\n\n\nal. (1999) \n\n\n\nEucalyptus 1666 20-30 L/tree/day 2.5-3.5 years 23.4-163.9*** \nLima (1984); Quartucci et al. \n\n\n\n(2015) \n\n\n\n*DM= Dry matter production (Stem).\n\n\n\n** Each Paulownia tree aged 5-7 years old can generate 1 m3 timber in a surface with density of 2000 plants/ha, offering a total production of 330 t/ha. \n\n\n\n***At 10 years, the highest total biomass in the plantation of 1111 stems/ha, reached about 146 t/ha. Additionally, 5425 Populus trees/ha (>1.6 m2 per \ntree), 440,000 L/ha of water are minimally necessary to grow this number of trees for the first 3 months under Iowa conditions (water usage would be far \ngreater under growing conditions in Central California).  \n\n\n\n**** The density of 1666 plants per hectare, harvest was also carried out at the age of 7 years. Total biomass produced (23.4-163.9 t/ha), of the age of 1-7 \nyears old.   \n\n\n\n4. CONCLUSION \n\n\n\nNowadays, recover and renew environment is really necessary, so climate \nchange and global warming are the great issue should be concerned by the \nresearchers, so this current study investigated to find other natural \nsources as alternative raw material usage in industrial applications, which \nwas to reduce global warming problems by avoiding of cutting trees. \nResults were showed that, growth and fiber yield/dry matter of all \nvarieties of kenaf plant could be considered as alternative materials and \nthen it will be the best strategies to prevent deforestation. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nAuthors would like to special thanks to the head of the both Departments; \nField Crops and Forestry in College of Agricultural Engineering Sciences, \nand also to all staffs from Grdarasha Research Field, Salahaddin \nUniversity-Erbil. Additionally, they would like to thank the Institute of \nTropical Forestry and Forest Production (INTROP) at the Universiti Putra \nMalaysia (UPM), for providing the kenaf seeds. \n\n\n\nREFERENCES \n\n\n\nAgbaje, G.O., Saka, J.O., Adegbite, A.A., & Adeyeye, O.O., 2008. Influence of \nagronomic practices on yield and profitability in kenaf (Hibiscus \ncannabinus L.) fibre cultivation. African Journal of \nBiotechnology, 7(5). \n\n\n\nAisyah, H.A., Paridah, M.T., Sahri, M.H., Anwar, U.M.K., & Astimar, A.A., \n2013. Properties of medium density fibreboard (MDF) from kenaf \n(Hibiscus cannabinus L.) core as function of refining \nconditions. Composites Part B: Engineering, 44(1), pp. 592-596. \n\n\n\nAtes, S., Ni, Y., Akgul, M., & Tozluoglu, A., 2008. Characterization and \nevaluation of Paulownia elongota as a raw material for paper \nproduction. African journal of biotechnology, 7(22). \n\n\n\nBaghban, M.H., & Mahjoub, R., 2020. Natural kenaf fiber and LC3 binder for \nsustainable fiber-reinforced cementitious composite: a \nreview. Applied Sciences, 10(1), pp. 357. \n\n\n\nBa\u00f1uelos, G.S., Shannon, M.C., Ajwa, H., Draper, J.H., Jordahl, J., & Licht, J., \n1999. Phytoextraction and accumulation of boron and selenium by \npoplar (Populus) hybrid clones. International Journal of \nPhytoremediation, 1(1), pp. 81-96. \n\n\n\nBa\u00f1uelos, G.S., Bryla, D.R., & Cook, C.G., 2002. Vegetative production of \nkenaf and canola under irrigation in central California. Industrial \ncrops and products, 15(3), pp. 237-245. \n\n\n\nBanwart, S., 2011. Save our soils. Nature, 474(7350), pp. 151-152. \n\n\n\nBasri, M.H.A., Abdu, A., Junejo, N., Hamid, H.A., & Ahmed, K., 2014. Journey \nof kenaf in Malaysia: A Review. Scientific Research and Essays, 9(11), \npp. 458-470. \n\n\n\nCampbell, B.T., Saha, S., Percy, R., Frelichowski, J., Jenkins, J.N., Park, W., \nMayee, C.D., Gotmare, V., Dessauw, D., Giband, M., & Du, X., 2010. Status \nof the global cotton germplasm resources. Embrapa Recursos \nGen\u00e9ticos e Biotecnologia-Artigo em peri\u00f3dico indexado (ALICE). \n\n\n\nChen, Y., Chiparus, O., Sun, L., Negulescu, I., Parikh, D.V., & Calamari, T.A., \n2005. Natural fibers for automotive nonwoven composites. Journal of \nIndustrial Textiles, 35(1), pp. 47-62. \n\n\n\nDanalatos, N.G., & Archontoulis, S.V., 2010. Growth and biomass \nproductivity of kenaf (Hibiscus cannabinus L.) under different \nagricultural inputs and management practices in central \nGreece. Industrial Crops and Products, 32(3), pp. 231-240. \n\n\n\nFang, S., Xue, J., & Tang, L., 2007. Biomass production and carbon \nsequestration potential in poplar plantations with different \nmanagement patterns. Journal of environmental management, 85(3), \npp. 672-679. \n\n\n\nGarc\u00eda-Morote, F.A., L\u00f3pez-Serrano, F.R., Mart\u00ednez-Garc\u00eda, E., Andr\u00e9s-\nAbell\u00e1n, M., Dadi, T., Candel, D., Rubio, E., & Lucas-Borja, M.E., 2014. \nStem biomass production of Paulownia elongata\u00d7 P. fortunei under \nlow irrigation in a semi-arid environment. Forests, 5(10), pp. 2505-\n2520. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 92-96 \n\n\n\nCite The Article: Rabar Fatah Salih, Ekhlass Mamand Hamad, Tara Namiq Ismail (2022). Commercial and Field Factors of Selecting Kenaf Fibers As Alternative \nMaterials in Industrial Applications. Journal of Sustainable Agricultures, 6(2): 92-96. \n\n\n\nGrigoriou, A., Passialis, C., & Voulgaridis, E., 2000a. Experimental \nparticleboards from kenaf plantations grown in Greece. Holz als Roh-\nund Werkstoff, 58(5), pp. 309-314. \n\n\n\nGrigoriou, A., Passialis, C., & Voulgaridis, E., 2000b. Kenaf core and bast \nfiber chips as raw material in production of one-layer experimental \nparticleboards. European Journal of Wood and Wood Products, 58(4), \npp. 290-291. \n\n\n\nHossain, M.D., Hanafi, M.M., Jol, H., & Jamal, T., 2011. Dry matter and \nnutrient partitioning of kenaf (Hibiscus cannabinus L.) varieties grown \non sandy bris soil. Australian Journal of Crop Science, 5(6), pp. 654-\n659. \n\n\n\nIcka, P., Damo, R., & Icka, E., 2016. Paulownia tomentosa, a fast growing \ntimber. Ann. Valahia Univ. Targoviste, Agric, 10(1), pp. 14-19. \n\n\n\nJuliana, A.H., Paridah, M.T., Rahim, S., Azowa, I.N., & Anwar, U.M.K., 2012. \nProperties of particleboard made from kenaf (Hibiscus cannabinus L.) \nas function of particle geometry. Materials & Design, 34, pp. 406-411. \n\n\n\nKamal, I.B., 2014. Kenaf for biocomposite: an overview. Journal of Science \nand Technology, 6(2). \n\n\n\nKimball, B.A., & Idso, S.B., 1983. Increasing atmospheric CO2: effects on \ncrop yield, water use and climate. Agricultural water \nmanagement, 7(1-3), pp. 55-72. \n\n\n\nLee, C. H., Khalina, A., Nurazzi, N. M., Norli, A., Harussani, M. M., Rafiqah, S., \n... & Ramli, N. (2021). The challenges and future perspective of woven \nkenaf reinforcement in thermoset polymer composites in malaysia: A \nreview. Polymers, 13(9), pp. 1390. \n\n\n\nLIMA, W.P., 1984. The hydrology of eucalypt forests in Australia. IPEF \n(Piracicaba), 28, pp. 11-32. \n\n\n\nMohanty, A.K., Misra, M., & Drzal, L.T. eds., 2005. Natural fibers, \nbiopolymers, and biocomposites. CRC press. \n\n\n\nParidah, M.T., Juliana, A.H., El-Shekeil, Y.A., Jawaid, M., & Alothman, O.Y., \n2014. Measurement of mechanical and physical properties of \nparticleboard by hybridization of kenaf with rubberwood \nparticles. Measurement, 56, pp. 70-80. \n\n\n\nQatu, M.S., 2011. Application of kenaf-based natural fiber composites in \nthe automotive industry (No. 2011-01-0215). SAE Technical Paper. \n\n\n\nQuartucci, F., Schweier, J., & Jaeger, D., 2015. Environmental analysis of \nEucalyptus timber production from short rotation forestry in \nBrazil. International Journal of Forest Engineering, 26(3), pp.225-239. \n\n\n\nRao, A.N., 1986. Paulownia in China: cultivation and utilization. \n\n\n\nRadzuan, N. A. M., Tholibon, D., Sulong, A. B., Muhamad, N., & Haron, C. H. \nC. (2020). New processing technique for biodegradable kenaf \ncomposites: A simple alternative to commercial automotive \nparts. Composites Part B: Engineering, 184, pp. 107644. \n\n\n\nSalih, R.F., 2016. Influence of Potassium, Boron and Zinc on Growth, Yield \nand Fiber Quality of Two Kenaf (Hibiscus cannabinus L.) Varieties. \n\n\n\nSalih, R.F., & Qader, N.A., 2020. Environmental Effect on Growth and Yield \nParameters of Ten Kenaf Varieties (Hibiscus cannabinus L.) in \nErbil. Zanco Journal of Pure and Applied Sciences, 32(4), pp. 169-173. \n\n\n\nSalih, R.F., Abdan, K., & Wayayok, A., 2014a. Growth responses of two kenaf \nvarieties (Hibiscus cannabinus L.) applied by different levels of \npotassium, boron and zinc. Journal of Agricultural Science, 6(9), pp. \n37-45. \n\n\n\nSalih, R.F., Abdan, K., Wayayok, A., & Hashim, N., 2014b. Effect of \npotassium, boron and zinc on nitrogen content in bast and core fibers \nfor two kenaf varieties (Hibiscus cannabinus L.). International Journal \nof Development Research, 4(12), pp. 2581-2586. \n\n\n\nXu, X., Wu, Q., & Zhou, D., 2013. Influences of layered structure on physical \nand mechanical properties of kenaf core \nparticleboard. BioResources, 8(4), pp. 5219-5234. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 01-06 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2022.01.06 \n\n\n\n \nCite The Article: Chuen Khee Pek, Fang Ee, Foo (2022). Agricultural Multifunctionality for Sustainable Development in Malaysia: A Contingent Valuation Method \n\n\n\nApproach. Malaysian Journal of Sustainable Agricultures, 6(1): 01-06. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2022.01.06 \n\n\n\n\n\n\n\nAGRICULTURAL MULTIFUNCTIONALITY FOR SUSTAINABLE DEVELOPMENT IN \nMALAYSIA: A CONTINGENT VALUATION METHOD APPROACH \n \nChuen Khee Pek*, Fang Ee, Foo \n \nFaculty of Business and Management, UCSI University \n*Corresponding author E-mail: pekck@ucsiuniversity.edu.my \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 13 July 2021 \nAccepted 15 August 2021 \nAvailable online 23 August 2021 \n\n\n\n\n\n\n\nAgriculture multifunctionality highlights the importance of non-food benefits as joint products of agriculture. \nA study on the non-food benefits of agriculture is important to explore the potential of innovating the \nagricultural industry for sustainable development.  The societal influence of agriculture multifunctionality \n(AMF), for instance job opportunities, more environmental-friendly crop-production methods and food \nsecurity, is not much known, especially in Malaysia. Thus, the objectives of this study are to estimate the \neconomic value and the factors influencing willingness-to-pay (WTP) for AMF. Additionally, there are vast \npotentials in AMF to support the UN Sustainable Development Goals 2, 8 and 12. A contingent valuation (CV) \nwith two payment solicitation formats was commissioned on respondents in Kuala Lumpur to study the WTP \nan agricultural premium of the purchase values of agricultural products to support AMF. The findings support \nthe direction of innovating the agricultural industry through AMF as one of the forerunners of sustainable \ngrowth for developing countries like Malaysia. Although only half of the respondents vowed their WTP for \nAMF, it is observed that households, which spend more on agricultural products such like vegetables, fruits \nand related goods are having higher odds ratio of WTP for AMF. The odds ratios change to values of more \nthan 1.00 with spending, which was three times the mean value. And that payment card format begets higher \nodds ratio of WTP for AMF compared to open-ended CV method format. The findings encourage \nentrepreneurs, especially the youth to venture into the innovative non-food benefits of agriculture for more \nresponsible usage of our natural resources and decent economic growth. \n\n\n\nKEYWORDS \n\n\n\nagricultural multifunctionality, sustainable development goals, non-food benefits \n\n\n\n1. INTRODUCTION \n\n\n\nAgriculture is an industry that could survive financial crisis as evident in \nthe 1997 Malaysian economic crisis. Agriculture provides both food \nproduces and also non-food benefits such like food security, \nenvironmental protection, landscape and cultural preservation and rural \nemployment, known as agricultural multifunctionality (AMF). This term \nwas first shared at the earth Summit in Rio de Janeiro in 1992 and was \nreferring to the provision of a framework to understanding and addressing \nvarious developments and changes in global agriculture activities. The \nOrganisation for Economic Co-operation and Development (OECD) has \nlong worked on the non-commodity output of agriculture mentioned \nearlier. AMF refers to the fact that an agriculture activity may have \nmultiple outputs and, by virtue of this, may contribute to several societal \nobjectives at once (OECD, 2001). This articulates well that agriculture as \nan industry has rich contributions to the United Nations (UN) Sustainable \nDevelopment (SD) Goals (SDGs), especially Goal 2- Zero Hunger, Goal 8- \nDecent Work and Economic Growth and Goal 12- Responsible \nConsumption and Production. \n\n\n\nThe SD Goal 2 aims at ending hunger, achieve food security and promote \nsustainable agriculture. The first target of this goal is to enhance \nagricultural productivity and getting more diverse group of the \ncommunity to be involved to create this economic value. In order to attract \n\n\n\nmore participation, especially the youth, the concept of AMF as \ncomplementary benefits of agriculture helps. One of the main non-trade \nbenefits of AMF is food security and the main indicator of the said target is \nto counter severe food insecurity occurrences. It is also the aim of this \ntarget of expanding agricultural yields to protect reasonable agricultural \narea for sustainable agriculture. With a healthy agriculture industry, the \ncountry can promote sustained, inclusive, and sustainable economic \ngrowth and decent work for all. This helps to create more job \nopportunities for both women and men, young and people with special \nneeds and worthy to be mentioned single mothers and refugees residing \nin the country. Annual growth rate and unemployment rates, especially \namong the youth can be improved for social balance and cohesion. SD Goal \n8 covers these concerns and AMF plays a role in supporting those targets \nof the goal. \n\n\n\nAgriculture, like all other industries involves consumption and production \nof their respective goods and services. SD Goal 12 looks into ensuring \nsustainable consumption and production patterns in the country. It is the \ntargets of the goal that sustainable management, efficient use of natural \nresources and halving global food waste are achieved by 2030. AMF \nencourages reduction of pollutants as it promotes more environmental-\nfriendly crop production methods and better consumption habits of the \npeople through the appreciation of the non-food based invaluable positive \nexternalities. AMF provides positive externalities in terms of non-food \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 01-06 \n\n\n\n\n\n\n\n \nCite The Article: Chuen Khee Pek, Fang Ee, Foo (2022). Agricultural Multifunctionality for Sustainable Development in Malaysia: A Contingent Valuation Method \n\n\n\nApproach. Malaysian Journal of Sustainable Agricultures, 6(1): 01-06. \n\n\n\n\n\n\n\nbenefits with significant economic impacts and, also social and cultural \nbenefits, which are crucial information for policy makers to consider \n(Weersink, 2002; Wilson, 2007). However, AMF is a very broad concept, \nand its societal importance is little known (Heringa et al., 2013). As such, \nan economic valuation exercise would help inform the policy makers the \nvalue of these positive externalities, which support the SDGs mentioned \nearlier.  \n\n\n\n1.1  Objectives of study \n\n\n\nThe main objective of this study is to estimate the economic value of AMF \nas a catalyst of sustainable development (SD) using the contingent \nvaluation method (CVM), which elicits the willingness-to-pay (WTP) of \nMalaysians using two different payment solicitation formats. The \nsecondary objectives are to identify the factors influencing WTP for AMF \nand understand the perspectives of the public on AMF. Although AMF is \nnot a new enhanced alternative, the awareness and understanding of \nMalaysian about it is rather low. This paper showcases a fundamental \nstudy of AMF, adding to the limited literature of such work in Malaysia. \nThe agriculture industry contributes to around 7.1 percent of the gross \ndomestic product (GDP) of the country in 2019. The trend has been falling \nsince 2012 and there is a need to rejuvenate this industry, which has a \ngreat potential ahead.  \n\n\n\nThis study monetises the non-food benefits of agriculture and opens wider \nperspectives to agriculturalists in both the public and private sectors. \nThere are several scientific contributions of AMF, and two of the more \nsignificant are lower temperature and air pollution in the areas \nsurrounded by paddy fields. With climate change, the average \ntemperature is rising and affecting crop yields. AMF may be a catalyst to \nstart the planters, public and policy makers to see the importance of \nreviving the agriculture industry in Malaysia. The findings may be useful \nto the relevant authorities like the Ministry of Agriculture and Agro-based \nIndustries, Ministry of Natural Resources and Environment, Ministry of \nInternational Trade and Industry, and other related agencies. \n\n\n\n1.2 Literature review on agricultural multifunctionality \n\n\n\nContinuous works on agricultural multifunctionality (AMF) are observed \nglobally. However, not many current studies have been added into the \nstock of literature as evident also by the work of Tohidyan and Rezaei-\nMoghaddam (2019), which cited literatures older than five years or more. \nThe work of Tohidyan and Rezaei-Moghaddam reported multifunctional \nagriculture as an approach for entrepreneurship development for the \nindustry and is one of the closest to the research focus of this paper. The \nstudy of the authors also lists the characteristics of AMF. The philosophy \nof AMF entails multiple progress and sustainability through social \ninnovation and take a more holistic agricultural point of view. AMF focuses \non a balance between food security and self-sufficiency through increasing \nproduction of farm yields and environmental protection. Farmers should \nbe entrepreneurial, and farms can be managed alike a multifunctional-\nrural companies.  \n\n\n\nThese characteristics converge to show that agricultural growth for \neconomic development through AMF supports sustainable development \nespecially SD Goal 8. Most of the literature record works on policies related \nto AMF, the appropriate AMF framework and valuing the output of AMF \n(Randall, 2002; Heringa et al., 2013; Dominguez-Torrero and Solino, 2011; \nMoxey et al., 1999; Paarlberg et al., 2002; Bjorkhaug and Richards, 2008; \nCairol et al., 2009; Caron et. al., 2008). A group researcher looked at how \nAMF is reflected in policies, made a comparative analysis of AMF between \nNorway and Australia, a studied how AMF affects agricultural trade \nnegotiations, and considered the efficient design for agri-environmental \npolicies (Cairol et al., 2009; Bjorkhaug and Richards, 2008; Paarlberg et al., \n2002; Moxey et al., 1999).  \n\n\n\nSome researchers argued the need to refocus the concept in addressing \nAMF (Caron et al., 2008). Heringa, van der Heide & Heijman looked at the \neconomic impact of AMF in the Netherlands using an input-output model, \na studied the implications for valuing outputs of AMF using different status \nquo in choice experiments, and valued the outputs of AMF (Heringa, van \nder Heide and Heijman, 2013; Dominguez-Torrero and Solino, 2011; \nRandall, 2002). In addition, a group researcher works on promoting \nagricultural multifunctionality in Germany as a new approach as they see \nthis as an area of great benefits to the economy (Lehmann et al., 2009). \nSome researchers works on the tools and impact assessment of \nagricultural multifunctionality (Zander et al., 2007). In the more recent \nworks such like care farming was studied as having roles in \nmultifunctional agriculture and recommended politicians to mandate an \neconomic environment, which supports the care farms (Custance et al., \n2015). In the local context, work on AMF is very minimal. This study adds \n\n\n\ninto the local AMF literature, especially into looking ways the non-trade \nbenefits of agriculture can play a significant role in sustainable \ndevelopment in Malaysia. \n\n\n\n2. METHODOLOGY \n\n\n\nContingent valuation method (CVM) is an economic and environmental \nvaluation technique, which uses a surrogate market by directly eliciting \nconsumers\u2019 preferences and willingness-to-pay (WTP) for some proposed \nmarket conditions, which offer potential improvements or avoid potential \ndamages to the environment and/or society. It is grouped under the family \nof non-market environmental valuation stated preference technique, \nwhich aims to quantify the environmental goods or services of non-market \nattributes into monetary or market values. CVM elicits the maximum WTP \nof individual respondent to obtain improvement or avoid damages on \nenvironmental goods and services in a hypothetical market (Sellar et al., \n1985; Bergstrom and Stoll, 1989). In this study respondents were asked \ntheir WTP to pay for agricultural multifunctionality (AMF) in order to \npreserve the non-food benefits of agriculture. As such the framework of \ncompensating surplus (CpS) is employed. The CpS of a price increase (in \nthe context of higher prices of agricultural products) is the amount of \nincome, when taken away from the consumer, will leave him/her as well \noff as without the price change as if it had occurred. This will keep the \nconsumer on his/her post-change utility level. The following is the \nexpenditure function to illustrate the CpS: \n\n\n\nU (xi\u201d, xj\u2019 \u2013 WTP gainji [xi\u2019, xi\u201d, xj\u2019]) = U (xi\u2019, xj\u2019) \n\n\n\nThe public\u2019s WTP, socio-economic and attitudinal variables can be \nspecified as: \n\n\n\nWTPi = Xi\u2019\u03b2 + ei \n\n\n\nwhere Xi = vector of explanatory variables and \u03b2 = vector of coefficients. \nThe ei term is assumed to be independent, identically normally distributed \nrandom variable with zero mean and variance \u03c32, i = 1, 2, \u2026, n denotes \nrespondents in the sample. The conditional distribution of the WTP is \ngiven by: \n\n\n\nWTPi \u2502Xi ~ N(Xi\u2019\u03b2, \u03c32),  i = 1, 2, \u2026, n \n\n\n\nThe CVM technique has been widely used to estimate WTP due to its \nflexibility in application, allowing it to value almost everything. It can even \nvalue goods and services with no observable behaviour but are easily \nunderstood and identified by respondents. Its direct approach of eliciting \nthe WTP to obtain improvement or abstain from degradation of \nenvironmental goods and services provides defensible estimates and are \neasy to analyse and describe. CVM is famously used to value total economic \nvalue, including the use and non-use values of an environmental good or \nservice. Although CVM has been widely used in economic valuation, \ncritiques are sceptical of its ability to accurately and adequately measuring \nthe WTP for any environmental goods or services (Diamond and Hausman, \n1994). However, the CVM results can be reliable if the recommendations \nreported by The National Oceanic and Atmospheric Administration\u2019s \n(NOAA) Panel, are closely followed. The validity and accuracy of CVM can \nbe further enhanced by respondents\u2019 familiarity with the issues through \ndetailed explanation and interviewed by well-trained interviewers (Yoo \nand Kwak, 2009). This paper follows those conditions as closely as \npossible to ensure reliability of the findings. \n\n\n\n2.1 Survey instrument  \n\n\n\nThe CVM questionnaire was designed to elicit the value of the proposed \npolicy of improving agricultural sustainability through multifunctionality. \nThe respondents were asked their WTP an agricultural premium for \nsupporting agricultural sustainability projects lead by a hypothetical \nMalaysian Agricultural Multifunctionality Board (MAMB) and endorsed \nand monitored by the Ministry of Agriculture and Agro-based Industry. A \ntotal of 800 households were interviewed in the Klang Valley, an area in \nMalaysia comprising Kuala Lumpur and its suburbs, and adjoining cities \nand towns in the state of Selangor. It is also known as the Greater Kuala \nLumpur. The survey was undertaken between July to December of 2019. \nThis sample size is comfortable for use in surveys on environmental \nvaluation studies in the Malaysian context as shown in the works (Pek and \nJamal, 2010; 2011).  \n\n\n\nThe survey was conducted on head of households, normally the \u2018father\u2019 but \nin the absence of this person, the \u2018mother\u2019 was interviewed. Otherwise, the \nhousehold will be skipped. The finalisation of the questionnaires was done \nafter a pre-test and a pilot study. These served to check and ensured if the \nideas and questions were understood and acceptable to the public. After \ntaking into consideration the comments from these exercises, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 01-06 \n\n\n\n\n\n\n\n \nCite The Article: Chuen Khee Pek, Fang Ee, Foo (2022). Agricultural Multifunctionality for Sustainable Development in Malaysia: A Contingent Valuation Method \n\n\n\nApproach. Malaysian Journal of Sustainable Agricultures, 6(1): 01-06. \n\n\n\n\n\n\n\nimprovements were made on the questionnaires before were used in the \nactual survey. The survey was conducted by face-to-face interviewing as \nthe other methods such like mail or telephone interviews could not allow \nthe interviewers to explain the actual issue in a detailed and clear manner \nto the interviewees. Face-to-face interview is expected to obtain more \naccurate and complete responses. The average time to complete the \nquestionnaires was about 30 to 45 minutes.  \n\n\n\nThe interviewers were properly trained through mock interviews in the \ntraining workshop organised. Some innovations to present the green \nmarket to help respondents better understand AMF was applied into the \ntraditional open-ended and payment card CVM format question \nrespectively. Before the CVM questions were presented to the \nrespondents, a description of agricultural sustainability was explained. \nThe respondents were asked introductory questions like \u2018are they \nconcerned about agricultural sustainability\u2019 and \u2018are they member(s) of \n(any) environmental organisations\u2019. They were also asked to rank their \nconcern, based on importance, on a series of environmental issues like \nagricultural sustainability, environmental sustainability, food security and \nclimate change. Next, the proposed state with improvements were \npresented in terms of the non-marketable roles of agriculture such like \ncontribution to employment growth, provision of healthy products and \nfood security. \n\n\n\n2.2 Hypothetical market and choice set \n\n\n\nThe hypothetical market for AMF is presented to the respondents in the \nfollowing narrative. \u201cA Malaysian Agricultural Multifunctionality Board \n(MAMB) will be established, and agriculture and agro-based companies \nwill be required to register themselves. The members will have to identify \nand existing or launch a new agricultural product line to participate in \nMAMB\u2019s efforts in the country. These identified agricultural goods must be \ntheir top quality green products and be sold at a higher price to the \nconsumers. The prices of all non-participating produces will remain \nunchanged. The selected agricultural products will be identified by a \nhologram sticker tagged onto them. When you purchase any of these \nparticipating agricultural products, it would mean that you are supporting \nthe \u2018buy agricultural multifunctionality products\u2019 by paying an extra price, \nknown as the agri-premium, which is denominated in percentages. \nSuppose the price of a participating good is RM10.00 and your agri-\npremium is 2 percent, this would mean you are willing to pay 20 sen more \nto support agricultural multifunctionality. All agri-premium collected by \nthe companies as MAMB members must be used to fund agricultural \nsustainability projects endorsed and monitored by the Ministry of \nAgriculture and Agro-based Industry of Malaysia.\u201d The respondents were \nthen explained the improvements on the roles of AMF such like the \ncontribution to employment growth from current state of -1 percent to a \nproposed improved state of 1 percent, provision of healthy products by \nconventional agriculture to more environmental-friendly organic farming, \nalike the care farming, and reduction of the country\u2019s rice reliance on \nimports from 33 percent to 25 percent. The choice set of the CVM question \nis shown as Figure 1 (Custance et. al., 2015). \n\n\n\nFigure 1: Choice set of CVM question \n\n\n\nThe solicitation of respondents\u2019 WTP was done using two sets of payment \nformats; the open-ended and payment card. In the open-ended format \nCVM question (see Figure 2), respondents were asked for their WTP \ndirectly and followed by a next question for confirmatory purpose.  \n\n\n\n\n\n\n\nFigure 2: Open-ended CVM question \n\n\n\nThe respondents surveyed using the payment card method (see Figure 3) \nwere first shown a payment card with ranges of WTP for selection and \nthen followed by the next question confirming the actual WTP value within \nthe selected range.  \n\n\n\n\n\n\n\nFigure 3: Payment card CVM question \n\n\n\nFollowing the key WTP questions, socio-demographic information about \nthe household was recorded. These include asking questions like their age, \ngender, income, qualification, type of profession and ownership of the \nhouse they are residing. The open-ended CVM format was used in this \nstudy, and it allows respondents the full autonomy to state their maximum \nWTP. Critics on the wide range of WTP replies can be rebuked by the use \nof payment card with reasonable ranges of WTP obtained through focus \ngroup discussions.  Enumerators are reminded not to influence the \nrespondents in choosing the values of WTP to minimise \u201cstarting-point\u201d \nbias. The respondents were told explicitly that if they decided to choose \nthe improved plan, they would need to pay an agri-premium, which is a \npercentage of the price of agriculture products purchased. Supposed the \nparticipating good, identified by a hologram sticker tagged onto it, is \nRM10.00 and the agri-premium WTP is 2 percent, the respondent will \nhave to pay 20 sen more in support of AMF. They were also being informed \nthat agreeing to pay the extra cost would mean reducing their disposal \nincome.  \n\n\n\nIt is recognised that the open-ended CVM would put pressures on the \nrespondents to state their WTP and this gives rise to high level of protest \nbids (Yoo and Kwak, 2009). However, to minimise this concern, then \npayment card format was used on a different group of respondents to \ncheck the validity of this claim. Enumerators were also told to give \nsufficient time and space for the respondents to think and reconsider \ncarefully of the issue and their WTP. This study is fully aware of the several \nconcerns of using CVM. Respondents may not be familiar with the \nenvironmental goods posed to them for WTP elicitation. This information \nbias would influence their stating of the true monetary values. Besides, \nthese respondents may have just revealed their opinions on the scenario \ngiven to them than expressing value for the good. Respondents may state \nagreement to WTP to show their support for sustainable agriculture, but \nnot the monetary values they give to the environmental good itself. \nHypothetical bias occurs when the actual payments by the respondents \nare lower than the hypothetical values pledged (List and Gallet, 2011). \nStrategic bias occurs when CVM respondents supply biased answers in \norder to influence some outcomes in line with their personal agenda.  \n\n\n\n2.3 Model specification \n\n\n\nThe Binary Logistics Regression (BLR) was used for estimation to predict \nthe probability of the outcome of a categorical (non-numerical) dependent \nvariable influenced by the change(s) in one or more independent \nvariables. The odd ratios were then estimated from the log of odds ratios \nand predicted probabilities. \n\n\n\nThe BLR is based on a linear model for the natural logarithm of the odds \n(known as log-odds) in favour of Y=1: \n\n\n\n\uf028 \uf029\n\uf028 \uf029\n\n\n\n1\n\n\n\n1\n\n\n\n1 1\n\n\n\n1\n\n\n\n1| ,...,\n\n\n\n11 1| ,...,\n\n\n\n...\n\n\n\np\n\n\n\ne e\n\n\n\np\n\n\n\np\n\n\n\np p j j\n\n\n\nj\n\n\n\nP Y X X\nLog Log\n\n\n\nP Y X X\n\n\n\nX X X\n\n\n\n\uf070\n\n\n\n\uf070\n\n\n\n\uf061 \uf062 \uf062 \uf061 \uf062\n\uf03d\n\n\n\n\uf0e9 \uf0f9\uf03d \uf0e9 \uf0f9\n\uf03d\uf0ea \uf0fa \uf0ea \uf0fa\uf02d\uf02d \uf03d \uf0eb \uf0fb\uf0ea \uf0fa\uf0eb \uf0fb\n\n\n\n\uf03d \uf02b \uf02b \uf02b \uf03d \uf02b\uf0e5\n \n\n\n\n\u03c0 is a conditional probability of the form P(Y=1| X1,...,Xp ). That is, it is \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 01-06 \n\n\n\n\n\n\n\n \nCite The Article: Chuen Khee Pek, Fang Ee, Foo (2022). Agricultural Multifunctionality for Sustainable Development in Malaysia: A Contingent Valuation Method \n\n\n\nApproach. Malaysian Journal of Sustainable Agricultures, 6(1): 01-06. \n\n\n\n\n\n\n\nassumed that \"success\" is more or less likely depending on combinations \nof values of the predictor variables. The log-odd, as defined above, is also \nknown as the logit transformation of \u03c0 and the analytical approach \ndescribed here is sometimes known as logit analysis. \n\n\n\nThe logistic function takes the form of: \n\n\n\n\uf028 \uf029\n1\n\n\n\n1\n\n\n\n11| ,...,\n\n\n\n1\n\n\n\np\n\n\n\nj j\n\n\n\nj\n\n\n\np\n\n\n\nj j\n\n\n\nj\n\n\n\nX\n\n\n\np\nX\n\n\n\ne\nP Y X X\n\n\n\ne\n\n\n\n\uf061 \uf062\n\n\n\n\uf061 \uf062\n\n\n\n\uf03d\n\n\n\n\uf03d\n\n\n\n\uf02b\n\n\n\n\uf02b\n\n\n\n\uf0e5\n\n\n\n\uf03d \uf03d\n\uf0e5\n\n\n\n\uf02b  \n\n\n\nwhich can also be transformed into: \n\n\n\n\uf028 \uf029\n1\n\n\n\n1\n\n\n\n1\n1| ,...,\n\n\n\n1\n\n\n\np\n\n\n\nj j\n\n\n\nj\n\n\n\np\nX\n\n\n\nP Y X X\n\n\n\ne\n\uf061 \uf062\n\n\n\n\uf03d\n\n\n\n\uf02d \uf02d\n\n\n\n\uf03d \uf03d\n\uf0e5\n\n\n\n\uf02b  \n\n\n\nThe non-response probability is: \n\n\n\n\uf028 \uf029 \uf028 \uf029\n1\n\n\n\n1 1\n\n\n\n1\n0 | ,..., 1 1| ,...,\n\n\n\n1\n\n\n\np\n\n\n\nj j\n\n\n\nj\n\n\n\np p\nX\n\n\n\nP Y X X P Y X X\n\n\n\ne\n\uf061 \uf062\n\n\n\n\uf03d\n\n\n\n\uf02b\n\n\n\n\uf03d \uf03d \uf02d \uf03d \uf03d\n\uf0e5\n\n\n\n\uf02b\n \n\n\n\nUsing the set of predictors, the BLR equation for the log-odds in favour of \nthe dependent variable is estimated to be: \n\n\n\n0log\n1\n\n\n\ni\ni i\n\n\n\ni\n\n\n\np\nb b X\n\n\n\np\n\n\n\n\uf0e9 \uf0f9\n\uf03d \uf02b \uf0b4\uf0ea \uf0fa\n\n\n\n\uf02d\uf0eb \uf0fb  \n\n\n\nwith the partial coefficients, bi, informing the change to log odds of \nagreeing to the dependent variable. \n\n\n\n3. FINDINGS AND DISCUSSION \n\n\n\nThe statistical analysis of the socio-economic profile of the respondents is \nshown in Table 1. The parameter values like the mean, standard deviation, \nminimum and maximum of each variable are listed. \n\n\n\nTable 1: Statistical analysis of respondents\u2019 socio-economic profile \n\n\n\nVariables Mean Standard \ndeviation \n\n\n\nMinimum Maximum \n\n\n\nAge 37.62 11.42 19 69 \n\n\n\nSpending on \nagricultural \nproducts per \nweek (MYR) \n\n\n\n178.45 86.31 15 480 \n\n\n\nNumber of \nhouseholds \n\n\n\n4.14 1.75 1 9 \n\n\n\nAgri-premium \n(%) \n\n\n\n2.99 4.80 0 30 \n\n\n\nNumber of kids 0.73 1.03 0 4 \n\n\n\nHousehold \nincome  \n\n\n\n(mid-range \nvalues) \n\n\n\n6,200.00 2,143.00 1,501.00 10,001.00 \n\n\n\nNumber of \nhouseholds \nworking \n\n\n\n2.03 0.85 1 6 \n\n\n\n*Exchange rate MYR4.04 : USD1.00 (December 2019) \n\n\n\nThe preliminary results from the just completed survey exercise reveal a \nmixed response to agricultural multifunctionality (AMF) by the \nrespondents. It is surprising to see that on average, agricultural \nsustainability is the least concerned socioeconomic issue ranked by the \nrespondents. However, they have given a high importance (second \nplacing) to food security in the following question asking for their \npreference of areas within agricultural sustainability. This may mean that \n\n\n\nthe public is still uncertain about what AMF is and that food security may \nbe joint-product of agriculture. This outcome is less expected as \nenumerators had explained the concept of AMF thoroughly to the \nrespondents before they were asked to rank their preferences. This \npattern of response may also be due to the fact that Malaysians have never \nexperienced food shortages. \n\n\n\nWhen the respondents were asked of their willingness-to-pay (WTF) an \nagri-premium for AMF, a 50:50 response was received. This is rather \nsimilar to the finding of Pek et. al (2014) on the WTP of Malaysians for a \nreduction of rice subsidy to fund more climate change mitigation projects. \nThis may mean that more effort to create awareness of AMF is much \nneeded. The respondents on the average are willing to pay an agri-\npremium of 3 percent. On average a household spends about RM720.00 \nper month for vegetables, fruits, nuts, grains and rice. Hence, a household \nis willing to pay RM22 per month to support AMF, a decent figure. The \nWTP in terms of agri-premium (%) of the respondents are shown in Table \n2. Majority of those who are WTP stated their agri-premium between one \nto five percent (28.2 percent of the total respondents), low but reasonable \nas the level of awareness and acceptance of AMF is still low in the country.  \n\n\n\nTable 2: WTP in agri-premium (%) for AMF \n\n\n\nAgri-premium \n(%) \n\n\n\n0 1 to 5 6 to \n10 \n\n\n\n11 \nto \n15 \n\n\n\n16 \nto \n20 \n\n\n\n21 \nto \n25 \n\n\n\n26 \nto \n30 \n\n\n\nCount 423 226 103 28 14 4 2 \n\n\n\nPercentage 52.9 28.2 12.9 3.5 1.8 0.5 0.2 \n\n\n\nBased on the BLR category prediction result, the model correctly predicted \n66.3 percent of cases (0 = not WTP, 1 = WTP for AMF), where the \npredictions are correct 530 times out of 800 times. The Nagelkerke R \nsquare value is 0.152. The results of the BLR model is shown in Table 3. \n\n\n\nTable 3: Results of the binary logistic regression model \n\n\n\n B S.E. Wald df Sig. Exp(B) \n\n\n\nFormat .400 .158 6.454 1 .011 1.492 \n\n\n\nCareAgri -.093 .227 .167 1 .683 .911 \n\n\n\nMbrEnv -.517 .358 2.085 1 .149 .596 \n\n\n\nSpdWk .004 .002 5.452 1 .020 1.004 \n\n\n\nNoHH -.075 .084 .799 1 .372 .928 \n\n\n\nAge .015 .007 4.036 1 .045 1.015 \n\n\n\nGender -.665 .153 18.987 1 .000 .514 \n\n\n\nKids -.397 .088 20.180 1 .000 .672 \n\n\n\nJobHH -.285 .138 4.298 1 .038 .752 \n\n\n\nConstant -.594 .479 1.534 1 .216 .552 \n\n\n\nThe results show that format of the CV questions; open-ended vs payment \ncard has a significant influence on the respondents\u2019 WTP for AMF, with a \n1.5 times higher probability if the payment solicitation format is payment \ncard with all other factors held constant. Female has a 0.5 lower \nprobability than male in their significance in affecting WTP for AMF from \nthe gender perspective (male n= 392; female n= 408). The household \nspending on agricultural products per week (SpdWk) and age have \npositive and significant influence in WTP for AMF. The number of kids in \nthe household (Kids) and number of working household members \n(JobHH) are negatively related to WTP of AMF and the parameters are \nsignificant. The other factors like care for agricultural sustainability \n(CareAgri: n= 107 do not care; n= 693 care), member of environmental \norganisations (MbrEnv: n= 760 non-member; n= 40 member) and number \nof members in the household (NoHH) do not have any significant influence \non the probability of WTP for AMF. The significance level for all \ninterpretations of the parameters and variables is 0.05. \n\n\n\nThe results from the BLR are used to make predictions on the probability \nof WTP for AMF focusing on the different payment solicitation formats \nwith changes in the amount of spending on agricultural products by \nhouseholds per week. The amount of SpdWk changes from one mean value \nto five mean values. All the continuous dependent variables (NoHH, Age, \nKids and JobHH) are assigned their fixed mean values in the computations \nof the odd ratios. For the categorical dependent variables, Gender- male, \nCareAgri- Yes and MbrEnv- Non were the observed category in the \nrespective dummy variables. Table 4 and 5 report the changes in odds \nratios of WTP for AMF when the weekly spending on agricultural products \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 01-06 \n\n\n\n\n\n\n\n \nCite The Article: Chuen Khee Pek, Fang Ee, Foo (2022). Agricultural Multifunctionality for Sustainable Development in Malaysia: A Contingent Valuation Method \n\n\n\nApproach. Malaysian Journal of Sustainable Agricultures, 6(1): 01-06. \n\n\n\n\n\n\n\nby households change under the two payment solicitation formats. The \ninterpretation of odds ratio 3, for example, is that there is a 3 to 1 chance \nof WTP for AMF.  \n\n\n\nTable 4: Predicted probability and odds ratios with open-ended \nformat \n\n\n\nSpdWk (MYR) 178.45 356.9 535.35 713.8 892.25 \n\n\n\nLog of odds \nratio \n\n\n\n-1.253 -0.539 0.175 0.889 1.602 \n\n\n\nPredicted \nprobability \n\n\n\n0.222 0.368 0.544 0.709 0.832 \n\n\n\nOdds ratio 0.288 0.583 1.191 2.431 4.965 \n\n\n\n\n\n\n\nTable 5: Predicted probability and odds ratios with payment card \nformat \n\n\n\nSpdWk (MYR) 178.45 356.9 535.35 713.8 892.25 \n\n\n\nLog of odds \nratio \n\n\n\n-0.853 -0.139 0.575 1.289 2.002 \n\n\n\nPredicted \nprobability \n\n\n\n0.299 0.465 0.640 0.784 0.881 \n\n\n\nOdds ratio 0.426 0.870 1.777 3.628 7.407 \n\n\n\nIn both the payment formats, the log of odds ratios changes from negative \nto positive values, and the odds ratios are greater than 1.000 when the \namount of SpdWk is three mean values. Hence, when SpdWk takes the \namount of MYR535.35, there is a 1.2 to 1 chance of WTP for AMF under the \nopen-ended format versus 1.8 to 1 under the payment card format. A more \nsignificant difference is observed when the SpdWk is five mean values \nwith 5 to 1 versus 7.5 to 1 chance respectively under the two different \npayment solicitation formats. \n\n\n\n4. CONCLUSION \n\n\n\nThe results from the study are rather encouraging although half the \nrespondents did not vow their support in WTP for AMF. This may reflect \nthat the respondents are generally less educated and aware of AMF and \nthe benefits it can bring economically, socially and culturally. The non-\nfood benefits of agriculture such like employment opportunities, organic \nfarming production method and food security are crucial for AMF to be \npreserved and given substantial policy priorities in Malaysia. The findings \nfrom household spending on agricultural products per week (SpdWk) \nshow possible opportunities to encourage higher public participation in \nAMF. The observation that higher weekly spending on agricultural \nproducts increases the odds ratio of WTP AMF shows that households, \nwho spend more on agricultural products notice the importance of \nkeeping the core function and non-trade benefits of agriculture.  \n\n\n\nThe characteristics of AMF in this study, contribution to employment \ngrowth, provision of healthy products using greener method and food \nsecurity fit well into SD Goals 2, 8 and 12. And with the observed potential \nto get more Malaysians to participate in AMF, it is hoped that the \nagricultural can be sustainable and continue to support the economic \ngrowth of the country. The policy makers such like the Ministry of \nAgriculture and Agro-based Industries, Ministry of Natural Resources and \nEnvironment, Ministry of International Trade and Industry, and other \nrelated agencies can strategise more balanced agriculture policies to \nsupport AMF such like tax incentives and enhancing the framework of \nsustainable agriculture. Malaysia is a country with abundant land and \nagriculture can be reinstated with its glory of the early 70s being the major \ncontributor to the GDP of the country. \n\n\n\nThere are green movements such like MyHijau and interested youths to \npromote and venture into eco-tourism and green start-ups with vegetable \nand organic crop plantations in small and medium scales. In order to \nunderstand this niche group of people, this study presents some revelation \nof their preferences. With some revelation of the attitude and behaviour of \nthe respondents towards AMF in Malaysia, the government would need to \neducate the public with more AMF awareness creation campaigns and \nprojects. One of the fruitful places to start with is at the school and \nuniversity levels. The United Nations\u2019 SDG 2030 is a timely framework for \ninstitutions of learning to promote, implement and achieve sustainable \ndevelopment goals. AMF awareness promotion efforts are necessary as \nMalaysia needs to move into sustainable development with agriculture \nsustainability as one of the main pillars of green growth.  \n\n\n\nThe findings support the direction of innovating the agricultural industry \n\n\n\nthrough AMF as one of the forerunners of sustainable growth for \ndeveloping countries like Malaysia. Although only half of the respondents \nvowed their WTP for AMF, it is observed that households, which spend \nmore on agricultural products such like vegetables, fruits and related \ngoods are having higher odds ratio of WTP for AMF. The findings \nencourage entrepreneurs, especially the youth to venture into the \ninnovative non-food benefits of agriculture for more responsible usage of \nour natural resources and decent economic growth. Malaysia is on the \nright track to achieve the SDG indicators and agricultural \nmultifunctionality is a viable option for the government to achieve SDG 2, \n8 and 12 come 2030.  \n\n\n\nREFERENCES \n\n\n\nBergstrom, J.C., Stoll, J.R., 1989. Application of experimental economics \nconcepts and precepts to CVM field survey procedures. Western \nJournal of Agricultural Economics, 14, Pp. 98-109. \n\n\n\nBjorkhaug, H., Richards, C.A., 2008. Multifunctional agriculture in policy \nand practice? A comparative analysis of Norway and Australia. Journal \nof Rural Studies, 24, Pp. 98-111. \n\n\n\nCairol, D., Coudel, E., Knickel, K., Caron, P., Kroger, M., 2009. \nMultifunctionality of agriculture and rural areas as reflected in \npolicies: The importance and relevance of the territorial view. Journal \nof Environmental Policy and Planning, 11 (4), Pp. 269-289. \n\n\n\nCaron, P., Reig, E., Roep, D., Hediger, W., Le Cotty, T., Barthelemy, D., \nHadynsky, A., Hadynski, J., Oostindie, H.A., Sabourin, E., 2008. \nMultifunctionality: Refocusing a spreading, loose and fashionable \nconcept for looking at sustainability? International Journal of \nAgricultural Resources, Governance and Ecology, 7 (4/5), Pp. 301-\n318.  \n\n\n\nCustance, P., Walley, K., Tate, G., Armagan, G., 2015. Agricultural \nmultifunctionality and care farming: Insight from the UK. South Asian \nJournal of Business and Management Cases, 4 (1), Pp. 74-86. \n\n\n\nDiamond, P., Hausman, J., 1994. Contingent valuation: is some number \nbetter than no number? Journal of Economic Perspectives, 8, Pp. 45-\n64. \n\n\n\nDominguez-Torreiro, M., Solino, M., 2011. Provided and perceived status \nquo in choice experiments: Implications for valuing the outputs of \nmultifunctional rural areas. Ecological Economics, 70, Pp. 2523-2531. \n\n\n\nHeringa, P.W., van der Heide, C.M., Heijman, W.J.M., 2013. The economic \nimpact of multifunctional agriculture in Dutch regions: An input-\noutput model. NJAS- Wageningen Journal of Life Sciences, 64-65, Pp. \n59-66. \n\n\n\nLehmann, P., Schleyer, C., W\u00e4tzold, F., W\u00fcstemann, H., 2009. Promoting \nMultifunctionality of Agriculture: An Economic Analysis of New \nApproaches in Germany. Journal of Environmental Policy & Planning, \n11 (4), Pp. 315-332. \n\n\n\nList, J.A., Gallet, C.A., 2001. What experimental protocol influence \ndisparities between actual and hypothetical stated values? Evidence \nfrom a meta-analysis. Environmental and Resource Economics, 20, Pp. \n241-254. \n\n\n\nMoxey, A, White, B., Ozanne, A., 1999. Efficient contract design for agri-\nenvironmental policy. Journal of Agricultural Economics, 50, 187-202. \n\n\n\nOECD (Organisation for Economic Co-operation and Development). 2001. \nMultifunctionality: Towards an Analytical Framework. Paris: OECD. \n\n\n\nPaarlberg, P.L., Bredahl, M., Lee, J., 2002. Multifunctionality and \nagricultural trade negotiations. Review of Agricultural Economics, 24 \n(2), Pp. 322-335. \n\n\n\nPek, C.K., Jamal, O., 2010. Household demand in solid waste disposal \noptions in Malaysia. International Journal of Environmental, Earth \nScience and Engineering, 4 (7), Pp. 377-40. \n\n\n\nPek, C.K., Jamal, O., 2011. A choice experiment analysis for solid waste \ndisposal option: A case study in Malaysia. Journal of Environmental \nManagement, 92 (11), Pp. 2993-3001.  \n\n\n\nPek, C.K., Lim, Y.M., Choong, C.K., Tan, K.E., 2014. The economic impact of \nclimate change on food security in Malaysia. International Journal of \nAcademic Research Part B, 6 (3), Pp. 195-199. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 01-06 \n\n\n\n\n\n\n\n \nCite The Article: Chuen Khee Pek, Fang Ee, Foo (2022). Agricultural Multifunctionality for Sustainable Development in Malaysia: A Contingent Valuation Method \n\n\n\nApproach. Malaysian Journal of Sustainable Agricultures, 6(1): 01-06. \n\n\n\n\n\n\n\nRandall, A., 2002. Valuing the outputs of multifunctional agriculture. \nEuropean Review of Agricultural Economics, 29 (3), Pp. 289-307. \n\n\n\nSellar, C., Stoll, J.R., Chavas, J.P., 1985. Validation of empirical measures of \nwelfare changes: A comparison of nonmarket techniques. Land \nEconomics, 61, Pp. 15\u201364. \n\n\n\nTohidyan, F,S., Rezaei-Moghaddam, K., 2019. Multifunctional agriculture: \nAn approach for entrepreneurship development of agricultural sector. \nJournal of Global Entrepreneurship Research. Available at \nhttps://doi.org/10.1186/s40497-019-0148-4/ (accessed 27 August \n2019). \n\n\n\nWeersink, A., 2002. Policy options to account for the environmental costs \nand benefits of agriculture. Canadian Journal of Plant Pathology, 24, \nPp. 265-273. \n\n\n\nYoo, S.H., Kwak, S.Y., 2009. Willingness to pay for green electricity in \nKorea: A contingent valuation study. Energy Policy, 37, Pp. 5408-5416. \n\n\n\nZander, P., Knierim, A., Groot, J.C.J., Rossing, W.A.H., 2007. \nMultifunctionality of agriculture: Tools and methods for impact \nassessment and valuation. Agriculture, Ecosystems and Environment, \n120, Pp. 1\u20134. \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1186/s40497-019-0148-4/\n\n\n\n"
"\n\n Malaysian Journal of Sustainable Agriculture (MJSA)1(2) (2017) 06-08\n\n\n\nCONSUMPTIVE USE OF WATER BY SELECTED CASH CROPS IN MALAYSIA\nSiti Norliyana Haruna1, Marlia M. Hanafiah1*\n\n\n\n1School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan \n\n\n\nMalaysia, 43600 Bangi, Selangor, Malaysia\n\n\n\n*Corresponding author email: mhmarlia@ukm.edu.my\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 September 2017 \nAccepted 19 October 2017 \nAvailable online 30 October 2017\n\n\n\nKeywords: \n\n\n\nWater Footprint; Water stress index; \nWater deprivation; Agriculture sector; \nMalaysia\n\n\n\nABSTRACT\n\n\n\nA rapid development in economic sectors has induced the water depletion in most of the developing country, \nparticularly for Malaysia. This study estimates the consumptive water use of cultivating 5 cash crops, i.e. cassava, \nmaize, rice, sugarcane and sweet potato in Peninsular Malaysia. The consumptive water uses for cultivating these 5 \ncrops was determined based on the CROPWAT 8.0 and Penman Monteith model. CROPWAT 8.0 model was used to \ncompute the evapotranspiration and crop water requirement (effective rainfall and irrigation requirement) of the \ncash crops grown in Peninsular Malaysia from 2005-2013 (9 years). It was found that the green water uses for \ncultivating cassava, maize, paddy, sugarcane and sweet potato for Peninsular Malaysia is higher than the blue water \nuse. In conclusion, water use for cultivating agricultural crops will accelerate the competition on the consumption of \nclean water with the other sectors. However, the availability of water resource in Peninsular Malaysia is sufficient to \nfulfil the demands for water at the present time.\n\n\n\nCite this article as: Siti Norliyana Haruna, Marlia M. Hanafiah  (2017).  Consumptive Use Of Water \nBy Selected Cash Crops In Malaysia.   Malaysian Journal of Sustainable Agriculture,  1(2):06-08.\n\n\n\n1. INTRODUCTION\n\n\n\nWater consumption has growing faster than the population growth [1]. This \ncontinuous trend unfortunately has led to an issue of water scarcity and it is \nestimated that about half of the world population will face problem \naccessing safe water supply to support their daily routine by year 2025 [1]. \nDomestic, agriculture and industrial sectors are the largest consumers of \nwater will further accelerate the problem of water shortage [2]. According \nto the World Water Assessment Programme, agriculture is one of the \nhighest users for water consumption [3]. It is predicted that the global \nwater consumption for agriculture to reach to 8,515 km3 per year or to \nincrease 19% by year 2025.\n\n\n\nAs in Malaysia, agriculture sector plays an important role in national \neconomic development. It helps secure the national food security and also \narouses public incomes especially for people living in rural areas. Given its \nnatural advantages, agriculture and livestock sub-sectors play an important \nrole in ensuring Malaysian food security. Cassava, maize, rice, sugarcane and \nsweet potato are considered as cash crops in Malaysia. For the past 50 \nyears, the government has allocated billions of Ringgits to maximise \nMalaysian agriculture production [4]. Due to this scenario, farmers across \nthe world including Malaysia have taken a step further in strengthening its \nfood security.\n\n\n\nThe water footprint analysis can be used to determine the actual amount of \nwater used for the entire process of producing agricultural product. The \namount of blue water use from agriculture production can help us to \ndetermine the total amount of surface water consumed for producing crops. \nThe results from this study are expected to convey the overall amount of \nconsumptive water used by the selected cash crops in Malaysia. This study \nwill be a starting point to assess the amount of water consumed from crops \nfarming using a comprehensive and holistic water footprint approach. This \nstudy is also expected to provide benefits to agencies, policy makers and \nindustries pertaining to the cash crops sector. This baseline information can \nbe used to identify which area that needs to be conserved and what type of \nrecommendation that should be drawn. Furthermore, it offers a number of \nbenefits such as to identify the \u2018hotspots\u2019 in the value chain of activities and \nenhance initiatives towards sustainable agricultural practice and wise water \nmanagement in Malaysia.\n\n\n\n2. METHODOLOGY\n\n\n\nIn this research, data were compiled from a various secondary data sources \nsuch as books, publications, reports, government agencies including the \nDepartment of Irrigation and Drainage, Malaysian Meteorological \nDepartment, Department of Agriculture for Peninsular Malaysia, \nDepartment of Statistic, MARDI and National Water Services Commission \nthat related to the field of studies; Malaysian rice cultivation and Malaysian \nhydrological information. Meanwhile, foreground data were obtained \nthrough a series of site visits by communicating with data providers, and \ndeveloping questionnaires.\n\n\n\nISSN: 2521-2931 (Print)\nISSN: 2521-294X (Online)\n\n\n\nFor water footprint analysis, climatic data consists of minimum and \nmaximum temperature, humidity, sunshine, rainfall rate and wind speed for \n9 years (2005 to 2013) was used to estimate the crop water use. Crop water \nrequirements and irrigation requirements were calculated using CROPWAT \n8.0 Model, a decision support tool developed by the Land and Water \nDevelopment Division of FAO. Besides climatic data, other parameters such \nas crop data and soil condition were included in the CROPWAT 8.0 model to \nestimate crop performance under both rainfed and irrigated conditions.\n\n\n\nIn this study, the evapotranspiration rate was determined using the \nPenman-Monteith method. A statistical analysis was also used to estimate \nthe rainfall deficit for irrigation water requirements based on long-term \nrainfall records. This analysis was determined as part of the rainfall which \neffectively contributes to cover crop water requirements (CWR). \nInformation on inventory database was compiled and all data were used to \nmodel a water footprint of the selected cash crops cultivation in Malaysia. \nFinally, a set of recommendations and suggestions was given.\n\n\n\n2.1 Assessment of Water Footprint\n\n\n\nProper management of water resources is important, specifically when \nresources are limited [5]. Availability of water for population and its \neconomic activities is important when managing the optimum use of water \nresources. Estimating consumptive water use by crops cultivation can be \nconducted using the method of water footprint. Water footprint is a tool that \nhas been introduced by a researcher to assess and quantify the water \nrequired for production of a product [6]. Water footprint can also serve as a \nmedium to assess the potential environmental impacts related to water \n[7,8,9].\n\n\n\nWater footprint consists of three components which are specified \ngeographically and temporally; green, blue and grey water. Green water \nfootprint refers to the total rainwater evapotranspiration and water \nincorporated into the harvested crops used in the production of goods or \nservices. The blue water footprint is defined as the volume of surface and \ngroundwater consumed during the production of a product. Meanwhile, grey \nwater footprint refers to the volume of freshwater required to dilute \npollutants so that the quality of the polluted water complies with ambient \nwater quality standards, i.e. the Interim National Water Quality Standards \nfor Malaysia (INWQS). Figure 1 illustrates the inputs and outputs of growing \nthe selected crops.\n\n\n\nContents List available at RAZI Publishing \nMalaysian Journal of Sustainable Agriculture (MJSA)\n\n\n\nJournal Homepage:  : http://www.razipublishing.com/journals/malaysian- journal- \nofsustainable-agriculture-mjsa/\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.06.08\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.06.08\n\n\n\n\n\n\n7  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 06-08  \n\n\n\nFigure 1: Framework of growing crops in water footprint approach\n\n\n\nIn agricultural context, the water footprint is a tool to quantify how much \nwater has been consumed in growing a crop. The water footprint method of \ngrowing a crop is applicable to both annual and perennial crops [10]. The \ntotal water footprint of the crop cultivation process (WFcrop) was adapted \nfrom the general formula, as follows:\n\n\n\nFigure 2: Total water footprint of selected cash crops in 9 states in \nPeninsular Malaysia. (a) cassava, (b) maize, (c) rice main season, (d) rice \noff season, (e) sugarcane and (f) sweet potato.\n\n\n\nMeanwhile, Table 1 shows the comparison of blue and green water \nrequirements of the five studied cash crops grown in Malaysia. It was \nfound that rice cultivation in main season has the highest green water \nfootprint (1536.60 m3/ton) compared to other selected cash crops \ncultivation in peninsular Malaysia, while sweet potato has the lowest \ngreen water footprint (409.52 m3/ton). The highest blue water footprint \nwas recorded for rice in off season (949.31 m3/ton), whereas the lowest \nblue water footprint was estimated for cassava with 40.07 m3/ton.  \n\n\n\nSince rice is one of the most important crops in diet of Malaysian people, \nhigh production of rice was produced yearly in Malaysia [12]. In Thailand, \nrice has higher water requirement because of the paddy field cultivation is \nunder flood condition. A research reported that rice is one of the largest \nwater consumers in the world and requires large areas to irrigate the \npaddy field [13]. The country that consumed the largest volume of water \nfor rice sector is India, followed by China, Indonesia, Bangladesh, \nThailand, Myanmar, Vietnam and Philippines and Brazil [13]. However, \nthe total water footprint of rice in Malaysia is lower compared to Thailand \nand Indonesia as shown in Table 2.  \n\n\n\nTable 1: The green, blue and total water footprint of selected cash crops \nin Malaysia.\n\n\n\nWFcrop = WFcrop,green+ \nWFcrop,blue \n[m3/ton]   \n\n\n\nWFcrop \uf03d CWUYcrop,green \uf03d CWUcrop,blue \uf03d\uf03dtonm3 //haha\uf03d\uf03d\uf03d \n\n\n\nY \ncrop crop \uf03d \n\n\n\nThe green water in the process of water footprint of crop (WFcrop,green, \nm3/ton) was calculated by dividing the green component in water use of \ncrop with the crop yield (Y, ton/ha). The blue component (WFcrop,blue) \nwas calculated in a similar way as the green water footprint and expressed \nin m3/ton.   \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nFigure 2(a-f) shows the result of total water footprint of 5 selected cash \ncrops grown in 9 states in Peninsular Malaysia. The results indicate that per \nton of crop produced, rice has the highest water requirement with 2317.24 \nm3/ton in off season and 2292.99 m3/ton for main season, followed by \nsugarcane (1457.34 m3/ton), maize (819.86 m3/ton), cassava (746.28 m3/\nton) and sweet potato (496.71 m3/ton), respectively. Off season refers to \nthe rice grown in March-July, considered as dry months in Malaysia, while \nthe main season refers to the rice grown in August to February, known as \nrainy or wet season [11]. \n\n\n\nCassava Maize \n\n\n\n(a) (b) \n\n\n\nRice Main Season Rice Off Season \n\n\n\nState (c)\n(d)\n\n\n\nSugarcane Sweet \n2500 Potato \n\n\n\nState State \n   (e) (f) \n\n\n\n0 \n\n\n\n500 \n\n\n\n1000 \n\n\n\n1500 \n\n\n\n2000 \n\n\n\nThe largest contribution to the total water footprint of each crop is the \ngreen component. For cassava, the green component contributes 95% to \nthe total water footprint (blue and green water footprints), followed by \nsugarcane (84%), sweet potato (82%), maize (79%) and paddy (63%). \nThis implies that most crops in Malaysia are mainly grown using \nrainwater. Malaysia received average rainfall of 2500 mm per year and \nthis condition helps in growing crops in Malaysia. Blue water consumption, \ni.e. consumptive use of groundwater or surface water, generally has a \nlarger effect on the environment than green water consumption [14]. \nTherefore, high dependent on surface water will contribute to water \nscarcity and some arid regions are highly vulnerable to water shortage \ninduced by climate variability, such as prolonged drought event and \ndeficiency of the rainfall rate.\n\n\n\nTable 2 presents the previous studies on water footprint of some crops \nthat were reported in other countries in Southeast Asia. In Thailand, the \nmajor rice consumed the largest water footprint compared to another \ncrops cultivation [12]. A scientist also found that the water footprint of rice \nwas also the highest as compared to maize and cassava [15]. The results \nfrom the previous studies were similar to the present study. The slight \nregional differences in water footprint of crops were caused by differences \nin climate and agricultural practices [15]. Hence, agricultural practices \ndetermine the yield, thus affecting the water footprint of product [15]. The \nvariation in crop water requirements between countries also determined \nby the availability of modelling parameters, assumptions, limitations and \ninput data needed for the assessment of water footprint of crops \n[16,17,18].  \n\n\n\nCite this article as: Siti Norliyana Haruna, Marlia M. Hanafiah  (2017).  Consumptive Use Of Water \nBy Selected Cash Crops In Malaysia.   Malaysian Journal of Sustainable Agriculture,  1(2):06-08.\n\n\n\n\n\n\n\n\n8  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 06-08  \n\n\n\n4. CONCLUSION AND RECOMMENDATION\n\n\n\nBased on the study that has been carried out, the government needs to have \nan appropriate measurement to manage the sustainable use of water \nresource in order to avoid the issue of water scarcity due to the rising \ndemand on the agricultural practices especially in the production of rice. A \nrelated agencies and departments involved in the agricultural industry \nneed to further increase the efficiency of water consumption by revising \nthe policy related in agriculture production that has been prepared for the \nfarmers as a guideline in cultivating the crops. The productivity of \nagriculture can be improved by improving the agronomic management; in \nterm of straw mulching, nutrient management and pest controls. In \naddition, by reducing the water use for land preparation through land \nlevelling or reducing the period of land preparation can also help a wise \nwater management in Malaysia.   \n\n\n\nIn future, it is recommended that a comprehensive study needs to be \nconducted to assess the impacts of nutrient enrichment on freshwater \nresources by including grey water in the water footprint assessment. Since \nthis study only included states in Peninsular Malaysia, other crops growing \narea in Sabah and Sarawak could be included as well in the future study. In \nfuture, LCA-based water footprint could be conducted to further assessing \nthe impact of water consumption on areas of protection such as human \nhealth damage, ecosystem quality damage and natural resources depletion. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nMarlia Mohd Hanafiah was financed by research grants: FRGS/2/2013/\nSTWN01/UKM/03/1 and TD2014-012.  \n\n\n\nREFERENCES  \n\n\n\n[1] United Nations Development Programme (UNDP). 2006. Beyond \nscarcity: Power, poverty and the global water crisis. Human Development \nReport 2006. United Nations Development Programme. New York, USA. \n\n\n\n[2] Lloyd\u2019s 360\u00b0 Risk Insight. 2010. Global Water Scarcity: risks and \nchallenges for business. London, Lloyd\u2019s.\n\n\n\n[3] World Water Assessment Programme (WWAP). 2012. The United \nNations World Water Development Report 4: Managing Water under \nUncertainty and Risk. Paris, UNESCO.\n\n\n\n[4] Zaim, F., Bahaman, A. S. and Haslinda, A. 2013. Paddy Industry and \nPaddy Farmers Well-being: A Success Recipe for Agriculture Industry in \n\n\n\nMalaysia. Asian Social Science, 9, 177-181.\n\n\n\n[5] Abdul Samad, M. N. S., Hanafiah, M.M., Abdul Hasan, M. J., Mohd Ghazali, \nN. F., and Harun S. N. 2017. Ratio of Water Withdrawal to Availability in \nKelantan Watersheds, Malaysia. Journal of Clean Water, Air & Soil (J Clean \nWAS), 1(1), 40-44.\n\n\n\n[6] Hoekstra, A. Y., and Hung, P. Q. 2002. Virtual water trade: A \nquantification of virtual water flows between nations in relation to \ninternational crop trade. Value of Water. Research Report Series No. 11 \n(UNESCO-IHE): the Netherlands.\n\n\n\n[7] Babel, M. S., Shrestha, B., and Perret, S. 2010. Hydrological impact of \nbiofuel production: A case study of the Khlong Phlo Watershed in \nThailand,\u201d Agricultural Water Management, 101, 8-26.\n\n\n\n[8] Hoekstra, A.Y. 2003. Virtual Water Trade: Proceedings of the \nInternational Expert Meeting on Virtual Water Trade; Value of Water \nResearch Report Series No. 12; The United Nations Educational, Scientific \nand Cultural Organization-International Institute for Hydraulic and \nEnvironmental Engineering (UNESCO-IHE): Delft, the Netherlands.\n\n\n\n[9] Chapagain, A. K., and Hoekstra, A. Y. 2004. Water footprints of nations. \nValue of Water Research Report Series Vol. 16. Delft, The \nNetherlands.UNESCO-IHE.\n\n\n\n[10] Hoekstra, A.Y. 2011. The global dimension of water governance: Why \nthe river basin approach is no longer sufficient and why cooperative action \nat global level is needed. Water, 3(1), 21-46.\n\n\n\n[11] Kemubu Agricultural Development Authority (KADA). 2017. Paddy \nPlanting Schedule. Retrieved from http://www.kada.gov.my/en/web/\nguest/jadual-tanaman-padi.\n\n\n\n[12] Gheewala, S. H., Silalertruksa, T., Nilsalab, P., Mungkung, R., Perret, S. \nR., and Chaiyawannakarn, N. 2014. Water Footprint and Impact of Water \nConsumption for Food, Feed, Fuel Crops Production in Thailand. Water, 6, \n1698-1718.\n\n\n\n[13] Chapagain, A. K. and Hoekstra, A. Y. 2011. The blue, green and grey \nwater footprint of rice from both a production and consumption \nperspective. Ecological Economics, 70, 749-758.\n\n\n\n[14] Falkenmark, M., and Rockstr\u00a8om, J. 2004. Balancing water for humans \nand nature: The new approach in ecohydrology. London, UK: Earthscan.\n\n\n\n[15] Bulsink, F., Hoekstra, A. Y. and Booij, M. J. 2010. The water footprint of \nIndonesian provinces related to the consumption of crop products. \nHydrology and Earth System Sciences, 6 (4), 5115\u20135137.\n\n\n\n[16] Aminordin, M. M., and Hanafiah, M. M. 2014. Water Footprint \nAssessment of Oil Palm in Malaysia: A Preliminary Study. AIP Conference \nProceedings, 1614, 803-807.\n\n\n\n[17] Mohd Ghazali, N. F., and Hanafiah, M. M. 2016. Malaysian water \nfootprint accounts: Blue and green water footprint of rice cultivation and \nthe impact of water consumption in Malaysia. AIP Conference Proceedings, \n1784, 060025.\n\n\n\n[18] FAO. 2000. Climate change, water and food security. FAO Water \nReports 36. Rome, Italy.\n\n\n\nTable 2: Water footprint of crops reported in other countries of Southeast \nAsia \n\n\n\nCite this article as: Siti Norliyana Haruna, Marlia M. Hanafiah  (2017).  Consumptive Use Of Water \nBy Selected Cash Crops In Malaysia.   Malaysian Journal of Sustainable Agriculture,  1(2):06-08.\n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 16-21 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.16.21 \n\n\n\n \nCite the Article: Kushal Giri, Suraj Gurung, Sujan Pokharel, Rupak Karn, Ananta Prakash Subedi (2020). Effect Of Different Plant Extracts On Sprouting, Storability And \n\n\n\nPost-Harvest Loss Of Potato In Baglung District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(1): 16-21. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2020.16.21 \n\n\n\n\n\n\n\n \nEFFECT OF DIFFERENT PLANT EXTRACTS ON SPROUTING, STORABILITY AND \nPOST-HARVEST LOSS OF POTATO IN BAGLUNG DISTRICT, NEPAL \n\n\n\n \nKushal Giri*, Suraj Gurung, Sujan Pokharel, Rupak Karn, Ananta Prakash Subedi  \n \nAgriculture and Forestry University, Rampur Chitwan, Nepal \n*Corresponding Author Email: kushalgiri975@gmail.com  \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 01 December 2019 \nAccepted 06 January 2020 \nAvailable online 05 February 2020 \n\n\n\n\n\n\n\nAn experiment was conducted to investigate the effect of different plant extracts on sprouting, storability and \n\n\n\npost-harvest loss of potato at ambient condition in Baglung district, Nepal. The parameters observed were \n\n\n\nsprouting percentage, sprout number, sprout length, sprout mass, weight loss percentage and damage score. \n\n\n\nThe experiment consists of ten different treatments namely; Zingiber officinale rhizome, Azadirachta indica \n\n\n\nleaves, Atimisia spp. leaves, Utica dioica leaves, Control, Acorus calamus rhizome, Brassica campestris oil, \n\n\n\nCymbopogon spp. oil, Azadirachta indica oil and Mentha spp. oil. The experiment was conducted in a \n\n\n\nCompletely Randomized Design with four replications. Cympopogan oil, Mentha oil and Acorus powder \n\n\n\ncompletely suppressed the sprouting until 60 days with Cympopogan oil being the most effective to suppress \n\n\n\nsprouting throughout the experiment. The highest sprout number was recorded from Mentha oil (1.92) and \n\n\n\ncontrol (1.79) after 90 days. The lowest sprout number was recorded from Cymbopogon oil (0.38). The \n\n\n\naverage sprout length of Mentha spp. oil (13.47 mm) and control (13.19 mm) was maximum with minimum \n\n\n\nbeing that of Cymbopogon oil (2.5 mm) and Acorus (5.63 mm). The sprout mass measured at the end was \n\n\n\nmaximum in control (2 gm) and minimum in Cymbopogon oil (0.25 gm). The weight loss percentage was \n\n\n\nmaximum in control (10.66 %) with minimum in Cymbopogon oil (6.8 %). The tubers treated with Brassica \n\n\n\ncampestris oil were damage to least (1.46) and highest damage score was recorded from control (1.88). A \n\n\n\nstrong correlation was obtained between weight loss and sprout length (r=0.85), sprout number (r=0.78) \n\n\n\nand sprout mass (r=0.70). \n\n\n\n\n\n\n\nKEYWORDS \n\n\n\nCymbopogon, weight loss, sprouting, maximum, minimum. \n\n\n\n1. INTRODUCTION \n\n\n\nPotato is one of the most important crops in Nepal. The total area of potato \n\n\n\nproduction is 1,85,879 hectare and total production is 25,91,686 mt ton \n\n\n\nwith the productivity of 13.94 ton/ha (ABPSD, 2017). It occupies the fifth \n\n\n\nposition in area coverage, second in total production and first in \n\n\n\nproductivity among the food crops grown in Nepal (ABPSD, 2017). It is \n\n\n\nused as a major vegetable in terai and mid hills and as a staple food in high \n\n\n\nhills of Nepal (NPRP, 2011). Baglung district accounts for 1.61 % of \n\n\n\nnational potato producing area and 1.37 % of National production; yet it\u2019s \n\n\n\nproductivity (11.86 mt/ha) is below national average (14.03 mt/ha) \n\n\n\n(ABPSD, 2017). Further farmers often face disease infestation, weight loss, \n\n\n\nshrinkage, sprouting during storage which impede their earning. \n\n\n\n\n\n\n\nPost-harvest loss refers to any change in the commodity after harvest that \n\n\n\nhinders its normal consumption. Potato have a higher water content as \n\n\n\ncompared to grains, so its long-term storage is difficult (Ishwori, 2016). \n\n\n\nEven in cold storage a loss of 8-10 % is incurred during the storage period \n\n\n\nof 7-8 months (Ishwori, 2016). A year-round supply of potatoes is made \n\n\n\npossible by storage where sprouting is suppressed and damage is \n\n\n\nminimized either by cooling, refrigeration or by using sprout suppressant \n\n\n\n(Pinhero and Yada, 2016). Post-harvest technology of potatoes helps to \n\n\n\nensure quality of potatoes, checking its loss and fulfilling the year-round \n\n\n\ndemand of processing and consumption market. The main objectives of \n\n\n\npotato storage is to save both quality and quantity of potatoes (NPRP, \n\n\n\n2004). The main factors affecting potato storage are temperature, \n\n\n\nventilation, relative humidity, diffused light, cultivation techniques, post-\n\n\n\nharvest handling, curing, grading, packing and insect pest during storage \n\n\n\n(Bhattarai, 2018). Losses during post-harvest storage of potato range from \n\n\n\n15-20% (Blenkinsop et al., 2003). \n\n\n\n\n\n\n\nLow temperature storage of potato causes undesirable sweetening in \n\n\n\ntubers due to the conversion of starch into sugar. This high level of \n\n\n\nreducing sugar causes undesirable browning during frying operation \n\n\n\n(Blenkinsop et al., 2003). This is known as maillard reaction which is the \n\n\n\nreaction between amino acids and reducing sugar during frying resulting \n\n\n\n\nmailto:kushalgiri975@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 16-21 \n\n\n\n\n\n\n\n \nCite the Article: Kushal Giri, Suraj Gurung, Sujan Pokharel, Rupak Karn, Ananta Prakash Subedi (2020). Effect Of Different Plant Extracts On Sprouting, Storability And \n\n\n\nPost-Harvest Loss Of Potato In Baglung District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(1): 16-21. \n \n\n\n\n\n\n\n\nbrowning (Pinhero and Yada, 2016). These darkened fries or chips \n\n\n\ncontains high amount of  acrylamide which is linked to many different \n\n\n\ncancer (Chuda et al., 2003). Similarly, chemical sprout suppressant like \n\n\n\nCIPC effectively suppress the sprouting during storage thereby reducing \n\n\n\npostharvest loss but its residue which is left in the treated tuber is harmful \n\n\n\nfor human body (Aml et al., 2014). NPRP, 2074 recommend the best \n\n\n\ntemperature for long term storage of seed potato is 2 - 4oC, for fresh \n\n\n\nconsumption 4 - 5oC, for chips making 7 - 10oC and for frying 5 - 8oC. Due \n\n\n\nto insufficient cold store, maintaining such temperature is not feasible for \n\n\n\nNepal (Sharma et al., 2016). Due to insufficient cold storage and harmful \n\n\n\neffect in use of chemical suppressant, there is a need of storing potatoes at \n\n\n\nambient temperature using different plant extracts. \n\n\n\n\n\n\n\nCommonly available methods of potato storage like cold storage and \n\n\n\nchemical treatment have their own drawbacks. With these drawbacks, \n\n\n\nresearchers are interested toward other alternatives like herbs/shrubs, \n\n\n\ntheir essential oils (Song et al., 2008). In past times, many plant extracts \n\n\n\nand volatile oils were tested for their efficacy in potato sprout suppression \n\n\n\nand postharvest life and were effective to many extents (Song et al., 2008; \n\n\n\nElsadr and Waterer, 2005; Tartoura et al., 2015). Potato is one of the main \n\n\n\ncash earning crop of this place. A number of people are involved in potato \n\n\n\nproduction. Farmers in this place are following traditional storage \n\n\n\nmethods and modern storage methods are not suitable for them. So, an \n\n\n\ninvestigation is needed to solve their problem of storage at their level, \n\n\n\nusing the products available locally/easily which raise their income and \n\n\n\ndon\u2019t hamper potato quality and their health. This study aims to evaluate \n\n\n\nthe efficacy of different plant extracts on suppressing undesirable \n\n\n\nsprouting and damage during the storage of potatoes. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nThe experiment entitled \u201cEffect of different plant extract on Sprouting, \n\n\n\nStorability and post-harvest loss of Potato in Baglung district, Nepal\u201d was \n\n\n\nconducted under rudimentary conditions which portrays the real storage \n\n\n\ncondition of the farmers in Bodygad Rural Municipality, ward no 5, \n\n\n\nKhaular Bajar, Baglung, Nepal at 28o15\u2019 N latitude and 83o11\u2019 E longitude \n\n\n\nand 750 meter above sea level (masl). The variety used in the study was \n\n\n\n\u2018Kufri Sindhuri\u2019. The freshly harvested potatoes were sorted out and \n\n\n\nhealthy tuber of medium sized with no external sign of disease and \n\n\n\ndamage were selected for the study. Harvesting was done on Feb 22th 2019 \n\n\n\nand cured for about 10 days on the wooden floor. Six potatoes were used \n\n\n\nper treatment for study. The tubers were placed in a plastic containers and \n\n\n\nprecaution was taken to facilitate the gas exchange in and out of container. \n\n\n\nThe experiment was carried out in Completely Randomized Design (CRD) \n\n\n\nwith ten treatments replicated four times. \n\n\n\n\n\n\n\nTable 1: Applied treatments, their common names and abbreviation \n\n\n\nTreatments (Scientific names) Common name Abbreviation \n\n\n\nZingiber officinale rhizome \n\n\n\npowder \n\n\n\nGinger T1 \n\n\n\nAzadirachta indica leaves \n\n\n\npowder \n\n\n\nNeem T2 \n\n\n\nArtemisia spp. Leaves powder Mugwort (Titepati) T3 \n\n\n\nUtrica dioica leaves powder Nettle (Sisnu) T4 \n\n\n\nControl Local/control T5 \n\n\n\nAcoros calamus rhizome \n\n\n\npowder \n\n\n\nSweet flag (Bojho) T6 \n\n\n\nBrassica campestris oil Mustard (Tori) T7 \n\n\n\nCymbopogon spp. oil Lemongrass T8 \n\n\n\nAzadirachta indica oil Neem T9 \n\n\n\nMentha spp. oil Mint T10 \n\n\n\n\n\n\n\nThe grounded leaves and bulbs of different treatments were spread \n\n\n\nuniformly over the tubers at a dose of 1 gm/6 tubers. The essential oils \n\n\n\nwere applied as wick application methods by placing a blotting paper \n\n\n\nsaturated with essential oil at a dose of 1ml/6 tubers. These treatments \n\n\n\ndose were based on the previous study (Elsadr and Waterer, 2005). The \n\n\n\napplication method of the essential oils were based on the previous study \n\n\n\n(Frazier et al., 2004). Precaution were taken to avoid the direct contact of \n\n\n\noil with the tubers. These amounts were in excess of the amount \n\n\n\ntheoretically sufficient to saturate the atmosphere in the container. The \n\n\n\nstudy lasted for 100 days starting from harvesting at 22th February to final \n\n\n\nreading at 3rd June. The parameters observed were sprouting percentage, \n\n\n\nweight loss percentage, damage score, length of sprout, number of sprout \n\n\n\nand sprout mass. The observation was taken after 60 days and then at 15 \n\n\n\ndays interval with 3 observation at total. The data obtained were analyzed \n\n\n\nusing MS-excel and R-studio. \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Weight loss percentage \n\n\n\nThe weight loss percentage recorded in different treatments over three \n\n\n\ndifferent period is presented in Table 1. After 60 days of experiment, the \n\n\n\nhighest weight loss was recorded from control (3.9%) and Azadirachta \n\n\n\nindica oil (3.8%). The lowest weight loss was recorded from Acoros \n\n\n\ncalamus (3.2%) followed by Cymbopogon (3.3%) and Mentha oil (3.3%). \n\n\n\nAt the end of the experiment, highest weight loss was recorded from \n\n\n\ncontrol (8.5 %) and Mentha oil (8.3%) with lowest being that of \n\n\n\nCymbopogon oil (5.6 %).  \n\n\n\n\n\n\n\nTartoura, Gamily, Shall & El-Sharqawy, 2015 also recorded maximum \n\n\n\nweight loss in control followed by Azadirachta indica extract in potato in \n\n\n\nan experiment of using medicinal and aromatic plant extracts on potato at \n\n\n\nambient temperature. Sprouting occurs in tubers in expense of the tuber \n\n\n\ncontent i.e. its weight. The varying weight losses of tuber in different \n\n\n\ntreatments can be accounted for their relative efficacy in suppressing the \n\n\n\nsprout growth. Thus, tubers maximum sprout growth (numbers, mass, \n\n\n\nlength) loses more weight than tubers with low sprout growth.  \n\n\n\n\n\n\n\nTable 2: Effect of different treatments on weight loss percentage of \n\n\n\npotato in Baglung district \n\n\n\nTreatments Weight loss percentage after \n\n\n\n60 days 75 days 90 days \n\n\n\nGingiber officinale 3.41abc 6.23a 7.84bc \n\n\n\nArtimisia 3.44abc 5.43cd 6.38d \n\n\n\nAzadirachta indica 3.38bc 5.49cd 6.59d \n\n\n\nUtrica dioica 3.63abc 6.13a 7.92bc \n\n\n\nAcoros calamus 3.20c 4.36e 5.51e \n\n\n\nBrassica campestris oil 3.46abc 4.96d 7.61c \n\n\n\nCymbopogon oil 3.27c 4.15e 5.65e \n\n\n\nAzadirachta indica oil 3.77ab 6.09ab 7.97bc \n\n\n\nMentha oil 3.29c 5.57bc 8.28ab \n\n\n\nControl 3.88a 6.35a 8.47a \n\n\n\nCV, % 8.40 6.50 4.28 \n\n\n\nSEM 0.15 0.18 0.22 \n\n\n\nLSD 0.42* 0.52** 0.45** \n\n\n\nGrand mean 3.47 5.48 7.22 \n\n\n\nFigures in the column with the same letter are not significantly different at \n\n\n\n5% level of significance according to DMRT. CV = Coefficient of Variation, \n\n\n\nLSD = Least significant difference, and S.Em (\u00b1) = Standard error of mean \n\n\n\ndifference \n\n\n\n3.2 Sprouting percentage \n\n\n\nThe sprouting percentage recorded in different treatments over three \n\n\n\ndifferent period is presented in Table 2. After 60 days of experiment, \n\n\n\nhighest sprout percentage was recorded in control (45.75%) followed by \n\n\n\nUtrica dioica (41.5 %) and Azadirachta indica oil (41.5%). The tubers \n\n\n\ntreated with Cymbopogon, Mentha and Acoros calamus had no sprout at all. \n\n\n\nAfter 75 days of experiment, Cymbopogon oil (8.25%) had sprouting \n\n\n\npercentage significantly lower than rest of the treatments. The highest was \n\n\n\nrecorded in control (87.4%). Cymbopogon oil was recorded the most \n\n\n\neffective to suppress the sprouting during storage with the minimum \n\n\n\nsprouting of 13% at the end of 90 days. \n\n\n\n\n\n\n\nFarooqi et al. also recorded the maximum sprout inhibition with lemon \n\n\n\ngrass oil and minimum inhibition in control while screening volatile \n\n\n\nessential oils for sprout suppression. Cymbopogon essential oil consists of \n\n\n\ncitral which is very effective in suppressing sprout growth [14]. Similarly, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 16-21 \n\n\n\n\n\n\n\n \nCite the Article: Kushal Giri, Suraj Gurung, Sujan Pokharel, Rupak Karn, Ananta Prakash Subedi (2020). Effect Of Different Plant Extracts On Sprouting, Storability And \n\n\n\nPost-Harvest Loss Of Potato In Baglung District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(1): 16-21. \n \n\n\n\n\n\n\n\ncaravon, a terpenoid is found naturally in Mentha essential oil which is  \n\n\n\nsown to be an effective sprout suppressant in storage (Farooqi et al., \n\n\n\n2001). Acorus calamus consists of monoterpene menthol is recorded to \n\n\n\ninhibit germination (Oosterhaven et al., 1995). Mentha essential oil cause \n\n\n\ndamage to the vascular tissue causing necrosis of the emerging bud \n\n\n\nthereby inhibiting sprouting leaving black necrotic symptoms in tubers \n\n\n\n(Nawamaki and Kuroyanagi, 1996).  \n\n\n\n\n\n\n\nSurprising effect was observed with Mentha oil which completely inhibit \n\n\n\nsprouting till 60 days of treatment induce massive sprouting of 70.3% at \n\n\n\nthe 75 days of treatment and 92 % at the end of experiment (90 days). \n\n\n\nFrazier came up with the assertion that single application of Mentha oil \n\n\n\ninduces enhanced sprouting after certain duration of sprout inhibition \n\n\n\nthereby signifying the importance of repeated application.  \n\n\n\n\n\n\n\nTable 3: Effect of different treatments on Sprouting percentage of \n\n\n\npotato in Baglung district \n\n\n\nTreatments Sprouting percentage after \n\n\n\n60 days 75 days 90 days \n\n\n\nGingiber officinale 0.29b 0.58cd 1.00a \n\n\n\nArtimisia 0.29b 0.46de 1.00a \n\n\n\nAzadirachta indica 0.25b 0.62bc 1.00a \n\n\n\nUtrica dioica 0.42a 0.58cd 1.00a \n\n\n\nAcoros calamus 0.00c 0.37e 0.84b \n\n\n\nBrassica campestris oil 0.29b 0.42e 1.00a \n\n\n\nCymbopogon oil 0.00c 0.08f 0.13c \n\n\n\nAzadirachta indica oil 0.42a 0.75ab 1.00a \n\n\n\nMentha oil 0.00c 0.70bc 0.92ab \n\n\n\nControl 0.46a 0.88a 1.00a \n\n\n\nCV, % 30.9 18.3 10.6 \n\n\n\nSEM 0.03 0.04 0.04 \n\n\n\nLSD 0.11** 0.15** 0.14** \n\n\n\nGrand mean 0.24 0.54 0.89 \n\n\n\nFigures in the column with the same letter are not significantly different at \n\n\n\n5% level of significance according to DMRT. CV = Coefficient of Variation, \n\n\n\nLSD = Least significant difference, and S.Em (\u00b1) = Standard error of mean \n\n\n\ndifference \n\n\n\n3.3 Sprout number \n\n\n\nThe effect of different treatments on the sprout number of tubers is shown \n\n\n\nin table no 3. Cymbopogon oil, Mentha oil and Acorus powder had zero \n\n\n\nsprout till 60 days of experiment. The control had the highest sprout \n\n\n\nnumber (0.46). Maximum sprout number was recorded again from control \n\n\n\n(1.04) at 75 days of experiment followed by Mentha oil (1.0). Mentha oil \n\n\n\n(1.1.92) was recorded with highest sprout number at the end of \n\n\n\nexperiment followed by control (1.79). \n\n\n\n\n\n\n\nEnhanced sprout number in Mentha oil treated tuber must be due to the \n\n\n\nreduced inhibitory effect of Mentha oil with time that induces axillary bud \n\n\n\nto sprout with reduced apical dominance (Nawamaki and Kuroyanagi, \n\n\n\n1996). Further, longer sprout suppression by Mentha oil requires the need \n\n\n\nof repeated application, single application of which causes enhanced \n\n\n\nsprouting (Bamnolker et al., 2010; Evenari, 1949; Pryor et al., 1940; Joshi \n\n\n\nand Bashyal, 2019). Thus, this unusual increased sprout number due to \n\n\n\nMentha oil application is due to the single application of treatment which \n\n\n\nenhance sprouting with reduced apical dominance that causes axillary bud \n\n\n\nto sprout. \n\n\n\n\n\n\n\nTable 4: Effect of different treatments on Sprout number of potato in \n\n\n\nBaglung district June 2019 \n\n\n\nTreatments Sprout number after \n\n\n\n60 days 75 days 90 days \n\n\n\nGingiber officinale 0.33bc 0.75bcd 1.42bcd \n\n\n\nArtimisia 0.29c 0.63cd 1.13d \n\n\n\nAzadirachta indica 0.29c 0.63cd 1.33cd \n\n\n\nUtrica dioica 0.42ab 0.68cd 1.38bcd \n\n\n\nAcoros calamus 0.00d 0.54d 1.08d \n\n\n\nBrassica campestris oil 0.38abc 0.75bcd 1.46bcd \n\n\n\nCymbopogon oil 0.00d 0.21e 0.38e \n\n\n\nAzadirachta indica oil 0.42ab 0.83abc 1.63abc \n\n\n\nMentha oil 0.00d 1.00ab 1.92a \n\n\n\nControl 0.46a 1.04a 1.79ab \n\n\n\nCV, % 26.3 23.6 20.1 \n\n\n\nSEM 0.03 0.04 0.08 \n\n\n\nLSD 0.10** 0.24** 0.39** \n\n\n\nGrand mean 0.26 0.71 1.35 \n\n\n\nFigures in the column with the same letter are not significantly different at \n\n\n\n5% level of significance according to DMRT. CV = Coefficient of Variation, \n\n\n\nLSD = Least significant difference, and S.Em (\u00b1) = Standard error of mean \n\n\n\ndifference \n\n\n\n3.4 Average Sprout length \n\n\n\nThe average sprout length recorded in different treatments over three \n\n\n\ndifferent period is presented in Table 4. After 60 days of treatment, the \n\n\n\nlongest sprout length was recorded in the Azadirachta indica oil while the \n\n\n\ntubers treated with Cymbopogon, Mentha and Acoros calamus had zero \n\n\n\nsprout i.e. zero length. After 75 days of treatment, Azadirachta indica oil \n\n\n\nhad the longest sprout length (7.864) which was significantly at par with \n\n\n\ncontrol (7.824). The lowest sprout length was recorded from Cymbopogon \n\n\n\n(1.536) and Acoros calamus (3.008). At the end of the experiment (90 days \n\n\n\nafter treatments) the longest sprout length was recorded from Mentha oil \n\n\n\n(13.47), control (13.19) and Azadirachta indica oil (13.29). The lowest \n\n\n\nsprout length was recorded from Cymbopogon (2.5) followed by Acoros \n\n\n\ncalamus (5.63). \n\n\n\n\n\n\n\nThe abrupt rise in sprout length of tuber treated with Mentha oil can be \n\n\n\ndue to decrease in its effect with concentration over time. Complete \n\n\n\ninhibition of sprout by Mentha oil is followed by stimulation of \n\n\n\ngermination with reduced concentration. Inhibition of germination is \n\n\n\nfollowed by stimulation of germination; sometimes they appear in \n\n\n\ndifferent concentration and sometimes inhibition is followed by stimulus \n\n\n\nwith decrease in concentration over time. The enhanced length of sprout \n\n\n\ndue to Mentha oil can be explained from this implication. \n\n\n\n\n\n\n\nTable 5: Effect of different treatments on average sprout length of \n\n\n\npotato in Baglung district \n\n\n\nTreatments Average sprout length after \n\n\n\n60 days 75 days 90 days \n\n\n\nGingiber officinale 1.46b 6.71ab 10.60b \n\n\n\nArtimisia 1.59ab 6.05b 10.50b \n\n\n\nAzadirachta indica 1.76ab 6.75ab 10.29b \n\n\n\nUtrica dioica 1.63ab 6.20ab 11.58ab \n\n\n\nAcoros calamus 0.00c 3.01c 5.63c \n\n\n\nBrassica campestris oil 1.46b 6.78ab 12.39ab \n\n\n\nCymbopogon oil 0.00c 1.54c 2.50d \n\n\n\nAzadirachta indica oil 2.04a 7.86a 13.29a \n\n\n\nMentha oil 0.00c 7.52ab 13.47a \n\n\n\nControl 1.75ab 7.82ab 13.19a \n\n\n\nCV, % 21.5 18.1 16.0 \n\n\n\nSEM 0.13 0.353 0.59 \n\n\n\nLSD 0.36** 1.58** 2.38** \n\n\n\nGrand mean 1.17 6.02 10.3 \n\n\n\nFigures in the column with the same letter are not significantly different at \n\n\n\n5% level of significance according to DMRT. CV = Coefficient of Variation, \n\n\n\nLSD = Least significant difference, and S.Em (\u00b1) = Standard error of mean \n\n\n\ndifference \n\n\n\n3.5 Sprout mass \n\n\n\nThe Sprout mass recorded at the end of the experiment is presented in \n\n\n\nTable 5. The lower sprout mass was recorded from tubers treated with \n\n\n\nCymbopogon oil (0.25 gm). The highest being that of control (2 gm).  Higher \n\n\n\nthe growth of sprout higher will be its mass; the lower sprout mass from \n\n\n\nCymbopogon oil is due to lower sprouting (13%) and lower sprout number \n\n\n\n(0.38). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 16-21 \n\n\n\nCite the Article: Kushal Giri, Suraj Gurung, Sujan Pokharel, Rupak Karn, Ananta Prakash Subedi (2020). Effect Of Different Plant Extracts On Sprouting, Storability And \nPost-Harvest Loss Of Potato In Baglung District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(1): 16-21. \n\n\n\nTable 6: Effect of different treatments on sprout mass of potato in \n\n\n\nBaglung district \n\n\n\nTreatments Sprout mass at the end \n\n\n\nGingiber officinale 1.50ab \n\n\n\nArtimisia 1.25ab \n\n\n\nAzadirachta indica 1.25ab \n\n\n\nUtrica dioica 1.50ab \n\n\n\nAcoros calamus 1.00b \n\n\n\nBrassica campestris oil 1.50ab \n\n\n\nCymbopogon oil 0.25c \n\n\n\nAzadirachta indica oil 1.75ab \n\n\n\nMentha oil 1.75ab \n\n\n\nControl 2.00a \n\n\n\nCV, % 34.5 \n\n\n\nSEM 0.10 \n\n\n\nLSD 0.69** \n\n\n\nGrand mean 1.38 \n\n\n\nFigures in the column with the same letter are not significantly different at \n\n\n\n5% level of significance according to DMRT. CV = Coefficient of Variation, \n\n\n\nLSD = Least significant difference, and S.Em (\u00b1) = Standard error of mean \n\n\n\ndifference \n\n\n\n3.6 Damage score \n\n\n\nThe damage score recorded in different treatments over three different \n\n\n\nperiod is presented in Table 6. The damage score of tubers for different \n\n\n\ntreatment was found not significant at 60 days of treatments; It ranged \n\n\n\nfrom 1.42 \u2013 1.13. After 75 days of treatments, the damage score was higher \n\n\n\nin control (1.58). The lowest damage score was obtained from Brassica \n\n\n\ncampestris oil (1.29) and Acorus calamus (1.29).  At the end of the \n\n\n\nexperiment, the damage was higher in the control (1.88) with lowest being \n\n\n\nthat of Brassica campestris oil (1.46). This might be due to the peculiar \n\n\n\nconstituent of Brassica campestris oil that keeps pests and other damaging \n\n\n\nagent away from it. The Brassica campestris oil and its vapor are highly \n\n\n\ntoxic to different fungi in lower concentration of 10 ppm. Further, Acorus \n\n\n\ncalamus is well known for having antibacterial property. The essential oil \n\n\n\nfrom dry powder of Acorus calamus is showed to have a good potentiality \n\n\n\nfor acting as an antibacterial agent. The least damage score due to Brassica \n\n\n\ncampestris oil and Acorus calamus can be understood by the points above. \n\n\n\nTable 7: Effect of different treatments damage score of potato in \n\n\n\nBaglung district June 2019. \n\n\n\nTreatments Damage score after \n\n\n\n60 days 75 days 90 days \n\n\n\nGingiber officinale 1.21 1.42bc 1.75ab \n\n\n\nArtimisia 1.21 1.38bc 1.67abc \n\n\n\nAzadirachta indica 1.21 1.38bc 1.58bc \n\n\n\nUtrica dioica 1.33 1.46ab 1.75ab \n\n\n\nAcoros calamus 1.21 1.29c 1.58bc \n\n\n\nBrassica campestris oil 1.13 1.29c 1.46c \n\n\n\nCymbopogon oil 1.17 1.38bc 1.58bc \n\n\n\nAzadirachta indica oil 1.21 1.38bc 1.75ab \n\n\n\nMentha oil 1.167 1.38bc 1.71ab \n\n\n\nControl 1.42 1.58a 1.88a \n\n\n\nCV, % 10.4 6 .92 8.59 \n\n\n\nSEM 0.02 0.02 0.03 \n\n\n\nLSD 0.18NS 0.14* 0.21* \n\n\n\nGrand mean 1.23 1.39 1.67 \n\n\n\nFigures in the column with the same letter are not significantly different at \n\n\n\n5% level of significance according to DMRT. CV = Coefficient of Variation, \n\n\n\nLSD = Least significant difference, and S.Em (\u00b1) = Standard error of mean \n\n\n\n3.7 Correlation between weight loss and sprout growth \n\n\n\nThe correlation between weight loss and sprout growth parameters viz; \n\n\n\nsprout number, sprout length and sprout mass are shown in table 7. A \n\n\n\nsignificant correlation (P<0.01) is found between weight loss and all \n\n\n\nparameters of sprout growth. Strong correlation was found between \n\n\n\nweight loss and sprout length (r = 0.85**) which signifies tuber with longer \n\n\n\nsprout length lose weight faster than that of small length tuber. Similarly, \n\n\n\nstrong correlation was found between weight loss and sprout number (r = \n\n\n\n0.78**) which signifies, tuber with higher sprout number lose weight \n\n\n\nfaster than that of tubers with lower sprout number. And, a strong \n\n\n\ncorrelation of weight loss and mass of sprout (r = 70**) signifies, sprout \n\n\n\ngrows in expense of tuber mass. \n\n\n\nTable 8: Correlation matrix between weight loss, sprout length, sprout \n\n\n\nnumber and sprout mass. \n\n\n\nWeight \n\n\n\nloss \n\n\n\nSprout \n\n\n\nlength \n\n\n\nSprout \n\n\n\nnumber \n\n\n\nSprout \n\n\n\nmass \n\n\n\nWeight loss 1 \n\n\n\nSprout \n\n\n\nlength \n\n\n\n0.85** 1 \n\n\n\nSprout \n\n\n\nnumber \n\n\n\n0.78** 0.90** 1 \n\n\n\nSprout \n\n\n\nmass \n\n\n\n0.70** 0.75** 0.73** 1 \n\n\n\n** correlation coefficient is significant at 0.01 probability level \n\n\n\n4. CONCLUSION \n\n\n\nIt can be concluded that wick application of Cymbopogon oil significantly \n\n\n\ndecreases the sprouting percentage, sprout number, length of sprout and \n\n\n\nweight loss during storage. The use of Brassica campestris oil can decrease \n\n\n\nthe damage of tubers during storage. Acorus calamus can be used \n\n\n\nsuccessfully for short term preservation that check sprouting and hence \n\n\n\nweight loss and damage to tubers. Mentha oil can be used as a good sprout \n\n\n\nsuppressant but it needs repeated application for its efficacy. Locally \n\n\n\nfound natural compounds like Acorus calamus, Artimisia spp., Symbopogon \n\n\n\nspp., Azadirachta indica etc. can be used as additives during potato storage \n\n\n\nto check undesirable sprouting and loses. Use of such natural herbs during \n\n\n\nstorage can significantly decrease the post-harvest loss during storage. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nAuthors would like to thank Agriculture and Forestry University, Rampur, \n\n\n\nChitwan; Prime Minister Agriculture Modernization Project (PMAMP); \n\n\n\nSudip Khatiwada, Senior Agriculture Development Officer for guiding \n\n\n\nthroughout the study period. \n\n\n\nAUTHORS CONTRIBUTIONS \n\n\n\nAnanta Prakash Subedi supervised the experiment. Kushal Giri conducted \n\n\n\nthe experiment and recorded data, analyzed and created the final \n\n\n\nmanuscript. Suraj Gurung, Sujan Pokharel and Rupak Karn helped during \n\n\n\ndata observation and manuscript preparation. \n\n\n\nREFERENCES \n\n\n\nABPSD., 2017. Statistical information on Nepalese agriculture 2016/17. \nAgri-business Promotion and Statistic Division, Kathmandu, Nepal. \n\n\n\nAml, A.E.A., Moghazy, A.M., Gouda, A.E.A.,  Elshatoury, R.S.A., 2014. \nInhibition of Sprout Growth and Increase Storability of Processing \nPotato by Antisprouting Agent. Trends Hortic. Res. \n\n\n\nBamnolker, P.T., Dudai, N., Fischer, R., Belausov, E., Zemach, H., Shoseyov, \nO., Eshel, D., 2010. Mint essential oil can induce or inhibit potato \nsprouting by differential alteration of apical meristem. Planta, 232 (1), \nPp. 179\u2013186. \n\n\n\nBhattarai, D.R., 2018. Postharvest horticulture in Nepal. Hortic. Int. J., 2 (6), \nPp. 458\u2013460. \n\n\n\nBlenkinsop, R.W.,  Copp, L.J.,  Yada, R.Y., Marangoni, A.G., 2003. A proposed \nrole for the anaerobic pathway during low-temperature sweetening in \ntubers of Solanum tuberosum, Physiol. Plant.  \n\n\n\nChuda, Y., Ono, H., Yada, H., Ohara -Takada, A., Matsuura-endo, C., Mori, M., \n2003. Effects of Physiological Changes in Potato Tubers ( Solanum \ntuberosum L.) after Low Temperature Storage on the Level of \nAcrylamide Formed in Potato Chips. Biosci. Biotechnol. Biochem., 67 \n(5), Pp. 1188\u20131190. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 16-21 \n\n\n\n\n\n\n\n \nCite the Article: Kushal Giri, Suraj Gurung, Sujan Pokharel, Rupak Karn, Ananta Prakash Subedi (2020). Effect Of Different Plant Extracts On Sprouting, Storability And \n\n\n\nPost-Harvest Loss Of Potato In Baglung District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(1): 16-21. \n \n\n\n\n\n\n\n\nElsadr, H.,  Waterer, D., 2005. Efficacy of Natural Compounds to Suppress \nSprouting and Fusarium Dry Rot in Potatoes. Univ. Saskatchewan, 306. \n\n\n\nEvenari, M. 1949. Germination Inhibitors. New York Bot. Gard. Press, 15, \nPp. 153\u2013194. \n\n\n\nFarooqi, A.H.A.,  Agarwal, K.K.,  Fatima, S., Ahmad, A., Sharma, S., Kumar, S., \n2001. Anti-sprouting agent for potato tuber and a method for producing \nthe same. \n\n\n\nFrazier, M.J.,  Olsen, N., Kleinkopf, G., 2004. Organic and Alternative \nMethods for Potato Sprout Control in Storage. Univ. Idaho Ext., 73 \n(1120), Pp. 1013\u20131018. \n\n\n\nIshwori, G., 2016. Seed and Ware Potato Storage and processing status in \nNepal, Nepal. \n\n\n\nJoshi, S., Bashyal, S., 2019. Study of the chemical constituents and \nantibacterial activity of the essential oil of Acorus calamus L. rhizomes \nof Rupandehi district (Nepal). J. Inst. Sci. Technol., 23 (1), Pp. 57\u201360. \n\n\n\nNawamaki, K., Kuroyanagi, M., 1996. Sesquiterpenoids from Acorus \ncalamus as germination inhibitors. Phytochemistry, 43 (6), Pp. 1175\u2013\n1182. \n\n\n\nNPRP. 2011. Annual Report 2010/11. National Potato Research Program \nPp, 158 \n\n\n\nNPRP., 2074. Potato Storage Technology in Nepal. National Potato \nResearch Program, MoALD Government of Nepal, Pp. 44.  \n\n\n\nOosterhaven, K., Poolman, B., Smid, E.J., 1995. S-carvone as a natural \npotato sprout inhibiting, fungistatic and bacteristatic compound, Ind. \nCrops Prod. \n\n\n\nPinhero, R.G., Yada, R.Y., 2016. Postharvest Storage of Potatoes. Advances \nin Potato Chemistry and Technology: Second Edition., Elsevier Inc. Pp. \n283-314. https://doi.org/10.1016/B978-0-12-800002-1.00010-8 \n\n\n\nPryor, D.E., Walker, J.C., Stahmann, M.A., 1940. Toxicity of Allyl \nIsothiocyanate Vapor to Certain Fungi. Am. J. Bot., 27 (8), Pp. 30\u201338. \n\n\n\nSharma, M.D.,  Gautam, I.P.,  Khatri, B.B., 2016. Yield , Storability and \nProcessing Quality of Potato, September. \n\n\n\nSong, X., Bandara, M., Tanino, K.K., 2008. Fruit, Vegetable and Cereal \nScience and Biotechnology Potato Dormancy Regulation: Use of \nEssential Oils for Sprout Suppression in Potato Storage. Fruit, Veg. \nCereal Sci. Biotechnol., 2 (1), Pp. 110\u2013117. \n\n\n\nTartoura, E.A.A.,  Gamily, E., Shall, Z.A.E., El-Sharqawy, M.M.S., 2015. Effect \nof Some Medicinal and Aromatic Plants Extracts on Storability of Potato \nAt the Ambient Temperature. J. Plant Prod., 6 (6), Pp. 977\u2013993.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 32-37 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2023.32.37 \n\n\n\n \nCite The Article: Anirup Sengupta, Mohanasundari Thangavel (2023). Impact of Climate Change on Sugarcane Production in Uttar Pradesh,  \n\n\n\nIndia: A District Level Study Using Statistical Analysis and Gis Mapping. Journal of Sustainable Agricultures, 7(1): 32-37. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.32.37 \n\n\n\n\n\n\n\nIMPACT OF CLIMATE CHANGE ON SUGARCANE PRODUCTION IN UTTAR \nPRADESH, INDIA: A DISTRICT LEVEL STUDY USING STATISTICAL ANALYSIS AND \nGIS MAPPING \n\n\n\nAnirup Senguptaa, Mohanasundari Thangavelb* \n\n\n\na Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, Mohanpur-741252, Nadia West Bengal, India. \nb School of Humanities & Social Sciences, Indian Institute of Technology, Indore, Khandwa Road, Simrol \u2013 453552 \n*Corresponding Author email: jespa.anirup@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 13 December 2022 \nRevised 16 January 2023 \nAccepted 19 February 2023 \nAvailable online 01 March 2023 \n\n\n\n Sugarcane is a cash crop typically cultivated for sugar. Due to climate change, there is a rise in temperature, \ndisruption in the rainfall patterns and cycle of seasons. Such changes in weather parameters affect sugarcane \nproduction as well as sugar recovery from the canes. The study was conducted in Uttar Pradesh, India using \nGIS (Geographic Information System) based models and statistical multiple linear regression from district-\nwise data on yield and climatic parameters over the study period (1986 to 2015). The GIS models reveal that \nclimatic factors like rainfall, temperature and evapotranspiration changed significantly throughout the study. \nThe multiple linear regression model shows that such changes in climatic parameters have a significant \nimpact on the yield of sugarcane. Graphical analysis of yearly data on temperature and sugar recovery (%) \nshowed that temperature affects the amount of sugar recovered from the canes. The study aims to illustrate \nthe evidence of climate change and its impact on sugarcane production in Uttar Pradesh. \n\n\n\nKEYWORDS \n\n\n\nClimate change, Remote Sensing, GIS, Multiple linear regression, Sugarcane cultivation \n\n\n\n \n1.   INTRODUCTION \n\n\n\nSugarcane (Saccharum officinarum) is an important cash crop and the \nsource of raw material for the second largest agro-based enterprise in \nIndia. Uttar Pradesh is the largest sugarcane-producing state having an \narea of about 22.77 lakh ha with the production of 135.64 million-ton \ncanes (Farmer\u2019s portal, 2021). Climate change is a severe global \nenvironmental issue resulting in rising temperatures, variability in \nrainfall, and changes in the normal cycle of seasons. The latest scientific \nresearches reveal that the earth's climate has changed significantly over \nthe last fifty years (Balasubramanian and Birundha, 2012). The \nIntergovernmental Panel on Climate Change has projected that the earth's \nmean temperature will rise around 1.8 to 5.7 oC by 2100 (IPCC et al., 2021). \nThese variations in temperature and rainfall patterns severely impact \nagriculture by exposing the plants to various abiotic stresses and reducing \nthe economic yield. This effect is more adverse in tropical regions like \nIndia (Sathaye et al., 2006). To obtain an optimum yield in sugarcane, the \nmaximum temperature should lie between 32-33oC. Very high and low \ntemperatures deteriorate the quality of juice that affects the sugar quality. \nVery few studies delineate climate change on a regional scale using \nmodern GIS techniques and studying its impact on economically important \ncrops like sugarcane. This study aims to illustrate the changes in maximum \nand minimum temperature; rainfall patterns over a period of 30 years \nfrom 1986 to 2015 using GIS techniques and further statistically analyzes \nits impact on the productivity of sugarcane and sugar recovery in Uttar \nPradesh. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1   Study Area \n\n\n\nThe study area (Figure 1) is the state of Uttar Pradesh located between \n\n\n\n23\u00b052'N and 31\u00b028'N latitudes and 77\u00b03' and 84\u00b039'E longitudes \n(Agriculture mechanization guide for Uttar Pradesh, 2022), comprising 75 \ndistricts having a total land area of 240,928 km2. The state shares \nboundaries with Rajasthan in the west, Haryana, Himachal Pradesh and \nDelhi to the northwest, Uttarakhand and Nepal (international border) to \nthe north, Bihar to the east, Madhya Pradesh to the south, and the states of \nJharkhand and Chhattisgarh to the southeast. There are three major agro-\nclimatic zones in Uttar Pradesh- Middle Gangetic Plains region; Upper \nGangetic Plains region; Central Plateau and Hills region. The climate is \nprimarily subtropical; however, the weather varies significantly in \ndifferent seasons at different locations. Based on the altitude, the average \ntemperatures range between 12.5\u201317.5 \u00b0C in winter and 27.5\u201332.5 \u00b0C in \nsummer months. The average annual rainfall is around 1,000\u20132,000 mm \nin the east to 600\u20131,000 mm in the west. The maximum amount of rain \noccurs during the Southwest Monsoon (Agriculture mechanization guide \nfor Uttar Pradesh, 2022). The soil is generally neutral in reaction having \nmoderate proportions of clay as well as low organic carbon content. \n\n\n\n2.2   Collection of Data \n\n\n\nThe geo-climatic informatics of the state of Uttar Pradesh has been \ndeveloped using GADM, Terra Climate and CHIRPS. Secondary data based \non the official statistics published by ICRISAT on the district-wise yearly \nyield of sugarcane in Uttar Pradesh, precipitation, and temperature for the \nperiod 1986 to 2015 has also been used in this study. The data on the \npercentage of sugar recovery in different years has been obtained from Co-\noperative Sugar, 2021. Table 1 illustrates the variables and their \ncorresponding data sources used in the present study. \n\n\n\n2.3   Multiple Linear Regression Model \n\n\n\nMultiple linear regression is a statistical tool used for predicting the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 32-37 \n\n\n\n\n\n\n\n \nCite The Article: Anirup Sengupta, Mohanasundari Thangavel (2023). Impact of Climate Change on Sugarcane Production in Uttar Pradesh,  \n\n\n\nIndia: A District Level Study Using Statistical Analysis and Gis Mapping. Journal of Sustainable Agricultures, 7(1): 32-37. \n\n\n\n\n\n\n\nrelationship between a continuous dependent variable and two or more \nindependent variables. The independent variables are either continuous \nor categorical (dummy). It is helpful in studying the effects or impacts of \nchanges, i.e. it denotes the rate of changes in the dependent variable \ncorresponding to the changes in the independent variables. The model \nassumes that: \n\n\n\nThere should be a normal distribution of the regression residuals. \nThe relationship between the dependent variable and the \nindependent variables is linear. \nThe residuals are homoscedastic and approximately rectangular-\nshaped. \nThere is the absence of multicollinearity, that is, independent \nvariables are not too highly correlated. \n\n\n\nThis study considers the major climatic factors affecting the yield of \nsugarcane, namely annual rainfall and mean annual temperatures \n(maximum and minimum). District-wise yearly yield of sugarcane was \ntaken as the dependent variable while annual rainfall and annual mean \nmaximum and minimum temperatures were taken as independent \nvariables. A multiple linear regression model was formulated following \nthe ordinary least squares procedure, expressed as: \n\n\n\nYield = f (Max. temperature, Min. temperature, Annual rainfall) \n\n\n\nYield: District-wise annual yield of cotton. \nMax. temperature: Average of the monthly maximum temperatures \nof a district for a year. \nMin. temperature: Average of the monthly minimum temperatures \nof a district for a year. \nAnnual rainfall: Sum of monthly rainfalls of a district for a year. \n\n\n\nSoftwares like Microsoft Office Excel and STATA were used to run the \nmodel and descriptive statistics. It is evident that even when the \nindependent variables are zero there can be some amount of sugarcane \nyield due to irrigation and other factors. Thereafter, the model needs a \nconstant. Hence, it becomes: \n\n\n\ny\ud835\udc56\ud835\udc52\ud835\udc59\ud835\udc51 = \ud835\udefc0 + \ud835\udefc1 (max. temperature) + \ud835\udefc2 (min. temperature) + a3 (rainfall) \n\n\n\nWhere \u2236 \ud835\udefc0, is the constant;  \n\ud835\udefc1, \ud835\udefc2, a3 are the contribution coefficients to be \nestimated. \nrainfall: annual rainfall \nmax. temperature: annual maximum temperature \nmin. temperature: annual minimum temperature \nyield: district-wise yield of sugarcane per year \n\n\n\n\n\n\n\nFigure 1: Study area (Uttar Pradesh, India). \n\n\n\nTable 1: List of Variables and Data Sources. \n\n\n\nVariables Data Source Time Period \n\n\n\nDistrict level map of Maharashtra GADM (https://gadm.org/) - \n\n\n\nMaximum and Minimum Temperature \nTerra Climate (http://climateengine.org/data) \n\n\n\nICRISAT [District-wise] (http://data.icrisat.org/dld/src/crops.html) \n1986-2015 \n\n\n\nPrecipitation \nTerra Climate (http://climateengine.org/data) \n\n\n\nICRISAT [District-wise] (http://data.icrisat.org/dld/src/crops.html) \n1986-2015 \n\n\n\nYield of Cotton ICRISAT [District-wise] (http://data.icrisat.org/dld/src/crops.html) 1986-2015 \n\n\n\nSugar recovery (%) \nCo-operative Sugar Vol.53, NOVEMBER 2021 \n\n\n\nUpdated November 2021; Vol. 53, No.3. (Published in Cooperative Sugar) \n1986-2015 \n\n\n\n\n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\nTemperature is an important parameter regulating plant growth, \ndevelopment and sugar content of canes. The optimum range of \ntemperature needed for biochemical and metabolic activities in plants is \ncalled the thermal kinetic window (TKW) (Burke et al., 1988). \nTemperatures above or below the TKW result in stress that limit plant \ngrowth and yield. The TKW range for sugarcane is 30-340 C (Machado, \n1987). 32 to 33 \u00b0C is optimum for germination of the stem cuttings. It slows \ndown below 25\u00b0 (Galdos et al., 2009). Figure 2a and Figure 2b illustrate \nthe changes in the temperature in Uttar Pradesh across the study period \n(1986-2015) by taking into account data obtained from Terra Climate. It \nis observed that even though there were fluctuations in the maximum and \nminimum temperatures, it showed an increasing trend.  \n\n\n\nBesides temperature, rainfall is also an essential factor affecting the yield \nof crops. The annual rainfall should be 1100-1500 mm for perennial cash \n\n\n\ncrops like sugarcane. Around 1200 mm of evenly distributed rainfall is \nsufficient for a higher yield (Srivastava and Rai, 1970). The ripening period \nrequires less rainfall in order to have good quality juice hence better sugar \nrecovery, less vegetative growth and reduced tissue moisture. Heavy \nrainfall may lead to disruptions of these conditions. In course of time, the \nrainfall has become erratic (Figure 3). In some parts, there is a scarcity of \nrain leading to moisture stress; in others there are excessive downpours, \nhampering economic yield. \n\n\n\nStatistical analysis of yield and climate data using multiple linear \nregression reveals the extent of such variability in climatic factors projects \non sugarcane production. Table 2 delineates the model output which \nshows that the F value is about 144.65 and significant at P < 0.001. Hence \nthe null hypothesis (H0) has to be rejected. Therefore, a statistically \nsignificant relationship exists between the dependent and the \nindependent variables. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 32-37 \n\n\n\n\n\n\n\n \nCite The Article: Anirup Sengupta, Mohanasundari Thangavel (2023). Impact of Climate Change on Sugarcane Production in Uttar Pradesh,  \n\n\n\nIndia: A District Level Study Using Statistical Analysis and Gis Mapping. Journal of Sustainable Agricultures, 7(1): 32-37. \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Temperature in Uttar Pradesh from 1986 to 2015 (a) Maximum temperature, (b) Minimum temperature \n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Spatial distribution of annual rainfall in Uttar Pradesh (a) 1986, (b) 1990, (c) 2000, (d) 2015. \n\n\n\nTable 2: Model outputs: Relevance of the equation ANOVAa \n\n\n\nModel SS DF MS F \n\n\n\nRegression 326155295 3 108718432 144.65 \n\n\n\nResidual 989115596 1,316 751607.596   \n\n\n\nTotal 1315270891 1,319 997172.776   \n\n\n\na. Dependent variable: Yield of cotton \n\n\n\nFrom Table 3, the value of the multiple correlation coefficients is 0.4980. \nR\u2248 0.50 indicates that the data set is well adjusted to the model so far the \nstudy is concerned. The value R\u00b2 \u2248 0.25 depicts that the independent \n\n\n\nvariables explain around 25% of the variability of the model. The value of \nadjusted R\u00b2 \u2248 0.25. It infers the robustness to be about 25% even if another \nsample from the same population is considered. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 32-37 \n\n\n\n\n\n\n\n \nCite The Article: Anirup Sengupta, Mohanasundari Thangavel (2023). Impact of Climate Change on Sugarcane Production in Uttar Pradesh,  \n\n\n\nIndia: A District Level Study Using Statistical Analysis and Gis Mapping. Journal of Sustainable Agricultures, 7(1): 32-37. \n\n\n\n\n\n\n\nTable 3: Model\u2019s Output Summarya \n\n\n\nNumber of observations R R-squared Adjusted R-squared Root MSE \n\n\n\n1,320 0.4980 0.2480 0.2463 866.95 \n\n\n\na. Dependent variable: Yield of cotton \n\n\n\n\n\n\n\nTable 4: Model outputs: Regression Coefficients. \n\n\n\nModel \nNon-Standardized Coefficient Standardized Coefficient \n\n\n\nt P-value \nA Standard Error Beta \n\n\n\n1 \n\n\n\nMax. Temperature -662.5485 42.24163 -0.6123202 -15.68 <0.001 \n\n\n\nMin. Temperature 194.8139 52.63412 0.1426535 3.70 <0.001 \n\n\n\nAnnual Rainfall -0.2217234 0.0806665 -0.0673141 -2.75 0.006 \n\n\n\nConstant 22570.12 855.8335  26.37 <0.001 \n\n\n\n \nFrom Table 4, the following results can be derived: \n\n\n\nThe last column implies that all coefficients except that of annual rainfall \nare significant with a p-value <0.001 and the coefficients for annual rainfall \nare significant at a p-value of 0.006. Therefore, there is a significant \ncontribution of the independent variables for the explanation of the \nvariability of the model (Pendergrass et al., 2017; Tabari, 2020). The \nstandardized coefficients of \"Max. Temperature\" and \u201cAnnual Rainfall\" \ncontribute negatively to the explanation of yield variability with an \napproximate relative weight of 61.2% and 7 % respectively. On the other \nhand, the standardized coefficient for minimum temperature contributes \npositively with a relative weight of 14.3%. As per the non-standardized \ncoefficients (A) the regression line can be reconstructed as \n\n\n\nYield = 22570.12 - 662.5485(max. temperature) + 194.8139(min. \ntemperature) - 0.2217234(rainfall) \n\n\n\nThe model output confirms the results of previous studies on factors \naffecting sugarcane yield variability. The residual plots for temperature \nand rainfall also reveal that the residual values are symmetrical to the \norigin and close to the line of zero, which suggests that the model fits \nperfectly (Figure 4a, Figure 4b and Figure 4c). It is to be noted that the \nmodel explains more than 61.2 % of yield variability and production, \ntechnology explains the rest (Rasheed et al., 2011). From Figure 5a and \nFigure 5b it is evident that higher temperature corresponds to lesser sugar \nrecovery (Rasheed et al., 2011). This may be because temperatures \nbeyond 32 \u00b0C lead to more number of nodes, shorter internodes, higher \nstalk fiber, therefore, lower sucrose (Bonnett et al., 2006). If night \ntemperature is higher, there is more number of flowering which in turn \nhampers the internodal vegetative growth resulting in lesser cane yield \nand sucrose content (Clowes and Breakwell, 1998). \n\n\n\n\n\n\n\nFigure 4: Residual plot (a) Maximum temperature; (b) Minimum temperature and (c) Annual rainfall. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 32-37 \n\n\n\n\n\n\n\n \nCite The Article: Anirup Sengupta, Mohanasundari Thangavel (2023). Impact of Climate Change on Sugarcane Production in Uttar Pradesh,  \n\n\n\nIndia: A District Level Study Using Statistical Analysis and Gis Mapping. Journal of Sustainable Agricultures, 7(1): 32-37. \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Sugar recovery in relation to (a) Maximum temperature; (b) Minimum temperature. \n\n\n\n4. CONCLUSION \n\n\n\nOur earth's climate is changing slowly yet steadily due to both natural and \nanthropogenic factors. GIS modeling and mapping of a place over a certain \nperiod of time provide evidence of changes in the spatial distribution of \nrainfall and temperature. Such changes affect crop production \nconsiderably. Especially the production of cash crops like sugarcane is \nhampered significantly if temperature and rainfall go beyond the \noptimum. Higher temperature and higher rainfall during ripening lead to \nreduced sucrose content. The study reveals that a large section of \nsugarcane production in Uttar Pradesh, India is affected by the changing \nclimate. The regression analysis shows that the results illustrate a \nsignificant linkage between independent variables, such as temperature, \nrainfall and the dependent variable, sugarcane yield. From the study, it has \nbeen found that a 1% rise in maximum temperature may cause a 61.2% \ndecrease in sugarcane yield.  \n\n\n\nHowever, 1% rise in minimum temperature might increase the cane yield \nby 14.2%. Again, higher maximum and minimum temperatures reduce \nsucrose content thereafter affecting sugar recovery. Furthermore, the \nresult shows that only a 1% increase in rainfall may result in a 7% \ndecrease in yield. The reason behind this observation is that precipitation \nvariability increases with the rise in temperature, which reduces the \neconomic yield of the crop. This means that the incidence of extreme \nprecipitation events increases as global warming increases. Therefore, at \nsome places, there is heavy rainfall while others are experiencing drought. \nNevertheless, such differences are not discrete. Temperatures have a \nhigher coefficient than others, suggesting that hot seasons are more \nfrequent and have a greater impact on yield. Hence, it is important to \nconstantly monitor climate variability, minimize anthropogenic factors \nresponsible for global warming. Agriculturists must come up with climate-\nresilient varieties and promote climate-smart agricultural practices to \nescalate crop production and ensure economic benefit of the farmers. \n\n\n\nACKNOWLEDGMENTS  \n\n\n\nThe authors are thankful to Indian Institute of Technology, Indore for \nhelping them with technical assistance and research facilities. \n\n\n\nDECLARATION OF INTEREST STATEMENT \n\n\n\nThe authors declare that they have no competing interest. \n\n\n\nREFERENCES \n\n\n\nAgriculture mechanization guide for Uttar Pradesh, 2022. Department of \nAgriculture & Cooperation and Farmers Welfare, Ministry of \nAgriculture and Farmers Welfare, Government of India. (Accessed \n15 February 2022). \nhttps://farmech.dac.gov.in/FarmerGuide/UP/index1.html \n\n\n\nBalasubramanian, M., and Birundha, V.D., 2012. Climate Change and Its \nImpact on India. IUP J. Environ. Sci., Pp. 6. \n\n\n\nBonnett, G.T., Hewitt, M.L., Glassop, D., 2006. Effects of high temperature \non the growth and composition of sugarcane internodes. Australian \nJournal of Agricultural Research, 57 (10), Pp. 1087-1095. \n\n\n\nBurke, J.J., Mahan, J.R., Hatfield, J.L., 1988. Crop-Specific Thermal Kinetic \nWindows in Relation to Wheat and Cotton Biomass Production. \nAgron. J., 80. \nhttps://doi.org/10.2134/agronj1988.00021962008000040001x \n\n\n\nClowes, M.J., Breakwell, W.L., 1998. Zimbabwe Sugarcane Production \nManual. Chiredzi, Zimbabwe: Zimbabwe Sugar Association \n\n\n\nCo-operative Sugar 53, 2021. Sugar statistics. \nhttps://coopsugar.org/about-us/  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 32-37 \n\n\n\n\n\n\n\n \nCite The Article: Anirup Sengupta, Mohanasundari Thangavel (2023). Impact of Climate Change on Sugarcane Production in Uttar Pradesh,  \n\n\n\nIndia: A District Level Study Using Statistical Analysis and Gis Mapping. Journal of Sustainable Agricultures, 7(1): 32-37. \n\n\n\n\n\n\n\nFarmer\u2019s portal, 2021. Sugarcane. Department of Agriculture & \nCooperation and Farmers Welfare, Ministry of Agriculture and \nFarmers Welfare, Government of India. (Accessed 15 February \n2022). https://farmer.gov.in/cropstaticssugarcane.aspx  \n\n\n\nGaldos, M., Cerri, C.C., Cerri, C., Paustian, K., Antwerpen, R., 2009. \nSimulation of Soil Carbon Dynamics under Sugarcane with the \nCENTURY Model. Soil Science Society of America Journal, 73. \n802.10.2136/sssaj2007.0285.  \n\n\n\nIPCC, Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., P\u00e9an, C., \nBerger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., \nReitzel, K., Lonnoy, E., Matthews, J.B.R., Maycock, T.K., Waterfield, T., \nYelek\u00e7i, O., Yu, R.B.Z., 2021. Climate Change 2021: The Physical \nScience Basis. Contribution of Working Group I to the Sixth \nAssessment Report of the Intergovernmental Panel on Climate \nChange, Cambridge University Press. \n\n\n\nMuchow, R.C., Spillman, M.F., Wood, A.W., Thomas. M.R., 1994. Radiation \ninterception and biomass accumulation in a sugarcane crop grown \nunder irrigated tropical conditions. Aust. J. Agric. Res., 45, Pp. 37\u2013\n49. \n\n\n\nPendergrass, A.G., Knutti, R., Lehner, F., Deser, C., Sanderson, B.M., 2017. \nPrecipitation variability increases in a warmer climate. Sci. Rep., Pp. \n7. https://doi.org/10.1038/s41598-017-17966-y \n\n\n\nRasheed, R., Wahid, A., Farooq, M., Hussain, I., Basra, S.M.A., 2011. Role of \nproline and glycine betaine pretreatments in improving heat \ntolerance of sprouting sugarcane (Saccharum sp.) bud. Plant Growth \nRegulation, 65, Pp. 35-45. \n\n\n\nSathaye, J., Shukla, P.R., Ravindranath, N.H., 2006. Climate change, \nsustainable development and India:Global and national concerns. \nCurr. Sci., Pp. 90. \n\n\n\nSrivastava, A.K., Rai, M.K., 1970. Review: Sugarcane production: Impact of \nclimate change and its mitigation. Biodiversitas J. Biol. Divers., Pp. \n13. https://doi.org/10.13057/biodiv/d130407 \n\n\n\nTabari, H., 2020. Climate change impact on flood and extreme \nprecipitation increases with water availability. Sci. Rep., Pp. 10. \nhttps://doi.org/10.1038/s41598-020-70816-2 \n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 97-100 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.97.100 \n\n\n\nCite The Article: Sambed kumar Chaube, Saugat Pandey (2022). Trichoderma: A Valuable Multipurpose Fungus for Sustainable Agriculture. \nMalaysian Journal of Sustainable Agricultures, 6(2): 97-100. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n\n\n\nREVIEW ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.97.100 \n\n\n\nTRICHODERMA: A VALUABLE MULTIPURPOSE FUNGUS FOR SUSTAINABLE \nAGRICULTURE \n\n\n\nSambed kumar Chaube, Saugat Pandey \n\n\n\nInstitute Of Agriculture Sciences, Banaras Hindu University, Varanasi, 221005, U.P, India \n*Corresponding Author email: bivekkumarchaube122@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 March 2022 \nAccepted 15 April 2022 \nAvailable online 18 April 2022\n\n\n\nA most valuable fungus that has multiple benefits in the agriculture production system. Around the Globe, \nFramers and scientists have taken the benefit of knowledge on trichoderma use. It is also called as \nmultipurpose fungus because of its use as bio fertilizer as well as biofungicide (Bio agent). Trichoderma is \nbeing able to produce volatile compound and ability to solubilize phosphate making them available to plants, \nit's used as biofertilizer. As a bioagent it control various pathogens such as Rhizoctonia, Phytophthora, \nSclerotinia spp. Presently a large number of Trichoderma based formulations/ Products are available in the \nglobal market which are dominated by Trichoderma harzianum & Trichoderma viridae. Its application in \ndeveloped countries is increasing rapidly replacing the chemical products, while in developing countries still \nit is lagging behind because of awareness among the peoples. This article is also with purpose to disseminate \nthe awareness among the peoples about its beneficial aspects in crop production process and its contribution \nin the environment. The main Ambition of this review paper is also to enlight the importance of trichoderma \nas a biofertilizer and bio agents. \n\n\n\nKEYWORDS \n\n\n\nBCA (biological control agents), Trichoderma, Bio fertilizer, Bio agents, Phytopathogen \n\n\n\n1. INTRODUCTION \n\n\n\nHuge agriculturally important crops face losses due to the disease caused \nby fungus and bacteria.   These diseases are more preferably controlled by \nthe use of chemical method and using such chemicals cause impact on \nhuman health and environment and develop resistance in the pathogen \nwhich later becomes difficult to control. Today's approach is to control \nthese diseases by using biological control agents (BCA). BCA are the \norganisms that suppress the pathogen or it's activity is referred to as BCA. \nAdopting biological control is potent mean of minimizing the damage \ncaused by pathogen and environmental safety. The species of Trichoderma \nis globally accepted for their bio control ability in addition to plant growth \npromotion and development (Vinale et al., 2014). Trichoderma (BCA) is a \ngenus of fungi that are present sufficiently in the soils. They are fast \ngrowing, highly adoptable fungi that form symbiotic relationships with \nplant root making them suitable to use to control phytopathogens. The bio \ncontrol potential in Trichoderma species is only due to their complex \ninteraction with phyto pathogens either by parasitizing them, secreting \nantibiotics or by competing for space and nutrients availability. Important \nSpecies of Trichoderma are; Trichoderma viridae, Trichoderma harzianum, \nTrichoderma virens, Trichoderma asperellum etc. are being used as bio \npesticide to control plant disease.  \n\n\n\n2. CHARACTERISTICS OF TRICHODERMA\n\n\n\nTrichoderma spp. are mostly found wherever decaying plant materials are \navailable and are mainly of cellulosic materials. Trichoderma spp. are \nmainly characterized by branched conidiophore bearing bright green \nconidia as in Figure 1. According to a study, the light green color of conidia \nof T. harzianum are globose to subglobose (Shah et al., 2012).  \n\n\n\nFigure 1: (a) Trichoderma harzianum; (b) Trichoderma viride (Viewed in \nUniversity de Bretagne Occidentale website) \n\n\n\n3. TRICHODERMA AS BIOFERTILIZER\n\n\n\nUsing Trichoderma spp as bio fertilizer is very excellent amendments to \nboost crop production alternative to the chemical fertilizers. Trichoderma \nhas inborn potential to enhance/ stimulate the plant growth in different \nways, so it is used as bio fertilizer in many agriculturally important crops. \nIt minimizes the employment of traditional chemical-based fertilizers and \nalso improve the uptake of micronutrients to plants, solubilization of (P) \nphosphate in soil and make available to plants. \n\n\n\nTrichoderma as a biofertilizer was reported high to be utilized in vegetable \nproduction process and was found most effective in tomato. However, a \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 97-100 \n\n\n\nCite The Article: Sambed kumar Chaube, Saugat Pandey (2022). Trichoderma: A Valuable Multipurpose Fungus for Sustainable Agriculture. \nMalaysian Journal of Sustainable Agricultures, 6(2): 97-100. \n\n\n\npositive response was also recorded from other crops like as cauliflower, \nonion, peas, tobacco, Bengal gram, Potato, soya bean and sunflower etc. \nMany attributes are there that qualify it to be used as an alternative to \nenhance fertilization and agriculture sustainability. Among these some \nare: - \n\n\n\n3.1  Macro and Micronutrient Uptake \n\n\n\nThe presence of Trichoderma within the rhizosphere accelerates the \nnutrient uptake and availability. This is the most important attribute \nwhich indicate that the presence of Trichoderma in the root zone helps to \nconvert the unavailable form of nutrients to the available form and hence \nimprove the nutrient uptake. It is reported that in Acidic soil the applied \nchemical fertilizers won't be available in sufficient quantity to the crop. \nThis is because of conversion into unavailable form and forming complex \n(Aluminum complexes) that can be even more toxic to the plant.  The \nadditive advantage is that   Trichoderma colonizes the rhizosphere and \nenable the crops to take more from the soil. It is found that sugarcane crop \ninoculated with Trichoderma viridae gained more Nitrogen, Phosphorus, \nPotassium and organic Carbon content. \n\n\n\n3.2 Phosphate Solubilization \n\n\n\nPhosphorus is the most crucial nutrient that the crop needs for its growth \nand development. Our soil has phosphorus content but is in unavailable \nform to the crops. Acidic soil restricts the availability of phosphorus by \nbinding it and forming complexes. At this case Trichoderma spp. only can \nmediate the process by solubilizing and converting them back into \navailable form for crop production. Phytase enzyme produced by some \nspecies of trichoderma is responsible to conversion of unavailable \nphosphorus into plant available form. \n\n\n\n3.3 Production of Phytohormones and Volatile compound \n\n\n\nPhytohormones are responsible for root and shoot development of plant. \nThe growth promotion found to be assisted by auxin production by the \nfungus, which lowers down the high levels of ethylene that gets \naccumulated during various stresses. In rhizosphere the presence of \nTrichoderma enhances the production of Phytohormones such as Auxin, \nIAA and gibberellic acid which ultimately promote growth of plants. \nTrichoderma also enhance germination percentage and improves seedling \nvigor, which is an advantage for the crop. \n\n\n\n3.4  Enhance Greater Root System and Boost Up Plant Health.  Also Provides More Niches for Growth of The Fungus. \n\n\n\nTable 1: Trichoderma as bio fertilizer and mode of application \n\n\n\nBio fertilizer Crops Method of application Yield \n\n\n\nTrichoderma asperellum Rice Seed Inoculation Increased by 30% \n\n\n\nT. harzianum Mustard and Tomato Inoculation in Soil \n50% Nitrogen and Trichoderma enriched increased yield by 108 \n\n\n\n& 203% over control \n\n\n\nCucumber Inoculation in Soil N/A but improved fruit quality and crop growth \n\n\n\nChilli Inoculation in Soil Increase in yield by 11 quintal/ha, than that of control (58 q/ha) \n\n\n\nBarley Seed Inoculation Increase in yield by 17% \n\n\n\nT. viridae Wheat \nSoil and Seed \nInoculation \n\n\n\n75.8% with NPK and by 41.8% with well rotten FYM. \n\n\n\nPotato Inoculation in Soil \n16.25 tubers/plant than that of  control which was 2.25 tubers/ \n\n\n\nplant \n\n\n\nRed Beet Cabbage Seed Inoculation Increased by 29% \n\n\n\n4. TRICHODERMA AS BIOAGENT/FUNGICIDE\n\n\n\nAgriculture is the crucial part of any country to feed huge populations. \nHowever, agriculturally important crops are attacked by several \npathogens which in turn results serious yield reductions threatening \nglobal security. In order to control these pathogens, amplified use of \nsynthetic chemicals occurred which have caused negative impact on \nenvironmental quality and resulted in increasing trend of many living \nforms which are resistant with the chemicals. Concerning these \nthreatening issues, the search of non-chemical alternatives has been \nfocused which result in emergence of Bio control agent as vital component \nof plant disease management. Trichoderma spp. naturally free-living \nfungus and cosmopolitan in distribution that are found abundantly in soil, \ndecaying organic matter. In early 1930s, Trichoderma first reported as \nbiocontrol agents for the control of root rot causing Amrillaria mellae in \ncitrus. \n\n\n\nAlmost 20 species of the genus Trichoderma act as bio agents against \nAlmost 20 species of the genus Trichoderma act as bio agents against \n\n\n\nmost of soil-borne pathogen as well as foliar plant pathogens. T. \nharzianum, T. koningii, T. viride, T. atroviride, T. pseudokoningii, T. \n\n\n\nlongibrachiatum, T. hamatum & T. reesei are the most preferred species, \nwhich act as potential antagonists (Monaco et al., 1991). \n\n\n\n4.1  Mechanism of Pathogen Control/ Bio control \n\n\n\nTrichoderma have potential to compete for the nutrient and ecological \nniche. Some of the major biological mechanisms that are involved \nindependently or together in antagonistic activity against phyto pathogen \nare given below: \n\n\n\n4.1  Mycoparasitism/Hyperparasitism \n\n\n\nMycoparasitism refer to the parasitic interaction between two fungi in \nwhich one fungus (Host) is parasitized by the mycelia of other fungus \n(Parasite). Mycoparasitism is the common mechanism shown by \nTrichoderma spp. After identifying the host, Trichoderma hyphae attach to \nthe host hyphae by twisting and then penetrate host cell wall through \nsecretion of cell wall degrading enzymes and take nutrients from host and \nuse it for growing. In a study, it was reported that some strains of T. \nharzianum have capicity to parasitize on nematodes and their egg masses, \nwhere it coiled around the second stage juveniles of Nematode and then \npenetrated them by forming haustoria like structures and disable them \n(Sahebani and Hadavi, 2008). \n\n\n\n4.2 Antibiosis \n\n\n\nAntibiosis states to the antagonistic association between two \nmicroorganisms, in which one is adversely affected by metabolites or \nantibiotics released by another. It is the important attributes for deciding \nthe saprophytic ability of the fungus. Antibiotics such as trichodermin, \nsuzukacillin and alamethicin produced by T. harzianum affect \nmorphological or physiological sequences leading to its successful \npenetration. Trichoderma can also produce a multitude of compounds that \nhave antagonistic properties including cell wall degrading enzymes like as \ncellulase, xylanase, pectinase, glucanase, lipase, amylase and protease, \nvolatile metabolites such as 6-npentyl-2H-pyran-2-one (6-PAP) etc. \n\n\n\n4.3 Competition \n\n\n\nThe most common cause of death of microorganisms is starvation, so the \ncompetition for macro, micro-nutrients and spaces, results in the \nbiocontrol of fungal phytopathogens. Trichoderma exhibits a better \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 97-100 \n\n\n\nCite The Article: Sambed kumar Chaube, Saugat Pandey (2022). Trichoderma: A Valuable Multipurpose Fungus for Sustainable Agriculture. \nMalaysian Journal of Sustainable Agricultures, 6(2): 97-100. \n\n\n\npotentiality of absorption and mobilization of nutrients from the limited \navailable substrate from environment comparative to other rhizospheric \nmicroorganisms; therefore, the bio control of fungal pathogens using \nTrichoderma involves the coordination of numerous strategies, such as the \ncompetition for nutrients and space which is considered among the most \nimportant one.  Thanasoulopolos found that T koningii as rhizosphere \ncompetent microbe, when tomato seed is treated with conidial suspension \nof T. koningii was sown, resulted in reduced damping-off pathogen \n(Thanasoulopolos, 2002). \n\n\n\n4.4 Bio Priming of Resistance Mechanism in Host Plant \n\n\n\nPlant- Pathogen interaction leads to develop wide range of defense \nmechanism in plant, which can prevent plant from attack by invading \npathogen. Among many, some of the bio-control strains of Trichoderma sp. \nare known to induce systemic resistance (ISR) and host plant defense \nagainst a variety of phytopathogen which is mediated by jasmonic acid \n(JA)/ethylene. The rhizosphere competent nature of Trichoderma species \npermit it to colonize roots, triggering the plant immune system and pre-\nactivation of the molecular mechanisms of defense against several potent \nphyto pathogens and the stress challenged situations. \n\n\n\nFigure 2: (A) Mycoparasitism confrontation assay; (B) Coiling of Trichoderma (T) hyphae around the phyto pthogen Rhizoctonia solani (R) (Image source- \nGoogle) \n\n\n\n5. TRICHODERMA SPP. IN BIOREMEDIATION\n\n\n\nAfter the green revolution there occurred blind application of chemical \nfertilizers and pesticides that contaminated and caused soil related \nconstraints. For the remediation scientists tried to use Trichoderma like \nmicroorganisms in soil and reported a surprising result that these \nmicroorganisms degraded the chemical contaminants present in the soil. \nMicrobes in Bioremediation and phytoremediation are excellent and \ninnovative technology that has the potential to remediate various soil \nrelated constraints. Trichoderma releases some enzymes that act upon \nchemicals and metal contaminants which ultimately improve the physical, \nchemical and biological properties of soil. Trichoderma not only degrade \nthe chemical contaminants but also make the nutrients available to plants \nfrom those chemicals\u2019 contaminants too.  \n\n\n\n6. DELIVERY (APPLICATION) METHOD OF TRICHODERMA\n\n\n\nFORMULATION \n\n\n\nSite specific delivery of the Trichoderma is very essential to enhance the \n\n\n\nefficiency. The most common and effective methods of application of \n\n\n\nTrichoderma are mentioned below: \n\n\n\n6.1 Seed Treatment \n\n\n\nMost effective method to prevent the soil borne pathogen and enhance the \ngermination of seed. The seed must be coated with the powder just before \nsowing and for that, the seed should be moistened by the molasses \nsolution (Enhance The stickiness) and Trichoderma powder is to be \nsprayed@ 10gm per Kg of seed. Mix the powder properly and spread in \nshade to dry. Propagules of bio control agents germinate over the surface \nof seed and colonize roots of germinated seedlings and rhizosphere. \n\n\n\n6.2 Seed Bio Priming \n\n\n\nIt is the process of treating the seed with Bioagents i.e. Trichoderma and \nthen incubating it in warm and moisten condition until just prior to the \nemergence of the radicle. It has potent advantage over the simple/normal \ncoating of the seed as it results in rapid and uniform emergence. \nBioprimed seed will be surrounded by a layer of Trichoderma conidia and \nsuch seed can tolerate adverse conditions. Bioprimed seed also show \nhigher germination percentage. \n\n\n\n6.3 Soil Treatment \n\n\n\nIn direct broadcasting, 300g of Trichoderma powder is mix uniformly in 6 \nkg of FYM now broadcast in irrigated land of area 1 hectare. Another \nfurrow application is highly effective mode of treatment in which root \ncrops like potato, ginger, turmeric are highly benefited. Here, uniform \nmixture of 300g+6FYM is applied at the time of earthing up or after 28-30 \ndays of planting. Trichoderma is capable of colonizing farmyard manure \n(FYM) and therefore the application of colonized FYM to the soil is more \nappropriate and is beneficial. It is the most suited method of application of \nTrichoderma particularly for the management of soil-borne diseases.    \n\n\n\n6.4 Cutting and Seedling Root Dip \n\n\n\nTrichoderma powder of about 10grams should mix with 100g of well \nrotten farmyard manure per liter of water and dipping the cuttings and \nseedling in Trichoderma suspension just before transplanting. Dipping in \nantagonist\u2019s suspension not only inhibits the disease severity but also \ninduce the seedling growth and development. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 97-100 \n\n\n\nCite The Article: Sambed kumar Chaube, Saugat Pandey (2022). Trichoderma: A Valuable Multipurpose Fungus for Sustainable Agriculture. \nMalaysian Journal of Sustainable Agricultures, 6(2): 97-100. \n\n\n\n6.5 Nursery Treatment \n\n\n\nFormulate a suspension by adding 250 gm in 50 liters of water then drench \nin nursery bed soil of area 400 sq.m. Application of FYM manures and \nneem cake before treatment induced the efficiency of the Trichoderma. \n\n\n\nFigure 3: Comparison among Trichoderma applied crop and not applied \ncrops. \n\n\n\n7. CONCLUSION\n\n\n\nTrichoderma spp are the boon for the agriculture world. In the disease \nprevention as well as nutrient availability, it has demonstrated best of its \npotential to be used as biofertilizer as well as bioagents. It has also shown \nits sustainability and various mechanisms of availing the crop with \nnutrients and disease prevention. Moreover, it has various control \nmechanisms for phyto pathogens, and gives it as additive advantage when \ncompared to other plant pathogen control mechanisms. Hence this can be \nthe best option for farmers to use for sustainable cropping, increase yields \nand quality of the produce to feed the global population. \n\n\n\n REFERENCES \n\n\n\nAbdullah, M.T., Ali, N.Y., Suleman, P., 2008. Bio control of Sclerotinia \nsclerotiorum, de Bary with Trichoderma harzianum & Bacillus \namyloliquefaciens. Crop Prot., 27, Pp. 1354-1359 \n\n\n\nAtanasova, L., Le-Crom, S., Gruber, S. Coulpier, F., SeidlSeiboth, V., Kubicek, \nC.P., and Druzhinina, I., 2013. Comparative transcriptomics show \ndifferent strategies of Trichoderma mycoparasitism. BMC Genomics, \n14, Pp. 121. \n\n\n\nBalakrishnan, S., Gomathi, D., Kalaiselvi, M., Ravikumar, G., Arulraj, C., and \nUma, C., 2012. Production and purification of chitinase by \n\n\n\nStreptomyces sp. from soil. J. of Advanced Scientific Research project, \n3 (3), Pp. 2-29. \n\n\n\nBhagat, S., Pan, S., 2010. Biological mgmt... of root and collar rot \n(Rhizoctonia solani) of French (Rajma) bean. Indian Journal Agric Sci., \n80 (1), Pp. 42\u201350.  \n\n\n\nBissett, J., 1984. A revision of the genus Trichoderma.I. Section Longi \nbrachiatum. \n\n\n\nCasas-Flores, S., Rios- Momberg, M., Rosales-Saavedra, T., Mart\u00ednez-\nHern\u00e1ndez, P., Olmedo-Monfil, V., Herrera-Estrella, A., 2006. Cross talk \nbetween fungal blue light perception system and the cyclic AMP \nsignaling pathway. Eukaryotic Cell, 5 (3), Pp. 499\u2013506. \n\n\n\nGardener, M.B.B., and Fraevel, D.R., 2002. Biological control of phyto \npathogens: Research, commercialization, and application in the \nUnited State of America. Online. Plant Health Progress, doi: \n10.1094/PHP2002-0510-01-RV. \n\n\n\nHarman, G.E., Howell, C.R., Viterbo, A., Chet, and Lorito, M., 2004. \nTrichoderma species-are Opportunistic, avirulent plant symbionts. \nNature Rev., 2, Pp. 43-56. \n\n\n\nHarman, G.E., Taylor, A.G., Stasz, T.E., 1989. is combining effective strains \nof Trichoderma harzianum and solid matrix priming to improve and \nenhance bio control seed treatment. Phytopathology, 73, Pp. 631\u2013637. \n\n\n\nKoul, A., 2011. Microbial bio pesticides: opportunities and challenges. CAB \nReviews: Perspectives in Agriculture, Nutrition and Natural \nResources, 6 (56), Pp. 1-26. \n\n\n\nNovy, V., Schimd, M., Eibinger, M., Petrasek, Z., 2016. The \nmicromorphology of Trichoderma reesei analyzed in cultivations on \nlactose and solid lignocellulosic substrate, and their relationship with \ncellulase production. Biotechnol for Biofuels 9. \nhttps://doi.org/10.1186/s13068-0160584-0 \n\n\n\nSankar, P., Jeyarajann, R., 1996. Biological control of sesamum root rot by \nbiological seed treatment with Trichoderma spp. and Bacillus subtilis. \nIndian Jo. Mycol. Plant Pathol., 26, Pp. 147-53. \n\n\n\nSharma, K.K., 2017. Induction of systemic resistance against sheath blight \nof rice caused by Rhizoctonia solani Kuhn using seed treatment with \nTrichoderma. Journal of Applied and Natural Science, 9 (3), Pp. 1861-\n1865.  \n\n\n\nSharma, K.K., 2017. Qualitative enzyme description and sclerotia \nparasitization by fungal antagonist i.e. Trichoderma. The Bioscan \n(Supplement on Plant Pathology), 11 (4), Pp. 2867-2872. \n\n\n\nSpiegel, Y., Chet, I., 1998. Estimation of the Trichoderma spp. as a biological \ncontrol agent against soil borne pathogen like fungi and plant parasitic \nnematodes. Integrated Pest Managemnt Revis., 3 (3), Pp. 169\u2013175. \n\n\n\nWeller, D.M., 1988. Biological control of soil borne phyto pathogens in the \nrhizosphere with bacteria. Annual Rev Phytopathol, 26, Pp. 379\u2013407. \n\n\n\nYadav, S.K., Dave, A., Sarkar, A., Singh, H.B., Sharmaa, B.K., 2013. Co \ninoculated bio priming with Trichoderma, Pseudomonas and \nRhizobium improves crop growth in Cicer arietinum and Phaseolus \nvulgaricus. Int. J. of Agric. Biol., 6 (2), Pp. 255-259. \n\n\n\nZehra, A., Dubey, M.K., Meena, M., Upadhyay, R.S., 2017. Effect of diff.  \nenvironment conditions on growth and sporulation of some \nTrichoderma species. J. of Environ Biol., 38, Pp. 197\u2013203. \n\n\n\nZeilinger, S., Oman, M., 2007. Trichoderma bio control: signal transduction \npathways involved in host sensing and mycoparasitism. Gene Regul \nSyst Bio., 1, Pp. 227\u2013234. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 40-43 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.40.43 \n\n\n\n \nCite the Article: Achille Dargaud Fofack  and Enow Asu Derick (2020). Evaluating The Bidirectional Nexus Between Climate Change And Agriculture From A  \n\n\n\nGlobal Perspective. Malaysian Journal of Sustainable Agriculture, 4(1): 40-43. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2020.40.43 \n\n\n\n\n\n\n\n \nEVALUATING THE BIDIRECTIONAL NEXUS BETWEEN CLIMATE CHANGE AND \nAGRICULTURE FROM A GLOBAL PERSPECTIVE \n \nAchille Dargaud Fofacka* and Enow Asu Derickb \n \naCyprus International University, Nicosia, Cyprus \nbDepartment of Plant Production Cyprus International University \n\n\n\n*Corresponding Author E-mail: adfofack.irlaem@gmail.com \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 18 December 2019 \nAccepted 23 January 2020 \nAvailable online 13 February 2020 \n\n\n\n\n\n\n\nUnderstanding the complex and dynamic nexus between climate change and agriculture has become crucial \n\n\n\nfor our civilization. Thus, this paper aims at estimating the impact of those two concepts on one another using \n\n\n\nworld data spanning from 1980 to 2018. On the one hand, the results show that the rising sea level inherent \n\n\n\nto climate change has a positive and significant impact on arable land and a negative and significant impact \n\n\n\non livestock production. It is also found that rising sea level and global temperature constitute significant \n\n\n\nobstacles to crop production while a surge in greenhouse gas emissions significantly boosts it. On the other \n\n\n\nhand, the paper reveals that livestock production significantly increases greenhouse gas emissions while \n\n\n\nagricultural activities \u2013crop production, livestock production and arable land\u2013 are found to have a negative \n\n\n\nand significant impact on global temperature. Finally, as agriculture is both a cause and a victim of climate \n\n\n\nchange, some adaptation (shift in farming timing, intercropping) and mitigation (carbon sequestration, \n\n\n\norganic farming) strategies are recommended. \n\n\n\n\n\n\n\nKEYWORDS \n\n\n\nGlobal warming, agricultural emissions, greenhouse gas, adaptation, mitigation. \n\n\n\n1. INTRODUCTION \n\n\n\nThe Earth is warmer than it should be due to a natural greenhouse effect \n\n\n\ntaking place within its atmosphere (IPCC, 2019). Over time, the emission \n\n\n\nof greenhouse gases (GHG) \u2013like carbon dioxide (CO2), nitrous oxide (N2O) \n\n\n\nand methane (CH4)\u2013 resulting from human activity have reinforced that \n\n\n\ngreenhouse effect and triggered a global dynamic environmental threat \n\n\n\nknown as climate change. The scientific literature abounds with robust \n\n\n\nevidence proving the existence of human-induced climate change \n\n\n\n(Backlund et al., 2019). In line with that literature, the Intergovernmental \n\n\n\nPanel on Climate Change (IPCC) estimates that on average, human \n\n\n\nactivities have led to an increase in global temperature equivalent to 1.0oC \n\n\n\nabove pre-industrial levels (IPCC, 2018). Furthermore, the panel argues \n\n\n\nthat global warming could reach 1.5oC between 2030 and 2052 if GHG \n\n\n\nemissions keep on increasing at the current pace. \n\n\n\nClimate change and agriculture are intrinsically connected because \n\n\n\nagricultural activities are contingent on climatic conditions (Ellis, 2015; \n\n\n\nRosegrant et al., 2008). Thus, number of studies argue and/or prove that \n\n\n\nby altering temperature, precipitation, sea level and the volume of CO2 in \n\n\n\nthe atmosphere among others, climate change negatively affects \n\n\n\nagricultural activities (Keane et al., 2009; Nelson et al., 2009; Ignaciuk, \n\n\n\n2015; OECD, 2016; IPCC, 2007). Changes in precipitation patterns for \n\n\n\ninstance, have substantial repercussions on water resources, irrigation \n\n\n\nmechanisms, droughts, insect outbreaks and forest fires. Meanwhile, \n\n\n\nchanges in atmospheric CO2 affect the expansion of the flora through its \n\n\n\nimpact on photosynthesis. \n\n\n\nAs highlighted in the literature, the effects of climate change on agriculture \n\n\n\nare not uniform across regions. Indeed, it is argued that upsurges in \n\n\n\ntemperature slightly improve yields in mid to high latitudes and depress \n\n\n\nthem in tropical and sub-tropical regions (Keane et al., 2009; Nelson et al., \n\n\n\n2009; Ignaciuk, 2015). Thus, in sub-Saharan Africa, South-East Asia and \n\n\n\nLatin America, climate change increases the exposure and vulnerability of \n\n\n\nfarmers to environmental disasters. For instance, it is projected that by \n\n\n\n2080, Africa will host up to 75% of the world population exposed to food \n\n\n\ninsecurity; while, a country like Guinea-Bissau is expected to lose almost a \n\n\n\nthird (32.7%) of its agricultural production. Overall, it is estimated that the \n\n\n\ndeveloping world needs some additional $7 billion worth of annual \n\n\n\ninvestment in rural infrastructure, research, and irrigation to offset the \n\n\n\nadverse effects of climate change on living conditions. \n\n\n\nThe nexus between climate change and agriculture becomes even more \n\n\n\ncomplex when taking into consideration the feedback effect of agriculture \n\n\n\non climate change. Indeed, agriculture substantially contributes to climate \n\n\n\nchange as enteric fermentation, forest conversion, rice cultivation, \n\n\n\nagrochemicals, livestock, and manure management are associated with \n\n\n\nGHG emissions, water, air and soil pollution (Bellarby et al., 2019; Paul et \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 40-43 \n\n\n\n\n\n\n\n \nCite the Article: Achille Dargaud Fofack  and Enow Asu Derick (2020). Evaluating The Bidirectional Nexus Between Climate Change And Agriculture From A  \n\n\n\nGlobal Perspective. Malaysian Journal of Sustainable Agriculture, 4(1): 40-43. \n \n\n\n\n\n\n\n\nal., 2009; Wreford et al., 2010; INTRACEN, 2008). Rosegrant et al. reveal \n\n\n\nthat agriculture accounted for 13% of global anthropogenic GHG \n\n\n\nemissions in 2000 while the OECD estimates that agriculture directly \n\n\n\naccounted for 17% of those emissions in 2015 (Keane et al., 2009; IPCC, \n\n\n\n2007). Moreover, the OECD reveals that agriculture was also indirectly \n\n\n\nresponsible for an additional 7 to 14% of global GHG emissions through \n\n\n\nland use changes (IPCC, 2007). \n\n\n\nIt should be noted that agriculture mainly releases N2O and CH4 in the \n\n\n\natmosphere and that it accounts for more than half of the global emission \n\n\n\nof those non-CO2 gases (Keane et al., 2009). The fact that the global \n\n\n\nwarming potential of those two gases is significantly higher than that of \n\n\n\nCO2 highlights the feedback effect of agriculture on climate change (IPCC, \n\n\n\n2007). The ITC estimates the GHG emissions associated with some \n\n\n\nagricultural activities and reveals that the production of a kg of beef is \n\n\n\nassociated with more than 10000 g of CO2 equivalent emissions (Prais et \n\n\n\nal., 2019). It is followed by pork, poultry and egg production (2000 - 3000 \n\n\n\ng of CO2 equivalents), milk production (1000 of CO2 equivalents) and plant \n\n\n\nproduction (500 g of CO2 equivalents). \n\n\n\nUnderstanding the complex and dynamic nexus between climate change \n\n\n\nand agriculture has become crucial for our civilization because we ought \n\n\n\nto provide enough food for the growing population without degrading \n\n\n\nfuture environmental prospects. Thus, this paper aims at estimating the \n\n\n\nimpact of those two concepts on one another using world data spanning \n\n\n\nfrom 1980 to 2018. The remainder of the paper is organized as follows: \n\n\n\nthe methodology is presented in the next section; the main findings are \n\n\n\nreported and discussed in section 3 and section 4 respectively; and section \n\n\n\n5 concludes the study with some recommendations. \n\n\n\n2. MATERIAL AND METHODS \n\n\n\nGlobal data related to climate change and agriculture are obtained from \n\n\n\nthe National Aeronautics and Space Administration (NASA) and the World \n\n\n\nBank. The data set covers each year from 1980 to 2018 and is composed \n\n\n\nof seven variables. As reported in table 1, arable land, crop production \n\n\n\nindex, and livestock production index account for agricultural activities \n\n\n\nwhile total GHG emissions, land-ocean temperature index, global mean sea \n\n\n\nlevel, and sea ice extent account for climate change. \n\n\n\nThe data are analyzed using the estimation method developed by \n\n\n\n(Cochrane et al., 1949). This generalized least squares method is suitable \n\n\n\nfor the estimation of parameters in a linear regression model in which \n\n\n\nerrors are assumed to follow a first-order autoregressive process. The \n\n\n\nmodel is transformed using the consistent estimates proposed by a \n\n\n\nresearcher and a search is performed for the value of the autoregressive \n\n\n\nparameter minimizing the sum of squared errors in the transformed \n\n\n\nmodel (White, 1980). Finally, robust standard errors are obtained using \n\n\n\nthe approach developed by a researcher (Fischer et al., 2002). \n\n\n\nTable 1: Description of variables \n\n\n\n Variables Definition Source \n\n\n\nA\ng\n\n\n\nri\ncu\n\n\n\nlt\nu\n\n\n\nre\n \n\n\n\nArable Arable land expressed as \npercentage of total land area \n\n\n\nWorld \nBank \n\n\n\nCrop Crop production index (2004-06 = \n100) \n\n\n\nWorld \nBank \n\n\n\nLivestock Livestock production index (2004-\n06 = 100) \n\n\n\nWorld \nBank \n\n\n\nC\nli\n\n\n\nm\na\n\n\n\nte\n C\n\n\n\nh\na\n\n\n\nn\ng\n\n\n\ne\n \n\n\n\nGhg Total greenhouse gas emissions (in \nKt of CO2 equivalent) \n\n\n\nWorld \nBank \n\n\n\nTemperature Land-ocean temperature index \n(1951-1980 base period) \n\n\n\nNASA \n\n\n\nSea Global mean sea level variations NASA \n\n\n\nIce Sea ice extent measures the area of \nocean containing some sea ice (in \nmillions of square Km) \n\n\n\nNASA \n\n\n\nTable 2 reports some descriptive statistics related to the data set while \n\n\n\nequation 1 and equation 2 represent the models used to estimate the \n\n\n\nimpact of climate change on agriculture and the impact of agriculture on \n\n\n\nclimate change respectively. \n\n\n\n\n\n\n\nTable 2: Characteristics of the variables \n\n\n\nVariables Obs. Mean Std. Dev. Min Max \n\n\n\nArable 39  10.755   0.145  10.313   10.991 \n\n\n\nCrop 39  88.653 24.689  52.133 129.447 \n\n\n\nLivestock 39  88.563 23.131  53.845 127.035 \n\n\n\nSea 39 -13.263 35.308 -69.509   51.890 \n\n\n\nTemperature 39    0.490   0.232    0.110     1.01 \n\n\n\nGhg 39 4.30 x 107 7,280,291 3.24 x 107 5.48 x 107 \n\n\n\nIce 39 6,107,627 1,141,376 3,404,543 7,862,303 \n\n\n\n\n\n\n\nAgriculturet =  \u03b1t +  \u03b21tGhgt + \u03b22tSeat + \u03b23tTemperaturet + \u03b24tIcet\n\n\n\n+ Trend\ud835\udc61 + \u03b5t    (1) \n\n\n\nWhere Agriculture stands for arable land, crop production or livestock \n\n\n\nproduction; t and Trend stand for time and time trend respectively; \u03b1 and \n\n\n\n\u03b2i (i = 1, 2, 3, 4) are parameters to be estimated; and \u03b5 is the error term. \n\n\n\n \nClimatet =  \u03b3t + \u03b41tArablet +  \u03b42t\ud835\udc36\ud835\udc5f\ud835\udc5c\ud835\udc5dt + \u03b43t\ud835\udc3f\ud835\udc56\ud835\udc63\ud835\udc52\ud835\udc60\ud835\udc61\ud835\udc5c\ud835\udc50\ud835\udc58t + Trend\ud835\udc61\n\n\n\n+ \u03bct                            (2) \n\n\n\nWhere Climate stands for total GHG emissions and land-ocean \n\n\n\ntemperature index; \u03b3 and \u03b4j (j = 1, 2, 3) are parameters to be estimated; \n\n\n\nand \u03bc is the error term. \n\n\n\n3. RESULTS \n\n\n\n\n\n\n\nTable 3: Unit root tests \n\n\n\n \nVariables \n\n\n\nADF PP NgP \n\n\n\nI TI I TI I TI \n\n\n\nLevel \n\n\n\nGhg -0.163 -3.780** 0.901 -3.164 1.173 -12.032 \n\n\n\nSea -3.959** -5.249** -6.849** -13.502** 1.025 -0.001 \n\n\n\nIce -0.243 -5.007** -1.913 -5.004** -2.284 -18.480** \n\n\n\nTemperature -1.560 -1.696 -1.856 -5.005** -0.690 -1.519 \n\n\n\nLand -3.117** -2.618 -3.117** -2.618 -0.284 -3.026 \n\n\n\nCrop -1.176 -2.993 -1.298 -3.128 -1.560 -13.069 \n\n\n\nLivestock -1.802 -0.415 -1.827 -0.415 -4.260 1.941 \n\n\n\nFirst difference \n\n\n\nGhg -5.908** -5.819** -10.788** -10.477** -17.695** -18.216** \n\n\n\nSea -13.386** -13.502** -14.233** -16.259** 0.402 0.036 \n\n\n\nIce -6.643** -6.532** -16.077** -15.846** -0.733 -1.979 \n\n\n\nTemperature -6.636** -6.833** -11.294** -10.956** -27.723** -0.135 \n\n\n\nLand -5.190** -5.558** -5.316** -5.653** -17.567** -17.906** \n\n\n\nCrop -7.847** -7.869** -8.200** -8.365** -18.175** -17.343** \n\n\n\nLivestock -5.626** -6.150** -5.669** -6.152** -18.321** -18.301** \n\n\n\nNotes: ** denotes significance at the 5 percent level; I stands for intercept and TI stands for trend and intercept. ADF, PP and NgP stand for augmented \nDickey-Fuller, Phillips-Perron and Ng-Perron unit root tests respectively. \n  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 40-43 \n\n\n\n\n\n\n\n \nCite the Article: Achille Dargaud Fofack  and Enow Asu Derick (2020). Evaluating The Bidirectional Nexus Between Climate Change And Agriculture From A  \n\n\n\nGlobal Perspective. Malaysian Journal of Sustainable Agriculture, 4(1): 40-43. \n \n\n\n\n\n\n\n\nTo avoid spurious regressions or misleading statistical evidence \n\n\n\ndescribing the relationship between climate change and agriculture, the \n\n\n\nstationarity of the variables is tested using the augmented Dickey-Fuller, \n\n\n\nPhillips-Perron and Ng-Perron unit root tests. The outputs of those tests \n\n\n\nreported in table 3 reveal that the data are all stationary at first difference. \n\n\n\nThus, equation 1 is estimated on the first difference of the data using Prais-\n\n\n\nWinsten regressions with Cochrane-Orcutt consistent estimates, \n\n\n\nminimization of the sum of squared errors in the transformed models and \n\n\n\nrobust standard errors. \n\n\n\n\n\n\n\nTable 4: Estimated impact of climate change on agriculture \n\n\n\nVariables Arable Crop Livestock \n\n\n\nGhg  0.001 (0.021)  0.131 (0.068)*  0.033 (0.074) \nSea  0.031 (0.010)*** -0.060 (0.020)*** -0.038 (0.017)** \n\n\n\nIce -0.007 (0.005)  0.028 (0.021) -0.005 (0.016) \n\n\n\nTemperature -0.001 (0.002) -0.049 (0.008)*** -0.018 (0.012) \nTrend  0.001 (0.001) -0.001 (0.001) -0.001 (0.000)*** \n\n\n\nConstant -0.002 (0.002)  0.041 (0.009)***  0.039 (0.006)*** \nObservations  37  37  37 \n\n\n\nR-squared  0.381  0.517  0.419 \nF-statistic  4.05**  7.40**  5.51** \n\n\n\nNotes: * denotes significance at the 10 percent level; ** denotes \nsignificance at the 5 percent level; *** denotes significance at the 1 percent \nlevel. \n\n\n\nTable 4 reporting the estimated impact of climate change on agriculture \n\n\n\nreveals that GHG emissions and sea level both have a positive impact on \n\n\n\narable land. The impact of GHG emissions is insignificant while that of sea \n\n\n\nlevel is significant. Furthermore, it is found that land-ocean temperature \n\n\n\nand sea ice extent both have a negative and insignificant impact on arable \n\n\n\nland. \n\n\n\nTable 4 also shows that land-ocean temperature and sea level both have a \n\n\n\nnegative and significant impact on crop production while GHG emissions \n\n\n\nand sea ice extent both have a positive impact on crop production. The \n\n\n\nimpact of GHG emissions is found to be significant while that of sea ice \n\n\n\nextent is insignificant. Besides, it is found that sea level, land-ocean \n\n\n\ntemperature and sea ice extent negatively affect livestock production; with \n\n\n\nthe impact of sea level being significant. Finally, it is found that GHG \n\n\n\nemissions have a positive and insignificant impact on livestock production. \n\n\n\n\n\n\n\nTable 5: Estimated impact of agriculture on climate change \nVariables GHG Temperature \nCrop -0.028 (0.212) -8.351 (1.834)*** \nArable  0.495 (1.028) -16.555 (7.546)** \nLivestock  0.548 (0.239)** -9.239 (3.534)** \nTrend - -0.005 (0.003) \nConstant -  0.574 (0.154)*** \nObservations  37  37 \nR-squared  0.140  0.458 \nF-statistic  3.61**  10.09** \n\n\n\nNotes: * denotes significance at the 10 percent level; ** denotes \nsignificance at the 5 percent level; *** denotes significance at the 1 percent \nlevel. \n\n\n\nTable 5 reporting the estimated impact of agricultural activities on climate \n\n\n\nchange shows that arable land and crop production do not significantly \n\n\n\naffect global GHG emissions. Contrarily, livestock production significantly \n\n\n\ncontributes to global GHG emissions. The table also reveals that livestock \n\n\n\nproduction, arable land and crop production significantly reduce land-\n\n\n\nocean temperature. \n\n\n\n4. DISCUSSIONS \n\n\n\nThe results reported in table 4 shows that sea level is the only climate \n\n\n\nchange parameter having a significant impact on arable land. It is found \n\n\n\nthat an increase in sea level induces an increase in arable land as higher \n\n\n\nsea level could irrigate more lands and make them more conducive for \n\n\n\nagriculture. \n\n\n\nIt is also found that GHG emissions are positively and significantly \n\n\n\nassociated with crop production. Indeed, as argued by a researcher, the \n\n\n\nvolume of CO2 in the atmosphere which is a fundamental input for \n\n\n\nphotosynthesis affects the growth of crop plants (Nelson et al., 2009). \n\n\n\nThus, an increase in GHG emissions leading to more CO2 in the atmosphere \n\n\n\ncould well boost crop production. As for sea level and temperature, they \n\n\n\nare found to be associated with a fall in crop production. Indeed, the \n\n\n\nformer could flood cultures while the latter could lead to droughts, forest \n\n\n\nfires, insects and diseases outbreaks (Nelson et al., 2009; Prais et al., \n\n\n\n2019). Moreover, these findings are in line with several studies who argue \n\n\n\nthat climate change will lead to a fall in the yield of cereal and horticultural \n\n\n\ncrops respectively (Rashid et al., 2010; Backlund et al., 2008). \n\n\n\nFocusing on livestock production, it is found that sea level is the only \n\n\n\nclimate change parameter exhibiting a significant impact. Indeed, a rising \n\n\n\nsea level is associated with a fall in livestock production. This could be due \n\n\n\nto the fact that an increase in sea level could inundate farms and create an \n\n\n\nenvironment suitable for insect and disease outbreaks. Everything being \n\n\n\nequal, the proliferation of pathogens and parasites will negatively affect \n\n\n\nlivestock production. \n\n\n\nPaying attention to the impact of agriculture on climate change, table 5 \n\n\n\nreveals that livestock production significantly increases GHG emissions. \n\n\n\nThis finding is in line with the abundant literature highlighting the \n\n\n\nsubstantial contribution of livestock to GHG emissions. The IPCC even \n\n\n\nreveals that livestock is responsible for about a third of global \n\n\n\nanthropogenic emissions of CH4. \n\n\n\nTable 5 also reveals that crop production and arable land have a negative \n\n\n\nand significant impact on temperature. This is in line with the well-known \n\n\n\nheat island effect according to which a built up area is often significantly \n\n\n\nwarmer than its surrounding rural neighborhood because vegetation has \n\n\n\na natural cooling effect (Dinesh, 2019). Finally, it is found that livestock \n\n\n\nproduction also has a negative and significant impact on temperature. This \n\n\n\ncounter-intuitive finding might be due to the fact that livestock production \n\n\n\nusually takes place in rural area and is often associated with crop \n\n\n\nproduction for animal feed. \n\n\n\nIn sum, agriculture is both suffering from the consequences of climate \n\n\n\nchange and reinforcing climate change through GHG emissions. Thus, \n\n\n\nagriculture is both a part of the problem and a credible solution to climate \n\n\n\nchange. This inherent duality of agriculture gives rise to two different \n\n\n\npolicy responses to climate change, namely adaptation and mitigation. As \n\n\n\ndefined by the IPCC, adaptation recommends to adjusting ecological, social \n\n\n\nand economic systems in order to take advantage of the positive effects of \n\n\n\nclimate change on agricultural activities and/or minimize the negative \n\n\n\nones. As for mitigation, it consists to reduce the impact of climate change \n\n\n\nby cutting down GHG emissions and/or by boosting carbon sinks. These \n\n\n\ntwo policy approaches are those upon which are built the \n\n\n\nrecommendations formulated in this paper. \n\n\n\n5. CONCLUSION \n\n\n\nOn the adaptation front, a wide range of on-farm measures could be \n\n\n\nimplemented in agriculture. For instance, farmers should choose crops \n\n\n\nand varieties that are suitable to the shifts in growing season, temperature, \n\n\n\nand precipitation induced by climate change. They should also adjust the \n\n\n\ntiming of planting, treatment, and sowing operations to suit current \n\n\n\nweather conditions. Finally, farmers should preserve landscapes \n\n\n\nproviding shelter to animal, improve the ventilation system of livestock \n\n\n\nshelters and invest in efficient irrigation, water storage and recycling \n\n\n\nsystems. \n\n\n\nDinesh describes some successful on-farm adaptation strategies \n\n\n\nimplemented around the world. Among others, he talks about coffee-\n\n\n\nbanana inter-cropping implemented in Rwanda, Burundi and Uganda (UN, \n\n\n\n2015). He reveals that this measure is effective in adapting to the rising \n\n\n\ntemperatures which negatively affect coffee production in those countries. \n\n\n\nIndeed, compared to mono-cropping, it is found that the combination of \n\n\n\nthose two crops can lead to a 50% increase in income. \n\n\n\nIn spite of the appealing nature of on-farm adaptation strategies, their \n\n\n\neffectiveness is often limited by market failures, access to information, \n\n\n\naccess to credit, and harmful subsidies. Thus, public authorities are invited \n\n\n\nto incentivize those strategies through the adoption of suitable policies. \n\n\n\nOn the mitigation front, the Paris Agreement reached in 2015 under the \n\n\n\nUnited Nations Framework Convention on Climate Change dealing with \n\n\n\nGHG emissions, mitigation, adaptation, and finance acknowledges the \n\n\n\nactive role of agriculture in the reduction of GHG emissions. Indeed, \n\n\n\nagriculture can help mitigate climate change through carbon \n\n\n\nsequestration and on-farm GHG emissions reduction. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 40-43 \n\n\n\n\n\n\n\n \nCite the Article: Achille Dargaud Fofack  and Enow Asu Derick (2020). Evaluating The Bidirectional Nexus Between Climate Change And Agriculture From A  \n\n\n\nGlobal Perspective. Malaysian Journal of Sustainable Agriculture, 4(1): 40-43. \n \n\n\n\n\n\n\n\nCarbon sequestration is a mitigation strategy that consists to boost and \n\n\n\nprotect carbon sinks. Non-tillage agriculture is one method through which \n\n\n\ncarbon sequestration can be implemented. As revealed by, in the absence \n\n\n\nof tillage, soil carbon is not released and agriculture helps reduce GHG \n\n\n\nemissions. Technically, in non-tillage agriculture, seeds are often sowed \n\n\n\ninto the residues of the previous crops and weeds are eliminated with \n\n\n\nherbicides. This mitigation strategy has been implemented on a large scale \n\n\n\n(17 million hectares) in Argentina but its environmental outcomes are \n\n\n\ndubious. \n\n\n\nAs for on-farm GHG emissions reduction, organic farming appears to be \n\n\n\nthe most sustainable strategy. Indeed, as argued by previous researcher, \n\n\n\nthe global warming ability of organic farming is substantially below that of \n\n\n\nconventional farming. The ITC argues that organic farming is a symbiosis \n\n\n\nof low external input, recycling mechanisms, and high output boosting soil \n\n\n\nfertility and making soils less vulnerable to erosion. Thus, under weather \n\n\n\nconditions characterized by high water stress, organic plants outperform \n\n\n\nconventional ones for each crop area as well as for each harvested crop \n\n\n\nunit. Furthermore, it is found that by being self-sufficient in nitrogen, \n\n\n\norganic farming releases less N2O in the atmosphere; and by focusing on \n\n\n\nanimal longevity, organic cattle husbandry is associated with less CH4 \n\n\n\nemissions. However, as in the case of adaptation strategies, mitigation \n\n\n\nstrategies and especially organic farming have to be incentivized by public \n\n\n\nauthorities. \n\n\n\nGiven that the main limitation of this paper is related to the nature of the \n\n\n\ndata used (aggregate world data), future studies should preferably be \n\n\n\ndone with data collected at the level of an ecosystem, a climatic zone or a \n\n\n\ncountry. The studies should also take into consideration some control \n\n\n\nvariables related to that specific ecosystem, climatic zone or country. \n\n\n\nFinally, future researches on the nexus between climate change and \n\n\n\nagriculture in developing countries should pay a particular attention to the \n\n\n\nquality of governance. \n\n\n\nREFERENCES \n\n\n\n2019. Intergovernmental Panel on Climate Change, Climate change: the \n\n\n\nIPCC scientific assessment, report prepared for IPCC by Working Group \n\n\n\n1. \n\n\n\nBacklund, P., Janetos, A., Schimel, D., Hatfield, J., Boote, K., Fay, P., Hahn, L., \n\n\n\nIzaurralde, C., Kimball, B.A., Mader, T., Morgan, J., Ort, D., Polley, W., \n\n\n\nThomson, A., Wolfe, D., Ryan, M.G., Archer, S.R., Birdsey, R., Dahm, C., \n\n\n\nHeath, L., Hicke, J., Hollinger, D., Huxman, T., Okin, G., Oren, R., \n\n\n\nRanderson, J., Schlesinger, W., Lettenmaier, D., Major, D.,  Poff, L., \n\n\n\nRunning, S., Hansen, L., Inouye, D., Kelly, B.P., Meyerson, L., Peterson, B., \n\n\n\nShaw, R. The effects of climate change on agriculture, land resources, \n\n\n\nwater resources, and biodiversity in the United States, U.S. Climate \n\n\n\nChange Science Program and the Subcommittee on Global Change \n\n\n\nResearch, U.S. Department of Agriculture, Washington, DC., USA, 2008,  \n\n\n\nhttps://www.usda.gov/oce/climate_change/SAP4_3/CCSPFinalReport\n\n\n\n.pdf (accessed June 2019) \n\n\n\nBellarby, J., Foeried, B., Hastings, A., Smith, P. 2019. Cool farming: climate \n\n\n\nimpacts of agriculture and mitigation potential, Greenpeace, 2008,  \n\n\n\nhttp://www.greenpeace.org/international/press/reports/cool-\n\n\n\nfarming-full-report (accessed June 2019). \n\n\n\nCochrane, D., Orcutt, G.H. 1949. Application of Least Squares Regression to \n\n\n\nRelationships Containing Auto-Correlated Error Terms, J. Am. Sta. Ass. \n\n\n\n44, 32\u201361. \n\n\n\nDinesh, D. 2019. Agricultural practices and technologies to enhance food \n\n\n\nsecurity, resilience and productivity in a sustainable manner, CCAFS \n\n\n\nWorking Paper no. 146. Copenhagen, Denmark: CGIAR Research \n\n\n\nProgram on Climate Change, Agriculture and Food Security (CCAFS), \n\n\n\n2016, http://hdl.handle.net/10568/71050 (accessed June 2019) \n\n\n\nEllis, S. 2015. The changing climate for food and agriculture: a literature \n\n\n\nreview, Institute for Agriculture and Trade Policy, Minneapolis, \n\n\n\nMinnesota, 2008 European Commission, EU agriculture and climate \n\n\n\nchange, 2015, \n\n\n\nhttps://ec.europa.eu/agriculture/sites/agriculture/files/climate-\n\n\n\nchange/factsheet_en.pdf (accessed June 2019) \n\n\n\nFischer, G., Shah, M., Velthuizen, H. 2002. Climate change and agricultural \n\n\n\nvulnerability, a special report, prepared by the International Institute \n\n\n\nfor Applied Systems Analysis under United Nations Institutional \n\n\n\nContract Agreement No. 1113 on Climate Change and Agricultural \n\n\n\nVulnerability as a contribution to the World Summit on Sustainable \n\n\n\nDevelopment, Johannesburg 2002. \n\n\n\nIgnaciuk, A. (2015-06-05), Adapting Agriculture to Climate Change: A Role \n\n\n\nfor Public Policies, OECD Food, Agriculture and Fisheries Papers 85 \n\n\n\n(2015), OECD Publishing, Paris, \n\n\n\nhttp://dx.doi.org/10.1787/5js08hwvfnr4-en \n\n\n\nIntergovernmental Panel on Climate Change, Climate Change 2007: \n\n\n\nChapter 8 Agriculture, 2007,   https://www.ipcc.ch/pdf/assessment-\n\n\n\nreport/ar4/wg3/ar4-wg3-chapter8.pdf (accessed June 2019) \n\n\n\nIntergovernmental Panel on Climate Change, Global warming of 1.5\u00b0C: an \n\n\n\nIPCC Special Report on the impacts of global warming of 1.5\u00b0C above \n\n\n\npre-industrial levels and related global greenhouse gas emission \n\n\n\npathways, in the context of strengthening the global response to the \n\n\n\nthreat of climate change, sustainable development, and efforts to \n\n\n\neradicate poverty, summary for policymakers, 2018, \n\n\n\nhttps://www.ipcc.ch/site/assets/uploads/sites/2/2019/05/SR15_SP\n\n\n\nM_version_report_LR.pdf (accessed June 2019) \n\n\n\nInternational Trade Center, Organic farming and climate change, Technical \n\n\n\npaper, 2008, \n\n\n\nhttps://www.intracen.org/Organics/documents/Organic_Farming_an\n\n\n\nd_Climate_Change.pdf (accessed June 2019) \n\n\n\nKeane, J., Page, S., Kergna, A., Kennan, J. 2009. Climate change and \n\n\n\ndeveloping country agriculture: an overview of expected impacts, \n\n\n\nadaptation and mitigation challenges, and funding requirements, the \n\n\n\nInternational Centre for Trade and Sustainable Development, The \n\n\n\nInternational Food & Agricultural Trade Policy Council, 2. \n\n\n\nNelson, G.C., Rosegrant, M.W., Koo, J., Robertson, R., Sulser, T., Zhu, T., \n\n\n\nRingler, C.,  Msangi, S., Palazzo, A., Batka, M., Magalhaes, M., Valmonte-\n\n\n\nSantos, R., Ewing, M., Lee, D. 2009. Climate change: impact on \n\n\n\nagriculture and costs of adaptation, International Food Policy Research \n\n\n\nInstitute, Washington D.C. \n\n\n\nOECD. 2016. Agriculture and climate change: towards sustainable, \n\n\n\nproductive and climate-friendly agricultural systems, OECD Meeting of \n\n\n\nAgriculture Ministers Background Note, April. \n\n\n\nPaul, H., Semino, S., Lorch, A., Hessellund, B., Gura, S., Ernsting, A. 2009. \n\n\n\nAgriculture and climate change: Real problems, false solutions, \n\n\n\npreliminary version prepared for the Bonn Climate Change Talks, June \n\n\n\n2009, http://www.econexus.info/pdf/agriculture-climate-change-\n\n\n\njune-2009.pdf (accessed June 2019) \n\n\n\nPrais, S.J., Winsten, C.B. 2019. Trend estimators and serial correlation, \n\n\n\nWorking paper 383, Cowles Commission, 1954, \n\n\n\nhttp://cowles.econ.yale.edu/P/ccdp/st/s-0383.pdf. \n\n\n\nRashid, Z.A., Junid, S.A., Thani, S.K. Trees Cooling Effect on Surrounding Air \n\n\n\nTemperature Monitoring System: Implementation and Observation, I. J. \n\n\n\nof Simulation, 15, 70-77, https://doi.org/10.5013/IJSSST.a.15.02.10 \n\n\n\nRosegrant, M.W., Ewing, M., Yohe, G., Burton, I., Huq, S., Valmonte-Santos, \n\n\n\nR. 2008. Climate change and agriculture: threats and opportunities, \n\n\n\nFederal ministry of Economic Cooperation and Development. \n\n\n\nUnited Nations Framework Convention on Climate Change, Paris \n\n\n\nAgreement, 2015, \n\n\n\nhttps://unfccc.int/sites/default/files/english_paris_agreement.pdf \n\n\n\n(accessed June 2019) \n\n\n\nWhite, H.L. 1980. A heteroskedasticity-consistent covariance matrix \n\n\n\nestimator and a direct test for heteroskedasticity, Econometrica, 48, \n\n\n\n817\u2013838. \n\n\n\nWreford, A., Moran, D., Adger, N. 2010. Climate Change and Agriculture: \nImpacts, Adaptation and Mitigation, OECD Publishing, Paris, \n2010, https://doi.org/10.1787/9789264086876-en.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 12-14 \n\n\n\nCite the article: Galal A. El Toum, Yassin M. I. Dagash, Sami A. Mahagoub (2018). Nitrogen Use Efficiency Of Three Maize (Zea Mays L.) Cultivars .  \nMalaysian Journal of Sustainable Agriculture, 2(1) : 12-14. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ    \n\n\n\n   ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nNitrogen use efficiency is one of the key issues in farming and fertilization, it is defined as the amount of product \nproduced per unit of resource used. A split plot arrangement in randomized complete block design with four \nreplications was used to compare the yield, nitrogen use efficiency and some quality characters of three maize (Zea \nmays L.) cultivars. The analysis of variance revealed that both nitrogen and maize cultivars were significantly \ndifferences in yield, nitrogen use efficiency, crude protein and crude fibre content in both seasons. This study \nrevealed that improving nitrogen use efficiency can help in optimizing nitrogen use in maize.  \n\n\n\n KEYWORDS \n\n\n\nMaize cultivars, Nitrogen use efficiency, Zea mays, Crude protein, Crude fibre.\n\n\n\n1. INTRODUCTION \n\n\n\nMaize also known as Corn (Zea mays L.) is a grain crop that belongs to the \nfamily Poaceae. The origin of this crop remains unknown; however, many \nhistorians believe that maize was first domesticated in Mexico's Tehuacan \nvalley, then introduced to Africa by the Portuguese in the sixteenth \ncentury and has become Africa's most important staple food crop [1]. \nMaize is the most important cereal crop in the world after wheat and rice. \nIt has great yield potential and attained the leading position among cereals \nbased on production as well as productivity and that is why it is called \n\"queen of cereals\" [2]. Nitrogen use efficiency parameters are high under \nlow nitrogen levels and decrease with increasing nitrogen level. Decreased \nnitrogen use efficiency at high nitrogen is attributed to higher losses \nbecause the plant is unable to absorb all of nitrogen applied [3]. Maize is \nnitro positive and needs ample quantity of nitrogen to attain high yield. \nNitrogen deficiency is a key factor for limiting maize yield [4]. Low yield of \nmaize can be attributed to many constraints but NPK fertilizer application \nis one of the major factors [5]. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nA pot experiment was carried out during two consecutive summer seasons \n2013/2014 and 2014/2015 at the Demonstration Farm of the Faculty of \nAgricultural Sciences \u2013University of Dongola-Sudan (Latitude 19\u02da 11\u02dd N \nand Longitude 30\u02da 29\u02dd E). The Northern State occupies the distant \nnorthern part of the Sudan and is within the desert region of the Sudan \nwhich has extremely high temperature and radiation in summer and low \ntemperature in winter. In general, in Dongola rainfall is scarce and wind \nprevails from the north. A Split plot arrangement in randomized complete \nblock design with four replications was used to execute the experiment \nwhere the three cultivars assigned to the main plots and the four nitrogen \nlevels to the sub plots. Nitrogen levels )0, 43, 86and 129kg/ha  ( used for \nthe treatment were notified as N0, N1, N2 and N3, respectively. Three to \nfour seeds were sown per hole and then thinned to one plant per hole \nthree weeks after sowing in both seasons. The total grain yield was \ncalculated according to the following formula: Total grain yield (tons/ha) \n= grain weight(g)/m2 / 100 [6]. Nitrogen use efficiency calculated as \nfollows: NUE = grain yield/ actual amount of nitrogen added [7]. Seeds \ncrude protein and crude fibre contents were determined following the \nstandard methods of the Association of Official American Analytical \nChemists [8]. The organic nitrogen content was determined using the \nmicro-Kjeldahal method, and an estimate of the crude protein content was \nestimated by multiplying the organic nitrogen content by a factor of 6.25% \n[9]. Two different samples were analyzed in triplicate. The data were  \n\n\n\nsubmitted to standard procedure of analysis of variance, means were \nseparated using Duncan Multiple Range Test (DMRT) as described by [10]. \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1 Effect of Nitrogen on the Yield, Nitrogen Use Efficiency and Quality \n\n\n\nResults in table-1 and table-2 showed that nitrogen caused highly \nsignificant differences in the yield and yield efficiency in both seasons. \nMaize yield is high when responsive to nitrogen fertilizer [11]. Also, this \nresult was similar to those reported by a group researcher who all found \nthe same result [12-18]. Similarly, the result of this study indicated a \nhighly significant effect of nitrogen on nitrogen use efficiency (NUE) in \nboth seasons. Nitrogen use efficiency decreased significantly with the \nincrease of nitrogen rate. This could probably be attributed to the inability \nof plants to assimilate all of nitrogen taken up. Similar result was reported \nby a researcher who indicated that nitrogen use efficiency decreased with \nthe increase of nitrogen rate because the plants were unable to assimilate \nall of nitrogen taken up [3]. Furthermore, nitrogen caused highly \nsignificant differences in crude protein and crude fiber content in both \nseasons. Similar results were obtained by some scientist who all found the \nsame result [19-22]. The increase in crude protein due to nitrogen can be \nattributed to the fact that nitrogen often plays a great role in the synthesis \nof protein. \n\n\n\nTable 1: F-values for the yield, nitrogen use efficiency and quality of maize \ncultivars during the summer season 2013/2014 and 2014/2015 \n\n\n\nCharacters \nNitrogen Cultivars Interaction \n\n\n\n1st \nSeason \n\n\n\n2nd \nseason \n\n\n\n1st \nSeason \n\n\n\n2nd \nseason \n\n\n\n1st \nSeason \n\n\n\n2nd \nseason \n\n\n\nCrude \nprotein \n\n\n\n42.78** 27.03** 7.45* 15.56* 0.63 n.s 5.90* \n\n\n\nCrude fibre 46.53** 34.67** 3.14 * 9.11* 1.63 n.s 3.14 * \n\n\n\nYield 18.27** 14.23** 39.94** 31.11**  1.26 n.s 1.12 n.s \n\n\n\nNitrogen use \nefficiency \n\n\n\n31.16** 29.76** 24.37** 20.12** 5.94* 5.00* \n\n\n\n*significant at 5% level, ** significant at 1% level, ns: non- significant at\n5% level \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.01.2018.12.14 \n\n\n\nNITROGEN USE EFFICIENCY OF THREE MAIZE (ZEA MAYS L.) CULTIVARS \n\n\n\nGalal A. EL Toum1, Yassin M. I. Dagash2, Sami A. Mahagoub1 \n\n\n\n1 Department of Agronomy, College of Agric. Sciences, University of Dongola, Sudan \n2 Department of Agronomy, College of Agricultural Studies, University of Science and Technology, Sudan  \n*Corresponding author: galaleltoum1234@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited.\n\n\n\n\nmailto:galaleltoum1234@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 12-14 \n\n\n\nCite the article: Galal A. El Toum, Yassin M. I. Dagash, Sami A. Mahagoub (2018). Nitrogen Use Efficiency Of Three Maize (Zea Mays L.) Cultivars .  \nMalaysian Journal of Sustainable Agriculture, 2(1) : 12-14. \n\n\n\nTable 2: Effect of nitrogen on quality, yield and nitrogen use efficiency of maize during summer seasons 2013/2014 and 2014/2015. \n\n\n\nTreatment \n\n\n\nCharacters  \nCrude protein Crude fibre yield Nitrogen use efficiency \n\n\n\n1st Season 2nd season 1st Season 2nd season 1st Season 2nd season 1st Season 2nd season \n\n\n\nN0 10.23d 10.88d 4.08 d 3.60c 3.97c 2.93b - - \n\n\n\nN1 10.31 c 11.20c 4.10 c 3.94b 6.24a 4.94a 114.88a 145.20a \n\n\n\nN2 10.23 b 11.47b 4.12 b 4.05a 5.49b 4.85a 56.40b 63.81b \n\n\n\nN3 10.35 a 11.61a 4.14 a 4.14a 5.85ab 4.99a 38.68c 45.35c \n\n\n\nmean 10.31 11.29 4.11 4.48 5.39 4.43 69.99 84.79 \n\n\n\nLSD 00.02 00.14 0.10 0.09 0.23 0.11 05.22 08.46 \n\n\n\nSE 00.04 00.04 0.06 0.03 0.66 0.30 02.14 02.98 \n\n\n\nC.V 01.93 02.15 3.57 4.32 21.16 26.97 31.23 26.53 \n\n\n\nMeans followed by the same letters within each column for each treatment are not significantly different at 5% level of probability. \n\n\n\n3.2 Performance of Cultivars in Yield, Nitrogen Use Efficiency and \nQuality \n\n\n\nTable (3) shows that cultivars differ significantly in grains yield in both \nseasons.  Differences among maize cultivars with respect to yield have \nbeen reported by some researcher which they found the same result \n[23,24]. The variation in grain yield among cultivars could be attributed to \ndifferences in genetic makeup, environment and interaction between \nthese aspects. Also, the same cultivars show different nitrogen use \nefficiency when subjected to different levels of nitrogen in both seasons. \nThe differences in nitrogen use efficiency between the three cultivars of \nmaize may be due to the fact that improved cultivars usually have higher \nnutrient use efficiency than traditional cultivars. Similarly, the result of \nthis study indicated significant differences among cultivars of maize in \ncrude protein and crude fibre content in both seasons. This result was in \nline with that reported found significant differences in crude protein and \ncrude fibre content between maize cultivars [19,25].  \n\n\n\nTable 3: Performance of cultivars in quality, yield and nitrogen use \nefficiency of maize in summer season of 2013/2014 and 2014/2015.  \n\n\n\nTreatment \n\n\n\nCharacters  \n\n\n\nCrude \nprotein \n\n\n\nCrude fibre yield \nNitrogen use \n\n\n\nefficiency \n\n\n\n1st \nSeason \n\n\n\n2nd \nseason \n\n\n\n1st \nSeason \n\n\n\n2nd \nseason \n\n\n\n1st \nSeason \n\n\n\n2nd \nseason \n\n\n\n1st \nSeason \n\n\n\n2nd \nseason \n\n\n\nV1 10.33 a 10.73c 3.99 a 3.56b 6.07a 5.44a 63.26a 69.50a \n\n\n\nV2 10.19 b 11.42b 3.77 b 4.10a 3.79b 2.49c 28.95b 46.92b \n\n\n\nV3 10.38 a 11.70a 4.07 a 4.13a 6.31a 4.86b 56.51a 74.36a \n\n\n\nmean 10.30 11.28 3.94 3.93 5.39 4.26 49.57 63.59 \n\n\n\nLSD 00.15 00.18 00.10 0.24 0.22 0.05 06.70 10.26 \n\n\n\nSE 00.14 00.46 0.72 0.06 0.76 0.18 03.64 02.97 \n\n\n\nC.V 01.93 02.15 3.57 4.32 21.16 26.97 31.23 26.53 \n\n\n\nMeans followed by the same letters within each column for each treatment \nare not significantly different at 5% level of probability. \n\n\n\nLSD= least significant difference. SE\u00b1 = standard error. C.V% = coefficient \nof variation \n\n\n\n4. CONCLUSIONS \n\n\n\nIn summary, the results of our study revealed that maize requires low \nnitrogen fertilization to optimize yield and improved cultivars have higher \nnutrient use efficiency than traditional cultivars. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThis study was conducted in the Demonstration Farm of the Faculty of \nAgricultural Sciences, University of Dongola-Sudan. We gratefully thank \nour research team for their helps on data processing and suggestions to \nimprove experimental design. \n\n\n\nREFERENCES \n\n\n\n[1] FAO, 2005. Current world fertilizer trends and outlook. Rome. Special \nReport. \n\n\n\n[2] Turi, N.A, Shah, S.S, Ali, S, Rahman, H. Ali T, Sajjad, M. 2007. Genetic \nvariability for yield parameters in maize (Zea mays) Genotypes. Journal of \nAgriculture and Biological Sciences, 2 (4), 1-3. \n\n\n\n[3] Giambalvo, D., Ruisi, P., Miceli, G.D., Frenda, A.S., Amato, G. 2009. \nNitrogen use efficiency and nitrogen fertilizer recovery of durum wheat \ngenotypes as affected by inter specific competition. Agronomy Journal, \n102, 707-715. \n\n\n\n[4] Alvarez, R., Grigera, S. 2005. Analysis of soil fertility and management \neffects on yields of wheat and corn in the rolling pampa of Argentina. \nJournal of Agronomy and Crop Sciences, 191, 321\u2012329. \n\n\n\n[5] Witt, C., Pasuquin, J.M.C.A., Doberman, A. 2008. Site \u2013specific nutrients \nmanagements for maize in favorable tropical environments of Saia. Proc. \n5th Inter. Crop Sci. Cong., April 13-18, Jeju, Korea, pp:1-4. \n\n\n\n[6] Baada, A. A. A. 1995. Evaluation of some exotic and local maize (Zea \nmays L.) Genotypes.M.Sc. Thesis. University of Khartoum, Sudan. \n\n\n\n[7] Greenwood, D.J., Lemaire, G., Goose, G., Cruz, P., Draycott, A. 1989. \nApparent recovery of fertilizer nitrogen by vegetable crops. Soil Science. \nPlant Nutrition, 35, 367-381. \n\n\n\n[8] AOAC. 1990. Association of Official Analytical Chemist. Official \nMethods of Analysis, 15thcdn. Washington, DC: AOAC. \n\n\n\n[9] Sosulski, F.W., Imafidon, G. I. 1990. Amino acid composition and \nnitrogen to protein conversion factors for animal plant foods. Journal of \nAgricultural and Food Chemistry, 38, 1351 \u20131356. \n\n\n\n[10] Gomez, K.A., Gomez, A.A. 1984. Statistical Procedures for \nAgricultural Research. 3rd Edition. John Wiley. New York. \n\n\n\n[11] Moose, S., Below, F.E. 2008. Biotechnology approaches to improving \nmaize nitrogen use efficiency. In: Molecular genetics approaches to maize \nimprovement. Kriz, A.L. and B.A. Larkins (ends). Springer Berlin \nHeidelberg (Publisher).Volume 63. Part II. \n\n\n\n[12] Doberman, A.R. 2005. Nitrogen use efficiency \u2013 state of the art.\nAgronomy \u2013 Faculty Publications. Agronomy and Horticulture \nDepartment of Nebraska \u2013 Lincoln. \n\n\n\n[13] Badr, M.M., Authman, A.S. 2006.  Effect of plant density, organic \nmanure, bio and mineral nitrogen fertilizers on maize growth and yield \nand soil fertility. Journal of Agricultural Sciences, Moshtohor, 44 (1), 75\u2013\n88. \n[14] Bakhet, J.S., Ahmad, I.M., Tariq, H.A., Shafai, M. 2006. Response of \nmaize to planting methods and fertilizer. Journal of Agricultural and \nBiological Science, 1(3), 1\u201314. \n\n\n\n[15] Delibaltova, V., Kirchev, H., Sevov, A., Matev, A., Yordanova, N. 2010. \nGenotypic response of maize hybrids to different nitrogen applications\nunder climatic conditions of Plovdiv region. Balwois-Ohrid, Rep. of \nMacedonia. May 25: 29. \n\n\n\n13\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 12-14 \n\n\n\nCite the article: Galal A. El Toum, Yassin M. I. Dagash, Sami A. Mahagoub (2018). Nitrogen Use Efficiency Of Three Maize (Zea Mays L.) Cultivars .  \nMalaysian Journal of Sustainable Agriculture, 2(1) : 12-14. \n\n\n\n[16] Hammad, H.M., Ahmad, A., Khaliq, T., Farhad, W., Mubeen, M. 2011. \nOptimizing rate of nitrogen application for higher yield and quality in \nmaize under semiarid environment. Crop and Environment, 2 (1), 38\u201341. \n\n\n\n[17] Wasaya, A. 2011. Growth and yield response of maize (Zea mays L.) \nto nitrogen management and tillage practices. Ph.D. Department of \nAgronomy Faculty of Agriculture. Faisalabad. 203. \n\n\n\n[18] Khan, N.W., Ijaz, N.K., Khan, A. 2012. Integration of Nitrogen \nFertilizer and Herbicides for Efficient Weed Management In Maize Crop. \nSarhad Journal of Agriculture, 28 (3), 458-463. \n\n\n\n[19] Ayub, M., Ahmed, R., Nadeem, M.A., Ahmed, B., Khan, R.M.A. 2003. \nEffect of different levels of nitrogen and seed rate on growth, yield and \nquality of maize fodder. Pakistan Journal of Agricultural Sciences, 40, 140\u2013\n143. \n\n\n\n[20] Almodares, A., Jafarinia, M., Hadi, M.R. 2009. The effect of nitrogen \nfertilizer on chemical composition in Corn and Sweet Sorghum. American \n\u2013Eurasian Journal of Agriculture and Environment Science, 6 (4), 441\u2013446.\n\n\n\n[21] Nadeem, M.A. Z., Igbal, M. A., Mubeen, K., Ibrahim, M. 2009. Effect of \nnitrogen application on forage yield and quality of maize sown alone and \nin mixture with legumes. Pakistan Journal of Life Sociology Science, 7 (2), \n161-167. \n\n\n\n[22] Reddy, D.M., Bahnumurthy, V.B. 2010. Fodder grain, grain yield, \nnitrogen uptake and crude protein of forage maize as influenced by \ndifferent nitrogen management practices. International journal of \nBiological Research, 1, 69-71. \n\n\n\n[23] Ayub, M., Awan, T.H., Tanveer, A., Nadeem, M. A. 2001. Studies on \nfodder yield and quality of maize cultivars. Pakistan Journal of Agricultural \nEngineering and Veterinary sciences, 17 (1-2), 28-32. \n\n\n\n[24] Bertoia, L., Lopez, C., Burak, R. 2006. Biplot analysis of forage \ncombining ability in maize landraces. Crop Science, 46 (3), 1346-1353. \n\n\n\n[25] Altin, G.W., Hunter, R.B. 1994. Comparison of growth, forage yield \nand nutritional quality of diploid and autotetraploid maize synthesis. \nCanadian Journal of Plant Science, 64 (3), 593\u2013598. \n\n\n\n14\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 16-20 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.16.20 \n\n\n\nCite the Article: M.N. Amin, B.C. Kundub, M. Rahman, M.M. Rahman and M.M. Uddin (2021). Promising Early Planting and Stress-Tolerant Potato Genotypes For \nNorthern Bangladesh. Malaysian Journal of Sustainable Agriculture, 5(1): 16-20. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2021.16.20 \n\n\n\nPROMISING EARLY PLANTING AND STRESS-TOLERANT POTATO GENOTYPES FOR \nNORTHERN BANGLADESH \n\n\n\nM.N. Amina*, B.C. Kundub, M. Rahmana, M.M. Rahmana and M.M. Uddinc \n\n\n\na Scientific Officer, Breeder Seed Production Center, Bangladesh Agricultural Research Institute, Debiganj, Panchagarh-5020.      \nb Principal Scientific Officer, Tuber Crop Research Center, Bangladesh Agricultural Research Institute, Debiganj, Gazipur -1701 \nc Chief Scientific Officer, Breeder Seed Production Center, Bangladesh Agricultural Research Institute, Debiganj, Panchagarh-5020 \n*Corresponding author email:  nurul01141@yahoo.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 21 October 2020 \nAccepted 25 November 2020 \nAvailable online 07 December 2020\n\n\n\nPotato is the third major food crop in the world. In Northern Bangladesh, potato production outside the \n\n\n\nregular growing season can contribute to farmers\u2019 profit and prices can be very favorable as consumers\u2019 \n\n\n\ndemand for potatoes is greater than the decreased, off-season supply. However, potato production may be \n\n\n\nnegatively affected by increased pest and disease pressure and higher soil temperature.  We hypothesized \n\n\n\nthat some potato varieties would have smaller tuber yield reduction when they are grown outside the normal \n\n\n\nseason. The objective of this experiment was to find out promising genotypes for earlier cultivation prior to \n\n\n\nmid of November, cultivation in northern regions of Bangladesh. The trials, corresponding to very early, early, \n\n\n\nnormal and late growing seasons were planted using a randomized complete block design with three \n\n\n\nreplications. Germination percent, plant height, stems per hill, marketable tuber yield at 65 days, marketable \n\n\n\ntuber yield at 90 days were recorded. For yield and components of yield contributing characters Clone 13.17, \n\n\n\nBARI Alu 7(Diamant) and Arizona outperformed in all growing condition and had wider adaptability and \n\n\n\nstability of tuber yield based on Additive main effects and multiplicative interaction (AMMI).  \n\n\n\nKEYWORDS \n\n\n\nPotato, Growing season, Region, Yield, Stable.\n\n\n\n1. INTRODUCTION \n\n\n\nPotato is the number one vegetable crop, have higher economic returns \nthan cereals such as High Yielding Variety (HYV) rice.  Potato yields are \nstill comparatively low in Bangladesh. This is caused by numerous factors, \nthe most important of which include low levels of applied technology and \nmechanization, fragmentation of production plots, production under dry \nconditions with high summer temperatures, and limited use of certified \nseed material, used on only 10-12% of the planted area. In addition, potato \nyields in Bangladesh are very unstable and very susceptible to abiotic \nstresses.  Similar conditions limiting potato production were found in \nMontenegro (Jovovic et al.,2011). The appropriate selection of varieties \ncan help diminish the adverse impacts on production, especially the \nwater-air regime of soil, high air temperatures and short growing season \nfor potato cultivation (Jovovic et al., 2002). Potatoes for human \nconsumption must have acceptable organoleptic quality in order to meet \nconsumer demands.  The tubers should be nicely shaped with shallow \nbuds, and be healthy, strong and uniformly sized, Tuber skin and flesh \ncolor are not essential quality components, but significantly influence \ncustomers\u2019 interest. Variety productivity is a function of its ability to \nprovide stable high yields in different agro-ecological conditions.  \nTherefore, it is very important to develop varieties that will be able to \nprovide consistent high yields across a wide range of environmental \nconditions. Principal component analysis (PCA) can improve selection \nefficiency in crop improvement programs (Johnson and Wichern, 2007). \n\n\n\nThrough this method, fewer variables explaining variations among \nindividuals can be screened among various characters (Shimelis et al., \n2013). Principal component analysis helps to provide a three-dimensional \nrepresentation of genotype environment pattern which allows the \nresponse of each environment to be directly identified (Mahajan and \nPrasad, 1986). In addition, the AMMI model can be used to identify high \nquality and stable genotypes under different environmental conditions. \nAMMI combined with PCA can be used to systematically evaluate different \nvarieties of potatoes in different environments is conditions provide \nstable yields, either on a higher or lower level. When farmers in Northern \nBangladesh plant potatoes early, yields are lower than when they are \nplanted in the normal season. Higher temperatures restrict yield by \nreducing the partitioning of assimilates to tubels, Whether or not potato \nplant will form tubers depends largely on the minimum night temperature \nand not on the average daily temperature. Potatoes planted prior to mid-\nOctober typically have lower yields due to high temperatures.  However, \nfarmers will plant this early because higher prices for earlier potatoes can \noffset the lower yields.  Identifying suitable varieties for early planting \nfrom the existing genetic materials of Tuber Crop Research Center (TCRC) \nmay provide farmers with higher yields and help ensure food security in \nBangladesh. Therefore, the objective of this study was to investigate the \npotential of eleven potato genotypes and examine their reactions to the \nspecific conditions of Northern regions of Bangladesh, in order to identify \ngenotypes that high and stable yields. The second objective of this \nexperiment is expected to extend potato cultivation to non-traditional \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 16-20 \n\n\n\nCite the Article: M.N. Amin, B.C. Kundub, M. Rahman, M.M. Rahman and M.M. Uddin (2021). Promising Early Planting and Stress-Tolerant Potato Genotypes For \nNorthern Bangladesh. Malaysian Journal of Sustainable Agriculture, 5(1): 16-20. \n\n\n\nareas and seasons, thus bringing more area under potato cultivation in \nBangladesh. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nEleven genotypes of potato including two clones \u201813.17\u2019 and \u201813.19\u2019, six \nexotic cultivars \u2018Arizona\u2019, \u2018Deliared\u2019, \u2018Hind II\u2019, \u2018HZD1249\u2019, \u2018Ottawa\u2019, and \n\u2018Prada\u2019, two late blight tolerant cultivars \u2018Twinner\u2019 and \u2018Twister\u2019, and one \npopular local variety \u2018BARI Alu 7\u2019(Diamant) were evaluated at the Breeder \nSeed Production Center, Debiganj (26.107852\u00b0N,88.773642\u00b0E,36 masl) \nBangladesh during the 2019-2020 crop year.  Plots were 5 m-2 and were \nplanted on 60cm x 25cm centers using a Randomized complete block \ndesign with 3 replicates. The entire experiment was planted on each of \nfour planting dates: 22 September 2019; 07 October 2019, 20 November \n2019, and 31 December 2019. These planting dates are termed very early, \nearly, normal, and late, respectively. Fertilizers: urea,325 kg ha-1, \ntrisodium phosphate,320 kg ha-1, muriate of potash,250 kg ha-1, and \ngypsum,120 kg ha-1 were applied. Half of urea and full dose of other \nfertilizers were applied before planting the seed in furrows and mixed \nproperly with the soil. The rest amount of urea was applied as side \ndressing at 35 days after planting. Necessary intercultural operations \nwere done as per TCRC recommendation. Data recorded included % \nemergence, plant height, number of stems plant-1, number of tubers plant-\n\n\n\n1, weight of all the tubers of each plant weight of tuber plant-1 and tuber \nyield (t ha-1). Plots were evaluated for presence of mites \n(Polyphagotarsonemus latus), Black leg (Pectobacterium carotovorum \nsubsp. Carotovorum), Rhizoctonia (Rhizoctonia solani K\u00fchn.) and Late \nBlight (Phytophthora infestans) at 60 days after planting (vegetative \nstage). Data were analyzed using R statistical software using agricolae \npackage. AMMI utilize ordinary ANOVA to estimate the main effects i.e. \nadditive part and Principal Component Analysis (PCA) to estimate the \nnon-additive error which is not counted by the ANOVA. Temperature and \nrelative humidity data were obtained from temperature sensor (James et \nal., 1971) (Figure 1) located 3 meters above the ground within each plot. \nGrowing degree days (GDD) were calculated throughout the season for \neach planting date (Table 1) using the formula GDD = [(min T + max T)/2-\nTb]. Tb, the base temperature/or minimum threshold temperature, for \npotato is 4.5 (Narayan et al., 2014). Disease and insect scoring (at 60 Days \nafter planting) was done on the basis of disease progress curve and \npercent yield loss following (James et al., 1971). \n\n\n\nTable 1: Growing Degree Days (GDD), Temperature and Rainfall for \nfour different planting dates \n\n\n\nVery \nEarly \n\n\n\nEarly Normal Late \n\n\n\nGrowing \nDegree Days \n\n\n\n1672.57 1716.53 1479.32 1468.56 \n\n\n\nMin Temp 7.041 7.041 6.81 6.81 \nMax Temp 35.904 35.904 35.47 33.36 \nRainfall,mm 29.4mm 15.6mm 6mm 68.4 mm \n\n\n\nFigure 1: Max-Min Temperature and photoperiod during the potato \n\n\n\ngrowing period \n\n\n\n3. RESULTS \n\n\n\nAnalysis of variance indicated that there was significant genotypic \nvariance among genotypes for tuber yield. There was significant variation \nbetween genotypes, environments, E+(G\u00d7E), Environment(linear), and \nG\u00d7E(linear) mean sums of squares (Table 2). The normal planting date had \nthe highest tuber yield (42.44 t ha-1) followed by late season (26.26 t ha-1). \nAmong the eleven genotypes, the mean tuber yield of Arizona, 32.29 t ha-\n\n\n\n1, was significantly greater across planting dates and significantly greater \nthan most genotypes at the normal planting date (Table 3). Because of the \nsignificant variation in genotype x environment interaction, the additive \nmain effects and multiplicative interaction (AMMI) model was used to \nidentify genotypes with stable, marketable tuber yield across \nenvironments. The estimate of deviations from regression(S2d) implies \nthe degree of reliance which should be put to linear regression in \nexplanation of the data. If these values are significantly deviating from \nzero, the expected phenotype cannot be predicted satisfactorily. When, \ndeviations(S2d) are not significant the assumption may be drawn by the \njoint consideration of mean, yield and regression coefficient (bi) values \n(Finlay and Wilkinson, 1963 and Eberhart and Russell, 1966).  \n\n\n\nTable 2: Mean squares of combined analysis of variance of \nmarketable tuber yield(tha-1) of 11 genotypes evaluated across \n\n\n\nseasons \nParameters df Mean Sum of Square \nSeason (Planting) 3 6643.4*** \nRep (Planting) 8 39.6 \nGenotype 10 197.4*** \nPlanting: genotype 30 161.3*** \nResiduals (error) 80 36.8 \n\n\n\nTable 3: Mean and AMMI stability of marketable tubers (tha-1) of 11 \ngenotypes evaluated over four planting dates during the 2019-2020 \ncropping year. \n\n\n\nVariety \nPlanting Date Mean \n\n\n\nVery Early Normal Late Early \n\n\n\n13.17 11.43 38.98 26.1 20.11 24.16 \n\n\n\n13.19 6.971 43.6 22.32 24.7 24.4 \n\n\n\nArizona 8.443 59.6 36.36 24.77 32.29 \n\n\n\nDeliared 4.03 21.51 23.81 15.12 16.12 \n\n\n\nDiamant 6.339 37.22 23.56 18.48 21.4 \n\n\n\nHindII 8.2 55.69 21.65 20.53 26.52 \n\n\n\nHZD-\n1249 \n\n\n\n17.2 49.96 10.81 29.35 26.83 \n\n\n\nOttawa 8.863 43 35.47 16.7 26.01 \n\n\n\nPrada 8.879 37.64 15.41 27.49 22.36 \n\n\n\nTwinner 4.557 34 36.64 18.77 23.49 \n\n\n\nTwister 3.492 45.64 36.73 22.35 27.05 \n\n\n\nSite \nMeans \n\n\n\n8.037 42.44 26.26 21.67 24.6 \n\n\n\nSite \nindex \n\n\n\n11.54 2.544 3.745 7.605 6.358 \n\n\n\nTo generate a principal component of a biplot, genotype scores were \nmultiplied by a set of environment scores. A high PC1 value and a low PC2 \nvalue was associated with genotypic stability. Genotypes with PC1 scores \ngreater than zero were considered as high yielding, stable and genotypes \nwith PC1 scores less than zero were considered as low yielding, unstable \nyielding (Figure 2). Grouping of lines in 3D biplots had much more \nconformity for each principal component with the results from 3D biplot \nand showed more importance of the first principal component, which \njustifies much of total variance (Figure 2). The PCA revealed three main \nprincipal components representing 100 % of total variance among the 11 \ngenotypes of potato (Table 4). The genotypes Deliared (16.12 t ha-1) and \nTwinner (23.49 t ha-1) performed well in the late planting period and had \nbelow average response with nonsignificant bi values (0.517, 0.889) \n(Table 4).  Principal component scores, regression coefficient (bi) and \ndeviation from regression (S2di) of the individual genotypes are shown in \n(Table 4). However, deviation from regression line was found to be non-\nsignificant for all genotypes. Genotype Diamant had overall mean (21.4 \n\n\n\ntha-1) non-significant 'bi'(0.90) and 'S2di' value (0.086) and could be \npredicted as stable. In addition, the genotypes 13.17 had i.e., overall mean \n\n\n\n(24.16t/ha), non-significant 'bi'(0.812) and 'S2di' value (0.537) and\n\n\n\nArizona overall mean (32.29 t/ha), non-significant 'bi'(1.50) and 'S2di' \nvalue (1.93) also somewhat satisfy the parameters of stability (Table 4). \nThus, Diamant, Clone 13.17 and Arizona genotypes could also be predicted \nas stable genotypes. Black leg and Mite damage were a serious problem in \nearly planted potato crop. Most of the genotypes were susceptible to those \ndiseases (Table 5). In Normal and late planted crop, late blight and \n\n\n\n9:36:00\n\n\n\n10:04:48\n\n\n\n10:33:36\n\n\n\n11:02:24\n\n\n\n11:31:12\n\n\n\n12:00:00\n\n\n\n12:28:48\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\n23-Sep 23-Oct 23-Nov 23-Dec 23-Jan 23-Feb\n\n\n\nP\nh\n\n\n\no\nto\n\n\n\np\ner\n\n\n\nio\nd\n\n\n\n,h\nr:\n\n\n\nM\nin\n\n\n\n:S\nec\n\n\n\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\nre\n\n\n\n,\u2103\n\n\n\nDate\n\n\n\nMax-Min Temperature and photoperiodand  during the potato \ngrowing period\n\n\n\nMax Tem Min Tem Daylength\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 16-20 \n\n\n\nCite the Article: M.N. Amin, B.C. Kundub, M. Rahman, M.M. Rahman and M.M. Uddin (2021). Promising Early Planting and Stress-Tolerant Potato Genotypes For \nNorthern Bangladesh. Malaysian Journal of Sustainable Agriculture, 5(1): 16-20. \n\n\n\nrhizoctonia were a threat but schedule spray reduced the risk of damage. \nIn addition, Twister and Twinner were found tolerant to late blight disease \n(Table 5). Hence, these four genotypes can be recommended for general \n\n\n\ncultivation in the Northern region of Bangladesh after detailed and further \ntesting. \n\n\n\nTable 4: Environment wise performance along with stability parameters for marketable yield at 90 days in potato \n\n\n\nVariety PC1 PC2 PC3 s\u00b2d\u1d62 bi s\u00b2d\u1d62 AR SD \n\n\n\n13.17 -0.164 0.769 -1.117 0.537 0.812 2 3.56 2.28 \n\n\n\n13.19 0.780 0.053 0.958 3.736 1.038 4 3.69 1.85 \n\n\n\nArizona -0.182 -2.678 0.380 1.938 1.508 3 4.38 3.14 \n\n\n\nDeliared -1.540 2.445 -0.262 10.573 0.517 5 7.88 3.48 \n\n\n\nDiamant -0.162 0.462 -0.304 0.086 0.900 1 3.38 3.72 \n\n\n\nHindII 1.588 -2.073 -0.641 13.760 1.380 7 6.63 1.82 \n\n\n\nHZD-1249 3.574 0.736 -0.507 58.755 0.891 11 8.25 3.21 \n\n\n\nOttawa -1.561 -0.637 -1.389 14.504 1.043 8 5.06 1.69 \n\n\n\nPrada 1.614 1.589 1.288 17.881 0.777 9 7.69 1.92 \n\n\n\nTwinner -2.436 0.501 0.347 26.719 0.889 10 8.56 1.82 \n\n\n\nTwister -1.510 -1.166 1.247 11.531 1.245 6 6.38 2.68 \n\n\n\nEarly 1.430 1.918 2.106 \n\n\n\nLate -4.823 -0.565 0.058 \n\n\n\nNormal 2.219 -3.712 -0.181 \n\n\n\nVery 1.175 2.359 -1.983 \n\n\n\nPrincipal component (PC), Deviation from regression(S2di), Regression coefficient(bi), Kang\u2019s rank-sum (AR), average of sum of ranks (ASR), and standard \ndeviation (SD) \n\n\n\nTable 5: Disease Reactions in different potato planting period \n\n\n\nVariety \n\n\n\nVary early planting Early planting Normal planting Late planting \n\n\n\nMite Blackleg Mite Black leg Late blight Rhizoctonia Late blight Rhizoctonia \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \nIncidence, \n\n\n\n% \nSeverity, \n\n\n\n% \n\n\n\n13.17 9.67 0.33 0 0 7.14 0.33 0 0 0.67 0.33 0 0 1.33 0.67 0 0 \n\n\n\n13.19 19.58 1.66 0 0 16.67 1.33 0 0 0.33 0.23 0 0 1.00 0.33 0 0 \n\n\n\nArizona  14.33 1.00 6.33 0.67 13.53 0.66 3.33 0.67 0.33 0.13 0 0 0.66 0.24 0 0 \n\n\n\nDeliared  10.47 0.67 3.33 0.67 8.33 0.33 1.33 0.33 0.23 0.12 0 0 0.33 0.12 0 0 \n\n\n\nHind II 7.63 0.67 1.67 0.33 5.63 0.33 1.67 0.33 0.46 0.15 0 0 0.66 0.22 0.33 0.11 \n\n\n\nHZ06-\n1249 \n\n\n\n16.38 1.67 2.67 0.67 14.38 1.33 1.67 0.33 0.33 0.08 0 0 0.33 0.11 0 0 \n\n\n\nOttowa  7.76 0.67 1.33 0.33 4.76 0.33 1.00 0.33 0.11 0.14 0 0 1.67 0.66 0.33 0.14 \n\n\n\nPrada  7.69 0.97 1.00 0.33 4.33 0.33 0.67 0.33 0.33 0.12 0 0 1.00 0.33 0 0 \n\n\n\nTwinner  26.19 1.67 0.87 0.33 15.19 1.33 0.33 0.13 0 0 0 0 0 0 0 0 \n\n\n\nTwister  9.52 1.33 0.67 0.33 6.67 0.33 0 0 0 0 0 0 0 0 0 0 \n\n\n\nDiamant  20.25 1.33 0.67 0.33 20.25 1.33 0 0 0.67 0.33 0.33 0.13 1.0 0.33 \n\n\n\nFigure 2: AMMI 1 triplot for yield (ton ha-1) of 11 potato genotypes and \n\n\n\nfour planting dates using genotypic and environmental scores. (Where 1. \n\n\n\nClone 13.17; 2. Clone 13.19; 3. Arizona;  4. Deliared; 5. Diamant; 6. HindII; \n\n\n\n7. HZD1249; 8. Ottawa; 9. Prada; 10. Twinner; 11.Twister)\n\n\n\n4. DISCUSSION\n\n\n\nThere is a need for high and stable yielding potato varieties to meet \nincreased demand for food in Asia and Africa. As a result of changing agro-\nclimatic conditions in Bangladesh, suitable potato varieties will be \nrequired for planting early and to enable potatoes to be incorporated into \ndiversified cropping systems. These potato varieties will provide \nflexibility in planting and harvesting times without exerting additional \npressure on declining resources. The northern region, which is located at \nthe highest altitude of Bangladesh, proved to be the most suitable area for \nthe production of early and medium early varieties of potatoes (Byuro, \n2011). We found that temperature and precipitation in September had \nsignificant influence on declining tuber yield in the analyzed series of field \ntrials. Similar results were obtained by, who found that cultivars behaved \ndifferently under higher temperature and longer photoperiod, depending \non their earliness behavior. Barman et al., (2019) noted that potatoes \nsown during the first week of December had higher tuber yield and quality \nas compared to earlier or later sown potatoes (Table 4). Hancock et al., \n(2014) found that temperature has a clear effect on the assimilate \npartition in potato which helps haulm growth and inhibits tuber growth. \nFurther, most potato varieties required more than 800 GDDs to reach \nmaturity but a minimum of 70-90 days of favorable cool temperature is \nrequired for profitable yields. This Principal component (PC) was very \nimportant to select high yielding clones and parents for breeding program. \nIn the evaluation of diversity among potato cultivars using agro-\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 16-20 \n\n\n\nCite the Article: M.N. Amin, B.C. Kundub, M. Rahman, M.M. Rahman and M.M. Uddin (2021). Promising Early Planting and Stress-Tolerant Potato Genotypes For \nNorthern Bangladesh. Malaysian Journal of Sustainable Agriculture, 5(1): 16-20. \n\n\n\nmorphological and yield components we found maximum 61.9% of \nvariation was explained by the first principal component. Ahmadizadeh \nand Felenji (2011) observed that three components explained 80.1% of \nthe total variation among traits. The authors reported that the first PC was \nhighly correlated with yield, tuber weight, dry matter content and harvest \nindex.  \n\n\n\nFigure 3: AMMI 1 biplot of 11 potato genotypes evaluated in four \n\n\n\nplanting dates for marketable tubers(tha-1) at Debiganj, Panchagarh, \n\n\n\nBangladesh \n\n\n\nThe distance from the origin (0,0) is indicative of the amount of interaction \nthat was exhibited by genotypes either over environments or \nenvironments over genotypes, van Eeuwijk et al., (2002). We found that \nDiamant and Clone 13.17 had low genotype-by-environment interaction \nand close to the origin., proposed using the \u201cregression coefficient\u201d(bi) to \nevaluate genotype adaptability and  the \u201cregression deviation\u201d(S2di) to \nevaluate the stability, which indicates the probability of the genotypes to \ndeviations in the environment. In our results, Diamant, Clone 13.17 and \nArizona genotypes had high mean yields, regression coefficients equal to \none (bi = 1), and small regression deviation mean square (S2di= 0) and \nwould be predicted as stable genotypes. In a study in New York, Tai (1971) \nreported Katahdin as a stable variety using a similar regression model. \nHassanpanah and Hassan (2014)  found three superior clones with good \nyield and quality traits out of eighteen promising potato clones in Ardabil \nprovince of Iran. Further, Mukherjee  and Naskar, (2013) conducted an \nexperiment evaluating phenotypic stability of tuber yield in sweet potato \nand identified a suitable variety for Orissa State using a regression model. \nBased on our study, we recommend Diamant, Clone 13.17 and Arizona for \nearly planting in the northern region of Bangladesh using similar \nregression model. Those early planting potato varieties will explore \nsustainable cultivation practices, meet market demand and impact of \nintroducing potato on cereal-based cropping system. \n\n\n\n5. CONCLUSION \n\n\n\nThe variety Prada was adapted to early season planting while genotypes \nHind II, Twister and Ottawa were found suitable for the normal season. \nDiamant, Clone 13.17 and Arizona were selected for wide production as \nthey had stable and high mean marketable tuber yields across seasons.  \nEarly planting potato genotypes Diamant, Clone 13.17 and Arizona with a \npositive response to an expected range of temperature variation seems \npertinent to adapt to climate change conditions, and will contribute to food \nsecurity. Further evaluation of current varieties in different environments \nis necessary for more precise selection and will be useful for crop \nmodeling.  \n\n\n\nACKNOWLEDGMENTS \n\n\n\nI would also like to extend my thanks to Rebecca J. McGee, Ph.D., Research \nGeneticist, Grain Legume Genetics and Physiology Research, USDA \u2013 ARS, \nWashington State University, Pullman, Washington 99164 for providing \nguidance on editing this manuscript. \n\n\n\nFUNDING SOURCES \n\n\n\nThis work was financially supported by National Agricultural Technology \nProgram, NATP-Phase II, PIU, BARC, Project ID:20 and Bangladesh \nAgricultural Research Institute, Ministry of Agriculture, Bangladesh. \n\n\n\nREFERENCES \n\n\n\nAhmadizadeh, M. and Felenji, H., 2011. Evaluating diversity among potato \n\n\n\ncultivars using agro-morphological and yield components in fall \n\n\n\ncultivation of Jiroft area. American-Eurasian Journal of Agricultural & \n\n\n\nEnvironmental Sciences, 11(5), pp.655-662. \n\n\n\nAnnicchiarico, P., 2002. Genotype x environment interactions: challenges \n\n\n\nand opportunities for plant breeding and cultivar recommendations \n\n\n\n(No. 174). Food & Agriculture Org. \n\n\n\nBarman, B., Goswami, B., Hussain, R., Khanikar, P.G., Deka, R.L., Singh, K.S.G. \n\n\n\nand Singh, A., 2019. Influence of GDD in different growth parameters of \n\n\n\npotato at agroclimatic condition of Jorhat. \n\n\n\nBangladesh. Parisa\u1e43khy\u0101na Byuro, 2011. Preliminary Report on \n\n\n\nHousehold Income & Expenditure Survey--2010. Bangladesh Bureau of \n\n\n\nStatistics, Statistics Division, Ministry of Planning. \n\n\n\nMendiburu, F., 2014. Statistical Procedures for Agricultural Research. R \n\n\n\npackage version 1.2-1. \n\n\n\nEberhart, S.T. and Russell, W.A., 1966. Stability parameters for comparing \n\n\n\nvarieties 1. Crop science, 6(1), pp.36-40. \n\n\n\nFinlay, K.W. and Wilkinson, G.N., 1963. The analysis of adaptation in a \n\n\n\nplant-breeding programme. Australian journal of agricultural research, \n\n\n\n14(6), pp.742-754. \n\n\n\nGauch, H.G., 1993. MATMODEL Version 2.0: AMMI and related analyses for \n\n\n\ntwo-way data matrices. Soil, Crop, and Atmospheric Sciences, Cornell \n\n\n\nUniversity. \n\n\n\nHaldavankar, P.C., Joshi, G.D., Bhave, S.G., Khandekar, R.G. and Sawant, S.S., \n\n\n\n2009. Stability of Yield and Yield Attributing Phenotypic Characters in \n\n\n\nSweet potato (Ipomoea batatas (L.) Lam.). Journal of Root Crops, 35(1), \n\n\n\npp.28-35. \n\n\n\nHancock, R.D., Morris, W.L., Ducreux, L.J., Morris, J.A., Usman, M., Verrall, \n\n\n\nS.R., Fuller, J., Simpson, C.G., Zhang, R., Hedley, P.E. and Taylor, M.A., \n\n\n\n2014. Physiological, biochemical and molecular responses of the potato \n\n\n\n(S olanum tuberosum L.) plant to moderately elevated temperature. \n\n\n\nPlant, cell & environment, 37(2), pp.439-450. \n\n\n\nHassanpanah, d. And hassan, a.h., 2014. Evaluating quantitative and \n\n\n\nqualitative traits of promising potato clones and commercial cultivars \n\n\n\nusing the gge bi-plot and ammi models. \n\n\n\nJames, W.C., Shih, C.S., Callbeck, L.C. and Hodgson, W.A., 1971. A method \n\n\n\nfor estimating the loss in tuber yield caused by late blight of potato. \n\n\n\nAmerican Potato Journal, 48(12), pp.457-463. \n\n\n\nJohnson, R.A. and Wichern, D.W., 2007. Applied multivariate statistical \n\n\n\nanalysis.. PrenticeHall International. INC., New Jersey. \n\n\n\nJovovi\u0107, Z., Kova\u010devi\u0107, D. and Milo\u0161evi\u0107, D., 2011. Uticaj na\u010dina suzbijanja \n\n\n\nkorova na fotosintetsku aktivnost i veli\u010dinu organske produkcije \n\n\n\nkrompira. XVI savetovanje o biotehnologiji. Zbornik radova, pp.391-\n\n\n\n396. \n\n\n\nJovovic, z., milic, v., postic, d., velimirovic, a., silj, m. And strunjas, k., 2012. \n\n\n\nProductivity testing of early and medium erly potato varieties in agro-\n\n\n\necological conditions in northern montenegro. In third international \n\n\n\nscientific symposium\" agrosym jahorina (pp. 200-204). \n\n\n\nMahajan, R.K. And Prasad, A.R., 1986. Principal Component Analysis of \n\n\n\nGenotype-Environment Interactions in Rice. \n\n\n\nMasarirambi, M.T., Mandisodza, F.C., Mashingaidze, A.B. and Bhebhe, E., \n\n\n\n2012. Influence of Plant Population and Seed Tuber Size on Growth and \n\n\n\nYield Components of Potato (Solanum tuberosum). International \n\n\n\nJournal of Agriculture & Biology, 14(4). \n\n\n\nMinhas, J.S., Singh, B., Kumar, D., Joseph, T.A. and Prasad, K.S.K., 2001. \n\n\n\nSelection of heat tolerant potato genotypes and their performance \n\n\n\nunder heat stress. Journal of the Indian Potato Association (India). \n\n\n\nMukherjee, A. and Naskar, S.K., 2013. Performance of orange and purple-\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 16-20 \n\n\n\nCite the Article: M.N. Amin, B.C. Kundub, M. Rahman, M.M. Rahman and M.M. Uddin (2021). Promising Early Planting and Stress-Tolerant Potato Genotypes For \nNorthern Bangladesh. Malaysian Journal of Sustainable Agriculture, 5(1): 16-20. \n\n\n\nfleshed sweet potato genotypes in coastal locations of Odisha. Journal \n\n\n\nof Root Crops, 38(1), p.26. \n\n\n\nRios, S.D.A., Paes, M.C.D., Bor\u00e9m, A., Cruz, C.D., Guimar\u00e3es, P.D.O., Schaffert, \n\n\n\nR.E., Cardoso, W.S. and Pacheco, C.A.P., 2009. Adaptability and stability \n\n\n\nof carotenoids in maize cultivars. Embrapa Milho e Sorgo-Artigo em \n\n\n\nperi\u00f3dico indexado (ALICE). \n\n\n\nShahabuddin, Q. and Dorosh, P.A., 2002. Comparative advantage in \n\n\n\nBangladesh crop production (No. 595-2016-39980). \n\n\n\nShimelis, H.A., Mashela, P.W. and Hugo, A., 2013. Principal agronomic and \n\n\n\nseed oil traits in the industrial oil crop vernonia (Centrapalus \n\n\n\npauciflorus var. ethiopica). South African Journal of Plant and Soil, \n\n\n\n30(3), pp.131-137. \n\n\n\nTai, G.C., 1971. Genotypic stability analysis and its application to potato \n\n\n\nregional trials. Crop science, 11(2), pp.184-190. \n\n\n\nvan Eeuwijk, F., Igartua, E. and Romagosa, I., 2002. Genotype by \n\n\n\nenvironment interaction and adaptation in barley breeding: basic \n\n\n\nconcepts and methods of analysis. Barley Sci, 12, p.205. \n\n\n\nVreugdenhil, D., Bradshaw, J., Gebhardt, C., Govers, F., Taylor, M.A., \n\n\n\nMacKerron, D.K. and Ross, H.A. eds., 2011. Potato biology and \n\n\n\nbiotechnology: advances and perspectives. Elsevier. \n\n\n\nWang-Pruski, G., 2018. Achieving sustainable cultivation of potatoes \n\n\n\nVolume 1: Breeding improved varieties. Burleigh Dodds Science \nPublishing.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 22-28 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2022.22.28 \n\n\n\n \nCite the Article: Md. Sohanur Rahman, Fakhar Uddin Talukder, Md. Nazrul Islam (2022). Pesticidal Effect of Selected Plant Extracts on Polyphagotarsonemus Latus \n\n\n\n(Banks) Infestation in Corchorus Olitorius L. Jute. Malaysian Journal of Sustainable Agriculture, 6(1): 22-28. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2022.22.28 \n\n\n\n\n\n\n\n \nPESTICIDAL EFFECT OF SELECTED PLANT EXTRACTS ON Polyphagotarsonemus \nlatus (BANKS) INFESTATION IN Corchorus olitorius L. JUTE \n\n\n\n \nMd. Sohanur Rahmana*, Fakhar Uddin Talukderb, Md. Nazrul Islama \n \naDepartment of Entomology, Bangladesh Jute Research Institute, Bangladesh \nbDepartment of Plant Pathology, Bangladesh Jute Research Institute, Bangladesh \n\n\n\n*Corresponding Author E-mail: sohanbau2010@gmail.com    \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 26 July 2021 \nAccepted 28 August 2021 \nAvailable online 19 September 2021 \n\n\n\n Many plant extracts could be considered as natural effective tool against yellow mite instead of synthetic \nchemicals. The research was aimed at studying the effects of plant extracts on Polyphagotarsonemus latus \nBanks infestation and jute yield production. This experiment was conducted in Manikganj, Bangladesh \nfollowing randomized complete block design with three replications. Treatments were Neem oil 3% (T1), \nNeem leaf extract @ 1:30 (T2), Mahagoni seed extract @ 1: 10 (T3) Turmeric powder extract @ 1: 40 (T4), \nGarlic paste extract @ 1:30 (T5) and control (T6). Percent reduction of mite population was found in neem oil \n(87.94%), neem leaf extract (85.76%) and garlic paste extract (86.48%) at 72 hrs after spray. After 7 days of \nspraying, Neem oil treated plot received the best reduction (89.05 %) followed by neem leaf extract (87.03%), \nMahogany (79.60%), turmeric (78.02%), and garlic (80.06%), respectively. Neem oil treated plot showed \nhighest fibre yield (2.95tha-1). Control plot showed highest mite infestation with lowest yield contributing \nattributes. Neem oil/leaf extract and mahogany seed extracts were found effective to control yellow mite \ninfestation resulting higher fibre yield production.  \n\n\n\nKEYWORDS \n\n\n\nPlant extracts, Polyphagotarsonemus latus, corchorus olitorius. \n\n\n\n1. INTRODUCTION \n\n\n\nJute is a vital sustainable natural fibre crop next to cotton (Das et al., 2014). \nIt is a prime fibre crop in the world. It positions second to the cotton among \nall the natural fibre manufacture (Talukder et al., 1989). About 90% of the \nworld\u2019s jute is produced in India and Bangladesh (Atwal and Dhaliwal, \n2007). It is the most important cash crop and the chief foreign exchange \nrecipient of Bangladesh. Bangladesh became the second largest producer \nof jute in the world with annual production estimated at 68.19 lakh metric \ntons in 6.66 lakh hectare lands in FY 2019-20 which covers 42% of the \ntotal jute production of the world. Raw Jute export earnings are US$ 130 \nmillion in FY 2019-2020 covering 0.39% of total export. At the same time, \njute goods scored US$ 752 million in FY 2019-2020 covering 2.23% of total \nexport (BER, 2020). Jute is attacked by about 40 species of insects and \nmites at all stages of the growth from seedling to harvest (Kabir,1975). \nAmong them yellow mite, Polyphagotarsonemus latus (Banks) is one of the \nmost common and a serious pest of jute. It sucks cell sap from young apical \nleaves causing in wrinkle and curly appearance of tender leaves. About \n38% of fiber yield of jute is reduced due its attack under field condition. \nYellow mite, Polyphagotarsonemus latus Banks is one of the major \ndestructive pests of jute (Rahman and Khan, 2006) and the loss caused by \nP. latus is reported to the extent of 10.00 - 42.00% depending on the level \nof infestation (Pandit et al., 2002). Both yield and quality of fibre are \nreduced due to the attack of this pest (Kamruzzaman et. al., 2013). Mite \nattacks the softer portions of the jute plants (Hath, 2000). It is very small \nand quite impossible to see without a 10X or stronger hand lens (Pena and \nCampbell, 2005). The mite's toxic saliva causes twisted, hardened and \n\n\n\ndistorted growth in the terminal bud of the plant (Baker, 1997). The \nblooms abort and plant growth is stunted when large populations are \npresent (Denmark, 1980; Wilkerson et al., 2005). \n\n\n\nGenerally, chemical acaricides are used to control mite pest of jute. The \nindiscriminate use of synthetic chemicals for the control of mite pests \ncreates several problems in agro-ecosystem such as direct toxicity to \nbeneficial insects, fishes and human (Goodland et al., 1985; Pimentel, 1980 \nand Munakata, 1977) gain resistance to chemicals (Schmutterer et al., \n1983; Waiss and Chen, 1981) out breaks of secondary pests (Hagen and \nFranz, 1973). health hazards (Bhaduri et al., 1989), environmental \npollution (Fishwick, 1988, Kavadia et al., 1986) susceptibility of crop \nplants to insect pests (Pimentel, 1977) and increases environmental and \nsocial cost (Pimentel et al., 1981). Pesticidal control of mite is very \nexpensive and unselective and recurrent use of pesticides causing several \nhazards (Goodland et al., 1985; Devi et al., 1986; Fishwick, 1988; Bhaduri \net al., 1989). The application of pesticide revises pest and \npredator/parasitoid ratio in the agro ecosystem imposing more harm \nrather than worthy. In general, control of pests by applying different \nchemical pesticides is very health risky, expensive and threat to their \nparasite, predators and create imbalance in environment. Therefore, \ntoday\u2019s scientists are trying their level best to find out suitable eco-friendly \npest control measures. Alternative or biodegradable substitutes are now \nintensely sensed in many developed countries to minimize the loss of \nsynthetic chemicals in mite control. The naturally active natural plant \nextracts can play a significant role in this regard. These plant products may \nhelp to keep the drawbacks of conventional methods within bounds. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 22-28 \n\n\n\n\n\n\n\n \nCite the Article: Md. Sohanur Rahman, Fakhar Uddin Talukder, Md. Nazrul Islam (2022). Pesticidal Effect of Selected Plant Extracts on Polyphagotarsonemus Latus \n\n\n\n(Banks) Infestation in Corchorus Olitorius L. Jute. Malaysian Journal of Sustainable Agriculture, 6(1): 22-28. \n \n\n\n\n\n\n\n\nDifferent botanical insecticides like pyrethroids, rotenoids, nicotiniods \nand unsaturated isobutyl amides have been studied extensively and \ndocumented information relating to structure-activity relationships of \nthese compounds (Crosby, 1971). More than 2000 plant species from \ndifferent families and genera have been described to contain toxic \ncompounds and a multitude of chemical compounds holding diverse and \nnovel types of structural patterns being isolated from various plants \n(Adityachaudhury et al., 1985). Recently, the derivatives of neem \n(Azadirachta indica) have become the most promising source of natural \ninsecticides to world scientists (Saxena, 1989). It has been reported that \nneem has repellent, toxicant, antifeedent, insect growth inhibitors, chemo-\nsterillant and anti oviposition activities (Gujar, 1992). Oils and/or vapors \nin garlic are directly toxic to insects suggested by some studies (Park and \nShin 2005, Zhao et al., 2013). Upadhyay and Singh (2012) believe that the \nlectins or lectin like compounds (ASAL) in garlic may interfere with \ndifferent aspects of the insect life cycle. Lectins serve as plant defences \nagainst insects, viruses, fungi, bacteria and mites (Peumans and Van \nDamme 1995, Saha et al. 2007, Roy et al., 2008, Chakraborti et al. 2009). \nThese garlic compounds are toxic to many insects and can be a strong \ndeterrent to feeding and egg laying behavior (Michiels et al., 2010). Some \nplant product acts as antifeedant, some as repellant, some as insecticides, \nhomicidal and growth inhibiting factor against many species of insect pest \n(Soudarajan et al., 2012). The fresh juice, alcoholic and aqueous extracts, \nand essential oils of Curcuma longa L. have demonstrated insecticidal \neffects against a number of insect pests, and also repelled mosquitoes \n(Tavares, W. S. et al., 2013; Iqbal, J. et al., 2010; Sukari, M. A. et al., 2010; \nDamalas, C. A. 2011). Natural products from this turmeric plant also have \nanalgesic, antibacterial, antifungal, anti-inflammatory, antioxidant, and \ndigestive properties (Chattopadhyay, I. et al., 2004; Ali, B. H. et al., 2006). \n\n\n\nIn Bangladesh, a very few studies have been piloted on the efficacy of plant \nextracts against yellow mite infestation. In view of the above mentioned \nfacts and insufficiency of linked knowledge on the performance of natural \nplant products, this study was investigated with the objective to assess the \ncomparative efficacy of different plant extracts against yellow mite and its \nimpact on yield contributing attributes of jute. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nPesticidal efficacy of selected plant extracts on yellow mite, \nPolyphagotarsonemus latus (Banks) experiment was led during April-\nAugust 2019 in the experimental field of Jute Agriculture Experimental \nStation (JAES), Manikgonj, Bangladesh following randomized complete \nblock design (RCBD) with three replications. Treatments were Neem oil \n3% (T1), Neem leaf extract @ 1:30 (T2), Mahagoni seed extract @ 1: 10 (T3) \nTurmeric powder extract @ 1: 40 (T4), Garlic paste extract @ 1:30 (T5) and \ncontrol (T6). The plot size was 2\u00d72.1 m2 having 1 m space between the \nplots and 30 cm between the lines. Germination test of Corchorus olitorius \njute variety O- 9897 was done before sowing at entomology laboratory in \nthe main field. Above 80% germinated seeds of Corchorus olitorius jute \nvariety-09897 were sown into the experimental field on April 2019 in 18 \nplots. Normal agronomic practices like weeding, thinning, irrigation etc. \nwere done as per recommendation. Manures and fertilizers were applied \nas per recommendation of Agronomy division of BJRI. For the preparation \nof neem leaf extract, green leaves were collected from the neem tree and \nthen dried for 7 days. Dry neem leaves of 10 g were soaked in 300 ml water \nfor overnight and the extracts were filtered through fine lilen cloth to get \n1:30 neem leaf extract. The dried turmeric was grinded with the help of an \nelectric blender and 10 g turmeric powder was soaked in 400 ml water for \novernight to make 1:40 turmeric powder extract. Garlic was collected from \nthe local market, sun dried and then crushed with the help of an electric \nblender. 300 ml water was dissolved into 10 gm garlic paste and kept for \novernight to make 1:30 of garlic paste extract. Mahogany seeds were \ncollected from mahogany plants and then sun dried and crushed by an \nelectric blender.10 g mahagoni seed paste was dissolved in 100 ml water \nand then kept for overnight to make 1: 10 mahagoni seed extract. Neem oil \nused in this experiment was collected from local market, Dhaka and its \nconcentration was 100%. From this stock, 3% neem oil solution was \nprepared by adding 97 parts of distilled water with 3 parts neem oil. The \nemulsion of neem oil in water was prepared by adding 1% liquid nikalin \ndetergent (emulsifier) as described by Mariappan and Saxena (1983). Two \ntimes of spraying was done. The first spraying was done at 50 DAS and the \nsecond was done at 65 DAS. Spraying was done with the help of a hand-\noperated sprayer in the afternoon giving attention to avoid sunlight and \ndrift caused by strong wind. The number of yellow mite/cm2 leaf was \ncounted with the help of sterio-microscope before spray and 24 hrs, 48 hrs \nand 72 hrs after spray.The number of mite infested plants was counted \nbefore spray and at 3 and 7 days after spray. Five plant of each plot was \nselected randomly to count plant height, base diametet and fibre yield. At \n120 days after sowing, plant height and base diameter were determined. \n\n\n\nPlant height was measured with a meter scale from the ground level to the \ntop of the plants and expressed in meter. Base diameter was measured \nwith a slide callipers scale from the base of the plants and expressed in \ncentimeter. After harvest, the total yield was calculated in ton per hectare. \nPercent reduction of mite infestation per plot and per cm2 leaf area, \npercent increase of height, diameter and yield over control was estimated. \nPercent reduction/ percent reduction over control of yellow mite was \nrecorded using the following formula- \n\n\n\n(%) Reduction = \nA\u2212B\n\n\n\nA\n \u00d7100 --------------------------- (i) \n\n\n\nHere, \n\n\n\nA = No. of mite per cm2 leaf /mite infested plant in control plot \n\n\n\nB = No. of mite per cm2 leaf / mite infested plant in treated plot \n\n\n\nData were analyzed by using Statistix10 software for analysis of variance. \nMean values were ranked by LSD at 5% level of significance which was \nused to compare the mean differences among the treatments (Gomez and \nGomez, 1984). \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Effect of different plant extracts on yellow mite population at \ndifferent hours after spraying \n\n\n\nEffect of different plant extracts showed significant difference on the \nsurvival of P. latus after 24, 48 and 72 hours of spraying. The mean number \nof jute yellow mite received lowest (14.27/ cm2 leaf) survival in the plot \ntreated with Neem oil at 24hrs (Table 1). At 72 hrs after spraying, Neem \noil showed lowest (5.22/ cm2 leaf) population of yellow mite which was \nstatically similar with Garlic paste extract (5.83/ cm2 leaf) and Neem leaf \nextract (6.12/ cm2 leaf) treated plot (Table 1). Other plant extracts showed \ndifferent performance against P. latus survival. Control plot in every case \nshowed highest survival of P. latus in jute crops. It clearly indicates that \ndifferent plant extracts has toxic effect to control yellow mite population. \nAmong them, neem oil showed highest toxic effect on jute yellow mite. \n\n\n\nThis result is strongly supported by Rahman et al., (2016) who stated that \nneem oil (4.67/ cm2 leaf) and mahogany (7.33/ cm2 leaf) oil showed lowest \nsurvival of yellow mite after 3 days of spraying. Hossain et al., (2013) \nreported that green neem leaf extract, dry neem leaf extract, neem oil, \nTurmeric powder extract and garlic paste extract of same dose had \nsignificant influence in reducing the mean number of jute yellow mite \ngiving 6.33, 5.33, 4.00, 6.67, 5.33 and 7.00/cm2 leaf of jute yellow mite \nsurvival after 3 days of spraying. Chari et al., (1999) observed that neem \noil at 1% concentration was highly effective in the reduction of yellow \nmite. Islam et al., (2019) and Akter B et al., (2019) found the same findings \nwhen they tested some plant extracts on jute yellow mite. This result is in \nagreement with (Singh, 2003) who found that 1 % neem oil was highly \neffective against survival of jute yellow mite. Anil et al., (2001) reported \nthat mahogany and karanja oils of same dose had significant influence in \nreducing the infestation of jute yellow mite. \n\n\n\n \nTable 1: Effect of different plant extracts on the survival of yellow \n\n\n\nmite population \nTreatments Mean no. of yellow mite population at \n\n\n\ndifferent hours \n Before \n\n\n\nSpraying \nAfter Spraying \n\n\n\n  24 hrs 48 hrs 72 hrs \n\n\n\nNeem oil 63.25a 14.27c 8.49d 5.22d \n\n\n\nNeem leaf extract 62.91a 16.87b 10.26c 6.12bcd \n\n\n\nMahogany seed \nextract \n\n\n\n56.26b 18.90b 11.09bc 7.56b \n\n\n\nTurmeric powder \nextract \n\n\n\n54.18b 17.24b 12.01b 7.35bc \n\n\n\nGarlic paste extract 57.96b 17.96b 11.75bc 5.83cd \n\n\n\nControl 54.82b 50.32a 47.16a 43.19a \n\n\n\nCV 5.62 7.62 6.81 9.71 \n\n\n\nLSD (5%) 4.32 2.27 1.51 1.61 \n\n\n\nSE 2.07 1.09 0.72 0.77 \n\n\n\n3.1.1 Survival percent of mean no. of yellow mite population \n\n\n\nSurvival percent of P. latus after 24, 48 and 72 hours of spraying showed \nthat there was a significant difference in the effect of different plant \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 22-28 \n\n\n\n\n\n\n\n \nCite the Article: Md. Sohanur Rahman, Fakhar Uddin Talukder, Md. Nazrul Islam (2022). Pesticidal Effect of Selected Plant Extracts on Polyphagotarsonemus Latus \n\n\n\n(Banks) Infestation in Corchorus Olitorius L. Jute. Malaysian Journal of Sustainable Agriculture, 6(1): 22-28. \n \n\n\n\n\n\n\n\nextracts. The population of jute yellow mite received lowest in the plot \ntreated with Neem oil (22.60%) at 24hrs after spraying (Figure 1). Other \nplant extracts showed various level of yellow mite population. At 48 hours \nafter spraying, lowest mite population was found in neem oil (13.40%). \nNeem leaf extract, mahogany seed extract, turmeric powder extract and \ngarlic paste extract showed 16.27%, 19.86%, 22.19% and 20.31% \nrespectively. At 72 hours after spraying, Neem oil (8.27%) showed lowest \npopulation of yellow mite which was statically similar with neem leaf \nextract (9.77%) and garlic paste extract (10.06%) treated plot (Figure 1). \nIn every case, the highest population of yellow mite was found in control \nplot at different hours after spraying. \n\n\n\n\n\n\n\nFigure 1: Effect of different plant extracts on the survival (%) of mean \nno. of yellow mite population \n\n\n\n3.1.2 Percent reduction of mite population over control at different \nhour\u2019s interval \n\n\n\nThe effect of different plant extracts on reduction of yellow mite \npopulation was determined at different time intervals after spraying. The \nhighest percent reduction of mite population over control was found in \nneem oil (71.63%), which was statically different with neem leaf extract \n(66.49%), mahogany seed extract (62.45%), turmeric powder extract \n(65.75%) and garlic paste extract (64.29%) after 24 hours of spraying \n(Table 2). Similarly, after 48 hours of spraying, neem oil showed better \nperformance (81.97%) in percent reduction of mite population over \ncontrol while the other treatments also significantly reduced mite \npopulation over control. After 72 hours of spraying, the highest percent \nreduction of mite population over control was found in neem oil (87.94%), \nwhich was statically similar with neem leaf extract (85.76%) and garlic \npaste extract (86.48%) and the other treatments had significant effect on \nmite population reduction over control. Therefore, all the plant extracts \nshowed the significant effect for the control of yellow mite population. \nAmong them, neem oil at 3% concentration had the better performance \nfor controlling mite population at all the three times of spraying (Table 2). \n\n\n\nHossain et al., (2013) stated that green neem leaf extract, dry neem leaf \nextract, neem oil, turmeric powder extract and mahogany powder extract \nof same dose had significant influence in percent reduction of jute yellow \nmite population over control giving 62.29%, 64.13%, 64.37%, 64.30% and \n52.46% reduction of jute yellow mite after 24 hrs of spraying. They also \nobserved that after 72 hours of spraying, the highest percent reduction of \nmite population over control was found in neem oil (93.47%). The results \nare supported by the findings of Isman (1993), who told that 1% neem oil \nand green neem leaf extracts were very much effective for reducing mite \npopulation in jute. Sanguanpong and Schmutterer (1992) revealed that \ncold pressed neem oil reduced the fecundity of mites on treated plants and \nthe survival of nymph hatched from treated eggs and thus reduced the \nmite population. Therefore, this finding supported the results obtained in \nthe present study. Chari et al., (1999) detected that neem oil at 1% \n\n\n\nconcentration was highly effective in the reduction of yellow mite. Banu et \nal., (2007) reported that green neem leaf extract and dry neem leaf were \nfound to be effective and gave 74.6 and 70.8% mortality at 72 hrs after \ntreatment in greenhouse condition in jute. Islam (2007)  reported that \nGreen neem leaf extract @ 1: 20 and Neem seed kernel extracts as very \nmuch effective against jute yellow mite. Karuppuchamy and \nMohanasundaram (1987) reported that 1% neem leaf extract and 5% \nneem seed kernel were effective against Tetranychus neocaledonicus and \nTetranychus urticae. Islam et al., (2019) and Akter B et al., (2019) found \nthe same findings when they tested some plant extracts on jute yellow \nmite. \n\n\n\n \nTable 2: Effect of different plant extracts on the reduction of yellow \n\n\n\nmite population over control \n\n\n\nTreatments \n% reduction of yellow mite \n\n\n\npopulation \nover control after spraying \n\n\n\n 24 hrs 48 hrs 72 hrs \nNeem oil 71.63a 81.97a 87.94a \nNeem leaf extract 66.49b 78.24b 85.76a \nMahogany seed extract 62.45b 76.42bc 82.45b \nTurmeric powder extract 65.75b 74.53c 82.92b \nGarlic paste extract 64.29b 75.06c 86.48a \nCV 5.69 2.93 2.14 \nLSD (5%) 5.04 3.04 2.44 \nSE 2.38 1.43 1.15 \n\n\n\n3.2 Effect of different plant extracts on yellow mite infested plant at \ndifferent days after spraying \n\n\n\n3.2.1 Survival (%) after treatment application \n\n\n\nSignificant variation of mite infested plant was noticed in different plant \nextracts to yellow mite attack. The lowest mite infested plant was found in \nneem oil (19.20) treated plot having no significant difference with neem \nseed extract (25.60) and turmeric powder extract (27.80) after 3 days of \nspraying (Table 3). Plot with untreated showed highest number of mite \ninfested plant (99.60), which was significantly different from that of the \nother treatments. In case of  percent plant infestation, neem oil exhibited \nthe best performance having minimum mite infestation (9.89%), which \nwas statically similar with neem leaf extract (13.06%), mahogany seed \nextract (14.22%), turmeric powder extract (15.50%) and garlic paste \nextract (15.34%) after 3 days of spraying.  In case of 7 days after spraying \nwith different plant extracts, the minimum number of mite infested plant \n(10.20) was found in the neem oil treated plot which was statistically \nidentical with neem leaf extract (12.20). Mahogany seed extract (19.20), \nturmeric powder extract (20.60) and garlic paste extract (18.80) were \nstatistically identical. Control plot received highest (93.80) plant \ninfestation (Table 3). Neem oil received lowest percent (5.17%) plant \ninfestation having statistically similar percent infestation with neem leaf \nextract (6.26%) and garlic paste extract (9.07%). Control plot was in \nhighest (51.43%) position considering percent plant infestation, which \nwas significantly different from other treatments after 7 days of spraying. \n\n\n\nHossain et al., (2013) examined the effect of neem (azadirachta indica) and \nother plant extracts on yellow mite of jute. In their study, they found mite \ninfested plant and percent in neem oil 22.67(11.01%), mahagoni seed \nextract 24.33 (12.71%), garlic paste extract 28.66 (14.55%), turmeric \npowder extract 31 (15.10%) and green neem leaf extract 28.67(15.15%), \nrespectively after 3 days of spraying. At 7 days after spraying, they found \nmite infested plant and percent in neem oil 9 (4.36%), mahagoni seed \nextract 10.33 (5.40%), garlic paste extract 16.33 (8.29%), turmeric \npowder extract 19.67 (9.58%) and green neem leaf extract 17(8.95%) \nrespectively. The findings of this study are fully supported by Hossain et \nal., (2013) and Akter B et al., (2019) findings. \n\n\n\n \nTable 3: Effects of plant extracts on survival of mite infestation at days after spraying \n\n\n\nTreatments Before Spray 3 days after spray 7 days after spray \n No. of mite \n\n\n\ninfested plant \n% infestation \n\n\n\nNo. of mite \ninfested plant \n\n\n\n% infestation \nNo. of mite \n\n\n\ninfested plant \n% infestation \n\n\n\nNeem oil 93.60a 52.38b 19.20c 9.89b 10.20c 5.17d \nNeem leaf extract 84.40ab 56.95ab 25.60bc 13.06b 12.20c 6.26cd \nMahogany seed extract 80.20ab 61.53a 28.80b 14.22b 19.20b 9.21bc \nTurmeric powder extract 85.80ab 52.56b 27.80bc 15.50b 20.60b 11.37b \nGarlic paste extract 78.80b 62.32a 31.40b 15.34b 18.80b 9.07bcd \nControl 82.20ab 55.05ab 99.60a 54.42a 93.80a 51.43a \nCV 12.23 11.02 17.68 22.29 10.51 19.65 \nLSD (5%) 13.58 8.26 4.33 6.00 4.04 4.00 \nSE 6.51 3.96 9.04 2.88 1.94 1.92 \n\n\n\n0.00\n\n\n\n10.00\n\n\n\n20.00\n\n\n\n30.00\n\n\n\n40.00\n\n\n\n50.00\n\n\n\n60.00\n\n\n\n70.00\n\n\n\n80.00\n\n\n\n90.00\n\n\n\n100.00\n\n\n\n0 1 2 3 4 5 6 7\n\n\n\nz\n\n\n\nSelected plant extracts\n\n\n\n24hrs 48hrs 72hrs\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 22-28 \n\n\n\n\n\n\n\n \nCite the Article: Md. Sohanur Rahman, Fakhar Uddin Talukder, Md. Nazrul Islam (2022). Pesticidal Effect of Selected Plant Extracts on Polyphagotarsonemus Latus \n\n\n\n(Banks) Infestation in Corchorus Olitorius L. Jute. Malaysian Journal of Sustainable Agriculture, 6(1): 22-28. \n \n\n\n\n\n\n\n\n3.2.2 Percent reduction over control \n\n\n\nIn case of percent reduction of yellow mite infested plant over control, the \nbest results were found by the application of neem oil (80.74%) followed \nby neem leaf extracts (74.27%) after 3 days of spraying (Figure 2). After 7 \ndays of spraying, Neem oil treated plot received the best efficacy against \nyellow mite attack and reduced mite infested plants 89.05 % which was \nstatically identical with neem leaf extract (87.03%). Other plant extracts, \nmahogany, turmeric and garlic showed better performance in reducing \nyellow mite infestation of jute giving 79.60, 78.02 and 80.06 % \nrespectively (Figure 2). On the other hand, the lowest results (68.43%) \nand (78.02%) were found in garlic extract and turmeric extract after 3 \ndays and 7 days of application, respectively. Das and Singh (1998) \nreported the efficacy of neem oil and green neem leaf extracts against \nyellow mite which supported these findings. The findings of this study are \ndirectly supported by Hossain et al., (2013 findings. Islam et al., (2012) \nindicated that neem oil. nembicidine and neem leaf extract were most \netfective against jute yellow mite. It has also been reported that neem \nproducts were controlled various insect pests (Pasini  et a1., 2003; \nNaganagouda et al., 1987). Banu et al., (2007) reported that green neem \nleaf extract and dry neem leaf were found to be effective and gave 67.7 and \n72.2% reduction of infestation at 7th day after spray in field condition in \njute. Islam et al., (2019) reported that neem seed kernel extract, mahogany \nseed extract, green neem leaf extract and turmeric powder extract \nsignificantly reduced the plant infestation by 70.1%, 68.05%, 61.5% and \n61.0%, respectively over control. \n\n\n\n\n\n\n\nFigure 2: Effect of different plant extracts on the reduction of yellow \nmite infested Plant over control \n\n\n\n3.3 Yield contributing characters \n\n\n\nDifferent plant extracts showed significant variation in influencing yield \ncontributing characters of jute. The highest plant height (3.06 m) was \nobserved in neem oil treated plot which was significantly similar with \nneem leaf (2.97m) and mahogany extract (2.92m) treated plot. Untreated \nplot received lowest plant height (2.33m) which was significantly different \nfrom other treatments. The highest base diameter (15.18 mm) was found \nin neem oil, which was significantly similar with neem leaf extract (14.64 \nmm), mahogany seed extract (14.17 mm) and garlic paste extract (14.26 \nmm) but significantly different from other treatments (Table 4). In \ncontrast, lowest base diameter (10.48 mm) was found in untreated \ncontrol, which was meaningfully different from other treatments. Neem oil \ntreated plot showed highest fibre yield (2.95 t ha-1) followed by neem leaf \n(2.89 t ha-1), mahogany (2.87 t ha-1) and turmeric (2.76 t ha-1). Garlic paste \nextract gave the fibre yield 2.62 t ha-1, which was statically similar with \nmahogany and turmeric treatments. The lowest fibre yield (1.87 t ha-1) \nreceived in control plot (Table 4). The findings definitely indicated that all \nthe plant extracts have the significant effect on the increase of fibre yield \nof jute. However, neem oil followed by neem leaf extract and mahagoni \nseed extract showed the best performance. Neem oil effect on plant height \ngrowth as observed in the present study is in conformity with findings \nreported by Palaniswamy and Ragini (2003) against yellow mite on chilli. \nThey observed that 5% aqueous extract of neem leaf reduced mite \npopulation on chilli and increased plant height. These results are \nsupported by the findings of Das and Singh (1998) who reported the \nhighest efficacy of neem oil against jute mite. Neem products effectiveness \nin the present study was in accordance with the findings observed by \nPande et al., (1987). Yeasmin et al., (2013) also found simiilar results after \napplication of Neem oil which increased 24.64% plant height, 27.87% base \ndiameter over control and gave the highest amount of fibre yield (2.68 t \nha-1). Hossain et al., (2013) and Rahman et al., (2016) found similar results \nwhich are strongly agreed with these findings. Chari et al. (1999), who \nobserved that neem oil at 1% concentration was highly effective in \nincreasing the growth of jute through the control of jute yellow mite. Akter \nB et al., (2019) reported the same results when they evaluated some plant \nmaterials against Jute Yellow Mite on Corchorus Olitorius. \n\n\n\nTable 4: Effect of different plant extracts on yield contributing \ncharacters of jute \n\n\n\nTreatments \nPlant \n\n\n\nheight(m) \nBase \n\n\n\nDiameter(mm) \nFibre Yield \n\n\n\n(t/ha) \nNeem oil 3.06a 15.18a 2.95a \nNeem leaf extract 2.97ab 14.64ab 2.89a \nMahogany seed \nextract \n\n\n\n2.92ab 14.17ab 2.87ab \n\n\n\nTurmeric powder \nextract \n\n\n\n2.87bc 13.64b 2.76ab \n\n\n\nGarlic paste extract 2.70c 14.26ab 2.62b \nControl 2.33d 10.48c 1.87c \nCV 4.91 6.95 7.52 \nLSD (5%) 0.18 1.26 0.26 \nSE 0.09 0.60 0.13 \n\n\n\n3.3.1 Percent increase of yield contributing characters of jute over \ncontrol \n\n\n\nPercent increase of plant height over control was presented in Table 5. The \nhighest percent increase of plant height over control (31.92%) was \nobserved in neem oil treatment followed by neem (27.86%), mahogany \n(25.83%) and turmeric (23.96%). On the other hand, the lowest plant \nheight percent increase over control (16.13%) was observed in garlic \npaste extract, which was significantly different from other treatments. \nNeem oil treated plot received highest percent increase of base diameter \nover control (46.48%) and lowest was observed in turmeric powder \nextract (31.68%). The highest percent increase over control of fibre yield \nwas found in neem oil treated plots followed by neem leaf extract \n(55.31%), mahogany (554.17%) and turmeric (48.47%) (Table 5). Garlic \npaste extracts received lowest percent increase over control (40.48%) of \nfibre yield parameter. These findings are in conformity of the findings of \nHossain et al. (2013); Akter B et al., (2019) and Islam et al., (2019). \n \n\n\n\nTable 5: Effect of different plant extracts on increase (%) of yield \ncontributing characters of jute over control \n\n\n\nTreatments \nPlant \n\n\n\nheight(m) \n\n\n\nBase \nDiameter \n\n\n\n(mm) \n\n\n\nFibre \nYield \n\n\n\n(t/ha) \nNeem oil 31.92a 46.48a 58.25a \nNeem leaf extract 27.86a 41.53ab 55.31ab \nMahogany seed extract 25.83a 36.72ab 54.17ab \nTurmeric powder extract 23.96ab 31.68b 48.47ab \nGarlic paste extract 16.13b 37.52ab 40.28b \nCV 25.49 23.83 22.64 \nLSD (5%) 8.59 12.39 15.57 \nSE 4.05 5.85 7.35 \n\n\n\n3.4 Principal component analyses \n\n\n\nPrincipal component analysis (PCA) is a statistical tool that permits \nsummarizing the information content in large data tables by means of a \nsmaller set of \u201csummary indices\u201d that can be more easily visualized and \nanalyzed.  In the 1st principal component, all the characters received \nnegative values where neem, mahogany and turmeric showed high vector \nvalues and the other characters showed low vector values (Table 6). In the \n2nd principal component, all the characters except mahogany and turmeric \ntested positive values where neem oil showed high vector values and the \nother characters showed low vector values. In 3rd component, all the \ncharacters received negative value except neem oil and turmeric where \nturmeric showed high vector values and the other characters showed low \nvector values. In 4th component, all other characters received negative \nexcept neem leaf where neem leaf showed high vector values. In the last \ncomponent, turmeric and garlic showed negative values and all other \ncharacters received positive values where mahogany and garlic received \nhigh vector values and the other characters showed low vector values. \n \n\n\n\nTable 6: Vector components of different plant extracts \n\n\n\nFactors       1       2       3       4       5 \n\n\n\nNeem oil (1) -0.2303 0.9110 0.0808 -0.1736 0.2834 \n\n\n\nNeem leaf extract (2) -0.5028 0.0455 -0.1252 0.8541 0.0038 \n\n\n\nMahogany seed \nextract (3) \n\n\n\n-0.4981 -0.3661 -0.2868 -0.3187 0.6588 \n\n\n\nTurmeric powder \nextract (4) \n\n\n\n-0.4043 -0.1793 0.8891 -0.0978 -0.0656 \n\n\n\nGarlic paste extract \n(5) \n\n\n\n-0.5316 0.0416 -0.3241 -0.3596 -0.6938 \n\n\n\n80.74 74.27 71.05 71.68 68.43\n\n\n\n89.05 87.03\n79.60 78.02 80.06\n\n\n\n0.00\n\n\n\n20.00\n\n\n\n40.00\n\n\n\n60.00\n\n\n\n80.00\n\n\n\n100.00\n\n\n\nNeem oil Neem leaf\n\n\n\nextract\n\n\n\nMahogany seed\n\n\n\nextract\n\n\n\nTurmeric\n\n\n\npowder extract\n\n\n\nGarlic paste\n\n\n\nextract\n\n\n\n%\n r\n\n\n\ned\nu\nct\n\n\n\nio\nn\n o\n\n\n\nf \nm\n\n\n\nit\ne \n\n\n\no\nv\ner\n\n\n\n\n\n\n\nco\nn\ntr\n\n\n\no\nl\n\n\n\nSelected plant extracts\n\n\n\n3DAS 7DAS\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 22-28 \n\n\n\n\n\n\n\n \nCite the Article: Md. Sohanur Rahman, Fakhar Uddin Talukder, Md. Nazrul Islam (2022). Pesticidal Effect of Selected Plant Extracts on Polyphagotarsonemus Latus \n\n\n\n(Banks) Infestation in Corchorus Olitorius L. Jute. Malaysian Journal of Sustainable Agriculture, 6(1): 22-28. \n \n\n\n\n\n\n\n\nThe eigenvalues, variabilities and cumulative variabilities among the \nprincipal components in PCA analyses were shown in Table 7 and scree \nplot analysis (Figure 3). The variabilities increased with increasing the \neigenvalues, but the variation in cumulative variability is vice-versa \n(Figure 3). In this analysis, the first two principal components having \nEigenvalues \u2265 1.0, accounting for 63.0% of total variations. The first \nprincipal component recorded variations about 60.0%; while the second \n19.7%, third 11.4%, fourth 5.3% and fifth 0.6% of the total variations \n(Table 7). \n \n\n\n\nTable 7: Eigenvalues, percent of variance and Cumulative percent of \nvariance of different plant extracts in PCA (Based on Correlation \n\n\n\nMatrix) \n\n\n\nEigenfactors Eigenvalue \nVariance \n\n\n\n(%) \nCumulative \n\n\n\nvariability (%) \nNeem oil  3.15093 63.0 63.0 \nNeem leaf extract  0.98660 19.7 82.8 \nMahogany seed \nextract  \n\n\n\n0.57001 11.4 94.2 \n\n\n\nTurmeric powder \nextract \n\n\n\n0.26384 5.3 99.4 \n\n\n\nGarlic paste \nextract  \n\n\n\n0.02861 0.6 100.0 \n\n\n\nA scree plot displays how much variation each principal component \ncaptures from the data. A scree plot is a diagnostic tool to check whether \nPCA works well on data or not. Principal components are created in order \nof the amount of variation they cover. PC1 captures the most variation, PC2 \n\u2014 the second most, and so on. Each of them contributes some information \nof the data. In a PCA, there are as many principal components as there are \ncharacteristics. Leaving out PCs and lose information. The primary y axis \nis eigenvalues and secondary y axis is cumulative variability (%). X axis \nindicates components (factors) (Figure 3). An ideal curve should be steep \nand then bends at an \u201celbow\u201d. This is called cutting-off point and after that \nit flattens out. In Figure 7, just PC 1, 2, and 3 are enough to describe the \ndata. \n\n\n\n\n\n\n\nFigure 3: Eigenvalues and cumulative variability (%) in PCA scree plot \n\n\n\n3.5 Box and Whisker Plots \n\n\n\nA box and whisker plots displays the five-number summary of a set of data. \nMinimum, first quartile, median, third quartile, and maximum are the five \nnumber summaries. A box is drawn from the first quartile to the third \nquartile. A vertical line goes through the box at the median. The whiskers \ngo from each quartile to the minimum or maximum. The first quartile (Q1) \nis the median of the data points to the left of the median. The third quartile \n(Q3) is the median of the data points to the right of the median. The \nminimum and maximum is the smallest and largest data point \nrespectively. The five-number summary divides the data into sections that \neach contains approximately 25 % of the data in that set. Five plants \nextracts effect on reduction of yellow mite infestation after spraying are \nsummarized with this plots. \n\n\n\nIn case of hours after spray data of neem oil, median =82.5, minimum = \n69.71, maximum=88.95, Q1=72.85 and Q3=87.3. Since Q1=72.85, it means \nthat about 25% data is lower than 72.85 and 75% data is above than 72.85. \nSince Q3=87.3, it means that about 75% data is lower than 87.3 and 25% \ndata is above than 87.3. So, the five number summaries are 69.71(min.), \n72.85(Q1), 82.5 (median), 87.3 (Q3) and 88.95 (max.) (Fig.3). Similarly, for \nneem leaf extracts= 65.17 (min.), 66.42(Q1), 77.33 (median), 85.8 (Q3) and \n88.14 (max.); mahogany = 57.21 (min.), 64.59 (Q1), 77.63 (median), 81.36 \n(Q3) and 85.03 (max.); turmeric= 60.86(min.), 68.45 (Q1), 74.15 (median), \n80.75 (Q3) and 85.9 (max.) and garlic= 57.65(min.), 67.09(Q1), 75.36 \n(median), 86.21 (Q3) and 87.3 (max.) (Figure 4). \n\n\n\n\n\n\n\nIn case of days after spraying data, neem oil = 69.31(min.), 80.84 (Q1), \n86.81 (median), 89.90 (Q3) and 90.53 (max.); neem leaf extracts= 70.33 \n(min.), 72.30 (Q1), 82.96 (median), 87.44 (Q3) and 88.10 (max.); mahogany \n= 63.83 (min.), 67.51 (Q1), 77.21 (median), 81.62 (Q3) and 85.15 (max.); \nturmeric= 63.92 (min.), 69.42 (Q1), 76.04 (median), 78.93 (Q3) and 83.67 \n(max.) and garlic= 60.64 (min.), 67.51 (Q1), 75.38 (median), 81.14 (Q3) and \n84.21 (max.) (Figure 4).  \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 4: Box and whisker plots of selected plant extracts on reduction of \nmite infestation \n\n\n\n4. CONCLUSION \n\n\n\nThe most important issue is pest in crop production because pests can \nreduce the production of both qualitative and quantitative. Percent \nreduction of mite population over control was found in neem oil (71.63%), \nneem leaf extract (66.49%), mahogany seed extract (62.45%), turmeric \npowder extract (65.75%) and garlic paste extract (64.29%) after 24 hours \nof spraying. Percent reduction of mite population over control was found \nin neem oil (89.05%), neem leaf extract (87.03%), mahogany seed extract \n(79.60%), turmeric powder extract (78.02%) and garlic paste extract \n(80.06%) after 7 days of spraying. Neem oil treated plot showed highest \nfibre yield (2.95 t ha-1) followed by neem leaf (2.89 t ha-1), mahogany (2.87 \nt ha-1), turmeric (2.76 t ha-1) and garlic (2.62 t ha-1).  \n\n\n\nIt is concluded that untreated plot tested highest pest infestation with \nlowest yield attributes. Neem oil/ neem leaf received lowest mite \ninfestation with higher plant height, base diameter and finally fibre yield \nfollowed by mahogany, turmeric and garlic extracts. It is highly \nrecommended to use these plant extracts for controlling most dangerous \npest of jute called yellow mite. Further investigation is strongly suggested \nto explore their toxic effect to yellow mite and other agricultural pests \nbecause the use of botanical pesticides is recyclable, comparatively safe \nfor environment, humans and non-target animals. \n\n\n\nCONFLICT OF INTEREST \n\n\n\nThe author declares that there is no conflict of interest to disclose. \n\n\n\nDATA AVAILABILITY STATEMENT \n\n\n\nThe datasets generated during and/or analyzed during the current study \nare available from the corresponding author on reasonable request. \n\n\n\nACKNOWLEDGEMENT   \n\n\n\nAuthor acknowledges the Entomology Department, Bangladesh Jute \nResearch Institute, Bangladesh for providing laboratory facilities. Authors \nalso show gratitude to all the researchers and technical staff of the \nEntomology Department for their cooperation in the research conduction \nand data collection. This study received no fund from any financial \ninstitutes except regular revenue research budget of BJRI. \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\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 1 2 3 4 5 6\n\n\n\nC\nu\n\n\n\nm\nm\n\n\n\nu\nla\n\n\n\nti\nve\n\n\n\n v\nar\n\n\n\nia\nb\n\n\n\nili\nty\n\n\n\n (\n%\n\n\n\n)\n\n\n\nEi\nge\n\n\n\nn\nva\n\n\n\nlu\ne\n\n\n\nFactors (components)\n\n\n\nScree plot\n\n\n\nEigenvalue Cummulative variability\n\n\n\nBox and Whisker Plots (Hour After Spray)\n\n\n\n57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nS\ne\nle\n\n\n\nc\nte\n\n\n\nd\n p\n\n\n\nla\nn\nt \n\n\n\ne\nx\ntr\n\n\n\na\nc\nts\n\n\n\n% reduction of mite infestation over control (Hours After Spray)\n\n\n\nBox and Whisker Plots (Days After Spray)\n\n\n\n60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nS\ne\nle\n\n\n\nc\nte\n\n\n\nd\n P\n\n\n\nla\nn\nt \n\n\n\ne\nx\ntr\n\n\n\na\nc\nts\n\n\n\n% reduction of mite infestation over control (Days After Spray)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 22-28 \n\n\n\n\n\n\n\n \nCite the Article: Md. Sohanur Rahman, Fakhar Uddin Talukder, Md. Nazrul Islam (2022). Pesticidal Effect of Selected Plant Extracts on Polyphagotarsonemus Latus \n\n\n\n(Banks) Infestation in Corchorus Olitorius L. Jute. Malaysian Journal of Sustainable Agriculture, 6(1): 22-28. \n \n\n\n\n\n\n\n\nREFERENCES  \n \nRahman, A., Islam, K. S., Jahan, M., Islam, N. 2016. Efficacy of three \n\n\n\nbotanicals and a microbial derivatives acaricide (Abamectin) on the \ncontrol of jute yellow mite, Polyphagotarsonemus latus (Bank). Journal \nof Bangladesh Agricultural University. 14(1): 1\u20136. \nhttps://doi.org/10.3329/jbau.v14i1.30588 \n\n\n\n \nKamruzzaman, A. S. M., Zinnatul Alam, M., Ramiz Uddin Miah, M., \n\n\n\nSiddiquee, S. 2013. Impact of yellow mite (Polyphagotarsonemus latus \n[Banks]) density on host's (Corchorus capsularis L.) phenology and \nassessment of yield loss under field conditions. Entomological Research. \n43(4): 243-252, https://doi.org/10.1111/1748-5967.12025 \n\n\n\n \nAdityachaudhury, N.A., Bhattacharyya, A., Chowdhury, Pal, S., 1985. \n\n\n\nChemical constituents of plants exhibiting insecticidal, anti-feeding and \ninsect growth regulating activities. Journal of Scientific Industrial \nResearch. 44(2):85-101. https://agris.fao.org/agris-\nsearch/search.do?recordID=US201301466031  \n\n\n\n \nAli, B. H. et al., 2006. Some biological properties of curcumin: A review. Nat. \n\n\n\nProd. Commun. 1: 509\u2013521  \n \nAnil, B., Bhadauria, N.S., Jakhmola, S.S. and Bhatragar, A. 2001. Efficacy of \n\n\n\nvegetable oils against yellow mite, (Polyphagotarsonemus latus) in jute. \nIndian Journal of Entomology. 63(3): 237-239. \n\n\n\n \nAtwal, A.S., Dhaliwal, G.S., 2007. Agricultural pests of South Asia and their \n\n\n\nmanagement. Kalayani publishers, Ludhiana, India, 235. \n \nAkter, B., Ali, M. and Islam, M. N. 2019. Effectiveness of Some Plant \n\n\n\nMaterials against Jute Yellow Mite on Corchorus Olitorius. Journal of \nEnvironmental Sciences & Natural Resources, 12(1&2):165-170, \nhttps://doi.org/10.3329/jesnr.v12i1-2.52031 \n\n\n\n \nBaker, J.R. 1997. Cyclamen mite and broad mite. Ornamental and Turf \n\n\n\nInsect Information Notes, 6: 41-46. \n \nBangladesh Economic Review, 2020. \n\n\n\nhttps://mof.portal.gov.bd/site/page/28ba57f5-59ff-4426-970a-\nbf014242179e/Bangladesh-Economic-Review-2020 \n\n\n\n \nBanu, H., Sultana, K., Islam, M.N., Polan, M.S. and Kamruzzaman, A.S.M. \n\n\n\n2007. Comparative study of non- chemical approach and chemical \npesticides for the management of jute pests. International Journal of \nSustainable Agricultural Technology. 3: 37-40. \n\n\n\n \nBhadhuri, N., Ram, S. and Patil, B. D. 1989. Evaluation of some plant extract \n\n\n\nas protectant against the pulse beetle, Callosobruchus maculatus (Fab.) \ninfesting cowpea seeds. Journal of Entomological Research. 9(2): 183 -\n187. \n\n\n\n \nChakraborti D, Sarkar A, Mondal HA, Das S. 2009. Tissue specific \n\n\n\nexpression of Allium sativum leaf agglutinin (ASAL) in important pulse \ncrop chickpea (Cicer arietinumL.) to resist the phloem feeding Aphis \ncraccivora. Transgenic Research.  18:529\u2013544.  \n\n\n\n \nChari, M.S., Ramprasad, G., Sitramaiah, S., Murthy,P.S.N. and Singh, R.F. \n\n\n\n1999. Laboratory and field evaluation of neem (Azadirachta indica A.) \nextracts and commercial neem formulation against jute mite. Central \njute research institute, Fajahmundry. 23(2): 111-129. \n\n\n\n \nChattopadhyay, I. et al,. 2004. Turmeric and curcumin: Biological actions \n\n\n\nand medicinal applications. Current Science. 87:44\u201353  \n \nCrosby, D.G., 1971. Naturally occurring insecticide. Marcel Dekker, New \n\n\n\nYork, 73-100. \n \nDamalas, C. A. 2011. Potential uses of turmeric (Curcuma longa) products \n\n\n\nas alternative means of pest management in crop production. Plant \nOmics 4:136\u2013141 \n\n\n\n \nDas, H., Poddar, P., Haque, S., Pati, S., Poddar, R., & Kundu, C.K. 2014. Seed \n\n\n\nyield and economics of white jute as influenced by different dates of \nsowing, spacing and topping schedule in Terai region of West Bengal. \nInternational Journal of Farm Sciences. 4(4): 51-58. \n\n\n\n \nDas, L. K.  and Singh, B.1998. Integrated Management of Jute Pest. \n\n\n\nEnvironment and Ecology. 16(1): 218-219. \nhttps://www.cabi.org/ISC/abstract/19981105497 \n\n\n\nDenmark, H.A. 1980. Broad mite, Polyphagotarsonemus latus (Banks). \nFDACS-DPI Bureau of Entomology. Circular No, 213. 2. \n\n\n\n \nDevi, D. A., Mohandas, N. and Vistakshy, A. 1986. Residues of fenthion, \n\n\n\nquinophos and malathion in paddy grains following surface treatment \nof gunny bags. Agricultural Research Journal of Kerala. 24(2): 222-224. \n\n\n\n \nFishwick, R. B. 1988. Pesticide residues in grain arising from post harvest \n\n\n\ntreatments. Aspects of Applied Biology. 17(2): 37-46. \n \nGomez, K.A., Gomez, A.A., 1984. Statistical Procedures for Agricultural \n\n\n\nResearch. 2nd edition. International Rice Research Institute, John Willey \nand Sons, Inc. Singapore, 139-240. \n\n\n\n \nGoodland R, Watson C and Ledac G. 1985. Biocides bring poisoning and \n\n\n\npollution to third world. The Bangladesh Observer. 16th and 17th \nJanuary. \n\n\n\n \nGujar, G.T., 1992. Neem (Azadirachta indica, A. Juss), a natural insecticide: \n\n\n\nStatus and need for bioassay standards. Journal of Pesticide Research. \n4(2): 69-79. \nhttps://www.indianjournals.com/ijor.aspx?target=ijor:prj&volume=4\n&issue=2&article=001 \n\n\n\n \nHagen, K. S., and Franz. J. M. 1973. A history of biological control. In \n\n\n\n\u201cHistory of Entomology\u201d (R. Smith and T. C. Mittler, Eds.), pp. 433\u2013476. \nAnnual Review, Palo Alto, CA. \n\n\n\n \nHath, T. K. 2000. Distribution of yellow mite(Polyphagotarsonemus latus \n\n\n\nBanks) population on leaves of different jute varieties. Environment and \nEcology, 18:578-580. \nhttps://www.indianjournals.com/ijor.aspx?target=ijor:ijfs&volume=4\n&issue=4&article=007 \n\n\n\n \nIqbal, J., Jilani, G. & Aslam, M. 2010. Growth inhibiting effects of plant \n\n\n\nextracts against the grain moth, Sitotroga cerealella (Oliv.) (Gelechiidae: \nLepidoptera). Pakistan Journal of  Zoology. 42(5): 597\u2013601. \nhttps://www.proquest.com/openview/fed3c21e9d2a2e4712f0535a6a\ne8a443/1?pq-origsite=gscholar&cbl=616534 \n\n\n\n \nIslam MN. 2007. Use of neem products for the control of jute yellow mite, \n\n\n\nPolyphagotarsonemus latus (Banks) and Red mite, Tetranychus \nbioculatus (Wood-Mason). MS Thesis, p. 71. \n\n\n\n \nIsman, M.B., 1999. Growth inhibitory and antifeedant effects of \n\n\n\nazadirachtin on six noctuids of regional economic importance. Pesticide \nScience. 38(1): 57-63. https://doi.org/10.1002/ps.2780380109 \n\n\n\n \nKabir, A.K.M.F., (1975). Jute pest of Bangladesh. Annual Report. \n\n\n\nBangladesh Jute Research Institute, Dhaka, Bangladesh, 28-33. \n \nKaruppuchamy P and Mohanasundaram M. 1987. Bioecology and control \n\n\n\nof chilli muranai mite, Polyphagotarsonemus Latus (Banks) \n(Tarsonemidae: Acari). Indian Journal of Plant Protection, 15: 1-4. \nhttps://doi.org/10.32473/edis-in340-2005 \n\n\n\n \nKavadia. V. S.; B. L. Pareek and K. P. Sharma. (1986). Residues of malathion \n\n\n\nand carbonyl in stored sorghum. Bull. Crain Tech. 22 (3):247-250. \n \nIslam, M. N., Bhuiyan, M. S., Banu, H., Haque, S. M. A. 2012. Effect of neem \n\n\n\nproducts on jute yellow mite and its impact on jute growth. International \nJournal of Sustainable Agricultural Technology. 8(8):7-12. \nhttps://www.cabdirect.org/cabdirect/abstract/20123365362 \n\n\n\n \nHossain, M. D., Yasmin, S., Latif M. A. and Akhter, N. 2013. Effect of Neem \n\n\n\n(Azadirachta indica) and other Plant Extracts on Yellow Mite of Jute. \nInternational Journal of Bio-resource and Stress Management, 4(3):412-\n417. \nhttp://www.indianjournals.com/ijor.aspx?target=ijor:ijbsm&volume=\n4&issue=3&article=006 \n\n\n\n \nUkari, M. A. S., Ashid, N. Y. R., Eoh, B. K. N., Bu, N. H. A., Akar, Ugeng, Iyanto. \n\n\n\n2010. Larvicidal Activity of Some Curcuma and Kaempferia Rhizome \nExtracts Against Dengue Fever Mosquito Aedes aegypti Linnaeus \n(Diptera: Culicidae). Asian Journal of  Chemistry. 22(10):7915\u20137919. \nhttp://www.asianjournalofchemistry.co.in/User/ViewFreeArticle.aspx\n?ArticleID=22_10_61  \n\n\n\n \nMichiels, K., Van Damme, E. J., Smagghe, G. 2010. Plant-insect interactions: \n\n\n\nwhat can we learn from plant lectins? Arch Insect Biochem Physiology. \n\n\n\n\nhttps://doi.org/10.1111/1748-5967.12025\n\n\nhttps://mof.portal.gov.bd/site/page/28ba57f5-59ff-4426-970a-bf014242179e/Bangladesh-Economic-Review-2020\n\n\nhttps://mof.portal.gov.bd/site/page/28ba57f5-59ff-4426-970a-bf014242179e/Bangladesh-Economic-Review-2020\n\n\nhttps://www.cabdirect.org/cabdirect/search/?q=au%3a%22Das%2c+L.+K.%22\n\n\nhttps://www.cabdirect.org/cabdirect/search/?q=au%3a%22Singh%2c+B.%22\n\n\nhttps://www.indianjournals.com/ijor.aspx?target=ijor:ijfs&volume=4&issue=4&article=007\n\n\nhttps://www.indianjournals.com/ijor.aspx?target=ijor:ijfs&volume=4&issue=4&article=007\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 22-28 \n\n\n\n\n\n\n\n \nCite the Article: Md. Sohanur Rahman, Fakhar Uddin Talukder, Md. Nazrul Islam (2022). Pesticidal Effect of Selected Plant Extracts on Polyphagotarsonemus Latus \n\n\n\n(Banks) Infestation in Corchorus Olitorius L. Jute. Malaysian Journal of Sustainable Agriculture, 6(1): 22-28. \n \n\n\n\n\n\n\n\n73:193-212. doi: 10.1002/arch.20351 \n \nIslam, M. N., Islam, K. S., Jahan, M., Amin, M. R. and Haque, M. M. 2019. Effect \n\n\n\nof plant extracts on jute yellow mite. Bangladesh Journal of Ecology, 1(2): \n91-95 \n\n\n\n \nMunakata, K. 1977. lnsect feeding deterrents in plants. In \"Chemical \n\n\n\ncontrol of insect behaviour\" (ede. Shorey, H. H and J. J. Mckelvey Jr.). John \nWiley and Sons. New York. 93-102. \n\n\n\n \nNaganagouda, A., Sreenivas, A. G., Rahman S. M. and Patil, B. V. 1997. \n\n\n\nEvaluation of some plant products against major pests of paddy in \nTungabhadra project area. Karnataka Journal of Agricultural Sciences. 10 \n(4): 1193-1196 \n\n\n\n \nPalaniswamy, S., Ragini, J.C. 2003. Influence of certain plant extracts on \n\n\n\nyellow mite polyphagotarsonemus latus (Banks) on chillies. Insect \nEnvironment, 6(1), 25-26. \n\n\n\n \nPande, Y.D., Majumdar, M., Roy, H.L. 1987. Laboratory evaluation of \n\n\n\nvarious concentration of neem (Azadirachta indica) leaf extract against \nTetranychus neocaledonicus Andre infesting okra in Tripura. First \nNatural Seminar on Acarology, Kalyani, West Bengal, Oct. 29-31. \n\n\n\n \nPandit, N.C., Rao, P.V. and Chakraborty, A.K. 2002. Studies on the biotic and \n\n\n\nabiotic factors on the incidence of yellow mite of jute. In: Annual Report: \n2000-2001 and 2001-2002, Central Research Institute for jute and \nAllied Fibres, Barrackpore, 71. \nhttps://doi.org/10.18311/jbc/2019/22708 \n\n\n\n \nPark, I. K., Shin, S. C. 2005. Fumigant activity of plant essential oils and \n\n\n\ncomponents from garlic (Allium sativum) and clove bud (Eugenia \ncaryophyllata) oils against the Japanese termite (Reticulitermes \nsperatus Kolbe). Journal of Agricultural Food Chemistry. 53(11):4388-\n92. https://doi.org/10.1021/jf050393r  \n\n\n\n \nPasini, A., Capelo, S. M. J. and Oliveira, R. C. 2003. Preliminary assays for \n\n\n\nefficiency evaluation of neem oil for control of Olygonychus yothersi \n(Acari: Tetranychidae). Semina londrina. 24 (2):315-316. \n\n\n\n \nPeumans, W. J., Van Damme, E. J. M. 1995. Lectins as plant defense proteins. \n\n\n\nPlant Physiology. 109 (2):347\u2013352. \nhttps://dx.doi.org/10.1104%2Fpp.109.2.347   \n\n\n\n \nPimentel, D. 1981. An overview of integrated pest management \n\n\n\n(Mimeograph). Dept. of Entomology, Scction of Ecology and Systematic, \nCornel University, Ithaca, New York. 52. \n\n\n\n \nPimentel, D., Andow, D., Dyson-Hudson, R., Gallahan, D., Jacobson, S., Irish, \n\n\n\nM., Kroop, S., Moss, A., Schreiner, I., Shepard, M., Thompson, T. and \nVinzant, B. 1980. Environmental and social costs of pesticides: a \npreliminary assessment. Oikos, 34:127-140. \nhttps://doi.org/10.2307/3544173 \n\n\n\n \nPimentel, D., Terhune, E.C., Dritschilo, W., Gallahan, D., Kinner, N., Nafus, \n\n\n\nD., Peterson, R., Zareh, N., Misiti, J. and Haber-Schaim, O. 1977. \nPesticides, insects in foods, and cosmetic standards. BioScience, 27:178-\n185. https://doi.org/10.2307/1297579 \n\n\n\n \nRahman, S. and Khan, M. R. 2006. Incidence of pests and avoidable yield \n\n\n\nlosses in jute. Annals of Plant Protection Sciences, 14:304-05. \nhttps://www.indianjournals.com/ijor.aspx?target=ijor:apps&volume=\n14&issue=2&article=008  \n\n\n\n \nRoy, A., Chakraborti, D., Das, S. 2008. Effectiveness of garlic lectin on red \n\n\n\nspider mite of tea. Journa lof Plant Interactions, 3(3): 157\u2013162. \n\n\n\nhttps://doi.org/10.1080/17429140701754195 \n \nSaha, P., Chakraborti, D., Sarkar, A., Dutta, I., Basu, D., Das, S. 2007. \n\n\n\nCharacterization of vascular-specific RSs1 and rolC promoters for their \nutilization in engineering plants to develop resistance against \nhemipteran insect pests. Planta. 226: 429\u2013\n442. https://doi.org/10.1007/s00425-007-0493-3 \n\n\n\n \nSanguanpong, U., Schmutterer, H. 1992. Laboratory stydies on the effect of \n\n\n\nneem oil and neem seed extracts on the spider mite Tetranychus urticae \nKoch (Acari: Tetranychidae). Zeitschrift Fiupfanzenkrankheiten \nPflanzenschutz. 99(6): 637-646. \nhttps://doi.org/10.1007/springerreference_87762 \n\n\n\n \nSaxena, R.C. 1989. In: Insecticides of plant origin Philogene B.J.R., Morand, \n\n\n\nP., ASC Symposium Series 387, Washington, D.C., 110-135. \n \nSchmutterer, H., Hedyde, J. V. D.  and Sarena, H. 1984. Effects of neem oil \n\n\n\non plant hopper and leaf hopper pests of rice, Oryza sativa. College, \nLaguna (Philippines). \n\n\n\n \nSingh, P. K. 2003. Effect of some oils against jute mite \n\n\n\nPolyphagotarsonemus latusi in infesting jute. Indian Journal of \nEntomology. 68(1), 56-58. \n\n\n\n \nSoudarajan, R. P. 2012. Biological control of bruchids Callosobruchus \n\n\n\nmaculatus (F) in blackgram, Journal of bio pesticides (supplementary), \n192-195. \n\n\n\n \nTalukder, D., Khan, A. R., & Hasan, M. 1989. Growth of Diacrisia obliqua \n\n\n\n[Lepidoptera:Arctiidae] with low doses of Bacillus thringiensis Var. \nKurstaki. Entomophaga. 34(4), 587-589. \nhttps://doi.org/10.1007/bf02374397 \n\n\n\n \nTavares, W.D., Freitas, S.D., Grazziotti, G.H., Parente, L.M., Li\u00e3o, L.M., \n\n\n\nZanuncio, J.C. 2013. Ar-turmerone from Curcuma longa (Zingiberaceae) \nrhizomes and effects on Sitophilus zeamais (Coleoptera: Curculionidae) \nand Spodoptera frugiperda (Lepidoptera: Noctuidae). Industrial Crops \nand Products, 46, 158-164. \nhttps://doi.org/10.1016/J.INDCROP.2013.01.023 \n\n\n\n \nUpadhyay, S. K., Singh, P. K. 2012. Receptors of garlic (Allium sativum) \n\n\n\nlectins and their role in insecticidal action. The Protein Journal. \n31(6):439-46. doi: 10.1007/s10930-012-9423-8 \n\n\n\n \nWaiss, A., Chan, B.G., Elliger, C., Dreyer, D.L., Binder, R.G., Gueldner, R. 1981. \n\n\n\nInsect Growth Inhibitors in Crop Plants. Bulletin of the Entomological \nSociety of America, 27, 217-221. \nhttps://doi.org/10.1093/BESA%2F27.3.217 \n\n\n\n \nWilkerson, J.L., Webb, S.E., and Capinera, J.L. 2005. Vegetable Pests II: Acari \n\n\n\n- Hemiptera - Orthoptera -Thysanoptera. UF/IFAS CD-ROM. SW 181. \n \nYasmin, S., Latif, M.A. and Akhter, N. 2013. Effect of Neem (Azadirachta \n\n\n\nindica) and other Plant Extracts on Yellow Mite of Jute. International \nJournal of Bio-Resource & Stress Management. 4(3): 412-417. \nhttp://www.indianjournals.com/ijor.aspx?target=ijor:ijbsm&volume=\n4&issue=3&article=006 \n\n\n\nZhao, N.N., Zhang, H., Zhang, X.C., Luan, X.B., Zhou, C., Liu, Q.Z, Shi WP, Liu \nZL. 2013. Evaluation of acute toxicity of essential oil of garlic (Allium \nsativum) and its selected major constituent compounds against \noverwintering Cacopsylla chinensis (Hemiptera: Psyllidae). Journal of \n\n\n\nEconomic Entomology. 106(3):1349-\n54. https://doi.org/10.1603/ec12191.\n\n\n\n \n\n\n\n\nhttp://dx.doi.org/10.1002/arch.20351\n\n\nhttps://doi.org/10.1007/bf02374397\n\n\nhttp://dx.doi.org/10.1007/s10930-012-9423-8\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.51.60 \n\n\n\nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.01.2021.51.60 \n\n\n\nFARM SIZE EFFICIENCY DIFFERENTIALS OF BIO-FORTIFIED CASSAVA \n\n\n\nPRODUCTION IN NIGERIA: A STOCHASTIC FRONTIER ANALYSIS APPROACH \n\n\n\nKolapo Adetomiwaa*, Raji, Ibraheem Adeyemib,Falana Kayodec, Muhammed, Opeyemi Abdulmuminc \n\n\n\na Department of Agricultural Economics, Faculty of Agriculture, Obafemi Awolowo University, Ile Ife, Osun State, Nigeria. \nb Department of Science Education (Biology), Faculty of Education, University of Ilorin, Ilorin, Kwara State, Nigeria \nc Department of Agricultural Economics and Extension, Ekiti State University, Ado-Ekiti, Nigeria. \n\n\n\n*Corresponding author\u2019s e-mail: kolapoadetomiwa@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 02 November 2020 \nAccepted 05 December 2020 \nAvailable online 06 January 2021\n\n\n\nThe study specifically investigated farm size efficiency differentials of bio-fortified cassava production in \n\n\n\nNigeria. Data were collected through a cross-sectional survey of bio-fortified cassava producers in Nigeria. \n\n\n\nThe estimated coefficients of the parameters of production variables for small scale bio-fortified cassava farm \n\n\n\nsize (land, herbicide and fertilizer) played a major role in bio-fortified cassava production on a small scale in \n\n\n\nNigeria. For the medium and large scale bio-fortified cassava farm size, production variables (land, labor and \n\n\n\nfertilizer) and (land, labor, herbicide and fertilizer) respectively played a major role in bio-fortified cassava \n\n\n\nproduction. The average economic efficiency of the small, medium and large scale bio-fortified cassava \n\n\n\nproducers was 42%, 54% and 63% respectively. Policies intended to increase the popularization and \n\n\n\ncultivation of bio-fortified cassava in Nigeria should be targeted toward the small and medium scale cassava \n\n\n\nfarmers since they carried the majority of the producer of bio-fortified cassava in Nigeria.  \n\n\n\nKEYWORDS \n\n\n\nEfficiency, Farm size, Stochastic Frontier Analysis, Bio-fortified cassava and Nigeria.\n\n\n\n1. INTRODUCTION \n\n\n\nAgriculture is an important component of a national economy including \n\n\n\nthe Nigeria economy, contributing to the latter approximately 21.91% of \n\n\n\nthe gross domestic product (GDP) (Plecher, 2020; Singh-Peterson and \n\n\n\nIranacolaivalu, 2018). In Nigeria, farming and other agricultural \n\n\n\nactivities constitute the principal sustainable livelihood of most Nigeria \n\n\n\npeople (Ibiremo et al., 2011). Among the cash crops important in Nigeria \n\n\n\nagriculture, cassava (Manihot esculenta) production plays an important \n\n\n\nrole in ensuring poor people\u2019s livelihood (Otekunrin and Sawicka, 2019). \n\n\n\nThe whole country has an area of approximately 824 thousand hectares \n\n\n\nplanted to cassava (FAO, 2018), with cassava yield growing by 9.94 million \n\n\n\ntonnes in 2018 (FAO, 2018). Cassava is an important staple food in Nigeria \n\n\n\n(Kolapo et al., 2020). Cassava is a starchy crop which contributes to the \n\n\n\nstaples of millions in sub-Saharan Africa (SSA). According to a study, about \n\n\n\n177,948 million tonnes of cassava were produced in Africa. Nigeria is \n\n\n\nregarded as the world\u2019s largest producer of cassava with a total of about \n\n\n\n20.4 percent of the world export in year 2017 (Otekunrin and Sawicka, \n\n\n\n2019; Otekunrin and Sawicka, 2019).  \n\n\n\nCassava is a major staple food crop in Nigeria. As defined by some \n\n\n\nresearcher, a staple crop is the one that is been eaten regularly and which \n\n\n\nalso provides larger proportions of the population\u2019s nutrients (Otekunrin \n\n\n\nand Sawicka, 2019). Cassava fulfil this purpose as it can be eaten raw or in \n\n\n\na processed form. Cassava is an essential component of the diet of about \n\n\n\n70 million Nigerians (FAO, 2013). Nigeria, being the largest producer of \n\n\n\ncassava in the world is producing an average annual estimate of 45 million \n\n\n\nmetric tons which had been translated into a major global market share of \n\n\n\nabout 19 percent (Hillocks, 2002); Phillips et al., 2004). The production of \n\n\n\nbiofortified vitamin-A cassava started in 2011 with the intervention of the \n\n\n\nInternational Center for Tropical Agriculture (CIAT) and the International \n\n\n\nInstitute of Tropical Agriculture (IITA) which were funded by Harvest Plus \n\n\n\nprogram (Kolapo and Fakokunde, 2020; Kolapo, Olayinka and \n\n\n\nMuhammed, 2020). Five years after the intervention program, statistics \n\n\n\nrevealed that over 1million of Nigerian farming households grows yellow \n\n\n\ncassava varieties that contains substantial quantities of vitamin-A even \n\n\n\nafter processing (Kolapo et al., 2020).  \n\n\n\nIn Nigeria diets today, yellow bio-fortified cassava represents additional \n\n\n\nsource of vitamin A (Saltzman et al., 2014). The production of bio-fortified \n\n\n\ncassava in Nigeria in on the increase and the producers were expected to \n\n\n\nbe efficient based on the attributes of the bio-fortified cassava varieties. \n\n\n\nEfficiency in agricultural production such as the production of bio-fortified \n\n\n\ncassava is defined as the measure of effectiveness that produces the \n\n\n\nminimum waste of resources, effort, and skill. The reason behind \n\n\n\nestimating efficiency is that if decision making units are not making \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n\n\n\nefficient use of existing technologies, then efforts designed to improve \n\n\n\nefficiency would be more cost effective than introducing a newtechnology \n\n\n\nas a means of increasing output (Shappiro, 1983). Efficiency measurement \n\n\n\nis important because it leads to a sustainable resource savings, which have \n\n\n\nimportant implications for both policy formulations and management \n\n\n\n(Bravo-Ureta and Evenson, 1994). However, despite the increase in \n\n\n\ncassava production in Nigeria, it remained well behind the population \n\n\n\ngrowth rate, but it is the only tropical root and tuber which plays some \n\n\n\nrole in world trade (Jochen, 1993; Awoyinka, 2009; Okigbo, 2007).  \n\n\n\nTherefore, based on the introduction of bio-fortified vitamin A cassava and \n\n\n\nthe quest to meet the demand of the growth in the population, there is the \n\n\n\nneed to examine the farm size efficiency of production of this important \n\n\n\ncrop to aid policy formulation, increase productivity and derive maximum \n\n\n\nbenefit from its production. Also, cassava has low input requirements and \n\n\n\nfarmers in Nigeria are poorly endowed with farm resources and as such \n\n\n\nthe available scarce inputs need to be efficiently utilized, hence the need \n\n\n\nto investigate the farm size efficiency of bio-fortified cassava production. \n\n\n\nResearch works bordering on farm size efficiency differentials of bio-\n\n\n\nfortified cassava production in Nigeria are scant or nonexisting. Few \n\n\n\nworks such as attempted such study but failed to ascertain the differentials \n\n\n\nof efficiency of bio-fortified cassava in the important agricultural zones in \n\n\n\nNigeria (Ogunleye et al., 2019).  They failed to ascertain and compare the \n\n\n\ntechnical, allocative and economic efficiency of the different farm size in \n\n\n\nbio-fortified cassava farming (Ogunleye et al., 2019). \n\n\n\nThere is a need for a study that will determine the efficiency determinants \n\n\n\nof factors influencing the technical, allocative and economic efficiency \n\n\n\namong these categories of bio-fortified cassava farms and compare these \n\n\n\ncoefficients in the various cassava zones of Nigeria to enable uniform \n\n\n\npolicy or specific policy frameworks be designed for boosting the \n\n\n\nproduction of bio-fortified cassava in Nigeria based on research findings. \n\n\n\nThe farm size in Nigeria is however categorized into three namely, small, \n\n\n\nmedium and large scale biofortified cassava farms. Specifically, the study \n\n\n\ndescribed the socio-economic characteristics of bio-fortified cassava \n\n\n\nproducers by farm size; determined the costs and returns of bio-fortified \n\n\n\ncassava production by farm size and analyzed the determining factors \n\n\n\ninfluencing the technical, allocative and economic efficiency of small, \n\n\n\nmedium and large-scale bio-fortified cassava farms. The need for this \n\n\n\nstudy was borne out of the desire to increase the level of productivity in \n\n\n\nbio-fortified cassava production and also to throw more light on the \n\n\n\nproblems associated with its production in Nigeria. The findings of this \n\n\n\nstudy help to provide information and a solution to the decreasing \n\n\n\nproductivity and yield of bio-fortified cassava per hectare, leading to an \n\n\n\nimprovement in bio-fortified cassava production. \n\n\n\n2. THEORETICAL FRAMEWORK \n\n\n\nThe review of relevant literature explains the techniques of estimating the \n\n\n\nfarm efficiency of agricultural production. The following two techniques \n\n\n\nexist: Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis \n\n\n\n(DEA). However, among the preference methods listed above, the \n\n\n\nStochastic Frontier Analysis (SFA) method is vastly recommended \n\n\n\nbecause of the following reasons; availability of data, types of data (cross-\n\n\n\nsection, time series and panel), convenience of the analysis, other \n\n\n\neconomic underpins and indeed advantages derived from the tools \n\n\n\n(Battese and Coelli, 1995).  Stochastic Frontier Analysis (SFA) has been \n\n\n\nwidely adopted and used by many researchers (Ajibefun et al., 2006; Coelli \n\n\n\nand Battese, 1996; Ogundele and Okoruwa, 2006; Ayinde et al., 2011; \n\n\n\nTaphee and Jongur, 2014; Ogunleye et al., 2019). This is because the \n\n\n\nStochastic Frontier Analysis (SFA) makes a distinction between statistical \n\n\n\nnoise and random noise around the obtained production frontier and \n\n\n\ninefficiency (Kebede, 2001; Oren and Alemdar, 2006). \n\n\n\nThe stochastic model specification not only address the noise problem \n\n\n\nassociated with earlier deterministic function, but also permit the \n\n\n\nestimation of standard errors and tests of hypotheses which were not \n\n\n\npossible with the early deterministic model because of the violation of the \n\n\n\nmaximum likelihood condition. However, the main criticism of stochastic \n\n\n\nfrontier is that there is no a-priori justification for the selection of any \n\n\n\nparticular distributional form Ui. In agricultural economics literature, the \n\n\n\nuse of Stochastic Frontier Analysis (SFA) is recommended because of the \n\n\n\ninherent nature of uncertainty/variability associated with agricultural \n\n\n\nproduction due to weather, fires, pests, diseases, etc (Coelli and Battese, \n\n\n\n1996; Coelli et al., 1998). The present study adopts the stochastic frontier \n\n\n\napproach already developed by earlier studies (Ani et al., 2013; Taphee \n\n\n\nand Jongur, 2014; Ogunleye et al., 2019).  \n\n\n\n3. METHODOLOGY \n\n\n\n3.1 Area of Study \n\n\n\nThis study was carried out in Nigeria. Nigeria is located in West Africa on \n\n\n\nthe Gulf of Guinea and has a total area of 923,768 km2 making it the world\u2019s \n\n\n\n32nd largest county. It shares a 4,047 km border with Benin(77km), \n\n\n\nNiger(1497km), chad (87km), Cameroon (1690km) and has a coastline of \n\n\n\na least 853km. Nigeria lies between latitude 4o and 14o North and \n\n\n\nlongitude 2o and 15o East.  The far South is defined by its tropical rain \n\n\n\nforest climate where annual rainfall is 60 to 80 inches (1524mm to \n\n\n\n2032mm) per year. The coastal plain are found in both the South-West and \n\n\n\nthe South-East, this forest zones most southerly portion is defined as salt \n\n\n\nwater swamp also known as the mangrove swamp. The tropical climate in \n\n\n\nthe area favors the growth of some varieties of annual crops such as \n\n\n\ngroundnut, yam, cassava, maize, rice, cowpea, plantain and banana and the \n\n\n\ntree crops include cocoa, kola nut and palm produce. There are two \n\n\n\ndistinct seasons in Nigeria, namely the rainy season which last from March \n\n\n\nto October and the dry season which comes up with harmattan and last \n\n\n\nfrom November to February. Nigeria is the most populous country in \n\n\n\nAfrica and account for about 18% of the continent total population. Nigeria \n\n\n\nwas one of the first country in Africa where bio-fortified cassava was \n\n\n\nintroduced in 2011, hence the choice of the study area. \n\n\n\n3.2 Sampling procedures and sample size \n\n\n\nMultistage sampling procedures were employed for the study. The first \n\n\n\nstage involved purposive selection of three States because the \n\n\n\nintroduction of bio-fortified cassava in 2011 started in these States. This \n\n\n\nincluded Oyo, Benue and Akwa-ibom State. The second stage involved \n\n\n\npurposive selection of two Local Government Areas (LGAs) because of the \n\n\n\nconcentration of bio-fortified cassava producers in the areas. The third \n\n\n\nstage involved purposive selection of three communities from each of the \n\n\n\nselected LGAs. At the third stage, ten bio-fortified cassava farmers were \n\n\n\npurposively selected from each community to make a total of 360 (Three \n\n\n\nhundred and sixty) respondents. Primary data were used for the study. \n\n\n\nThe primary data were sourced from cross-sectional survey of bio-\n\n\n\nfortified cassava farmers in the study area with the aid of well-structured \n\n\n\nquestionnaire to cover information about the socioeconomic \n\n\n\ncharacteristics of respondent and inputs and outputs of bio-fortified \n\n\n\ncassava production. Data were collected in June 2019- September, 2019. \n\n\n\n3.3 Analytical techniques \n\n\n\nThe data were analyzed using descriptive statistics, Farm budgeting \n\n\n\nanalysis and Stochastic Frontier Analysis (SFA) and ANOVA. \n\n\n\n3.4 Descriptive statistics \n\n\n\nDescriptive statistics was used to summarized the socio-economic \n\n\n\ncharacteristics of the bio-fortified cassava farmers. \n\n\n\n3.5 Budgetary technique  \n\n\n\nThe budgetary technique was used to estimate the costs and returns to the \n\n\n\nproduction of bio-fortified cassava. the various types of inputs used and \n\n\n\ntheir costs implication were analyzed using enterprise budget analysis. \n\n\n\nThe costs were divided into variable costs and fixed costs. \n\n\n\nThe enterprise budget equations are; \n\n\n\nGross Margin (GM), \n\n\n\nGM= \u2211\ud835\udc5d\ud835\udc56\ud835\udc5e\ud835\udc56\u2211\ud835\udc5f\ud835\udc56\ud835\ude39\ud835\udc56           (1) \n\n\n\nWhere  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\n\ud835\udc5d\ud835\udc56 =average price of bio-fortified cassava sold (N) \n\n\n\n\ud835\udc5e\ud835\udc56 =average quantity of bio-fortified cassava sold per production cycle  \n\n\n\n\ud835\udc5f\ud835\udc56 =average price of variable inputs(N) \n\n\n\n\ud835\ude39\ud835\udc56 =average quantity of variable inputs used (kg) \n\n\n\nSubsequently, the net returns were obtained from gross margin.  \n\n\n\nNet returns=GM\u2212TFC                                                                                                                     (2) \n\n\n\nNI=GM -TFC                                                                                                                                  (3)  \n\n\n\nROI = NFI/T                                                                                                                                    (4)  \n\n\n\nBCR = TR/TC                                                                                                                                 (5) \nTVC = Summation of all the variable cost which includes; Land \n\n\n\npreparation, Planting materials, Chemical used, Labour used (planting, \n\n\n\nweeding, fertilizer and pesticide application and harvesting), \n\n\n\nTransportation etc. Fixed costs include depreciation on fixed assets (e.g. \n\n\n\nbuilding, wheel barrow, tractor, plougher, cutlass, hoes etc.); this was \n\n\n\ncharged using straight-line method. \n\n\n\nWhere: \nGM = Gross margin; NFI = Net farm income; TC = Total cost incurred; ROI \n\n\n\n= Return on investment; BCR = Benefit cost ratio; TVC= Total variable cost \n\n\n\nincurred; TFC= Total fixed cost incurred; TR= Total revenue generated \n\n\n\nfrom production. \n\n\n\n3.6 Stochastic Frontier Model  \n\n\n\nStochastic frontier model was used to analyzed the factors affecting the \n\n\n\nefficiency of bio-fortified cassava production by farm size. The stochastic \n\n\n\nfrontier production model for the study is expressed in equation1 as: \n\n\n\n\ud835\udc4c\ud835\udc56 = \ud835\udefd\ud835\udc5c + \u2211 \ud835\udefd\ud835\udc58\n4\n\ud835\udc58=1 \ud835\udc4b\ud835\udc58\ud835\udc56 +\n\n\n\n1\n\n\n\n2\n\u2211 \u2211 \ud835\udefd\ud835\udc58\ud835\udc4b\ud835\udc58\ud835\udc56\n\n\n\n4\n\ud835\udc57=1\n\n\n\n4\n\ud835\udc58=1 \ud835\udc4b\ud835\udc57\ud835\udc56 + \ud835\udc49\ud835\udc56 \u2212 \ud835\udc48\ud835\udc56                                                               \n\n\n\n(6) \n\n\n\nwhere,  \n\n\n\n\ud835\udc4c\ud835\udc56stands for the observed individual ith producer\u2019s output (kg);  \n\n\n\nX1 represents the hectares of land used by the ith producer;  \n\n\n\nX2 indicates labor input consisting of family and hired labor (man-days per \nhour);  \n\n\n\nX3 shows the quantity of herbicides used (gram), and;  \n\n\n\nX4 represents quantity of fertilizer used on ith producer\u2019s farm (in Naira);  \n\n\n\n\ud835\udefd\ud835\udc5c= vector of unknown parameters estimated\n  \n\ud835\udc49\ud835\udc56= are random variables associated with random factors; and \n\n\n\n\ud835\udc48\ud835\udc56= which are non-negative random variables which are assumed to \naccount for technical inefficiency in production. \n \n\n\n\nAccordingly, technical efficiency (TE) of an individual producer is defined \n\n\n\nin terms of the ratio of the observed (\ud835\udc4c\ud835\udc56\u2217)output to the corresponding \n\n\n\nfrontier output (\ud835\udc4c\ud835\udc56), conditioned on the level of inputs used by the \n\n\n\nproducer. Technical inefficiency is therefore defined as the amount by \n\n\n\nwhich the level of production for the producer is less than the frontier \n\n\n\noutput. This is shown in the equation below:  \n\n\n\n\ud835\udc47\ud835\udc38\ud835\udc56 =\n\ud835\udc4c\ud835\udc56\u2217\n\n\n\n\ud835\udc4c\ud835\udc56\n=\n\n\n\n\ud835\udc53(\ud835\udc4b\ud835\udc56\ud835\udefd)exp\u2061(\ud835\udc49\ud835\udc56\u2212\ud835\udc48\ud835\udc56)\n\n\n\n\ud835\udc53(\ud835\udc4b\ud835\udc56\ud835\udefd)exp\u2061(\ud835\udc49\ud835\udc56)\n= exp\u2061(\u2212\ud835\udc48\ud835\udc56)                                                                                    \n\n\n\n(7) \nThe stochastic cost function which is the basis for estimating the allocative \n\n\n\nefficiency (AE) of the producer\u2019s farm is specified as follows: \n\n\n\n\ud835\udc36\ud835\udc56 = \ud835\udefc\ud835\udc5c + \u2211 \ud835\udefc\ud835\udc58\n4\n\ud835\udc58=1 \ud835\udc43\ud835\udc58\ud835\udc56 +\n\n\n\n1\n\n\n\n2\n\u2211 \u2211 \ud835\udefc\ud835\udc58\ud835\udc57\n\n\n\n\ud835\udc5b\n\ud835\udc56=0\n\n\n\n\ud835\udc5b\n\ud835\udc58=1 \ud835\udc43\ud835\udc58\ud835\udc56\ud835\udc43\ud835\udc57\ud835\udc56 + \ud835\udc49\ud835\udc56 \u2212\ud835\udc48\ud835\udc56                                                                 \n\n\n\n(8) \n\n\n\nwhere, \n\n\n\n\ud835\udc36\ud835\udc56\u2061stands for the total cost of producing the bio-fortified cassava output of \nith farm on per kg basis (\u20a6);  \n\n\n\nP1 indicates cost of land (\u20a6);  \n\n\n\nP2 indicates cost of total quantity of family and hired labour (man-days) \nrequired to perform various production activities on the ith producer\u2019s \nfarm (\u20a6);  \n\n\n\nP3 shows cost of quantity of herbicide used (gram), on ith producer\u2019s farm \n(\u20a6);  \n\n\n\nP4 represents the total cost of fertilizer used on ith producer\u2019s farm (\u20a6) and  \n\n\n\n\ud835\udefc\ud835\udc60are vector of unknown parameters to be estimated \nThe sources of technical inefficiency effects in equations (7) is modeled in \n\n\n\nterms of the farm\u2019s and producer\u2019s characteristics and specified as:  \n\n\n\n\ud835\udc48\ud835\udc56 = \ud835\udeff\ud835\udc5c \u2211 \ud835\udeff\ud835\udc5b\n10\n\ud835\udc56=0 \ud835\udc4d\ud835\udc56                                                                                                                                 (9) \n\n\n\nwhere,  \n\n\n\n\ud835\udc48\ud835\udc56= technical and allocative inefficiency effects of the equations (7) and (8) \nrespectively;  \n\n\n\nZ1 =Age of the producer in years;  \n\n\n\nZ2 =Gender of the producer (1=male, 0= otherwise) \n\n\n\nZ3 = Educational level (years);  \n\n\n\nZ4 = Years of production experience (years)  \n\n\n\nZ5 = Farm Size (ha);  \n\n\n\nZ6 = Membership of association (1=yes, 0=otherwise)  \n\n\n\nZ7 = Access to credit (Yes=1, 0= otherwise) \n\n\n\nZ8 = Access to planting materials (Yes=1, 0=otherwise)  \n\n\n\nZ9 = Access to extension agent (Yes=1; 0=otherwise) \n\n\n\nZ10= Training  (1=Yes, 0=otherwise) \n\n\n\n3.7 Economic Efficiency (EE) \n\n\n\nThis is the multiplication of technical and allocative efficiency, \n\n\n\nEE= TE\u00d7 AE   \n\n\n\n3.8 Analysis of Variance (ANOVA) \n\n\n\nAnalysis of Variance (ANOVA) was used to test for significant difference in \n\n\n\nthe profitability of bio-fortified cassava production among these \n\n\n\ncategories of bio-fortified cassava farmers. To stabilize the variance, data \n\n\n\ncollected was transformed using square root transformation while \n\n\n\npercentage data was transformed with angular transformation (arcsine), \n\n\n\nSignificant means was separated using Duncan\u2019s Multiple Range Test, \n\n\n\nDMRT at P<0.05. \n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\n4.1 Socio-economic characteristics of the biofortified cassava \n\n\n\nfarmers in Nigeria  \n\n\n\nThe result of the socio-economic characteristics of bio-fortified cassava \n\n\n\nfarmers by farm size were presented in Table 1. The result of the study \n\n\n\nshows that the mean ages of the small, medium and large scale biofortified \n\n\n\ncassava farmers were 47(\u00b113.77), 46(\u00b112.27) and 48(\u00b114.21) \n\n\n\nrespectively. This is no significant difference in the ages of the three \n\n\n\ncategories of farmers. This result implies that bio-fortified cassava farmers \n\n\n\nwere in their active and productive age (Oparinde et al., 2017). Majority \n\n\n\n(68.37% and 89.25%) of the medium and large scale bio-fortified cassava \n\n\n\nfarmers were male while about 46.38% of the small scale bio-fortified \n\n\n\ncassava farmers were male. This shows that production of bio-fortified \n\n\n\ncassava on medium and large scale were mainly popular among men while \n\n\n\nproduction on small scale were mostly common among the women. This \n\n\n\nmight be due to the fact that men are more prone to adopting new \n\n\n\ntechnology than women and might also be due to the drudgery nature of \n\n\n\nfarm practices involved in the production of bio-fortified cassava, hence, \n\n\n\nmen will be more involved than women. Majority (79%, 83.21% and \n\n\n\n85.31%) of the small, medium and large scale bio-fortified cassava \n\n\n\nrespectively were married.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\nThis implies that producers of bio-fortified cassava farmers were \n\n\n\nresponsible and also the use of family labour might be possible for the \n\n\n\nproduction of bio-fortified cassava. The mean years of formal education of \n\n\n\nsmall, medium and large scale biofortified cassava farmers were 14.39 \n\n\n\n(\u00b16.83), 14.34 (\u00b15.25) and 16.13 (\u00b17.29) respectively. This revealed that \n\n\n\nrespondents were literate and thus, can read and write. About 43.37% of \n\n\n\nthe small scale bio-fortified cassava farmers had access to credit while \n\n\n\n58.17% of the medium scale bio-fortified cassava farmers had access to \n\n\n\ncredit with majority (79.38%) of the large scale bio-fortified cassava \n\n\n\nfarmers having access to credit. This implies that large scale bio-fortified \n\n\n\ncassava farmers had more access to credit and this might have facilitated \n\n\n\nthem producing bio-fortified cassava on a large scale. The mean years of \n\n\n\nexperience of small, medium and large scale bio-fortified cassava farmers \n\n\n\nwere 14.62(\u00b16.92), 13.38(\u00b16.18) and 15.19(\u00b18.34) respectively. This \n\n\n\nshows that the respondents had been into cassava production for a long \n\n\n\ntime even before the introduction of new improved bio-fortified cassava \n\n\n\nin 2011. Majority (86.23%, 87.42% and 94.47%) of the small, medium and \n\n\n\nlarge scale bio-fortified cassava farmers respectively belong to one \n\n\n\nassociation or the other. This revealed that they might experience group \n\n\n\ndynamics and benefits such as credit facilities, farm inputs and etc. \n\n\n\n\n\n\n\nTable 1: Socio-economic characteristics of bio-fortified cassava \n\n\n\nfarmers by farm size \n\n\n\nVariables \nSmall \n\n\n\nscale \n\n\n\nMedium \n\n\n\nScale \n\n\n\nLarge \n\n\n\nScale \nPooled \n\n\n\nAge (years)                                      \n47 \n\n\n\n(\u00b113.77) \n\n\n\n46 \n\n\n\n(\u00b112.27) \n\n\n\n48 \n\n\n\n(\u00b114.21) \n\n\n\n47 \n\n\n\n(\u00b114.35) \n\n\n\nMale (%)                                         46.38 68.37 89.25 67.36 \n\n\n\nMarried (%)                                      79.00 83.21 85.31 81.57 \n\n\n\nFormal \n\n\n\neducation \n\n\n\n(years)           \n\n\n\n14.39 \n\n\n\n(\u00b16.83) \n\n\n\n14.34 \n\n\n\n(\u00b15.25) \n\n\n\n16.13 \n\n\n\n(\u00b17.29) \n\n\n\n14.92 \n\n\n\n(\u00b16.48) \n\n\n\nAccess to credit \n\n\n\n(%)                         \n43.37 58.17 79.38 61.71 \n\n\n\nYears of \n\n\n\nexperience \n\n\n\n(years)            \n\n\n\n14.62 \n\n\n\n(\u00b16.92) \n\n\n\n13.38 \n\n\n\n(\u00b16.18) \n\n\n\n15.19 \n\n\n\n(\u00b18.34) \n\n\n\n14.22 \n\n\n\n(\u00b16.43) \n\n\n\nMembership of \n\n\n\nassociation (%)       \n86.23 87.42 94.47 88.41 \n\n\n\nFigures in parentheses are standard deviation \n\n\n\n4.2 Farm specific characteristics by farm size \n\n\n\nPresented in Table 2 is the farm specific characteristics of the bio-fortified \n\n\n\ncassava farmers by farm size. From Table 2, majority (83.57%) of the \n\n\n\nsmall-scale bio-fortified cassava farmers had a farm size of between 1-2ha \n\n\n\nwhile 16.43% of them had a farm size of between 2.1-3ha. The implies that \n\n\n\nfarm size of between 1-2ha were more common among scale bio-fortified \n\n\n\ncassava farmers in Nigeria. Among the medium scale bio-fortified cassava \n\n\n\nfarmers, about 57.41% of them had a farm size of between 4.1-5ha while \n\n\n\n42.59% of the medium scale bio-fortified cassava farmers had a farm size \n\n\n\nof between 3.1-4ha. Considering the large-scale bio-fortified cassava \n\n\n\nfarmers, about 26.67% of them had a farm size of between 4.1-5ha while \n\n\n\nmajority (73.33%) had a farm size of   \u22655.1ha. The mean farm size for the \n\n\n\nsmall, medium and large-scale bio-fortified cassava farmers were \n\n\n\n1.2(\u00b10.37), 2.3(\u00b11.98) and 4.2(\u00b12.59) respectively. This result implies that \n\n\n\nproduction of bio-fortified cassava in Nigeria is still largely on small scale \n\n\n\nas majority (57.5%) of the respondents were small scale bio-fortified \n\n\n\ncassava farmers (Table 2).  \n\n\n\n\n\n\n\nAs regarding the mode of land acquisition of bio-fortified cassava farmers \n\n\n\nby farm size in Table 2, about 43% of the small scale bio-fortified cassava \n\n\n\nfarmers inherited their farm land, 5.8% purchased their farm land, 16.9% \n\n\n\nrented their farm land while 34.3% of the small scale bio-fortified cassava \n\n\n\nfarmers acquired their farm land through communal/gift. Considering the \n\n\n\nmedium scale bio-fortified cassava farmers, about 39.82% inherited their \n\n\n\nfarmland, 8.33% purchased their farm farmland, 31.48% rented their \n\n\n\nfarmland while 20.37% of the medium scale bio-fortified cassava farmers \n\n\n\nacquired their farmland through communal/gift. About 8.89% of the large \n\n\n\nscale bio-fortified cassava farmers acquired their farm land through \n\n\n\ninheritance, 40% got their farmland through purchase, 37.78% of them \n\n\n\nwere through rent while 13.33% of the large scale bio-fortified cassava \n\n\n\nfarmers got their farmland through communal/gift. This result implies \n\n\n\nthat acquisition of farmland through inheritance for the production of bio-\n\n\n\nfortified cassava were more common among the small scale bio-fortified \n\n\n\ncassava farmers in the study area while acquisition of farm land through \n\n\n\npurchase and rent were more common among medium and large scale bio-\n\n\n\nfortified cassava farmers in Nigeria. \n\n\n\n\n\n\n\nThe varieties of bio-fortified cassava grown by the farmers by farm size \n\n\n\nwere also presented in Table 2. Among the small scale bio-fortified cassava \n\n\n\nfarmers, about 29.5% grown TMS 01/1371, 16.9% grown TMS 01/1412, \n\n\n\n22.2% grown, TMS 01/1368, 9.2% grown TMS 01/0593, 11.6% grown \n\n\n\nTMS 01/0539 while 10.6% of the small scale bio-fortified cassava farmers \n\n\n\ngrown TMS 01/0220 varieties in Nigeria. Among the medium scale bio-\n\n\n\nfortified cassava farmers, about 7.4% grown TMS 01/1371, 4.6% grown \n\n\n\nTMS 01/1368, 14.8% grown TMS 01/0593, 30.6% grown TMS 01/0539 \n\n\n\nwhile 42.6% of the medium scale bio-fortified cassava farmers grown TMS \n\n\n\n01/0220 varieties in Nigeria. Among the large-scale bio-fortified cassava \n\n\n\nfarmers, about 13.3% of grown TMS 01/1412, 17.8% grown TMS \n\n\n\n01/0593, 40% grown TMS 01/0539 while 28.9% of the large scale bio-\n\n\n\nfortified cassava grown TMS 01/0220 varieties. This result implies that \n\n\n\ncultivation of bio-fortified cassava varieties released in 2011 (TMS \n\n\n\n01/1371, TMS 01/1412 and TMS 01/1368) were more popular among \n\n\n\nsmall scale bio-fortified cassava farmers in Nigeria while the cultivation of \n\n\n\nbio-fortified cassava varieties released in 2016 (TMS 01/0593, TMS \n\n\n\n01/0539 and TMS 01/0220 were more popular among the medium and \n\n\n\nlarge scale bio-fortified cassava farmers. This might be attributed to access \n\n\n\nto information and planting materials by the medium and large-scale bio-\n\n\n\nfortified cassava farmers in Nigeria. \n\n\n\n\n\n\n\nTable 2: Farm specific characteristics by farm sizes \n\n\n\nVariables \n \n\n\n\nSmall \nscale \n\n\n\nFreq (%) \n\n\n\nMedium \nScale \n\n\n\nFreq (%) \n\n\n\nLarge \nScale \n\n\n\nFreq (%) \n\n\n\nTotal \nFreq (%) \n\n\n\nFarm size (ha) \n\n\n\n1.0-2.0 173(83.57) -- -- 173(48.06) \n\n\n\n2.1-3.0 34(16.43) -- -- 34 (9.44) \n\n\n\n3.1-4.0 -- 46(42.59) -- 46(12.78) \n\n\n\n4.1-5.0 -- 62(57.41) 12(26.67) 74(20.56) \n\n\n\n\u22655.1 -- -- 33(73.33) 33(9.17) \n\n\n\nTotal \n207 \n\n\n\n(100.0) \n108 \n\n\n\n(100.00) \n45 \n\n\n\n(100.00) \n360 \n\n\n\n(100.00) \n\n\n\nMean 1.2(\u00b10.37) 2.3(\u00b11.98) 4.2(\u00b12.59)  \n\n\n\nMode of land acquisition \n\n\n\nInherited 89(43.0) 43(39.82) 4(8.89) 136(37.78) \n\n\n\nPurchase 12(5.8) 9(8.33) 18(40.0) 39(10.83) \n\n\n\nRent 35(16.9) 34(31.48) 17(37.78) 86(23.89) \n\n\n\nCommunal/Gift 71(34.3) 22(20.37) 6(13.33) 99(27.5) \n\n\n\nTotal \n207 \n\n\n\n(100.0) \n108 \n\n\n\n(100.0) \n45 \n\n\n\n(100.0) \n360 \n\n\n\n(100.00) \n\n\n\nVarieties grown \n\n\n\nTMS 01/1371 61(29.5) 8(7.4) -- 69(19.2) \n\n\n\nTMS 01/1412 35(16.9) -- 6(13.3) 41(11.4) \n\n\n\nTMS 01/1368 46(22.2) 5(4.6) -- 51(14.2) \n\n\n\nTMS 01/0593 19(9.2) 16(14.8) 8(17.8) 43(11.9) \n\n\n\nTMS 01/0539 24(11.6) 33(30.6) 18(40.0) 75(20.8) \n\n\n\nTMS 01/0220 22(10.6) 46(42.6) 13(28.9) 81(22.5) \n\n\n\nTotal \n207 \n\n\n\n(100.0) \n108 \n\n\n\n(100.0) \n45 \n\n\n\n(100.0) \n360 \n\n\n\n(100.0) \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\nTable 3: Average costs and returns to bio-fortified cassava \n\n\n\nproduction per season by farm size \n\n\n\nVariables \n\n\n\nSmall Scale \n\n\n\nAmount (\u20a6) \n\n\n\n% of total \n\n\n\ncosts \n\n\n\nMedium \n\n\n\nScale \n\n\n\nAmount (\u20a6) \n\n\n\n% of total \n\n\n\ncosts \n\n\n\nLarge Scale \n\n\n\nAmount (\u20a6) % \n\n\n\nof total costs \n\n\n\nA. Total \n\n\n\nrevenue \n\n\n\nVariable cost \n\n\n\n284159.27 823572.62 1585463.68 \n\n\n\nLand \n\n\n\npreparation \n\n\n\n42786.32           \n\n\n\n48.93 \n\n\n\n89854.43          \n\n\n\n24.42 \n\n\n\n128432.64         \n\n\n\n17.76 \n\n\n\nPlanting \n\n\n\nmaterial \n\n\n\n8620                    \n\n\n\n9.85 \n18743 \n\n\n\n64743                 \n\n\n\n8.95 \n\n\n\nFertilizer 1650 28451 39264 \n\n\n\nHerbicides 3400 19652 31753 \n\n\n\nLabor cost \n14370                \n\n\n\n16.43 \n\n\n\n49435              \n\n\n\n13.44 \n\n\n\n87934                \n\n\n\n12.16 \n\n\n\nB. Total \n\n\n\nVariable Costs \n\n\n\n(TVC) \n\n\n\n70826.32           \n\n\n\n81.00 \n\n\n\n206135.43       \n\n\n\n56.04 \n\n\n\n352126.64         \n\n\n\n48.68 \n\n\n\nFixed cost Rent \n\n\n\non land \n\n\n\n14850                \n\n\n\n16.97 \n\n\n\n96413              \n\n\n\n26.21 \n\n\n\n185632              \n\n\n\n25.66 \n\n\n\nDepreciation on \n\n\n\nsprayer \n843 9324 28461 \n\n\n\nDepreciation on \n\n\n\nwheelbarrow \n929 8329 33837 \n\n\n\nDepreciation on \n\n\n\ntractor \n-- \n\n\n\n18485              \n\n\n\n5.03 \n\n\n\n64962                  \n\n\n\n8.98 \n\n\n\nDepreciation on \n\n\n\nother equipment \n-- \n\n\n\n29148              \n\n\n\n7.92 \n\n\n\n58325                  \n\n\n\n8.06 \n\n\n\nC. Total fixed \n\n\n\ncosts (TFC) \n\n\n\n16622                \n\n\n\n19.00 \n\n\n\n161699             \n\n\n\n43.96 \n\n\n\n371217              \n\n\n\n51.32 \n\n\n\nD. Total costs \n\n\n\n(B+C) \n87448.32 367834.43 723343.64 \n\n\n\nE. Gross margin \n\n\n\n(A-B) \n213332.95 617437.19 1233337.04 \n\n\n\nF. Net Farm \n\n\n\nIncome (A-D) \n196710.95 455738.19 862120.04 \n\n\n\nReturn on \n\n\n\nInvestment \n\n\n\n(ROI) \n\n\n\n1.06 1.24 1.19 \n\n\n\nBenefit cost \n\n\n\nration \n2.06 2.24 2.19 \n\n\n\n4.3 Costs and returns to bio-fortified cassava production by farm \n\n\n\nsize in Nigeria \n\n\n\nIn other to ascertain the profitability of bio-fortified cassava production, \n\n\n\nthe average gross margin, net returns, rate of returns and benefit cost ratio \n\n\n\nof the bio-fortified cassava farmers were calculated by farm size. The input \n\n\n\nused, costs, output data generated from the bio-fortified cassava farmers \n\n\n\nwere used to compute the gross margin and net returns to bio-fortified \n\n\n\ncassava production. The average costs and returns for the bio-fortified \n\n\n\ncassava production by farm size were presented in Table 3. The result \n\n\n\nrevealed the revenue generated for one production season by small scale \n\n\n\nbio-fortified cassava farmers was \u20a6284159.27. From Table 8 Small scale \n\n\n\nfarmers, the cost of land preparation (\u20a642786.32) on individual cost \n\n\n\naccounted for a large proportion (48.93%) of the total costs with the total \n\n\n\nvariable costs (\u20a670826.32) accounting for the largest proportion (81%) of \n\n\n\nthe total costs. Rent on land (\u20a614850) accounted for a significant \n\n\n\nproportion 16.97% of the fixed cost with the total fixed costs accounting \n\n\n\nfor just 19%. The negligible small proportion of the fixed costs shows the \n\n\n\ncrude method of agricultural small-scale practices in Nigeria.  \n\n\n\n\n\n\n\nThe average net return (net farm income) from the production of bio-\n\n\n\nfortified cassava in Table 3 (small scale) was \u20a6196710.95. This implies that \n\n\n\nthe production of bio-fortified cassava in Nigeria is a profitable enterprise \n\n\n\neven on a small scale. The return on investment of small scale bio-fortified \n\n\n\ncassava farmers indicated that for every one naira invested in bio-fortified \n\n\n\ncassava production, the farmer gains \u20a61.06. The implication is that bio-\n\n\n\nfortified cassava production on a small scale in Nigeria is profitable. The \n\n\n\nresult agrees in the Profitability of investment and farm level efficiency \n\n\n\namong groups of Vitamin A cassava farmers in Oyo State, Nigeria who \n\n\n\nfound out that bio-fortified cassava production is a profitable business \n\n\n\nenterprise (Ogunleye et al., 2019). The benefit cost ratio of 2.06 among the \n\n\n\nsmall-scale cassava farmers shows that for every \u20a62.00 return to bio-\n\n\n\nfortified cassava production, 6k is been spent on the cost of producing the \n\n\n\nbio-fortified cassava. Also, the costs and returns for medium scale bio-\n\n\n\nfortified cassava farmers were calculated and presented in Table 3. The \n\n\n\naverage revenue generated by the medium scale bio-fortified cassava \n\n\n\nfarmers in one production cycle in Nigeria were \u20a6823572.62.  \n\n\n\n\n\n\n\nThe cost of land preparation \u20a689854.43 takes the largest proportion of the \n\n\n\nvariable cost while the total variable cost \u20a6206135.43 takes the largest \n\n\n\nshare of the total costs. The costs of rent on land \u20a696413takes the largest \n\n\n\nproportion of the fixed cost while the total fixed costs (\u20a6161699) \n\n\n\naccounted for 43.96% of the total costs of producing bio-fortified cassava \n\n\n\non a medium scale. The average total cost of producing bio-fortified \n\n\n\ncassava on a medium scale was \u20a6367834.43. The net farm income realized \n\n\n\nby the medium scale bio-fortified cassava farmers was \u20a6455738.19 which \n\n\n\nshows that production of bio-fortified cassava on a medium scale is a \n\n\n\nprofitable enterprise in Nigeria. The return on investment of medium scale \n\n\n\nbio-fortified cassava farmers indicated that for every one naira invested in \n\n\n\nbio-fortified cassava production, the farmer gains \u20a61.24. The implication \n\n\n\nis that bio-fortified cassava production on a medium scale in Nigeria is \n\n\n\nprofitable. The result agrees with in the Profitability of investment and \n\n\n\nfarm level efficiency among groups of Vitamin A cassava farmers in Oyo \n\n\n\nState, Nigeria who found out that bio-fortified cassava production is a \n\n\n\nprofitable business enterprise (Ogunleye et al., 2019).  \n\n\n\n\n\n\n\nThe benefit cost ratio of 2.24 among the medium scale bio-fortified cassava \n\n\n\nfarmers shows that for every \u20a62.00 return to bio-fortified cassava \n\n\n\nproduction, 24k is been spent on the cost of producing the bio-fortified \n\n\n\ncassava. The average revenue generated by the large-scale bio-fortified \n\n\n\ncassava farmers in one production cycle in Nigeria were \u20a61585463.68. The \n\n\n\ncost of land preparation \u20a6128432.64 also takes the largest proportion \n\n\n\n(17.76%) of the variable cost while the total variable cost \u20a6352126.64 \n\n\n\ntakes the largest share (48.68%) of the total costs. The costs of rent on land \n\n\n\n\u20a6185632 takes the largest proportion (25.66%) of the fixed cost while the \n\n\n\ntotal fixed costs (\u20a6371217) accounted for 51.32% of the total costs of \n\n\n\nproducing bio-fortified cassava on a large scale. The average total cost of \n\n\n\nproducing bio-fortified cassava on a large scale was \u20a6723343.64. The net \n\n\n\nfarm income realized by the large-scale bio-fortified cassava farmers was \n\n\n\n\u20a6862120.04 which shows that production of bio-fortified cassava on a \n\n\n\nlarge scale is a profitable enterprise. The return on investment of large-\n\n\n\nscale bio-fortified cassava farmers indicated that for everyone naira \n\n\n\ninvested in bio-fortified cassava production, the farmer gains \u20a61.19. The \n\n\n\nimplication is that bio-fortified cassava production on a large scale is \n\n\n\nprofitable (Ogunleye et al., 2019). The benefit cost ratio of 2.19 among the \n\n\n\nlarge-scale bio-fortified cassava farmers shows that for every \u20a62.00 return \n\n\n\nto bio-fortified cassava production, 19k is been spent on the cost of \n\n\n\nproducing the bio-fortified cassava. However, it can be implied that \n\n\n\nproduction of bio-fortified cassava on all the categories of farm size \n\n\n\nincluding small, medium and large scale were profitable, farmers in the \n\n\n\ncategory of medium and large scale however, realize more income in a \n\n\n\nproduction cycle than the small scale bio-fortified cassava farmers. \n\n\n\n4.4 Efficiency of Bio-fortified Cassava Production by Farm Size \n\n\n\n4.4.1 Technical efficiency of Bio-fortified Cassava Production by \n\n\n\nFarm Size  \n\n\n\nThe stochastic frontier model specified was estimated by the maximum \n\n\n\nlikelihood (ML) method using frontier 4.1 software developed (Coelli, \n\n\n\n1995). The ML estimates and inefficiency determinants of the specified \n\n\n\nfrontier were presented in Table 4 by farm size. The result of study \n\n\n\nrevealed that the generalized likelihood function was -16.23, -36.37 and -\n\n\n\n11.47 for small, medium and large bio-fortified cassava farm size \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\nrespectively. The likelihood function in Table 4 implies that inefficiency \n\n\n\nexist in the data set. The likelihood ratio value represents the value that \n\n\n\nmaximizes the joint densities in the estimated model. Thus, the functional \n\n\n\nform used in this estimation is an adequate representation of the data. The \n\n\n\nvalue of gamma (\u03b3) is estimated and it was highly significant at (p<0.01), \n\n\n\n(p<0.01) and (p<0.01) level of probability for small, medium and large bio-\n\n\n\nfortified cassava farm size respectively. This is consistent with the theory \n\n\n\nthat \u03b3-value should be greater than zero. This implies that 99% of random \n\n\n\nvariation in the output of the bio-fortified cassava farmers was due to the \n\n\n\nproducers\u2019 inefficiency in their respective production farms and not as a \n\n\n\nresult of random variability.  \n\n\n\n\n\n\n\nSince these factors are under the control of the producers, reducing the \n\n\n\ninfluence of the effect of \u03b3 will greatly enhance the technical efficiency of \n\n\n\nthe producers and increase their output. The value of sigma square (\u03c32) \n\n\n\nwas significantly different from zero at p<0.01, p<0.05 and p<0.01 level of \n\n\n\nprobability for small, medium and large bio-fortified cassava farm size \n\n\n\nrespectively. This indicates a good fit and correctness of the specified \n\n\n\ndistributional assumptions of the composite error terms while the \u03b3 \n\n\n\nindicates the systematic influences that are unexplained by the production \n\n\n\nfunction and the dominant sources of random error. This means that the \n\n\n\ninefficiency effects make significant contribution to the technical \n\n\n\ninefficiencies of bio-fortified cassava producers. However, the estimated \n\n\n\ncoefficients of the parameters of production variables for small scale bio-\n\n\n\nfortified cassava farm size (land, herbicide and fertilizer) were positive \n\n\n\nand significant at 1% level of probability each and hence play a major role \n\n\n\nin bio-fortified cassava production on a small scale. \n\n\n\n\n\n\n\nFor the medium scale bio-fortified cassava farm size, production variables \n\n\n\nsuch as land, labor and fertilizer were positive and significant at 1%, 1% \n\n\n\nand 5% probability level respectively and also play a major role in bio-\n\n\n\nfortified cassava production on a medium scale. For the large-scale bio-\n\n\n\nfortified cassava farm size, all the production variables (land, labor, \n\n\n\nherbicide and fertilizer) were positive and significant at 1% probability \n\n\n\nlevel each. This implies that all the production variables play a major role \n\n\n\nin bio-fortified cassava production on a large scale. The average technical \n\n\n\nefficiency for the small, medium and large scale bio-fortified cassava \n\n\n\nfarmers was 0.52, 0.64 and 0.73 respectively implying that, on average, the \n\n\n\nrespondents are able to obtain 52%, 64% and 73% of potential output \n\n\n\nfrom a given mixture of production inputs for the small, medium and large \n\n\n\nscale  bio-fortified cassava farmers.  \n\n\n\n\n\n\n\nThus, in a short run, there is minimal scope (48%, 36% and 27%) for small, \n\n\n\nmedium and large-scale farmers respectively of increasing the efficiency, \n\n\n\nby adopting the best management practices and techniques for the \n\n\n\nproduction of bio-fortified cassava. For small scale bio-fortified farm size, \n\n\n\nthe estimated coefficient for land was 0.042 which is positive and \n\n\n\nstatistically significant at 1% level of probability. The estimated (0.042) \n\n\n\ncoefficient of land implies that increasing land size by 1% will lead to \n\n\n\nincreased bio-fortified cassava output by 4.2% among the small-scale bio-\n\n\n\nfortified cassava farmers.  Furthermore, the coefficients of the quantity of \n\n\n\nherbicide used by the small-scale bio-fortified cassava farmers was 0.282 \n\n\n\nwhich is positive and statistically significant at 1% level of probability. \n\n\n\nThis implies that a 1% increase in herbicide used will increase bio-fortified \n\n\n\ncassava output by 28.2% among the small-scale bio-fortified cassava \n\n\n\nfarmers. Also, the coefficient of fertilizer used by the small-scale bio-\n\n\n\nfortified cassava farmers was 0.042 which was positive and significant at \n\n\n\n1% probability level. This implies that small scale bio-fortified cassava \n\n\n\nfarmers can increased their output by 0.42% if they increase fertilizer \n\n\n\nused by 1%.  \n\n\n\n\n\n\n\nRegarding medium scale bio-fortified cassava farmers, the estimated \n\n\n\ncoefficient (0.305) of land was positive and statistically significant at 1% \n\n\n\nlevel of probability. This implies that 1% increase in land size will leads to \n\n\n\n30.5% increase in output among the medium scale bio-fortified cassava \n\n\n\nfarmers.  In addition, the coefficients (0.931) of labor was positive and \n\n\n\nsignificant at 1% probability level among the medium scale bio-fortified \n\n\n\ncassava farmers. This implies that increasing labor used by 1% will \n\n\n\nincrease bio-fortified cassava output by 93.1% among the medium scale \n\n\n\nbio-fortified cassava farmers. Furthermore, the coefficients (0.006) of \n\n\n\nfertilizer used was positive and significant at 5% probability level. This \n\n\n\nimplies that medium scale bio-fortified cassava farmers could increase \n\n\n\ntheir output by 6% if they increase fertilizer used by 5%. For the large \n\n\n\nscale bio-fortified cassava farmers, the coefficient (0.068) of land was \n\n\n\npositive and significant at 1% probability level. This implies that the \n\n\n\noutputs of large scale bio-fortified cassava farmers could increase by 6.8% \n\n\n\nif they increase their land size by 1%.  \n\n\n\n\n\n\n\nFurthermore, the coefficient (0.024) of labor was positive and significant \n\n\n\nat 1% level of probability among the large scale bio-fortified cassava \n\n\n\nfarmers. This implies that the large scale bio-fortified cassava farmers can \n\n\n\nincrease their output by 2.4% if they increase labour use by 1%. Also, the \n\n\n\ncoefficient of herbicide (1.330) was positive and significant at 1% \n\n\n\nprobability level among the large scale bio-fortified cassava farmers. This \n\n\n\nimplies that the bio-fortified cassava outputs can be increased by 13.3% if \n\n\n\nthey increase herbicide used by 1%. In addition, the coefficient (0.369) of \n\n\n\nfertilizer used was positive and significant at 1% level of probability \n\n\n\namong the large scale bio-fortified cassava farmers. This implies that large \n\n\n\nscale  bio-fortified cassava farmers can increased their outputs by 36.9% \n\n\n\nif they increase their fertilizer used by 1%. The estimated result of the \n\n\n\ninefficiency model was presented in Table 4. Generally, a negative sign on \n\n\n\na coefficient means that the variable increases technical inefficiency, while \n\n\n\na positive sign will decrease technical inefficiency. The result present in \n\n\n\nTable 4 shows that among the small-scale bio-fortified cassava farmers, \n\n\n\nthe technical inefficiency variables such as age, education and access to \n\n\n\ncredit was significant.  \n\n\n\n\n\n\n\nAmong the medium-scale bio-fortified cassava farmers, the technical \n\n\n\ninefficiency variables such as gender, membership in association and \n\n\n\naccess to credit were significant. Among the large-scale bio-fortified \n\n\n\ncassava farmers, the technical inefficiency variables such as, age and \n\n\n\neducation were significant. Age in bio-fortified cassava production was \n\n\n\npositive and significant at 5% and 1% respectively for the small and large \n\n\n\nscale bio-fortified cassava farmers in Nigeria. This shows that increase in \n\n\n\nage in bio-fortified cassava production would reduce technical \n\n\n\ninefficiency. Producers\u2019 age could be associated with skill accumulation \n\n\n\nover years which could enhance productivity and resource allocations \n\n\n\nthereby reducing technical inefficiency. Education in bio-fortified cassava \n\n\n\nproduction was positive and significant at 1% each for small and large-\n\n\n\nscale bio-fortified cassava producers. This shows that increase in the years \n\n\n\nof education in bio-fortified cassava production would decrease technical \n\n\n\ninefficiency. As the producers attained more education in their enterprise, \n\n\n\nthey tend to be more productive and efficiently allocate their resources \n\n\n\nthereby increasing their technical efficiency.  \n\n\n\n\n\n\n\nAccess to credit was positive and significant at 5% and 1% for small and \n\n\n\nmedium scale bio-fortified cassava farmers. This implies that access to \n\n\n\ncredit will reduce technical inefficiency among the small and medium scale \n\n\n\nbiofortified cassava producers. Gender was negative and significant at 5% \n\n\n\nfor medium scale bio-fortified cassava producers. This shows that the \n\n\n\ngender of the small-scale producers could increase technical inefficiency. \n\n\n\nMale producers might be more technical efficient than their male \n\n\n\ncounterparts because they are more involved in training and skill \n\n\n\nacquisition which may help them in reducing their technical inefficiency. \n\n\n\nMembership in association was positive and significant at 1% level of \n\n\n\nprobability for the medium scale bio-fortified cassava farmers. The \n\n\n\npositive coefficients for membership in association implies that \n\n\n\nmembership in association reduces technical inefficiency in bio-fortified \n\n\n\ncassava production among the medium scale producers. Membership in \n\n\n\nassociation could afford the producers the opportunity of sharing \n\n\n\ninformation on effective management practices by interacting with other \n\n\n\nproducers. A group researcher noted that the increase in efficiency effects \n\n\n\nthrough producers belonging to association is as a result of association \n\n\n\nbeing a source of quality inputs, information and organized marketing of \n\n\n\nproducts (Abass et al., 2019). This implied that medium scale bio-fortified \n\n\n\ncassava producers can market their produce through association in other \n\n\n\nto have access to higher income. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\nTable 4: Maximum Likelihood Estimates of Stochastic Frontier Model (Technical Efficiency) of Bio-fortified cassava Production by Farm Size \n\n\n\nVariables Parameters \nSmall Scale Medium Scale Large Scale \n\n\n\nCoeff. Std. Err t-ratio Coeff. Std. Err t-ratio Coeff. Std. Err t-ratio \n\n\n\nConstant \u03b20 0.110*** 0.007 15.74 6.659*** 0.439 15.16 0.011*** 0.001 11.01 \n\n\n\nLand \u03b21 0.042*** 0.007 5.63 0.305*** 0.026 11.73 0.068*** 0.010 6.80 \n\n\n\nLabor \u03b22 -0.045 0.029 -1.52 0.931*** 0.322 2.89 0.024*** 0.008 3.01 \n\n\n\nHerbicide   \u03b23 0.282*** 0.058 4.48 0.005 0.335 0.01 1.330*** 0.193 6.89 \n\n\n\nFertilizer \u03b24 0.042*** 0.007 5.63 0.006** 0.003 2.01 0.369*** 0.060 6.15 \n\n\n\nInefficiency model \n\n\n\nConstant   Z0               0.013        0.021        0.63                           2.097*    1.276          1.69                    1.593*** 0.303          5.25 \n\n\n\nAge Z1              0.610**    0.273        2.24                           4.353      4.518          0.96                    0.492*** 0.090          5.46 \n\n\n\nGender Z2 0.020        0.020        1.01                         -2.695** 1.165         -2.31                     1.061       4.488          0.23 \n\n\n\nEducational \nlevel                         \n\n\n\nZ3             0.282*** 0.058        4.48                           0.023       0.234         0.09                     0.639*** 0.103          6.20    \n\n\n\nYears of \nexperience                     \n\n\n\nZ4    0.197        0.139        1.42                           1.219       1.711        0.71                      4.353        4.518         0.96 \n\n\n\nFarm Size                                    Z5 4.286        4.281         1.34                           0.031        0.057       0.54                      0.531       0.745          0.71 \n\n\n\nMembership of \nassociation         \n\n\n\nZ6 0.044        0.054         0.82                           2.798*** 0.648       4.31                      0.086       0.080          1.07 \n\n\n\nAccess to \ncredit                          \n\n\n\nZ7              0.318**    0.141         2.25                           0.803*** 0.152       5.28                      0.022       0.078          0.28 \n\n\n\nAccess to \nplanting \nmaterial        \n\n\n\nZ8 7.686       7.054          1.08                           0.027        0.052        0.51                      0.004       0.157          0.02 \n\n\n\nAccess to \nextension \n\n\n\nagent          \nZ9     0.040        0.066          0.61                          0.731        0.143        5.11                      0.031       0.057          0.54 \n\n\n\nTraining Z10 0.001        0.005          0.28                           0.004        0.157       0.02                       0.027       0.052          0.51 \n\n\n\nSigma-square                             \u03c32          0.045*** 0.017          2.65                           0.057**    0.024       2.37                      0.639*** 0.103          6.20 \n\n\n\nGamma \u03b3            0.947*** 0.103         9.19                            0.931*** 0.322       2.89                      1.594*** 0.290          5.49     \n\n\n\nLog likelihood \nfunction            \n\n\n\nL/f        -16.23                                                             -36.37                                            -11.47   \n\n\n\nLR test                                                    12.31                                                               41.38                                                     32.15   \n\n\n\nMean \nefficiency                                     \n\n\n\n 0.52                                                                 64   73   \n\n\n\n***,**,* significant at 1%, 5% and 10% level \n \n\n\n\nTable 5: Maximum Likelihood Estimates of Frontier Cost function (Allocative efficiency) for Bio-fortified Cassava Production by Farm Size \n\n\n\nVariables \n \n\n\n\nParameters \nSmall Scale Medium Scale Large Scale \n\n\n\nCoeff. Std. Err t-ratio Coeff. Std. Err t-ratio Coeff. Std. Err t-ratio \nConstant   \u03b20 0.305*** 0.026          11.73                    0.031        0.057          0.54 0.531       0.745          0.71 \n\n\n\nLand \u03b21    0.070        5.397          0.01                              0.318**    0.141          2.25                   1.593*** 0.303         5.25 \nLabor \u03b22                  0.012*** 0.003          4.00                      0.639*** 0.103          6.20                   2.695**    1.165         2.31    \n\n\n\nHerbicide \u03b23 0.931*** 0.322          2.89                              1.594*** 0.290          5.49                   0.282*** 0.058         4.48 \nFertilizer \u03b24 0.879*** 0.138          6.37                       0.057**    0.024         2.37                   0.042*** 0.007         5.63                          \n\n\n\nSigma-square                               \u03c32              1.220** 0.566         2.16                     0.027        0.052          0.51                   0.057**    0.024         2.37 \nGamma \u03b3                0.998      2.041         0.48                     0.040        0.066          0.61                   0.004        0.157         0.02 \n\n\n\nLog likelihood \nfunction               \n\n\n\nL/f             35.462                                                  49.442                                            53.128   \n\n\n\nLR test                                                           21.538                                                  28.478                                            32.924   \nMean efficiency                                             0.56                                                      0.66                                                0.75   \n\n\n\n***,**,* significant at 1%, 5% and 10% level \n \n4.4.2 Estimated Stochastic Frontier Production Cost Function \n\n\n\n(Allocative Efficiency) by farm size \n\n\n\nThe Maximum Likelihood (ML) estimates of the stochastic frontier \n\n\n\nproduction cost parameters (allocative efficiency) for bio-fortified cassava \n\n\n\nproduction by farm size were presented in Table 5. For the small scale \n\n\n\nproduction cost function, the sigma (\u03c32= 1.22) and the gamma (\u03b3=0.99) are \n\n\n\nquite high and highly significant at 1% level of probability. The high and \n\n\n\nsignificant value of the sigma square (\u03c32) indicate the goodness of fit and \n\n\n\ncorrectness of the specified assumption of the composite error terms \n\n\n\ndistribution (Abass et al., 2019). The gamma (\u03b3 = 0.99) shows that 99% of \n\n\n\nthe variability in the output of small scale bio-fortified cassava producers \n\n\n\nthat are unexplained by the function is due to allocative inefficiency. For \n\n\n\nthe medium scale production cost function, the sigma (\u03c32= 0.027) and the \n\n\n\ngamma (\u03b3=0.40) are considerably high although not significant. The \n\n\n\ngamma (\u03b3 = 0.40) shows that 44%% of the variability in the output of \n\n\n\nmedium scale bio-fortified cassava producers that are unexplained by the \n\n\n\nfunction is due to allocative inefficiency.  \n\n\n\nFor the large scale production cost function, the sigma (\u03c32= 0.057) and the \n\n\n\ngamma (\u03b3=0.004) are also considerably high and significant at 5% level of \n\n\n\nprobability. The gamma (\u03b3 = 0.004) shows that 4% of the variability in the \n\n\n\noutput of large scale bio-fortified cassava producers that are unexplained \n\n\n\nby the function is due to allocative inefficiency. In Table 5. The estimated \n\n\n\ncoefficients of the parameters of the cost function (labor, herbicide and \n\n\n\nfertilizer) for small scale bio-fortified cassava farmers were positive and \n\n\n\nstatistically significant at 1% level of probability each respectively. The \n\n\n\nestimated coefficients of the parameters of the cost function (land, labor, \n\n\n\nherbicide and fertilizer) for medium scale bio-fortified cassava farmers \n\n\n\nwere positive and statistically significant at 5%, 1%, 1% and 5% level of \n\n\n\nprobability respectively. \n\n\n\nFurthermore, the estimated coefficients of the parameters of the cost \nfunction (land, labor, herbicide and fertilizer) for large scale bio-fortified \ncassava farmers were positive and statistically significant at 1%, 5%, 1% \nand 1% level of probability respectively. This implies that majority of the \ninput variables were important in bio-fortified cassava production \nirrespective of the farm size. The implication of these finding is that if there \nis an increase in any of the variable input the total cost of production will \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\nincrease. This shows that the cost of production is influenced by the cost \nof variable input incurred in the production cycle. \n\n\n\n4.4.3 Elasticity and Return to Scale \n\n\n\nTable 6 presents the production elasticities of the inputs used in bio-\n\n\n\nfortified cassava production by farm size. In the Stochastic frontier model, \n\n\n\nthe coefficients are direct elasticities of the variables. The elasticities for \n\n\n\nthe small scale farm size were 0.042, 0.045, 0.282 and 0.042 for land, labor, \n\n\n\nherbicide and fertilizer respectively. The return to scale calculated for the \n\n\n\nsmall scale farm size bio-fortified cassava farmers were 0.411. This implies \n\n\n\na decreasing return scale for the small scale bio-fortified cassava farmers. \n\n\n\nThe result suggest that resources were under utilized by the small scale \n\n\n\nbio-fortified cassava farmers. The elasticities for the medium scale farm \n\n\n\nsize were 0.305, 0.931, 0.005 and 0.006 for land, labor, herbicide and \n\n\n\nfertilizer respectively. The return to scale calculated for the medium scale \n\n\n\nfarm size bio-fortified cassava farmers were 1.247 which implies an \n\n\n\nincreasing return to scale for the medium scale bio-fortified cassava \n\n\n\nfarmers. The results suggest that medium scale bio-fortified cassava \n\n\n\nfarmers could enlarge their production scale by about 1.2% on average, in \n\n\n\norder to adequately expand productivity, given their disposable resources.  \n\n\n\nThe elasticities for the large scale farm size were 0.068, 0.024, 1.330 and \n0.369 for land, labor, herbicide and fertilizer respectively. The return to \nscale calculated for the large scale farm size bio-fortified cassava farmers \nwere 1.791 which implies an increasing return to scale for the large scale \nbio-fortified cassava farmers. The results suggest that medium scale bio-\nfortified cassava farmers could enlarge their production scale by about \n1.79% on average, in order to adequately expand productivity, given their \ndisposable resources. This result suggests that the medium and large scale \nbio-fortified cassava farmers were still in stage one of the production \nprocess. Large scale farmers were however closer to the rational stage \n(stage two) of the production process than other categories of farms. \n\n\n\n \nTable 6: Elasticities of production of input variable in bio-fortified \n\n\n\ncassava production by farm size \n\n\n\nVariable \nElasticities \n\n\n\nSmall scale Medium scale Large scale \n\n\n\nLand 0.042 0.305 0.068 \n\n\n\nLabor 0.045 0.931 0.024 \n\n\n\nHerbicide 0.282 0.005 1.330 \n\n\n\nFertilizer 0.042 0.006 0.369 \n\n\n\nReturn to scale 0.411 1.247 1.791 \n\n\n\n \nTable 7: Frequency Distribution of Technical, Allocative and \n\n\n\nEconomic Efficiency from the Stochastic Frontier Model for Small \nscale Bio-Fortified Cassava Production \n\n\n\nClass \n\n\n\nTechnical \nEfficiency \n\n\n\nAllocative \nEfficiency \n\n\n\nEconomic \nEfficiency \n\n\n\nFrequency % Frequency % Frequency % \n\n\n\n<0.2                0 0 0 3.4                           29                  14.0 \n\n\n\n0.21-\n0.40       \n\n\n\n12 5.8                      18 8.7                          56 27.0 \n\n\n\n0.41-\n0.60       \n\n\n\n178   86.0                   173                       83.6                        102 49.3 \n\n\n\n0.61-\n0.80       \n\n\n\n17 8.2                       9                         4.3                           20                   9.7 \n\n\n\n0.81-\n1.00        \n\n\n\n0 0 0 0 0 0 \n\n\n\nTotal 207 \n10\n0 \n\n\n\n207 \n10\n0 \n\n\n\n207 100 \n\n\n\nMean   0.52                                              0.56                                                    0.42  \n\n\n\nMinimum 0.38                                              0.20                                                   0.20                      \n\n\n\nMaximum   0.71                                              0.62                                                   0.61  \n\n\n\n\n\n\n\n\n\n\n\nTable 8: Frequency Distribution of Technical, Allocative and \nEconomic Efficiency from the Stochastic Frontier Model for Medium \n\n\n\nscale Bio-Fortified Cassava Production \n\n\n\nClass \nTechnical \nEfficiency \n\n\n\nAllocative \nEfficiency \n\n\n\nEconomic \nEfficiency \n\n\n\nFrequency % Frequency % Frequency % \n<0.2 0 0 0 0 8 7.4 \n\n\n\n0.21-0.40 0 0 15 13.9 13 12.0 \n0.41-0.60 92 85.2 12 11.1 72 66.7 \n0.61-0.80 12 11.1 81 75.0 15 13.9 \n0.81-1.00 4 3.7 0 0 0 0 \n\n\n\nTotal 108 100 108 100 108 100 \nMean 0.60  0.61  0.54  \n\n\n\nMinimum 0.53  0.39  0.20  \nMaximum 0.82  0.73  0.65  \n\n\n\n \nTable 9: Frequency Distribution of Technical, Allocative and \n\n\n\nEconomic Efficiency from the Stochastic Frontier Model for Large \nscale Bio-Fortified Cassava Production \n\n\n\nClass \nTechnical \nEfficiency \n\n\n\nAllocative \nEfficiency \n\n\n\nEconomic \nEfficiency \n\n\n\nFrequency % Frequency % Frequency % \n<0.2                0 0 0 0                               0 0 \n\n\n\n0.21-0.40        0                         0 0 0 0 0 \n0.41-0.60        6 13.3                   12                        26.7                          11 24.4 \n0.61-0.80        32 71.1                   29 64.4                          31 68.9 \n0.81-1.00        7 15.6                    4 8.9                            3 6.7 \n\n\n\nTotal 45                       \n10\n0 \n\n\n\n45                      100                           45 100 \n\n\n\nMean 0.73                                               0.75                                                   0.63  \nMinimum 0.58                                                0.59                                                   0.51             \nMaximum 0.87                                                0. 82                                                  0.81  \n\n\n\n4.5 Distribution of bio-fortified cassava producers according to \n\n\n\ntechnical, allocative and economic efficiencies of bio-fortified \n\n\n\ncassava production by farm size \n\n\n\n4.5.1 Distribution of bio-fortified cassava processors according to \n\n\n\ntechnical efficiency by farm size \n\n\n\nThe frequency distribution of the technical efficiency estimates for small \n\n\n\nscale bio-fortified cassava producers as obtained from the stochastic \n\n\n\nfrontier model were presented in Table 7. It was observed from the study \n\n\n\nthat 8.2% of the small-scale bio-fortified cassava producers had a technical \n\n\n\nefficiency (TE) of between 0.61-0.80 while 91.8% of the small scale bio-\n\n\n\nfortified cassava producers operated at less than 0.60 technical efficiency \n\n\n\nlevels as indicated in Table 7. The small scale producers with the best and \n\n\n\nleast practice had technical efficiencies of 0.71 and 0.38 respectively. This \n\n\n\nimplies that on the average, output fell by 8.2% from the maximum \n\n\n\npossible level attainable due to inefficiency. The study also suggests that \n\n\n\nfor the average small scale bio-fortified cassava producers to achieve \n\n\n\ntechnical efficiency of his most efficient counterpart, he could realize about \n\n\n\n29 % cost savings while on the other hand, the least technically efficient \n\n\n\nproducer will have about 62% cost savings to become the most efficient \n\n\n\nproducer.  \n\n\n\nThe frequency distribution of the technical efficiency estimates for \n\n\n\nmedium scale bio-fortified cassava producers as obtained from the \n\n\n\nstochastic frontier model were presented in Table 8. It was observed from \n\n\n\nthe study that 3.7% of the medium scale bio-fortified cassava producers \n\n\n\nhad a technical efficiency (TE) of 0.81 and above while 96.3% of the \n\n\n\nmedium scale bio-fortified cassava producers operated at less than 0.80 \n\n\n\ntechnical efficiency levels as indicated in Table 8. The medium scale \n\n\n\nproducers with the best and least practice had technical efficiencies of 0.82 \n\n\n\nand 0.53 respectively. This implies that on the average, output fell by 3.7% \n\n\n\nfrom the maximum possible level attainable due to inefficiency by medium \n\n\n\nscale farmers. The study also suggests that for the average medium scale \n\n\n\nbio-fortified cassava producers to achieve technical efficiency of his most \n\n\n\nefficient counterpart, he could realize about 18 % cost savings while on \n\n\n\nthe other hand, the least technically efficient producer will have about \n\n\n\n47% cost savings to become the most efficient producer. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\nThe frequency distribution of the technical efficiency estimates for the \n\n\n\nlarge scale bio-fortified cassava producers as obtained from the stochastic \n\n\n\nfrontier model were presented in Table 9. It was observed from that 15.6% \n\n\n\nof the large scale bio-fortified cassava producers had a technical efficiency \n\n\n\n(TE) of 0.81 and above while 13.3% of the large scale bio-fortified cassava \n\n\n\nproducers operated at less than 0.40 technical efficiency levels as \n\n\n\nindicated in Table 9. The large scale producers with the best and least \n\n\n\npractice had technical efficiencies of 0.87 and 0.58 respectively. This \n\n\n\nimplies that on the average, output fell by 15.6% from the maximum \n\n\n\npossible level attainable due to inefficiency by medium scale farmers. The \n\n\n\nstudy also suggests that for the average large scale bio-fortified cassava \n\n\n\nproducer to achieve technical efficiency of his most efficient counterpart, \n\n\n\nhe could realize about 13% cost savings while on the other hand, the least \n\n\n\ntechnically efficient producer will have about 42% cost savings to become \n\n\n\nthe most efficient producer. \n\n\n\n4.5.2 Distribution of bio-fortified cassava producers according to \n\n\n\nallocative efficiency by farm size \n\n\n\nThe distribution of the allocative efficiency estimates of the small scale \n\n\n\nfarmers presented in Table 7, indicate that it ranged from 0.20 to 0.80; the \n\n\n\nmean allocative efficiency of the small scale farmers was 0.56. The result \n\n\n\nindicates that average small scale bio-fortified cassava producers would \n\n\n\nenjoy cost saving of about 38% if he or she attains the level of the most \n\n\n\nefficient producer among the small scale bio-fortified cassava producers. \n\n\n\nThe most allocative inefficient small scale bio-fortified cassava farmer will \n\n\n\nhave an efficiency gain of 80% in bio-fortified cassava production if he or \n\n\n\nshe is to attain the efficiency level of most allocative efficient bio-fortified \n\n\n\ncassava producer on a small scale. \n\n\n\nThe distribution of the allocative efficiency estimates of the medium scale \n\n\n\nfarmers presented in Table 8, indicate that it ranged from 0.21 to 0.80; the \n\n\n\nmean allocative efficiency of the medium scale farmers was 0.61. The \n\n\n\nresult indicates that average medium scale bio-fortified cassava producers \n\n\n\nwould enjoy cost saving of about 27% if he or she is to attains the level of \n\n\n\nthe most efficient producer among the medium scale bio-fortified cassava \n\n\n\nproducers. The most allocative inefficient medium scale bio-fortified \n\n\n\ncassava farmer will have an efficiency gain of 61% in bio-fortified cassava \n\n\n\nproduction if he or she is to attain the efficiency level of most allocative \n\n\n\nefficient bio-fortified cassava producer on a medium scale. \n\n\n\nThe distribution of the allocative efficiency estimates of the large scale \n\n\n\nfarmers presented in Table 9, indicate that it ranged from 0.41 to 1.0; the \n\n\n\nmean allocative efficiency of the large scale farmers was 0.75. The result \n\n\n\nindicates that average large scale bio-fortified cassava producers would \n\n\n\nenjoy cost saving of about 18% if he or she is to attains the level of the most \n\n\n\nefficient producer among the large scale bio-fortified cassava producers. \n\n\n\nThe most allocative inefficient large scale bio-fortified cassava farmer will \n\n\n\nhave an efficiency gain of 41% in bio-fortified cassava production if he or \n\n\n\nshe is to attain the efficiency level of most allocative efficient bio-fortified \n\n\n\ncassava producer on a large scale. \n\n\n\n4.5.3 Distribution of bio-fortified cassava producers according to \n\n\n\neconomic efficiency by farm size \n\n\n\nThe frequency distribution of the economic efficiency estimates for small \n\n\n\nscale bio-fortified cassava producers obtained from the stochastic frontier \n\n\n\nmodel were presented in Table 7. It was observed from that none of the \n\n\n\nsmall scale bio-fortified cassava producers had economic efficiency (EE) \n\n\n\nof 0.81 and above while 100% of the small scale producers operate at less \n\n\n\nthan 0.8 efficiency level. The mean economic efficiency of the sampled \n\n\n\nsmall scale bio-fortified cassava producers was 0.42. The small scale bio-\n\n\n\nfortified cassava producers with the best and least practice had economic \n\n\n\nefficiencies of 0.61 and 0.20 respectively. This implies that on the average, \n\n\n\noutput fall by 39% from the maximum possible level due to inefficiency \n\n\n\namong the small scale bio-fortified cassava farmers. The study also \n\n\n\nsuggests that for the average small scale bio-fortified cassava producer to \n\n\n\nachieve economic efficiency of his most efficient counterpart, he could \n\n\n\nrealize about 39% cost savings while on the other hand, the least economic \n\n\n\nefficient small scale producers will have about 80% cost savings to become \n\n\n\nthe most efficient producer on a small scale. However, the average \n\n\n\neconomic efficiency of the small scale bio-fortified cassava producers was \n\n\n\n42%. This indicates that small scale bio-fortified cassava producers were \n\n\n\noperating on less than average economic efficiency level. \n\n\n\nThe frequency distribution of the economic efficiency estimates for \n\n\n\nmedium scale bio-fortified cassava producers in the study area as obtained \n\n\n\nfrom the stochastic frontier model were presented in Table 8. It was \n\n\n\nobserved that the medium scale bio-fortified cassava producers had \n\n\n\neconomic efficiency (EE) ranges between 0.20-0.80 efficiency level. The \n\n\n\nmean economic efficiency of the sampled medium scale bio-fortified \n\n\n\ncassava producers was 0.54. The medium scale bio-fortified cassava \n\n\n\nproducers with the best and least practice had economic efficiencies of \n\n\n\n0.65 and 0.20 respectively. The study also suggests that for the average \n\n\n\nmedium scale bio-fortified cassava producer to achieve economic \n\n\n\nefficiency of his most efficient counterpart, he could realize about 35% \n\n\n\ncost savings while on the other hand, the least economic efficient medium \n\n\n\nscale producers will have about 80% cost savings to become the most \n\n\n\nefficient producer on a medium scale. The average economic efficiency of \n\n\n\nthe medium scale bio-fortified cassava producers was 54%. This indicates \n\n\n\nthat medium scale bio-fortified cassava producers were operating above \n\n\n\nthe average economic efficiency level and are thus economic efficient. \n\n\n\nThe frequency distribution of the economic efficiency estimates for large \n\n\n\nscale bio-fortified cassava producers as obtained from the stochastic \n\n\n\nfrontier model were presented in Table 9. It was observed that the large \n\n\n\nscale bio-fortified cassava producers had economic efficiency (EE) ranges \n\n\n\nbetween 0.41-1.00 efficiency level. The mean economic efficiency of the \n\n\n\nsampled large scale bio-fortified cassava producers was 0.63. The large \n\n\n\nscale bio-fortified cassava producers with the best and least practice had \n\n\n\neconomic efficiencies of 0.81 and 0.51 respectively. The study further \n\n\n\nsuggests that for an average large scale bio-fortified cassava producer to \n\n\n\nachieve economic efficiency of his most efficient counterpart, he could \n\n\n\nrealize about 19% cost savings while on the other hand, the least economic \n\n\n\nefficient large scale producers will have about 49% cost savings to become \n\n\n\nthe most efficient producer on a large scale. The average economic \n\n\n\nefficiency of the large scale bio-fortified cassava producers was 63%. This \n\n\n\nindicates that large scale bio-fortified cassava producers were also \n\n\n\noperating above the average economic efficiency level and are thus \n\n\n\neconomic efficient. \n\n\n\n4.5.4 Analysis of Variance (ANOVA) \n\n\n\nPresented in Table 10 is the result of the Analysis of variance (ANOVA). \n\n\n\nThe result showed that the efficiency of small scale bio-fortified cassava \n\n\n\nfarmers were significantly different from medium scale efficiency at 1% \n\n\n\nprobability level. Furthermore, small scale efficiency was significantly \n\n\n\ndifferent from large scale efficiency of bio-fortified cassava farmers at 1% \n\n\n\nlevel of probability. Consequently, the medium scale efficiency was \n\n\n\nsignificantly different from large scale efficiency of bio-fortified cassava \n\n\n\nfarmers at 5% probability level. This implies that there is a significant \n\n\n\ndifference in the level of efficiency of small, medium and large scale bio-\n\n\n\nfortified cassava farmers in Nigeria as Large scale were more efficient than \n\n\n\nmedium and small scale farmers while medium scale farmers were more \n\n\n\nefficient than small scale farmers in Nigeria. \n\n\n\n\n\n\n\nTable 10: ANOVA result on the profitability level among the different \ncategories of bio-fortified cassava producers \n\n\n\nFarm \nScale \n\n\n\nModel Df \nSum of \n\n\n\nSquares \nMean \n\n\n\nSquare \nF-cal Sig \n\n\n\nSmall X \nmedium \n\n\n\nRegression \nResidual \n\n\n\n25    \n290 \n\n\n\n6.542 \n19.531 \n\n\n\n0.435  \n0.634 \n\n\n\n19.46 0.01*** \n\n\n\nSmall X \nLarge \n\n\n\nRegression \nResidual \n\n\n\n18   \n234 \n\n\n\n3.258  \n32.636 \n\n\n\n0.625 \n0.383 \n\n\n\n32.87 0.01*** \n\n\n\nMedium \nX Large \n\n\n\nRegression \nResidual \n\n\n\n15   \n138 \n\n\n\n2.693  \n41.249 \n\n\n\n0.462 \n0.815 \n\n\n\n26.47 0.04** \n\n\n\n***,**,* significant at 1%, 5% and 10% level \n\n\n\n5. CONCLUSIONS  \n\n\n\nThe study specifically looked at the farm size efficiency differentials of bio-\n\n\n\nfortified cassava production in Nigeria. Our study concluded that the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 51-60 \n\n\n\n\n\n\n\n \nCite the Article: Kolapo Adetomiwa, Raji, Ibraheem Adeyemi,Falana Kayode, Muhammed, Opeyemi Abdulmumin (2021). Farm Size Efficiency Differentials of Bio-\n\n\n\nFortified Cassava Production In Nigeria: A Stochastic Frontier Analysis Approach . Malaysian Journal of Sustainable Agriculture, 5(1): 51-60. \n \n\n\n\n\n\n\n\nproduction of bio-fortified cassava in Nigeria is profitable enterprise \n\n\n\nirrespective of the scale of production, although large scale bio-fortified \n\n\n\ncassava farmers realized more profit than the other categories. However, \n\n\n\nthe estimated coefficients of the parameters of production variables for \n\n\n\nsmall, medium and large scale bio-fortified cassava farm size played a \n\n\n\nmajor role in bio-fortified cassava production in Nigeria. The study \n\n\n\nconcluded that small scale bio-fortified cassava producers were operating \n\n\n\non less than average economic efficiency level in Nigeria, the medium scale \n\n\n\nbio-fortified cassava producers were operating slightly above the average \n\n\n\neconomic efficiency level in Nigeria and are thus are averagely economic \n\n\n\nefficient. The large scale bio-fortified cassava producers were operating \n\n\n\nabove the average economic efficiency level in Nigeria and are thus \n\n\n\neconomic efficient. It was therefore recommended that policies intended \n\n\n\nto increase the popularization and cultivation of bio-fortified cassava in \n\n\n\nNigeria should be targeted toward the small and medium scale biofortified \n\n\n\ncassava farmers as they carried the majority of the producer of bio-\n\n\n\nfortified cassava in Nigeria. Furthermore, agencies, stakeholders and \n\n\n\ngovernment should made available the production inputs such as bio-\n\n\n\nfortified cassava stems, herbicide, labor and fertilizers for the bio-fortified \n\n\n\ncassava farmers in Nigeria at a subsidized rate.  \n\n\n\nACKNOWLEDGEMNT \n \n\n\n\nThe authors will like to appreciate the effort of ADP staffs in respective \n\n\n\nStates where the data for the study were collected and also, Cassava \n\n\n\nfarmers association for their help rendered during the time of data \n\n\n\ncollection. \n\n\n\nREFERENCES \n\n\n\nAjibefun, I.A., Daramola, A.G. and Falusi, A.O., 2006. Technical Efficiency of \nSmall-scale Farmers: An Application of the Stochastic Frontier \nProduction to Rural and Urban Farmers in Oyo State, Nigeria. Internal \nEconomics Journal, 20 (1), Pp. 87-107. \n\n\n\nAyinde, T.B., Omolehim, R.A., Ibrahim, U., 2011. Efficiency of Resource use \nin Hybrid and Open-pollinated Maize production in Giwa LGA of Kaduna \nState, Nigeria. American Journal of Experimental Agriculture, 1 (3), Pp. \n86-95. \n\n\n\nAwoyinka Y.A., 2009. Effects of presidential initiative on cassava \nproduction efficiency in Oyo State, Nigeria. Ozean journal of applied \nsciences, 2 (2), Pp. 7-15. \n\n\n\nBattese, G.E., and Coelli, T.J., 1995. A Model of Technical Inefficiency Effects \nin a Stochastic Frontier Production for Panel Data. Emperical Economics, \n1 (39), Pp. 387-399. \n\n\n\nBravo-Ureta, B.E., and Evenson, R.E., 1994. Efficiency in Agricultural \nProduction. The Case Study of Peasant Farmers of Eastern Paraguay. The \nJournal of International Association of Agricultural Economics, 14, Pp. \n23-30. \n\n\n\nCoelli, T.J., and Battese, G.E., 1996. Identification of Factors which Influence \nthe Technical Inefficiency of Indian Farmers. Australia Journal of \nAgricultural Economics, 40, Pp. 103-128. \n\n\n\nCoelli, T.J., 1995. Estimators and hypothesis test for a Stochastic Frontier \nFunction. A Monte-cerlo. Analysis. Journals of productivity analysis, 6, \nPp. 247-268. \n\n\n\nCoelli, T., Rao, D.S.P., and Battese, G.E., 1998. An Introduction to Efficiency \nProductivity Analysis. Boston: Kluwer Academic Publishers. \n\n\n\nFAO. 2013. Food and Agriculture Organization (FAO) website. \nwww.fao.org. \n\n\n\nFAO. 2018. Food and Agriculture Organization (FAO) website. \nwww.fao.org. \n\n\n\nHillocks, R., 2002. \u201cCassava in Africa\u201d. CABI Publishing. \n\n\n\nIbiremo, O.S., Daniel, M.A., Iremiren, G.O. and Fagbola, O., 2011. Soil \nFertility Evaluation for Cocoa Production in Southeastern Adamawa \nState, Nigeria. World Journal of Agricultural Sciences, 7 (2), Pp. 218-223. \n\n\n\n\n\n\n\nJochen, K., 1993. Traditional storage of yams and cassava and its \nimprovement, GTZ post-harvest project, Pckhuben, 4.  \n\n\n\nKebede, T.A., 2001. Farm Household Technical Efficiency: A Stochastic \nFrontier Analysis (A Case of Rice Producers in Mardi Watershed in the \nWestern Development Region of Nepal). Unpublished Masters\u2019 Thesis \nDepartment of Economics and Social Science. Agricultural University of \nNorway. \n\n\n\nKolapo, A., and Abimbola, E.I., 2020. Consumers\u2019 Preferences and \nWillingness to Pay for BioFortified Vitamin-A Garri in South Western, \nNigeria: A Conjoint Analysis and Double-Hurdle Model Estimation. \nWorld Research Journal of Agricultural Sciences, 7 (2), Pp. 221-229. \n\n\n\nKolapo, A., and Fakokunde, A.O., 2020. Economic Efficiency of Bio-fortified \n\n\n\ncassava processing in South Western, Nigeria. International journal of \n\n\n\nagriculture, environment and bioresearch, 5 (3), Pp. 191-203. \n\n\n\nKolapo, A., Olayinka, J.Y. and Muhammed, O.A., 2020. Market Participation \n\n\n\nand Food Security Status of Bio-Fortified Cassava Processors in South \n\n\n\nWestern Nigeria. International Journal of Sustainable Agricultural \n\n\n\nResearch, 7 (3), Pp. 174-184. \n\n\n\nKolapo, A., Ologundudu, O.M., and Adekunle, I.A., 2020. Gender, \nMembership in Farmers\u2019 Association and Adoption of Biofortification In \nNigeria: The Case of Bio-Fortified Cassava. SSRG International Journal of \nAgriculture & Environmental Science (SSRG-IJAES), 7 (3), Pp. 38-45. \n\n\n\nOgundele, O.O., and Okoruwa, V.O., 2006. Technical Efficiency Differentials \nin Rice Production Technologies in Nigeria. African Economic Research \nConsortium Research Paper No. 154. \n\n\n\nOgunleye, A.S., Bamire, A.S., Awolola, O., 2019. Profitability of Investment \nand Farm Level Efficiency Among Groups of Vitamin A Cassava Farmers \nin Oyo State Nigeria. American Journal of Environmental and Resource \nEconomics, 8 (1), Pp. 14-19. doi: 10.11648/j.eco.20190801.13  \n\n\n\nOkigbo, B.N., 2007. Keynote Address: Roots and Tubers in the African Food \nCrisis, in: Terry, E.R. (edi.). Tropical Root Crops, Root Crops and the \nAfrican Food Crisis, Nigeria, Pp. 9-20.  \n\n\n\nOparinde, A., Abdoulaye, T., Mignouna, D.B., Bamire, A.S., 2017. Will farmers \nintend to cultivate Provitamin A genetically modified (GM) cassava in \nNigeria? Evidence from a k-means segmentation analysis of beliefs and \nattitudes. PLoS ONE, 12(7), Pp.e0179427.  \n\n\n\nOren, M.N., and Alemder, T., 2006. Technical Efficiency Analysis of Tobacco \nFarming in Southeastern Anatolia, Turkey. Journal of Agriculture and \nForestry, 30, Pp. 164-172. \n\n\n\nOtekunrin, O.A., and Sawicka, B., 2019. Cassava, a 21st century staple crop: \nHow can Nigeria harness its Enormous Trade potentials?. Acta Scientific \nAgriculture, 3 (8), Pp. 194-202. \n\n\n\nPhillips, T.P., Taylor, D.S., Sanni, L., and Akoroda, M.O., 2004. A cassava \nindustrial revolution in Nigeria: The Potential for a new industrial crop\u201d. \nInternational Institute of Tropical Agriculture, Ibadan, Nigeria\u201d. \nInternational Fund for Agricultural Development, Food and Agriculture \nOrganization of the United Nations, Rome, Italy. \n\n\n\nPlecher, H., 2020. Nigeria: Distribution of gross domestic product (GDP) \nacross economic sectors from 2009-2019. Economy and politics.  \n\n\n\nSaltzman, A., Birol, E., Bouis, H.E., Boy, E., De Moura, F.F., Islam, Y., Pfeiffer, \nW.H., 2014. Biofortification: Progress towards a more nourishing future. \nBread and Brain, Education and Poverty. Pontifical Academy of Sciences, \nVatican City. Scripta varia 125. Retrieved from: www.pas. \nva/content/dam/accademia/pdf/sv125/sv125-bouis.pdf  \n\n\n\nShappiro, K.H., 1983. Efficiency Deficiency Differentials in peasant \nAgriculture and their implications for Development policies. Journal of \nDevelopment Studies, 19, Pp. 179 \u2013 190. \n\n\n\nSingh-Peterson, L., and Iranacolaivalu, M., 2018. Barriers to market for \nsubsistence farmers in Fiji\u2013A gendered perspective. Journal of Rural \nStudies, 60, Pp. 11\u201320. doi:10.1016/j.jrurstud.2018.03.001. \n\n\n\nTaphee, G.B., and Jongur, A.A.U., 2014. Productivity and Efficiency of \nGroundnut Farming in Northern Taraba State, Nigeria. Journal of \nAgriculture and Sustainability, 5 (1), Pp. 45-56.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 10-15 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.10.15 \n\n\n\nCite the Article: Sagar Bhandari, Ruchita Bhattarai , Krishna Raj Pandey, Safal Adhikari (2021). Assessment Of Infestation Of Spodoptera Frugiperda (J.E. Smith) On \nMaize And Its Implemented Management Practices With Their Efficacy In Kailali, Nepal . Malaysian Journal of Sustainable Agriculture, 5(1): 10-15. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2021.10.15 \n\n\n\nASSESSMENT OF INFESTATION OF SPODOPTERA FRUGIPERDA (J.E. SMITH) ON \nMAIZE AND ITS IMPLEMENTED MANAGEMENT PRACTICES WITH THEIR \nEFFICACY IN KAILALI, NEPAL \n\n\n\nSagar Bhandari, Ruchita Bhattaraia\uf02a , Krishna Raj Pandey, Safal Adhikari \n\n\n\na Agriculture and Forestry University, Nepal. \n*Corresponding Author Email: theruchita@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 September 2020 \nAccepted 28 October 2020 \nAvailable online 23 November 2020\n\n\n\nFall armyworm has been recently introduced to Nepal. In a very less time, the invasive pest has rapidly spread \nthroughout the country causing 21% of yield loss in the total production of maize. It has the potential to attack \nall the crop stages of maize. If the effect of fall armyworm is neglected, it can result in the loss of 53% in the \ntotal production threatening food security and living of millions of farming households. Keeping the fact in \nmind, two blocks from the Tikapur Municipality of Kailali district, Nepal were surveyed to understand the \ninfestation status of FAW in maize, farmer's perception, implemented management practices at the local level \nfor its control, and its efficacy. Lack of knowledge regarding the identification and control measures has led \nto more than 50% of the household being infected by FAW. The average yield loss has reached 129.058 kg/ha \nin block 10 and 93.052 kg/ha in block 24. The average percentage of infestation has drooped to only 4.15% \nwhen all the measure of management was applied in an integrated way. Our study concluded that there is a \npivotal need for extension knowledge to farmers on the identification of the pest, its life cycle, effective \nmanagement practices, and tie for its implementation.  \n\n\n\nKEYWORDS \n\n\n\nFall armyworm, infestation, maize, control measures, farmers.\n\n\n\n1. INTRODUCTION \n\n\n\nFall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), \n\n\n\nis a devastating pest of cereal crops native to the western hemisphere -\n\n\n\nespecially tropical and subtropical regions of America (FAO, 2017; CABI, \n\n\n\n2017; Smith, 2017; Capinera J., 2001). It is highly polyphagous in nature \n\n\n\nwhich feeds on 186 plant species from 42 families (Hoy, 2013; Early et al., \n\n\n\n2018). However, corn and rice are the major hosts (Hoy, 2013). On the \n\n\n\nother hand, revealed that there are 353 fall armyworm larval host plants \n\n\n\nspecies belonging to 76 plant families with the greatest number of hosts \n\n\n\ntexa in the family Poaceae (106 texa), followed by Asteraceae and \n\n\n\nFabaceae (31 texa) each (Montezano et al., 2018). It is called fall \n\n\n\narmyworm because it does not reach the more northerly regions until late \n\n\n\nin the summer or early in the fall (Luginbill, 1990). This pest is a strong \n\n\n\nflier with migratory and localized dispersal habit and can fly up to 500km \n\n\n\nbefore oviposition (Prasanna et al., 2018). Restriction fragment length \n\n\n\npolymorphism (RFLP) analysis of generic DNA identified two groups \n\n\n\ngenerally consistent with the R-strain (Rice strain), which is most \n\n\n\nconsistently found in millet and grass species associated with Pasture \n\n\n\nhabitats, and C-strain (corn strain), which prefers maize and sorghum (Lu \n\n\n\net al., 1992). \n\n\n\nRecognizing FAW is the first step for management. A study reported that \n\n\n\nthe face of matured larva is marked with inverted \u2018Y', epidermis is rough \n\n\n\nor granular in texture and the egg is of fine shaped with flattened base \n\n\n\nmeasuring 0.4mm in diameter and 0.3mm in height (Prasanna et al., 2018).  \n\n\n\nAccording to a study, the four black dots arranged in a square on the back \n\n\n\nof the last abdominal segment are also distinctive to FAW larvae (CABI, \n\n\n\n2017). Spines bearing dark colored elevated spots occur dorsally on the \n\n\n\nbody (CABI, 2017). Luginbill reveals that adults are active during hot and \n\n\n\nhumid evening favoring night for oviposition (Luginbill, 1928). Studying \n\n\n\nabout the life cycle of FAW, it completes its life cycle in 30 days in summer, \n\n\n\n60 and 80 to 90 days during spring and winter respectively with the \n\n\n\nabsence of diapause (Capinera, 2002). Luginbill has mentioned that moth \n\n\n\ndeposits eggs in a mass of two or three layers, or decks or in heaps; all the \n\n\n\neggs from a fertilized female are fertile, covered by scales and hatch \n\n\n\n(Luginbill, 1928).  \n\n\n\nCapinera also agreed to the furry or moldy appearance of egg due to a layer \n\n\n\nof greyish scales between the eggs and over the egg mass (Capinera, 2002). \n\n\n\nTotal egg production (per female) counts to an average of 1500 with 100 \n\n\n\nto 200 eggs per mass (Capinera, 2002). Egg stage lasts for two to three \n\n\n\ndays in summer (Capinera, 2002). The activity of larva can be observed in \n\n\n\nthe early morning and in the late evening (Luginbill, 1928). After having \n\n\n\nown shells as their first meal, larva begins to scatter in all directions in \n\n\n\nsearch of food (Luginbill, 1928). Capinera has talked about six instars of \n\n\n\nlarva. 1st instar is greenish with a black head (Capinera, 2002). Head \n\n\n\nturned orangish in 2nd instar. Dorsal surface of the body becomes \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 10-15 \n\n\n\nCite the Article: Sagar Bhandari, Ruchita Bhattarai , Krishna Raj Pandey, Safal Adhikari (2021). Assessment Of Infestation Of Spodoptera Frugiperda (J.E. Smith) On \nMaize And Its Implemented Management Practices With Their Efficacy In Kailali, Nepal . Malaysian Journal of Sustainable Agriculture, 5(1): 10-15. \n\n\n\nbrownish and lateral white lines begin to form in 3rd instar. Whereas the \n\n\n\nhead is reddish-brown, moulted with white in 4th to 6th instar. Also, the \n\n\n\nbrownish body bears white subdorsal and lateral lines (Capinera, 2002). \n\n\n\nLarva stage lasts for 14 days in summer and 30 days in winter (Capinera, \n\n\n\n2002).  \n\n\n\nSimilarly, found out the mean development time to be 3.3, 1.7, 1.5, 1.5, 2.0 \n\n\n\nand 3.7 days for instars 1 to 6 respectively when the larvae were reared at \n\n\n\n25 \u00b0C (Pitre and Hogg, 1983). Soil is the preferred medium for pupation \n\n\n\nfrom depth of 1 to 3 inches (Luginbill, 1928). Cocoon is formed by tying \n\n\n\nsoil particles with silk (Luginbill, 1928). Capinera revealed that leaf debris \n\n\n\nand other materials may be used by larva to form cocoon on the soil \n\n\n\nsurface when the soil is too hard (Capinera, 2002). Pupa is about 14 to 18 \n\n\n\nmm in length, about 4.5 mm in width and reddish-brown in color \n\n\n\n(Capinera, 2002). Depending on the temperature, the pupa turns into an \n\n\n\nadult in 8 to 9 days in summer and 20 to 30 days in winter (Capinera, \n\n\n\n2002). Adult stage lasts for about 10 days, with a range of about 7 to 21 \n\n\n\ndays (Capinera, 2002). The dark grey color makes them unremarkable in \n\n\n\nsurroundings (Luginbill, 1928). \n\n\n\nIn maize, FAW attacks all crop stages from seedling emergence through to \n\n\n\near development (Sisay et al., 2019 b). The first study, based on surveys \n\n\n\nestimated that FAW had the potential to cause maize yield losses ranging \n\n\n\nfrom 8.3 to 20.6 million tons per annum (21-53% of production), if left \n\n\n\nuncontrolled (Abrahams, et al., 2017; Day et al., 2017). Recent estimates \n\n\n\nby Centre for Agriculture and Biosciences International (CABI) in 12 maize \n\n\n\nproducing countries showed that without control FAW can cause maize \n\n\n\nyield losses ranging from 4.1 to 17.7 million tonnes per year which is \n\n\n\nequivalent to an estimated loss between US $1088 and US $4661 million \n\n\n\nannually (Rwomushana et al., 2018). Yield losses were recorded the \n\n\n\nhighest in Argentina (72%), and it was 34% in Brazil, threatening the food \n\n\n\nand nutrition security and livelihood of millions of farming households \n\n\n\n(Murua et al., 2006; Cruz et al., 1999). It is rapidly spreading across Africa, \n\n\n\ncurrently affecting 44 countries (Rwomushana et al., 2018). A study \n\n\n\nreported its infestation in Yemen and Karnataka state of India by July 2018 \n\n\n\n(Ganiger et al., 2018).  Furthermore, it was confirmed in five Asian \n\n\n\ncountries including China by 2019 (FAO, 2018). \n\n\n\nIn Nepal, the Spodoptera frugiperda has been recorded for the first time on \n\n\n\nmaize form Nawalpur district (N 27\u00b0 42\u2019 16.67\u201d, E 84\u00b0 22\u2019 50.61\u201d) on 9th \n\n\n\nMay 2019 (Bajracharya et al., 2019). The samples collected from \n\n\n\nNawalparasi district were sent for the molecular identification to National \n\n\n\nBureau of Agricultural Insect Resources (NABIR), Bangaluru, India on 20th \n\n\n\nJuly 2019, which confirmed to be Spodoptera frugiperda on 9th of August \n\n\n\n2019 (MOALD, 2019). Fall armyworm has spread across 58 districts of \n\n\n\nNepal so far, causing yield loss of 21% of the total maize production of the \n\n\n\ncountry (Online Khabar, 2020). Maize is the second most important crop \n\n\n\nafter rice in terms of area and production in Nepal (KC et al., 2015). A total \n\n\n\nmaize production and yield have been reported 2,713,635 tons and 2.84 \n\n\n\nt/ha in Nepal and 18,334 tonnes and 2.87 t/ha respectively in Kailali \n\n\n\n(MoALD, 2020). A study reported that maize demand has been constantly \n\n\n\ngrowing by about 5% annually in the last decade (Sapkota and Pokhrel, \n\n\n\n2010). Among cereals, it contributes about 26.8% of the total feed \n\n\n\nrequirement (Sapkota and Pokhrel, 2010). As we know Maize is the second \n\n\n\nmost important crop and is preferred by the C-strain FAW, in this regard \n\n\n\nthis invasive pest is paving its way for threatening food security and \n\n\n\nlivelihood of the people especially living in the mid-hills and high hills of \n\n\n\nNepal. This research survey is carried with an aim to understand \n\n\n\ninfestation status of FAW in maize, farmer's perception, implemented \n\n\n\nmanagement practices at the local level for its control and its efficacy in \n\n\n\nTikapur municipality of Kailali district. \n\n\n\n2. MATERIAL AND METHODS \n\n\n\n2.1 Study site \n\n\n\nThe survey was conducted in Kailali district of Sudurpaschim province of \n\n\n\nNepal. Ward no. 1 of Tikapur municipality was selected as a study area. \n\n\n\nTwo blocks namely, block no 10 and block no 24 from ward no. 1 were \n\n\n\nrandomly selected. \n\n\n\nFigure 1: Map of Study Area \n\n\n\n2.2 Sampling design and analysis \n\n\n\n25 households of maize growing farmers from each block were chosen by \n\n\n\nrandom sampling technique and scheduled interviews were carried out in \n\n\n\neach selected household. The study was conducted during July to August, \n\n\n\n2020. In both blocks, surveys covered growing period of maize from 35 to \n\n\n\n65 days after sowing (DAS). In each surveyed farm, an area of 3m \u00d7 3m \n\n\n\nwas randomly selected and total numbers of plants and damaged plants \n\n\n\nwere counted (Sisay et al., 2019 a). \n\n\n\nThen, percentage field infestation was calculated as follows: \n\n\n\n% Field infested = (Number of FAW infested fields)/ (total number of field \nsurveyed) \u00d7 100 \n\n\n\nPercentage plant infestation was calculated as follows: \n\n\n\n% plant infestation = (Number of FAW infested plants)/ (Total number of \nplants observed) \u00d7 100 \n\n\n\nYield loss was calculated as follows: \n\n\n\nYield loss (kg/ha) = Yield before FAW infestation (kg/ha) \u2013 Yield after \nFAW infestation (kg/ha) \nData analysis was done by using Microsoft Excel (MS Excel). Independent \n\n\n\n\u2018t test\u2019 was computed to test whether the average of different variables \n\n\n\nbetween two blocks are significantly different or not. Chi square test was \n\n\n\ncomputed to examine whether there is significant relation between \n\n\n\ndifferent variables and adoption status of management practices. \n\n\n\n3. RESULTS \n\n\n\n3.1 Socioeconomic characteristics of household head \n\n\n\nTable 1 gives the summary of the demographic and socio-economic \n\n\n\ncharacteristics of household head. The average age is found to be 47.46 \n\n\n\nyears old for block no.10 and 54.92 years old for block no.24. The average \n\n\n\nage for block no 10 is found to be significantly different with that of Block \n\n\n\nno 24 at 1% significance level. The average family size is 4.92 (block no.10) \n\n\n\nand 4.8 (block no.24). In both blocks the majority of household head are \n\n\n\nmale. More than half of the household head were found without secondary \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 10-15 \n\n\n\nCite the Article: Sagar Bhandari, Ruchita Bhattarai , Krishna Raj Pandey, Safal Adhikari (2021). Assessment Of Infestation Of Spodoptera Frugiperda (J.E. Smith) On \nMaize And Its Implemented Management Practices With Their Efficacy In Kailali, Nepal . Malaysian Journal of Sustainable Agriculture, 5(1): 10-15. \n\n\n\nlevel of education in both blocks. Majority of the household head were \n\n\n\nfound to be engaged in off farm activities in both blocks. Very less \n\n\n\nhouseholds have access to credit with an average of 0.16 in block 10 and \n\n\n\n0.28 in block 24. Household size is about 0.12 ha for block 10 and 0.0847 \n\n\n\nha for block 24 with maize area of 0.04 ha and 0.02 ha in Block no 10 and \n\n\n\n24 respectively. The average total land size and maize area of block no 10 \n\n\n\nare found to be significantly different with that of block no 24 at 1% \n\n\n\nsignificance level.  Random planting is less practiced in both blocks. \n\n\n\nSequential cropping is more practiced over crop rotation and mono-\n\n\n\ncropping in both blocks with an average of 0.49 and 0.51 in block 10 and \n\n\n\nblock 24 respectively. In case of cropping pattern, sole cropping and mixed \n\n\n\ncropping were found in majority in both blocks. Both the blocks are \n\n\n\ndominated by Brahmin community followed by Chhetri, Janajati and \n\n\n\nDalits.\n\n\n\nTable 1: Demographic and Socio-economic characteristics of household head \nVariable Block No.10 (n=25) Block no.24 (n=25) T test value \n\n\n\nMean Standard \ndeviation \n\n\n\nMean Standard \ndeviation \n\n\n\nAge (years) 47.76 7.48 54.92 11.01 2.69 *** \nFamily size 4.92 1.07 4.8 1.29 0.358 \nGender (male = 1, female = 0) 0.8 0.4 0.68 0.47 0.97 \nEducation (secondary = 1, otherwise = 0) 0.44 0.5 0.36 0.49 0.57 \nOff farm activity (yes = 1, no = 0) 0.72 0.45 0.64 0.49 0.601 \nCredit access (yes = 1, no = 0) 0.16 0.37 0.28 0.46 1.016 \nHousehold size (ha) 0.12 0.04 0.0847 0.034 3.36*** \nMaize area (ha) 0.04 0.025 0.02 0.014 3.49*** \nPlanting method (Random = 1, otherwise = 0) 0.33 0.066 0.41 0.49 0.81 \nCropping System \na. (Mono cropping = 1, otherwise = 0) 0.32 0.064 0.37 0.71 0.351 \nb. (Sequential Cropping = 1, otherwise = 0) 0.49 0.098 0.51 0.52 0.19 \nc. (Crop Rotation = 1, otherwise = 0) 0.43 0.086 0.48 0.6 0.412 \nCropping pattern \na. (Sole cropping = 1, otherwise = 0) 0.45 0.09 0.49 0.63 0.314 \nb. (Inter Cropping with legumes = 1, otherwise = 0) 0.27 0.054 0.33 0.32 0.924 \nc. (Mixed Cropping =1, otherwise = 0) 0.5 0.1 0.48 0.02 0.98 \n Days after sowing 60.48 2.58 61.32 2.35 1.203 \n\n\n\nNote: *** indicates 1% level of significance. \n\n\n\nFigure 2: Ethnicity of Block 10 and 24 of Tikapur Municipality, Kailali \n\n\n\n3.2 Infestation status, implemented management practices and \n\n\n\nConstraints in controlling FAW \n\n\n\nField infestation, implemented management practices and problems \n\n\n\nfacing in controlling FAW are presented in table 2. Field infestation was \n\n\n\nseen in more than half of the household surveyed in both villages. It was \n\n\n\n60% in block no 10 and 52% in block no 24. Among household infested \n\n\n\nwith FAW, most of them adopted management practices. But those \n\n\n\nhousehold where infestation is not yet seen has not adopted any \n\n\n\npreventive measures against FAW. Among implemented management \n\n\n\npractices most of them had adopted cultural method (40% in block 10 and \n\n\n\n36% in block 24) of pest management followed by the use of locally \n\n\n\navailable materials toxic to FAW (24% in block 10 and 16% in block 24) \n\n\n\nand chemical (16% in block 10 and 8% in block 24). Very few households \n\n\n\nwere found to receive extension facilities about FAW and its control \n\n\n\nmeasures. It was 12% in block 10 and 8% in block 24. According to our \n\n\n\nstudy the main problem faced by farmers in controlling FAW was lack of \n\n\n\nsufficient knowledge about FAW. It was 32% in block 10 and 36% in block \n\n\n\n24. As this is the problem of those households infested with FAW the \n\n\n\npercentage of this problem was 53.33% in block 10 and 69.23% in block \n\n\n\n24 among FAW infested household.  \n\n\n\nTable 2: Farmer\u2019s knowledge and perception on FAW \n\n\n\nVariable Block No.10 \n(n=25) \n\n\n\nBlock No.24 \n(n=25) \n\n\n\nField infestation 15 (60.0%) 13 (52.0%) \n\n\n\nAdopters of management \npractices  \n\n\n\n12 (48%) 9 (36%) \n\n\n\n  A. Male adaptors  9 (36%) 7 (28%) \n\n\n\n  B. Female adopters  3 (12%) 2 (8%) \n\n\n\nImplemented management \npractices  \n1.Cultural 10 (40%) 8 (32%) \n\n\n\n     A. Handpicking of egg masses \nand larva \n\n\n\n6 (24%) 4 (16%) \n\n\n\n  B. Frequent weeding 2 (8%) 1 (4%) \n\n\n\n  C. Early planting 0 (0%) 1 (4%) \n\n\n\n  D. Crop Rotation 2 (8%) 2 (8%) \n\n\n\n2. Chemical 4 (8%) 2 (8%) \n\n\n\n3. Locally available materials toxic \nto FAW \n\n\n\n6 (24%) 4 (16%) \n\n\n\n  A. Ash 4 (16%) 3(12%) \n\n\n\n  B. Neem products 1 (4%) 0 (0%) \n\n\n\n  C. Lime 1 (4%) 1 (4%) \n\n\n\nReceiving Extension facilities on \nFAW \n\n\n\n3 (12%) 2 (8%) \n\n\n\nProblems facing in controlling \nFAW \nA. Lack of enough budget 1 (4%) 3 (12%) \n\n\n\nB. Lack of sufficient knowledge on \ncontrolling FAW \n\n\n\n8 (32%) 9 (36%) \n\n\n\nC. Unavailability of pesticides in \ntime \n\n\n\n3 (12%) 1 (4%) \n\n\n\nNote: Figure in parenthesis () indicates percentage. \n\n\n\n3.3 Adoption status of management practices \n\n\n\nTable 3 describes the adoption of management practices among different \n\n\n\nsocioeconomic characteristics. Chi square value is calculated. No \n\n\n\nsignificant relation was observed in case of sex, education and age of the \n\n\n\nhousehold head with adoption status in both the blocks.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 10-15 \n\n\n\nCite the Article: Sagar Bhandari, Ruchita Bhattarai , Krishna Raj Pandey, Safal Adhikari (2021). Assessment Of Infestation Of Spodoptera Frugiperda (J.E. Smith) On \nMaize And Its Implemented Management Practices With Their Efficacy In Kailali, Nepal . Malaysian Journal of Sustainable Agriculture, 5(1): 10-15. \n\n\n\nTable 3: Adoption status of management practices among different socioeconomic characteristics \nVariables Block No.10 (n =25) Block No.24 (n =25) \n\n\n\nAdopters Non adopters  Chi square value Adopters  Non adopters Chi square value \nSex 0.01 0.115 \n    A. Male 9 11 7 10 \n    B. Female 3 2 2 6 \nEducation 4.116 0.973 \n    A. Illiterate 2 7 3 9 \n    B. Primary 3 2 2 2 \n    C. Secondary 6 1 4 3 \n    D. Higher secondary 1 3 0 2 \nAge 0.431 0.423 \n    A. Below 34 Years 1 2 2 1 \n    B. 40 to 60 years 10 10 4 10 \n    C. Above 60 years 1 1 3 5 \n\n\n\nNote: * indicates 10% level of significance. \n\n\n\n3.4 Infestation and yield loss perceived by farmers due to FAW in \n\n\n\nmaize \n\n\n\nAverage percentage infestation and yield loss in maize were found to be \n\n\n\ngreater in block 10 than that of block 24. Table 4 shows average \n\n\n\npercentage infestation and yield loss of both villages. Average percentage \n\n\n\nplant infestation was 11.372% in block 10 with standard deviation of \n\n\n\n4.328 and it was found to be 8.913% in block 24 with standard deviation \n\n\n\nof 2.473. Average yield loss in block 10 was found to be 129.058 kg/ha and \n\n\n\nthat was 93.052 kg/ha in block 24. \n\n\n\nTable 4: Assessment of infestation and yield loss \nVariable  Block No.10 Block No.24 \n\n\n\nMean Standard \nDeviation \n\n\n\nMean Standard \nDeviation \n\n\n\n% infestation 11.372 4.328 8.913 2.473 \n\n\n\nYield loss \n(kg/ha) \n\n\n\n129.058 58.997 93.052 27.733 \n\n\n\n3.5 Efficacy of different management practices against FAW \n\n\n\nimplemented by farmers \n\n\n\nThe efficacy of different implemented management practices in both \n\n\n\nvillages is shown in table 5. These management practices are ranked \n\n\n\naccording to their efficacy to control FAW infestation. The management \n\n\n\npractice which results minimum plant infestation is ranked as first and \n\n\n\nvice versa. Those households which do not adopt any management \n\n\n\npractices have highest infestation (18.17%). Among management \n\n\n\npractices cultural method is the least effective with average percentage \n\n\n\ninfestation of 13.15%. Use of locally available materials toxic to FAW \n\n\n\nseems to be more effective than the cultural method with average \n\n\n\npercentage infestation of 8.27%. Table 5 reveals that a combination of two \n\n\n\nor more management practices control FAW better than individual \n\n\n\npractice. Use of all three management practices that are chemical, cultural \n\n\n\nand locally available materials are found to be the most effective in \n\n\n\ncontrolling FAW with an average percentage infestation of only 4.15%. \n\n\n\nTable 5: Assessment of the efficacy of implemented management \npractices against FAW \n\n\n\nImplemented \nmanagement practices  \n\n\n\nAverage Percentage \nplant infestation  \n\n\n\nRank (Efficacy) \n\n\n\nNo adoption of \nmanagement practices  \n\n\n\n18.17 VIII \n\n\n\nCultural method 13.15 VII \nChemical pesticides  6.85 IV \nLocally available \nmaterials toxic to FAW  \n\n\n\n8.27 VI \n\n\n\nCultural + chemical  6.25 III \nCultural + locally \navailable materials toxic \nto FAW  \n\n\n\n7.34 V \n\n\n\nChemical + locally \navailable materials toxic \nto FAW  \n\n\n\n4.93 II \n\n\n\nCultural + chemical + \nlocally available \nmaterials toxic to FAW  \n\n\n\n4.15 I \n\n\n\n4. DISCUSSION\n\n\n\nMajority of the household surveyed were patriarchal and joint family. This \n\n\n\nfinding of our study is in consistent with that of CBS. Their farming system \n\n\n\nwas subsistence type so involved in nonagricultural works for generating \n\n\n\nincome. Lack of any facilities of subsidies from government was the reason \n\n\n\nbehind practicing subsistence farming. The problem of land fragmentation \n\n\n\nwas prevalent there that hindered them from adopting modern methods \n\n\n\nof cultivation. Most of them were found to give continuation to the \n\n\n\ntraditional methods of cropping systems and cropping patterns. This \n\n\n\nrevealed that they are unaware of the advantages of crop rotation and \n\n\n\nintercropping. Lack of agricultural knowledge and extension services were \n\n\n\nthe main reasons behind these problems. \n\n\n\nThough the percentage of plant infestation seems to be less, it is necessary \n\n\n\nto apply control measures. Fernandez also recommended to apply \n\n\n\nappropriate management practices on maize if 5% of the seedlings are cut \n\n\n\nor 20% of whorls of small plants are damaged by FAW (Fernandez, 2002). \n\n\n\nMost of the farmers failed to recognize FAW and thus they were unable to \n\n\n\nadopt appropriate management practices. Very few farmers were familiar \n\n\n\nwith the larval stage of FAW. Koffi also reported that maize producers in \n\n\n\nGhana were familiar with the larval stage of FAW due to its visible injuries \n\n\n\nin maize plant (Koffi, 2020). Implementation of management practices \n\n\n\nwas found to be greater in Block no 10 than that of Block no 24 due to \n\n\n\nhigher literacy rate in Block no 10. Greater number of male farmers had \n\n\n\nadopted management practices as they had greater access to agricultural \n\n\n\nextension services and also had higher literacy rate than women. A group \n\n\n\nresearchers also reported that most of the Nepalese women especially of \n\n\n\nrural part of the country are illiterate and limited in agricultural and \n\n\n\nhousehold activities (Bhandari et al., 2015).  \n\n\n\nMost of the household surveyed were of smallholder farmers owing less \n\n\n\nthan a hectare of land. Nearly half of the Nepalese farmers had less than \n\n\n\n0.5 hectare of land (CBS, 2011). Due to small land size, agriculture \n\n\n\nmechanization was restricted and hence, they were not able to adopt \n\n\n\ncommercialization in agriculture. Most of them used cultural method of \n\n\n\npest management. CABI also reported that smallholders\u2019 farmers practice \n\n\n\nhandpicking, destroying egg masses and larvae and putting sand mixed \n\n\n\nwith lime or ash in the whorl of infested maize to kill the larvae (CABI, \n\n\n\n2017). Early planting helps to create asynchrony between the pest and \n\n\n\ncritical crop growth stages. Intercropping of maize with leguminous crops \n\n\n\nresults in a significantly lower FAW infestation, compared with mono-\n\n\n\ncropping (Hailu et al., 2018). Similarly, crop rotation helps to break the \n\n\n\ncontinuous life cycle of the pest. FAO reported that crop rotation of maize \n\n\n\nwith non host crops such as bean, sunflower helps to reduce infestation of \n\n\n\nFAW (FAO, 2018). There are many other cultural methods of pest \n\n\n\nmanagement including deep ploughing, increasing ground cover, grown of \n\n\n\nmaize hybrids with tight husk cover will reduce ear damage by FAW \n\n\n\n(Firake et al., 2019). But they were found to use very few options of \n\n\n\ncultural pest management because of lack of sufficient knowledge about \n\n\n\nFAW and its control measures. Very few farmers used to go with the \n\n\n\nchemical method of pest management.  \n\n\n\nThis finding of our study is in consistent with the research conducted in \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 10-15 \n\n\n\nCite the Article: Sagar Bhandari, Ruchita Bhattarai , Krishna Raj Pandey, Safal Adhikari (2021). Assessment Of Infestation Of Spodoptera Frugiperda (J.E. Smith) On \nMaize And Its Implemented Management Practices With Their Efficacy In Kailali, Nepal . Malaysian Journal of Sustainable Agriculture, 5(1): 10-15. \n\n\n\nAfrica (Abate et al., 2000). It might be due to less percentage of plant \n\n\n\ninfestation and/or may be due to unavailability of pesticide in time. \n\n\n\nFarmers of Block no 10 had easy access to the market so that they were \n\n\n\nfound to use chemical pesticides more than that of Block no 24. Some of \n\n\n\nthe farmers in both villages used locally available materials toxic to FAW \n\n\n\nfor its control such as sand, lime powder and neem products. They used to \n\n\n\napply ash and lime powder by dusting and neem oil by spraying into the \n\n\n\ninfested plants. A Group Researchers reveals that neem seed powder is \n\n\n\nefficient in killing FAW larvae causing over 70% of mortality (Maredia et \n\n\n\nal., 1992). Such products can be made easily in house and are somehow \n\n\n\neffective in controlling pest so many of them are interested towards these \n\n\n\nproducts. Schmutterer revealed that because of availability and \n\n\n\naffordability many botanical pesticides have been used in developing \n\n\n\ncountries for centuries (Schmutterer, 1985). But they were unaware of the \n\n\n\nappropriate dose, method and time for application. Some of the farmers \n\n\n\nshowed finance as a major problem in controlling FAW. These farmers had \n\n\n\nvery less land area as compared to others and are illiterate. The male from \n\n\n\nsuch household used to go to India to live from hand to mouth. They are \n\n\n\neven unable to afford pesticides and also have no idea to control the pest. \n\n\n\nThis lacking of knowledge can be linked with lack of facilities of extension \n\n\n\nservices. Nevertheless, among overall farmers, lack of sufficient \n\n\n\nknowledge about FAW and its control measures was the major problem.  \n\n\n\nAccording to our study, the infestation of FAW is in increasing rate in both \n\n\n\nvillages but a bit more in Block no 10. This increasing rate of FAW \n\n\n\nincidence is due to lack of any preventive measures, seeds from \n\n\n\nunauthorized sources and careless by the farmers especially of small land \n\n\n\nsize. They are not aware that this invasive pest will cause a serious threat \n\n\n\nto food security in nearby future. Percentage plant infestation seems to be \n\n\n\nmore in Block no 10 though there were more numbers of adopters than in \n\n\n\nblock no 24. This was due to the fact that the percentage field infestation \n\n\n\nwas more in Block no 10. Due to this very reason, they were seriously \n\n\n\nconcerned about infestation and most of the farmers had adopted some of \n\n\n\nthe management practices but they were not so effective. The yield loss \n\n\n\nreported was about 4% of the average yield of the region. A study reported \n\n\n\nyield loss of 11.57% on maize from a study conducted in the eastern \n\n\n\nZimbabwe (Baudron et al., 2019). The less yield loss in our case is due to \n\n\n\nthe fact that the pest has recently been introduced in the region. \n\n\n\nOur study revealed that the implementation of more than one method of \n\n\n\nmanagement practices results in effective control of FAW. This \n\n\n\nemphasizes on Integrated Pest Management (IPM). FAO states that \n\n\n\nIntegrated Pest Control is a pest management system that, in the context \n\n\n\nof the associated environment and the population dynamics of the pest \n\n\n\nspecies, utilizes all suitable techniques and methods in as compatible a \n\n\n\nmanner as possible and maintains the pest population at levels below \n\n\n\nthose causing economic injury (FAO, 1975). It includes cultural, physical, \n\n\n\nchemical, biological and mechanical methods. But they were found to use \n\n\n\nonly a few of these methods. This is due to lack of knowledge and extension \n\n\n\nfacilities. Most of the farmers were not aware about IPM. Nonetheless, they \n\n\n\nconsciously or unconsciously implemented more than one method of pest \n\n\n\nmanagement.  \n\n\n\n5. CONCLUSION \n\n\n\nMajority of the farmers surveyed were infested with FAW, nonetheless the \n\n\n\ninfestation is not so severe yet. The pest is in the state of rapid and \n\n\n\nsubstantial expansion in the region. Though the farmers have \n\n\n\nimplemented some of the management practices, the result is not so \n\n\n\nsatisfactory. Those farmers who implemented different management \n\n\n\npractices in an integrated way are able to control pest effectively. They are \n\n\n\nable to do so because the pest has recently been introduced in the region \n\n\n\nand the infestation is not so severe. But when it spreads thoroughly across \n\n\n\nthe region their present management methods will not be as effective as \n\n\n\nthey are at present. Most of the farmers are unknown about this new \n\n\n\ninvasive pest due to which they cannot adopt appropriate management \n\n\n\npractices. They are deprived of agricultural extension services. Most of \n\n\n\nthem are practicing traditional system of cultivation. Mechanization and \n\n\n\nscientific production technologies are lacking in the region. At present, as \n\n\n\nthe infestation rate is low in maize field, farmers are ignoring this pest. If \n\n\n\nthis situation continues, this invasive pest will cause considerable yield \n\n\n\nloss threatening the livelihood of the maize growing farmers.  \n\n\n\nSo, it is a high time to adopt effective management strategies for its control \n\n\n\nto prevent further expansion. We can learn from other countries where it \n\n\n\nhas been a serious threat and their management strategies to tackle it. \n\n\n\nThere is an urgent need to spread awareness among farmers about the \n\n\n\nidentification of the pest, its life cycle, effective management practices and \n\n\n\ntime for their implementation. Extension services should be provided at \n\n\n\nthe grassroots level. Pesticides and other necessary tools used in pest \n\n\n\ncontrol should be made available in time. The government should \n\n\n\nrecognize those farmers who cannot adopt management practices due to \n\n\n\ntheir poor economic condition and subsidy should be provided for such \n\n\n\nneedy farmers. Effective regulation of plant quarantine check post should \n\n\n\nbe done at Nepal India border to prevent import of infested planting \n\n\n\nmaterials. Focuses should be given towards Integrated Pest Management \n\n\n\n(IPM) for sustainable management which is cost-effective and \n\n\n\nenvironmentally safer. At the same time, resistant varieties against FAW \n\n\n\nshould be developed. \n\n\n\nREFERENCES \n\n\n\nAbate, T., van Huis, A., Ampofo, J., 2000. Pest management strategies in \ntraditional agriculture: An African perspective. Annual Review of \nEntomology, 45 (1), Pp. 631\u2013659. doi: 10.1146/annurev.ento.45.1.631. \n\n\n\nAbrahams, P., 2017. Fall Armyworm: Impacts and Implications for Africa. \nEvidence Note (2). CABI. \n\n\n\nBajracharya, A., Bhat, B., Sharma, P., Shashank, P., Meshram, N., Hashmi, \nT.R., 2019. First record of fall army worm Spodoptera frugiperda (J. E. \nSmith) from Nepal. Indian Journal of Entomology, 81 (4), Pp. 635-639. \n\n\n\nBaudron, F., Zaman-Allah, M.A., Chaipa, I., Chari, N., Chinwada, P., 2019. \nUnderstanding the factors influencing fall armyworm (Spodoptera \nfrugiperda J.E. Smith) damage in African smallholder maize fields and \nquantifying its impact on yield. A case study in Eastern Zimbabwe. Crop \nprotection, 120, Pp. 141-150. \nhttps://doi.org/10.1016/j.cropro.2019.01.028 \n\n\n\nBhandari, N.B., Bhattarai, D., Aryal, M., 2015. Cost, Production and Price \nSpread of Cereal Crops in Nepal: A Time Series Analysis 2071/72 \n(2014/15). Ministry of Agriculture Development, Department of \nAgriculture. Hariharbhawan, Lalitpur: Market Research & Statistics \nManagement Program. Retrieved from http://mrsmp.gov.np/ \n\n\n\nCABI. 2017. Invasive Species Compendium. Retrieved from Invasive \nSpecies Compendium: https://www.cabi.org/isc/datasheet/29810 \n\n\n\nCapinera, J., 2001. Handbook of Vegetable Pests. Elsevier. \n\n\n\nCapinera, J.L., 2002. Fall Armyworm, Spodoptera frugiperda (J.E. Smith) \n(Insecta: Lepidoptera: Noctuidae). EDIS, (7). \n\n\n\nCBS (Central Bureau of Statistics). 2011. National Population and Housing \nCensus 2011. Kathmandu: National Planning Commission, Nepal. \n\n\n\nCruz, I., Figueiredo, M., Oliveira, A., Vasconcelos, C., 1999. Damage of \nSpodoptera frugiperda (Smith) in different maize genotypes cultivated \nin soil under three levels of aluminium saturation. International Journal \nof Pest Management, 45 (4). \n\n\n\nDay, R., Abrahams, P., Bateman, M., Beale, T., 2017. Fall Armyworm: \nImpacts and Implications for Africa. Outlooks on Pest Management, 28 \n(5), Pp. 196-201. \n\n\n\nEarly, R., Moreno, P.G., Murphy, S., Day, R., 2018. Forecasting the global \nextent of invasion of the cereal pest Spodoptera frugiperda, the fall \narmyworm. NeoBiota, 40, Pp. 25-50. \n\n\n\nFAO. 2017. Food Chain Crisis. Retrieved from http://www.fao.org/food-\nchain-crisis/how-we-work/plantprotection/fall-armyworm/en/ \n\n\n\nFAO. 2018 a. Fall Armyworm Monitoring and Early Warning System \n(FAMEAS). FAO. \n\n\n\nFAO. 2018 b. Integrated management of the fall army-worm on maize a \nguide for farmer field schools in Africa. Retrieved from \nhttp://www.fao.org/faostat/en/. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 10-15 \n\n\n\nCite the Article: Sagar Bhandari, Ruchita Bhattarai , Krishna Raj Pandey, Safal Adhikari (2021). Assessment Of Infestation Of Spodoptera Frugiperda (J.E. Smith) On \nMaize And Its Implemented Management Practices With Their Efficacy In Kailali, Nepal . Malaysian Journal of Sustainable Agriculture, 5(1): 10-15. \n\n\n\nFern\u00e1ndez, J., 2002. Nota corta: Estimaci\u00f3n de umbrales econ\u00f3micos para \nSpodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae) en el \ncultivo del ma\u00edz. Invest. Agric. Prod. Prot. Veg, 17, Pp. 467\u2013474. \n\n\n\nFirake, D., Behere, G., Babu, S., Prakash, N., 2019. Fall Armyworm: \nDiagnosis and Management. An Extension Pocket Book. Umiam-793 \n103, Meghalaya, India: ICAR Research Complex for NEH Region. \n\n\n\nGaniger, P., Yeshwanth, H., Muralimohank, V., Kumar, A., Chandrashekara, \nK., 2018. Occurrence of the new invasive pest, fall armyworm, \nSpodoptera frugiperda (J.E. Smith) (Lepidoptera; Noctuidae), in the \nmaize field of Karnataka, India. Current Science, Pp. 621-623. \n\n\n\nHailu, G., Niassy, S., Zeyaur, K., Ochatum, N., Subramanian, S., 2018. Maize\u2013\nLegume Intercropping and Push\u2013Pull for Management of Fall \nArmyworm, Stemborers, and Striga in Uganda. Agronomy Journal, Pp. \n2513-2522. \n\n\n\nHoy, M., 2013. Insect population ecology and molecular genetics. In M. Hoy, \nInsect Molecular Genetics Third Edition ed., Pp. 591-659.  \n\n\n\nKC, G., Karki, T., Shrestha, J., Achhami, B., 2015. Status and prospects of \nmaize research in Nepal. Journal of Maize Research and Development, 1 \n(1), Pp. 1-9. doi:DOI: http://dx.doi.org/10.5281/zenodo.34284 \n\n\n\nKoffi, D., Kyerematen, R., Eziah, V.Y., Osei-Mensah, Y.O., Afreh-Nuamah, K., \nAboagye, E., Osae, M., Meagher, R.L., 2020. Assessment of impacts of fall \narmyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) on maize \nproduction in Ghana. Journal of Integrated Pest Management, 11 (1), 20. \nhttps://doi.org/10.1093/jipm/pmaa015 \n\n\n\nLu, Y.J., Adang, M., Isenhour, D., Kochert, G., 1992. RFLP analysis of genetic \nvariation in North American populations of the fall armyworm moth \nSpodoptera frugiperda (Lepidoptera: Noctuidae). Molecular Ecology. \n\n\n\nLuginbill, P., 1928. The Fall Army Worm (Vol. 34). U.S. Department of \nAgriculture. \n\n\n\nLuginbill, P., 1990. Habits and Control of the Fall Armyworm (Issue 1990 \nof Farmers' Bulletin, United States. Department of Agriculture ed.). U.S. \nGovernment Printing Office, 1950. \n\n\n\nMaredia, K., Segura, O., Mihm, J., 1992. Effects of neem, Azadirachta indica \non six species of maize insect pests. Tropical Pest Management, 38 (2), \nPp. 190-195. \n\n\n\nMoALD (Ministry of Agricultural and Livestock Development); Planing and \n\n\n\ndevelopment cooperation coordination division, 2020. Statistical \nInformation on Nepalese Agriculture 2018/19. Singha Durbar, \nKathmandu, Nepal. \n\n\n\nMOALD (Ministry of Agriculture and Livestock Development). 2019. Plant \nQuarantine and Pesticide Management Centre. Hariharbhawan, \nLalitpur, Nepal. \n\n\n\nMontezano, D., Specht, A., Sosa-G\u00f3mez, D., V.F., R.S., Sousa-Silva, J., Paula-\nMoraes, S., Hunt, T., 2018. Host Plants of Spodoptera frugiperda \n(Lepidoptera: Noctuidae) in the Americas. African Entomology, 26 (2), \nPp. 286-300. \n\n\n\nMurua, G., Molina-Ochoa, J., Coviella, C., 2006. Population dynamics of the \nfall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) and \nits parasitoids in northwestern Argentina. The Florida Entomologist. \ndoi: 10.1653/0015-4040(2006)89[175:pdotfa]2.0.co;2  \n\n\n\nOnline Khabar. 2020. Fall armyworms have spread across 58 districts of \nNepal so far: Agriculture Ministry. Nepal. \n\n\n\nPitre, H., Hogg, D., 1983. Development of the fall armyworm on cotton, \nsoybean and corn [Spodoptera frugiperda]. Journal of the Georgia \nEntomological Society. \n\n\n\nPrasanna, B., Huesing, J., Eddy, R., Peschke, V., 2018. Fall armyworm in \nAfrica: a guide for integrated pest management. Handbook. M\u00e9xico: \nCIMMYT; USAID. \n\n\n\nRwomushana, I.B., 2018. Fall armyworm: impacts and Implications for \nAfrica. Evidence Note Update. Oxfordshire, UK: CABI. \n\n\n\nSapkota, D., Pokhrel, S., 2010. Community based maize seed production in \nthe hills and mountains of Nepal: A review. Agronomy Journal of Nepal, \n1, Pp. 107-112. doi: https://doi.org/10.3126/ajn.v1i0.7550 \n\n\n\nSchmutterer, H., 1985. Which insect pests can be controlled by application \nof neem seed kernel extracts under field conditions? Zeitschrift f\u00fcr \nangewandte Entomologie, 100 (1-5), Pp. 468\u2013475. doi:10.1111/j.1439-\n0418.1985.tb02808.x \n\n\n\nSisay, B., Tefera, T., Wakgari, M., Ayalew, G., 2019 b. The Efficacy of Selected \nSynthetic Insecticides and Insects. \n\n\n\nSisay, B., Simiyu, J., Mendesil, E., Likhayo, P., Ayalew, G., Mohamed, S., \nTefera, T., 2019 a. Fall Armyworm, Spodoptera frugiperda Infestations \n\n\n\nin East Africa: Assessment of Damage and Parasitism. Insects.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 71-74 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.71.74 \n\n\n\nCite the Article: Nisha Paneru, Pragya Adhikari, Puja Tandan (2020). Management Of Purple Blotch Complex Of Onion (Allium Cepa Cv Red Creole) Under Field \nCondition In Rukum-West, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 71-74. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.71.74 \n\n\n\nMANAGEMENT OF PURPLE BLOTCH COMPLEX OF ONION (ALLIUM CEPA CV RED \nCREOLE) UNDER FIELD CONDITION IN RUKUM-WEST, NEPAL \n\n\n\nNisha Paneru*, Pragya Adhikari, Puja Tandan \n\n\n\nAgriculture and Forestry University, Rampur, Chitwan. \n*Corresponding Author Email: nishapaneru23@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 03 March 2020\n\n\n\nA field experiment was conducted in Chaurjahari Municipality, Rukum-west, Nepal during the rabi season of \n2019 to study the management of purple blotch complex of onion through chemicals and bio fungicides. The \nexperiment was laid out in a Randomized Complete Block Design (RCBD) with three replications of each \ntreatment. Six different chemical fungicides; Hexaconazole, Tebuconazole, Mancozeb+Cymoxanil, \nDimethomorph, Chlorothalonil, Carbendazim and one biological fungicides Trichoderma were evaluated in \nfield condition against Alternaria porri and Stemphylium vesicarium for effective control of purple blotch \ncomplex. The fungicides were spread in fortnight interval and data were collected on biometric parameters \nlike plant height, number of umbel stalk, umbel diameter; yield contributing characters like thousand seed \nweight and yield of onion.  Likewise, data on disease incidence and disease severity were also recorded. \nHexaconazole and Mancozeb + Cymoxanil were proved to be best in controlling this complex with the Percent \nDisease Control (PDC) of 84.45 percent and 80.00 percent respectively. The highest yield 878.7 kg/ha and \nthousand seed weight 3.72gm were recorded from Hexaconazole treated plot followed by Mancozeb + \nCymoxanil with yield and thousand seed weight of 878.3kg/ha and 3.64gm respectively. The economic \nanalysis of fungicides was also done where Hexaconazole at 0.1 percent concentration was found most \neconomic with the Benefit Cost ratio of 3.02. Similarly, the study of weather parameter in relationship to \ndisease occurrence was done, the coefficient of multiple determinants (R2) obtained was 0.7858 indicating \n78.58 percent of variation in purple blotch development explained by the different weather parameter under \nstudy.  \n\n\n\nKEYWORDS \n\n\n\nPurple blotch Complex, Fungicides, Hexaconazole, Mancozeb + Cymoxanil, Yield.\n\n\n\n1. INTRODUCTION \n\n\n\nOnion (Allium cepa L.) (2n=16) often called as \u201cQueen of kitchen\u201d is one of \n\n\n\nthe oldest known and an important vegetable crop grown in Nepal (Pareek \n\n\n\net al., 2017; Galande and Simon, 2019). It is valued for its distinctive \n\n\n\npungent smell (Ravichandra, 2012). Medicinal values of onion are \n\n\n\ninnumerable and is one of the ancient crops being utilized in medicine. \n\n\n\nExtract of onion is used as antibacterial, antifungal, anti-helmenthic, anti-\n\n\n\ninflammatory, anti-septic and anti-spasmodic (Jhala. Over years, onion has \n\n\n\ngained the importance of a cash crop rather than vegetable crop because \n\n\n\nof its very high export potential. \n\n\n\nSeveral factors have been identified for the low productivity of onion in \n\n\n\nNepal. The most important factors responsible are the diseases like purple \n\n\n\nblotch, downy mildew, Stemphylium blight, basal rot and storage rots and \n\n\n\nnon-availability of varieties resistant to biotic and abiotic stresses (Savitha \n\n\n\net al., 2014; Meena et al., 2017). Purple blotch, commonly known as leaf \n\n\n\nblotch, caused by Alternaria porri is noted as the most serious disease of \n\n\n\nonion affecting both bulb and seed production by breaking floral stalks \n\n\n\n(Islam and Faruq, 2006; Veeraghanti et al., 2017).  Nowadays, \n\n\n\nStemphylium botryosum, the causal agent of white blotch of onion are \n\n\n\nindirectly involved with the causation of purple blotch of onion. \n\n\n\nStemphylium botryosum initiate the infection and Alternaria porri \n\n\n\nfacilitates for causing purple blotch and hence the disease is treated as \n\n\n\npurple blotch complex (Nainwal and Vishunavat, 2016; Akter et al., 2015; \n\n\n\nAli et al., 2016). \n\n\n\nIn Nepal, few attempts have been made to find out the suitable control \n\n\n\nmeasures of this disease.  The varieties grown in the country are highly \n\n\n\nsusceptible to the disease. Role of environmental factors on disease \n\n\n\ndevelopment has not yet been studied systematically. Therefore, quite a \n\n\n\nlittle information is available on fungicidal control; and mostly those are \n\n\n\non bulb production only but not on seed production. Some of the \n\n\n\nfungicides used worldwide and found effective are Chlorothalonil 75% \n\n\n\nWP, Mancozeb 75% WP, Propineb 70% WP, Difenconazole 25% EC, \n\n\n\nPropiconazole 25% EC and Hexaconazole 5% EC (Bachkar and Bhalckar, \n\n\n\n2018). A good number of fungicides are yet to be assayed against this \n\n\n\ndisease. Thus, the present study was undertaken to screen out the effective \n\n\n\nfungicides for the management of the purple blotch complex of onion for \n\n\n\nseed production. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 71-74 \n\n\n\nCite the Article: Nisha Paneru, Pragya Adhikari, Puja Tandan (2020). Management Of Purple Blotch Complex Of Onion (Allium Cepa Cv Red Creole) Under Field \nCondition In Rukum-West, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 71-74. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nIn order to study the effectiveness of fungicides in controlling the disease, \n\n\n\neight different treatments were included in the trial. The treatments were \n\n\n\nreplicated three times in a Randomized Complete Block Design. Thus, total \n\n\n\n24 plots were there. The size of individual plot was 3 \u00d71.5 m. The spraying \n\n\n\nwas done as soon as the plant has shown the initial symptoms. The \n\n\n\nsprayings were done at fortnightly intervals (15 days) till the crop \n\n\n\nmatured and onion seed was harvested. The treatments are tabulated. \n\n\n\nTable 1: List of treatments with their trade names, recommended \n\n\n\ndoses and active ingredients \n\n\n\nTreatments Chemical name Trade name Dose \n\n\n\n(%) \n\n\n\nActive \n\n\n\ningredient \n\n\n\nT1 (control) Plain Water - - - \n\n\n\nT2 Carbendazim Bavistin 0.2% 50%WP \n\n\n\nT3 Tebuconazole Carapace 0.1% 25.9%EC \n\n\n\nT4 Hexaconazole Roshan plus 0.1% 5%SC \n\n\n\nT5 Chlorothalonil Royal care 0.2% 75%WP \n\n\n\nT6 Dimethomorph Kingstival 0.25% 50%WDG \n\n\n\nT7 Mancozeb+ \n\n\n\nCymoxanil \n\n\n\nReal-mil 0.3% 64%WP \n\n\n\n+8% WP \n\n\n\nT8 Tricoderma \n\n\n\nliquid \n\n\n\nTrivi 0.3% SL \n\n\n\nThe 0 to 5 disease scoring scale was used to estimate the disease severity \n\n\n\n(PDI-Percent Disease Index) of purple blotch complex of onion for each \n\n\n\nunit plot under each treatment. The scale followed was given as described \n\n\n\nbelow (Islam, 2013): \n\n\n\nTable 2: Disease scoring scale used in purple blotch complex at \n\n\n\nChaurjahari Municipality, Rukum-West, Nepal, 2019 \n\n\n\nS.N Score Severity \n\n\n\n1 0 no disease symptoms in the plant  \n\n\n\n2 1 a few spots towards the tip, covering less than 10% leaf \n\n\n\narea   \n\n\n\n3 2 several dark purplish brown patches covering less than \n\n\n\n20% leaf area  \n\n\n\n4 3 several patches with paler outer zone, covering up to \n\n\n\n40% leaf area  \n\n\n\n5 4 long streaks covering up to 75% leaf area or breaking \n\n\n\nof leaves / stalks from the center \n\n\n\n6 5 complete drying of the leaves/ stalks or breaking of the \n\n\n\nleaves / stalks from the base \n\n\n\nData were collected on leaf infection and percent leaf area diseased and \n\n\n\ncalculated in terms of disease incidence and disease severity (PDI) by \n\n\n\nfollowing formulae- \n\n\n\n2.1 Disease Incidence (DI) \n\n\n\nDI (%) = \\frac{no.of\\ infected\\ leaf}{Total\\ no.of\\ inspected\\ leaf}\u00d7 100% \n\n\n\n2.2 Percent Disease Index (PDI) or Percent Disease Severity \n\n\n\nPDI=\\frac{Total\\ sum\\ of\\ numerical\\ ratings\\ \\ }{\\ Number\\ of\\ \n\n\n\nobservation\\ X\\ Maximum\\ disease\\ rating\\ in\\ the\\ scale\\ }\u00d7 100% \n\n\n\n2.3 Percent Disease Control (PDC) \n\n\n\nPDC (%) \\frac{PDI\\ in\\ Control-PDI\\ in\\ treatment}{PDI\\ in\\ Control}\u00d7 \n\n\n\n100% \n\n\n\n2.4 Area under Diseased progress Curves (AUDPC)  \n\n\n\nArea under Disease Progress Curves (AUDPC) was calculated using the \n\n\n\nformula given by Das et al. (1992)   \n\n\n\n  AUDPC= i=1n[{Yi+Y(i+1)/ 2}\u00d7(ti+1-ti)] \n\n\n\nWhere, Yi=disease severity on the ith date \n\n\n\n  Y(i+1)= disease severity on the i+1th date \n\n\n\n  n = number of dates \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Effects of fungicides on biometric parameters of onion \n\n\n\nThere was both significant and insignificant relationship found between \n\n\n\napplied fungicides and biometric parameters of onion. The applied \n\n\n\nfungicides showed insignificant differences with umbel diameter and \n\n\n\nnumber of umbel per hill while significant differences was seen in plant \n\n\n\nheight, thousand seed weight and yield. The highest plant height was \n\n\n\nrecorded from Hexaconazole (130.8cm) treated plot at par with \n\n\n\nMancozeb+ Cymoxanil and Dimethomorph. Likewise, the maximum \n\n\n\nthousand seed weight was obtained from Hexaconazole (3.727gm) \n\n\n\nsprayed plot followed by Mancozeb + Cymoxanil and Tebuconazole. \n\n\n\nSimilarly, maximum yield was seen in Hexaconazole (878.7kg/ha) sprayed \n\n\n\nplot   which was found statistically similar with Mancozeb + Cymoxanil \n\n\n\n(878.3kg/ha). \n\n\n\nThe above finding collaborated with the result of the different other \n\n\n\nresearchers around the world. Some researchers found out the positive \n\n\n\nand significant impact of fungicides on plant height and also observed \n\n\n\nstatistically significant variation in terms of umbel diameter for the \n\n\n\neffectiveness of different chemicals and environmentally friendly \n\n\n\ncomponents against purple blotch of onion (Haque, 2015). Similarly, Islam \n\n\n\net al. 2013, had also shown remarkable influence of different treatments \n\n\n\non height of onion seed stalk (cm) (Islam and Faruq, 2006). Likewise, \n\n\n\nAhmed, 2018  had shown insignificant relationship between number of \n\n\n\numbel/hill and applied fungicides against purple blotch of onion (Ahmed \n\n\n\net al., 2018).  \n\n\n\nNote: SEm\\pm, Standard Error of mean; CV, Coefficient of variation; LSD, \n\n\n\nLeast significant difference. Means in the column with the same letter (s) \n\n\n\nin superscript indicate no significant difference between treatments at \n\n\n\n0.05 level of significance: \u2018***\u2019 Significant at 0.001 level of Significance \n\n\n\n3.2 Effects of fungicides on disease parameters \n\n\n\nDisease incidence percentage was significantly influenced by the \n\n\n\napplication of the different fungicides against purple blotch complex. The \n\n\n\ndata on disease incidence of onion varies throughout the observation \n\n\n\nperiod. At 145 DAP the lowest disease incidence (31.11%) was recorded \n\n\n\nfrom Hexaconazole treated plot which is followed by \n\n\n\nMancozeb+Cymoxanil plot. The highest disease incidence (87.78%) was \n\n\n\nseen from control plot which was found statistically similar with \n\n\n\nTable 3: Influence of different fungicides on biometric parameters in \n\n\n\nred creole variety of onion at different dates of observation at \n\n\n\nChaurjahari Municipality, Rukum-West, 2019 \n\n\n\nTreatment \n\n\n\nBiometric Parameters \n\n\n\nPlant \n\n\n\nHeight \n\n\n\n(cm) \n\n\n\nUmbel \n\n\n\ndiameter \n\n\n\n(cm) \n\n\n\nNo. of \n\n\n\numbel/ \n\n\n\nhill \n\n\n\nThousand \n\n\n\nseed \n\n\n\nWeight \n\n\n\n(gm) \n\n\n\nYield \n\n\n\n(kg/ha) \n\n\n\nControl 106.1 c 3.70 2.467 2.523d 458.5e \n\n\n\nCarbendazim 122.0 ab 4.24 2.952 3.007 c 692.4 c \n\n\n\nTebuconazole 125.2ab 4.58 2.962 3.533ab 839.3ab \n\n\n\nHexaconazole 130.8a 4.87 3.467 3.727 a 878.7a \n\n\n\nChlorothalonil 123.9ab 4.46 3.086 3.423 b 773.7b \n\n\n\nDimethomorph 130.7 a 5.03 3.152 3.433 b 847.6 ab \n\n\n\nMancozeb + \n\n\n\nCymoxanil \n\n\n\n129.0 a 5.01 3.333 3.643ab 878.3a \n\n\n\nTrichoderma 117.8 b 4.50 3.048 2.927 c 573.1d \n\n\n\nSEm (\u00b1) 2.643 0.320 0.2187 0.0731 25.54 \n\n\n\nLSD (=0.05) 8.017 NS NS 0.2217 77.47 \n\n\n\nCV% 3.7 12.2 12.4 3.9 6.0 \n\n\n\nGrand mean 123.20 4.55 3.058 3.277 742.7 \n\n\n\nP value <.001*** 0.140 0.153 <.001***  <.001*** \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 71-74 \n\n\n\nCite the Article: Nisha Paneru, Pragya Adhikari, Puja Tandan (2020). Management Of Purple Blotch Complex Of Onion (Allium Cepa Cv Red Creole) Under Field \nCondition In Rukum-West, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 71-74. \n\n\n\nTricoderma treated plot. Similarly, at 175 DAP the reduction on the \n\n\n\ndisease incidence was highest on the Mancozeb+Cymoxanil treated plot \n\n\n\nwith DI% of (43.33%). In case of 205 DAP, Mancozeb+ cymoxanil fungicide \n\n\n\nwas found to be more effective against Purple blotch complex with disease \n\n\n\nincidence percentage of (21.1%). \n\n\n\nPurple blotch complex was suppressed significantly due to the application \n\n\n\nof different fungicides on the field condition. Data on Percent Disease \n\n\n\nIntensity of onion by fungus was influenced by different fungicides. It was \n\n\n\ncalculated based on a rating scale 0-5 and found out the significant \n\n\n\nrelationship between applied fungicides and percent disease intensity. At \n\n\n\n145 DAP, the lowest PDI (21.3%) was obtained from Hexaconazole treated \n\n\n\nplot which was statistically similar with Mancozeb + Cymoxanil treated \n\n\n\nplot. Likewise, the highest PDI was recorded from untreated control plot. \n\n\n\nAt 175 DAP, Hexaconazole treated plot showed the lowest disease severity \n\n\n\nof purple blotch (25.33%) which was found statistically similar with \n\n\n\nDimethomorph (26.67%), Mancozeb+ Cymoxanil (28.00%) and \n\n\n\nTebuconazole (29.33%). Again, at 205 DAP Hexaconazole (9.33%) was \n\n\n\nfound to be more effective in controlling disease severity at par with \n\n\n\nMancozeb + Cymoxanil (12.00%). \n\n\n\nA group researchers tested different fungicides and found 100% inhibition \n\n\n\nin Hexaconazole followed by Difenaconazole (83.91%) and Mancozeb \n\n\n\n(63.58%) (Wanggikar et al., 2014). Some researchers also examined \n\n\n\ndifferent concentrations of systemic fungicides in vitro and reveled that \n\n\n\nHexaconozole was found most effective with highest mean inhibition of \n\n\n\nradial growth (98.21%) followed by Propiconazole (97.32%) and \n\n\n\nDifenaconazole (91.23%) against the purple blotch complex of onion \n\n\n\n(Yadav et al., 2017). \n\n\n\nFigure 1: Percentage disease severity of purple blotch complex of onion \n\n\n\nwith respect to different treatment at different dates of observation \n\n\n\nTable 4:  Influence of different fungicides on disease parameters in \n\n\n\nred creole variety of onion at different dates of observation at \n\n\n\nChaurjahari Municipality, Rukum-West, 2019 \n\n\n\nTreatments \n\n\n\n     Disease Incidence (%)  Disease Severity (%) \n\n\n\n145 \n\n\n\nDAP \n\n\n\n175 \n\n\n\nDAP \n\n\n\n205 \n\n\n\nDAP \n\n\n\n145 \n\n\n\nDAP \n\n\n\n175 \n\n\n\nDAP \n\n\n\n205 \n\n\n\nDAP \n\n\n\nControl 69.84a \n\n\n\n(87.78) \n\n\n\n89.94a \n(100.00) \n\n\n\n89.99a \n\n\n\n(100.0) \n\n\n\n7.713a \n\n\n\n(59.3) \n\n\n\n7.604a \n\n\n\n(57.33) \n\n\n\n7.778a \n\n\n\n(60.00) \n\n\n\nCarbendazim 50.12b \n\n\n\n(58.89) \n\n\n\n61.24c \n\n\n\n(76.68) \n\n\n\n44.36c \n\n\n\n(48.9) \n\n\n\n6.564bc \n\n\n\n(42.7) \n\n\n\n6.024c \n\n\n\n(36.00) \n\n\n\n4.654c \n\n\n\n(21.33) \n\n\n\nTebuconazoe 37.90cd \n\n\n\n(37.78) \n\n\n\n43.06f \n\n\n\n(46.67) \n\n\n\n33.81de \n\n\n\n(31.1) \n\n\n\n5.801c \n\n\n\n(33.3) \n\n\n\n5.443d \n\n\n\n(29.33) \n\n\n\n4.062d \n\n\n\n(16.00) \n\n\n\nHexaconazoe 33.85d \n\n\n\n(31.11) \n\n\n\n43.70ef \n\n\n\n(47.78) \n\n\n\n29.46ef \n\n\n\n(24.4) \n\n\n\n4.654d \n\n\n\n(21.3) \n\n\n\n5.079d \n\n\n\n(25.33) \n\n\n\n3.122e \n\n\n\n(9.33) \n\n\n\nChlorothaloil 41.12c \n\n\n\n(43.34) \n\n\n\n56.07d \n\n\n\n(68.89) \n\n\n\n38.51cd \n\n\n\n(38.9) \n\n\n\n6.569bc \n\n\n\n(42.7) \n\n\n\n6.245c \n\n\n\n(38.67) \n\n\n\n4.528cd \n\n\n\n(20.00) \n\n\n\nDimethomorph 39.87c \n\n\n\n(41.12) \n\n\n\n47.52e \n\n\n\n(54.45) \n\n\n\n35.19de \n\n\n\n(33.3) \n\n\n\n5.928c \n\n\n\n(34.7) \n\n\n\n5.200d \n\n\n\n(26.67) \n\n\n\n4.062d \n\n\n\n(16.00) \n\n\n\nMancozeb + \n\n\n\nCymoxanil \n\n\n\n36.57cd \n\n\n\n(35.56) \n\n\n\n41.14f \n\n\n\n(43.33) \n\n\n\n26.84f \n\n\n\n(21.1) \n\n\n\n4.809d \n\n\n\n(22.7) \n\n\n\n5.339 d \n\n\n\n(28.00) \n\n\n\n3.536e \n\n\n\n(12.00) \n\n\n\nTrichoderma 65.20a \n\n\n\n(82.23) \n\n\n\n78.19b \n\n\n\n(95.76) \n\n\n\n52.10b \n\n\n\n(62.2) \n\n\n\n7.304ab \n\n\n\n(53.3) \n\n\n\n6.850b \n\n\n\n(46.67) \n\n\n\n6.024b \n\n\n\n(36.00) \n\n\n\nSEm (\u00b1) 1.607 1.357 2.163 0.2612 0.1888 0.1555 \n\n\n\nLSD (=0.05) 4.874 4.116 6.560 0.7921 0.5728 0.4715 \n\n\n\nCV% 5.9 4.1 8.6 7.3 5.5 5.7 \n\n\n\nGrand mean 46.81 57.61 43.78 6.168 5.973 4.721 \n\n\n\nP value <.001*\n\n\n\n** \n\n\n\n<.001*\n\n\n\n** \n\n\n\n<.001*\n\n\n\n** \n\n\n\n<.001*\n\n\n\n** \n\n\n\n<.001*\n\n\n\n** \n\n\n\n<.001*\n\n\n\n** \n\n\n\nNote: SEm\\pm, Standard Error of mean; CV, Coefficient of variation; LSD, \n\n\n\nLeast significant difference; Means in the column with the same letter (s) \n\n\n\nin superscript indicate no significant difference between treatments at \n\n\n\n0.05 level of significance; \u2018***\u2019 Significant at 0.001 level of Significance; \u2018**\u2019 \n\n\n\nSignificant at 0.01 level of Significance; \u2018*\u2019 Significant at 0.05 level of \n\n\n\nSignificance. Value in the Parenthesis indicate mean value while without \n\n\n\nparenthesis indicate Arcsine transformed value (ASIN \n\n\n\n(SQRT(X/100))\u00d7180/(22/7)) and Square root transformed value \n\n\n\n(SQRT(X+0.5)) \n\n\n\n3.3 Economic analysis of different fungicides \n\n\n\nThe benefit cost ratio was analyzed to figure out the most economic \n\n\n\nmethod of purple blotch complex management under farmer field \n\n\n\ncondition.  It was calculated using the formulae: \n\n\n\nB:C = \n\ud835\udc47\ud835\udc5c\ud835\udc61\ud835\udc4e\ud835\udc59 \ud835\udc56\ud835\udc5b\ud835\udc50\ud835\udc5c\ud835\udc5a\ud835\udc52 \ud835\udc53\ud835\udc5f\ud835\udc5c\ud835\udc5a \ud835\udc5c\ud835\udc5b\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc60\ud835\udc52\ud835\udc52\ud835\udc51 \ud835\udc5d\ud835\udc5f\ud835\udc5c\ud835\udc51\ud835\udc62\ud835\udc50\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b\n\n\n\n\ud835\udc47\ud835\udc5c\ud835\udc61\ud835\udc4e\ud835\udc59  \ud835\udc38\ud835\udc65\ud835\udc5d\ud835\udc52\ud835\udc5b\ud835\udc51\ud835\udc56\ud835\udc61\ud835\udc62\ud835\udc5f\ud835\udc52 \ud835\udc53\ud835\udc5c\ud835\udc5f \ud835\udc5c\ud835\udc5b\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc60\ud835\udc52\ud835\udc52\ud835\udc51 \ud835\udc5d\ud835\udc5f\ud835\udc5c\ud835\udc51\ud835\udc62\ud835\udc50\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b\n\n\n\nHexaconazole was found to be most economic among all the tested \n\n\n\nfungicides with highest B:C ratio of 3.02 and the lowest AUDPC value of \n\n\n\n2240. It was found statistically similar with Mancozeb + Cymoxanil and \n\n\n\nTebuconazole with B:C ratio of 2.92 and 2.8 respectively.  This result is \n\n\n\nsupported by the result, that Cymoxanil 8% + Mancozeb 64% WP may be \n\n\n\nused as a better choice in managing the disease and also to overcome \n\n\n\nresistance development in the pathogen (Rao et al., 2014). However, \n\n\n\nTreatments in this research varies, the result can be correlated. \n\n\n\nTable 5:  AUDPC and B:C ratio calculation of different fungicides in \n\n\n\nred creole variety of onion at different dates of observation at \n\n\n\nChaurjahari Municipality, Rukum-West, Nepal, 2019 \n\n\n\nTreatment Total AUDPC B:C ratio \n\n\n\nControl 6800e 1.187d \n\n\n\nCarbendazim 3990c 2.320b \n\n\n\nTebuconazole 3030b 2.807a \n\n\n\nHexaconazole 2240a 3.020a \n\n\n\nChlorothalonil 3980c 2.683ab \n\n\n\nDimethomorph 3140b 2.683ab \n\n\n\nMancozeb + \n\n\n\nCymoxanil \n\n\n\n2320a 2.920a \n\n\n\nTrichoderma 5410d 1.673c \n\n\n\nSEM (\u00b1) 190.5 0.1302 \n\n\n\nLSD(=0.05) 577.8 0.3950 \n\n\n\nCV% 8.5 9.4 \n\n\n\nGrand mean 3864 2.412 \n\n\n\nP value <.001*** <.001*** \n\n\n\nNote: SEm\\pm, Standard Error of mean; CV, Coefficient of variation; LSD, \n\n\n\nLeast significant difference. Means in the column with the same letter (s) \n\n\n\nin superscript indicate no significant difference between treatments at \n\n\n\n0.05 level of significance; \u2018***\u2019 Significant at 0.001 level of Significance. \n\n\n\nFigure 1:  Economic analysis of different treatment along with disease \n\n\n\nincidence and disease severity \n\n\n\n3.4 Relationship between disease severity and weather condition \n\n\n\nThe multiple liner regression equation was fitted to the data and the \n\n\n\nequation arrived for the weather parameters was: \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\n85 DAP 115 DAP 145 DAP 175 DAP 205 DAP\n\n\n\nD\nis\n\n\n\ne\na\nse\n\n\n\n S\ne\nv\ne\nr\nit\n\n\n\ny\n (\n\n\n\n%\n)\n\n\n\nIncreasing Time\n\n\n\nControl\n\n\n\nCarbendazim\n\n\n\nTebuconazole\n\n\n\nHexaconazole\n\n\n\nChlorothalonil\n\n\n\nDimethomorph\n\n\n\nMancozeb +\n\n\n\ncymoxanil\n\n\n\nTrichoderma\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\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\nB\n:C\n\n\n\n r\na\n\n\n\nti\no\n\n\n\n%\n d\n\n\n\nis\ne\na\n\n\n\nse\n s\n\n\n\ne\nv\ne\nr\nit\n\n\n\ny\n, \n%\n\n\n\n d\nis\n\n\n\ne\na\n\n\n\nse\n i\n\n\n\nn\nc\nid\n\n\n\ne\nn\n\n\n\nc\ne\n a\n\n\n\nn\nd\n\n\n\n\n\n\n\n%\n y\n\n\n\nie\nld\n\n\n\n i\nn\n\n\n\nc\nr\ne\na\n\n\n\nse\n\n\n\nTreatments\n\n\n\n% Disease Severity % yield increase % Disease incidence B:c ratio\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 71-74 \n\n\n\nCite the Article: Nisha Paneru, Pragya Adhikari, Puja Tandan (2020). Management Of Purple Blotch Complex Of Onion (Allium Cepa Cv Red Creole) Under Field \nCondition In Rukum-West, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 71-74. \n\n\n\nY=-137.65*-1.175X1+1.13X2*+5.41X3-4.43X4+1.41X5 \n\n\n\nWhere, Y= Percentage Disease Index (PDI) \n\n\n\n  X1=Precipitation (mm/day) \n\n\n\n  X2=Relative humidity at 2m (%) \n\n\n\n  X3=T max at 2m (\u00b0C) \n\n\n\n  X4= T min at 2m (\u00b0C) \n\n\n\n  X5= Long wave radiation flux (MJ/m2/day) \n\n\n\nIn year 2019, the coefficient of multiple determinants (R2) was 0.7858 \n\n\n\nindicating 78.58% of variation in purple blotch development explained by \n\n\n\nthe set of variable in the study. When there was increase in one unit of \n\n\n\nRelative humidity (1%), maximum temperature (1\u00b0C) and longwave \n\n\n\nradiation flux (MJ/m2/day), the Percent diseased index increased by 1.13, \n\n\n\n5.41 and 1.41 units respectively. While when there was increase in one \n\n\n\nunit of precipitation (mm/day) and minimum temperature (\u00b0C), the \n\n\n\nPercentage disease index decreased by 1.17 and 4.43 units respectively. \n\n\n\n4. CONCLUSION \n\n\n\nThe study inferred the best effective fungicides that can be easily adopted \n\n\n\nby the farmers to control purple blotch havoc in their field. Influence of \n\n\n\ntreatment on biometric characters like plant height, number of umbel \n\n\n\nstalk, and umbel diameter at different dates of observation was found \n\n\n\nstatistically significant as well as insignificant. But the influence of the \n\n\n\ntreatments on the disease parameters was found highly significant in each \n\n\n\ndates of observation. Hexaconazole gave the maximum yield (878.7 kg/ha) \n\n\n\nwhich was found statistically correspondent with Mancozeb + Cymoxanil. \n\n\n\nLikewise, same fungicides Hexaconazole scored maximum thousand seed \n\n\n\nweight (3.727 gm). Regarding the disease incidence and disease severity, \n\n\n\nHexaconazole and Mancozeb+Cymoxanil performed better with lowest \n\n\n\ntotal AUDPC value of 2240 and 2320 respectively. The Percentage disease \n\n\n\ncontrol at 205 DAP was found highest (84.45%) in Hexaconazole treated \n\n\n\nplot followed by Mancozeb+Cymoxanil treated plot. The economic \n\n\n\nanalysis of tested fungicides was done where Hexaconazole was proved to \n\n\n\nmost efficacious with B:C ratio of 3.02. Similarly, the study of weather \n\n\n\nparameter in relationship to disease occurrence was done and found out \n\n\n\nthat the minimum temperature, maximum as well as minimum relative \n\n\n\nhumidity proved to be congenial for disease development.  On the basis of \n\n\n\nthe present finding it is concluded that Hexaconazole @ 0.1% \n\n\n\nconcentration is the best remedy to control the purple blotch complex of \n\n\n\nonion. \n\n\n\nRECOMMENDATION \n\n\n\nMost effective measure to control the Purple blotch complex is use of the \n\n\n\nresistant cultivars and good cultural management practices against the \n\n\n\ndisease, but in case of their unavailability, then only solution of the farmers \n\n\n\nis to use the chemical fungicides for the management of purple blotch \n\n\n\ncomplex.  Hence it is recommended to use Hexaconazole @ 0.1% \n\n\n\nconcentration among all other fungicides for the control and management \n\n\n\nof purple blotch complex in the study site. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nAuthors would like to express heartfelt thanks to Agriculture and Forestry \n\n\n\nUniversity, Rampur, Chitwan; Prime Minister Agriculture Modernization \n\n\n\nProject (PMAMP); Ram Hari Timilsina, Assistant Professor for guiding \n\n\n\nthroughout the study period.  \n\n\n\nAUTHOR CONTRIBUTIONS \n\n\n\nNisha Paneru conducted the experiment and recorded data, analyzed and \n\n\n\ncreated the final manuscript. Pragya Adhikari, Puja Tandan and Sushil \n\n\n\nChandra Sapkota helped during data observation and manuscript \n\n\n\npreparation. \n\n\n\nREFERENCES \n\n\n\nAhmed, S., Quddus, A., Kamrozzaman, M., Sarker, R., Uddin, M., 2018. \n\n\n\nIntegrated approaches for controlling purple blotch of onion for true \n\n\n\nseed production in Faridpur of Bangladesh. Fundam. Appl. Agric., 3 (1), \n\n\n\nPp. 1. \n\n\n\nAkter, R.I., Sarifun, U., Rashid, H., Rahman, A., 2015. Effect of the \n\n\n\nTreatments in Controlling Purple Blotch Complex of Onion (Allium cepa \n\n\n\nL.). Acad. J. Plant Sci., 7 (2), Pp. 14\u201319. \n\n\n\nAli, H., Ara, H., Nisha, C., Hossain, B., Islam, R., 2016. Evaluation of \n\n\n\nCombined Effect of Micronutrients (ZnSO 4 + Borax) and Fungicides to \n\n\n\nControl the Purple Blotch Complex of Onion (Allium cepa). Am. J. Plant \n\n\n\nSci., 7, Pp. 715\u2013723. \n\n\n\nBachkar, S.A.C.P.B., Bhalckar, M.N. 2018. Control of Alternaria porri \n\n\n\nCausing Purple Blotch of Onion during Kharif Season. J. Allium Res., 1, \n\n\n\nPp. 57\u201359. \n\n\n\nGalande, D.R., Simon, S., 2019. Effect of Intercropping on Purple Blotch \n\n\n\n(Alternaria porri) of Onion (Allium cepa L.). Int. J. Curr. Microbiol. Appl. \n\n\n\nSci., 8 (02), Pp. 1105\u20131111. \n\n\n\nHaque, M., 2015. Effect of fungicides and plant extracts on growth and \n\n\n\nyield of Onion (Allium Cepa L.). J. Basic Appl. Sci. Res., 5, Pp. 25\u201328. \n\n\n\nIslam, R., Faruq, A.N., 2006. Evaluation of Fungicides against Purple Blotch \n\n\n\nComplex of Onion (Alternaria porri and Stemphylium botryosum) for \n\n\n\nSeed. J. agric. educ. technol., 9, Pp. 83\u201386. \n\n\n\nIslam, Z., 2013. Seed Yield Loss Assessment for Purple Blotch Complex of \n\n\n\nOnion. \n\n\n\nJhala, P., Mali, B.L., 2017. Effective Management of Purple Blotch of Onion \n\n\n\nCaused by Alternaria porri (Ellis) Through Host Resistance, Fungicides \n\n\n\nand Botanicals. Int. J. Curr. Microbiol. Appl. Sci., 6(5), Pp. 1737\u20131745. \n\n\n\nMeena, L., Verma, A.K., Rishi, R., College, G., Rajasthan, A., 2017. Fungal \n\n\n\nDisease of Onion and Their Biological Management: A Review. Int. J. \n\n\n\nRecent Sci. Res., 8, Pp. 19441\u201319445. \n\n\n\nNainwal, D., Vishunavat, K., 2016. Management of purple blotch and \n\n\n\nStemphylium blight of onion in Tarai and Bhabar regions of \n\n\n\nUttarakhand, India. J. Appl. Nat. Sci., 8, Pp. 150\u2013153. \n\n\n\nPareek, S., Sagar, N.A., Sharma, S., Kumar, V., 2017. Onion (Allium cepa L.), \n\n\n\nFruit Veg. Phytochem. Chem. Hum. Heal. Second Ed., 2, Pp. 1145\u20131161. \n\n\n\nRao, A.S., Ganeshan, G., Ramachandra, Y.L., Chethana, B.S., 2014. Field \n\n\n\nevaluation of fungicides against Alternaria porri (Ellis) Cif., causing \n\n\n\npurple blotch of onion (Allium cepa L.). Int. J. Agric. Environ. Biotechnol., \n\n\n\n8 (1), Pp. 89. \n\n\n\nRavichandra, S., 2012. Epidemiology and Management of Purple Blotch of \n\n\n\nOnion Caused by Alternaria porri.  \n\n\n\nSavitha, A.S., Ajithkumar, K., Ramesh, G., 2014. Integrated disease \n\n\n\nmanagement of purple blotch [Alternaria porri (Ellis) Cif] of onion. Pest \n\n\n\nManag. Hortic. Ecosyst., 20 (1), Pp. 97\u201399. \n\n\n\nVeeraghanti, K.S., Naik, B.G., Hegde, K.T., 2017. Management of Purple \n\n\n\nblotch disease of Onion under field condition,\u201d J. Pharmacogn. \n\n\n\nPhytochem., 6 (6), Pp. 1768\u20131769.  \n\n\n\nWanggikar, A.A., Wagh, S.S., Kuldhar, D.P., Pawar, D.V., 2014. Effect of \n\n\n\nfungicides, botanicals and bioagents against purple blotch of onion \n\n\n\ncaused by Alternaria porri. Int. J. Plant Prot., 7 (2), pp. 405\u2013410. \n\n\n\nYadav, Y.K., Singh, A., Jain, S., Dhatt, A.S., 2017. Management of Purple \n\n\n\nBlotch Complex of Onion in Indian Punjab. Int. J. Appl. Sci. Biotechnol., \n\n\n\n5, Pp. 454\u2013465. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 16-19 \n\n\n\nCite The Article: Jubaidur Rahman, Monira Yasmin, Fouzia Sultana Shikha, Majharul Islam, Mukaddasul Islam Riad (2019). Intercropping Of \nPotato With Brinjal.  Malaysian Journal of Sustainable Agriculture, 3(2): 16-19. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 4 January 2019 \nAccepted 19 February 2019 \nAvailable online 21 February 2019 \n\n\n\nABSTRACT\n\n\n\nA field experiment was conducted to find out the spacing of potato - brinjal intercropping system and land utilization \n\n\n\nand economic return at the Regional Agricultural Research Station, Jamalpur during rabi 2017-2018. The \n\n\n\nexperiment was laid out in randomized complete block design (RCBD) with three (3) replications and six \n\n\n\ntreatments. Cultivation of potato with brinjal at potato (60 cm \u00d7 25 cm) + brinjal (120 cm \u00d7 75 cm), Potato (50 cm \n\n\n\n\u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm), Potato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 75 cm) might be agronomically \n\n\n\nfeasible and economically profitable for potato and brinjal intercropping system as compared to sole treatment. \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) best performed in gross return, gross margin and potato \n\n\n\nequivalent yield (PEY 49.14 tha-1) compared with the other treatments. The total yield of intercropped crops was \n\n\n\ngreater than sole cropping, shown by LER>1. The overall advantage of intercropping ranged from 73 to 92%. The \n\n\n\nhighest land equivalent value of 92% was recorded for Potato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) \narrangements indicated a yield advantage of 92% over sole crop. Viable agronomic option in increasing land use \n\n\n\nefficiency and increased food security. It is, therefore, imperative to demonstrate the best treatment under farmer\u2019s \n\n\n\ncondition. \n\n\n\nKEYWORDS \n\n\n\nPotato, Brinjal, Intercropping, Land utilization \n\n\n\n1. INTRODUCTION \n\n\n\nBangladesh is one of the major horticultural countries in South Asia. \nBrinjal (Solanummelongena L.) is an important vegetable for its \ncommercial and nutritional value in the world as well as in Bangladesh. \n\"Begoon\" (Brinjal or Eggplant) is a very common and favorite vegetable in \n\n\n\nBangladesh which has a link with the social, cultural and economic lives of \nrural people. Brinjal is the most important vegetable of the country. Brinjal \n(Solatium melongena), Linnaeus belongs of the family Solanaceae is also \n\n\n\nknown as eggplant or aubergine is a popular nutritious and grown \n\n\n\nvegetable in Bangladesh as well as in the world and has got multifarious \nuse as a dish item It is thought to be originated in Indian subcontinent \nbecause of maximum of genetic diversity and closely related species of \nsolanuin are grown in this reason. Potato is the number one vegetable crop \n\n\n\nof Bangladesh both in terms of area and production [1]. It alone constitutes \nmore than 50% of the total annual vegetable production in the country [2]. \nIn northern part of Bangladesh Potato-Maize-Transplant aman rice \n\n\n\nturning into a major cropping pattern nowadays [3]. Also, Jamalpur region \n\n\n\nPotato is the most important crop in area and production in Bangladesh \n\n\n\nespecially in char. Jamalpur district which most potato growing area of \nBangladesh 1643 acres area produced 6238 MT [4]. The use of an \n\n\n\nintercropping system is one method of increasing crop productivity and \n\n\n\nintensity of crops [5]. Intercropping has several advantages such as \n\n\n\nadditional income from companion crops, insurance against crop failure, \nincrease productivity, stability of production, and maximization of  \n\n\n\nproducts, soil fertility and pest control [6-8]. The inventory of main river \nchar lands estimated their total area at 8,444 km2 or almost 6% of \nBangladesh [9]. Due to decreasing cultivable land, some farmers of char \nareas (river flood plain) under greater Mymensingh district (together five \n\n\n\ndistrict) in Bangladesh have been practicing garden pea with onion, \ncoriander with onion and vegetables, pulse and oilseed crops with wheat \nare common practice to the farmers of char areas [10-12]. Brinjal and \n\n\n\npotato are the most important crop in area and production in Bangladesh \n\n\n\nespecially in char. Jamalpur district which most brinjal production area of \nBangladesh 6987 acres area produced 25449 MT [13]. Farmers of this area \n\n\n\npracticed intercropped potato with brinjal and after harvest. But they do \n\n\n\nnot know suitable combination of intercropped system. This suggests that \nthe system can help to raise productivity to achieve food security, but the \n\n\n\nsystem has never been researched and no studies have been made to \n\n\n\nimprove the productivity of the system. To this effect, an experiment was \n\n\n\nconducted at Jamalpur to assess the compatibility of the companion crops \n\n\n\nand identify best cropping ratio that maximize land use efficiency. \n\n\n\n2. METHODS AND MATERIALS \n\n\n\nThe district lies between 24\u00b034\u00b4 and 25\u00b026\u00b4 north latitudes and between \n\n\n\n89\u00b040\u00b4 and 90\u00b012\u00b4 east longitudes and it is situated at elevation 23 meters \nabove sea level. The annual average temperature of this district varies \nfrom maximum 33.3\u00b0C to minimum 12\u00b0C. Annual average rainfall is 2174 \n\n\n\nmm. The experimental site was of medium high land belonging to the agro-\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.02.2019.16.19 \n\n\n\n   RESEARCH ARTICLE \n\n\n\nINTERCROPPING OF POTATO WITH BRINJAL \n\n\n\nJubaidur Rahman1*, Monira Yasmin2, Fouzia Sultana Shikha3, Majharul Islam4, Mukaddasul Islam Riad5 \n\n\n\n1Scientific Officer, Agronomy Division, Bangladesh Agricultural Research Institute, Jamalpur-2000, Bangladesh \n2Scientific Officer, Soil Science Division, Bangladesh Agricultural Research Institute, Jamalpur-2000, Bangladesh \n3Scientific Officer, Soil Science Division, Bangladesh Agricultural Research Institute, Jamalpur-2000, Bangladesh \n4Scientific Officer, Soil Science Division, Bangladesh Institute of Nuclear Agriculture, Mymensingh-2200, Bangladesh \n5Scientific Officer, Plant Genetic Resources Centre, Bangladesh Agricultural Research Institute, Jamalpur-2000, Bangladesh \n\n\n\n*Corresponding Author Email: jubaidurjp@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:jubaidurjp@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)16-19 \n\n\n\necological zone Old Brahmaputra Floodplain under Agro-Ecological Zone \n\n\n\n9 [14]. The experiment was conducted at the Regional Agricultural \n\n\n\nResearch Station, Jamalpur during rabi 2017-2018 to find out the spacing \n\n\n\nof potato - brinjal intercropping system and land utilization and economic \n\n\n\nreturn. Design of the experiment was RCB with 03 (three) replications \n\n\n\nhaving the unit of plot 3m \u00d7 3.75m. BARI Alu \u2013 25 (Asterix) and BARI \nBegun-8 were used as a variety in the experiment. Treatments included in \n\n\n\nthe experiment were: T1 = Sole potato (50 cm \u00d7 20 cm), T2 = Sole brinjal \n(100 cm \u00d7 75 cm), T3 = potato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 60 cm), \nT4 = potato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm), T5 = potato (50 cm \n\n\n\n\u00d7 20 cm) + Brinjal (100 cm \u00d7 75 cm), T6 =  potato (60 cm \u00d7 25 cm) + Brinjal \n\n\n\n(120 cm \u00d7 75 cm). Fertilizers were applied for sole potato: N:P2O5:K2O:S @ \n\n\n\n160:100:160:20 kg /ha and sole brinjal 80-24-60-10-1.0-0.3 kg/ha N-P-K-\nS-Zn-B fertilizers were applied in the form of Urea, triple super phosphate, \nMuriate of potash, Zypsum, Zinc Sulphate and Boric acid respectively [15]. \nFor sole potato at the time of final land preparation cow dung @ 10 t/ha \n\n\n\nwas applied and other fertilizers were applied as following doses. For sole \n\n\n\nbrinjal: Half cow dung should be applied during the final land preparation. \nRemaining cowdung and full phosphorus, sulphur, zinc and boron should \n\n\n\nbe applied in three equal splits 21, 35 and 50 days after transplanting. \nPotato sown on November 23, 2017 and Brinjal: November 30, 2017. \n\n\n\nIntercultural operations like watering, weeding and spraying insecticides \nwere followed as and when necessary. One pheromone trap was used for \neveryone decimal land to control of brinjal fruit and shoot borer. Irrigation \n\n\n\nwas applied two times during the potato growing period and brinjal was \ngrown when necessary. Yield of potato and yield of brinjal was calculated \n\n\n\nin t ha-1 considering the whole plot harvest area. Five plants of brinjal in \n\n\n\neach plot were selected randomly to collect data on yield components. \nCollected data were analyzed statistically with the help of STAR software \n\n\n\nand mean separation was done as per LSD test at 5% level of significance. \n\n\n\nEconomic analysis was performed considering the price of potato and \n\n\n\nbrinjal prevailed at the harvesting period in the local market. Potato \n\n\n\nequivalent yield (PEY) was also calculated considering the local market \nprice at the harvesting time following the formula as stated by a researcher \n[16]. LER indicates the efficiency of intercropping for using the resources \n\n\n\nof the environment compared with mono-cropping [17]. The LER was \n\n\n\ncalculated as follows: Land equivalent ratio (LER) = (YAB/YAA) + \n\n\n\n(YBA/YBB) [18]. \n\n\n\nWhere: \nYAB=yield of crop A (potato) when intercropped with crop B (brinjal) \nYBA=Yield of crop B (brinjal) when intercropped with crop A (potato) \nYAA=Yield from sole planted crop A (potato) \nYBB=Yield from sole planted crop B (brinjal) \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 EFFECT OF POTATO \n\n\n\nYield and yield components like plant height, plant m-2, no. of stem plant-1, \nno. of tuber plant-1, tuber weight plant-1, 10 tuber weight and tuber yield \n\n\n\ndiffered significantly influenced by different intercropping system (Table \n\n\n\n1). The highest plant height was found in Potato (50 cm \u00d7 20 cm) + Brinjal \n\n\n\n(100 cm \u00d7 75 cm) due to intercrop competition for densely population. No. \nof tuber plant-1 was found from sole potato (50 cm \u00d7 20 cm) might be due \n\n\n\nto free space. 10 Tuber wt was the highest in potato (60 cm \u00d7 25 cm) + \n\n\n\nBrinjal (120 cm \u00d7 75 cm) because of higher potato spacing than others \ntreatment. The highest yield was observed in sole potato due to less \ncompetition to nutrient supply. Several authors have reported the \n\n\n\nsuperiority of maize/potato intercropping to sole [19,20]. \n\n\n\nTable 1: Yield and yield components of potato in potato-brinjal intercropping system (Jamalpur 2017-18) \n\n\n\nTreatment \ncombination \n\n\n\nPlant \nheight(cm) \n\n\n\nPlant/m2 No.of \nstem/ \nplant \n\n\n\nNo.of \ntuber/plant \n\n\n\nTuber \nwt./plant \n(gm) \n\n\n\n10 Tuber \nwt.(gm) \n\n\n\nTuber \nyield \n(t/ha) \n\n\n\nPLERP \n\n\n\nT1  71.5 6.33 5 10.47 597 730 25.78 1 \nT2 - - - - - - - - \nT3 67.3 6.67 4 7.7 616 817 22.31 0.87 \nT4 68.9 6.33 4 7.7 520 800 24.13 0.94 \nT5 74 6.33 4 8.2 537 533 22.61 0.88 \nT6 71.2 7 5 9.1 602 838 21.93 0.89 \nLSD0.05 4.2 1.8 0.6 0.72 26.14 23.96 1.27 - \nCV (%) 2.48 11.93 5.83 3.55 7.5 1.36 2.31 - \n\n\n\nT1 = Sole potato (50 cm \u00d7 20 cm), T2 = Sole brinjal (100 cm \u00d7 75 cm), T3 = \npotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 60 cm), T4 = potato (50 cm \u00d7 \n20 cm) + Brinjal (100 cm \u00d7 70 cm), T5 = potato (50 cm \u00d7 20 cm) + Brinjal \n(100 cm \u00d7 75 cm), T6 =  potato (60 cm \u00d7 25 cm) + Brinjal (120 cm \u00d7 75 cm) \n\n\n\n3.2 Effect of brinjal \n\n\n\nYield and yield components like plant height, single fruit weight, fruit \nlength, fruit breadth, no. of fruit/plant, weight of fruit/plant and yield \n\n\n\ndiffered significantly influenced by different intercropping system (Table \n\n\n\n2). The highest plant height was found in Potato (50 cm \u00d7 20 cm) + Brinjal \n\n\n\n(100 cm \u00d7 75 cm) due to closer spcing of brinjal population and the lowest \nfrom potato (50 cm \u00d7 20 cm) + brinjal (100 cm \u00d7 70 cm). Single fruit weight \nwas obtained from sole brinjal (100 cm \u00d7 75 cm) might be due to over \nspace to increased side branching. Fruit length, fruit breadth, no. of \nfruit/plant, weight of fruit/plant was highest from sole brinjal (100 cm \u00d7 \n\n\n\n75 cm). The highest yield was observed in sole brinjal (100 cm \u00d7 75 cm) \nwhich was statistically similar to potato (50 cm \u00d7 20 cm) + brinjal (100 cm \n\n\n\n\u00d7 70 cm). \n\n\n\nTable 2: Yield and yield components of brinjal in potato - brinjal intercropping system (Jamalpur 2017-18) \n\n\n\nTreatment \ncombination \n\n\n\nPlant \nheight(cm) \n\n\n\nSingle fruit \nwt.(gm) \n\n\n\nFruit \nlength (cm) \n\n\n\nFruit breadth \n(cm) \n\n\n\nNo. of \nfruit/5 \nplant \n\n\n\nWt.of \nfruit/5 \nplant (gm) \n\n\n\nYield \n(t/ha) \n\n\n\nPLERB \n\n\n\nT1  - - - - - - - - \nT2 93.3 84.93 20.11 3.75 127.7 9720 19.1 1 \nT3 92.83 80.27 19.57 3.39 93.33 7983 16.45 0.86 \nT4 90.02 81.37 19.55 3.49 91.33 8533 18.76 0.98 \nT5 94.23 83.57 19.58 3.59 101.7 8536 17.62 0.92 \nT6 91.99 81.47 19.75 3.62 100 8570 18.58 0.97 \nLSD0.05 5.3 6.3 1.54 0.45 4.24 86.12 1.81 - \nCV (%) 2.4 3.23 3.31 5.34 1.74 0.42 4.22 - \n\n\n\nT1 = Sole potato (50 cm \u00d7 20 cm), T2 = Sole brinjal (100 cm \u00d7 75 cm), T3 = \npotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 60 cm), T4 = potato (50 cm \u00d7 \n20 cm) + Brinjal (100 cm \u00d7 70 cm), T5 = potato (50 cm \u00d7 20 cm) + Brinjal \n(100 cm \u00d7 75 cm), T6 =  potato (60 cm \u00d7 25 cm) + Brinjal (120 cm \u00d7 75 cm) \n\n\n\n3.3 Combined yield \n\n\n\nThe highest combined yield of the crops (42.89 t ha-1) was obtained from \n\n\n\npotato (50 cm \u00d7 20 cm) + brinjal (100 cm \u00d7 70 cm) (Table 3). The highest \nyield in the intercropping treatment could be attributed to growing spaces \nbeing varied; temporal growth variance between two varying crops; a \n\n\n\ncombined increase in making better use of light, soil moisture content and \n\n\n\nnutrients as discussed by a researcher. The highest yield in intercropping \n\n\n\nCite The Article: Jubaidur Rahman, Monira Yasmin, Fouzia Sultana Shikha, Majharul Islam, Mukaddasul Islam Riad (2019). Intercropping Of \nPotato With Brinjal.  Malaysian Journal of Sustainable Agriculture, 3(2): 16-19. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)16-19 \n\n\n\nas opposed to sole cropping was supported by several studies. Partial LERs \nfor potato and brinjal grown in the intercropping systems are less than \n\n\n\nunity (Table 3) indicating that both potato and brinjal are compatible for \nintercropping under different cropping intensities. The highest PLER for \npotato (0.94) and brinjal (0.98) was recorded for potato (50 cm \u00d7 20 cm) \n\n\n\n+ brinjal (100 cm \u00d7 70 cm) compared with Potato (50 cm \u00d7 20 cm) + Brinjal \n\n\n\n(100 cm \u00d7 75 cm) treatment combination. The highest TLER for potato \n\n\n\n(0.94) and brinjal (0.98) was recorded from (50 cm \u00d7 20 cm) + brinjal (100 \n\n\n\ncm \u00d7 70 cm) \n\n\n\nTable 3: Effect of arrangement of potato-brinjal intercropping on combined yield and land equivalent ratios of the component crops \n\n\n\nTreatment combination Combined yield \n(t ha-1) \n\n\n\nPLER P PLERB TLER \n\n\n\nSole potato (50 cm \u00d7 20 cm) 25.78 1 - 1 \nSole brinjal (100 cm \u00d7 75 cm) 19.1 - 1 1 \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 60 cm) 38.76 0.87 0.86 1.73 \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) 42.89 0.94 0.98 1.92 \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 75 cm)  40.23 0.88 0.92 1.8 \npotato (60 cm \u00d7 25 cm) + Brinjal (120 cm \u00d7 75 cm) 40.51 0.89 0.97 1.86 \n\n\n\nPLERP = partial land equivalent ratio potato, PLERB = partial land \nequivalent ratio brinjal. TLER= total land equivalent ratio \n\n\n\n3.4 Land Equivalent Ratio (LER) \n\n\n\nTotal LER was significantly different from 1.00 in all intercropping \n\n\n\ntreatments, which shows an advantage over pure stands in terms of the \n\n\n\nuse of environmental resources for Plant growth as reported by previous \nresearcher [21-30]. In this study, TLER ranged from 1.73 to 1.92. The \n\n\n\nintercropped yield advantage in terms of total LER indices was greatest in \n\n\n\nthe cases of Potato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) \nintercropping arrangement (1.92) which might be attributed to more \n\n\n\nefficient total resource exploitation and greater overall production as \nopposed to the other intercropping treatments [31-38]. This indicated that \nadditional 0.92 ha (92%) more area would have been needed to get equal \n\n\n\nyield to planting potato and brinjal in pure stands. This result is in \n\n\n\nagreement with the findings of several other intercropping studies, a \n\n\n\nresearcher demonstrated the advantages of intercropping systems where, \n\n\n\nLER of greater than 1 was recorded [39-41]. This might indicate that in a \n\n\n\nsuitable combination plants can complement each other in a more efficient \nuse of environmental resources, mainly light, water and nutrients. The \n\n\n\ncurrent intercropping systems demonstrate that farmers could benefit by \n\n\n\ngrowing the Potato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) in Jamalpur \nregion. \n\n\n\n3.5 Economics \n\n\n\nIntercropping of potato with brinjal was more profitable than sole \n\n\n\ncropping of brinjal. The maximum cost of cultivation Tk. 135000 ha-1 was \n\n\n\nfound in potato brinjal intercropping system while the minimum cost of \ncultivation Tk. 110000 ha-1 was found in sole potato cultivation systems. \nThe maximum gross return Tk. 737100 ha-1 was obtained from Potato (50 \n\n\n\ncm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) followed by the potato (50 cm \u00d7 \n\n\n\n20 cm) + brinjal (100 cm \u00d7 75 cm). The maximum gross margin Tk. 607100 \n\n\n\nha-1 was obtained from potato (50 cm \u00d7 20 cm) + brinjal (100 cm \u00d7 75 cm). \n\n\n\nTable 3: Economics of potato-brinjal intercropping system during rabi 2017-2018 \n\n\n\nTreatment combination PEY Cost of \ncultivation (Tk. \nha-1) \n\n\n\nGross return (Tk. \nha-1) \n\n\n\nGross margin (Tk. \nha-1) \n\n\n\nBCR \n\n\n\nSole potato (50 cm \u00d7 20 cm) 25.78 110000 386700 276700 3.52 \nSole brinjal (100 cm \u00d7 75 cm) 25.46 120000 381900 261900 3.18 \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 60 cm) 44.24 135000 663600 528600 4.92 \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) 49.14 130000 737100 607100 5.67 \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 75 cm) 46.10 125000 691500 566500 5.53 \nPotato (60 cm \u00d7 25 cm) + Brinjal (120 cm \u00d7 75 cm) 46.70 123000 700500 577500 5.70 \n\n\n\nPotato: 15 Tk/kg and Brinjal: 20 Tk/kg \n\n\n\n4. CONCLUSION\n\n\n\nFrom the result indicated that cultivation of potato with brinjal at potato \n\n\n\n(60 cm \u00d7 25 cm) + brinjal (120 cm \u00d7 75 cm), Potato (50 cm \u00d7 20 cm) + \n\n\n\nBrinjal (100 cm \u00d7 70 cm), Potato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 75 \n\n\n\ncm) might be agronomically feasible and economically profitable for \n\n\n\npotato and brinjal intercropping system as compared to sole treatment. \nPotato (50 cm \u00d7 20 cm) + Brinjal (100 cm \u00d7 70 cm) best performed in gross \n\n\n\nreturn, gross margin. \n\n\n\nREFERENCES \n\n\n\n[1] Anowar, M., Parveen, A., Ferdous, Z., Kafi, A.H., Kabir, M.E. 2015. \nBaseline survey for farmer livelihood improvement at farming system \n\n\n\nresearch and development, Lahirirhat, Rangpur. International Journal of \nBusiness, Management and Social Research, 2, 92\u2013104. \n\n\n\n[2] Anwar, M., Ferdous, Z., Sarker, M.A., Hasan, A.K., Akhter, M.B., Zaman, \nM.A.U., Haque, Z., Ullah, H. 2017. Employment generation, increasing \n\n\n\nproductivity and improving food security through farming systems \n\n\n\ntechnologies in the Monga Regions of Bangladesh. Annual Research & \n\n\n\nReview in Biology, 16(6), 1-15. DOI: 10.9734/ARRB/2017/35645 \n\n\n\n[3] Ahmed, N.U., Ferdous, Z., Mahmud, N.U., Hossain, A., Zaman, M.A.U. \n2017a. Effect of split application of nitrogen fertilizer on the yield and \n\n\n\nquality of potato (Solanum tuberosum). International Journal of Natural \nand Social Sciences, 4(2), 60\u201366. \n\n\n\n[4] Bangladesh Bureau of Statistics (BBS). 2016. Yearbook of Agricultural \n\n\n\nStatistics. \n\n\n\n[5] Sullivan, P. 2000. Intercropping Principles and Production Practices. \nAgronomy Systems Guide. Appropriate technology transfer for rural areas, \nhttp://www.attra.ncat.org/attra-pub/intercrop.html. 09/06/03. \n\n\n\n[6] Samsuzzaman, S.M., Ali, A., Malik, M.D., Karim, A.S., Khan, M.R., Islam, \nM.N. 1999. Effect of Intercropping maize population with tomato on their \nproductivity and profitability in the farmers field. Bangladesh Journal. \n\n\n\n[7] Beyenesh, Z. 2008. Effect of variety and plant population density on \n\n\n\nyield and yield components of barley and wheat mixed cropping (Hanfets) \nin Tigray, Ethiopia. M.Sc. Thesis, Mekelle University, College of Dryland \n\n\n\nAgriculture and Natural Resources, Mekelle, Ethiopia. \n\n\n\n[8] Sharaiha, R., Battikhi, A. 2002. A study on Potato- Corn intercropping \n\n\n\nmicroclimate modification and yield advantages. Agricultural Sciences, 2, \n\n\n\n97-108. \n\n\n\n[9] FAP 16/19. 1993a. Charland Study Overview: Summary Report, Flood \n\n\n\nPlain Coordination Organization, Ministry of Irrigation Water \nDevelopment and Flood Control, Dhaka. \n\n\n\nCite The Article: Jubaidur Rahman, Monira Yasmin, Fouzia Sultana Shikha, Majharul Islam, Mukaddasul Islam Riad (2019). Intercropping Of \nPotato With Brinjal.  Malaysian Journal of Sustainable Agriculture, 3(2): 16-19.\n\n\n\n\nhttp://www.attra.ncat.org/attra-pub/intercrop.html.%2009/06/03\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)16-19 \n\n\n\n[10] Rahman, J., Talukder, A.H.M.M.R., Nahar, L., Rahman, M.M., Kayser, \nN. 2015. International Journal of Applied Research, 1(2), 79-82. \n\n\n\n[11] Talukder, A.H.M.M.R., Rahman, J., Nahar, L., Rahman, M.M., Kayser, \nN. 2015. Mixed cropping onion with different plant population of sweet \ngourd. IOSR Journal of Agriculture and Veterinary Science, 45-50. \n\n\n\n[12] Talukder, A.H.M.M.R., Rahman, J., Rahman, M.M., Biswas, M., \nAsaduzzaman, M. 2015. Optimum ratio of coriander intercropping with \n\n\n\nonion. International Journal of Plant & Soil Science, 4(4), 404-410. \n\n\n\n[13] Bangladesh Bureau of Statistics (BBS). 2011. District Statistics, \nJamalpur. \n\n\n\n[14] UNDP \u2013 FAO. 1988. Land resources appraisal of Bangladesh for \nagricultural development. Report to Agro-ecological regions of \nBangladesh. UNDP- FAO, BGD/81/ 035 Technical Report, 2, 570. \n\n\n\n[15] FRG. 2012. Fertilizer Recommendation Guide, Bangladesh \n\n\n\nAgricultural Research Council (BARC), Farmgate, Dhaka, 1215, 274. \n\n\n\n[16] Aujeneyulu, V.R., Singh, S.P., Ali, M. 1982. Effect of competition free \n\n\n\nperiod technique and pattern pearl millet planting in growth and yield of \nmungbean and total productivity in solid pearl millet and pearl millet \nmungbean intercropping system. Indian Journal of Agronomy, 27, 219-\n226. \n\n\n\n[17] Mead, R., Willey, R.W. 1980. The concept of land equivalent ratio and \n\n\n\nAdvantages in yields from intercropping. Experimental Agriculture, 16, \n217-228. \n\n\n\n[18] Willey, R.W., Osiru, D.S.O. 1972. Studies on mixtures of maize and \n\n\n\nbeans (Phaseolus vulgaris) with particular reference to plant population. \nJournal of Agricultural Science, 79, 517-529. \n\n\n\n[19] Amini, B.A., Mazaheri, D. 1996. The Effect of plant density of Maize-\nPotato in Sole Cropping and Intercropping. Fifth Iranian Congress on Crop \n\n\n\nProduction and Breeding. Karaj. \n\n\n\n[20] Tesfay, A., Yigzaw, D., Ermias, A. 2006. Review of Potato and Sweet \nPotato variety Development and Agronomic Research Achievements in \n\n\n\nNorthern Ethiopia In Proceedings of the Inaugural & First Ethiopian \n\n\n\nHorticultural Science Society Conference, Addis Ababa, Ethiopia. \n\n\n\n[21] Motiur, A.H.M., Talukder, R., Ahmed, B., Nahar, L., Hossain, K.M.F., \nRahman, J., Paul, S.K. 2016. Enhancement of farm productivity through \n\n\n\nintercropping of vegetables, pulse and oilseed crops with wheat at Jamuna \n\n\n\nChar area of Islampur in Jamalpur District. International Journal of Applied \n\n\n\nResearch, 2(2), 71-76. \n\n\n\n[22] Ahmed, F., M.A. Rahman, M.A.H.S. Jaahn, M. Ahmed and M.A. Khayer. \n2006. Effect of different planting system in maize/spinach-red amaranth \n\n\n\nintercropping. Bangladesh J. Agric and Environ. 2(2): 69-79 \n\n\n\n[23] Awal, M.A., Kothi, H., Ikeda, T. 2006. Radiation interception and use \n\n\n\nby maize/peanut intercrop canopy. Agricultural and Forest Meteorology, \n\n\n\n139, 73-84. \n\n\n\n[24] Francis, C.A. 1986. Multiple cropping systems. Macmillan Publ. Co, \nNew York, 383. \n\n\n\n[25] Gardiner, T.R., Craker, L.E. 1981. Bean growth and light interception \n\n\n\nin maize-bean intercrop. Field Crops Research, 4, 313-320. \n\n\n\n[26] Hiebsch, C.K. 1980. Principles of intercropping: Effects of N \n\n\n\nfertilization, plant population and crop duration on equivalent ratios in \n\n\n\nintercrop versus monoculture. Ph.D. dissert. North Carolina State \n\n\n\nUniversity. Raleigh, 413, 4337. \n\n\n\n[27] Islam, M.N. 2002. Competitive interference and productivity in \n\n\n\nmaize-bushbean intercropping system. A Ph D. Dissertation, Dept. of \nAgronomy, Bangabandhu Sheikh Mujibur Rahman Agricultural University, \nGazipur. \n\n\n\n[28] Javanmard, A., Mohamadi Nasab, A.D., Javanshir, A., Moghaddam, M., \nJanmohammadi, H. 2009. Forage yield and quality in intercropping maize \n\n\n\nwith different legumes as double-cropped. Journal of Food Agriculture and \n\n\n\nEnvironment, 7, 163-166. \n\n\n\n[29] Li, L., Sun, J., Zhang, F., Li, X., Yang, S., Rengel, Z. 2001. Wheat/maize \n\n\n\nor wheat/soybean strip intercropping I. Yield advantage and interspecific \n\n\n\ninteractions on nutrients. Field Crops Research, 71, 123-137. \n\n\n\n[30] Myaka, F.A. 1995. Effect of time of planting and planting pattern of \ndifferent cowpea cultivars on yield of intercropped cowpea and maize in \n\n\n\ntropical sub-humid environment. Tropical Science, 35, 274-279. \n\n\n\n[31] Ofori, F., Stern, W.R. 1987. Cereal-legume intercropping systems. \nAdvances in Agronomy, 41, 41-90. \n\n\n\n[32] Prabhakar, B.S., Shukla, V. 1990. Crop land use efficiency in \n\n\n\nsequential intercropping systems with vegetables. Indian Journal of \nHorticulture, 47, 427-430. \n\n\n\n[33] Reddy, M.S., Willey, R.W. 1981. Growth and resource use studies in \n\n\n\nan intercrop of pearl millet/groundnut. Field Crops Research, 4, 13-24. \n\n\n\n[34] Reedy, T.Y., Reddi, G.H.S. 1992. Principles of Agronomy, kalyanim \n\n\n\npublishers. New Delhi-110002. India, 423. \n\n\n\n[35] Pal, S. 2012. \"Jamalpur District\". In Sirajul Islam and Ahmed A. Jamal. \nBanglapedia: National Encyclopedia of Bangladesh (Second ed.). Asiatic \n\n\n\nSociety of Bangladesh. \n\n\n\n[36] Santalla, M., Rodino, A.P., Casquero, P.A., De Ron, A.M. 2001. \nInteractions of bush bean intercropped with field and sweet maize. \nEuropean Journal of Agronomy, 15, 185-196. \n\n\n\n[37] Sheehan, W. 1986. Response of specialist and naturalist natural \nenemies to agroecosystem diversification. A selective review. \nEnvironmental Entomology, 15, 456-461. \n\n\n\n[38] Tsegay, A., Vanuytrecht, E., Abrha, B., Deckers, J., Gebrehiwot, K., \nRaes, D. 2015. Sowing and irrigation strategies for improving rainfed tef \n(Eragrostistef (Zucc.) Trotter) production in the water scarce Tigray \n\n\n\nregion, Ethiopia. Agricultural Water Management,150, 81-91. \n\n\n\n[39] Vandermeer, J. 1989. The ecology of intercropping. Cambridge \n\n\n\nUniversity Press, Great Britain, 237. \n\n\n\n[40] Vandermer, J.H. 1989. The ecology of intercropping. Cambridge: \nCambridge University Press. \n\n\n\n[41] Vohra, S., Rizaman, B., Khan, J.A. 1994. Medical uses of common \n\n\n\nIndian vegetables. Planta medea, 23, 381-393. \n\n\n\nCite The Article: Jubaidur Rahman, Monira Yasmin, Fouzia Sultana Shikha, Majharul Islam, Mukaddasul Islam Riad (2019). Intercropping Of \nPotato With Brinjal.  Malaysian Journal of Sustainable Agriculture, 3(2): 16-19.\n\n\n\n\nhttp://en.banglapedia.org/index.php?title=Jamalpur_District\n\n\nhttps://en.wikipedia.org/wiki/Asiatic_Society_of_Bangladesh\n\n\nhttps://en.wikipedia.org/wiki/Asiatic_Society_of_Bangladesh\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 45-51 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2023.45.51 \n\n\n\nCite The Article: Ibrahim Abdulwaliyu, Stanley I.R. Okoduwa, Shefiat O. Arekemase, Abdulkadir Muhammad, Musa L. Batari, Razaq A. Mustapha, Joseph F. Itiat (2023). Impact \nof Security Challenges on Food and Nutrition Security in Nigeria: The Role of Food Production Focus. Malaysian Journal of Sustainable Agricultures, 7(1): 45-51. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-294X (Online) \nCODEN:MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.01.2023.45.51\n\n\n\nIMPACT OF SECURITY CHALLENGES ON FOOD AND NUTRITION SECURITY IN \nNIGERIA: THE ROLE OF FOOD PRODUCTION FOCUS \n\n\n\nIbrahim Abdulwaliyua, Stanley I.R. Okoduwab, Shefiat O. Arekemasec, Abdulkadir Muhammadd, Musa L. Bataria, Razaq A. Mustaphae, Joseph F. \nItiatf \n\n\n\na Scientific and Industrial Research Department, National Research Institute for Chemical Technology, Zaria, Nigeria \nb Department of Biochemistry, School of Basic Medical Sciences, Babcock University, Ilishan-Remo, Nigeria \nc Petrochemical and Allied Department, National Research Institute for Chemical Technology, Zaria, Nigeria \nd Department of Biochemistry, Ahmadu Bello University, Zaria, Nigeria \ne Department of Nutrition and Dietetics, Rufus Giwa Polytechnic, Owo, Nigeria \nf Department of Home Economics (Nutrition Unit), Federal College of Education (Technical) Gusau, Nigeria \n*Corresponding author email: abdulwaliyui@yahoo.com; abdulwaliyui80@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 26 November 2022 \nRevised 06 December 2022 \nAccepted 11 January 2023 \nAvailable online 13 January 2023\n\n\n\nIntakes of adequate and quality food on sustainable basis are prerequisite for good health. However, food \n\n\n\nsecurity cannot be achieved amidst security challenges. In the recent decade food production in Nigeria has \n\n\n\nbeen a challenge. This is owing to many reasons, including security challenges. The aim of this study was to \nprovide information on the deplorable state of food production in Nigeria, as triggered by security challenges. \n\n\n\nThis study also highlights impacts of the security challenges on food affordability, and consequently its effects \n\n\n\non hunger and malnutrition in Nigeria. And by extension, this study conceptualized food production focus \nmodel (FPF) that if adopted, could improve rural food production in Nigeria. Databases such as Google \n\n\n\nScholar, African Journals Online, Nigerian Journals Online, Scopus, Medline and Pubmed were used to search \n\n\n\nfor relevant information on the impacts of the security challenges on farmers productivity. Findings from \nliterature search revealed that access to sufficient, safe and quality food is now a serious concern in Nigeria \n\n\n\nin recent time due to the security challenges, coupled with increased population growth, rural-urban \n\n\n\nmigration. Although migration may be multifaceted, however, security challenges play substantial roles, and \nhave seriously affected the food production in several rural communities in Nigeria. And most worrisome is \n\n\n\nincreased unemployment rate due to lost of jobs and investment phobia caused by the security challenges in \n\n\n\nNigeria. Furthermore, the scourge of COVID-19 pandemic amidst the security challenges have further \n\n\n\naggravated food and nutrition insecurity in Nigeria. \n\n\n\nKEYWORDS \n\n\n\nInsecurity; Food Security; Food Production Focus; Hunger; Poverty \n\n\n\n1. INTRODUCTION \n\n\n\nInsecurity in Nigeria is now a national concern. It is also a pervasive \nmenace that has made many Nigerians worried with a simple question, \nwhich is \u201cwhy are the security challenges so difficult to arrest?\u201d. This has \ntranslated to the notion that the security of persons living in Nigeria is now \nthe primary responsibility of the individual. Ironically, this is contrary to \nthe constitution of any nation, which includes Nigeria (Barker and Lamble, \n2009). Regrettably the Nigerian government has loss control over the \nsecurity situation, as it is now blatant that even security agents in Nigeria \nare one of the endanger species hunted by criminal elements (Okoli and \nOkpaleke, 2014). Historically, it is known that a problem identified at an \nearly stage and acknowledged accordingly without delay is easy to arrest, \nand would certainly not developed anarchy and acrimony beyond remedy \n(Ivancik et al., 2014). Unfortunately, Nigerian leaders hardly acknowledge \na problem until it has deteriorated beyond remedy. Moreso, the failure of \ntraditional conflict resolution mechanisms to adapt to governance system \nof communities can exacerbate competition for natural resources leading \nto political and conflict fragility (Hendriks et al., 2021). This is the case in \nseveral countries of the world, where inter-communal violence, armed \n\n\n\nconflicts, and other localized tensions create insecurity (FSIN, 2020). \n\n\n\nSecurity, being a basic need, is a non-negotiable factor for adequate food \nproduction. It is a safety guard for every human activity including farming, \nand a necessary tool for adequate food production. Sadly, farming \nactivities in many rural communities (especially in the northern part of \nNigeria) have been halted due to security challenges. By implication, food \nproduction has been halted. This may account for the triggered significant \nlosses in food production and increased prices of food stuff beyond \npurchasing power for many Nigerians (Nwazor et al., 2019). A group of \nresearchers also documented that the national insecurity undermines \nfarming activities, shooting food prices, thereby worsening poverty and \nhunger in Nigeria (Ilo et al., 2019). The livelihood of millions of farmers \nresiding especially in rural communities in northern Nigeria is seriously \naffected, as a result of the security challenges. This is owing to the fact that \nthe majority of the rural dwellers depend solely on farming activities \n(Adebisi et al., 2017; Mgbenka et al., 2015). The effects of insecurity on \nfarming activities has overwhelming effects on household incomes and \nconsequently food insecurity (Ayanlade and Radeny, 2020; B\u00e9n\u00e9, 2020). \nObviously, this would undeniably drive the nation into poverty and hunger \ncycle. And considering the quantum of damage so far, it may take longer \n\n\n\n\nmailto:abdulwaliyui@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 45-51 \n\n\n\nCite The Article: Ibrahim Abdulwaliyu, Stanley I.R. Okoduwa, Shefiat O. Arekemase, Abdulkadir Muhammad, Musa L. Batari, Razaq A. Mustapha, Joseph F. Itiat (2023). Impact \nof Security Challenges on Food and Nutrition Security in Nigeria: The Role of Food Production Focus. Malaysian Journal of Sustainable Agricultures, 7(1): 45-51. \n\n\n\n(many years) than necessary (even with government commitment) to pull \npeople out of poverty and hunger cycle, even if insecurity is averted. \n\n\n\nUnfortunately, amidst the security challenges, COVID-19 surfaces. The \nInsecurity amidst COVID-19 could be term as double strokes with multiple \ntragedies. It is evidence that as insecurity causes displacement, COVID-19 \nhalt activities around the globe, including Nigeria (Pak et al., 2020; \nInegbedion, 2021). It has been estimated that not less than 150 million \npersons would be affected by food insecurity due to the incessant \ninsecurity amidst COVID-19 pandemic (Walker et al., 2020). The insecurity \nsituation amidst the pandemic would consequently increase mortality rate \nin Sub Saharan Africa, including Nigeria (Walker et al., 2020). Insecurity \nalone is expected to worsening all forms of malnutrition that is too weighty \nfor Nigeria (Headey et al., 2020). Insecurity has affected food systems, and \nreduced income for food accessibility and affordability (Laborde et al., \n2020). \n\n\n\nThe trends in the security situation in Nigeria and failure to tackle the \nmenace are a serious concern for both the political and economic \narchitecture of the nation. Therefore, the objectives of this study was to \nprovide an overview on some of the security challenges and their effects \non poverty, hunger and malnutrition. To provide some key contributions \nsuch as implication of increasing security challenges and its impact on \nfuture food production, effects on local and foreign direct investment, \nincreased poverty, hunger, and malnutrition rate in Nigeria. And \nconceptualized framework that could improved food production in \nNigeria for sustainable food security, human and national development. \nInformation from this study could interest the government and relevant \nstakeholders to take aggressive action to avoid irreversible complications \nin the nearest future. Furthermore, it could necessitate policies and \nprograms formulation aimed at strengthening food production in the rural \ncommunities, and reducing undernutrition among children in Nigeria. \n\n\n\n2. METHODOLOGY \n\n\n\n2.1   Literature Search \n\n\n\nThe present study is based on integrative literature review. Published \npeer-reviewed research articles were identified from Google scholar, \nAfrican Journals Online, Nigerian Journals Online, Scopus, Medline and \nPubmed and Local and non-indexed literature. The following words:  food \nsecurity, insecurity, hunger, malnutrition, COVID-19 pandemic, Nigeria, \nwere used separately or combined to seek for the relevant information. \nThe purpose of the literature search (study) was to provide information \non the deplorable state of food production in Nigeria, as caused by security \nchallenges in Nigeria. And also, how the security challenges has aggravated \nfood insecurity and malnutrition, especially among children in the affected \nrural communities in Nigeria. The literature search and research (review \nproces) were conducted between July 2020 to May 2022, at various \ninstitutions/organizations of the authors.  \n\n\n\n2.1.1   Inclusion Criteria \n\n\n\nSome articles with relevant information related to security challenges, \nfood security, and malnutrition in Nigeria were used  \n\n\n\n2.1.2   Exclusion Criteria \n\n\n\nArticles with confounding and irrelevant information were not considered \nin this study. \n\n\n\n2.1.3   Security Challenges in Nigeria \n\n\n\nMost of the security challenges in Nigeria have their root cause emanating \nfrom the high level of poverty and inequality in the country (Awojobi et al., \n2014). Global Terrorism Index 2018, rank Nigeria 1st out of 54 African \nnations examined and 3rd in the world, as it is considered as the home base \nof over 70% of small arms and light weapons (Udoh et al., 2019). \nFurthermore, security updates from a global perspective revealed that \nNigeria is one among the crime ravaging countries worldwide (Varrella, \n2021). The following crime attributes were indicated: banditry, \nkidnapping, raping, Boko-Haram, unlawful possession of arms, armed \nrobbery, and attempted murder among others (Afolabi et al., 2015). \n\n\n\nThe security challenges due to Boko haram insurgency, banditry, farmers-\nherders clash, cattle rustling, kidnapping among other pockets of security \nchallenges are a menace worsening food crisis, poverty, hunger and \nmalnutrition in Nigeria (Olanrewaju et al., 2019). The Boko haram \ninsurgency and banditry are possibly the same group, considering their \nmode of operation, but may only differ in nomenclature. For instance, \nBoko-haram insurgents and bandits have invaded many communities \nwhere lives were loss, houses razed down, quantum of foods carted away, \n\n\n\nand people taken away, in other word \u201ckidnapped\u201d in exchange for money. \nFollowing their mode of operations one could infer that Boko-haram \ninsurgency has taken a new dimension that has spread to other part of the \ncountry (Olanrewaju et al., 2019). Perhaps, the destructive inversion by \nthe bandits is being used for sustaining the activities of Boko haram. \nKidnapping on the other hand has practically become a daily business \naffair in Nigeria. It is almost a norm and the second most profitable \nbusiness after politics in Nigeria.  \n\n\n\nAs a result of the security challenges in Nigeria, thousands have been \nkilled, millions have been displaced and hundreds of communities where \nfoods are produced are no longer habitable (Olanrewaju et al., 2019). \nNigeria is one of the countries with consistent high levels of displaced \npersons with over 1.8 million internally displaced persons in 2018, largely \ndue to insecurity (OCHA, 2018). The menace has also made thousands of \nfarmers to stick to the choice of staying at home without any means of \nlivelihood, rather than risk going to farm. Others go as far as to negotiate \nby paying ransom to be allowed to carry-out farming activities (Maigari et \nal., 2021). This poses a consequential effects such as weakening the \nfinancial strength and mental health of the farmers. Obviously, the \ninsecurity in Nigeria has graduated into \u201chard row to hoe\u201d situation that \nseems to have no solution or difficult to proffering solution. The worst \nimplication of this would be self-help, the possession of arms and \nammunitions by the individual members of the community in order to \nprotect themselves against terrorist. Of course, this likelihood of self-\ndefense would be extremely hazardous to virtually everyone from the \nindividuals\u2019 perspective down to the communities and, to the country at \nlarge. \n\n\n\nThe security challenges in Nigeria had been taken undue advantage of, as \na political tool or campaign instrument. As long as insecurity in Nigeria \nremains a campaign tool, efforts to quell insecurity in Nigeria would be \nfutile. Reason being that, attempted effort maybe frustrated by any \npolitical opposition. Since 2003, election in Nigeria has been perceived as \na must win affair. Must win election syndrome (MWES) by any means is \nperhaps one of the factors that encouraged importation of firearms and \nammunitions into the country. It breeds thugs and hooligans thus \nworsening insecurity in Nigeria (Adegbami et al., 2013). \n\n\n\nAt present, government efforts in tackling security challenges in Nigeria is \nyet to yield significant positive result. Perhaps the security agents in \nNigeria have become incapable of handling the situation (Adegbami et al., \n2013). Some of the most affected states have resolved to negotiation and \nrehabilitation of the organized criminals, yet it has remained  futile efforts. \nIt is not only a futile effort, possibly effort that is refueling the insecurity \nrate. The money paid as ransom to bandits in exchange for peace is rather \nused to strengthen the destructive activities. This suggests that rather than \nnegotiation, there is need for critical intelligence analysis that would yield \na positive solution, as it is now glaring that negotiation and rehabilitation \nare never the solution to the menace. In fact, negotiation and rehabilitation \nare breeding tools for more criminal elements and worsening security \nchallenges. More so, the government is trying her possible best to \nrebuilding the destroyed communities and gradually returning some of \nthe internally displaced persons (IDPs) to their ancestral homes. Though \nan applause effort, however that maybe be a waste of energy, resources, \nand worst of all endangering the lives of the returnees. If the destructive \nactivities are not halted, the security of the returnees cannot be \nguaranteed.  Although the need to return the IDPs to their homes is \nimperative, the IDPs camps are however breeding ground for more hunger \nand malnutrition. \n\n\n\n2.2   Impacts of Security Challenges on Food Production in Nigeria \n\n\n\nGood health is desired by all human and can be largely assured from \nadequate intake of quality food. Due to increased security challenges in \nNigeria, availability of sufficient food for everyone has become a major \nchallenge in Nigeria. Also, population is swiftly increasing while food \nproduction is declining. Nigeria is faced with population growth and is \nexpected to double by 2050, and by 2100, the population is predicted to \nreach 800 million (Olowe, 2020). The assumption is that in subsequent \nyears there will be limited land for food production (Olowe, 2020). The \nexpected growth is tremendously worrisome, as no mechanistic approach \nhas been projected to address its probable consequences in Nigeria. This \nmay exacerbate the current fragile food security caused by especially \ninsecurity, and future food security may not be guarantee.  \n\n\n\nTo ensure sustainable (present and future) food security in Nigeria, \npopulation growth must significantly translate to food production \nsufficient to ensure national and household food security (Abdulrahaman, \n2013). This implies that food production at all times must exceeds (double \nor triple) the desired food requirement by the population. Furthermore, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 45-51 \n\n\n\nCite The Article: Ibrahim Abdulwaliyu, Stanley I.R. Okoduwa, Shefiat O. Arekemase, Abdulkadir Muhammad, Musa L. Batari, Razaq A. Mustapha, Joseph F. Itiat (2023). Impact \nof Security Challenges on Food and Nutrition Security in Nigeria: The Role of Food Production Focus. Malaysian Journal of Sustainable Agricultures, 7(1): 45-51. \n\n\n\nthis may help overcome the problem of food and nutrition insecurity in \norder to achieve a sustainable development goal for \u201cZero hunger\u201d \n(Wongnaa and Awunyo-Vitor, 2018). However, with little emphasis on \nagriculture (as it has been in Nigeria), particularly amidst security \nchallenges coupled with population growth, the United Nations (UN) effort \ntowards achieving the sustainable development goal for \u201cZero hunger\u201d \nwould be impossible in Nigeria, even beyond 2030.  \n\n\n\nAgricultural sector is one of the least priorities of the government, as oil \nsector is perceived as the only redeemer of the Nigeria economy (Okoi, \n2019). Over the years it has been proven that the oil sector is not the only \nway forward. Yet the Federal Government of Nigeria (FGN) has not \ndeemed it necessary to give agriculture a top most priority (Olowa and \nOlowa, 2014). Although the oil sector got all it could to take Nigeria beyond \ndevelopment, unfortunately it hasn\u2019t for obvious reasons. The over \ndependent on oil alone may further increase restiveness, insecurity, \nvolatility and fragility of Nigeria economy. \n\n\n\nIn recent times an increasing number of persons in rural communities are \nshunning agriculture for white-collar jobs in the urban cities, largely due \nto security challenges in rural communities. Many of the migrants \noriginate from rural areas, and young people constitute a larger \npercentage, leaving women and aged members of the society to constitute \nthe labor force of rural communities (World Bank, 2011). Consequently, \nfood production decreases. Reasons for the migration maybe justifiable, \nthough on a more complex reason, people migrate or force to leave due to \ncultural, demographic, socio-economic, ethnic conflicts, environmental, \nnatural disasters, or political (large scale infrastructure projects and \nresettlement), and largely as a result of security challenges (Tanle et al., \n2020). \n\n\n\n On the other hand, it could be the preference for industrial sectors or \nurban jobs. This has over the years contributed to a substantial decrease \nin food production, and consequently increases in food prices, food \ninsecurity and security challenges in Nigeria (Figure 1). A group of \nresearchers highlighted that rural-urban migration has a deteriorative \nimpact on rural economy, a major cause of poor food production, and \nconsequently food insecurity (Babi et al., 2017). The economy of the rural \ncommunities is driven by agricultural activities by the people living in \nthem (rural communities). Hence, the more rural-urban migration surface, \nthe more will be the food insecurity, and poverty rate in Nigeria (Figure 1). \nIncreased population growth and decreased farming activities caused by \nthe security challenges, are conjugal factors that have contributed \nsignificantly to decline food production, and consequently national food \ninsecurity in Nigeria (Figure 1). \n\n\n\nFigure 1: Conceptual framework showing dimensional pathways by \nwhich insecurity affects food security. Source; Authors' conceptualization \n\n\n\nLow food production with devastating effects on food prices has \nenormously reduced the purchasing power of food by millions of \nNigerians. Implying many people living in Nigeria do not have financial \naccess to quality food, to quench hunger, which is the primary reason for \neating food (Abdulwaliyu et al., 2019). Lack of access to quality and \nsufficient food could increase the risk of anxiety and depression, and poor \nmental health (Fang et al., 2021; Jones, 2017). This may have devastating \neffects on economic growth, unemployment and poverty rate, and most \n\n\n\nworrisome, security challenges (Adebayo and Ojo, 2012) (Figure 1). As \npoverty breeds insecurity, insecurity in turn worsens poverty and this \nmanifest in viscous cycle (Figure 2).  \n\n\n\nFigure 2: Vicious cycle of Insecurity (Source: Authors' \nconceptualization) \n\n\n\n2.3   Impacts of Security Challenges on Foreign and local direct \nInvestment in Nigeria \n\n\n\nNo country thrives economically amidst insecurity. The security of any \nnation is a tool for promoting investment opportunities. However, the \nrelationship between peace and economic development is not as strong as \nthe relationship between insecurity and underdevelopment (Denney, \n2013). Insecurity disrupts development, and peace may not drive \ndevelopment, unless there is peace in the atmosphere of strong political \nwill. A climate of insecurity frightens local and foreign investors, hinders \nbusiness activities, and consequently retards socio economic activities \n(Ewetan and Urhie, 2014). It also discourages investors who are interested \nin carrying out meaningful development programmes, thus limits \ncommunity and national development, and people\u2019s ability to develop \neconomically. In Nigeria, the activities of Boko haram in the North, \nMilitancy in the Niger-Delta, and Fulani herders in the Middle Belt \nweakens the economic and business climate, and most volatile in recent \ntime is the banditry that has taken over some states in Nigeria (Udoh et al., \n2019). \n\n\n\nAside its effects on direct investment opportunities, it has affected the \neconomic activities of people living in the country, especially those from \nthe most security risk zones.  Thus affects, several businesses, especially \nsmall and medium enterprises, which contribute significantly to economic \ngrowth of Nigeria. Small and medium enterprises world over have been \nthe major employers. They (small and medium enterprises) serve as the \nmainstream for employment generation, poverty reduction, leading to \neconomic development (Hassan et al., 2020). Regrettably, in the recent \nyears, the enabling environment for small and medium enterprises to \nthrive efficiently, no longer exist in Nigeria. It is therefore, not amazing to \nnote that most Nigerians are trapped in the vortex circle of abject poverty, \nthereby diminishing Nigeria status to a nation with poorest people in the \nworld (Umaru et al., 2015). Furthermore, dislocation of people diminishes \nthe manufacturing of export goods, and a reduction in employment and \nrenumeration (Onime, 2018). The effects of insecurity on business \ninvestment create more unemployment. This inturn causes extreme \npoverty, which further instigates crime that fosters to insecurity. \n\n\n\n2.4 Impacts of Security Challenges on Poverty, Hunger and \nMalnutrition in Nigeria \n\n\n\nThe effects of security challenges has the tendencies of increasing \nhousehold food insecurity, malnutrition, cognitive deficit and poor \nperformance, poor economic growth that in turn increase unemployment \nrate (Figure 1).  Insecurity affects women and girls, making them more \nvulnerable and having low ability to handle the socio-economic in addition \nto health aspect as a result of insecurity induce tremor (FSIN, 2020). The \nPrediction by the World Food Program (WFP) shows that the number of \npersons in developing nations being exposed to severe level of food \ninsecurity have increased in millions, in 2020, with high consequences on \nmalnutrition compared to stable nations (WFP, 2020; GNR, 2020). Millions \nof Nigerians are food insecure due to reasons like the poverty rate in the \ncountry (FMARD, 2016). By the perspectives of national standard, over \n82.9 million Nigerians are considered poor, as 4 out of 10 individuals in \nNigeria has real per capita expenditures below 137,430 Naira per year \n(NBS, 2020).  \n\n\n\nMost worrisome is the poverty rate in Northern Nigeria that is quite \nalarming despite the enormous land for food production (Jaiyeola and \nChoga, 2020). Unfortunately, the onset of security challenges has further \nworsened poverty, hunger and malnutrition rate, especially in northern \nNigeria (Okolie et al., 2019). The menace if not curtailed, soonest may \n\n\n\nPoverty\n\n\n\nHunger\n\n\n\nInsecurity\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 45-51 \n\n\n\nCite The Article: Ibrahim Abdulwaliyu, Stanley I.R. Okoduwa, Shefiat O. Arekemase, Abdulkadir Muhammad, Musa L. Batari, Razaq A. Mustapha, Joseph F. Itiat (2023). Impact \nof Security Challenges on Food and Nutrition Security in Nigeria: The Role of Food Production Focus. Malaysian Journal of Sustainable Agricultures, 7(1): 45-51. \n\n\n\nresult to the security challenges chain reaction, and may be difficult to \ncontrol. Also, it could develop to intergenerational transmission of \npoverty, hunger and malnutrition in Nigeria. However, with focus on food \nproduction, the poverty, hunger and malnutrition dilemma maybe \nreduced or averted in nearest future. This would upsurge affordability of \nfoods by all (food security), and consumer\u2019s preferences for healthy diets. \nFood insecurity at the household level has been reported as one of the \ncauses of hunger, leading to loss of lives of children yearly (Drammeh et \nal., 2019). It has been reported also that the risk of malnutrition increases \nby 12% among children from food insecured household (Mutisya et al., \n2015).  \n\n\n\nThe prevalence of under nutrition and food insecurity in Nigeria are \namong the worst worldwide (Fadare et al., 2019; Nwozor et al., 2019). The \ntotal number of under-malnourished Nigerians increased from 9.1 million \n(2004-2006) to 25.6 million (2016-2018) (FAO et al., 2019). The observed \nincrement may be associated with an increased rate of insecurity. This \nimplies that, persistence security challenges in Nigeria are a menace \nworsening malnutrition in the country.  \n\n\n\n2.5 The Role of Food Production Focus (FPF) Framework to \nStrengthen Food Production in Nigeria \n\n\n\nThe FPF is a framework that would require honesty, commitment of the \ngovernment and farmers\u2019 cooperation to necessitate actions that should at \nall times strengthen agricultural activities in Nigeria. To achieve the \ndesired results, government would need to focus on rural agricultural \nactivities (Ajani et al., 2015). Furthermore is the need to evaluate, educate, \nand empower (triple E as shown in Figure 3) people with a profitable and \ntechnologically advanced agricultural input. This should specifically be for \npeople living in rural communities. \n\n\n\nFigure 3: Food production focus.  Source; Authors' conceptualization \n\n\n\nThe FPF should entails evaluation of minor and complex problems \nassociated with farming activities or problems faced by farmers, especially \nin the rural communities. This requires situational approach, as different \nproblems, at different places, in different seasons, at different times \nrequires a different solution. In the evaluation, education and \nempowerment processes, triple A cycle (Assessment, Analysis and Action) \nis a necessary model in achieving desired results. The evaluation requires \nassessment of problems faced by farmers and the cause of the problem(s) \n(analysis), while the action entails educating and empowering the farmers.  \n\n\n\nThe assessment of problems faced by farmers in different part of the \ncountry would enable government take necessary and decisive actions. \nHowever, the necessary actions cannot be met until there is a progressive \nand uninterrupted interaction with farmers, suggesting the need to engage \nthem in the evaluation process. Among the farmers, young people \nincluding women in particular, should be the primary target, as they seem \nto be the game changers and key players for sustainable food security in \nNigeria. Moreso, Farmers should be educated on newer and best farming \npractices, for better harvest and that would positively affect their standard \nof living. In that way farming in Nigeria would look attractive, and a lot \nmore people would key into it. \n\n\n\nEducation enhances farm productivity (Paltasingh and Goyari, 2018). \nStudy affirmed that, as educational level increases, output increases as \nwell (Eric et al., 2014).  Education alone is empowerment, as it would \nempower farmers with the knowledge, skills and values needed for better \noutput. However, empowerment is beyond educating farmers. It includes \nalso newer scientific tools for better outputs, and health status of the \nfarmers, since they may be susceptible to various kinds of diseases, \nparticularly during the farming season (Ali et al., 2020).  \n\n\n\nEmpowerment, also include good roads (especially in rural communities) \nfor ensuring free flow of food via food supply chain. Food supply chain \nentails food production, harvesting, processing, storage, distribution and \nconsumption. Food insecurity occurs at any points of the food supply \nchain, and it is a global problem. A group of researchers highlighted that \nabout 1.3 billion tons of food (one third of the food produced) are wasted \nand two-third of it occur in the food supply chain (Zhong et al., 2017). \nHowever, the security challenges in Nigeria have further retarded the free \n\n\n\nflow of food via the food supply chain. From the point of food production \nand distribution, farmers are being kidnapped or killed (Ladan and \nMatawalli, 2020).  \n\n\n\nIn this study, the recommendation of FPF may be ineffective in the light of \nthe security challenges, on sustainable food security in Nigeria. The \natmosphere of insecurity would impede FPF. Therefore, adequate security \nis a necessary catalyst for attainment of FPF (Figure 3). According to a \nstudy, to create a healthy environment for sustainable development, the \ngovernment of Nigeria should put in place good governance, workable \nanti-terrorism procedures and build strong legitimate establishments that \ncan effectively curtail the menace of corruption and poverty (Umaru et al., \n2015).  \n\n\n\nSecurity challenges of a nation, like Nigeria, could lead to the disruption of \nthe entire food system. This may ultimately affects the availability, \naccessibility and utilization of foods, which in return has a negative impact \non the general wellbeing of the populace. It may also have a socio-\neconomic impact at both individual and national level. Also, disruption of \nfood systems by insecurity has made the actualization of the SDGs 2030 in \nNigeria to be a mirage. As a result, a lot of persons will become very poor \nand lack food due to displacement and lack of access to their farm lands.  \n\n\n\n2.6   Impacts of Insecurity amidst COVID-19  on Food security in \nNigeria \n\n\n\nAmidst security challenges in Nigeria, COVID-19 surfaces, and was \ndeclared a public health emergency, and a pandemic, on 30th January \n2020, and 11th March 2020 respectively. The scourge of COVID-19 amidst \nsecurity challenges in Nigeria has affected all aspects of human life. It \nplaced havoc on health, education, socioeconomic and agricultural sectors \n(Nicola et al., 2020;  Rajhans et al., 2020;  Stephens et al., 2020; Tadesse \nand Muluye, 2020;  Chaturvedi et al., 2021; Mulugeta et al., 2021; \nOnyemachi and Okoduwa, 2022). The need to curb the onslaught of the \nCOVID-19 started in December 2019, and as of 10th May 2020, the COVID-\n19 pandemic has enthralled many countries across the globe (Salami et al., \n2021; Rajhans et al., 2020). The wake of the disease further deepens a \ndecline in food production (small and large scales) and disrupted food \nsupply chain in Nigeria (Ilesanmi et al., 2021). This had profound \nimplication on food and nutrition security, as obvious in the decay of food \nsecurity in many households in Nigeria (Amare et al., 2021; FAO, 2020). \nStudy affirmed that more than half of the households in Nigeria are \nconfronted with extreme food insecurity (Ibukun and Adebayo, 2021). \nThis may be attributed to Government\u2019s order of restrictions (movement, \neconomic and social activities restriction) mainly to mitigate the spread of \nthe pandemic (Balana et al., 2020). As a result, hunger and deaths due to \nlockdown measures outweighed those due to infection (Kalu et al., 2020). \nAlso, the measures (although necessary) have made many Nigerians \nvulnerable to food insecurity, hunger (Martinez, 2021) and consequently \nincreased malnutrition (Figure 5).  \n\n\n\nPrior to the outbreak of the COVID-19 pandemic, Nigeria already has many \npeople with precarious nutritional status (malnutrition) (Onyeaka et al., \n2021).  The emergence of COVID-19 further exacerbated the malnutrition \nlevel in Nigeria (Aborode et al., 2021). It is not surprising to note that the \nCOVID-19 pandemic was tagged disease of hunger in Nigeria (Kalu, 2020). \nThis was due to the fact that the impact of the COVID-19 pandemic placed \nadditional burden on the vulnerable communities where insecurity is \nseverely hampering food production (ICRC, 2020). Just like the insecurity \nthat may affects vision 2030 of \u201cZero hunger\u201d (as mentioned earlier), the \nCOVID-19 is also stalling efforts to achieve sustainable development Goal \n(SDG2) of \u201cZero hunger\u201d (FAO, 2020).  \n\n\n\nFigure 5: Conceptual framework showing how COVID-19 pandemic \naffects food security, and consequently hunger and malnutrition. Source; \n\n\n\nAuthors' conceptualization \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 45-51 \n\n\n\nCite The Article: Ibrahim Abdulwaliyu, Stanley I.R. Okoduwa, Shefiat O. Arekemase, Abdulkadir Muhammad, Musa L. Batari, Razaq A. Mustapha, Joseph F. Itiat (2023). Impact \nof Security Challenges on Food and Nutrition Security in Nigeria: The Role of Food Production Focus. Malaysian Journal of Sustainable Agricultures, 7(1): 45-51. \n\n\n\n3. CONCLUSION \n\n\n\nInsecurity in Nigeria is not only a very stern problem but also a growing \none. It has affected some components of food security and food system. It \nis (insecurity) a serious impediment to food production, and availability \nby many Nigerians, especially those in the rural communities. Food supply \nchain has been interrupted. Hence, food inflation is on the increase \nespecially in regions where foods are not sufficiently produced. The \nsecurity challenges in Nigeria have negatively remodelled her economy \nsystem. Some companies had shutdown, and proliferation of investment \nphobia and unemployment rate is prevalent. It has become more alarming \nat a difficult time the country is trying to get to its feet. The insecurity \nsituation is yet to be abated, but has however, assumed a different \ndimension, which pose more serious threats to co-existence of the people \nresiding in Nigeria. It is a menace worsening poverty, hunger and \nmalnutrition. However, if maximum attention is sincerely concerted on \nagriculture (especially in rural farming) by effectively implementation of \nthe adopted FPF policy, the atmosphere of security, perhaps zero hunger \nmaybe achieved soonest.   \n\n\n\nConflict of Interest: The authors declare that they have no conflicts of \ninterest. \n\n\n\nFunding Statement: This research did not receive any specific grant from \nfunding agencies in the public, commercial, or not-for-profit sectors. \n\n\n\nEthical Approval: This article does not contain any studies with human \nparticipants or animals performed by any of the authors. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThe Authors express sincere appreciation to the technical staff of the \nTraining and Consultancy Service Department, SIRONigeria Global \nLimited, Abuja, Nigeria for the typesetting and formatting of the \nmanuscript. \n\n\n\nREFERENCES \n\n\n\nAbdulrahaman, S., 2013. Population growth and food security in \nNigeria. Arabian Journal of Business and Management Review \n(Nigerian Chapter) 1 (3). \n\n\n\nAbdulwaliyu, I., Arekemase, S.O., Adudu, J.A., Batari, M.L., Egbule, M.N., \nOkoduwa, S.I.R., 2019. Investigation of the Medicinal Significance \nof Phytic acid as an Indispensable anti-nutrient in Diseases. \nClinical Nutrition Experimental, 28, Pp. 42-61. \n\n\n\nAborode, A., Fajemisin, E., Awoniyi, O., Akinremi, Q., 2021. The Impact of \nCOVID-19 on Child Malnutrition in Nigeria: Focus on Strategic \nSolutions. Under Considerations and Submitted to Science Open \nPreprints for Open Peer Review. DOI: 10.14293/S2199-\n1006.1.SOR-PPG517K.v1.  \n\n\n\nAdebayo, P.F., and Ojo, E.O., 2012. Food security in Nigeria: An \noverview. European journal of sustainable development, 1 (2), \nPp. 199-199. \n\n\n\nAdebisi, S.A., Azeez, O.O., Oyedeji, R., 2017. Appraising the effect of Boko \nharam Insurgency on the Agricultural sector of Nigerian Business \nEnvironment. Journal of Law and governance, 11 (1), Pp. 14-25. \n\n\n\nAdekola, G., and Enyiche, C.C., 2017. Effects of Insecurity on Community \nDevelopment Projects in Ogba/Egbema/Ndoni and Ahoada East \nLocal Government Areas of Rivers State, Nigeria. Journal of \nEducation and Practice, 8 (14), Pp. 34-39. \n\n\n\nAdwgbami, A., 2003. Insecurity: A threat to human existence and economic \ndevelopment in Nigeria. J. Public policy and administration \nresearch, 3 (6). \n\n\n\nAfolabi, A., 2015. The impact of insecurity on growth and development in \nNigeria. Available at SSRN: https://ssrn. \nCom/abstract=2643608. \n\n\n\nAjani, E.N., Mgbenka, R.N., and Onah, O., 2015. Empowerment of youths in \nrural areas through agricultural development programmes: \nImplications for poverty reduction in Nigeria. International \nJournal of Research, Pp. 34. \n\n\n\nAli, Z., Rosemary, G., Robert, B.Z., Siyabusa, M., Amanda, P., Andrew, M.P., \nAndy, H., Alan, D.D., and Pauline, F.D.S., 2020. Long-Term Impact \nof West African Food System Responses to COVID-19. Nature \n\n\n\nFood, 1 (12), Pp. 768\u201370. https://doi.org/10.1038/s43016-020-\n00191-8. \n\n\n\nAmare, M., Abay, K.A., Tiberti, L., and Chamberlin, J., 2021. COVID-19 and \nFood Security: Panel Data Evidence From Nigeria. Food Policy, \n101, Pp. 102099.  \n\n\n\nAwojobi, O.N., 2014. Poverty and Insecurity in Nigerian Democratic \nDispensation. International journal of innovative research & \ndevelopment, 3 (6), Pp. 357-365. \n\n\n\nAyanlade, A., and Maren, R., 2020. COVID-19 and Food Security in Sub-\nSaharan Africa: Implications of Lockdown during Agricultural \nPlanting Seasons. Npj Science of Food, 4 (1), Pp. 13. \nhttps://doi.org/10.1038/ s41538-020-00073-0. \n\n\n\nBabi, M.L.A., Guogping, X., and Ladu, J.L.C., 2017. Causes and consequences \nof rural-urban migration: The case of NJuba Metropolitan, \nRepublic of South Sudan IOP Conf. Series: Earth and \nEnvironmental Science, 8, Pp. 012130 . do i :10.1088/1755-\n1315/81/1/012130. \n\n\n\nBalana, B.B., Oyeyemi, M.A., Ogunniyi, A.I., Fasoranti, A., Edeh, H., Aiki, J., \nand Andam, K.S., 2020. The Effects of COVID-19 Policies on \nLivelihoods and Food Security of Small holder Farm Household\u2019s \nin Nigeria. Descriptive Results from a Phone Survey. IFPRI \nDiscussion Papers, Pp. 34; https://doi.org/10.2499/p15738 coll \n2.1341. \n\n\n\nB\u00e9n\u00e9, C., 2020. Resilience of Local Food Systems and Links to Food Security \n\u2013 A Review of Some Important Concepts in the Context of COVID-\n19 and Other Shocks. Food Security, 12 (4), Pp. 805\u201322. \nhttps://doi. org/10.1007/s12571-020-01076-1. \n\n\n\nBarker, N., and Lamble, S., 2009. From social security to individual \nresponsibility: Sanctions, conditionality and punitiveness in the \nWelfare Reform Bill 2009 (Part One) Current \ndevelopments. Journal of social welfare & family law, 31 (3), Pp. \n321-332. \n\n\n\nChaturvedi, K., Vishwakarma, D.K., and Singh, N., 2020. COVID-19 and its  \nImpact on Education, Social Life and Mental  Health of Students: \nA Survey. Child and Youth Services Review, 121, Pp. 105866. \nhttps://doi.org/10.1016/j.cchildyouth.2020.105866. \n\n\n\nDenney, L., 2013. Insecurity disrupts development but peace doesn\u2019t drive \nit. Retrieved from Drammeh W, Hamid NA and Rohana AJ (2019). \nDeterminants of household food insecurity and its association \nwith child malnutrition in Sub-saharan Africa. A review of the \nliterature. Curr Res Nutr Food Sci., 7 (3). \nDoi:http://dx.doi.org/10.12944/CRNFSJ.7.3.02. \n\n\n\nEric, O., Prince, A.A., and Elfreda, A.N., 2014. Effect of evaluation on the \nagriculture productivity of farmers in the offinso municipality. \nInternational journal of development research, 4 (9), Pp. 1951-\n1960. European Scientific Journal, 14(4). Doi: \n10.19044/esj.2018.v14n4p377. \n\n\n\nEwetan, O.O., and Urhie, E., 2014. Insecurity and Socio-Economic \nDevelopment in Nigeria. Journal of Sustainable Development \nStudies, 5 (1), Pp. 40-63. \n\n\n\nFadare, O., Akerele, D., Mavrotas, G., and Ogunniyi, A., 2019. Effect of \nconflict and food price shocks on calorie intake and acute \nmalnutrition in Nigeria: A micro-panel data analysis. Paper \npresented at the 93rd Annual Conference of the Agricultural \nEconomics Society, University of Warwick, England. \n\n\n\nFang, D., Thomsen, M.R., and Naygya, R.M., 2021. The Association between \nFood Insecurity and Mental Health during the COVID-19 \nPandemic. BMC Public Health, 21, Pp. 607. \nhttps://doi.org/10.1186/s12889-021-10631-0. \n\n\n\nFAO (Food and Agricultural Organization) IFAD (International Fund for \nAgricultural Development), UNICEF (United Nations Children\u2019s \nFund), WFP (World Food Programme) and WHO (World Health \nOrganization). 2019. The state of food security and nutrition in \nthe world 2019: Safeguarding against economic slowdowns and \ndownturns. Rome: FAO. \n\n\n\nFAO (Food and Agriculture Organization). 2020. Impact of COVID-19 on \nFood Security and Nutrition:  Developing Effective Policy \nResponses to Address the Hunger and Malnutrition Pandemic \n\n\n\n\nhttps://ssrn/\n\n\nhttps://doi.org/10.1038/\n\n\nhttps://doi.org/10.2499/p15738%20coll%202.1341\n\n\nhttps://doi.org/10.2499/p15738%20coll%202.1341\n\n\nhttps://doi/\n\n\nhttps://doi.org/10.1016/j.cchildyouth.2020.105866\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 45-51 \n\n\n\nCite The Article: Ibrahim Abdulwaliyu, Stanley I.R. Okoduwa, Shefiat O. Arekemase, Abdulkadir Muhammad, Musa L. Batari, Razaq A. Mustapha, Joseph F. Itiat (2023). Impact \nof Security Challenges on Food and Nutrition Security in Nigeria: The Role of Food Production Focus. Malaysian Journal of Sustainable Agricultures, 7(1): 45-51. \n\n\n\nFMARD. 2016. The agriculture promotion policy (2016 \u2013 2020): Building \non the successes of the ATA, closing key gaps. Retrieved from \nhttps://fscluster.org/sites/default/files/documents/2016-\nnigeria-agric-sector- \n\n\n\nFSIN (Food Security Information Network). 2020. 2020 Global Report on \nFood Crises: Joint analysis for better decisions. Rome: FSIN. \n\n\n\nGNR (Global Nutrition Report). 2020. 2020 Global Nutrition Report: Action \non equity to end malnutrition. Bristol, UK: Development \nInitiatives. \n\n\n\nHassan, A.E., Akor, K., Bamiduro, E.O., and Rauf, I., 2020. Impact of \ngovernment policy and insecurity factors on small and medium \nenterprises (smes) productivity in Nigeria. European Journal of \nBusiness and Management Research,  5 (6), \ndoi:http://dx.doi.org/10.24018/ejbmr.2020.5.6.545. \n\n\n\nHeadey, D., Heidkamp, R., Osendorp, S., Ruel, M., Scott, N., Flory, A., 2020. \nImpacts of COVID-19 on childhood malnutrition and nutrition-\nrelated mortality. The Lancet, 396, Pp. 519 - 521. \n\n\n\nHendriks, S., Soussana, J.F., Cole, M., Kambugu, A., and Zilberman, D., 2021. \nEnsuring Access to Safe and Nutritious Food for All Through \nTransformation of Food Systems. Action Track, 1. \n\n\n\nIbukun, C.O., and Adebayo, A.A., 2021. Household Food Security and the \nCOVID-19 Pandemic in Nigeria. African Developmental Review. \nhttps://doi/10.1111/1467-8268. \n\n\n\nIlesanmi, F.F., Ilesanmi, O.S., and Afolabi, A.A., 2021. The Effects of the \nCOVID-19 Pandemic on Food Losses in the Agricultural Value \nChains in Africa. The Nigerian Case Study. Public Health in \nPractice, 2, Pp. 100087. \nhttps://doi.org/10.1016/j.puhip.2021.100087. \n\n\n\nIlo, U.J., Jonathan-Chaver, I., Adamolekun, Y., 2019. The deadliest conflict \nyou\u2019ve never heard of: Nigeria\u2019s cattle herders and farmers wage \na resource war. Foreign Affairs. Retrieved from \nhttps://www.foreignaffairs.com/articles/nigeria/2019-01-\n23/deadliest-conflict-youve-never-heard. \n\n\n\nInegbedion, H., 2021. Impact of COVID-19 on Economic Growth in Nigeria: \nOpinions and Attitudes. Heliyon, 7 (5). \nhttps://doi.org/10.1016/j.heliyon.2021.e06943. \n\n\n\nICRC (International Committee of the Red Cross). 2020. Nigeria: Sharp \nIncrease in Food Prices Caused by COVID-19 Raises fear of \nHunger. http://www.icre.org/ \n\n\n\nIvan\u010d\u00edk, R., Ne\u010das, P., and Jur\u010d\u00e1k, V., 2014. Theoretical View of Some \nCurrent Global Security Challenges. Incas Bulletin, 6 (1), Pp. 99 \u2013 \n107.  \n\n\n\nJaiyeola, A.O., and Choga, I., 2020. Assessment of poverty incidence in \nNorthern Nigeria, Journal of Poverty, DOI: \n10.1080/10875549.2020.1783424To link to this article: \nhttps://doi.org/10.1080/10875549.2020.1783424P. \n\n\n\nJones, A.D., 2017. Food Insecurity and Mental Health Status. A Global \nAnalysis of 149 Countries. American Journal Preventive \nMedicine, 53 (2), Pp. 264-273. \nhttps://doi.org/10.1016/j.amepre.2017.04.008. \n\n\n\nKalu, B., 2020. COVID-19 in Nigeria: a Disease of Hunger. The Lancet. \nRespiratory Medicine, 8 (6), Pp. 556-557. Doi: 10. 1016/s2213-\n2600(20)30220-4. \n\n\n\nLaborde, D., Will, M., and Rob, V., 2020. Impacts of COVID-19 on Global \nPoverty, Food Security and Diets. Agricultural Economics \nForthcoming. \n\n\n\nLadan, S.I., and Matawalli, B.U., 2020. Impact of Banditry on Food Security \nin Katsina State, Nigeria. Direct Research Journal of Agriculture \nand Food Science, 8 (12), Pp. 439-447.\nDoi:https://doi.org/10.26765/DRJAFS278190136. \n\n\n\nMaigari, M.A., Dantani, U., and Arafat, I., 2021. Negotiating with the Bandits \nand Endless Security Challenges in Katsina State, Nigeria (2019-\n2020). JSW (Jurnal Sosiologi Walisongo), 5 (1), Pp. 47-60. \n\n\n\nMulugeta, T., Tadesse, E., Shegute, T., and Desta, T.T., 2021. COVID-19: \nSocio-economic Impacts and Challenges in the Working Group. \nHeliyon, 7 (6), Pp. e07307. \nhttps://doi.org/10.1016/j.heliyon.2021.e07307. \n\n\n\nMartinez, E., 2021. Food Insecurity Amid the COVID-19 Lockdowns in \nNigeria: Do Impact on Food Insecurity Persist  after Lockdowns  \nEnd? Current Development in Nutrition, 5 (2). \nhttps://doi.org/10.1093/cdn/nzabo29_0369. \n\n\n\nMgbenka, R.N., Onwubuya, E.A., and Ezeano, C.I., 2015. Organic farming in \nNigeria: Need for popularization and policy. World Journal of \nAgricultural Sciences, 11 (6), Pp. 346-355. \n\n\n\nMutisya, M., Kandala, N., Ngware, M.W., and Kabiru, C.W., 2015. Household \nfood insecurity and nutritional status of urban poor children \naged 6-23 months in Kenya. BMC Public Health, 15, Pp. 1052. Doi: \n10.1186/s12889-015-2403-0. \n\n\n\nNational Bureau of Statistics (NBS). 2020. 2019 Poverty and Inequality in \nNigeria: Executive Summary. \n\n\n\nNicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, Agha, M., \nand Agha, R., 2020. The Socio-economic Implication of the \nPandemic (COVID-19); a Review. International Journal of  \nSurgery, 78, Pp. 185-193. \nhttps://doi.org/10.1016/j.ijsu.2020.04.018.  \n\n\n\nNwozor, A., Olanrewaju, J.S., and Ake, M.B., 2019. National Insecurity and \nthe Challenges of Food Security in Nigeria. Academic Journal of \nInterdisciplinary Studies, 8 (4). Doi: 10.36941/ajis-2019-0032. \n\n\n\nOCHA. 2018. Nigeria: Humanitarian Needs Overview 2019. Available at: \nhttps://reliefweb.in/sitest/reliefweb.int/files/resouces/01022\n019 ocha nigeria humanitarian needs overview pdf. \n\n\n\nOkoi, O., 2019. The Paradox of Nigeria\u2019s Oil Dependency. Economic \nDevelopment Oil. https://www.africaportal.org. \n\n\n\nOkolie, U.G., Onyema, O.A., Baseey, U.S., 2019. Poverty and Insecurity in \nNigeria: an Empirical  Study. International  Journal of Legal \nStidies, 6 (2), Pp. 247-261. doi: 10.5604/01.3001.7419 \n\n\n\nOkoli, A.C., and Okpaleke, F., 2014. Banditry and crisis of public safety in \nNigeria: Issues in national security strategies. European \nScientific Journal, 10 (4), Pp. 350-362. \n\n\n\nOlanrewaju, F.O., Olanrewaju, A., Omotoso, F., Alabi, J.O., Amoo, E., \nLoromeke, E., and Ajayi, L.A., 2019. Insurgency and the invisible \ndisplaced population in Nigeria: A Situational analysis. SAGE \nOpen, 9 (2), Pp. 2158244019846207. \n\n\n\nOlowe, V., 2020. Africa 2100: how to feed Nigeria in 2100 with 800 million \ninhabitants. Org. Agr. https://doi.org/10.1007/s13165-020-\n00307-1. \n\n\n\nOlowa, O.W., and Olowa, O., 2014. Policy interventions and public \nexpenditure reform for pro-poor agricultural development in \nNigeria. African Journal of Agricultural Research, 9 (4), Pp. 487-\n500. \n\n\n\nOnime, B.E., 2018. Insecurity and Economic Growth in Nigeria: A \nDiagnostic Review \n\n\n\nOnyeaka, H., Agbugba, I., Ekwebelem, O.C., Anumudu, C., Anyogu, A., \nOdeyemi, O., Agbagwa, S., 2021. Strategies to Mitigate the Impact \nof COVID-19 on Food Security and Malnutrition in Nigeria. \nEuropean Journal of Nutrition and Food Safety, 13 (2), Pp. 103-\n109.  \n\n\n\nOnyemachi, D.I., and Okoduwa, S.I.R. 2022. Impact of online media in \nmanagement of burden and psychological trauma associated \nwith COVID-19 pandemic and spread of false new. Information \nTechnology Journal, 21(1), 24-3 \n\n\n\nPark, A., Adegboye, O.A., Adekunle, A.I., Rahman, K.M., McBryde, E.S., Eisen, \nD.P., 2020. Economic Consequences of the COVID-19 outbreak:\nThe Need for Epidemic Preparedness. \nhttps://doi.org/10.3389/fpubh.2020.00241. \n\n\n\nPaltasingh,  K.R., and Goyari. P., 2018. Impact of farmer education on farm \nproductivity under varying technologies: case of paddy growers \nin India. Agricultural and Food Economics, 6, 7 \nhttps://doi.org/10.1186/s40100-018-0101-9. \n\n\n\n  policy-roadmap_june-15-2016_final1.pdf. \n\n\n\n\nhttps://doi/10.1111/1467-8268\n\n\nhttps://doi.org/10.1016/j.puhip.2021.100087\n\n\nhttp://www.icre.org/\n\n\nhttps://doi.org/10.1080/10875549.2020.1783424P\n\n\nhttps://doi.org/10.1016/j.heliyon.2021.e07307\n\n\nhttps://doi.org/10.1016/j.ijsu.2020.04.018\n\n\nhttps://reliefweb.in/sitest/reliefweb.int/files/resouces/01022019\n\n\nhttps://reliefweb.in/sitest/reliefweb.int/files/resouces/01022019\n\n\nhttps://www.africaportal.org/\n\n\nhttps://doi.org/10.1007/s13165-020-00307-1\n\n\nhttps://doi.org/10.1007/s13165-020-00307-1\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 45-51 \n\n\n\nCite The Article: Ibrahim Abdulwaliyu, Stanley I.R. Okoduwa, Shefiat O. Arekemase, Abdulkadir Muhammad, Musa L. Batari, Razaq A. Mustapha, Joseph F. Itiat (2023). Impact \nof Security Challenges on Food and Nutrition Security in Nigeria: The Role of Food Production Focus. Malaysian Journal of Sustainable Agricultures, 7(1): 45-51. \n\n\n\nRajhans, V., Memon, U., Patil, V., and Goyal, A., 2020. Impact of COVID-19 \non Academic Activities and way forward in Indian Optometry. \nJournal of Optometry, 13 (4), Pp. 216-226. \nhttps://doi.org/10.1016/j.optom.2020.06.002. \n\n\n\nSalami, U.V., Okoduwa, S.I.R., Chris,  A.O., Ayilara, S.I., and Okoduwa, U.J., \n2021. Opinion Review of Socioeconomic Impact of COVID-19 on \nWomen\u2019s Health. Front. Global Women\u2019s Health, 2, Pp. 647421, \ndoi: 10.3389/fgwh.2021.647421. \n\n\n\nStephens, E.C., Martin, G., Wijk, M., Timsina, J., and Snow, V., 2020. \nEditorial: Impact of COVID-19 on Agricultural and Food Systems \nWorldwide and on Progress to the Development Goals. Agric \nSystem, 183, Pp. 102873. Doi: 10.1016/j.agsy.2020.102873. \n\n\n\nTadesse, S., and Muluye, W., 2020. The Impact of COVID-19 Pandemic on \nEducation System in developing Countries: A Review. Open \nJournal of Social Sciences, 8, Pp. 159-170. \nhttps://doi.org/10.4236/jss.2020.810011. \n\n\n\nTanle, A., Ogunleye-Adetona, C.I., and Arthor, G., 2020. Rural-urban \nmigration and household livelihood in the Agona West \nMunicipality, Ghana. J. Geo. Reg. Plan., 13 (1), Pp. 1-18. \n\n\n\nUdoh, B.E., Prince, A.I., Udo, M., and Kelvin-Iioafu, L.E., 2019. Insecurity on \nEconomic and Business Climate; Empirical Evidence from \nNigeria. International Journal of Supply and Operations \nManagement, 6 (2), Pp. 182-187. \n\n\n\nUmaru, A., Pate, H.A., and Haruna, A.D., 2015. The Impact of Insecurity and \nPoverty on Sustainable Economic Development in Nigeria. \nInternational Journal of Humanities Social Sciences and \nEducation (IJHSSE), 2 (2), Pp. 32-48. \n\n\n\nWalker, Patrick, G.T., Charles, W., Oliver, J.W., Marc, B., Peter, W., Arran, H., \nBimandra, A.D., 2020. The Impact of COVID-19 and Strategies for \nMitigation and Suppression in Low- and Middle-Income \nCountries. Science, 369 (6502), Pp. 413\u201322. \nhttps://doi.org/10.1126/science.abc0035. \n\n\n\nWFP (world food programme), 2020. COVID-19 will double number of \npeople facing food crises unless swift action is taken. Retrieved \nfrom https://www.wfp.org/news/covid-19-will-double-\nnumber-people-facing-food-crises-unless-swift-actiontaken. \n\n\n\nWongnaa, C.A., and Awunyo-Vitor, D., 2018. Achieving sustainable \ndevelopment goals on no poverty and zero hunger: Does \ntechnical efficiency of Ghana\u2019s maize farmers \nmatter?. Agriculture & Food Security, 7 (1), Pp. 1-13. \n\n\n\nWorld Bank, 2011. Migration and Remittances Fact Book, 2011 Second \nEdition. Development Prospective Group, Washington DC, World \nBank. www. the brokenonline.eu/Blogs on 30th June, 2016. \n\n\n\nZhong, R.Y., Xu, X., and Wang, L., 2017. Food supply chain management: \nsystems, implementations, and future research. Industrial \nManagement and Data Systems, 117 (9), Pp. 2085-2114. DOI: \n10.1108/IMDS-09-2016-0391. \n\n\n\n\nhttps://doi.org/10.1016/j.optom.2020.06.002\n\n\nhttps://doi.org/10.4236/jss.2020.810011\n\n\nhttps://www.wfp.org/news/covid-19-will-double-number-people-facing-food-crises-unless-swift-actiontaken\n\n\nhttps://www.wfp.org/news/covid-19-will-double-number-people-facing-food-crises-unless-swift-actiontaken\n\n\nhttp://www.the/\n\n\nhttps://www.researchgate.net/journal/0263-5577_Industrial_Management_Data_Systems\n\n\nhttps://www.researchgate.net/journal/0263-5577_Industrial_Management_Data_Systems\n\n\nhttps://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1108%2FIMDS-09-2016-0391\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The \nGuinea Savanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n   ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 June 2019 \nAccepted 02 July 2019 \nAvailable online 31 July 2019 \n\n\n\nABSTRACT\n\n\n\nThis study was conducted in the Northern Region of Ghana to assess the growth performance of two different Hevea \n\n\n\nbrasiliensis clones namely IRCA 41 and GT 1 in the Guinea Savanna soil. The fresh rubber stumps which were used \n\n\n\nfor the experiment were collected from Ghana Rubber Estate Limited (GREL) which is located in the Western \n\n\n\nRegion. The research design employed the use of completely randomized design with thirty replicates each of the \n\n\n\ntwo Hevea brasiliensis species. Data was collected during the dry and rainy seasons. The results at the end of the \n\n\n\nproject for the two Hevea brasiliensis clones showed an average germination rate of 86.66% and 73.30% during \n\n\n\nthe rainy and dry seasons respectively. A mean height of 25.30cm, mean girth of 6.34mm and mean leaf number of \n\n\n\n28 were recorded during the rainy season whiles a mean height of 22.56cm, mean girth of 3.41mm and mean leaf \n\n\n\nnumber of 42 were recorded in the dry season for the two Hevea brasiliensis clones. When T-test was performed \n\n\n\non the two Hevea brasiliensis clones, it was revealed that, there was no significant difference (p>0.05) in height, \n\n\n\ngirth and number of leaves between the two different rubber clones during the rainy season and the dry season. \n\n\n\nHevea brasiliensis therefore has a greater potential for cultivation in Guinea Savanna soil. \n\n\n\nKEYWORDS \n\n\n\nHevea brasiliensis, rubber clones, Irca 41, Gt 1, growth rate of rubber, rubber trees, guinea savanna soil, germination \n\n\n\nrate of rubber \n\n\n\n1. INTRODUCTION \n\n\n\nThe common rubber tree (Hevea brasiliensis), also known as the Par\u00e1 \n\n\n\nrubber tree is an indigenous tree in Amazon, Brazil. It belongs to the \n\n\n\nFamily Euphorbiaceae, Order Malpighiales, Genus Hevea and Species \n\n\n\nbrasiliensis [1]. It is a commercial tree economically grown in plantations \n\n\n\nand a \u201chot\u201d commodity with worldwide consumption increasing at an \n\n\n\naverage rate of 5.8% per year since 1900 [1]. One most important use of \n\n\n\nHevea brasiliensis is the manufacture of jet-aircraft tires and truck tires. It \n\n\n\nis also used in the manufacture of industrial products which range from \n\n\n\nballs, containers and shoes to bands and a lot of other items. About 61 \n\n\n\ndifferent products have been reported to be made from rubber wood [2]. \n\n\n\nThese products are: furniture and furniture parts, parquet, panelling, \n\n\n\nwood-based panels (particleboard, cement and gypsum-bonded panels, \n\n\n\nmedium-density fibreboard, kitchen and novelty items, sawn timber for \n\n\n\ngeneral utility and fuel), among others.   \n\n\n\nHevea brasiliensis is grown mainly for latex production, while its wood is \n\n\n\nconsidered as a secondary product. However, its wood can also increase \n\n\n\nthe total productivity hence resulting in maximum productivity of the \n\n\n\nrubber plant. This is possible because wood selling can shorten the latex \n\n\n\ntapping period, after which trees can either be felled or used for further \n\n\n\ntapping depending on the current prices of latex and wood [3,4]. Recent \n\n\n\nimprovements in wood technology have led to Hevea brasiliensis becoming  \n\n\n\nincreasingly important as a source of wood products [5]. Hevea brasiliensis  \n\n\n\nhas also enjoyed an environmentally friendly reputation as a raw material,  \n\n\n\nbecause it is a by-product of latex production, and when grown in \n\n\n\nrenewable plantations, it can substitute timber from natural forests. Its \n\n\n\ntimber is moderately durable and light creamy in colour, which makes it \n\n\n\nattractive and popular among consumers. Hevea brasiliensis can also be a \n\n\n\nsubstitute for many species, including teak, oak and pine [6]. The role of \n\n\n\nHevea brasiliensis as a carbon sink has often been under-estimated. \n\n\n\nApparently due to its high leaf area index and the extra energy the tree \n\n\n\nrequires to produce latex, it acts as an effective carbon sink [6]. Due to the \n\n\n\nnumerous benefits that are often obtained from Hevea brasiliensis, it has \n\n\n\nbeen referred to as a woody agricultural crop together with oil palm and \n\n\n\ncoconut [7]. \n\n\n\nHevea brasiliensis was first introduced in Asia in 1876, when seeds were \n\n\n\nfirst shipped from the Amazon to the United Kingdom and further to \n\n\n\nCeylon where they were planted [8].  In the following year, rubber trees \n\n\n\nwere planted in Singapore and Malaya [9]. Although it was first an estate \n\n\n\ncrop, local individual farmers soon adopted the crop and so they were \n\n\n\ndrawn into the world commercial economy [10]. The commercial and \n\n\n\nlarge-scale exploitation of the tree did not begin until in the last quarter of \n\n\n\nthe 19th century where the arrival of cars and discovery of pneumatic tyre \n\n\n\nled to an increase in the prices of rubber which resulted in the increased \n\n\n\nproduction of rubber. Production of rubber in the world is mainly found in \n\n\n\ncontinents such as Asia, South America and Africa. It was introduced in \n\n\n\nAfrica early in the 20th century: in Uganda and Nigeria (1903), Congo \n\n\n\n(1904) and Liberia [11]. Countries like Nigeria, Ghana, D.R. Congo, C\u00f4te \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.02.2019.46.55 \n\n\n\n  RESEARCH ARTICLE \n\n\n\nASSESSING THE GROWTH PERFORMANCE OF TWO DIFFERENT HEVEA \nBRASILIENSIS CLONES (IRCA 41 AND GT 1) IN THE GUINEA SAVANNA SOIL IN \nTHE NORTHERN REGION OF GHANA \n\n\n\nDamian Felladam Tangonyire \n\n\n\nDepartment of Agriculture for Social Change, Regentropfen College of Applied Sciences, Kansoe-Bongo, Upper East Region, Ghana. \n\n\n\n*Corresponding Author Email: damian.tangonyire@recas-ghana.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nhttp://en.wikipedia.org/wiki/Family_%28biology%29\n\n\nhttp://en.wikipedia.org/wiki/Euphorbiaceae\n\n\nhttp://simple.wikipedia.org/wiki/Malpighiales\n\n\nmailto:damian.tangonyire@recas-ghana.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The Guinea \nSavanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\nd\u2019Ivoire, Liberia, Gabon and Cameroon are the major producers of rubber \n\n\n\nin the African continent [12].  \n\n\n\n\n\n\n\nHevea brasiliensis was introduced in Ghana in the botanic garden at Aburi \n\n\n\nnear Accra in 1898 [13]. However, rubber tree is continuously grown in \n\n\n\nonly certain parts of the country. It is widely cultivated in the Western \n\n\n\nRegion, with very few plantations found at Asamankese and Okumaning \n\n\n\nboth in the Eastern Region. It has recently been introduced to the Central \n\n\n\nand Ashanti regions by a division known as Rubber Out-growers \n\n\n\nAssociation [13].  Hevea brasiliensis grows best in a climate similar to that \n\n\n\nin its area of origin in the Amazons. The climate of this region is \n\n\n\ncharacterized by heavy rainfall and no distinct dry season. The optimal \n\n\n\nclimatic conditions for the genus Hevea are rainfall of 2000mm or more, \n\n\n\nevenly distributed throughout the year with no severe dry season and with \n\n\n\n125-150mm annual rainy days, maximum temperature of about 29-34 \u00b0C, \n\n\n\nminimum temperature of about 20 \u00b0C and a monthly mean temperature of \n\n\n\n25-28 \u00b0C, high atmospheric humidity of about 80% with moderate wind, \n\n\n\nand bright sunshine for about 2000 hours in a year [14]. Hevea brasiliensis \n\n\n\nis a light demanding tree species and requires moist soil and grows on \n\n\n\nmany soils, with the best options being well drained clayey and deep clay \n\n\n\nsoils. It requires deep soils, relatively stable high temperatures and \n\n\n\ncontinuous moisture throughout the year. An optimal soil pH value for \n\n\n\nhevea brasiliensis tree is at 5-6 [15].    \n\n\n\n\n\n\n\nHowever, the performance of Hevea brasiliensis can be restricted where \n\n\n\nthere is rocky surface, heavy drainage or soil pH values above 6.5 or below \n\n\n\n4 [16]. It has been revealed by researchers that, unfavorable \n\n\n\nenvironmental conditions would more drastically affect the latex yield \n\n\n\nthan the timber production of rubber [17]. In areas where rubber \n\n\n\ncultivation is less favored by environmental conditions, improved farming \n\n\n\nsystems such as agro forestry could be an option for increasing the \n\n\n\neconomical profitability as well as environmental and social benefits of \n\n\n\nrubber cultivation. \n\n\n\n\n\n\n\nStudies done by other researchers revealed that, Hevea brasiliensis reach \n\n\n\nmaturity at the age of about six and would have achieved a girth of about \n\n\n\n1.5m from the ground [18]. Hevea brasiliensis plantations are usually \n\n\n\nestablished using vegetative propagation and often improved planting \n\n\n\nmaterial. Rubber clones perform differently in response to stress from \n\n\n\nexternal factors such as drought [14]. In hevea brasiliensis plantations, the \n\n\n\ntrees are kept smaller, up to 78 feet (24 m) tall, so as to use most of the \n\n\n\navailable carbon dioxide for latex production.  In the wild, the Hevea \n\n\n\nbrasiliensis tree can reach a height of up to 100 feet (30 m). The white or \n\n\n\nyellow latex occurs in latex vessels in the bark, mostly outside the phloem. \n\n\n\nThese vessels spiral up the tree in a right-handed helix which forms an \n\n\n\nangle of about 30 degrees with the horizontal, and can grow as high as \n\n\n\n45 ft.  \n\n\n\n\n\n\n\nDiseases of Hevea brasiliensis are rampant in the nursery than in the field \n\n\n\nwhen they are planted. The major diseases at the nursery are mostly \n\n\n\nfungal diseases and these include; white rot caused by Fomes lignosus, \n\n\n\nbrown rot caused by Fomes noxious and red rot caused by Ganoderma \n\n\n\npseudoferreum. Some of the diseases that affect mature rubber trees \n\n\n\nmostly are known as panel diseases and these include; Mouldy rot, Black \n\n\n\nthread or black stripe disease, among others. Various pests such as \n\n\n\ntermites, caterpillars, mealy bug, aphids and rodents attack Hevea \n\n\n\nbrasiliensis but the most serious plant pest among them is the mistletoe \n\n\n\npest [19].  \n\n\n\n\n\n\n\nHevea brasiliensis has been targeted by several Western African countries \n\n\n\nas an opportunity for rural development and poverty alleviation. In line \n\n\n\nwith this, the Government of Ghana (GoG) in particular adopted a Rubber \n\n\n\nMaster Plan in 2001 with a view to expand the areas under rubber tree \n\n\n\ncultivation from 14,000ha of mainly industrial estates to 50,000ha [19]. \n\n\n\nSuch a significant expansion would only happen if many small private \n\n\n\nfarmers broadly adopt Hevea brasiliensis tree cultivation and grow it on \n\n\n\ntheir own as a business. This will then serve as a tool for economic \n\n\n\ndevelopment to the nation especially in the rural communities where \n\n\n\nthese Hevea brasiliensis plantations will be established such as \n\n\n\ninfrastructure, roads, schools, clinics, among others. \n\n\n\nA study conducted in the plant house on Nyankpala campus to assess the \n\n\n\ninitial growth performance of Hevea brasiliensis using different Guinea \n\n\n\nSavanna soils proved successful [20]. However, the growth performance \n\n\n\nof Hevea brasiliensis in the field has not been studied on Guinea Savanna \n\n\n\nsoils, hence the need for this study. The study aims at determining the \n\n\n\ngermination rate of two different Hevea brasiliensis clones, namely GT \n\n\n\n1and IRCA 41 as well as investigate the growth rate of these rubber clones \n\n\n\nin Guinea Savanna soil. The results from this study would be useful in \n\n\n\ndetermining which Hevea brasiliensis clones would be best suited for \n\n\n\nplanting in Guinea Savanna soils.    \n\n\n\n\n\n\n\n2.  METHODOLOGY \n\n\n\n\n\n\n\n2.1 Profile of the study area \n\n\n\n \nThe project work was carried out on Nyankpala campus of the University \n\n\n\nfor Development Students (UDS) in the Northern Region of Ghana. Field \n\n\n\nwork was carried out inside the mango plantation of the Faculty of \n\n\n\nRenewable Natural Resources from July, 2013 to March, 2014. Nyankpala \n\n\n\ncampus is located in the Tolon District in the Northern Region of Ghana. It \n\n\n\nlies between latitude 09\u030a 25\u2033N, longitude 0\u030a 58\u2033W in the Guinea Savanna \n\n\n\nwoodland ecological zone of Ghana [21]. The area is 183m above sea level. \n\n\n\nNyankpala campus is about 20km to the South-West of Tamale, the capital. \n\n\n\nThe vegetation of the area is Guinea Savanna comprising trees of varying \n\n\n\nsizes and density, dispersed in a ground cover of perennial bush, grasses \n\n\n\nand associated herbs [22]. These trees include shea (Paradoxa vitellaria), \n\n\n\nbaobab (Adansonia digitata), dawadawa (Parkia biglobosa), neem \n\n\n\n(Azadirachta indica), among others. Guinea Savanna woodland has a \n\n\n\nvegetation zone area of 147.9 square. km representing about 62% [23]. \n\n\n\n\n\n\n\n2.2 Climatic conditions and soil types \n\n\n\n\n\n\n\nThe area has a unimodal rainfall pattern which starts from May and ends \n\n\n\nin October. The peak rainfall occurs between June and September with the \n\n\n\ndry season usually running from November to April. Temperatures are \n\n\n\nrelatively constant throughout the year ranging between 25\u030aC and 32.4\u030aC \n\n\n\nwith a mean monthly minimum temperature of 23.1\u030aC and mean monthly \n\n\n\nmaximum temperature of 32.4\u030aC. Similarly, relative humidity figures for \n\n\n\nthe study area show high humidity from May to October with a mean \n\n\n\nmonthly minimum relative humidity of 53% and mean monthly maximum \n\n\n\nrelative humidity of 80% [24].  \n\n\n\n\n\n\n\nThe underlying rock is the Voltain sandstone with lower Birrimians chist. \n\n\n\nThe overlying soil is almost entirely Savanna Ochrosols. The soil is mainly \n\n\n\nloamy which is highly porous and has low moisture holding capacity [24]. \n\n\n\nThe chemical properties of the soil is as follows; pH ranges from 4.5 \u2013 6.7, \n\n\n\nPhosphorus (Mg/Kg soil) = 2.5 \u2013 10.0, total Nitrogen content = 0.02 \u2013 \n\n\n\n0.05%, Calcium Chlorine (Cacl\u2082) = 5.6, Organic carbon is 0.43% and \n\n\n\nOrganic matter carbon is 0.74% [22]. \n\n\n\n\n\n\n\n2.3 Land preparation  \n\n\n\n\n\n\n\nLand preparation is very important in the establishment of a good stand. \n\n\n\nIt is the only way to prevent the Hevea brasiliensis trees from attack by \n\n\n\ntermites and the deadly disease called fomes. Land preparation was done \n\n\n\nusing hoes and cutlasses.  The land was first of all measured and \n\n\n\ndemarcated, an area of 875m\u00b2 (25m\u00d735m) was used. Stumps within the \n\n\n\narea were uprooted and all dead wood were also collected. The vegetation \n\n\n\nwas sprayed, weeded, piled together and burnt. The spraying was done on \n\n\n\nthe 19th of July, 2013, after which weeding of the land followed three days \n\n\n\nlater. Weeding is an important weed control method practiced in many \n\n\n\ncrops. The removal of weeds is useful because they compete with the tree \n\n\n\ncrop for space, water and nutrients, increase transpiration and block \n\n\n\ncirculation of air.  \n\n\n\n\n\n\n\n2.4 Lining and pegging \n\n\n\n\n\n\n\nAfter the land was cleared and prepared, it was then pegged using garden \n\n\n\nlines, ranging poles, and pegs. The ranging poles were used to get a straight \n\n\n\nline. It is very important for the Hevea brasiliensis clones to be planted in \n\n\n\n\nhttp://en.wikipedia.org/wiki/Carbon_dioxide\n\n\nhttp://en.wikipedia.org/wiki/Bark\n\n\nhttp://en.wikipedia.org/wiki/Phloem\n\n\nhttp://en.wikipedia.org/wiki/Helix\n\n\nhttp://en.wikipedia.org/wiki/Angle\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The Guinea \nSavanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\nstraight lines for easy passage and proper functioning. The planting \n\n\n\ndistance was 3m within rows and 6m between rows.  \n\n\n\n\n\n\n\n2.5 Holing and labeling \n\n\n\n\n\n\n\nThe holes were dug using earth chisels (diggers) and cutlasses. A total of \n\n\n\n60 holes were obtained from an area of 875m\u00b2. The holes which were dug \n\n\n\nmeasured 40 centimeters from one end to the other (diameter) and 60 \n\n\n\ncentimeters deep. These dimensions of the hole were necessary to aid in \n\n\n\neasy planting and also enhance sprouting of roots into the soil. The topsoil \n\n\n\nwas separated from the sub soil and was later used to refill the hole after \n\n\n\nplanting. The digging of holes was done on the 27th of July 2013. After the \n\n\n\ndigging of holes was done, the total land area was divided into ten equal \n\n\n\nplots. Each plot contained six holes each measuring 6m\u00d76m. The planting \n\n\n\nmaterial was then planted according to the plots created. \n\n\n\n\n\n\n\n2.6 Planting and termiticide application \n\n\n\n\n\n\n\nPlanting of the Hevea brasiliensis clones was done on September 3, 2013 \n\n\n\nafter the digging of holes was completed. It was done very early in the \n\n\n\nmorning. Allocation of the planting materials to the plots created was done \n\n\n\nthrough the process of randomization. The planting material (Hevea \n\n\n\nbrasiliensis clones) was placed in the hole with the scion (eye of the bud) \n\n\n\nfacing North. This was done to prevent the scion from facing the sun \n\n\n\ndirectly which could inhibit survival. The topsoil was used to refill the hole \n\n\n\nafter planting the clones. Firming was done gently with a planting stick and \n\n\n\nstamping with the feet. This was done in order to make the plant firmer in \n\n\n\nthe ground. After this, a hoe was used to gather more earth soil around the \n\n\n\nplanting material to the level of the root collar. Re-firming was done after \n\n\n\nplanting. Re-firming is the process of making the plant firm in the ground. \n\n\n\nAt the same period of re-firming, a basin was created around each Hevea \n\n\n\nbrasiliensis clone with a hoe with the aim of trapping water for the plant. \n\n\n\nImmediately after planting the rubber clones, grounded camphor was \n\n\n\napplied around the base of the rubber clones. This was done in order to \n\n\n\nprevent the rubber clones from termites attack. \n\n\n\n\n\n\n\n2.7 Pruning and mulching \n\n\n\n\n\n\n\nThis was done in the course of sprouting. The purpose of pruning was to \n\n\n\nget rid of undesirable side shoots. The only shoot that was required was \n\n\n\nthat from the bud (eye bud). After planting, all other shoots which appear \n\n\n\nlooking darker than the desirable one was pruned to allow food reserves \n\n\n\nto be given to the scion alone. This reduced competition between the \n\n\n\ndesirable and undesirable shoots and enhanced the growth of the \n\n\n\ndesirable shoot. Also, all side shoots and branches of the scion were also \n\n\n\npruned to encourage terminal growth. All branches of the clone within 2m \n\n\n\nfrom the ground were cut. In the case of double sprouting, the weaker one \n\n\n\nwas pruned leaving the healthier one. Pruning is very good because it \n\n\n\nhelps keep the plant shorter and help the plant to branch out making a \n\n\n\nmore esthetic plant.  \n\n\n\n\n\n\n\nMulching was also carried out after pruning. A researcher defined \n\n\n\nmulching as a crop husbandry practice in which organic material is spread \n\n\n\nover the topsoil to influence the physical, chemical and biological \n\n\n\nproperties of the soil and its micro-climate with the aim of improving the \n\n\n\nproductivity of a site [25]. It is a form of soil conservation aimed at \n\n\n\nmaintaining, protecting and improving the soil for agricultural purposes. \n\n\n\nPlant productivity depends on the topsoil, where plant nutrients are \n\n\n\nconcentrated. As this rich layer is at the top of the soil surface, plant \n\n\n\nnutrients can easily be lost, removed or damaged by various natural \n\n\n\nprocesses. Local materials such as dried weeds were used as mulch for the \n\n\n\nproject. The objectives of mulching were to maintain and improve soil \n\n\n\nstructure, maintain organic material content in the soil, utilize available \n\n\n\nsoil water effectively, maintain soil fertility by reducing nutrient loss and \n\n\n\nto replace those that are lost and reduce erosion. Mulching also induces \n\n\n\nlower soil temperature and higher soil moisture retention and increases \n\n\n\nroot density [26]. \n\n\n\n\n\n\n\n   \n \n                                                                 Plate 1: Mulching of IRCA 41                                                               Plate 2:  Mulching of GT 1 \n \n\n\n\n2.8 History of the two Hevea brasiliensis clones used for the project \n\n\n\n \n2.8.1 GT 1 rubber (Hevea brasiliensis) clone \n\n\n\n\n\n\n\nGT 1 rubber clone originated from Malaysia. It is an approved cultivar \n\n\n\nwhich is classified under category II of the rubber board categories. \n\n\n\nCategory II consists of clones with consistent performance over a long \n\n\n\nterm in any one of the evaluation stages. It is recommended that these \n\n\n\nclones be used to plant up to 50% of the total area of any estate.  It has \n\n\n\nvariable branching habit, upright but slightly twisted. The main branches \n\n\n\nare long and acute angled, secondary branches are light. GT 1 has narrow \n\n\n\nglobular crown and dense dark green glossy foliage. Wintering and \n\n\n\nrefoliation is late and often partial, occurrence of tapping panel dryness \n\n\n\nand incidence of pink disease are mild, abnormal leaf fall is mild to medium \n\n\n\nand powdery mildew is medium to severe and requires fairly wind fast. \n\n\n\nThis clone shows rising yield trend with summer yield fairly high. The \n\n\n\nlatex gotten from GT 1 is usually white in colour. Clones RRIM 600, GT l, \n\n\n\nRRII 5, RRII 203, PB 28/59 PB 217, PB 312, PB 314, PB 255 and PB 280 are \n\n\n\nother clones in this category II [19].  \n\n\n\n\n\n\n\n2.8.2 IRCA 41 rubber (Hevea brasiliensis) clone \n\n\n\n\n\n\n\nIRCA 41 originated from C\u00f4te d'Ivoire from the experimental plantations \n\n\n\nof CNRA-Bimbresso research station, situated in Angu\u00e9d\u00e9dou, in \n\n\n\nSoutheast of C\u00f4te d\u2019Ivoire. It gives high yield in terms of productivity. IRCA \n\n\n\n41 is an approved cultivar which is classified under category III of the \n\n\n\nrubber board categories. This category III consists of clones on which \n\n\n\nthere is only limited data from experimental planting. These clones are \n\n\n\nrecommended for only small-scale experimental planting not to exceed \n\n\n\n15% of the total area in aggregate.  These clones have exhibited good \n\n\n\nperformance over a long period in small scale trials and/or over a short \n\n\n\nterm in large scale trials in India or abroad. Clones  RRII 50,  RRII 51,  RRII  \n\n\n\n52, RRII 118,  RRII 176,  RRII 208,  RRII  300, RRII 429, PR 107, PR  255, PR   \n\n\n\n261, PB  86, PB  5/51, PB 235,  PB 311, PB   330,  RRIM  605, RRIM 701,  \n\n\n\nRRIM  703, RRIM 712,  RRIC 100,  RRIC  102, RRIC 130, KRS 163, IRCA 111, \n\n\n\nIRCA    130,  SCATC 88-13, SCATC  93-114, Haiken 1, BPM    24   and \n\n\n\nPolyclonal seeds are all under this category [19]. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The Guinea \nSavanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\n2.8.3 Study approach and design \n\n\n\n \nThe experimental design was a randomized complete block with thirty \n\n\n\nreplicates, using two different Hevea brasiliensis tree species (IRCA 41 and \n\n\n\nGT 1), six Hevea brasiliensis clones were allocated per plot.  \n\n\n\n\n\n\n\n2.8.4 Field data collection and analysis \n\n\n\n\n\n\n\n Data was collected everyone week and was based on parameters such as \n\n\n\ndays of shoot emergence, germination percentage, stem diameter, shoot \n\n\n\nlength/height, shoot girth, number of leaves and basal area. The data \n\n\n\nwhich was collected was analyzed using Statistical Package for Social \n\n\n\nSciences at the end of the project period. The means were computed for \n\n\n\nthe two different clones and the differences between them tested using \n\n\n\nstudent\u2019s t test. The results were then represented in tables and figures. \n\n\n\n\n\n\n\n3.  RESULTS \n\n\n\n \n3.1 Germination Rate \n\n\n\n \nThe first evidence of germination is the emergence of an eye bud from the \n\n\n\nrubber clones. Data was taken for both the rainy and the dry seasons. The \n\n\n\ngermination rate of both IRCA 41 and GT1 rubber clones after planting \n\n\n\nduring the rainy and dry seasons is shown in figure 1. Week 4, week 8 and \n\n\n\nweek 12 constituted the rainy season while week 16, week 20 and week \n\n\n\n24 constituted the dry season. T test performed showed that, there was no \n\n\n\nsignificant difference (p>0.05) between the survival rate of IRCA 41 and \n\n\n\nGT 1 at the end of the rainy and dry seasons.  \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Germination rate of IRCA 41 and GT 1 rubber plants \n \n\n\n\nAt the end of the rainy season, a total survival rate of 86.66% representing \n\n\n\n52 Hevea brasiliensis plants and a mortality rate of 13.34% representing 8 \n\n\n\nHevea brasiliensis plants respectively were recorded. (Table 1)  \n\n\n\n\n\n\n\n \nTable 1: Survival rate of IRCA 41 and GT 1 Hevea brasiliensis clones during the rainy season. \n\n\n\n \nClone type Percentage (%)  survival Percentage (%)  mortality \n\n\n\nIRCA 41 43.33 6.67 \n\n\n\nGT 1 43.33 6.67 \n\n\n\nTotal 86.66 13.34 \n\n\n\n\n\n\n\nAlso, at the end of the dry season, a total survival rate of 73.30% and a \n\n\n\nmortality rate of 26.70% representing 44 and 16 Hevea brasiliensis plants \n\n\n\nrespectively were recorded. (Table 2) \n\n\n\n\n\n\n\n \nTable 2: Survival rate of IRCA 41 and GT 1Hevea brasiliensis clones at the end of the dry season \n\n\n\n \nClone type Percentage (%) survival Percentage (%) mortality \n\n\n\nIRCA 41 33.30 16.70 \n\n\n\nGT 1 40.00 10.00 \n\n\n\nTotal 73.30 26.70 \n\n\n\n\n\n\n\n3.2 Growth Performance Of The Rubber (Hevea Brasiliensis) Clones \n\n\n\n \n3.2.1 Height of plants \n\n\n\n\n\n\n\nData was taken on the height of both IRCA 41 and GT 1 during the rainy \n\n\n\nseason. The mean heights of both rubber clones increased gradually after \n\n\n\nplanting from week 4 to week 12. GT 1 had a higher mean height than IRCA \n\n\n\n41 only in week 4. IRCA 41 however, had a higher mean height than GT1 \n\n\n\nin week 8 and week 12 as shown in figure 2. There was no significant \n\n\n\ndifference (p>0.05) in height between GT 1 and IRCA 41 at the end of the \n\n\n\nrainy  \n\n\n\n20\n\n\n\n25 26 26 25\n\n\n\n20\n\n\n\n23\n\n\n\n26 26 26\n24\n\n\n\n24\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\nweek4 week 8 week 12 week 16 week 20 week 24\n\n\n\nN\nu\n\n\n\nm\nb\n\n\n\ner\n o\n\n\n\nf \ng\ner\n\n\n\nm\nin\n\n\n\na\nte\n\n\n\nd\n p\n\n\n\nla\nn\n\n\n\nts\n\n\n\nweeks after planting\n\n\n\nGt 1\n\n\n\nIrca 41\n\n\n\nclones\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The \nGuinea Savanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Change in growth of height of IRCA 41 and GT 1 during the rainy season. \n \n\n\n\nIn the dry season, there was also an increase in the mean height of both \n\n\n\nspecies from week 16 to week 24.  IRCA 41 however, recorded the higher \n\n\n\nmean height in all the weeks than GT 1. Figure 3 shows the mean heights \n\n\n\nof the two rubber clones during the dry season. Also, there was no \n\n\n\nsignificant difference (p>0.05) in height between GT 1 and IRCA 41 Hevea \n\n\n\nbrasiliensis clones at the end of the dry season.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Change in the growth of height of GT 1 and IRCA 41 during the dry season. \n \n\n\n\nHowever, at the end of the project, Irca 41 had a higher mean height both in the rainy and the dry seasons than Gt 1 as illustrated in figure 4.   \n\n\n\n\n\n\n\n \nFigure 4: Change in the growth of height of IRCA 41 and GT 1 within each season \n\n\n\n11.8\n\n\n\n26.12\n30.15\n\n\n\n7.06\n\n\n\n37.09\n39.59\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\nweek 4 week 8 week 12\n\n\n\nm\nea\n\n\n\nn\n h\n\n\n\nei\ng\n\n\n\nh\nt \n\n\n\n(c\nm\n\n\n\n)\n\n\n\nweeks after planting\n\n\n\nGt 1\n\n\n\nIrca 41\n\n\n\nclones\n\n\n\n5.45\n\n\n\n18.68\n\n\n\n38.42\n\n\n\n6.43\n\n\n\n27.71\n\n\n\n38.67\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\nweek 16 week 20 week 24\n\n\n\nm\nea\n\n\n\nn\n h\n\n\n\nei\ng\nh\n\n\n\nt(\ncm\n\n\n\n)\n\n\n\nweeks after planting\n\n\n\nGt 1\nIrca 41\n\n\n\nclones\n\n\n\n22.69 20.8527.91 24.27\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\nRainy Dry\n\n\n\nm\nea\n\n\n\nn\n h\n\n\n\nei\ng\n\n\n\nh\nt \n\n\n\n(c\nm\n\n\n\n)\n\n\n\nweeks after planting\n\n\n\nGt 1\nIrca 41\n\n\n\nclones\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The \nGuinea Savanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\n3.2.2 Girth of Hevea brasiliensis plants \n\n\n\n\n\n\n\nAt the end of the rainy season, week 12 recorded the highest mean girth \n\n\n\nwith week 4 having the least mean girth. IRCA 41 had a higher mean girth \n\n\n\nthan GT 1 in week 8 and week 12. GT 1 however, had a higher mean girth \n\n\n\nthan IRCA 41 only in week 4. Figure 5 shows the mean girths of both IRCA \n\n\n\n41 and GT 1 during the rainy season. \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Change in the girth growth of GT 1 and IRCA 41 during the rainy season \n \n\n\n\nBoth IRCA 41 and GT 1 recorded an increase in mean girth from week 16 to week 24 in the dry season. IRCA 41 had a higher mean girth in all the weeks \n\n\n\nthan GT 1. (Figure 6) \n\n\n\n\n\n\n\n \nFigure 6: Change in the girth growth of GT 1 and IRCA 41 during the dry season \n\n\n\n\n\n\n\n\n\n\n\nThere was no significant difference (p>0.05) in girth between GT 1 and \n\n\n\nIRCA 41 in the rainy and dry seasons. IRCA 41 had a higher mean girth \n\n\n\nthan GT 1 during the rainy and dry seasons. The mean girths of both IRCA \n\n\n\n41 and GT 1 for both the rainy and dry seasons are shown in figure 7. \n\n\n\n\n\n\n\n5.67\n\n\n\n6.19\n\n\n\n6.71\n\n\n\n5.15\n\n\n\n7.01\n7.27\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\nweek 4 week 8 week 12\n\n\n\nm\nea\n\n\n\nn\n g\n\n\n\nir\nth\n\n\n\n (\nm\n\n\n\nm\n)\n\n\n\nweeks after planting\n\n\n\nGt 1\n\n\n\nIrca 41\n\n\n\nclones\n\n\n\n1.28\n\n\n\n3.14\n\n\n\n4.25\n\n\n\n1.76\n\n\n\n4.4\n\n\n\n5.62\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\nweek 16 week 20 week 24\n\n\n\nm\nea\n\n\n\nn\n g\n\n\n\nir\nth\n\n\n\n (\nm\n\n\n\nm\n)\n\n\n\nweeks after planting\n\n\n\nGt 1\n\n\n\nIrca 41\n\n\n\nclones\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The \nGuinea Savanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\n \nFigure 7: Change in girth of IRCA 41 and GT 1 within each season \n\n\n\n \n3.2.3 Number of leaves of Hevea brasiliensis plants \n\n\n\n\n\n\n\nBoth IRCA 41 and GT 1 recorded increase in the mean number of leaves \n\n\n\nduring the rainy season. The mean leaves increased gradually from week  \n\n\n\n\n\n\n\n4 to week 12 with week 12 having the highest mean number of leaves and \n\n\n\nweek 4 having the least mean number of leaves seen see in figure 8. \n\n\n\n\n\n\n\nFigure 8: Change in the mean number of leaves of GT 1 and IRCA 41 during the rainy season \n \n\n\n\nSimilarly, there was an increase in the mean number of leaves during the \n\n\n\ndry season. The mean number of leaves increased from week 16 to week \n\n\n\n24. Week 24 had the highest mean number of leaves while week 16 \n\n\n\nrecorded the least mean number of leaves. IRCA 41 had a higher mean \n\n\n\nnumber of leaves throughout the dry season than GT 1 (figure 9). T test \n\n\n\nshowed no significant significance (p>0.05) in the mean number of leaves \n\n\n\nbetween IRCA 41 and GT 1 in the rainy and dry season respectively.  \n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Change in the mean number of leaves of GT 1 and IRCA 41 during the dry season \n\n\n\n6.19\n\n\n\n2.89\n\n\n\n6.48\n\n\n\n3.92\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\nRainy Dry\n\n\n\nm\nea\n\n\n\nn\n g\n\n\n\nir\nth\n\n\n\n (\nm\n\n\n\nm\n)\n\n\n\nweeks after planting\n\n\n\nGt 1\n\n\n\nIrca 41\n\n\n\nclones\n\n\n\n22\n\n\n\n28 28\n\n\n\n18\n\n\n\n32\n\n\n\n38\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\nweek 4 week 8 week 12\n\n\n\nm\nea\n\n\n\nn\n n\n\n\n\nu\nm\n\n\n\nb\ner\n\n\n\n o\nf \n\n\n\nle\na\nv\nes\n\n\n\nweeks after planting\n\n\n\nGt 1\n\n\n\nIrca 41\n\n\n\nclones\n\n\n\n7\n\n\n\n42\n\n\n\n74\n\n\n\n3\n\n\n\n42\n\n\n\n81\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\nweek 16 week 20 week 24\n\n\n\nm\nea\n\n\n\nn\n n\n\n\n\nu\nm\n\n\n\nb\ner\n\n\n\n o\nf \n\n\n\nle\na\n\n\n\nv\nes\n\n\n\nweeks after planting\n\n\n\nGt 1\nIrca 41\n\n\n\nclones\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The \nGuinea Savanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\nAt the end of the project, IRCA 41 recorded a higher mean number of leaves during both the rainy and dry seasons than GT 1. Figure 10 shows the mean \n\n\n\nnumber of leaves of both IRCA 41 and GT 1 during the rainy and dry seasons.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 10: Change in the mean number of leaves of IRCA 41 and GT 1 within each season \n \n\n\n\n4.  DISCUSSION \n\n\n\n \n4.1 Germination Rate  \n\n\n\n \nThe first evidence of germination is the emergence of an eye bud from the \n\n\n\nHevea brasiliensis clones. From Figure 1, germination increased from \n\n\n\nweek 4 to week 12 after which mortality set in. At the end of the raining \n\n\n\nseason, the results revealed, a total survival rate of 86.66%.  Both IRCA 41 \n\n\n\nand GT 1 rubber clones had the same survival rate. The highest survival \n\n\n\nrate in the rainy season was however, recorded in week 12 where both \n\n\n\nIRCA 41 and GT 1 rubber clones recorded 26 plants each. There was no \n\n\n\nsignificant difference (p>0.05) between Gt 1 and Irca 41 in their survival \n\n\n\nrates at the end of the rainy season. Also, at the end of the dry season, a \n\n\n\ntotal survival rate of 73.30% was recorded. Between the two Hevea \n\n\n\nbrasiliensis clones, GT 1 had a higher survival rate than IRCA 41. Statistical \n\n\n\nanalysis showed that, there was no significant difference (p>0.05) \n\n\n\nbetween the survival rates of the two rubber clones at the end of the dry \n\n\n\nseason. This indicates that, both GT 1 and IRCA 41 can be used in Guinea \n\n\n\nSavanna soil since they both recorded high germination rates. This high \n\n\n\nsurvival rate of the two Hevea brasiliensis clones agrees with the work of \n\n\n\nLemmens [15] that rubber can grow on many soils. \n\n\n\n\n\n\n\nThe high survival rate at the end of the project also agrees with the work \n\n\n\nof one researcher who carried out his work in the plant house on \n\n\n\nNyankpala campus and observed a survival percentage of about 75% for \n\n\n\nthe rubber plants [20]. This high survival rate can also be attributed to the \n\n\n\nfresh state of stumps collected and planted on the same day. The fresh \n\n\n\nstumps may possess the required amount of moisture needed for \n\n\n\ngermination. A recommendation by one research, was that, for high \n\n\n\npercentage of germination, Hevea brasiliensis clones should be planted as \n\n\n\nsoon as possible preferably within the first week [27]. Other researchers \n\n\n\n[28] also reported that, with fresh stumps, the percentage germination can \n\n\n\nbe about 70%.  \n\n\n\n\n\n\n\nThe high survival rate of the Hevea brasiliensis clones can also be \n\n\n\nattributed to the type of soil at the site as well as suitable climatic \n\n\n\nconditions such as adequate light, humidity and temperature. Wide \n\n\n\nspacing may have reduced competition for nutrients, moisture, and light. \n\n\n\nAlso line clearing around plants may have reduced weed invasion, water \n\n\n\ntranspiration, diseases, among others facilitating proper infiltration and \n\n\n\nthe growth of the plants. In general, suitable conditions for growth were \n\n\n\nmore favourable in the rainy season than in the dry season explaining why \n\n\n\nthere was a higher survival rate in the rainy season than in the dry season. \n\n\n\nThis confirms the studies done by some researchers who suggested that, \n\n\n\nwater assists in the germination and development of plants and also lack \n\n\n\nof water decreases the intensity of photosynthesis which results in \n\n\n\nmortality [18].  \n\n\n\n\n\n\n\n4.2 Mortality Rate \n\n\n\n\n\n\n\nResults of mortality rates for both the rainy season and the dry season \n\n\n\nwere 13.34 % and 26.70% respectively. This shows that mortality rate was \n\n\n\nhigher in the dry season than in the rainy season. Mortality rate was the \n\n\n\nsame for both IRCA 41 and GT 1 during the rainy season. However, IRCA \n\n\n\n41 recorded a higher mortality rate in the dry season than GT 1. Low water \n\n\n\navailability during the dry season can be attributed to the higher death \n\n\n\nrate of the rubber plants. The humidity at that time was low and the \n\n\n\ntemperature very high leading to high transpiration. This resulted in water \n\n\n\ndeficiency and reduction in the amount of photosynthates produced to \n\n\n\nsupport their growth. This confirms the findings of one researcher who \n\n\n\nrevealed that, water assists in the germination and development of plants \n\n\n\nand also lack of water decreases the intensity of photosynthesis which \n\n\n\nresults in mortality [18].   \n\n\n\n\n\n\n\nThe high mortality rate can also be attributed to termite infestation as \n\n\n\ntermite moulds were seen on the dead Hevea brasiliensis plants. The roots \n\n\n\nof dead trees also contained termites when they were pulled out. Termites \n\n\n\nwere found to be more devastating in the rainy season since the trees were \n\n\n\nyoung and still struggling to establish their roots as compared to the dry \n\n\n\nseason. Browsing by animals was also attributed to the death of the Hevea \n\n\n\nbrasiliensis plants. Animals that escaped into the site fed on the palatable \n\n\n\nleaves and branches. Some of the plants therefore found it difficult to \n\n\n\nsurvive at that time of the dry season where suitable conditions were less \n\n\n\nfavourable. IRCA 41 however, was severely affected by these mortality \n\n\n\nfactors accounting for its low survival rate at the end of the project in the \n\n\n\nGuinea Savanna soil. \n\n\n\n\n\n\n\n4.3 Assessment Of The Growth Performance Of The Rubber (Hevea \n\n\n\nBrasiliensis) Clones \n\n\n\n \n4.3.1 Plant height development \n\n\n\n\n\n\n\nThe results revealed that there was an increase in height of both Hevea \n\n\n\nbrasiliensis plants from week 4 to week 24 for both the rainy and dry \n\n\n\nseasons. IRCA 41 however, had a higher mean height both in the rainy and \n\n\n\ndry seasons than GT 1. The rainy season had a mean height of 25.30cm \n\n\n\n26\n\n\n\n41\n\n\n\n29\n\n\n\n42\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\nRainy Dry\n\n\n\nm\nea\n\n\n\nn\n n\n\n\n\nu\nm\n\n\n\nb\ner\n\n\n\n o\nf \n\n\n\nle\na\nv\nes\n\n\n\nweeks after planting\n\n\n\nGt 1\nIrca 41\n\n\n\nclones\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The Guinea \nSavanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\nwhiles the dry season recorded a mean height of 22.56cm. This shows that, \n\n\n\nthe rainy season performed better in height than the dry season. GT 1 \n\n\n\nrecorded a higher height than IRCA 41 only in week 4. This finding agrees \n\n\n\nwith the work of Addo- Quaye et al. [18], that rubber grows 25-30m tall in \n\n\n\nits natural distribution area. Between IRCA 41 and GT 1, there was no \n\n\n\nsignificant difference (p>0.05) during the rainy and dry seasons. There \n\n\n\nwas, however, significant difference in height of IRCA 41 between the rainy \n\n\n\nand dry seasons. GT 1 also had significant difference in height between the \n\n\n\nrainy and dry seasons. The difference in height between IRCA 41 and GT 1 \n\n\n\nin the rainy and dry seasons can be attributed to soil conditions such as \n\n\n\nsoil moisture. According to one researcher, the growth and latex yield of \n\n\n\nrubber trees are affected in different ways by soil moisture [17]. High \n\n\n\nmoisture was prevalent in the rainy season whereas the dry season had \n\n\n\nlower moisture content. From the findings of Rao and Vijayakumar [14], \n\n\n\nHevea brasiliensis clones perform differently in response to stress from \n\n\n\nexternal factors such as drought. Another researcher also argued that, in \n\n\n\nfavourable soils, rubber trees could tolerate a dry season of four to five \n\n\n\nmonths, during which less than 100mm of rain is received and within this \n\n\n\nperiod, two to three months with rainfall less than 50mm [29].  \n\n\n\n\n\n\n\nStudies carried out by prominent researchers revealed that, 74% of the \n\n\n\ndry matter of rubber plant is found in their roots indicating that shoot \n\n\n\ndevelopment is suppressed in the early stage of rubber stump growth [28]. \n\n\n\nThe findings from these researchers therefore can be attributed to the \n\n\n\ndifference in mean height between IRCA 41 and GT 1 in the rainy and dry \n\n\n\nseasons. The difference in the height of the rubber plants between the \n\n\n\nrainy and dry seasons can also be attributed to the mulch applied. The \n\n\n\nmulch was fully decomposed in the dry season and supported plants \n\n\n\ngrowth by improving soil structure, maintaining organic material content \n\n\n\nin the soil, utilizing available soil water effectively, maintaining soil \n\n\n\nfertility by reducing nutrient loss as well as replacing those that are lost \n\n\n\nand reducing erosion. According to one researcher, mulching also induces \n\n\n\nlower soil temperature, higher soil moisture retention and increases root \n\n\n\ndensity, hence resulting in an increase in the growth of the Hevea \n\n\n\nbrasiliensis plants [26]. \n\n\n\n\n\n\n\n4.3.2 Girth development \n\n\n\n\n\n\n\nThe results revealed that, IRCA 41 had a higher mean girth in the rainy \n\n\n\nseason than GT 1. Mean girth of IRCA 41 increased gradually from week 4 \n\n\n\nto week 12 in the rainy season. In the dry season, IRCA 41 again had a \n\n\n\nhigher mean girth than GT 1. The rainy season had a mean girth of 6.34mm \n\n\n\nwhereas the dry season recorded a mean girth of 3.41mm. Between IRCA \n\n\n\n41 and GT 1, there was no significant difference (p>0.05) in girth during \n\n\n\nthe rainy and dry seasons There was however, significant difference in \n\n\n\ngirth for IRCA 41 between the rainy and dry seasons. Significant difference \n\n\n\nin girth for GT1 between the rainy and dry seasons was also observed. \n\n\n\nThese differences can be attributed to the type of soil at the site as well as \n\n\n\nsuitable climatic conditions such as adequate light, humidity and \n\n\n\ntemperature. Wide spacing may have reduced competition for nutrients, \n\n\n\nmoisture, and light. Also line clearing around plants may have reduced \n\n\n\nweed invasion, water transpiration, diseases, among others facilitating \n\n\n\nproper infiltration and the growth of the plants. \n\n\n\n\n\n\n\nIn general, suitable conditions for girth growth of IRCA 41 and GT 1 were \n\n\n\nmore favourable in the rainy season than in the dry season and this \n\n\n\naccounts for a higher girth in the rainy season than in the dry season. This \n\n\n\nconfirms the findings of one researcher who observed that Savanna soil \n\n\n\nwithout NPK recorded a girth of 5.94mm when he conducted his studies \n\n\n\nin the plant house on Nyankpala campus [20].This also confirms the \n\n\n\nfindings by Addo- Quaye et al.[would have 18], that rubber plants reach \n\n\n\nmaturity at the age of about six years and achieved a girth of 1.5m from \n\n\n\nthe ground.  \n\n\n\n\n\n\n\n4.3.3 Leaves development \n\n\n\n\n\n\n\nThe results revealed that, there was an increase in the mean number of \n\n\n\nleaves from week 4 to week 20 during the rainy and dry seasons. The mean \n\n\n\nnumber of leaves for the rainy season was 28 whiles the dry season \n\n\n\nrecorded a mean leaf number of 42 with IRCA 41 recording a higher mean \n\n\n\nnumber of leaves in both seasons. Between IRCA 41 and GT 1, there was \n\n\n\nno significant difference (p>0.05) in the mean number of leaves during the \n\n\n\nrainy and dry seasons. There was however, significant difference (p<0.05) \n\n\n\nin the mean number of leaves for IRCA 41 between the rainy and dry \n\n\n\nseasons. GT 1 also had significant difference (p<0.05) in the mean number \n\n\n\nof leaves between the rainy and dry seasons. The difference in the number \n\n\n\nof leaves between the dry and rainy seasons can be attributed to the rapid \n\n\n\nstem development. The low number of leaves during the rainy season is \n\n\n\ndue to the inability of the plants to develop leaves immediately after \n\n\n\ngermination. The plants have to grow to a certain height before they start \n\n\n\nproducing leaves. As the height increased, the number of leaves increased. \n\n\n\nOne researcher revealed that, when Hevea brasiliensis clone germinates, \n\n\n\nit sends down a long tap root before producing leaves [30]. \n\n\n\n\n\n\n\nSoil moisture absorption by the two rubber clones can be attributed to the \n\n\n\ndifference in the number of leaves between IRCA 41 and GT 1 during the \n\n\n\nrainy and dry seasons. Soil moisture equally influenced the rate of plant \n\n\n\nnutrient absorption for leaf development. The higher the soil moisture \n\n\n\nabsorbed by a plant, the higher the number of leaves produced. This \n\n\n\nconfirms the assertion by a researcher who observed that the number of \n\n\n\nleaves produced by a plant is directly proportional to the photosynthates \n\n\n\nproduced [31]. Also, the high leaf area index of the Hevea brasiliensis plants \n\n\n\nand the extra energy the trees require to produce latex makes them \n\n\n\neffective carbon sinks [6]. Again, difference in the mean number of leaves \n\n\n\nof IRCA 41 and GT 1 during the rainy and dry seasons can be attributed to \n\n\n\ntheir leaf shedding ability. Several researchers observed that, rubber trees \n\n\n\n(Hevea brasiliensis) shed their leaves annually but the timing and intensity \n\n\n\nof leaf shedding depends on climatic conditions and varies between \n\n\n\ndifferent clones [15].   \n\n\n\n\n\n\n\n5.  CONCLUSION AND RECOMMENDATION  \n\n\n\n \nThe results obtained at the end of the project revealed that, both IRCA 41 \n\n\n\nand GT 1 rubber clones have a higher survival rate in Guinea Savanna soil. \n\n\n\nTheir ability for latex production could not be assessed since rubber tree \n\n\n\ntakes about six years to reach maturity. Hevea brasiliensis therefore has a \n\n\n\ngreater potential for cultivation in Guinea Savanna soil.  \n\n\n\n\n\n\n\nThe death of the Hevea brasiliensis plants during the project can be \n\n\n\nattributed to termite infestation as termite moulds were seen on the dead \n\n\n\nrubber plants. Also, browsing by animals and low water availability during \n\n\n\nthe dry season can be attributed to the death of the Hevea clones. The \n\n\n\nstudy therefore recommends that, more work should be carried on the \n\n\n\nrubber plants up to the tapping stage to assess its latex producing ability. \n\n\n\nFurther studies should also be conducted to determine the growth \n\n\n\nperformance of Hevea brasiliensis clones on Guinea Savanna soil using \n\n\n\ndifferent fertilizer treatments.  \n\n\n\n\n\n\n\nREFERENCES  \n\n\n\n \n[1] Rubber Board. 2005. Rubber Growers Companion. Government of \nIndia, Kottayam, Kerala, India, 115.  \n \n[2]  Salleh, M.N. 1984. Heveawood - timber of the future. The Planter, \n60(702), 370-381.  \n \n[3] Arshad, N.L., Mohamad Hassan, M.J., Ibrahim, A.G., Mahayana Bongkik, \nD.Y.M., Wahab, M.A. 1997. Viability of Hevea plantation for wood \nproduction. Pp 41-57. In: Yahya, A.Z., Ghani, A.R.A., Mahat, M.N., Wahab, \nM.A., Wickneswari, R. and Mahmood, N.Z.N (Editors) Proceedings of the \nSeminar on Commercial Cultivation of Teak, Sentang, Acacia and Hevea for \nTimber. Proceedings of the Seminar 9.1.1997, Kuala Lumpur. Forest \nResearch Institute Malaysia, Kuala Lumpur. 62p ISBN 938-9592-88-2. \n \n[4] Cl\u00e9ment-Demange, A. 2004. Rubber: Wood, Cropping and Research- \nRubberwood and biomass: adaptation of rubber cropping and rubber \nresearch in South-East Asia. Regional workshop for the building of a \nrubberwood research project, November 12.-14.2003. Scientific and \nTechnical report. 23 p.  \n[5]  Evans, J., Turnbull, J.W. 2004. Plantation Forestry in the Tropics. 3rd \nEdition. Oxford University Press, Oxford. 467 p. ISBN 0-19-852994-5 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 46-55 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Assessing The Growth Performance Of Two Different Hevea Brasiliensis Clones (Irca 41 And Gt 1) In The Guinea \nSavanna Soil In The Northern Region Of Ghana. Malaysian Journal of Sustainable Agriculture, 3(2): 46-55. \n\n\n\n \n[6] Balsiger, J., Bahdon, J., Whiteman, A. 2000. The utilization, processing \nand demand for rubberwood as a source of wood supply. Asia-Pacific \nForestry Sector Outlook Study Working paper series, Working paper No. \nABFSOS/WP/50. Forestry Policy and Planning Division, Rome and \nRegional Office for Asia and the Pacific, Bangkok. FAO, Rome. 56 p. \n \n[7] FAO (Food and Agriculture Organization. 2005. State of the World's \nForests, 168 p. ISBN 9251051879 \n \n[8] Dijkman, M.J. (1951).  Hevea:  Thirty years of research in the Far East. \nUniversity of Miami press, Florida, pp. 5-7.  \n \n[9] Hong, L.T. 1999. Introduction. Pp. 1-15. In: Hong, L.T & Sim, H.C. \n(Editors) 1999. Rubberwood - Processing and Utilisation. Malayan Forest \nRecords 39. Forest Research Institute Malaysia, Kuala Lumpur. 254 p. ISBN \n983-9592-27-0  \n \n[10] Courtenay, P.P. 1979. Commercial Agriculture. Pp. 108-133. In: Sien, \nC.L., McGee, T.G., Osborne, M.E., Courtenay, P.P., Neville, W., Swan, B. South-\nEast Asia: A Systematic Geography. Oxford University Press, Kuala \nLumpur. 214 p. ISBN 0-19-580393-0 \n \n[11] Firestone Tyre & Rubber Company. 1924. \nhttp://www.firestonenaturalrubber.com (assessed on 15th June 2014. \n[12] Prachaya, J. 2009. Rubber economist quarterly report. London and \nBangkok. Provincial Agriculture and Forestry Offices, Laos. Rubber \nPlantation in Lao PDR \n \n[13]  GREL News. 2012. 5 (2). http://www.grel.com. (assessed on 15th \nJune 2014) \n \n[14]  Rao, P., Vijayakumar, K.R. 1992. Climatic Requirements. Pp. 200-220. \nIn: Sethuraj, M.R. & Mathew, N.M. 1992. Natural Rubber: Biology, \nCultivation and Technology. Developments in Crop Science 23. Elsewier, \nNetherlands. 610 p. ISBN 0-444-88329-0 \n[15] Lemmens, R.H.M.J., Soerianegora, I., Wong, W.C. 1995. PROSEA Plant \nResources of South-East Asia 5(2), Timber Trees: Minor Commercial \nTimbers. Backhuys, Leiden. 655 p. ISBN 90-73348-44-7.  \n \n[16]  Krishnakumar, A.K., Potty, S.N. 1992. Nutrition of Hevea. Pp. 239-263. \nIn: Sethuraj, M.R. & Mathew, N.M. (Editors) 1992. Natural Rubber: Biology, \nCultivation and Technology. Developments in Crop Science 23. Elsewier, \nNetherlands. 610 p. ISBN 0-444-88329-0  \n \n[17] Grist, P., Menz, K., Thomas. 1998. Modified BEAM Rubber \nAgroforestry Models: RRYIELD and RRECON. ACIAR Technical Reports \nSeries 42. 43 p. ISBN 1-86320-225-0 \n\n\n\n \n[18] Addo-Quaye, A.A., Saah, M.K., C.K.B., Ibrahim A., Tetteh, J.P., Rockson-\nAkorly, V.K., Kitson, J. E. 1993. Ministry of Education, General Agriculture \nfor Senior Secondary School, H, Grangaram & Sons, Bombay, India 406pp. \n[19] Rubber Board. 2013. www.Rubber board.com Classification and \ndiseases of rubber clones \n \n[20] James Orhin (ud). 2013. Assessing the initial growth performance of \nrubber clone (Hevea brasiliensis) on guinea Savanna soil. A case study in \nthe Tolon District in Northern Region of Ghana. Unpublished BSc. Thesis \nsubmitted to Department of Forestry and Forest Resources Management, \nUDS, Tamale. \n \n[21] Meteorological Service. 2005. Tamale, Ghana  \n \n[22] Nyankpala Agriculture Station (NAS). 1993. Annual report, edited by \nArt Hum Rungemetzger and Lothar Diehl Velag Losef. Margrafin Imental \nstation and crop research institute, Ghana. Pp 5, 6 and 41 \n \n[23] Ministry of Lands & Forestry. 2001. Statistics, Research and \nInformation Directorate (SRID).  \n \n[24] Savanna Accelerated Research Institute (SARI). 2005. Annual report \n2005, Tamale, Ghana.  \n \n[25]  Muller-Samann, K.M., Kotschi, J. 1994. Sustaining growth: Soil fertility \nmanagement in Tropical small holdings. Magraf Verlag. Weikersheim. \n \n[26] Maurya, P.R., Lal, R. 1981. Effects of different mulch materials on soil \nproperties and root growth and yield of maize (Zea mays) and cowpea \n(Vigna unguiculata). Field Crops Res., 4(1), 33-45. \n \n[27] Ruyssen, B. 1957. Le Karate au Soudan. Agronomie Tropical, 12, 143-\n127, 297-306, 415- 440. In Hall et al., 1996. A monograph Pp.13-21 \n \n[28] Frimpong, E.B,, Adomako, D. 1986. Tree Germination and Mineral \nNutrition Studies. Annual Rep. Cocoa Res. Ins. Ghana 1985/86, Pp. 98-100 \n \n[29] Compagnon, P. 1987. Le Caoutchouc Naturel- Biologie, Culture, \nProduction. Techniques Agricoles et Productions Tropicales. G.-P. \nMaisonneuve & Larose, Paris. 595 p. ISBN 2-7068-0910-8 \n \n[30] Yayock, J.Y., Lombin, G., Owonubi, J.J. 1988. Crop Science and \nproduction in warm climates- Shea butter, pp. 150.  \n \n[31] Ridge, I. 1991. Plant Physiology. Hodder and Stoughon Educational \nPress. United Kingdom.  233p \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nhttp://www.firestonenaturalrubber.com/\n\n\nhttp://www.grel.com/\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.101.109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-293X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.101.109\n\n\n\nEFFICACY OF DIFFERENT HOMEMADE AND COMMERCIAL BAITS IN MONITORING \nOF FRUIT FLIES AT MARANTHANA, PYUTHAN, NEPAL \n\n\n\nAkash Guptaa*, Rajendra Regmib \n\n\n\na Faculty of Agriculture, Agriculture and Forestry University (AFU), Rampur, Chitwan, Nepal \nb Department of Entomology, Agriculture and Forestry University (AFU), Rampur, Chitwan \n*Corresponding author email: agriculture.akash@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 27 Aprils 2022 \nAccepted 30 May 2022 \nAvailable online 01 June 2022\n\n\n\nFruits & vegetable commodities incur huge loss in field & postharvest conditions due to infestation by \nTephritid Fruit Flies. The adult female flies lay eggs inside near maturity fruits & vegetables. The eggs hatch \ninto apodous larvae which feed on the pulp; making them unfit for human consumption and marketing. Using \nmale pheromone lures like Cue Lure & Methyl Eugenol Lure is one of novel techniques for annihilating male \nfruit flies only. Female flies can still mate & keep ovipositing fruits. So, an experiment was carried at \nMaranthana, Pyuthan, Nepal with 3 replications & 7 treatments to devise techniques for female fruit flies \nmanagement. The experiment comprised of commercially used pheromones like Cue Lure & Methyl Eugenol \nLure and 5 home based baits viz. Apple Cider Vinegar, Yeast fermented sugar, Tulsi Lure, Local Liquor Lure \n& Pumpkin Lure; all poisoned with Malathion, soaked in cotton wick and assembled in Lynfield traps. The \nexperiment was completed in two trappings; 2021/04/18 to 2021/05/09 and 2021/04/16 to 2021/07/07; \nwith similar results in both trappings. The commercial were able to attract the highest number of flies; all of \nwhich were male. Local liquor lure & tulsi lure attracted least number of male fruit flies. The Apple Cider \nVinegar Lure and Yeast Lure attracted both male & female flies while pumpkin lure attracted only female flies \nof genus Zeugodacus. Results revealed that female flies of genus Zeugodacus tau & Z. cucurbitae could be \nattracted efficiently by making use of Apple Cider vinegar and Pumpkin. \n\n\n\nKEYWORDS \n\n\n\nMalathion, Methyl Eugenol, Cue Lure, Apple Cider Vinegar, Pumpkin, Fruit Fly \n\n\n\n1. INTRODUCTION \n\n\n\nFruit Flies attacking cucurbits are the insects that belong to class Insecta, \norder Diptera & family Tephritidae. These insects lay eggs inside the near \nmature fruits & vegetables via sharp ovipositors where they hatch into \napodous larvae. The so hatched larvae feed on the pulp & inner fruit parts \nmaking it hub of secondary infections and eventually making them unfit \nfor human consumption (Dhillon et al., 2005). Later on, they jump to soil \nfor pupation where they form barrel shaped pupae which release adult \nforms at the end. Hence, their larval forms incur loss in field as well as \npostharvest conditions & stand as an economic insect pest of various fruits \n& vegetables.  These insects may have some extent of host specificity like \nBactrocera dorsalis mainly attack Mango & tropical fruits while B. \ncucurbitae (now Zeugodacus cucurbitae) mainly attacks vegetables like \nCucurbits (Kwasi, 2008; Bhowmik et al., 2014). \n\n\n\nIn Nepal, seventeen species of fruit flies have been reported by \nEntomological Division, Nepal Agriculture Research Council (Adhikari et \nal., 2019). The fruit flies in Nepal mainly attack Cucurbit fruits (79%), \nfruits (14%) and solanaceous fruits (6%) (Adhikari et al., 2018). Their \npopulation varies with season & climatic conditions. Their management \nremains as a big challenge for farmers growing different horticultural \ncrops; be it fruits or vegetables. Fruit Flies reduce the crop yield & \nmarketability of fruits. In many countries, the fruit flies serve as \nquarantine pest, thus reducing scope for foreign export of Nepalese \nproduce.  \n\n\n\nThe fruit flies are one of the most destructive insects that cause huge loss \nto horticultural commodities. These insects are usually managed by \nchemical insecticide sprays, botanicals sprays, food lures like Cue lure (in \ncucurbits), Methyl eugenol lure (in fruits) or by cultural methods like deep \nsummer tillage, soil solarization; Sanitary methods like destroying \ninfested fruits and by manual methods like fruit bagging or using nets. \nAmong all these methods, the food lure or para-pheromones are most \neffective & promising methods to control fruit flies as they leave no \nchemical residue on the environment & the fruits are free from \ninsecticides poisoning, offering organic fruits & vegetables. In addition, \nthey are less labor intensive than the cultural & manual methods. But the \ncommercially available lures & pheromones are very expensive to be used \nby farmers of underdeveloped countries like Nepal and moreover, these \nlures attract male flies only which remains as secondary fruit fly control \nmechanism. As not all males can be lured & killed, the female flies can mate \n& keep ovipositing on near ripe fruit & vegetables as it will still be able to \nmate. Thus, making lures that are cost effective & kill both sexes of flies by \nusing locally available resources can contribute largely to fruit flies \nmonitoring & control programs at farmer\u2019s level in low-income countries \nlike Nepal. \n\n\n\nThe volatile gases produced by products like Banana, Soybean \nhydrolysate, Apple Cider Vinegar, Locally Brewed Liquor, yeast sugar \nfermented products when poisoned with insecticides like Dimethoate, \nFenthion, Spinosad, Deltamethrin, Malathion or Carbofuran can make a \ngood trap for attracting fruit flies & their subsequent killing or knocking \n\n\n\n\nmailto:agriculture.akash@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\ndown (Bharathi et al., 2004; Mesquita et al., 2018; Hardy and Jessup, 2012; \nBhowmik et al., 2014). Also, Methyl Eugenol is naturally present in many \nplants and can be extracted from e.g., holy basil (Tulsi). Extracts may \ncontain as much as 80% Methyl Eugenol (Sumatra, 2012). These \nattractants are equipped in economical Lynfield traps made from used \ndrinking water plastic bottles. Thus, a cost-effective trap from locally \navailable resources can prove to be a sustainable solution for an \neconomically threatening problem & this research can stand as a stepping \nstone for sustainable & local resource based fruit fly management & \nmonitoring system. \n\n\n\nThe life cycle of fruit fly consists of stages viz. egg, maggots, pupa & adult. \nIts systematic position in animal kingdom is: \n\n\n\nDomain: Eukaryota \n\n\n\n    Kingdom: Metazoa \n\n\n\n  Phylum: Arthropoda \n\n\n\n  Subphylum: Uniramia \n\n\n\n  Class: Insecta \n\n\n\n  Order: Diptera \n\n\n\n  Family: Tephritidae \n\n\n\nB. dorsalis is said to develop well between temperatures 15-33\u00b0C. The eggs \nhatch in 1.46-4.31 days. The larval and pupal stages last for 7.14\u201325.67 \ndays & 7.18\u201331.50 days respectively (Kenfak, et al., 2021).  \n\n\n\nB. zonata; known as peach fruit fly has its total life duration from egg to \ndeath of the adult male and female varied between 34.5-61.5 days and \n35.5-65.0 days, respectively. The mean duration of prepupal and pupal \nstages last for 1.8 and 10.7 days. The mating period ranged between 4 and \n6 hours (Mir et al., 2015).  \n\n\n\nIn the pumpkin fruit fly (Z. tau) the female fly has a preovipositional period \nof 11.7 \u00b1 4.49 days. Then the whitish, shiny, elliptical eggs are hatched \ninside fruits which gradually tur darker. The larval period is 1.2 \u00b1 0.42, 1.7 \n\u00b1 0.48 and 4.0 \u00b1 0.94 days for first, second and third instars & pupation \noccurs inside soil/sand whose duration is 7.0 \u00b1 0.47 days. Overall, the life \ncycle spans for 14.2 \u00b1 1.69 days (Singh et al., 2010).  \n\n\n\nThe Zeugodacus cucurbitae flies lay 80.0\u00b120 eggs/life cycles beneath the \nskin of near ripe fruits which go on to hatch within 1.25\u00b10.25 days. The \napodous maggots feed on the pulp & fully fed within 5.93\u00b11.41 days \naccording to climate. The maggots pupate inside the soil by jumping off \nfrom infested fruits. The pupation takes place within 9.5\u00b10.5days. The \nadult fly may live for 1-3 months; up to 12 months in cooler climate & begin \nmating after 8-12 days (Sohrab, 2018). \n\n\n\nDifferent species of fruit flies have a huge number of host ranges. The flies \ndamage different horticultural commodities at near ripe conditions when \nthey are almost ready for marketing or at near marketing stages.  \n\n\n\nIn Ghana, 60% farmers reported loss due to fruit flies in Mango orchards \n(Kwasi, 2008). In Nepal, the fly has damaged 20-50% of the fruit every \nyear and resulted in a loss of millions of rupees in sweet orange. \nBactrocera dorsalis causes damage to fruit crops like rainy season guava \n(up to 100%), mango (87%), peach (78%) and pear (61%) (Sharma et al., \n2011; Sharma and Dahal, 2020). In Asia, Z. tau causes up to 90% loss in \ncucurbits & solanaceous crops like tomato (Sharma and Tiwari, 2020).  \nBactrocera minax is very serious insect in sweet oranges causing up to 97 \n% loss by the end of harvesting season (Sharma et al., 2015). Cucurbit fruit \nfly (Z. cucurbitae) infests flowers & fruits in Cucurbits and 9.7% loss of \nfemale flowers was seen. Cucurbit fruit fly resulted in more than one-\nfourth (26%) fruit drop or damaged just after set and 14.04% fruits were \ndamaged during harvesting stage, giving only 38.8% fruits of marketable \nquality (Sapkota et al., 2010). So, a substantial loss in horticulture industry \nhas been reported due to fruit fly. \n\n\n\nIn Nepal, different methods have been deployed for fruit fly management \nlike chemical (32%), mechanical (80%), indigenous (70%) (Jholmol) and \noften a combination of these (68%) methods have been used too in mid \nhills of Nepal (Sapkota et al., 2010). The most effective chemical in \nreducing the fruit infestation by melon fruit fly have been identified as \nSpinosad in Cucurbits (Bhowmik et al., 2014). Cue lure and Methyl eugenol \nbaited with Malathion were used for attracting & killing male fruit flies in \nSweet orange orchards at Sindhuli using Steiner trap (Sharma et al., 2015).  \n\n\n\nPhytosanitary measures & botanicals sprays are suggested by agriculture \n\n\n\ntechnicians for fruit fly management in Nepal (Adhikari et al., 2020). \nTrimedlure (for Ceratitis capitata, C. rosa) was suggested for use in Sri-\nLanka as para pheromone along with components like killing agent inside \nMcPhail Trap by technicians there and the trapping system introduced \nthere also made combined & solitary use of Torula Yeast Borax, Protein \nderivatives, Ammonium carbonate, Putrescine & Trimethylamine, \nAmmonium salts, Butyl hexanoate along with toxicants such as Dichlorvos, \nMalathion, Spinosad and Pyrethroids (such as Deltamethrin) along with \n1.5 to 2 g of borax or 10% propylene glycol were added to preserve \ncaptured flies in liquid based killing traps (Jiajiao, 2019). The easy trap \nbaited with Ammonium Acetate and Trimethylamine using Deltamethrin \nas killing agent was identified as the best trap against C. capitata in a \nmango orchard (Cohen, 2007). \n\n\n\nIn an experiment, Soybean hydrolysate, fishmeal, beef extract, \nbanana/grapes, bread and dog biscuit along with vinegar and beer (to \nenhance their attractiveness) were used as food baits for managing melon \nfruit flies. Results revealed that, banana and soybean hydrolysate were \n85\u201395% more attractive to adult Zeugodacus cucurbitae than other forth \nmentioned treatments. Similarly, among combined treatments, Grapes + \nbeer + palm oil was found to be 37% more attractive than the other \nadmixtures (Bharathi et al., 2004). \n\n\n\n2. MATERIAL AND METHODS\n\n\n\n2.1  Research Site \n\n\n\nPyuthan Municipality, Ward 9 & 10 was selected as the research site. The \nsite was selected due to abundance of farmers growing cucurbitaceous \nvegetable crops like cucumber, bitter gourd, bottle gourd, sponge gourd \nalong with occurrence of substantial amount of tropical & sub-tropical \nfruit tress like mango, sweet orange & mandarin. Thus, the baits could be \nused for trapping more species of fruit flies prevalent in Pyuthan & further \nefficiency & performance of such baits can be tested. \n\n\n\nFigure 1: Research Location (Ward 9 & 10 of Pyuthan Municipality) \n\n\n\n2.2  Treatments  \n\n\n\nSeven treatments were prepared with different ingredients as evident \nfrom the literature review. Among seven; 2 lures were commercially used \nlure namely Cue Lure & Methyl Eugenol Lure. The cue lure is used mainly \nfor cucurbitaceous vegetable crops while the latter is used for fruit fly \nmanagement in fruit orchards. The remaining 5 treatments were prepared \nfrom products that are used for human consumption. All the attractants \nmade use of Malathion to make baits and for knocking down the flies that \nwere attracted to the food component. All these treatments were installed \nin Cucumber fields (Ninja Variety) at fruiting height at fruiting initiation \nperiod. All treatments solutions were prepared, soaked in cotton wick for \n24 hours & equipped in Lynfield trap. Lynfield like traps can be made \nlocally by using abandoned packaged water bottles. After removing the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\nplastic label of such bottles, 4 holes of size 6-8 mm; just enough for entry \nof fruit flies only are pierced using hot GI wire. The holes are made towards \nthe upper 5cm band of the label on plastic bottle. Then, they were further \nequipped using GI-wires for hanging. A prototype Lynfield trap model is \nshown in Figure 2. \n\n\n\nTable 1: Different treatments & their ingredients to be used in the \nexperiment \n\n\n\nTreatment Treatment Name \nTreatment \n\n\n\nComposition/Ratio \n\n\n\nT1 \nCue Lure \n\n\n\n(Division, 2017a) \n\n\n\nCue Lure-4 ml \n\n\n\nEthyl Alcohol-6 ml \n\n\n\nMalathion-2 ml \n\n\n\nT2 \nMethyl Eugenol Lure \n\n\n\n(Division, 2017b) \n\n\n\nMethyl Eugenol-4 ml \n\n\n\nEthyl Alcohol-6 ml \n\n\n\nMalathion-2 ml \n\n\n\nT3 \nApple Cider Vinegar Lure \n\n\n\n(Maung et al., 2019) \n\n\n\nApple Cider Vinegar with \nmother-90ml \n\n\n\nMalathion-10ml \n\n\n\nT4 \nYeast Lure \n\n\n\n(Lloyd, 2003) \n\n\n\nBaker\u2019s Yeast-2gm \n\n\n\nSugar-8gm \n\n\n\nWater-90ml \n\n\n\nMalathion-10ml \n\n\n\nT5 \n\n\n\nTulsi Lure \n\n\n\n(Sumatra, 2012) \n\n\n\n(Mumford, 2006) \n\n\n\n50gm Tulsi paste \n\n\n\nJaggary-10gm \n\n\n\nWater-90ml \n\n\n\nMalathion-10ml \n\n\n\nT6 \nLocal Liquor Lure \n\n\n\n(Pi\u00f1ero et al., 2017) \n\n\n\nLiquor-90ml \n\n\n\nMalathion-10ml \n\n\n\nT7 \nPumpkin Lure \n\n\n\n(Mumford, 2006) \n\n\n\nMashed Pulp-100gm \n\n\n\nMalathion-10ml \n\n\n\nAll treatments solutions were prepared, soaked in cotton wick for 24 \nhours & equipped in Lynfield trap. Lynfield like traps can be made locally \nby using abandoned packaged water bottles. After removing the plastic \nlabel of such bottles, 4 holes of size 6-8 mm; just enough for entry of fruit \nflies only are pierced using hot GI wire. The holes are made towards the \nupper 5cm band of the label on plastic bottle. Then, they were further \nequipped using GI-wires for hanging. A prototype Lynfield trap model is \nshown in Figure 2. \n\n\n\nFigure 2: Lynfield trap model made from used plastic bottles equipped \nwith lure-soaked cotton wick \n\n\n\n2.3  Research Design  \n\n\n\nThe experiment design was Randomized Complete Block Design (RCBD). \nThree locations within Pyuthan Municipality Ward 9 & 10 were used as \n\n\n\nthree replications. The baits/lures were hanged at crop height (where \nfruiting occurs) using iron wires; usually 1.5m above ground level in \nCucumber fields. The distance between each trap was 5m (Mesquita et al., \n2018). Such spacing allowed the fruit flies to choose their favorite bait for \nfeeding. While feeding, they were subsequently knocked out due to \nMalathion which disrupts the nerve impulse system & after death got \ncollected at bottom of the Lynfield trap. After installation, the traps were \nleft in the field for 3 weeks. The entire experiment was done twice in \ndifferent durations to gain better confidence about the results.   \n\n\n\nFigure 3: Map showing replication sites R1, R2 and R3 at Maranthana, \nPyuthan, Nepal (Source: Google Maps) \n\n\n\n2.4 Data Collection  \n\n\n\nThe fruit flies collected in Lynfield trap were counted & categorized \naccording to sex & species after three weeks of trap placement. The \nspecimens were preserved by dry preservation techniques in insect \ncollection box. The sexes were distinguished based on presence or absence \nof sharp ovipositor while species were distinguished by virtue of \nmorphological features based on identification guidelines provided by: \n\n\n\n\u2022 THE AUSTRALIAN HANDBOOK FOR THE IDENTIFICATION OF \nFRUIT FLIES Version 3.1 (Schutze et al., 2018) \n\n\n\n\u2022 Field Guide for Identification of Fruit Fly Species of Genus \nBactrocera Prevalent in and around Mango Orchards (Choudhary et \nal., 2014) \n\n\n\n\u2022 Occurrences and field identities of fruit flies in sweet orange (Citrus \nsinensis) orchards in Sindhuli, Nepal (Adhikari and Joshi, 2018) \n\n\n\n2.5 Data Analysis  \n\n\n\nThe mean number of catches incurred by each trap were compared & \ncategorized by using Duncan\u2019s multiple range tests.  The number of male \nadult flies & female adult flies trapped in different lure were compared & \ncategorized similarly. All these analyses were performed by using MS \nExcel & RStudio software. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Comparison of Number of Adult Fruit Flies Trapped in Different \nBaits at 2 Different Trappings \n\n\n\nThe comparison between all the treatments for total fruit flies trapped, \ntotal male fruit flies trapped, and total female fruit flies trapped was done \nusing Duncan multiple range test. The results are presented in Table 2. \n\n\n\nTable 2 shows that the maximum number of adult fruit flies were trapped \nin commercial baits i.e. Cue Lure & Methyl Eugenol Lure; compared to \nhome based attractants poisoned with Malathion. The commercial lures \nshowed statistically similar results in first trapping while during second \ntrapping, the cue lure attracted maximum number of insects. During first \ntrapping, least number of adult fruit flies was found attracted to Local \nLiquor Lure which was statistically similar to Tulsi Lure & Ripe Pumpkin \nLure. During second trapping, the least fruit flies were found attracted to \nLocal Liquor Lure again, which was statistically similar to Tulsi Lure only.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\nTable 2: Adult Fruit Flies trapped in the experiment on different trappings \n\n\n\nTreatments \n\n\n\nNumber of Adult Fruit Flies Trapped \n\n\n\n2078/01/05 to 2078/01/26 2078/03/02 to 2078/03/23 \n\n\n\nTotal Male Female Total Male Female \n\n\n\nCue Lure 264.67a 264.67a 0.00c 132.33a 132.33a 0.00 \n\n\n\nMethyl Eugenol Lure 226.67a 226.67a 0.00c 122.33b 122.33b 0.00 \n\n\n\nApple Cider Vinegar Lure 146.67b 101.33b 45.33a 114.67c 86.67c 28.00ab \n\n\n\nYeast Lure 90.33c 51.67c 38.67ab 76.33d 50.33d 26.00b \n\n\n\nTulsi Lure 44.33d 44.33c 0.00c 11.00f 11.00e 0.00 \n\n\n\nLocal Liquor Lure 19.33d 19.33cd 0.00c 8.33f 8.33e 0.00 \n\n\n\nRipe Pumpkin Lure 32.00d 0.00d 32.00b 30.00e 0.00f 30.00a \n\n\n\nSEm (\u00b1) 13.96 13.19 2.37 1.37 1.15 0.77 \n\n\n\nF-Probability *** *** *** *** *** *** \n\n\n\nLSD (=0.05) 43.02 40.65 7.29 4.21 3.56 2.36 \n\n\n\nCV (%) 20.55 22.59 24.72 3.35 3.41 11.06 \n\n\n\nGrand Mean 117.71 101.14 16.57 70.71 58.71 12.00 \n\n\n\nNote: CV, Coefficient of variation; LSD, Least significant difference; SEm (\u00b1), Standard error of mean; Letters a, b, c, d, e, f represent the ranking of treatments \naccording to DMRT at 0.05 level of significance; *, **, *** denote significance at p=0.05, p=0.01, p=0.001 respectively \n\n\n\nIn regard to male fruit flies, all the treatments attracted male fruit flies \nexcept the Pumpkin Lure. Highest number of male fruit flies were attracted \nto Cue Lure in both trappings. During first trapping, male fruit flies \nattracted to Cue Lure were statistically similar to Methyl Eugenol Lure. \nThe pumpkin lure attracted only female fruit flies. In addition to pumpkin \nlure, Apple Cider Vinegar Lure & Yeast Lure also attracted female fruit flies \nviz. Zeugodacus tau & Zeugodacus cucurbitae. The highest number of \nfemale fruit flies during first trapping was found in Apple Cider Vinegar \nLure which was statistically similar to Yeast Lure. During second trapping, \nthe highest number of female fruit flies were found in Pumpkin Lure which \n\n\n\nwas statistically similar to Apple Cider Vinegar Lure.  \n\n\n\nThe number of insects trapped in second trapping were almost twice as \nlower compared to first trapping except in the case of pumpkin lure. The \nhigher precipitation during the second trapping might be the possible \nreason behind it. The higher catch in pumpkin lure might be due to faster \nmicrobial rotting of pumpkin which makes it more suitable for the fruit \nflies infestation. This is evident from the weather characteristics derived \nfrom NASA power & shown in figure.4 below: \n\n\n\nFigure 4: Weather data during research period at Maranthana, Pyuthan, Nepal (Source: NASA Power) \n\n\n\n3.2 Fruit Fly Species Trapped by Different Lures \n\n\n\nDuring experimentation, four species of fruit flies were seen in different \ntraps viz.  \n\n\n\n\u2022 Bactrocera dorsalis (Hendel) \u2013 Oriental Fruit Fly \n\n\n\n\u2022 Bactrocera zonata (Saunders) \u2013 Peach Fruit Fly \n\n\n\n\u2022 Zeugodacus tau (Walker) \u2013 Pumpkin Fruit Fly\n\n\n\n\u2022 Zeugodacus cucurbitae (Coquillet) \u2013 Melon Fruit Fly\n\n\n\n3.2.1  Cue Lure \n\n\n\nCue Lure is extensively used for cucurbitaceous vegetable crop\u2019s fruit fly \nmanagement on a commercial scale. The species attracted by this \npheromone lure were Zeugodacus tau & Zeugodacus cucurbitae. The \nnumber of Z. tau caught was higher compared to Z. cucurbitae during both \ntrappings as shown in figure.5.  The flies caught during second trapping \nwere lower as evident from weather characteristics at Maranthana as \nshown in figure.4. Only male adult flies were caught in this lure. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\nFigure 5: Different fruit fly species caught in cue lure \n\n\n\n3.2.2   Methyl eugenol lure  \n\n\n\nMethyl eugenol is used for fruit fly management specifically in fruit \norchards. In this experiment, the lure attracted mainly two male fruit fly \n\n\n\nspecies only viz. B. dorsalis & B. zonata. The number of B. dorsalis was \nhigher than B. zonata in both trappings as shown in Figure 6. The number \nof flies caught during second trapping was again lower due to above stated \nreasons. \n\n\n\nFigure 6: Different fruit fly species caught in methyl eugenol lure \n\n\n\n3.2.3  Apple cider vinegar lure \n\n\n\nThe apple cider vinegar was able to attract all the four species of fruit flies \nencountered in this experiment. The lure was able to attract both male & \n\n\n\nfemale fruit fly species. ACV attracted female fruit fly species of Z. tau & Z. \ncucurbitae. The male fruit flies attracted in this experiment belonged to all \nfour species stated above (Figure 7). The number of different flies caught \nin this lure is shown in bar diagram below: \n\n\n\nFigure 7: Different fruit fly species caught in apple cider vinegar lure \n\n\n\n31.33\n\n\n\n23.67\n\n\n\n0.00\n\n\n\n7.00\n\n\n\n36.33\n\n\n\n28.00\n26.00\n\n\n\n12.67\n\n\n\n33.67\n\n\n\n28.00\n\n\n\n19.33\n\n\n\n15.33\n\n\n\n0.00\n\n\n\n5.00\n\n\n\n10.00\n\n\n\n15.00\n\n\n\n20.00\n\n\n\n25.00\n\n\n\n30.00\n\n\n\n35.00\n\n\n\n40.00\n\n\n\nFirst Trapping Second Trapping\n\n\n\nB. dorsalis (male) B. zonata (male) Z. tau (male)\n\n\n\nZ. tau (female) Z. cucurbitae (male) Z. cucurbitae (female)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\n3.2.4  Yeast lure \n\n\n\nThe yeast lure attracted male & female fruit flies of species Z. tau & Z. \ncucurbitae. It didn\u2019t attract the fruit flies that are more evident in fruit \n\n\n\norchards. However, the number of insects trapped in different trappings \nare almost similar as shown in Figure 8 which is unlike the results \nobtained in other traps and remains unaffected by weather characteristics \nduring the research period. \n\n\n\nFigure 8: Different fruit fly species caught in yeast lure \n\n\n\n3.2.5  Tulsi lure \n\n\n\nTulsi is said to be a prominent source of Methyl Eugenol. Proving it aright, \n\n\n\nit was able to attract only male flies of species B. dorsalis & B. zonata \n(figure.9) which were similar to that attracted in Methyl Eugenol Lure. \nHowever the numbers were almost 4 times lower compared to the latter.  \n\n\n\nFigure 9: Different fruit fly species caught in tulsi lure \n\n\n\n3.2.6  Local liquor lure \n\n\n\nLocal liquor also attracted fly species similar to that of Methyl Eugenol \n\n\n\nLure. However, the number of flies attracted was least among all the lures \nused in the experiment which was almost 11 times lower than the \nnumbers figured in Methyl Eugenol Lure (Figure 10).  \n\n\n\nFigure 10: Different fruit fly species caught in local liquor lure \n\n\n\n0.00 0.000.00 0.00\n\n\n\n18.67\n20.67\n\n\n\n24.00\n\n\n\n10.67\n\n\n\n33.00\n\n\n\n29.67\n\n\n\n14.67 15.33\n\n\n\n0.00\n\n\n\n5.00\n\n\n\n10.00\n\n\n\n15.00\n\n\n\n20.00\n\n\n\n25.00\n\n\n\n30.00\n\n\n\n35.00\n\n\n\nFirst Trapping Second Trapping\n\n\n\nB. dorsalis (male) B. zonata (male) Z. tau (male)\n\n\n\nZ. tau (female) Z. cucurbitae (male) Z. cucurbitae (female)\n\n\n\n33.33\n\n\n\n9.67\n11.00\n\n\n\n1.33\n0.00 0.000.00 0.000.00 0.000.00 0.00\n\n\n\n0.00\n\n\n\n5.00\n\n\n\n10.00\n\n\n\n15.00\n\n\n\n20.00\n\n\n\n25.00\n\n\n\n30.00\n\n\n\n35.00\n\n\n\nFirst Trapping Second Trapping\n\n\n\nB. dorsalis (male) B. zonata (male) Z. tau (male)\n\n\n\nZ. tau (female) Z. cucurbitae (male) Z. cucurbitae (female)\n\n\n\n12.67\n\n\n\n5.67\n6.67\n\n\n\n2.67\n\n\n\n0.00 0.000.00 0.000.00 0.000.00 0.00\n0.00\n\n\n\n2.00\n\n\n\n4.00\n\n\n\n6.00\n\n\n\n8.00\n\n\n\n10.00\n\n\n\n12.00\n\n\n\n14.00\n\n\n\nFirst Trapping Second Trapping\n\n\n\nB. dorsalis (male) B. zonata (male) Z. tau (male)\n\n\n\nZ. tau (female) Z. cucurbitae (male) Z. cucurbitae (female)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\n3.2.7  Pumpkin lure \n\n\n\nThe pumpkin lure attracted only female fruit flies in the experiment. The \nfemale fruit flies attracted were Z. tau & Z. cucurbitae. The numbers in both \n\n\n\ntrappings were similar. During second trapping, number of Z. tau was \nhigher (Figure 11).  \n\n\n\nFigure 11: Different fruit fly species caught in yeast lure \n\n\n\nThe cue lure mainly caught Z. cucurbitae & Z. tau while the Methyl Eugenol \ncaught B. dorsalis & B. zonata which is in line to what recommended by \nNational Institute of Plant Health Management (Division, 2017a; Division, \n2017b). Though Tulsi (Ocimum sanctum) is source of Methyl eugenol, the \ncatch performed by commercial methyl eugenol was very high \ncomparatively. The major reasons behind this was quoted by Jhala, et al., \n2006 here he found that 1ml of Methyl Eugenol could be extracted from 46 \ngm of Black Tulsi (var. Krishna) while the same 1 ml was extracted from \n417 gm of Green Tulsi. As, local green Tulsi paste of 90 gm was used in the \nexperiment to prepare 3 traps; this makes the methyl eugenol content to \njust 0.071ml per trap & hence the catch obtained in Tulsi Lure might be \nlow. However, usage of smaller amount of Black Tulsi paste could prove \ngame changer for male fruit fly management. \n\n\n\nThe fruit flies management using male sex pheromone lures stand as an \nindirect method of fruit fly management. As being part of management \napproach and not the control approach; it doesn\u2019t kill all the male flies & \nhence the females still is able to mate & oviposit in the cucurbitaceous \nfruits and vegetables. The females have also show some extent of \ninterspecific mating (Schutze et al., 2013). Thus, management of female \nfruit flies seems better approach for reducing fruit fly infestation & fruit \nfly management. Similarly, as evident from Table 2 the grand mean of \nfemale flies is 6 times lower compared to male flies during first trapping & \nthis number stands 5 times during second trapping; showing the number \n\n\n\nof female flies is lower than males by many folds in the experiment & \nmanaging female flies could enhance the profitability of the \ncucurbitaceous vegetables.  \n\n\n\nThe baits made from apple cider vinegar, yeast lure & pumpkin was able \nto attract female fruit flies which was also depicted in the texts (Mumford, \n2006). About 13 volatile compounds were identified from yeast \nfermentation of minimal media to which the fruit flies prefer (Becher et al., \n2012). Similarly, Dipterans of Drosophilidae family were found attractive \nto Apple Cider Vinegar while in this experiment at Maranthana, Pyuthan, \nNepal; Dipterans of Tephritidae family were also found attractive (Kleiber, \n2013). Overall, as the catch produced by the home based attractants is \nlower than the commercials ones, to match the latter\u2019s performance, more \nnumber of homebased traps could be installed to catch more number of \nflies; still making the approach economical compared to the commercial \nones.   \n\n\n\n3.3 Economic Aspects of Building Different Traps Used in The \nExperiment \n\n\n\nThis experiment stresses on minimizing the cost of fruit fly management \nand hence an estimate for the cost of active ingredients used in the \nexperiment was devised in accordance of the existing market scenario \nduring experimentation. The result is presented in Table 3.  \n\n\n\nTable 3: Cost of active ingredients for making different lures used in the experiment \n\n\n\nTrap Name Ingredients Estimated Cost (NRs) Traps Made Cost of ai/trap (NRs) \n\n\n\nCue Lure  \n\n\n\nCue lure-4 ml \n\n\n\nEthyl alcohol-6 ml \n\n\n\nMalathion-2 ml \n\n\n\n80\u00d74+0.8\u00d76+1.54\u00d72 \n\n\n\n=327.9 \n3 109.3 \n\n\n\nMethyl Eugenol Lure  \nMethyl eugenol-4 ml Ethyl alcohol-6 \n\n\n\nml Malathion-2 ml \n\n\n\n75\u00d74+0.8\u00d76+1.54\u00d72 \n\n\n\n=307.9 \n3 102.6 \n\n\n\nApple Cider Vinegar Lure  \nApple cider vinegar-90ml \n\n\n\nMalathion-10ml \n\n\n\n0.84\u00d790+1.54\u00d710 \n\n\n\n=91 \n10 9.1 \n\n\n\nYeast Lure \n\n\n\nBaker\u2019s yeast-2gm \n\n\n\nSugar-8gm \n\n\n\nWater-90ml \n\n\n\nMalathion-10ml \n\n\n\n0.56\u00d72+0.09\u00d78+1.54\u00d710 \n\n\n\n=17.24 \n10 1.724 \n\n\n\nTulsi Lure \n\n\n\n50gm Tulsi paste \n\n\n\nJaggary-10gm \n\n\n\nWater-90ml \n\n\n\nMalathion-10ml \n\n\n\n50\u00d70.48+10\u00d70.15+1.54\u00d710 \n\n\n\n=40.9 \n10 4.1 \n\n\n\nLocal Liquor Lure \nLiquor-90ml \n\n\n\nMalathion-10ml \n\n\n\n0.2\u00d790+1.54\u00d710 \n\n\n\n=33.1 \n10 3.31 \n\n\n\nPumpkin Lure  \nMashed pulp-100gm \n\n\n\nMalathion-10ml \n\n\n\n0.06\u00d7100+1.54\u00d710 \n\n\n\n=21.4 \n3 7.13 \n\n\n\nTable 3 shows that the cost of commercially use traps viz. Cue Lure & Methyl Eugenol was NRs. 109.3 & NRs. 102.6 respectively. The effective home-based \ntraps like Apple Cider Vinegar, Yeast Lure & Pumpkin Lure costed NRs. 9.1, NRs. 1.724 and NRs. 7.13 respectively which is 12\u00d7, 63\u00d7, 15\u00d7 lower compared \nto Cue Lure & 11\u00d7, 59\u00d7, 14\u00d7 lower compared to Methyl Eugenol Lure respectively.  \n\n\n\n0.00 0.000.00 0.000.00 0.00\n\n\n\n16.00\n17.67\n\n\n\n0.00 0.00\n\n\n\n16.00\n\n\n\n12.33\n\n\n\n0.00\n\n\n\n5.00\n\n\n\n10.00\n\n\n\n15.00\n\n\n\n20.00\n\n\n\nFirst Trapping Second Trapping\n\n\n\nB. dorsalis (male) B. zonata (male) Z. tau (male)\n\n\n\nZ. tau (female) Z. cucurbitae (male) Z. cucurbitae (female)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\n4. CONCLUSION \n\n\n\nTephritid Fruit Fly Management is a big challenge for fruits & vegetable \nproduction in Nepal and around the globe. The current techniques revolve \naround the indirect method of fruit fly management which included use of \nmale sex pheromone lures like Cue Lure in cucurbitaceous vegetable crops \n& Methyl Eugenol in fruit orchards. Although there is substantial decrease \nin fruit fly infestation with this approach, but as not all male fruit flies are \nkilled, the female fruit flies is still able to mate with multiple male partners \nand in most of the cases with fruit flies of other species too due to \nabundance of male counter parts. Thus, after all the toiling effort, the \ninfestation of fruits and vegetables could still take place. Thus, the better \noption would stand as directly managing the female adult fruit flies. This \nliberty can be availed by making use of the home based lures used in this \nexperiment like Apple Cider Vinegar Lure and Yeast Lure which attracted \nboth male and female flies of diverse genre while the Pumpkin Lure was \nable to attract only female flies. However, these lures were able to attract \nfemale flies of genre Z. tau & Z. cucurbitae and no bait was able to attract \nthe female counter parts of B. dorsalis & B. zonata. As the formers ones are \nfound to be more abundant in cucurbitaceous field, the home based baits \nlike Apple Cider Vinegar, Yeast Lure & Pumpkin Lure can be suggested for \nuse in female adult fruit flies management in cucurbit vegetable \ncultivation. Similarly, the home based baits are very economical compared \nto the commercial ones. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThis study was part of B.Sc.Ag. Final semester LEE program. We would like \nto acknowledge PMAMP & AFU, IALDO-Pyuthan and farmers of \nMaranthana, Pyuthan for funding this research & proving logistics \nrespectively to complete the research; to Mr. Debraj Adhikari, PMAMP-\nSindhuli for guiding on methodology & insect identification. \n\n\n\nREFERENCES \n\n\n\nAbdullah, M.T., Ali, N.Y., Suleman, P., 2008. Bio control of Sclerotinia \nAdhikari, D., Joshi, S.L., 2018. Occurrences and field identities of fruit \nflies in sweet orange (Citrus sinensis) orchards in Sindhuli, Nepal. \nJournal of Natural History Museum, 30 (2015), Pp. 47\u201354. \nhttps://doi.org/10.3126/jnhm.v30i0.27511 \n\n\n\nAdhikari, D., Joshi, S.L., Thapa, R.B., Du, J.J., Sharma, D.R., GC, Y.D., 2019. \nNational Plant Protection Workshop on status and management of \nfruit fly in Nepal. \n\n\n\nAdhikari, D., Thapa, R.B., Joshi, S.L., Liang, X.H., Du, J.J., 2020. Area-Wide \nControl Program of Chinese Citrus Fly Bactrocera minax (Enderlein) \nin Sindhuli, Nepal. American Journal of Agricultural and Biological \nSciences, 15, Pp. 1-7(10.3844/ajabssp.2020.1.7). \nhttps://doi.org/10.3844/ajabssp.2020.1.7 \n\n\n\nAdhikari, D., Tiwari, D.B., Joshi, S.L., 2018. Population dynamics of fruit flies \nin sweet orange (Citrus sinensis L.) ochards in Sindhuli, Nepal. The \nJournal of Agriculture and Environment, 19, Pp. 9\u201316. \n\n\n\nBecher, P.G., Flick, G., Rozpedoeska, E., Schmidt, A., Hagman, A., Lebreton, \nS., Bengtsson, M., 2012. Yeast, not fruit volatiles mediate Drosophila \nmelanogaster attraction, oviposition, and development. Functional \nEcology, Pp. 822-828. \n\n\n\nBharathi, T.E., Sathiyanandam, V.K.R., David, P.M.M., 2004. Attractiveness \nof some food baits to the melon fruit fly, Bactrocera cucurbitae \n(Coquillett) (Diptera: Tephritidae). International Journal of Tropical \nInsect Science, 24 (2), Pp. 125\u2013134. \nhttps://doi.org/10.1079/IJT200412 \n\n\n\nBhowmik, P., Mandal, D., Chatterjee, M., 2014. Chemical Management of \nMelon Fruit Fly, Bactrocera cucurbitae Conquillett \n(Diptera:Tephritidae) on Bitter Gourd (Momordica charantia L.). \nPesticide Research Journal, 26 (1), Pp. 68\u201373. \n\n\n\nChoudhary, J.S., Naaz, N., Prabhakar, C.S., 2014. Field Guide for \nIdentification of Fruit Fly Species of Genus Bactrocera Prevalent in and \naround Mango Orchards. ICAR Research Complex for Eastern Region \nResearch Centre - Ranchi, R-43/Ranchi-16 Field. \n\n\n\nCohen, H., 2007. Development and evaluation of improved Mediterranean \nfruit fly attractants in Israel. Proceedings of a Final Research \nCoordination Meeting \u201cDevelopment of Improved Attractants and \nTheir Integration into Fruit Fly SIT Management Programmes\u201d, May \n2005, Pp. 237. \n\n\n\nDhillon, M.K., Singh, R., Naresh, J.S., Sharma, H.C., 2005. The melon fruit fly, \nBactrocera cucurbitae: A review of its biology and management. \nJournal of Insect Science, 5 (1). https://doi.org/10.1093/jis/5.1.40 \n\n\n\nDivision, P.B., 2017a. Fruit fly Management: Low-cost Traps & Lures \nPreparation. In National Institute of Plant Health Management-\nNIPHM, Telangana, Hyderabad. \n\n\n\nDivision, P.B., 2017b. Fruit fly Management: Low-cost Traps & Lures \nPreparation. In National Institute of Plant Health Management-\nNIPHM, Telangana, Hyderabad. \n\n\n\nDongmo, K.M.A., Fiaboe, K., Kekeunou, S., Nanga, S., Fotso Kuate, A., \nTonnang, H., Rachid, H., 2021. Temperature-based phenology model \nto predict the development, survival, and reproduction of the oriental \nfruit fly Bactrocera dorsalis. Journal of Thermal Biology, 97 (102877). \n\n\n\nHardy, S., Jessup, A., 2012. Managing Queensland fruit fly in citrus. \nDepartment of Primary Industries, NSW Government, Pp. 1\u20134. \n\n\n\nJhala, R., Patel, Z., Jagadale, V., Sisodya, D., Stonehouse, J., Verghese, A., \nMumford, J., 2006. Ocimum and Methyl-Eugenol in Fruit Fly Traps in \nSouthern and Central Gujarat. Imperial College London: United \nKingdom Department for International Development. \n\n\n\nJiajiao, W.U., 2019. Introduction of Trapping Systems for fruit flies\u2019 \nSurveys. In FAO-IPPC Project, Sri Lanka. \n\n\n\nKleiber, J.R., 2013. Comparison of Baits for Monitoring the Spotted Wing \nDrosophila, Drosophila suzukii. Comparison of Baits for Monitoring \nthe Spotted Wing Drosophila, Drosophila suzukii. United States of \nAmerica: Oregon State Univeristy. \n\n\n\nKwasi, W., 2008. Assessment of Fruit Fly Damage and Implications for the \nDissemination of Management Practices for Mango Production in the \nUpper West Region of Ghana. Journal of Developments in Sustainable \nAgriculture, 3 (2), Pp. 117\u2013134. https://doi.org/10.11178/jdsa.3.117 \n\n\n\nLloyd, A., 2003. Improved protein bait formulations for fruit fly control. \nKnow-How for Horticulture, 1 (0 7341 0861 3), Pp. 124. \n\n\n\nMaung, K.L., Mon, Y.Y., Khine, M.P., Chan, K.N., Phyoe, A., Soe, A.T., Khai, \nA.Y.A., 2019. Efficient Protein-based Bait Formulation for Attraction \nand Feeding Response of Fruit Flies (Diptera: Tephritidae) in \nMyanmar. Journal of Life Sciences, 13 (2), Pp. 18\u201324. \nhttps://doi.org/10.17265/1934-7391/2019.02.003 \n\n\n\nMesquita, P.R.R., Magalh\u00e3es-Junior, J.T., Cruz, M.A., Novais, H.O., Santos, \nJ.R.J., Carvalho, S.L., Rodrigues, F.D.M., Barbosa, C.D.J., Bravo, I.S.J., \nNascimento, A.S., 2018. Sources of Protein as Food Baits for \nAnastrepha obliqua (Diptera: Tephritidae): Tests in a Wind Tunnel \nand the Field. Florida Entomologist, 101 (1), Pp. 20\u201324. \nhttps://doi.org/10.1653/024.101.0105 \n\n\n\nMir, S.H., Mir, G.M., Dar, S.A., Haji, S.A., 2015. Biology and biometrics of the \npeach fruit fly, Bactrocera zonata on peach. Indian Journal of Plant \nProtection, Pp. 154-157. \n\n\n\nMumford, J.D., 2006. Integrated Management of Fruit Flies (Diptera: \nTephritidae) in India (Vol. 8440, Issue January 2002). \n\n\n\nPi\u00f1ero, J.C., Souder, S.K., Smith, T.R., Vargas, R.I., 2017. Attraction of \nBactrocera cucurbitae and Bactrocera dorsalis (Diptera: Tephritidae) \nto Beer Waste and Other Protein Sources Laced with Ammonium \nAcetate. Florida Entomologist, 100 (1), Pp. 70\u201376. \nhttps://doi.org/10.1653/024.100.0112 \n\n\n\nQureshi, Z., Hussain, T., Carey, J.R., Dowell, R.V., 1993. Effects of \ntemperature on development of Bactrocera zonata (Saunders) \n(Diptera: Tephritidae). Pan-Pacific Entomologist, Pp. 71-76. \n\n\n\nSapkota, R., Dahal, K.C., Thapa, R.B., 2010. Damage assessment and \nmanagement of cucurbit fruit flies in spring-summer squash. Journal \nof Entomology and Nematology, 2 (1), Pp. 7\u201312. \nhttp://www.academicjournals.org/jen/PDF/Pdf2010/ \nFebruary/Sapkota et al.pdf \n\n\n\nSchutze, M., McMahon, J., Krosch, M., Strutt, F., Royer, J., Bottrill, M., Woods, \nN., Cameron, S., Woods, B., Blacket, M. (Eds. ). 2018. The Australian \nHandbook for the Identification of Fruit Flies (Version 3.1). \nhttp://www.planthealthaustralia.com. au/wp-\ncontent/uploads/2018/10/The-Australian-Handbook-for-the-\nIdentification-of-Fruit-Flies-v3.1.pdf \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 101-109 \n\n\n\nCite The Article: Akash Gupta, Rajendra Regmi (2022). Efficacy of Different Homemade and Commercial Baits in Monitoring of \nFruit Flies at Maranthana, Pyuthan, Nepal. Malaysian Journal of Sustainable Agricultures, 6(2): 101-109. \n\n\n\nSchutze, M.K., Jessup, A., Ul-Haq, I., Vreysen, M.J., Wornoayporn, V., Vera, \nM.T., Clarke, A.R., 2013. Mating Compatibility Among Four Pest \nMembers of the Bactrocera. Journal of economic entomology, Pp. 695-\n707. \n\n\n\nSharma, D.R., Adhikari, D., Tiwari, D.B., 2015. Fruit Fly Surveillance in \nNepal. Agricultural and Biological Sciences Journal, 1 (3), Pp. 121\u2013125. \n\n\n\nSharma, D.R., Singh, S., Aulakh, P.S., 2011. Management of Fruit Flies in \nFruit Crops. Ludhiana: Department of Horticulture, Punjab \nAgriculture University. \n\n\n\nSharma, P., Dahal, B.R., 2020. Life cycle and eco-friendly management of \nChinese fruit fly (Bactrocera minax) in sweet orange (Citrus sinesis \nOsbeck) in Nepal. Archives of Agriculture and Environmental Science, \n5 (2), Pp. 168\u2013173. \nhttps://doi.org/10.26832/24566632.2020.0502013 \n\n\n\nSharma, S., Tiwari, S., 2020. Biology of Pumpkin Fruit Fly, Zeugodacus tau \nWalker (Diptera: Tephritidae) in cucumber in Kathmandu Nepal. \nJournal of Plant Protection Society, Pp. 100-107. \n\n\n\nSingh, S.K., Kumar, D., Ramamurthy, V.V., 2010. Biology of Bactrocera \n(Zeugodacus) tau (Walker) (Diptera: Tephritidae). Entomological \nResearch, Pp. 259-263. \n\n\n\nSohrab, C.P. and W.H., 2018. Study on the biology and life cycle of cucurbit \nfruit fly, Bactrocera cucurbitae (Coquillett). Journal of Pharmacognosy \nand Phytochemistry, 7 (1S), Pp. 223\u2013226. \n\n\n\nSumatra, N., 2012. Fruit Fly Management. ASEAN Sustainable Agrifood \nSystems (SAS), 1, Pp. 1\u201310. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 66-70 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.66.70 \n\n\n\nCite the Article: Muhammad Nurfaiz Abd. Kharim, Aimrun Wayayok, Ahmad Fikri Abdullah, Abdul Rashid Mohamed Shariff (2020). Effect Of Variable Rate Application On \nRice Leaves Burn And Chlorosis In System Of Rice Intensification. Malaysian Journal of Sustainable Agriculture, 4(2): 66-70. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.66.70\n\n\n\nEFFECT OF VARIABLE RATE APPLICATION ON RICE LEAVES BURN AND \nCHLOROSIS IN SYSTEM OF RICE INTENSIFICATION \n\n\n\nMuhammad Nurfaiz Abd. Kharima, Aimrun Wayayoka,b, Ahmad Fikri Abdullaha,b, Abdul Rashid Mohamed Shariffa,c  \n\n\n\na Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia. \nb SMART Farming Technology Research Center, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia. \nc Faculty of Agro Based Industry, University Malaysia Kelantan Campus Jeli, Locked Bag 100, 17600 Jeli, Kelantan, Malaysia. \nCorresponding author; e-mail: faizkharim@gmail.com; phone number: +60 134708094 \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 03 March 2020\n\n\n\nBoth nitrogen deficiency and over-fertilization result in rice leaf damage and effect on yield production. \nInsufficient nitrogen supply gives raise to yellow leaves, whereas spraying too high concentrations of \nfertilizers can be recognized by leaf burn. This study used variable rate application (VRA) of organic foliar \nfertilization to fertilize the System of Rice Intensification (SRI) cultivation without application of granular \nfertilizer and used Soil Plant Analysis Development (SPAD) chlorophyll meter as a tool to measure \nchlorophyll-nitrogen content for fertilizer calculation. Based on a greenhouse experiment the effect of \nnitrogen regime on rice leaves damage was assessed. The experiment consisted of four nitrogen regimes \n(50% fixed rate, 100% fixed rate,150% fixed rate and VRA) arranged in a randomized complete block design \n(RCBD) with four replications. Experimental result showed that none of the treatments resulted in leaf burn, \nwhereas chlorosis was observed for all the treatments. VRA had the lowest level of chlorosis with low \nsupplied of nitrogen compared to uniform treatments. Application of organic liquid fertilizer in variable rate \nform and using SPAD chlorophyll meter able to help to diagnose accurately the nitrogen content in the rice \nleaves for fertilizer application and capable to reduce chlorosis effect on the rice leaves.  \n\n\n\nKEYWORDS \n\n\n\nnutrients deficiency, leaf burn, chlorophyll-nitrogen content, SPAD meter, precision farming.\n\n\n\n1. INTRODUCTION \n\n\n\nShortage of major nutrients such as nitrogen is a serious issue in rice \ncultivation since the nutrients deficiency can lead to plant chlorotic \ncondition and limits the rice yields (Chen and Wang, 2014).  Nitrogen \ndeficiency on rice plant can be noticeable during vegetative and \nreproductive growth due to insufficient rates of nitrogen fertilization, and \nfertilizer loss to environment. In fact, nitrogen deficiency in soil was also \ncaused by other reasons such as loss of nitrogen due to soil erosion, \nleaching, volatilization, and denitrification (Alam et al., 2015). Since \nnitrogen is one of the key limiting nutrients in rice cultivation, therefore, \nknowing the rice nutrients status and finding accurate nitrogen level \nrequired by the rice is crucial in enhancing growth development and yield \n(Gholizadeh et al., 2017).  \n\n\n\nLeaf yellowing is the most frequently observed nutrient deficiency \nsymptom in rice production including in the System of Rice Intensification \n(SRI). Yellowing on leaves usually will change to yellow-pale or yellow \nwhite and if the deficiency is too severe the leaves will fully turn into \nyellow-brown colour. While other numerous symptoms are also shown by \nthe rice plant whenever having nitrogen deficiency such as reduction in \ntiller number, low panicle count, reduction in spikelet number and low \ngrain count (Fageria and Santos, 2015). Commonly, leaf yellowing is the \nchlorotic condition happened due to low rates of fertilizer supplied to the \nplant. Chlorosis is as a condition where the rice leaves produce low \nchlorophyll content and affect the green colour of the leaf therefore related \nto the leaf nitrogen content in the plant (Shayganya et al., 2011). \n\n\n\nChlorophyll is mainly responsible for effective photosynthesis process \nwhich use sunlight to produce foods or substances within the plant cell for \nplant growth and development. If nitrogen deficiency occurred in plant, it \nis unable to produce sufficient chlorophyll and caused leaves to become \nlight green-yellow pale and chlorotic at the tip especially for old leaves. \nWhile for young leaves, the leaves will become narrow, short, erect and \nyellowish. Leaf chlorosis is also a sign of inefficient photosynthesis within \nthe rice plant and photosynthesis decreases as the severity of nitrogen \ndeficiency increases (Muhidin et al., 2018).  \n\n\n\nUsually, nitrogen deficiency symptom is diagnosed by using visual \nmorphological diagnoses in the field but it is tedious and require vast \namount of experiences to accurately diagnose the deficiency (Lee and Lee, \n2013). Fortunately, a hand-held tool such as Soil Plant Analysis \nDevelopment (SPAD) chlorophyll meter is created and has the advantage \nto diagnose accurately the nitrogen deficiency in the plant since SPAD \nvalues has a relationship between leaf chlorophyll concentration with \nnitrogen content (Liu et al., 2015). Currently, SPAD chlorophyll meter \nwidely used in rice cultivation including SRI cultivation for fertilizer \nmanagement specially to improve nutrient-nitrogen based fertilizer \napplication (Ghosh et al., 2013).  \n\n\n\nFoliar fertilization is practiced widely in most cropping system as well as \nmaintaining soil fertilizer application within the cultivation due to its \nadvantages to minimize the soil-environment degradation by reducing the \namount of granular soil based application through reduction of nutrients \nleaching, runoff, drainage and ground water contamination (Wang et al., \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 66-70 \n\n\n\nCite the Article: Muhammad Nurfaiz Abd. Kharim, Aimrun Wayayok, Ahmad Fikri Abdullah, Abdul Rashid Mohamed Shariff (2020). Effect Of Variable Rate Application On \nRice Leaves Burn And Chlorosis In System Of Rice Intensification. Malaysian Journal of Sustainable Agriculture, 4(2): 66-70. \n\n\n\n2017). Hence, foliar fertilization capable to increase agronomic \nperformances of plant and increase plant yield compared to the granular \nmethod (Saberioon et al., 2013). Most of the time, foliar fertilization is \npracticed only to supplement micronutrient elements in rice cultivation to \ncorrect nutritional deficiency problem of the plant but the rates of the \napplication still in uniform rate (Rabin et al., 2016). However, leaf burn on \nthe plant can be observed after spraying the foliar fertilizer which could \nindicate high dosage of fertilizer rates used or improper handling during \nthe foliar fertilization process.  \n\n\n\nEven though there are many researches about foliar fertilization that \nshowed many advantages however there were less information on the \nleaves burn and chlorosis effect on the rice plant by using only foliar \nfertilization as solely fertilizer sources to supply macro nutrients in rice \ncultivation. In fact, there was less information on the application of \nprecision farming principle within the SRI fertilizer management since \nmost of the SRI cultivation relies on the uniform rates of fertilizer \napplication either for organic or inorganic type of fertilizer (Lee et al., \n2009). The aim of the study is to understand the actual needs of the \nnutrients that required by the rice plant before nutrient sources applied to \nprevent the incident of leaf burn and chlorosis symptoms. Thus, the main \nobjective of the study is to highlight the usage of variable rate application \ntechnique by using SPAD chlorophyll meter as a tool to apply precisely the \nvariable rate organic fertilizer in the SRI and to understand the close \ninteraction between the chlorosis-nitrogen deficiency symptom while \npreventing leaf burn effect on the rice leaves. \n\n\n\n2. MATERIAL AND METHODS \n\n\n\n2.1 Site and soil \n\n\n\nThe experiment was conducted at Ladang 2, Universiti Putra Malaysia \n(3.0087\u00b0 N, 101.7037\u00b0 E) from January 1, 2017 until April 30,2017. Rice \ncultivar MR219 was planted in pots of 40 cm height and 34 cm diameter \nsize filled with sandy clay loam soil in weight of 15 kg. The experimental \nsoil used have pH 6.8, soil organic matter 5.40%, total nitrogen 0.25%, \navailable phosphorus 230.8 kg/ha, available potassium 287.6 kg/ha and \nCEC 18.3 cmol kg-1 soil.  \n\n\n\n2.2 Treatment and cultural operations \n\n\n\nAn organic foliar fertilizer (fish based) was used in this experiment as \nfoliar spraying application with nitrogen, phosphorus, and potassium of \n4.8:4:3.5. Foliar spraying was performed at early crop establishment \u2013 15 \nDays After Transplanting (DAT), mid-tillering (35 DAT), panicle initiation \n(55 DAT) and flowering (65 DAT). This experiment only took into account \nof nitrogen content in the leaves for fertilizer amount calculation while \nphosphorus and potassium ratio were formulated according to supplier \nrecommendation. The experiment consisted four type of treatments; 50% \nfixed rate, 100% fixed rate, 150% fixed rate and variable rate application \n(VRA). Each treatment had four replications arranged in a randomized \ncompletely block design (RCBD).  \n\n\n\nTreatment of 50% fixed rate, 100% fixed rate, and 150% fixed were \napplied based on the supplier recommendation without using SPAD \nchlorophyll meter measurement. While the VRA treatment was performed \nbased on SPAD chlorophyll meter measurement to measure the rice leaf \nchlorophyll content which later transferred into nitrogen fertilizer \nformulation to recommend precise nitrogen amount that actually required \nby the rice plant. A handheld pump sprayer was used in the experiment to \nperformed foliar spraying on top of the canopy of the rice leaves. The rice \nseeds were pre-germinated in the SRI nursery tray for 10 days and then \ntransferred into the pot with one seedling planted per pot (Zubairu et al., \n2015). The pots were arranged in a line according to each group of \ntreatment with widely space and planting distance between the pots of 34 \ncm x 34 cm.  \n\n\n\nSRI planting practices were performed throughout the period of rice \ncultivation until the harvesting the process, where the water regime was \nmaintained at the moist condition level only and weeding were performed \nfor every planting pots. Water conditions in the pots were monitored \nevery day until the moist condition was maintained and whenever the soil \nin the pot looked too dry additional water was added. Repeated weeding \nin the pot was performed every 10 days by using hand and fork weeder to \naerate the soil, remove weeds and incorporate them into the soil as exactly \nas the SRI method practiced in the field. Adequate pest and disease control \nmeasures were taken throughout the plants\u2019 growth according to SRI \nrecommendation such as application of liquid neem-based bio-pesticide to \nspray all over the rice plant at every planting stages to reduce pest and \ndisease attack to avoid further variability. \n\n\n\n2.3 SPAD measurement \n\n\n\nChlorophyll meter (SPAD-502, Minolta Camera Co., Japan) was used in the \nexperiment for taking the SPAD reading before and after foliar fertilization \napplication; at the early crop establishment (15 & 18 DAT), midtillering \n(35 & 38 DAT), panicle initiation (55 & 58 DAT) & flowering (65 & 68 DAT). \nOutermost and fully expanded leaves of rice plant were taken at the tip, \nmidway and base of the leaf and then were averaged (Yuan et al., 2016). \nHence, SPAD reading for the rice plant were also measured for every 10 \ndays until 95 DAT before the harvesting process.  \n\n\n\n2.4 Nitrogen application formula \n\n\n\nThe nitrogen application formula was modified to determine the amount \nof nitrogen for VRA foliar fertilization treatment in the experiment to \ndevelop threshold chlorophyll values for rice plant in the region of \nMalaysia (Gholizadeh et al., 2011). At early crop establishment (15 DAT) \nuntil mid-tillering (35 DAT), formula (1) was used to determine nitrogen \ncontent; N (mg/L) = 0.80 + 0.93*SPAD. Then, at panicle initiation (55 DAT) \nand flowering (65) stages, formula (2) was used to determine nitrogen \ncontent; N (mg/L) = -2.61 + 0.98 *SPAD. Overall formula (3) to determine \namounts of foliar fertilization (mL) needed for liquid spraying was; (mL) \n= [A - (formula (1) or (2) / 1000)] / C, where: A is the threshold level of N \nin the rice leaves in mg/mL while C is the N amount in percentage of the \nfoliar fertilization used. \n\n\n\nAt early crop establishment (15 DAT) and mid-tillering (35 DAT)  \n\n\n\nNitrogen (mg/L) = 0.80 + 0.93*SPAD (modified after Gholizadeh et al. [16]) \n\u2026\u2026\u2026\u2026\u2026\u2026... (1) \n\n\n\nAt panicle initiation (55 DAT) and flowering (65 DAT) \n\n\n\nNitrogen (mg/L) = -2.61 + 0.98 *SPAD (modified after Gholizadeh et al. \n[16] \u2026\u2026...\u2026\u2026\u2026... (2) \n\n\n\nAmount of Liquid Fertilizer (mL) = [A - (1 or 2 / 1000)] / C \u2026\u2026\u2026\u2026\u2026\u2026 (3) \n\n\n\n2.5 Plant sampling  \n\n\n\nPlant sampling was performed to measure the number of chlorosis and \nleaf burns on rice leaves were conducted from 15 DAT until 95 DAT for all \nthe treatment. All the sampling was collected and recorded as parameter \ndata to determine the effect of chlorosis and leaf burn on rice leaves \nbetween VRA and uniform fertilizer rates. \n\n\n\n2.6 Statistical analysis \n\n\n\nFor statistical analysis of data, Statistical Analysis System Software (SAS \n9.1, SAS, USA) was employed. All data collected were subjected to analysis \nof variance (ANOVA) and mean values between treatment rates were \ncompared using Tukey\u2019s honest significant difference (HSD) test at 0.05.  \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 SPAD values \n\n\n\nFigure 1 (a) and (b) show the SPAD reading pattern of all the treatments \nwhich varied according to different DAT. Figure 1(a) show the SPAD \nreadings of rice plant that had been taken before and after foliar \nfertilization in order to measure the effectiveness of foliar spraying at \ndifferent rates to rice plant. Treatments of 100% fixed rate, 150% fixed \nrate and VRA show increase in SPAD readings from 15 DAT until 68 DAT \nwith less chlorosis deficiency symptom on the rice leaves. However, only \n50% fixed rate shows decline in SPAD readings after foliar fertilization \nbetween 55 - 58 DAT and 65 - 68 DAT where higher chlorosis count was \nobserved which indicate less amount of chlorophyll content in the rice \nleaves due to insufficient of nitrogen supply from the foliar fertilization to \nthe plant. Active process for panicle initiation is happened during period \nof 55 \u2013 68 DAT and require a lot of amount of nutrients especially nitrogen \nsince nitrogen is essential substance of amino acid, nucleic acid, \nchlorophyll, protein (enzymes) and others for panicle production and \nmultiplication (Foulkes and Murchie, 2011; Hu et al., 2014).  \n\n\n\nWhile during flowering period which happened during 65 \u2013 75 DAT, rice \nplant requires more nutrients of phosphorus and potassium however still \nrequired nitrogen for grain production since nitrogen can affects all \nparameters contributing to yield. While from Figure 1(b), VRA shows the \nhighest SPAD readings pattern throughout the planting period until 95 \nDAT compared to other treatments. However, treatments of 150%, 100% \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 66-70 \n\n\n\nCite the Article: Muhammad Nurfaiz Abd. Kharim, Aimrun Wayayok, Ahmad Fikri Abdullah, Abdul Rashid Mohamed Shariff (2020). Effect Of Variable Rate Application On \nRice Leaves Burn And Chlorosis In System Of Rice Intensification. Malaysian Journal of Sustainable Agriculture, 4(2): 66-70. \n\n\n\nfixed rates and VRA show decline in SPAD reading from 75 DAT until 95 \nDAT after the last cycle of foliar fertilization during 65 DAT. While, \ntreatment of 50% fixed rate shows the lowest SPAD readings pattern and \nshow decline trend in SPAD reading from 55 DAT until 95 DAT which \nindicate that it is unable to supply sufficient nutrients to rice plant for \ngrain production until grain maturity process. Sufficient nutrients source \nespecially nitrogen was essential to develop sufficient chlorophyll content \nin the leaf for efficient and effective photosynthesis process and if there \nwas deficiency, reduction of plant photosynthetic rate could happen and \ncause declining in plant growth and developme thus yield contribution \n(Amane, 2011).  \n\n\n\nFigure 1: SPAD reading of rice leaf for different fertilizer treatment rates \n(a) SPAD reading before and after foliar fertilization to the plant at four \n\n\n\nsplit time of fertilization (b) SPAD reading of rice leaf recorded on \nvarious DAT. \n\n\n\n3.2 Chlorosis occurrence on rice leaves \n\n\n\nChlorosis occurrence on the rice leaves was determined through visual \nobservation by detecting the discoloration on the leaves from green to \nyellow whether on certain leaf regions or complete region of leaf. \nCommonly, chlorosis occurrence occurred earlier at older leaves \ncompared to new leaves since nitrogen was a mobile type of nutrient \ndeficiency and because of its high mobility, it easily lost to the plant and \nsoil. However, after a few days of nitrogen fertilizer application, the rice \nleaves change back to green colour due to nitrogen high mobile ability. \nReduction of nitrogen content in leaf can be close relationship to leaf \nchlorosis and a sign of the reduction in photosynthesis process in the plant \ndue to less function of chlorophyll to reflect green light to produce green \ncolouration (Mona et al., 2012). As can be seen in the Table 1, number of \nchlorosis was observed and counted on every 10 days until 95 DAT before \nharvesting process. All the treatment rates had chlorosis occurrence on \nrice leaves in the early stages (15 DAT). \n\n\n\nThe occurrence of chlorosis on rice leaves keep happening throughout the \nplanting period until 95 DAT without showing any decline in chlorosis \ncount even after foliar fertilization was performed. The highest chlorosis \ncount was observed on rice leaves of 50% fixed rate treatment followed \nby 100% fixed rate, 150% fixed rates and VRA was the lowest in the \nchlorosis count as compared to other treatments. The chlorosis \noccurrence on the rice leaves might indicates the early sign of reduction in \nplant leaves chlorophyll contents and reduction of chloroplast number in \n\n\n\nleaf which was responsible for leaf yellowing due to nitrogen deficiency in \nthe rice plant. The increment in chlorosis count on the rice leaves from 15 \nDAT until 75 DAT was usually happened because rice requires nitrogen \nalmost throughout the vegetative cycle however more demanding during \nperiod of tillering and panicle initiation stages was in critical and need in \nlarge amount (Fageria and Santos, 2015).  \n\n\n\nThe accumulation of nitrogen was begun in the leaves during vegetative \nphase (15 \u2013 35 DAT), then migrates to panicles (55 -65 DAT) and last \nduring the grain maturity (75 \u2013 95 DAT) period. Mostly nitrogen was \nabsorbed by the rice in large quantities for sufficient growth, development, \nyield and during grain maturity stages, 75% of the nitrogen assimilates in \nthe grain. That could be the reason why during period of 85 \u2013 95 DAT \nchlorosis occurrence was increase dramatically while SPAD reading of rice \nleaf was declining for all the treatments since nitrogen was consumed in \nhigh amount by the rice plant during this period for the grain maturity \nprocess. From the Figure 1 and Table 1, it can be said that uniform \napplication such as 50%,100%, and 150% fixed rate of foliar fertilization \nunable to provide sufficient nutrients throughout the planting period of \nrice plant due to high accumulation of chlorosis count.  \n\n\n\nMeanwhile, rice plant that received foliar fertilization by using VRA \nmethod showed less chlorosis count during entire planting period because \nof accurate nutrients adjustment was performed based on actual needs by \nthe rice plant by using VRA-SPAD chlorophyll meter measurement \nmethod. SPAD chlorophyll meter was proven to predict accurately the \nnitrogen needed by rice plant based on current nutrients availability in the \nsoil and plant requirement for specific application of foliar fertilization \namount and concentration rates in SRI cultivation. Therefore, VRA foliar \nfertilization by using SPAD chlorophyll meter was useful during site \u2013 \nspecific fertilizer application especially for foliar fertilization to provide \nsufficient nutrients to the rice plant for healthy growth with less deficiency \noccurrence on the rice leaves. \n\n\n\nTable 1: Comparison of chlorosis counts between all the treatments \non various DAT (NOS) \n\n\n\nTreatments 15 35 55 65 75 85 95 \n\n\n\n50% 1.00a 2.50a 3.25a 4.00a 5.00a 8.00a 9.00a \n\n\n\n100% 1.00a 2.25a 2.75a 3.25ab 4.50a 6.00ab 8.5ab \n\n\n\n150% 1.00a 2.00a 2.50a 3.00ab 4.25a 5.50b 7.00ab \n\n\n\nVRA 1.00a 1.25b 2.25a 2.75b 3.75a 4.75b 6.00b \n\n\n\n*means separation in each column followed by the same letter are not \nsignificantly different at p = 0.05 \n\n\n\n3.3 Effect of weather patterns on chlorosis count \n\n\n\nWeather patterns such as relative humidity, solar radiation, sunshine \nhours, evapotranspiration and max temperature are important factor that \nplay significant role in rice productivity therefore could affect the rice \nphysiology growth during the study (Basak et al., 2010). In the study, \nweather pattern characteristics such as relative humidity, solar radiation, \nsunshine hours, monthly rain precipitation, evapotranspiration and max \ntemperature can be seen in Table 2. Weather patterns were varied \nthroughout the planting period according to the rice growth stages and \nsince the experiment was conducted in greenhouse-controlled experiment \nso weather characteristics such as rainfall, max and min temperature were \nsuitable and response accordingly as mentioned (Ablar et al., 2017). Lower \nsolar radiation and sunshine hours during the planting period could had \nreduced the photosynthesis process of the plant therefore less amount of \nchlorophyll content produce which can lead to yellowing of the leaf (Sun \net al., 2012). Hence, due to heavy clouding that caused shading might had \nlower the solar radiation and sunshine hours during the planting period \n(Chen et al., 2019).  \n\n\n\nWhile for relative humidity characteristic, high relative humidity was \nfound to be anticipated. High relative humidity can influence the \nevapotranspiration rate which high evapotranspiration rate may influence \nthe plant physiological process (Irmak, 2017). High relative humidity can \nreduce the evapotranspiration rate which later can turn liquid fertilizer \ndroplet into more atmospheric vapour. This can cause less absorption of \nliquid droplets into the leaves and cause the plant not sufficiently received \nenough nutrients from fertilizer spraying. So, this indirectly could cause \nmore chlorosis effect on the leaves which also indicate signed of nutrients \ndeficiency. Therefore, field weather pattern is important for consideration \nin planning the application of liquid fertilizer to ensure the optimization of \nliquid spraying effect to the plant in order to minimize the chlorosis \nsymptoms on the leaf which later can enhance the overall rice plant health \nand produce better yield. \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\n15 18 35 38 55 58 65 68\n\n\n\nSP\nA\n\n\n\nD\n r\n\n\n\nea\nd\n\n\n\nin\ng\n\n\n\nDays After Transplanting (DAT)\n\n\n\n50% 100% 150% VRA\n\n\n\n(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\n35\n\n\n\n40\n\n\n\n45\n\n\n\n50\n\n\n\n15 35 55 65 75 85 95\n\n\n\nSP\nA\n\n\n\nD\n r\n\n\n\nea\nd\n\n\n\nin\ng\n\n\n\nDays After Transplanting (DAT)\n\n\n\n50% 100% 150% VRA\n\n\n\n(b) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 66-70 \n\n\n\nCite the Article: Muhammad Nurfaiz Abd. Kharim, Aimrun Wayayok, Ahmad Fikri Abdullah, Abdul Rashid Mohamed Shariff (2020). Effect Of Variable Rate Application On \nRice Leaves Burn And Chlorosis In System Of Rice Intensification. Malaysian Journal of Sustainable Agriculture, 4(2): 66-70. \n\n\n\nTable 2: Pattern of meteorological data at Ladang 2, University Putra Malaysia from January until April 2017 (retrieved from CLIMWAT, FAO). \n\n\n\nMonth Rain precipitation \n\n\n\n(mm/Month) \n\n\n\nETo (mm/day) Solar radiation \n\n\n\n(MJ/m2/day) \n\n\n\nSunshine hours \n\n\n\n(h/day) \n\n\n\nRelative humidity \n\n\n\n(%) \n\n\n\nMax temperature \n\n\n\n(\u2103) \n\n\n\nMin temperature \n\n\n\n(\u2103) \n\n\n\nJanuary 162.8 3.54 17.82 6.11 85.48 31.9 22.1 \n\n\n\nFebruary 144.7 3.81 19.30 6.56 88.81 32.8 22.3 \n\n\n\nMarch 218.4 4.06 20.00 6.73 84.38 33.1 22.8 \n\n\n\nApril 284.8 3.92 19.41 6.56 87.8 33.0 23.4 \n\n\n\n4. CONCLUSION \n\n\n\nThe present study shows there was no any incidence of leaf burn on the \nrice leaves at all fertilization rates. By using SPAD chlorophyll meter \nmeasurement, less chlorosis occurrence was observed on the rice leaves \ncompared to treatment that used uniform fertilization without relies on \nSPAD chlorophyll meter measurement. It can be concluded that rice plant \nonly required specific amount of nitrogen at specific time of its growth \nstages and precise fertilization can benefit the SRI cultivation to lessen the \nchlorosis occurrence and to prevent leaf burn from excessive foliar \nfertilization.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors would like to acknowledge the Universiti Putra Malaysia for \nfunding this research project under Putra Grant, project code GP-\nIPS/2017/9573700 and SMART Farming Technology Research Centre, \nFaculty of Engineering, University Putra Malaysia for technical support. \n\n\n\nCONFLICT OF INTEREST \n\n\n\nThe authors declare that they have no conflict of interest. \n\n\n\nREFERENCES \n\n\n\nAblar, R.M., Kang, S.C., Nagabhatla, N., Macnee, R., 2017. Impacts of \n\n\n\ntemperature and rainfall variation on rice productivity in major \n\n\n\necosystem of Bangladesh. Agriculture & Food Security, 6, 10 \n\n\n\nAlam, Z., Sadekuzzaman, Md., Sarker, S., Hafiz, R.H., 2015. Reducing soil \n\n\n\napplication of nitrogenous fertilizer as influenced by liquid fertilization \n\n\n\non yield and yield traits of kataribhog rice. International Journal of \n\n\n\nAgronomy and Agricultural Research, 6 (1), 63-69. \n\n\n\nAmane, M., 2011. Photosynthesis, grain yield, and nitrogen utilization in \n\n\n\nrice and wheat. Plant Physiol, 155 (1), 125-129. \n\n\n\nhttps://doi.org/10.1104/pp.110.165076 \n\n\n\nBasak, J.K., Ali, M.A., Islam, N.M., Rashid, A.M., 2010. Assessment of the \n\n\n\nclimate change on boro rice production in Bangladesh using DSSAT \n\n\n\nmodel. Journal of Civil Engineering (IEB), 38 (2), 95\u2013108. \n\n\n\nChen, H., Li, Q.P., Zeng, Y.L., Deng, F., Ren, W.J., 2019. Effect of different \n\n\n\nshading materials on grain yield and quality of rice. Scientific Reports, \n\n\n\n9, 9992. \n\n\n\nChen, L.S., Wang, K., 2014. Diagnosing of rice nitrogen stress based on \n\n\n\nstatic scanning technology and image information extraction. Journal of \n\n\n\nSoil Science and Plant Nutrition 14(2), Pp. 382\u2013393. \n\n\n\nFageria, N.K., Santos, A.B., 2015. Yield and yield components of lowland \n\n\n\nrice genotypes as influenced by nitrogen fertilization. Communications \n\n\n\nin Soil Science and Plant Analysis, 46, 1723-1735.  \n\n\n\nFageria, N.K., Santos, A.B., 2015. Yield and yield components of lowland \n\n\n\nrice genotypes as influenced by nitrogen fertilization. Communications \n\n\n\nin Soil Science and Plant Analysis, 46, 1723-1735. \n\n\n\nhttps://doi.org/10.1080/01904169909365603 \n\n\n\nFoulkes, M.J., Murchie, E.H., 2011. Optimising canopy physiology traits to \n\n\n\nimprove the nutrient use efficiency in crops. In M. J. Hawkesford, & P. \n\n\n\nBarraclough, P. (Eds.). The molecular and physiological basis of nutrient \n\n\n\nuse efficiency in crops. (pp. 65\u201383) Chichester: Wiley-Blackwell. \n\n\n\nGholizadeh, A., Amin, M.S.M., Anuar, A.R., Aimrun, W., Saberioon, M.M., \n\n\n\n2011. Temporal variability of SPAD chlorophyll meter readings and its \n\n\n\nrelationship to total nitrogen in leaves within a Malaysian paddy field. \n\n\n\nAustralian Journal of Basic and Applied Sciences, 5 (5), 236-245. \n\n\n\nGholizadeh, A., Saberioon, M.M., Lubos\u02c7 Boru\u00b0 vka, Aimrun, W., Amin, \n\n\n\nM.S.M., 2017. Leaf chlorophyll and nitrogen dynamics and their \n\n\n\nrelationship to lowland rice yield for site-specific paddy management. \n\n\n\nInformation Processing in Agriculture, 4, 259-268. \n\n\n\nGhosh, M., Swain, D.K., Madan, K., Jha, Tewari, V.K., 2013. Precision \n\n\n\nnitrogen management using chlorophyll meter for improving growth, \n\n\n\nproductivity and N use efficiency of rice in subtropical climate. Journal \n\n\n\nof Agricultural Science, 5 (2), https://doi.org/10.5539/jas.v5n2p253 \n\n\n\nHu, Y., Jingping, Y., Yamin, L., Junjun, H., 2014. SPAD Values and Nitrogen \n\n\n\nNutrition Index for the Evaluation of Rice Nitrogen Status. Plant \n\n\n\nProduction Science, 17(1), 81-92. https://doi.org/10.1626/pps.17.81 \n\n\n\nIrmak, S., 2017. Evapotranspiration basic and estimating actual crop \n\n\n\nevapotranspiration from reference evapotranspiration and crop-\n\n\n\nspecific coefficient. Nebraska Extension. \n\n\n\nLee, J.H., Kang, C.S., Roh, A.S., Park, K.L., Lee, H.J., 2009. Assessment of N \n\n\n\ntopdressing rate at panicle initiation stage with chlorophyll meter-\n\n\n\nbased diagnosis in rice. Journal Crop Science Biotechnology, 12 (4), 195-\n\n\n\n200.  \n\n\n\nLee, K.J., Lee, B.W., 2013. Estimation of rice growth and nitrogen nutrition \n\n\n\nstatus using colour digital camera image analysis. European Journal of \n\n\n\nAgronomy, 48, 57-65. https://doi.org/10.1016/j.eja.2013.02.011 \n\n\n\nLiu, K., Li, Y., Hu, H., Zhou, L., Xiao, X., Yu, P. 2015. Estimating rice yield \n\n\n\nbased on normalized difference vegetation index at heading stage of \n\n\n\ndifferent nitrogen application rates in southeast of China. Journal of \n\n\n\nEnvironmental & Agricultural Sciences, 2, 13. \n\n\n\nMona, E.E., Ibrahim, S.A., Manal, M.F., 2012. Combined effect of NPK levels \n\n\n\nand foliar nutritional compounds on growth and yield parameters of \n\n\n\npotato plants (Solanum tuberosum L.). Afr J Micro Res., 6(24), 5100-\n\n\n\n5109. https://doi.org/10.5897/AJMR12.085 \n\n\n\nMuhidin, Syam\u2019un, E., Kaimuddin, Musa, Y., Sadimantara, G.R., Usman, \n\n\n\nLeomo, S., Rakian, R.C., 2018. The effect of shade on chlorophyll and \n\n\n\nanthocyanin content of upland red rice. IOP Conference Series: Earth \n\n\n\nand Environmental Science, 122.  \n\n\n\nRabin, M.H., Razzaque, Md. A., Zamil, S.S., Zaman, Kh, A., Siddik, Md. A., \n\n\n\n2016. Foliar application of urea and magic growth liquid fertilizer on \n\n\n\nthe yield and nutrient content of Aman rice cultivars. American-\n\n\n\nEurasian Journal of Agricultural & Environment Science, 16 (4), 737 - \n\n\n\n743. \n\n\n\nSaberioon, M.M., Amin, M.S.M., Wayayok, A., Gholizadeh, A., Anuar, A.R., \n\n\n\n2013. Assessment of colour indices derived from conventional digital \n\n\n\ncamera for deter-mining nitrogen status in rice plants. Journal of Food, \n\n\n\nAgriculture and Environment, 11 (2), 655\u2013662. \n\n\n\nShayganya, J., Peivandy, N., Ghasemi, S. 2011. Increased yield of direct \n\n\n\nseeded rice (Oryza sativa L.) by foliar fertilization through \n\n\n\nmulticomponent fertilizers. Archives of Agronomy and Soil Science 1-8. \n\n\n\nhttps://doi.org/10.1080/03650340.2011.570336 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 66-70 \n\n\n\nCite the Article: Muhammad Nurfaiz Abd. Kharim, Aimrun Wayayok, Ahmad Fikri Abdullah, Abdul Rashid Mohamed Shariff (2020). Effect Of Variable Rate Application On \nRice Leaves Burn And Chlorosis In System Of Rice Intensification. Malaysian Journal of Sustainable Agriculture, 4(2): 66-70. \n\n\n\nSun, Y.Y., Sun, Y.J., Chen, L., Xu, H., Ma, J., 2012. Effects of different sowing \n\n\n\ndates and low-light stress at heading stage on the physiological \n\n\n\ncharacteristics and grain yield of hybrid rice. Chinese Journal of Applied \n\n\n\nEcology, 23 (10), 2737\u20132744. \n\n\n\nWang, Y., Lu, J., Ren, T., Hussain, S., Guo, C., Wang, S., Cong, R., Li, X., 2017. \n\n\n\nEffects of nitrogen and tiller type on grain yield and physiological \n\n\n\nresponses in rice. AoB PLANTS 9. \n\n\n\nYuan, Z., Ata-Ul-Karim, S.T., Cao, Q., Lu, Z., Cao, W., Zhu, Y., 2016. Indicators \n\n\n\nfor diagnosing nitrogen status of rice based on chlorophyll meter \n\n\n\nreadings. Field Crops Res 185, 12\u201320. \n\n\n\nhttps://doi.org/10.1016/j.fcr.2015.10.003 \n\n\n\nZubairu, U.B., Aimrun, W., Amin, M.S.M., Mahadi, M.R., Bande, Y.M., 2015. \n\n\n\nSri-tray: Breakthrough in nursery management for the System of Rice\n\n\n\nIntensification. Jurnal Teknologi, 78 (1-2), 65 \u2013 71. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.131.141 \n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-293X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.02.2022.131.141 \n\n\n\n\n\n\n\nAN AGRICULTURAL \u2018SYSTEMS-BASED\u2019 FRAMEWORK FOR INDEXING POTENTIAL \nEXPOSURE TO FARMING PESTICIDES: TEST FINDINGS FROM ASIA-PACIFIC, AND \nASEAN \n \nEllis Wongsearaya \n\n\n\nThree Percent Earth Foundation, 101 Moo 6, Liap Klong Prapa Road, Ban Mai, Pakkret, Nonthaburi, Thailand, 11120. \n*Corresponding Author Email: info@threepercentearth.org \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 18 May 2022 \nRevised 13 July 2022 \nAccepted 11 October 2022 \nAvailable online 31 October 2022 \n\n\n\n\n\n\n\n The issue of ASEAN food security has led to chemical pesticides-driven policy directives as economic \nconvention for protecting crop yields while concomitantly conferring an implicit ecological and health risk-\nbased \u2018trade-off\u2019 that works to undermine SDG target indicators 2.4, 3.9, and 6.3. In this study the Pesticides \nConsumer-Environmental Indexing System (PCE-ISys), a conceptual heuristic \u2018systems-based\u2019 framework is \nproposed to explore needed policy-informing option(s) beyond the largely cost-externalising rubric of \nchemical crop protection management, by indexing (the potential for and magnitude of potential) pesticides \nexposure (EIR-IS) using a semi-quantitative tiered percentile-based, continuous-to-discrete variable \ntransform that captures the stochastic distribution arising from the \u2018generalisable\u2019 interconnectivity of \npolitical governance, agricultural economy, and natural environment. 1990-2016 indexing results revealed \n\u2018high\u2019 EIR-IS levels for 52% and 63% of Asia-Pacific and ASEAN nations, respectively, with 28% of Asia-Pacific \ncountries scoring at \u2018highest\u2019 indexing levels demonstrating pervasive and expansive pesticides-use and/or \ntonnage contrary to IPM sustainable agricultural practices. \n\n\n\nKEYWORDS \n\n\n\nAgricultural Economy; Food Systems; Pesticides Exposure; Public Health; Southeast Asia \n\n\n\n \n1.   INTRODUCTION \n\n\n\nAround the middle-to-late twentieth century rapid population growth, and \nincreased food demand helped drive the impetus for what would become \nthe green revolution, a set of broad technological, agricultural, and \neconomic policy directives aimed at ending food hunger across large \nswaths of the developing world such as Southeast Asia (ASEAN).  \nProducing sufficient crop output to feed millions of people (over a \nrelatively short-term) necessitated a shift from subsistence farming to \nhigh production volume agricultural methods dependent upon intensive \nuse of land and natural resources (Hazell 2009; Paddock, 1970; Pingali, \n2012).  Today, the aim of global food security is set forth in United Nations \n(UN) Sustainable Development Goal (SDG) 2 (United Nations Development \nProgramme, 2021).  \n\n\n\n1.1   Agroeconomic Systems, Pesticides, and Human Health Risk \n\n\n\nAgricultural intensification in line with green revolution policies was (and \nstill is) characterised by the adoption of pervasive chemical pesticides-use \nas economic convention for protecting crop yields (EU Parliament, 2021; \nFernandez-Cornejo et al., 1998; Glass and Thurston, 1978; Pingali, 2001; \nPinstrup-Andersen, 2002; Popp et al., 2013; Popp and Hantos, 2011; \nRepetto, 1986; Sharma et al, 2019; Zhang, 2018; Zilberman et al., 1991).  \nWorldwide, pesticides are largely managed by way of a cost-benefit \nderived regulatory approach defined by quantitation of risk that \nextrapolates \u2018safe\u2019 or \u2018acceptable\u2019 exposure levels from toxicity study data, \ni.e., MRLs.  The outcome has been (and is) a politically tolerated (if not \nwidely accepted) risk-based \u2018trade-off\u2019 with health and environment \ndespite the ever-growing repository of information demonstrating the \necological and public health impacts associated with their use (United \n\n\n\nStates International Trade Commission, 2020; Zilberman et al., 1991; \nZilberman and Millock, 1997).   \n\n\n\nThis compromise, however, arguably works to fundamentally undermine \nthe purpose of SDG target indicators such as 2.4, 3.9, and 6.3.    The \nconvergence of numerous factors including, pesticides physico-chemical \nproperties, conditions of climate, air, land, and water, as well as collective \npolitical and regulatory decision-making aimed at meeting food \nconsumption demand and economic growth objectives all potentially \ninfluence population-based risk from high production volume chemical \nfarming pesticides (Organisation for Economic Co-operation and \nDevelopment, 2018) commonly used within (highly complex) social-\nenvironmental systems of food and agriculture (Bonmatin et al., 2015; \nBoxall et al., 2009; Del Prado-Lu, 2015; Fernandez-Cornejo et al., 1998; \nGereslassie et al., 2019; Kennedy et al., 2019; Obilo et al., 2006; Rice et al., \n2007; Skevas, 2012; United Nations Environment Programme-HELI, \n2004).  Figure 1 depicts a diagram of a complex \u2018generalisable\u2019 \nagroeconomic environmental system. \n\n\n\nDevelopment-focused policy that guide systems of agricultural economy \nhave resulted in a strong propensity for pesticides exposure at the \npopulation-level (Aktar et al., 2009; Bonmatin et al., 2015; Economy and \nEnvironment Institute, 2017; EU Parliament, 2021; Gereslassie et al., \n2019; Lam et al., 2017; Pingali, 2001).  Conventional agroeconomic policy \nregimens rarely (if at all) take into account the health and environmental \nimpact(s) of likely residual concentration(s) found in food commodities, \nas well as almost certain contamination of air, soil, and water; a \nshortcoming further reinforced by risk-based regulatory policy  (Del \nPrado-Lu, 2015; Leach and Mumford, 2008; Pretty and Waibel, 2005; \nAktar et al., 2009; EU Parliament, 2021;  Obilo et al., 2006; Zillberman and \n\n\n\n\nmailto:info@threepercentearth.org\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nMillock, 1997).  An exploration of decision-support option(s) aimed at \nharmonising public health principles and measures with agricultural food \nsystems policy is logical for evolving beyond a largely \u2018cost-externalising\u2019 \ngovernance approach. \n\n\n\n\n\n\n\nFigure 1: Diagram of a \u2018generalisable\u2019 agroeconomic environmental \nsystem (g-AEES) \n\n\n\n1.2   Indexing as a Policy Decision-Support Tool  \n\n\n\nPolicy decision-support is an essential informational component for \nhelping guide governance-based decision making such as addressing the \nrole of agriculture in achieving economic goals tied to food production, \ntrade, and consumption (Lencucha et al., 2020; Rose et al., 2016; Udias et \nal., 2018). Indexing is a widely accepted, and reasonably transparent \nheuristic approach for evaluating relevant data in support of specific or \nbroad policy goals across a full range of social, economic, and \nenvironmental issues, such as air quality, monetary policy, and pesticides \nimpact(s) (Corporate Finance Institute, 2021; Gorai and Goyal, 2015; \nKovach et al, 1992; Sherrick, 2017; Surminski and Williamson, 2012; \nWorld Bank, 2021).  For pesticides this type of evaluation device is \n\n\n\nprincipally designed to comparatively score or rank chemicals, or \nchemical-related outcomes for purposes of risk reduction.   \n\n\n\nPesticides scoring frameworks including the environmental impact \nquotient (EIQ) used in support of IPM, and the pesticides environmental \nrisk indicator model (PERI) used in farm-level decision(s) to limit \npesticides groundwater contamination are designed to index based on \nphysico-chemical, environmental, and/or health related parameters, the \nformer using a simple (5=high, 3=medium, 1=low) coding system \ncorresponding to pesticides toxicity and physico-chemical reference \nvalues, and the latter relying on ecotoxicity and Kow values, as well as the \ngroundwater ubiquity score (GUS) (Benbrook and Davis, 2020; Chou et al., \n2019; Kookana et al., 2005; Kovach et al., 1992; Kromann et al., 2011; \nMuhammetoglu et al., 2010; Reus et al., 2002; Soudani et al., 2020; Van Bol \net al., 2005; Van Bol et al., 2003).   \n\n\n\nSome indexing models are strictly data-driven in methodology such as the \ndecoupling index used to address the \u2018connectivity\u2019 between agricultural \neconomy and agricultural pollution, while others such as the \nenvironmental performance index (EPI) evaluate multivariable complex \n\u2018systems,\u2019 by statistically modelling data from 32 sustainability indicators \nacross two broad evaluation categories, \u2018ecological vitality,\u2019 and \n\u2018environmental health,\u2019 measured against macroeconomic indicators for \n180 countries (Li et al., 2019; Wendling et al., 2020). The use of indexing \nmethods as policy decision-support in navigating, for example, the \ninherently complex milieu of pesticides use within agricultural food \nsystems offer regulators, policy makers, farmers, and civil society a \ntransparent and pragmatic evaluation approach from which to draw \npractical conclusions about risk reduction guidance measures, and \nstrategies for achieving ecological, and health-protective goals as a part of \nthe broader social-economic framework (Choi et al., 2019; Gorai and \nGoyal, 2015; Kookana et al., 2005; Kovach et al., 1992; Kromann et al., \n2011; Wendling et al., 2020). \n\n\n\nIn this project study a novel conceptual framework is proposed with the \naim of helping inform policy-relevant decision-making and aiding to \nfurther the sustainable agricultural development dialogue relating to \nreliance of pesticides within global/regional food systems, and its public \nhealth implications.  The Pesticides Consumer-Environmental Indexing \nSystem (PCE-ISys) is developed from a \u2018generalisable\u2019 agroeconomic \n\u2018systems-based\u2019 concept (g-AEES), and supported by semi-quantitative \nmethodology that draws on a \u2018three-tiered\u2019 (upper 25th percentile, median, \nand lower 25th percentile) data distribution coding designation to \ntransform continuous data variable(s) to discrete value(s) for a practical \nand transparent measure by which to index agricultural pesticides \npotential exposure defined as pesticides \u2018total exposure potential\u2019 (EIR-\nIS).  Analysis of the indexing functionality of PCE-ISys is showcased from \nthe perspective of select Asia-Pacific and ASEAN countries. \n\n\n\n2.   METHODS \n\n\n\n2.1 Rationale Supporting PCE-ISys \n\n\n\n\n\n\n\nFigure 2: Working Suppositions that Support the Rationale for the PCE-ISys Construct \n\n\n\n\n\n\n\n1. The potential for population-level pesticides exposure, and the magnitude of potential exposure (exposure potential) to chemical farming \n\n\n\npesticides arise from an agricultural economic system of crop cultivation, food production, trade, and consumption integrated with the \n\n\n\nbroader natural environment and political domain (g-AEES); and that the potential for exposure, and exposure potential do not occur in \n\n\n\nan environmentally or economically \u2018isolated,\u2019 or \u2018compartmentalised\u2019 manner.  In other words, the nature, and magnitude of pesticides \n\n\n\ntotal exposure potential is multi-factorial, and an integrated part of the system. \n\n\n\n2. There are many social and economic factors that contribute to the potential for human agricultural pesticides exposure, and exposure \n\n\n\npotential. Four key variables, however, are essential in order to conceptualise, quantify, and rate the potential for such presumed exposure. \n\n\n\nI) Use of chemicals for the express purpose of crop protection, II) A requisite goal of producing measurable crop outputs for commercial \n\n\n\nfood markets, and livestock productivity,  \n\n\n\nIII) Quantifiably discernible land-use allocated for agricultural production, and IV) A quantifiably discernible population cohort \n\n\n\nassociated with the given system of economic crop cultivation, food production, consumption, trade and natural environment. \n\n\n\n3. Economic, agricultural, and land-use policy decisions (characteristic of agroeconomic environmental systems) are governance variables \n\n\n\nthat affect the potential for population-level pesticides exposure, and exposure potential. \n\n\n\n4. Population-based potential for pesticides exposure, and exposure potential occurs from collective (multi-aggregated) pathways, i.e., the \n\n\n\nsummation of potential exposures from dietary intake of food products, drinking water, occupational, and non-occupational inhalation, \n\n\n\nand dermal routes. \n\n\n\n5. Physico-chemical properties of pesticides, and environmental variability and uncertainty are inherently associated with the nature and \n\n\n\ndegree of population-based potential for exposure, and exposure potential. \n\n\n\n6. The main source(s) for the potential for population-based pesticides exposure, and exposure potential arise from aggregate agricultural \n\n\n\nand economic policy decisions from within a population cohort\u2019s own country where pesticides are used as inputs for agricultural \n\n\n\nproduction. \n\n\n\n7. Population-based potential for pesticides exposure, and the magnitude of potential exposure (and risk) are continuously \u2018shifted\u2019 vis-\u00e0-\n\n\n\nvis food commodities consumed, and traded at local, regional, national, and international levels.  Thus, the \u2018distribution\u2019 of exposure and \n\n\n\nrisk potential across populations are constantly shifted from one geographical space to another, and that the net \u2018influx-efflux\u2019 of total \n\n\n\nexposure potential is in a state of variable commercial and ecological \u2018equilibrium.\u2019 This assumption is supported by the measurable \n\n\n\nubiquity of pesticides in food commodities, and the natural environment (on a global scale). \n\n\n\n8. The existence or absence of, and/or the degree of robustness in health-based regulatory policy (including, compliance and enforcement) \n\n\n\nimpact(s) the extent to which the potential for pesticides exposure, and exposure potential can occur. \n\n\n\n9. Agricultural pesticides usage, including intensity of use, and tonnage are not the only variables that contribute to population-based \n\n\n\nexposure potential, and total exposure potential, but [usage] is the primary agroeconomic systems-based input necessary for human \n\n\n\nexposure to occur, and for total exposure potential to be observed and evaluated. \n\n\n\n10. The Precautionary Principle \u2013 Chemical farming pesticides are inherently hazardous to all biological organisms, albeit to varying degrees.  \n\n\n\nThus, the basis for the PCE-ISys model output(s), i.e., the potential for exposure, and daily magnitude of potential exposure (per person) \n\n\n\nassumes that pesticides lower total exposure potential (by population) is always more favourable compared with higher total exposure \n\n\n\npotential.  Thus, the concept of \u2018total exposure potential\u2019 should be woven into policy strategy for reducing and/or preventing pesticides-\n\n\n\nrelated health and environmental impact(s) in lieu of (or in conjunction with) a risk-based approach.   \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nThe Pesticides Consumer-Environmental Indexing System (PCE-ISys) is a \npolicy evaluation framework built on principles, and indicators of \nagricultural economy supported by semi-quantitative methods.  The \nmodel is purposed to provide a data-driven screening of population-based \npesticides potential exposure associated with (presumed) economic \nmacro-policy decisions that impact key agricultural systems inputs and \noutcomes, i.e., pesticides-use, agricultural land-use, and crop productivity. \nThe PCE-ISys decision-support model is based on the principal \nassumption that the potential for population-level exposure together with \nthe magnitude of potential exposure, i.e., \u2018total exposure potential\u2019 arises \nfrom the summation use of pesticides across the broader system of crop \ncultivation, food production, trade, consumption, and environment.   \n\n\n\nThe PCE-ISys concept works by indexing the potential for pesticides \nexposure, and quantifying and indexing the magnitude of potential \nexposure on a \u2018per capita\u2019 basis.  The rationale for the indexing scheme is \nbased on the idea that substantially limiting, or preventing potential \nexposure at the macro-level is central to reducing pesticides related \nimpact(s), which can happen when consideration is given to integrating \nmeasures of public health into policy decision frameworks that promote \nagricultural economy in the context of sustainable development.  Figure 2 \nshows that the PCE-ISys concept is developed from, and buttressed by a \nseries of working suppositions that help form the basis for the index \nconstruct. \n\n\n\nThe ten working suppositions help illuminate how a \u2018generalisable\u2019 system \nof agricultural economy manifests the reality of pesticides usage, total \nexposure potential, and its likely public health implications. \n\n\n\n2.2 Index Scoring Methodology \n\n\n\nPCE-ISys is defined by the capacity to index the total exposure potential of \nchemical pesticides arising from industrialised systems of agricultural \neconomy (g-AEES), the key inputs of which include, total estimated land-\nuse for agricultural production, total estimated output from crop seeding \nand cultivation, and average total annual pesticides-use all in relation to \nthe total population cohort for a given country.  This makes PCE-ISys an \nevaluation scheme built on \u2018macro-level\u2019 agricultural indicators, the \npurpose and scope of which serves as a decision-support tool focusing on \ntotal exposure potential in the context of collective governance and farm-\nlevel decisions that may include policy directives such as implementation \nof GAP or IPM strategies, targeted crop, or pesticides subsidisation, or \nagricultural tax policy that incentivises, or limits specific farming methods \nand/or practices.   \n\n\n\n2.2.1 Methods-Driven Requirements for PCE-ISys Indexing \n\n\n\nIndicators that help explain complex systems (such as g-AEES) are \ninherently fraught with variability, uncertainty, and randomness arising \nfrom a host of factors ranging from environmental condition(s) to policy \ndecision-making processes; in turn, leading to challenges in how health \nand environmental outcomes stemming from those system(s) may be \ninterpreted.  The PCE-ISys model construct is both data-driven and \nstochastic, so interpretative applicability of its indexing results rely on \nthree key elements, \n\n\n\n\u2022 First, evaluation dataset(s) of adequate sample size.  Cochran\u2019s \nFormula for estimating sample size (modified for \u2018smaller\u2019 \npopulations) at 95% confidence is used to determine the minimum \nrequired sample size of nations for the project study (Bartlett II, et \nal., 2001; Pourhoseingholi et al., 2013). \n\n\n\n\n\n\n\nwith \u2018p\u2019 (the proportion of the population with the defining attribute) \ncharacterised by  crop production (by country), where p = 0.91892, \nZ = 1.96, e = .05, and N = 224, q = 1 - p   \n\n\n\n\u2022 Second is data transformation of g-AEES indicator(s) from \ncontinuous to discrete variable for indexing purposes to allow for \ndirect \u2018country-to-country\u2019 observational comparability on a relative \nbasis. \n\n\n\n\u2022 Third, a linear correlation assumption to allow for valid statistical \ninference in supporting the model indicator variable(s) within the \nPCE-ISys design construct, i.e., g-AEES, with the model\u2019s response \noutput variable, i.e., EIR-IS.  Figure 3. shows an (approximately \n\u2018normal-distribution shaped\u2019) histogram of pesticides total exposure \npotential scores for all country observations from 1990 - 2016.  \n\n\n\n\n\n\n\nFigure 3: Frequency Distribution of Pesticides Total Exposure Potential \n(EIR-IS), by Country Observation for Time Series 1990-2016 \n\n\n\nKey Parameters \n\n\n\nPercentile \nCategory \n\n\n\n(%) \n\n\n\nCoded \n\n\n\nPoints \nComments \n\n\n\nTotal Annual Pesticides \nUse Rate (PUR) (by \n\n\n\ncountry); Agricultural \npesticides-use is directly \nrelated to the potential \n\n\n\nfor human exposure \n\n\n\nUpper 25th 5 A higher \npesticide use \n\n\n\nrate is \nassociated with \na higher point \n\n\n\nscore within the \ncontext of g-\n\n\n\nAEES \n\n\n\nMiddle \n50th \n\n\n\n3 \n\n\n\nLower \n25th \n\n\n\n1 \n\n\n\nAnnual Crop \nProduction Index \n\n\n\n(CPIDX) (by country); \nTotal annual pesticides-\n\n\n\nuse rate is a fixed \naverage measure; \n\n\n\ntherefore, crop \nproductivity is inversely \n\n\n\nrelated to potential \npesticides exposure \n\n\n\nUpper \n25th \n\n\n\n1 \nThe higher the \ncrop output the \n\n\n\nlower the \npesticide use \n\n\n\ndistribution per \nunit crop, and \nthus the lower \n\n\n\nthe score. \n\n\n\nMiddle \n50th \n\n\n\n3 \n\n\n\nLower \n25th \n\n\n\n5 \n\n\n\nAnnual Estimated \nAgricultural Land Area \n(AGL) (by country); The \nproduct of pesticides use \n\n\n\nintensity and total \nestimated area of land \nused for agricultural \nfunction serves as a \ndirect indicator for \npesticides tonnage \n\n\n\nUpper \n25th \n\n\n\n5 Land-use is a \nkey factor in \nestimating \npesticides \n\n\n\ntonnage.  The \nmore land area \n\n\n\nused for \nagriculture the \n\n\n\nhigher the score. \n\n\n\nMiddle \n50th \n\n\n\n3 \n\n\n\nLower \n25th \n\n\n\n1 \n\n\n\nTotal Annual Estimated \nPopulation (AEP) \n\n\n\n(by country); Population \nsize affects the overall \n\n\n\npotential exposure \nimplications associated \n\n\n\nwith pesticides-use \nwithin a given a country \n\n\n\nUpper \n25th \n\n\n\n1 \nExposure \n\n\n\npotential is \n\u2018diluted\u2019 with \n\n\n\nincreasing \npopulation \n\n\n\nrelative to total \naverage \n\n\n\npesticides \ntonnage. \n\n\n\nMiddle \n50th \n\n\n\n3 \n\n\n\nLower \n25th \n\n\n\n5 \n\n\n\nFigure 4: PCE-ISys Three-tiered Percentile-based Coded Point Scheme \n\n\n\nPCE-ISys (population-based) pesticides total exposure potential is \nexpressed as an aggregated relative measure (by country) called the \nExposure Indicator Ratio-weighted Index Score, or \u2018EIR-IS\u2019 representing \nthe potential for exposure, \u2018weighted\u2019 by the average daily magnitude of \npotential exposure (called \u2018exposure potential\u2019) on a per capita basis,  \n\n\n\nEIR-IS (by country) = PCE-IS (by country) + EIRscore (by country)             (1) \n\n\n\nEIR-IS is derived from the sum of two relative indexing measures, \n\n\n\n\u2022 The Pesticides Consumer-Environmental Index Score (PCE-IS), and \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\n\u2022 The Exposure Indicator Ratio Score (EIRscore) \n\n\n\nBoth metrics are discrete numeric values that correspond to continuous \nindicator variables.   Where the variable(s) fall within one of three \npercentile categories (Upper 25th, Middle 50th, and Lower 25th) across the \ndistribution of continuous variables for a given evaluation dataset \ndetermines the index/indicator score (Han et al., 2012; Toppr, 2020). \n\n\n\n\u2022 Upper percentile = (3(n+1)/4) th term \n\n\n\n\u2022 Median percentile = ((n+1)/2) th term \n\n\n\n\u2022 Lower percentile = ((n+1)/4) th term \n\n\n\nAs shown in Figure 4 each percentile category is assigned a designated \nvalue, 1-,3-, or 5. \u20183\u2019 is the value \u2018coded\u2019 to continuous indicator variables \nthat fall within the middle 50th percentile of the distribution range.  An \nassigned point score of \u20181\u2019 or \u20185\u2019 corresponds to either the upper or lower \n25th percentile, depending on how the respective indicator variable is \nassumed to behave within g-AEES. \n\n\n\nPCE-IS represents the potential for pesticides exposure (by country) for \none calendar year.  The index score is expressed as,   \n\n\n\nPCE-IS = PUR (%ile) + CPIDX (%ile) + AGL (%ile) + AEP (%ile)                (2) \n\n\n\nand has a score range from 4 to 20 in incremental units of two.  The higher \nthe PCE index score the greater the potential for pesticides exposure.  \n\n\n\nThe EIRscore represents a discrete relative value for pesticides \u2018exposure \npotential.\u2019  The score is a numeric \u2018weighting\u2019 factor (when combined) with \nPCE-IS produces a total exposure potential score (EIR-IS), with an index \nscale of 5 to 25 (by country).  Similar to the PCE-IS scoring methodology, \nEIRscore is scored based on where the Pesticides Exposure Indicator Ratio \n(PiexpR) variable falls within the data distribution range of all PiexpR values \nfor the given evaluation dataset (by year), seen in Figure 5.   \n\n\n\nKey \nParameters \n\n\n\nPercentile \nCategory \n\n\n\n(%) \n\n\n\nEIR \nCoded \n\n\n\nPoints \n\n\n\nComments \n\n\n\nPiexpR (by \ncountry); a \n\n\n\nrelative measure \nof daily \n\n\n\npesticides \nexposure \n\n\n\npotential per \ncapita \n\n\n\nUpper 25th 5 \nEIRscore is the \n\n\n\nnumeric relative \nmeasure of \n\n\n\nPiexpR that is \nused to \u2018weight\u2019 \n\n\n\nPCE-IS \n\n\n\nMiddle 50th 3 \n\n\n\nLower 25th 1 \n\n\n\nFigure 5: PCE-ISys Percentile-based Coded Point Scheme for PiexpR \n\n\n\nThe higher the PiexpR value across the distribution range, the higher the \n(percentile category based) EIRscore.  PiexpR is a unitless continuous variable \nthat represents the relative average measure of daily pesticides exposure \npotential that is equal to, exceeds, or is below the Pesticides Reference \nIndicator Ratio (PirefR = PirefUC / PirefUC); and is expressed as follows: \n\n\n\nPiexpR = PiexpUC / PirefUC                                                                  (3) \n\n\n\nwhere PiexpUC (the unit-converted Pesticides Exposure Indicator) is a \nfunction of the product of the unit-converted pesticides-to-crop \nproductivity ratio (PCPr) and farmland area per capita (FLAC) \n\n\n\nPiexp = PCPr x FLAC (kgpesticides person-1 year-1) \n\n\n\n\u2193 \n\n\n\nPCPrcountry (kgpesticides / ha) x FLACcountry (ha/person) = Piexp \n\n\n\nAnnual (1-year) [unit conversion] \n\n\n\n\u2193 \n\n\n\n(kgpesticides /person-year) (1-year/365 days) (1*106 mg /1 kgpesticides) \n\n\n\n\u2193 \n\n\n\nPesticides Exposure Indicator (mgpesticides person-1 day-1) = PiexpUC (4) \n\n\n\nwhere, \n\n\n\nPesticides-to-Crop \nProductivity Ratio (PCPr) \n\n\n\n\u2193 \n\n\n\nTotal (Annual) Pesticides Use \nRate (kg/ha) \n\n\n\nFarmland Area per capita \n(FLAC) \n\n\n\n\u2193 \n\n\n\nTotal (Annual) Agricultural Land \nArea (ha) \n\n\n\nAnnual Crop Production Index Annual Estimated Country \nPopulation (per.) \n\n\n\nNext, the Pesticides Reference Indicator (PirefUC) denotes a benchmark \nlevel representing the 95% upper bound limit of the mean (unit-\nconverted) Pesticides Exposure Indicator value(s) for all countries (within \na given evaluation dataset) that have annual total average pesticides use \nrate(s) less than or equal to 0.5 kg/ha. \n\n\n\nPirefUC = 95% UCL of \u00b5PiexpUC (pesticides use rate \u2264 0.5 kg/ha, \u2018low\u2019)      (5) \n\n\n\nwhere,  \n\n\n\n\n\n\n\nPesticides use rate level(s) such as 0.5 kg/ha were adopted as a \nmodification of the Wachter & Staring guideline protocol for active \ningredient use rate(s) (World Health Organization, 1990), and correspond \nto the economic status, and regulatory sophistication of the given country.  \nAnnual use rates of 0.5 kg/ha to 0.1 kg/ha are graded as \u2018low\u2019 while <0.1 \nkg/ha, and \u2265 1 kg/ha annual pesticides active ingredient use rate are \ngraded as \u2018very low,\u2019 and \u2018high,\u2019 respectively.  PirefUC reflects the minimum \nthreshold of average daily pesticides exposure potential that is rated as a \n\u2018Lower Appreciable\u2019 public health concern. \n\n\n\n2.2.2 PCE-ISys Public Health Rating Scheme  \n\n\n\nThe PCE-ISys evaluation model is not a tool designed to reflect \nestimation(s) of risk or impact(s) associated with the use of crop \nprotection chemicals.  Instead, [it] is a data-driven construct that produces \na relative measure of pesticides-related potential exposure that \ncorresponds to a generic qualitative rating (supported by Working \nSupposition 10) termed \u2018public health concern.\u2019  The basis for the indexing \nsystem\u2019s public health-related rating scheme is centred on three basic \nprecepts,  \n\n\n\n\u2022 That chemical pesticides are engineered to produce target organism \nmortality, but also manifest varying degrees of \u2018collateral\u2019 toxicity to \nother biological species, including humans.  \n\n\n\n\u2022 That pesticides-related health impact(s) and risk are function(s) of \npesticides exposure. \n\n\n\n\u2022 That pesticides risk reduction through \u2018integrated\u2019 policy and \nplanning measure(s), over the long-term, are best accomplished, and \nmore cost-effective through prevention efforts than through (largely \ncost externalising) \u2018command and control\u2019 impact mitigation.    \n\n\n\nPCE-ISys public health rating categories correspond to pesticides total \nexposure potential as a function of EIR-IS or PCE-IS percentile-based \nscoring distribution(s).  Figure 6. illustrates that the public health \nclassification measure for the indexing system is a qualitative expression \ntermed \u2018Appreciable\u2019 public health concern.  \n\n\n\nThe scoring classifications across each data distribution range are ordered \naccording to percentile range: Upper 25th%ile = Highest Appreciable, \nMedian = Appreciable, and Lower 25th%ile = Lower Appreciable.  Based on \nthe index score distribution(s) for each of the 27 evaluation datasets, the \nyear-to-year threshold levels for each respective percentile range for this \nproject study was as follows (index score distributions were tabulated \nfrom data available at www.threepercentearth.org/reports-analysis/),  \n\n\n\n\u2022 EIR-IS \u2265 17 (Upper 25th), EIR-IS = 15 (Middle 50th), EIR-IS \u2264 13 \n(Lower 25th)  \n\n\n\n\u2022 PCE-IS \u2265 14 (Upper 25th), PCE-IS = 12 (Middle 50th), PCE-IS \u2264 10 \n(Lower 25th) \n\n\n\n\n\n\n\nX \n\n\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nResponse Output Percentile Category (%) \nTotal Exposure Potential \n\n\n\nRating or Exposure \nPotential Rating \n\n\n\nPublic Health Rating Comments \n\n\n\nEIR-IS or PCE-IS \n(by country) \n\n\n\nUpper 25th Highest \nHighest Appreciable Public \n\n\n\nHealth Concern \nEIR-IS or PCE-IS (by country) \n\n\n\nare categorised into three-tiered \npercentiles based on the \n\n\n\nstochastic distribution for each \nrespective evaluation dataset \n\n\n\nMiddle 50th Medium to High \nAppreciable Public Health \n\n\n\nConcern \n\n\n\nLower 25th Lower \nLower Appreciable Public \n\n\n\nHealth Concern \n\n\n\n  Figure 6: Public Health Rating Classification Scheme \n\n\n\n2.3 Data Methodology \n\n\n\n2.3.1 Data Sources   \n\n\n\nData that reflect the model input variables for the PCE-ISys model \nconstruct are available as open access from FAO and World Bank websites. \nPesticides use data was accessed from the FAO website, \nhttp://www.fao.org/faostat/en/?#data/RP, and separately downloaded \nin bulk as Excel files categorised by world regions Africa, Americas, Asia, \nEurope, and Oceania.  Data for the rest of the model input variables were \naccessed at the following World Bank websites by doing total bulk data \ndownloads as \u2018.csv\u2019 files then saved as Excel \u2018.xlsx\u2019 files, 1) for Crop \nProduction Index, \nhttps://data.worldbank.org/indicator/AG.PRD.CROP.XD, 2) for annual \ntotal population estimates by country, \nhttps://data.worldbank.org/indicator/SP.POP.TOTL, 3) for annual total \nland area by country, \nhttps://data.worldbank.org/indicator/AG.LND.TOTL.K2, and 4) for \nannual percent land area for agriculture, \nhttps://data.worldbank.org/indicator/AG.LND.AGRI.ZS.  Data from the \nWorld Bank site representing the model input variables were extracted \nfrom the original downloaded spreadsheets and collated into columns in \nnew worksheets that included data from 268 countries, world regions, and \nother world development classification categories. \n\n\n\n2.3.2 Organising the Data, and Data Testing the Model \n\n\n\nData collected from World Bank and FAO websites were organised and \nmanaged using Microsoft Office Excel version 16.43 with data analysis \nfunctionality.  The PCE-ISys working model was developed using Excel \nbecause of the transparency, and ease of use of the application\u2019s \nmathematical functions to generate the indicator, and index outputs by \ncountry, and by year. A \u2018source\u2019 dataset worksheet was used to \nconsolidate, and organise all raw, and processed data for the project.  The \nbasic steps for the worksheet data consolidation and organising process \nwere as follows, 1) all g-AEES (indicator variable) data were entered into \nthe source worksheet.  This included all 268 countries, world regions, and \nother world development classification categories, 2) all categories \n(except for individual countries) were culled from the source worksheet; \nuse of Cochran\u2019s Formula at 95% confidence (section 2.2.1) estimated the \nminimum required sample size (n) per evaluation dataset for the project \nstudy to be (at least) 76 countries, 3) the remaining individual countries \nwere further  screened    to   include   only   those    with    average   annual  \n\n\n\npesticides-use rate data (157 \u2265 n \u2265 133).   \n\n\n\nConsumption rate data (by country) were available from years 1990 \u2013 \n2016 (the evaluation time-series for the project study).  2016 was the \nterminal year of the time series because (at the time of data collection) \nthere was no crop production index data beyond that time frame. 4) g-\nAEES indicator variable, total land area was unit-converted from square \nkilometres to hectares in order to comport with the unit expression used \nin the PCE-ISys model, then total agricultural land area (by year) was \ndetermined by calculating total land area (by country) as a percent of land \narea allocated for agriculture (by country), 5) eight additional columns \nwere added to the source worksheet dataset.   \n\n\n\nThe four initial data columns included the land area unit conversion, then \ncalculation of total agricultural land, and PCPr and FLAC output \ncalculations, respectively. 6) The final four additional columns included \ncalculation and unit conversion of Piexp (kg/person-year) to PiexpUC \n(mg/person-day), inclusion of PirefUC values, calculation of PiexpR \n(PiexpUC/PirefUC), and then scale-adjusted by a factor of 10 (Adjusted-\nPiexpR = PiexpUC/PirefUC x 10).  Eighteen auxiliary columns were added to \nthe source worksheet, three columns for each g-AEES indicator variable \nand EIRscore percentile range i.e., Excel \u2018= percentile\u2019 function for upper 25th \n(\u201875th\u2019), 50th, and lower 25th; and the last three auxiliary columns for index \nscoring PCE-IS, totalling EIRscore, and index scoring EIR-IS.  All indicator \nvariable data (by country) for twenty-seven evaluation datasets in the \nproject study were indexed to generate PCE-IS and EIR-IS for all country \nobservations for the time series. \n\n\n\n2.4 PCE-ISys Model \u2013 Correlation and Variance \n\n\n\n2.4.1 Multivariate Test for EIR-IS and g-AEES  \n\n\n\nA regression analysis was conducted to determine the strength of \nassociation between the PCE-ISys weighted index score (output/response \nvariable), and its respective g-AEES (input) variables.  First, test(s) of \ncollinearity demonstrated no discernible correlation among indicator \n(input) variables.  Second, it should be noted that neither indexing output \ntrend nor model predictiveness was the focus of the regression exercise. \nIn addition, examining the effect(s) of PCE-ISys database outliers among \nthe 27 non-independent evaluation datasets (and its effect on model \noutput distribution(s)), or use of nested model(s) were considered, but \ndecided against for this particular study.   \n\n\n\n\n\n\n\nFigure 7: Average EIR-IS as a Function of g-AEES Linear Correlation and Variance \n\n\n\n\nhttp://www.fao.org/faostat/en/?#data/RP\n\n\nhttps://data.worldbank.org/indicator/AG.PRD.CROP.XD\n\n\nhttps://data.worldbank.org/indicator/SP.POP.TOTL\n\n\nhttps://data.worldbank.org/indicator/AG.LND.TOTL.K2\n\n\nhttps://data.worldbank.org/indicator/AG.LND.AGRI.ZS\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nPurposed as a \u2018screening-level\u2019 information dissemination tool the aim, \ninstead, was to offer a basic underpinning of whether a linear relationship \nexists between pesticides total exposure potential and its model input \nvariables in the context of g-AEES (not by country), but on a global and \ngeneralisable scale.  A standard multivariate analysis was employed using \ng-AEES input variable(s), and their respective index output(s), i.e., EIR-IS \naveraged for all data points within each annual evaluation dataset of the \ntime series (by country).  Figure 7. shows the EIR-IS linear response \noutput in relation to the four key indicator variables that define the g-\nAEES-based model. \n\n\n\nThe g-AEES regression model shown in Figure 7 demonstrated a \nmoderately strong association between average EIR-IS and average \npesticides consumption rate, Crop Production Index, agricultural land \n\n\n\narea, and estimated country population, with r=0.657789, and directional \ncorrelation for three of 4 input variables consistent with their assumed \nbehaviour within g-AEES (\u2018population\u2019 variable was equivocal).  \nMultivariate R2 indicated that the model explains between 42% to over \n43% of the variance in average pesticides total exposure potential (F-Test \nSignificance = 6.62 x 10-18).   \n\n\n\n2.4.2 Multivariate Test for EIR-IS and Exposure Potential  \n\n\n\nA second regression test was conducted to examine the strength of \nassociation between average EIR-IS, and indicator variables used to derive \nPiexpUC.  There was, again, no demonstrated collinearity.  Figure 8. shows \nthe EIR-IS linear response output in relation to the two indicator variables \nused to determine exposure potential, i.e., PCPr and FLAC.  \n\n\n\n\n\n\n\n\n\n\n\nFigure 8: Average EIR-IS as a function of PiexpUC Linear Correlation and Variance \n\n\n\nThe multivariate \u2018exposure potential\u2019 (PiexpUC) model also demonstrated a \nmoderately strong positive directional correlation between average \npesticides total exposure potential and average daily magnitude of \npotential exposure (per person) with r=0.699048.  Variance in EIR-IS as a \nfunction of \u2018exposure potential\u2019 was R2=0.49 (F-Test Significance = 3.72 x \n10-23) indicating that nearly half the variation in EIR-IS output(s) are \nexplained by pesticides use per unit of crop productivity and agricultural \nland area per capita (a surrogate indicator of pesticides tonnage). All \nindicator variables for both models were statistically significant, except \nfor \u2018Average Estimated (Country) Population,\u2019 which was marginally non-\nstatistically significant (p-value = 0.078810616); though this occurrence \nwas not altogether unexpected given the likely complexity of human \npopulation dynamics in relation to agricultural food systems.   \n\n\n\nThe fact, however, that FLAC was both strongly statistically significant and \nlinearly correlated to EIR-IS helps support the argument that country \npopulation (in relationship with agricultural land-use) is indeed a relevant \nvariable (in the context of pesticides tonnage) for interpreting pesticides \ntotal exposure potential, but poses a challenge in defining its contributing \ninfluence on EIR-IS from the perspective of g-AEES, indicating that a more \nin-depth computational modelling exercise is likely necessary to parse out \nthe effect of \u2018population\u2019 within this system.  \n\n\n\nThe machinations of politics, governance, and economic activity that \ncharacterise complex \u2018social-environmental\u2019 systems, create inherent \ndegrees of uncertainty, and variability in models that attempt to interpret \nsuch systems.  Thus, Pearson correlation levels tangibly above 0.5 for both \nregression tests arguably help inspire a reasonable degree of confidence \nfor the plausibility of PCE-ISys as a workable policy decision-support \nconcept (Samuel and Okey, 2015).  Similarly, R2 values for both regression \nmodels indicating that the index construct explains over 40% to nearly \n50% of any change in the EIR-IS response variable output is an \nencouraging prospect for the usefulness of PCE-ISys. Why? 1) The degree \nof model variance indicated in both g-AEES and PiexpUC models allude to \nPCE-ISys agroeconomic predictor variables as basic factors associated \nwith the modern agricultural food system, and that 2) by relying on a \nhighly parsimonious model  that precludes other potentially knotty \n(social, cultural, political, regulatory, and environmental) variables (that, \nalthough may \u2018capture\u2019 the remaining difference in model variance) \nminimises the risk of diminishing the transparency, and confounding the \ngeneral interpretative capacity of the index output(s) that would arguably \n\n\n\nundermine its main functional purpose and advantage as a broad-based \ninformation dissemination tool.   \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1 Pesticides Total Exposure Potential  \n\n\n\n3.1.1 Ranking Profiles \n\n\n\nThe primary analytical feature of PCE-ISys is the capacity to (semi-\nquantitatively) index the potential for exposure to pesticides, \u2018weighted\u2019 \nby the magnitude of potential exposure (per capita). One particular \napplication derived from this indexing construct is the ability to generate \na multi-output \u2018profile\u2019 that includes, EIR-IS, PCE-IS, and PiexpR used in \nproducing a numeric ranking structure for either a single evaluation \ndataset or averaged over a given time series.  Due to the high number of \ntotal individual indexing outputs and their associated g-AEES parameter \nvalues (~50000), all parameter-specific data and data-driven outputs \n(excluding PCPr, FLAC, Piexp and PiexpUC categories) for individual country \nobservations per evaluation dataset have been made available as open \naccess at www.threepercentearth.org/reports-analysis/. A time series-\naveraged EIR-IS ranking chart for 157 countries displayed in Table 1. \n(Next page) produced interesting results in terms of pesticides total \nexposure potential (by country) over the 27-year time frame.  Each \ncountry in the average ranking chart was colour-coded according to world \nregion. \n\n\n\n\n\n\n\nPercentile-based index score threshold levels were as follows: \n\n\n\n\u2022 Average EIR-IS = highest average total exposure potential (Highest \nAppreciable public health concern) \u2265 17.07407,  \n\n\n\n\u2022 17.07407 \u02c3 Average EIR-IS = medium-to-high total exposure \npotential (Appreciable public health concern) \u02c3 12.85185, and  \n\n\n\n\u2022 Average EIR-IS = Lower average total exposure potential (Lower \nAppreciable public health concern) \u2264 12.85185.   \n\n\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nTable 1: Time Series-Averaged Exposure Indicator Ratio-Weighted Index Score and Rank (1990 \u2013 2016) \n\n\n\n\n\n\n\nAlso, for the project study in this instance, the \u2018medium-to-high\u2019 index \ndistribution range was further subdivided: \n\n\n\n\u2022 \u2018high\u2019 (17.07407 \u02c3 Average EIR-IS (Higher Appreciable public health \nconcern) \u2265 15), and  \n\n\n\n\u2022 \u2018medium-to-lower medium\u2019 (15 \u02c3 Average EIR-IS (Medium \nAppreciable public health concern \u02c3 12.85185). \n\n\n\nOne noticeable observation gleaned from the time-averaged evaluation \nappears to reveal a broad, underlying consistency between average \nannualised pesticides-use per area of cropland, and pesticides total \nexposure potential, i.e., regions of the world where use rates are deemed \nconsistently high, or low such as in the Americas and Africa (FAOSTAT, \n2021), respectively saw overall correspondingly \u2018highest,\u2019 and \u2018lower\u2019 EIR-\nIS outputs for these same areas.  This analysis outcome also draws support \nfrom the g-AEES linear correlation and variance results. \n\n\n\nOf nations with the highest average pesticides total exposure potential for \nthe time series 38%, and 23% were from the Americas, and Asia-Pacific \nregion, respectively, while 63% of nineteen G7/G20 nations evaluated in \nthe project study demonstrated either \u2018high\u2019 or highest total exposure \npotential.  On first pass, a country-by-country global perspective appears \nto suggest that the size of a country\u2019s economy may be a   feasibly   reliable  \n\n\n\nindicator for pesticides total exposure potential.  However, a closer look at \nthe time series-averaged EIR-IS appears to indicate that developing \nnations with known \u2018transitioning\u2019 economies may be consistently more \nvulnerable to higher pesticides total exposure potential.  Examples of such \nplaces include, Belize, Malaysia, Ecuador, Colombia, Costa Rica, and Fiji, \ncountries that (generally speaking) place a relatively high premium on \neconomic growth and development, while in some cases discounting \nprinciples and applications of sustainability (Wendling et al., 2020). \n\n\n\nA wide-ranging, prospective policy analysis of the potential factors that \nmay affect EIR-IS, such as human development status, agricultural GDP, \ndegree of regulatory sophistication, or measures of socio-cultural \nattitudes toward pesticides may be useful in providing greater insight into \npossible solution(s) for reducing and/or preventing pesticides-related \nimpact(s) arising from politically driven, agroeconomic systems.  \n\n\n\n3.1.2 Asia-Pacific Region and ASEAN \n\n\n\nThe PCE-ISys evaluation study included twenty-nine countries located \nthroughout East Asia, South Asia, ASEAN, and Oceania, under the broad \nheading of \u2018Asia-Pacific.\u2019  Figure 9. illustrates a vertical EIR-IS index chart \nshowing pesticides total exposure potential across the Asia-Pacific region \nfrom 1990 \u2013 2016 (by country, region, and worldwide).  \n\n\n\n\n\n\n\nFigure 9: Time Series-Averaged EIR-IS for the Asia-Pacific Region (by country) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nAsia-Pacific as a regional designation produced an average EIR-IS of 14.94 \n(versus 15.02 globally) for the 27-year time series, indicating that \nagricultural food systems across this region are (on average) \ncharacterised by pesticides-related \u2018Appreciable\u2019 public health concern \nqualified with a sub-group rating of \u2018medium\u2019 total exposure potential. A \nmore detailed look at the Asia-Pacific indexing results reveal that ten of 29 \nnations (34.5%) across this region produced average EIR-IS ratings at, or \nbelow the 12.85 (lower total exposure potential) benchmark, of which \nHong Kong (EPI=N/A) is HDI-rated as \u2018very high\u2019 (0.949), and Indonesia \nEPI=37.8, HDI=0.718 is G20 designated.  Over half of the remaining \ndeveloping nations in this sub-group (excluding Mongolia EPI=32.2, \nHDI=0.737, Bhutan EPI=39.3, HDI = 0.654, and Bangladesh EPI=29, HDI = \n0.632) are largely rated as having medium HDI (on the lower range), and \nbelow average environmental performance index score(s) (EPI < 46.44), \nthey include, Lao PDR, Myanmar, Nepal, Pakistan, and Timor-Leste (UNDP \nHuman Development Index, 2021; Wendling et al., 2020).  \n\n\n\nThis indexing output \u2018dynamic\u2019 appears consistent with evidence that \ngenerally lesser developed countries that tend to maintain a relatively \nmore subsistence, or local market index of agriculture may be (on average) \nless apt to rely on pervasive pesticides-use (which, in some cases, limits \npesticides tonnage) compared with their more developed counterparts \n(World Health Organization, 1990).  Of the \u2018highest total exposure \npotential\u2019 sub-group of Asia-Pacific nations, the majority are recognised as \n\u2018developing\u2019 (New Zealand EPI=71.3, HDI=0.931 being the exception), and \nhaving \u2018transitioning\u2019 economies (with China being a G20).  They include, \nfor example, China, Fiji, Malaysia, Samoa, and Vietnam (UNDP Human \nDevelopment Index, 2021; Wendling et al., 2020).   \n\n\n\nAsian-Pacific countries undergoing rapid economic development are \nlargely faced with concomitant health and environmental impacts, \nespecially evident in China, and across most of ASEAN. Thus, it is not \nsurprising that governance and policy measures that direct agricultural \nfood systems within these same jurisdictions would also experience (on \naverage) consistently higher systemic pesticides-use rates. Table 2. \ndisplays environmental performance index scores, UN human \ndevelopment indexes, the time series-averaged pesticides-use rates, and \ntheir associated EIR-IS for the \u2018highest\u2019 and \u2018lower\u2019 total exposure potential \nAsia-Pacific sub-groups (Wendling et al., 2020). \n\n\n\nTable 2: Comparison of Asia-Pacific \u2018Highest\u2019 and \u2018Lower\u2019 Total \nExposure Potential Sub-Groups \n\n\n\nCountries with \n\n\n\n(\u2018highest total exposure \npotential\u2019) \n\n\n\nEPI HDI \nAverage \n\n\n\nPesticides-Use \nRate (kg/ha) \n\n\n\nAverage \nEIR-IS \n\n\n\nChina \n\n\n\nFiji \n\n\n\nMalaysia \n\n\n\nSamoa \n\n\n\nVietnam \n\n\n\n37.3 0.761 \n10.36 \n\n\n\n1.86 \n\n\n\n6.43 \n\n\n\n0.99 \n\n\n\n2.49 \n\n\n\n18.85 \n\n\n\n34.3 0.743 18.70 \n\n\n\n47.9 0.810 21.22 \n\n\n\n37.3 0.715 17.89 \n\n\n\n33.4 0.704 17.30 \n\n\n\nCountries with \n\n\n\n(\u2018lower total exposure \npotential\u2019) \n\n\n\nEPI HDI \nAverage \n\n\n\nPesticides-Use \nRate (kg/ha) \n\n\n\nAverage \nEIR-IS \n\n\n\nLao PDR \n\n\n\nMyanmar \n\n\n\nNepal \n\n\n\nPakistan \n\n\n\nTimor-Leste \n\n\n\n34.8 0.613 \n0.013 \n\n\n\n0.176 \n\n\n\n0.092 \n\n\n\n0.246 \n\n\n\n0.004 \n\n\n\n11.07 \n\n\n\n25.1 0.583 12.56 \n\n\n\n32.7 0.602 9.96 \n\n\n\n33.1 0.557 12.26 \n\n\n\n35.3 0.606 12.85 \n\n\n\nPCE-ISys indexing outputs broadly demonstrate that by-and-large, and \nirrespective of their EPI \u2018sustainability\u2019 rating higher HDI-rated \ndeveloping countries within the Asia-Pacific regional designation \n(generally associated with rapid economic expansion efforts), possess \nagricultural food systems that appear more reliant on pervasive chemical \npesticides use compared to less developed nations within the region.  Also, \nit can be reasonably surmised that the time series-averaged EIR-IS \noutcomes for each of the countries within their respective sub-groups are \nevidenced by rates of crop protection chemical use that are consistent with \nthe Wachter & Staring use-guideline criteria template that associate \nhigher or lower active ingredient use-rates with corresponding levels of \ncountry economic development, and regulatory capacity (World Health \nOrganization, 1990).  Evidence-based information derived from PCE-ISys \n\n\n\noutputs used in conjunction with other broad-based, empirically derived \nmetrics, such as HDI, GDP, or GNI, for example, may offer policymakers \nand/or regulatory analysts with a viable (policy analysis) alternative to \nthe dominant risk-benefit based approach to agroeconomic governance.    \n\n\n\n3.1.3 ASEAN Test Case \u2013 Malaysia \n\n\n\nAgriculture is an economic mainstay of ASEAN, with farming and fishing \nindustries in 2018 generating 10.6% of total GDP across the region, as well \nas contributing up to 72% of employment (by country) (Food and \nAgriculture Organization, 2020).  At the same time, across large swaths of \nthe region use of chemical pesticides, perceived as the policy solution of \nhighest convenience for \u2018ensuring\u2019 optimally higher crop yields in line with \nagricultural intensification goals is marked by surplus evidence of \ndeleterious health and environmental outcomes (Economy and \nEnvironment Institute, 2017; EU Parliament, 2021; Lam et al., 2017; Gupta, \n2012; FAO-Situation Analysis Report, 2021).   \n\n\n\nOne particularly glaring observation from the year-to-year, and time \nseries-averaged components of the project study was the exceedingly high \nEIR-IS value(s) for Malaysia.  In fact, worldwide, over the 26-year span, \nonly the nation of Belize produced a higher average EIR-IS (see Figure 9), \nwith their being only a 0.7% difference in average index between the two \ncountries.  Thus, Malaysia offers a comparative \u2018point-of-reference\u2019 test \ncase for ASEAN, and Asia-Pacific nations (if not, worldwide) in how EIR-IS \ncan be used to screen for the potential public health implications \nassociated with pesticides input(s) within agricultural food systems.   \n\n\n\nVietnam was selected for comparative analysis with Malaysia because of \nthe former\u2019s similar time series-averaged EIR-IS threshold level and \n\u2018Highest Appreciable\u2019 public health concern rating. A comparison with \nlower total exposure potential nations was also deemed necessary.  Lao \nPDR was chosen because of its below benchmark 11.07 time series-\naveraged EIR-IS (lowest among ASEAN nations), and its measurably lower \nHDI rating of 0.613; and Indonesia was selected for comparison, also due \nto its below benchmark rating of 12.70, and its G20 designation.  The \ndifference in HDI between the two nations, and Indonesia\u2019s comparatively \nlarger economy provide a more enhanced contrast when comparing \npesticides total exposure potential with Malaysia.  Figure 10. shows the \nyear-to-year trend in annual pesticides use rates for Malaysia, Vietnam, \nIndonesia, and Lao PDR. Data available at \nwww.threepercentearth.org/reports-analysis/. \n\n\n\n\n\n\n\nFigure 10: Annual Pesticides Use Rate Trends for Four ASEAN Nations \n\n\n\nThe average pesticides use rate for Malaysia from 1990-2016 was 6.43 \nkg/ha compared to Vietnam (2.49 kg/ha), Indonesia (0.045 kg/ha), and \nLao PDR (0.01333 kg/ha), exceeding Vietnam\u2019s average use rate by 61%, \nwhile surpassing Indonesia and Lao PDR use rate trends by an astounding \n14.34 and 48.2 orders of magnitude, respectively.  According to the \nWachter & Staring pesticides use-rating guideline, annual use rates of \npesticides active ingredient of, or exceeding 5 kg/ha is deemed \u2018very high\u2019 \n(World Health Organization, 1990).  Also, worth noting is that pesticides \nuse has historically been limited within Lao PDR\u2019s agricultural food system \n(including average use rates approaching zero from 1997-2008, 2010, \n2014-2016) possibly reflecting a lesser degree of industrialised farming \ncompared with neighbouring countries such as Indonesia, Malaysia, \nThailand, and Vietnam.  Next, Figure 11. illustrates the year-to-year \nrelationship(s) reflecting EIR-IS as a function of PCPr for all four countries. \n\n\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Pesticides Total Exposure Potential as a Function of the \nPesticides-to-Crop Productivity Ratio \n\n\n\nPCE-ISys \u2018average\u2019 correlation and variance tests point to annual \npesticides use rate and Crop Production Index as the strongest indicator \nvariables within the model, with Pesticides-to-Crop Productivity Ratio \n(PCPr) being the strongest contributing indicator of EIR-IS (regression \n\n\n\ncoefficient = 43.18, p-value = 1.70 x 10-20) (see Figure 7 and 8).  Analysis of \nthe scatter plot results show: \n\n\n\n1) (positive) moderately linear cluster(s) for all four nations, but with \nLao PDR, and Vietnam\u2019s respective profiles exhibiting visibly more \ndata dispersion, the latter of which could be interpreted as a function \nof the abrupt, then incremental decline in average annual pesticides \nusage observed after 1997 (see Figure 10).  \n\n\n\n2) a concentrated cluster pattern of higher EIR-IS levels for Malaysia \ncongruent with persistently concentrated levels of \u2018high-to-very \nhigh\u2019 pesticides use per unit of crop productivity (per hectare) over \nthe time series, with observed levels of pesticides use to crop \nproductivity for Indonesia\u2019s and Lao PDR\u2019s g-AEES consistently 1-2 \norders of magnitude below Malaysia\u2019s agroeconomic system (data \navailable at www.threepercentearth.org/reports-analysis/).  \n\n\n\n3) that for Vietnam, pesticides use levels relative to its crop production \nindices appeared more variable, but consistent with its decreased \nusage rates and increased productivity trends over the 26-year span \n(data available at www.threepercentearth.org/reports-analysis/).   \n\n\n\nNext, a comparison of highest and lowest EIR-IS to PCPr \u2018pair-function\u2019 \noffer additional perspective on interpreting the EIR-IS-PCPr scatter plots.  \nTable 3. shows the change in the difference in magnitude between highest \nand lowest PCPr for Vietnam, Indonesia, and Lao PDR compared to \nMalaysia for the time series.  \n\n\n\nTable 3: Highest and Lowest Scatter Plot EIR-IS to PCPr \u2018Pair-Function\u2019 PCPr Ratio Difference \n\n\n\nCOUNTRY \n\n\n\nHIGHEST SCATTER PLOT \n\n\n\n\u2018PAIR-FUNCTION\u2019 \n\n\n\n(red dots in Figure 11) \n\n\n\nLOWEST SCATTER PLOT \n\u2018PAIR-FUNCTION\u2019 (blue \n\n\n\ndots in Figure 11) \n\n\n\nHIGHEST \n\n\n\n\u2018PAIR-FUNCTION\u2019 \n\n\n\nPCPr RATIO \n(Malaysia relative to \ncomparison nation) \n\n\n\nLOWEST \n\n\n\n\u2018PAIR-FUNCTION\u2019 \nPCPr RATIO \n\n\n\n(Malaysia relative to \ncomparison nation) \n\n\n\nHIGHEST to LOWEST \n\u2018PAIR-FUNCTION\u2019 \n\n\n\nPCPr RATIO \nDIFFERENCE \n\n\n\n(Malaysia relative to \ncomparison nation) \n\n\n\nMALAYSIA EIR-IS=23, PCPr=0.101920 EIR-IS=17, PCPr=0.07146    \n\n\n\nVIETNAM EIR-IS=23, PCPr=0.05713 EIR-IS=15, PCPr=0.01202 1.78x 5.95x (4.17x) \n\n\n\nINDONESIA EIR-IS=13, PCPr=0.00069 EIR-IS=9, PCPr=0.00022 147.71x 324.81x (177.1x) \n\n\n\nLAO PDR EIR-IS=13, PCPr=0.00105 EIR-IS=9, PCPr=0.00015 97.07x 476.4x (379.3x) \n\n\n\nThe magnitude of difference in highest and lowest EIR-IS to PCPr \u2018pair-\nfunction\u2019 PCPr ratio for Malaysia relative to Vietnam (4.17x), Indonesia \n(177.1x), and Lao PDR (379.3x), respectively, is consistent with the latter \nthree nations (especially Vietnam) either reducing, or maintaining \nconsiderably lower pesticides consumption per unit of crop productivity \nin contrast to Malaysia (data available at \nwww.threepercentearth.org/reports-analysis/), whose collective \nagricultural policy goals likely stayed the economic course over the 27-\nyear time series.  \u2018Brackets in bold\u2019 indicate that the magnitude of \ndifference was driven by the lowest \u2018pair-function\u2019 ratio.  Table 3. results \ninterpretation draws support from results displayed in Table 4. showing \nthe highest and lowest EIR-IS to PCPr scatter plot \u2018pair-function\u2019 range \nprofiles for Malaysia, Vietnam, Indonesia, and Lao PDR.  \n\n\n\nTable 4: Comparison of the Range of Difference in Highest and \nLowest PCPr (by country) \n\n\n\nCOUNTRY \n\u2206 HIGHEST to \nLOWEST PCPr \n(by country) \n\n\n\n% \u2206 HIGHEST to \nLOWEST PCPr \n\n\n\nRELATIVE to HIGHEST \nPCPr (by country) \n\n\n\n\u2206 EIR-IS \n\n\n\nMALAYSIA 0.03046 29.89% -6 \n\n\n\nVIETNAM 0.04511 78.95% -8 \n\n\n\nINDONESIA 0.00047 68.59% -4 \n\n\n\nLAO PDR 0.00089 85.34% -4 \n\n\n\nHigher percentage change(s) between the difference in highest and lowest \nPCPr (by country) relative to highest PCPr (by country) denotes either a \ngreater range of reduction in pesticides use per unit of crop productivity \nfor each hectare of farmed land (per annum), or static use-rates relative to \ncrop output coupled with measurable crop productivity increases.  Table \n4 results show a comparatively sizable difference in the range of reduction \nin (or consistently lower) use of pesticides for Vietnam (+49%), Indonesia \n(+39%), and Lao PDR (+56%) compared to Malaysia.  Malaysia\u2019s six-point \nEIR-IS decrease at its minimum range turned out to be a statistical outlier \nreflecting a largely \u2018non-diminished\u2019 relative effect, i.e.,  pesticides use-\nrates data for the country (over the time series) remained consistently \n\u2018very high,\u2019 as did its overall index pattern with no change in its public \n\n\n\nhealth rating category, despite considerable increases in crop productivity \n(data available at www.threepercentearth.org/reports-analysis/).  \n\n\n\nFindings from the scatter plot analysis point to pesticides consumption \nrelative to crop productivity as a reasonable indicator corollary to total \nexposure potential.  The broad conclusion drawn from interpretation of \nthe EIR-IS to PCPr scatter plot results (corroborated by FAO and World \nBank-sourced data, www.threepercentearth.org/reports-analysis/) is \nthat the ratio of pesticides use to agroeconomic productivity for Malaysia, \nskewed by \u2018very high\u2019 average pesticides use rates, likely contribute to the \ncountry\u2019s persistently elevated pesticides total exposure potential.  \nMinistry coordinated policy analysis efforts targeting issue(s) of \u2018high \nproportional use-to-productivity\u2019 within Malaysia\u2019s agricultural food \nsystem may serve to reduce future (per capita) health and environmental \nimpact(s) from agricultural pesticides use.  \n\n\n\nFuture research and policy analysis aimed at validating the decision-\nsupport functionality of PCE-ISys may include, charting usage trends in \nconjunction with agricultural land-use changes, and/or evaluating similar \nscatter plot profiles for other Asia-Pacific nation sub-groups as a way of \nascertaining a more comprehensive picture of potential pesticides \nimpact(s) arising from regional food systems, and to what extent those \npotential impact(s) are shaped by conventional versus sustainable \nagricultural practices. Other research related consideration(s) for PCE-\nISys may involve examining annual PiexpUC trends relative to registered \ncrop protection chemicals (by country) as a way of extrapolating \nproportional risk from those select pesticides groupings.  \n\n\n\nMost indexing systems with policy and/or business application(s) are \ndesigned to disseminate \u2018units\u2019 of information on a broadly \u2018generalisable\u2019 \nscale, captured within a defined scope of time and space context in \naddressing a given social, economic, or environmental issue (Consumer \nFinance Institute, 2021; Gorai, AK. and Goyal, P, 2015; Kookana, RS., et al., \n2005; Kovach, J., et al., 1992).  In this respect, PCE-ISys is no different from \nother indexing models in that its algorithm processes data drawn from a \nlimited set of parameters, i.e., pesticides use, crop productivity, \nagricultural land, and population.  \n\n\n\nOne obvious limitation of this type of heuristic evaluation regimen is that \nthe indexing outputs do not necessarily allow for inferential interpretation \n\n\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\nhttp://www.threepercentearth.org/reports-analysis/\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nbeyond the scope of its defined parameters.  For PCE-ISys that would be g-\nAEES.  Concomitantly, however, what the PCE-ISys model lacks in \ncapability to, for example quantify pesticides impact is replaced by the \npower of its indexing output(s) to prospectively reframe the debate about \nthe types of measurement outcomes (qualitative vs. quantitative, or \nprecautionary vs. risk-based)  that should be prioritised in helping guide \nagricultural policy-based decision making, especially given that the \neconomic and environmental reality of the world is not \n\u2018compartmentalised,\u2019 but is in fact based on the interconnectivity [of, and \nwithin] social and ecological systems. \n\n\n\n4.   CONCLUSION \n\n\n\nAs the Asia-Pacific region, and more specifically ASEAN, begin remission \nfrom the COVID-19 pandemic, the resiliency of Southeast Asia\u2019s \nagricultural economy will be showcased.   A prime opportunity exists for \ngovernments, economic participants of food producing systems, and civil \nsociety to begin deliberating in earnest the existing limitations of current \nrisk-based pesticides management for the region.  Population across the \nASEAN region is projected to exceed 740 million people by 2035, of which \na monumental task lies ahead to forge sustainable agricultural food \nsystems that comport with UN SDG target indicators such as 2.4, 3.9 and \n6.3. The Pesticides Consumer-Environmental Indexing System (PCE-ISys) \nis a novel, semi-quantitative framework designed to be a broad-based, \ndecision-support screening tool that works by integrating salient \nevidence-based information into agroeconomic and environmental policy \nanalysis.   \n\n\n\nThis project study demonstrates the policy-relevant indexing \napplication(s) of PCE-ISys, painting a somewhat nuanced, yet concerning \npicture of pesticides use throughout ASEAN, and the Asia-Pacific region.  \nBy-and-large, agricultural pesticides use remains systemic and expansive, \nlikely posing continued health and environmental risk(s) for this area of \nthe world.  Alternatives to largely risk assessment-derived health-based \nregulatory policy are needed.  \u2018Systems-based\u2019 indexing models such as \nPCE-ISys can be employed to 1.) encourage governing bodies to transition \ntowards harmonised policy concepts that more readily foster sustainable \nagricultural food systems, and 2.) promote research to further the \ndiscourse in sustainable development policy, specifically in order to \nmeaningfully address the inefficient, yet enduring \u2018policy-\ncompartmentalising\u2019 of crop protection chemicals use in food systems, and \nits associated long-standing resultant impacts to ecological and human \nhealth.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nHeartfelt professional acknowledgements go to Ted Schettler and Sandie \nHa.  Dr. Schettler is the Science Director for the Science and Environmental \nHealth Network and sits on the advisory board for the Collaborative on \nHealth and the Environment. He offered his time to review, and critically \nevaluate the initial draft iterations of this research article.  Dr. Ha is an \nAssistant Professor with the Graduate Public Health Programme at the \nUniversity of California, Merced where she does epidemiological research \non the effects of air pollution, ambient heat, and agricultural pesticides on \nperinatal and postnatal outcomes.  She offered substantive guidance and \nconsultation regarding statistical testing of the PCE-ISys model. \n\n\n\nREFERENCES \n\n\n\nAktar, W., 2009. Impact of pesticides use in agriculture: their benefits and \nhazards. Interdisciplinary Toxicology, 2 (1), Pp. 1-12. \n\n\n\nBartlett, J.E., 2001. Organizational research: appropriate sample size in \nsurvey research. Information Technology, Learning, and \nPerformance Journal, 19 (1), Pp. 43-50. \n\n\n\nBenbrook, C.M., Davis, D.R., 2020. The dietary risk index system: a tool to \ntrack pesticide dietary risk. Environmental Health, 19 (103), Pp. 1-\n18. \n\n\n\nBonmatin, J.M., 2015. Environmental fate and exposure; neonicotinoids \nand fiprinol.  Environmental Science and Pollution Research, 22, Pp. \n35-67. \n\n\n\nBoxall, A.B.A., 2009. Impacts of climate change on indirect human \nexposure to pathogens and chemicals from agriculture. \nEnvironmental Health Perspectives, 117 (4), Pp. 508-514. \n\n\n\nChou, W.C., 2019. Prioritization of pesticides in crops with a semi-\nquantitative risk ranking method for Taiwan postmarket \nmonitoring program.  Journal of Food and Drug Analysis, 27, Pp. \n347-354. \n\n\n\nCorporate Finance Institute. \nhttps://corporatefinanceinstitute.com/resources/knowledge/eco\nnomics/consumer-price-index-cpi/ (accessed 31 March 2021). \n\n\n\nDel Prado-Lu, J.L., 2015. Insecticide residues in soil, water, and eggplant \nfruits and farmers\u2019 health effects due to exposure to pesticides. \nEnvironmental Health and Preventive Medicine, 20, Pp. 53-62. \n\n\n\nEconomy and Environment Partnership for Southeast Asia. 2017. The \nimpact of pesticide use on human health: a case study from \nMyanmar. EEPSEA, Ho Chi Minh City, Vietnam. (2017-PB3). \nhttps://eepseapartners.org/the-impact-of-pesticide-use-on-\nhuman-health-a-case-study-from-myanmar-2/ (Accessed 30 \nMarch 2021). \n\n\n\nEuropean Parliament Directorate-General for External Policies, Policy \nDepartment. 2021. The use of pesticides in developing countries \nand their impact on health and the right to food. DEVE committee, \nBrussels, Pp. 653.622. \n\n\n\nFernandez-Cornejo, J., 1998. Issues in the economics of pesticide use in \nagriculture: a review of the empirical evidence. Review of \nAgricultural Economics, 2 (2), Pp. 462-488. \n\n\n\nFood and Agriculture Organization of the United Nations, FAOSTAT.  \nhttp://www.fao.org/faostat/en/#data/EP/visualize (accessed 31 \nMarch 2021). \n\n\n\nFood and Agriculture Organization of the United Nations. 2020. Southeast \nAsian nations examine the state of food systems to ensure recovery \nand resilience in a post-COVID-19 era. FAO news. \nhttps://www.fao.org/asiapacific/news/detail-\nevents/en/c/1295300/  \n\n\n\nFood and Agriculture Organization. 2021.  Pesticides Monitoring \nProgramme in Association of Southeast Asian Nations (ASEAN). \nSituation Analysis Report, Bangkok.  \nhttps://doi.org/10.4060/cb4742en. \n\n\n\nGereslassie, T., 2019. Determination of occurrences, distribution, health \nimpacts of organochlorine pesticides in soils of central China. \nInternational Journal of Environmental Research and Public \nHealth,16 (146), Pp. 1-18. \n\n\n\nGlass, E.H., Thurston, D.H., 1978. Traditional and modern crop protection \nin perspective. BioScience, 28 (2), Pp. 109-115. \n\n\n\nGorai, A.K., and Goyal, P., 2015. A review on air quality indexing system.  \nAsian Journal of Atmospheric Environment, 9 (2), Pp. 101-113. \n\n\n\nHan, J., 2012.  Data Mining: Concepts and Techniques, Third ed. Elsevier, \nAmsterdam. \n\n\n\nHazell, P.B.R., 2009. The Asian Green Revolution. IFPRI Discussion Paper \n00911. Washington, D.C. International Food Policy Research \nInstitute (IFPRI). \n\n\n\nKennedy, M.C., 2019. Modelling aggregate exposure to pesticides from \ndietary and crop spray sources in UK residents. Environmental \nScience and Pollution Research International, 26 (10), Pp. 9892-\n9907. \n\n\n\nKookana, R.S., 2005. Pesticide impact rating index \u2013 a pesticide risk \nindicator for water quality. Water, Air, and Soil Pollution: Focus, 5, \nPp. 45-65. \n\n\n\nKovach, J., 1992. A method to measure the environmental impact of \npesticides. New York\u2019s Food and Life Sciences Bulletin, 139, Pp. 1-8. \n\n\n\nKroman, P., 2011. Use of the environmental impact quotient to estimate \nhealth and environmental impacts pesticide usage in Peruvian and \nEcuadorian potato production. Journal of Environmental \nProtection, 2, Pp. 581-591. \n\n\n\nLam, S., 2017. Emerging health risks from agricultural intensification in \nSoutheast Asia: a systematic review. International Journal of \nOccupational and Environmental Health, 23 (3), Pp. 250-260. \n\n\n\nLeach, A.W., and Mumford, J.D., 2008. Pesticide environmental accounting: \nA method for assessing the external costs of individual pesticide \napplications. Environmental Pollution, 151, Pp. 139-147. \n\n\n\nLencucha, R., 2020. Agricultural production: a scoping review to inform \nresearch and policy on healthy agricultural commodities. \nGlobalization and Health, 16, Pp. 1-15. \n\n\n\n\nhttps://corporatefinanceinstitute.com/resources/knowledge/economics/consumer-price-index-cpi/\n\n\nhttps://corporatefinanceinstitute.com/resources/knowledge/economics/consumer-price-index-cpi/\n\n\nhttps://eepseapartners.org/the-impact-of-pesticide-use-on-human-health-a-case-study-from-myanmar-2/\n\n\nhttps://eepseapartners.org/the-impact-of-pesticide-use-on-human-health-a-case-study-from-myanmar-2/\n\n\nhttp://www.fao.org/faostat/en/#data/EP/visualize\n\n\nhttps://www.fao.org/asiapacific/news/detail-events/en/c/1295300/\n\n\nhttps://www.fao.org/asiapacific/news/detail-events/en/c/1295300/\n\n\nhttps://doi.org/10.4060/cb4742en\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 131-141 \n\n\n\n\n\n\n\n \nCite The Article: Ellis Wongsearaya (2022). An Agricultural \u2018Systems-Based\u2019 Framework for Indexing Potential Exposure to  \nFarming Pesticides: Test Findings from Asia-Pacific, and Asean. Malaysian Journal of Sustainable Agricultures, 6(2): 131-141. \n\n\n\n\n\n\n\nLi, S., 2019. Analysis of the agricultural economy and agricultural pollution \nusing the decoupling index in Chengdu, China. International Journal \nof Environmental Research and Public Health, 16, Pp. 1-11. \n\n\n\nMuhammetoglu, A., 2010. Evaluation of the environmental impact of \npesticides by application of three risk indicators. Environmental \nForensics, 11, Pp. 179-186. \n\n\n\nNicolopoulou-Stamati, P., 2016. Chemical pesticides and human health: \nThe urgent need for a new concept in agriculture. Frontiers in Public \nHealth, 4 (148), Pp. 1-8.  \n\n\n\nObilo, O.P., 2006. Integrated pest management (IPM) and good agricultural \npractices (GAP) in relation to food security: need for government \npolicy for successful implementation. International Journal of \nAgriculture and Rural Development, 7 (1), Pp. 80-84. \n\n\n\nOrganisation of Economic Co-operation and Development. 2018. \nConsiderations for assessing the risks of combined exposure to \nmultiple chemicals. Environment, Health and Safety Division, \nEnvironment Directorate, Paris. (Series on Testing and Assessment \nNo. 296). \n\n\n\nPaddock, W.C., 1970. How green is the green revolution? BioScience, 20 \n(16), Pp. 897-902. \n\n\n\nPingali, P.L., 2001. Environmental consequences of agricultural \ncommercialization in Asia, Environment and Development \nEconomics, 6 (4), Pp. 483-502. \n\n\n\nPingali, P.L., 2012. Green revolution: impacts, limits, and the path ahead. \nProceedings of the National Academy of Sciences of the United \nStates of America, 109 (31), Pp. 12302-12308. \n\n\n\nPinstrup-Andersen, P., 2002. Food and agricultural policy for a globalizing \nworld: preparing for the future. American Journal of Agricultural \nEconomics, 84 (5), Pp. 1201-1214. \n\n\n\nPopp, J., 2013. Pesticides productivity and food security: A review. \nAgronomy for Sustainable Development, 33, Pp. 243-255. \n\n\n\nPopp, J., and Hantos, K., 2011. The impact of crop protection on agricultural \nproduction. Studies in Agricultural Economics, 113 (1), Pp. 1-22. \n\n\n\nPourhoseingholi, M.A., 2013. Sample size calculation in medical studies. \nGastroenterology and Hepatology from Bed to Bench, 6 (1), Pp. 14-\n17. \n\n\n\nPretty, J., and Waibel, H., 2005. Paying the price: The full cost of pesticides. \nIn J. Pretty (Eds.), Pesticides Detox: Towards a More Sustainable \nAgriculture.  Taylor & Francis Publishing., Pp. 40-54. \n\n\n\nRepetto, R., 1986. Pesticide subsidies in the third world: bad policy. Issues \nin Science and Technology, 2 (3), 20-21. \n\n\n\nReus, J., 2002. Comparison and evaluation of eight pesticide environmental \nrisk indicators developed in Europe and recommendations for \nfuture use. Agriculture, Ecosystems & Environment, 90, Pp. 177-\n187. \n\n\n\nRice, Pamela, J., 2007. Advances in pesticide environmental fate and \nexposure assessments. Journal of Agricultural and Food Chemistry, \n55 (14), Pp. 5367-5376. \n\n\n\nRose, D.C., 2016. Decision support tools for agriculture: towards effective \ndesign and delivery. Agricultural Systems, 149, Pp. 165-174. \n\n\n\nSamuel, M., Okey, L.E., 2015. The relevance and significance of correlation \nin social science research. International Journal of Sociology and \nAnthropology Research, 1(3), Pp. 22-28. \n\n\n\nSharma, A., 2019. Worldwide pesticide usage and its impact on ecosystem. \nSN Applied Sciences, 1 (1446).  https://doi.org/10.1007/s42452-\n019-1485-1 (Accessed 31 March 2021). \n\n\n\nSherrick, B., 2017. Updated farmland values and indexing tools \u2013 2017.  \nhttps://farmdocdaily.illinois.edu/2017/08/updated-farmland-\nvalues-indexing-tools-2017.html. (Accessed 31 March 2021). \n\n\n\nSkevas, T., 2012. Economic analysis of pesticide uses and environmental \nspill overs under a dynamic production environment. PhD Thesis. \nWageningen University. \n\n\n\nSoudani, N., 2020. Environmental risk assessment of pesticides uses in \nAlgerian agriculture. Journal of Applied Biology & Biotechnology, 8 \n(5), Pp. 36-47. \n\n\n\nSurminski, S., and Williamson, A., 2012. Policy indexes \u2013 what do they tell \nus and what are their applications? The case of climate policy and \nbusiness planning in emerging markets. Working Paper 101. Leeds, \nLondon: Centre for Climate Change Economics and Policy (CCCEP). \n[dataset] Three Percent Earth Foundation, Reports and Analysis.  \nhttps://www.threepercentearth.org/reports-analysis/ (accessed \n22 November 2021). \n\n\n\nThree Percent Earth Foundation. 2021. The Pesticides Consumer-\nEnvironmental Indexing System Project Study Report. Pakkret, \nNonthaburi, Thailand. (Accessible online at \nhttps://www.threepercentearth.org/reports-analysis)  \n\n\n\nToppr.  https://www.toppr.com/guides/maths-formulas/quartile-\nformula/#:~:text=First%20Quartile(Q1)%3D,known%20as%20th\ne%20upper%20quartile (accessed 31 March 2021).  \n\n\n\nUdias, A., 2018. A decision support tool to enhance agricultural growth in \nthe M\u00e9krou river basin (West Africa). Computers and Electronics in \nAgriculture, 154, Pp. 467-481. \n\n\n\nUnited Nations Development Programme, Human Development Reports. \nhttp://hdr.undp.org/en/content/latest-human-development-\nindex-\nranking?utm_source=EN&utm_medium=GSR&utm_content=US_UN\nDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=C\nENTRAL&c_src2=GSR  (accessed 19 November 2021). \n\n\n\nUnited Nations Environment Programme. 2004. Health & Environment, \ntools for effective decision-making. WHO-UNEP Health and \nEnvironment Linkages Initiative (HELI), Review of Initial Findings. \nGeneva: WHO/UNEP Health and Environment Linkages Initiative. \n\n\n\nUnited Nations Sustainable Development Goals.  \nhttps://www.un.org/sustainabledevelopment/hunger/ (accessed \n30 March 2021). \n\n\n\nUnited States International Trade Commission. 2020. Global economic \nimpact of missing and low pesticides maximum residue levels, vol. \n1. Office of External Relations, Washington D.C. (Publication No. \n5071). \n\n\n\nVan Bol, V., 2005. Pesticide indicators: a study case in Belgium using the \nindex of load. Technical Report. Brussels: Federal Public Service \nHealth, Food Chain Safety and Environment.  \n\n\n\nVan Bol, V., Claeys, S., Debongnie, P., 2003. \u2018Pesticide Indicators\u2019, Pesticide \nOutlook, 14 (4), Pp. 159-163. \n\n\n\nWendling, ZA., 2020. 2020 environmental performance index. New Haven: \nYale Center for Environmental Law & Policy. [dataset] World Bank \nData.  https://data.worldbank.org/indicator/AG.PRD.FOOD.XD \n(accessed 30 March 2021). \n\n\n\nWorld Health Organization. 1990. Public health impact of pesticides used \nin agriculture. Report. Geneva: World Health Organization (WHO). \n\n\n\nZhang, W., 2018. Global pesticide use: profile, trend, cost / benefit and \nmore. Proceedings of the International Academy of Ecology and \nEnvironmental Sciences, 8 (1), Pp. 1-27. \n\n\n\nZilberman, D., 1991. The economics of pesticides use and regulation. \nScience, 253 (5019), Pp. 518-522. \n\n\n\nZilberman, D., and Millock, K., 1997. Pesticides use and regulation: making \neconomic sense out of externality and regulation nightmare, Journal \nof Agricultural and Resource Economics, 22 (2), Pp. 321-332. \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1007/s42452-019-1485-1\n\n\nhttps://doi.org/10.1007/s42452-019-1485-1\n\n\nhttps://farmdocdaily.illinois.edu/2017/08/updated-farmland-values-indexing-tools-2017.html\n\n\nhttps://farmdocdaily.illinois.edu/2017/08/updated-farmland-values-indexing-tools-2017.html\n\n\nhttps://www.threepercentearth.org/reports-analysis/\n\n\nhttps://www.threepercentearth.org/reports-analysis\n\n\nhttps://www.toppr.com/guides/maths-formulas/quartile-formula/#:~:text=First%20Quartile(Q1)%3D,known%20as%20the%20upper%20quartile\n\n\nhttps://www.toppr.com/guides/maths-formulas/quartile-formula/#:~:text=First%20Quartile(Q1)%3D,known%20as%20the%20upper%20quartile\n\n\nhttps://www.toppr.com/guides/maths-formulas/quartile-formula/#:~:text=First%20Quartile(Q1)%3D,known%20as%20the%20upper%20quartile\n\n\nhttp://hdr.undp.org/en/content/latest-human-development-index-ranking?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GSR\n\n\nhttp://hdr.undp.org/en/content/latest-human-development-index-ranking?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GSR\n\n\nhttp://hdr.undp.org/en/content/latest-human-development-index-ranking?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GSR\n\n\nhttp://hdr.undp.org/en/content/latest-human-development-index-ranking?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GSR\n\n\nhttp://hdr.undp.org/en/content/latest-human-development-index-ranking?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GSR\n\n\nhttps://www.un.org/sustainabledevelopment/hunger/\n\n\nhttps://data.worldbank.org/indicator/AG.PRD.FOOD.XD\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 72-78 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2023.72.78 \n\n\n\n \nCite the Article: Mohamed I. Mohaseb, Magdy M. Shahin, Alaa Eldeen A. Shaheen, Rama T. Rashad (2023). Macronutrients  \n\n\n\nUse Efficiency in Sandy Soil Cultivated by Magnetically Treated Seeds Pre-Sowing and Sprayed by N-Fertilizer \n Dissolvedin Magnetized Water. Malaysian Journal of Sustainable Agriculture, 7(2): 72-78. \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-293X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.02.2023.72.78 \n\n\n\n\n\n\n\n \nMACRONUTRIENTS USE EFFICIENCY IN SANDY SOIL CULTIVATED BY \nMAGNETICALLY TREATED SEEDS PRE-SOWING AND SPRAYED BY N-FERTILIZER \nDISSOLVED IN MAGNETIZED WATER  \n\n\n\nMohamed I. Mohaseba, Magdy M. Shahinb, Alaa Eldeen A. Shaheena, Rama T. Rashada,* \n\n\n\naSoils, Water and Environment Research Institute, Agricultural Research Center, Giza, Egypt  \nbSoil and Water Department, Faculty of Agriculture, Al Azhar University Egypt  \n*Corresponding Author Email: rtalat2005@yahoo.com; rama.mostafa@arc.sci.eg \n\n\n\nThis is an open access journal distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 15 April 2023 \nRevised 18 May 2023 \nAccepted 21 June 2023 \nAvailable online 03 July 2023 \n\n\n\n Purpose: is to enhance the nutrients use efficiency (NUE) along with the crop yield and quality by magnetic \ntreatment (MT) of the groundnut (Arachis hypogaea L.) seeds pre-sowing as well as the MT of water used to \ndissolve the nitrogen (N) fertilizer under sandy soil field conditions. Methods: Treatments were distributed \nin a split-plots design in triplicates. The control CL has received the recommended dose RD of the N-fertilizer \nwhile other treatments received a 50% of the RD applied to the surface soil 30 days after planting. The main \nfactor (F1) was the N-application rates 1000, 2000, and 3000 mg kg-1 of urea dissolved in the magnetically \ntreated water (MTW) then sprayed on the soil in the liquid form five times after planting. The sub-factor (F2) \nwas the time of MT (15, 30, and 45 min) of the groundnut seeds exposed to a magnetic field MF 1.4 T intensity \nbefore planting.  Results: The soil available N, P, K, Fe, Mn, and Zn (mg kg-1) were increased significantly by \n34.8%, 23.0%, 3.49%, 9.4%, 22.2%, and 23.2% respectively, at P \u2264 0.05 by the 45 min MT and 3000 mg kg-1 \nN relative to its corresponding control (CL). The MT has increased the seeds yield (kg ha-1) significantly in the \norder 45 min >30 min >15 min for the N-rates 1000, 2000, and 3000 mg kg-1. At the 45-min time, it was \nincreased by 17.5, 15.3, and 14.8% for the N-rates, respectively. Conclusions: The 2000 mg kg-1 rate with MT \nof seeds for 15 min can be recommended for an acceptable nutrients use efficiency (NUE). \n\n\n\nKEYWORDS \n\n\n\nGroundnut; Magnetic treatment; Magnetism; Nutrients use efficiency (NUE); Pre-sowing seed treatment \n\n\n\n1. INTRODUCTION \n\n\n\nAgronomists and climatologists try to improve the crops\u2019 yield and quality \nfor sustainable agriculture using the eco-friendly and safe strategies \nespecially under the conditions of the reclaimed sandy soils.  \n\n\n\nModern methods to improve the crop production include the treatment by \ngrowth regulators, stimulation by the laser, UV, electric and magnetic \nfields (MF) of different intensities. Application of a MF to the plant seeds, \nfertilizers, or water used for irrigation and/or preparation of solutions of \nnutrients and fertilizers has improved the seeds germination, plant \ngrowth, yield, and yield parameters of many crops such as wheat and \ntomatoes under saline conditions (Hussain et al., 2020; Samarah et al., \n2021).  \n\n\n\nMagnetic treatment of seeds pre-sowing by the exposure to a MF may be \npreferred compared to the biological and chemical stimulators because it \nmay be free of toxic residues. It is an effective and secure method can \nimprove the post-germination of plant especially for the temperature \nsensitive seeds, stress tolerance, and crop production. The beneficial \nstimulation effect of the MF was usually seen when seeds germinated \nunder stress condition (Rochalska and Orzeszko-Rywka, 2005). It may \nwork in the energy spectrum of plants. Some theories were suggested \nbased on biological and biochemical changes (alternation of enzymes \nactivities) occur due to exposure of seeds to the MF but do not occur in the \nuntreated seeds. Any change in the concentration of ions within the cell or \n\n\n\nacross the membrane can alter the speed of plant processes and activities \nsuch as photosynthesis, growth, mineral nutrition, water, and ion \ntransport. Enhanced root and shoot system improve the nutrients and \nwater uptake for plants, and provide good support to plants (Hussain et \nal., 2020). Exposure to a MF may induce enzyme changes and regulates the \nexpression of different enzymes and the stimulation of proteins, which \naccelerate the germination of seeds (De Souza et al., 2014; Radhakrishnan, \n2019; Vashisth and Nagaraja, 2010). \n\n\n\nOn another hand, many studies have been introduced to minimize or \nreplace the inorganic chemical fertilizers, but their necessity still exists for \nthe crops production. Magnetized fertilizers were prepared and studied \nfor the sustainable agricultural purposes. For example, magnetized fly ash \nwas a compound fertilizer prepared by mixing the fly ash with a certain \namount of N, P, K, then treated with a MF. The magnetism in fertilizers may \nimprove soil porosity, prevent soil hardening, and increase the use \nefficiency of N and K taking into account the environmental consideration \n(Wang et al., 2017). Some other magnetic fertilizers were synthesized and \nstudied (Li et al., 2021; Li. et al., 2016). The magnetic susceptibility (\u03c7m, m3 \nmol-1) of chemical formulations possibly will play a role in their response \nto the MF and their influence upon use. Positive and negative values of \nsusceptibility (\u03c7m) are likely energetic property because of which a \nsubstance may behave differently if exposed to a MF. Some values are \navailable for chemicals used as fertilizers 1 such as aqueous ammonia NH3 \n(\u03c7m = \u201318.3 m3 mol-1), Ammonium nitrate NH4NO3 (\u03c7m = \u2013 33 m3 mol-1), \nammonium sulfate (NH4)2SO4 (\u03c7m = \u2013 67 m3 mol-1), white phosphorus P (\u03c7m \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 72-78 \n\n\n\n\n\n\n\n \nCite the Article: Mohamed I. Mohaseb, Magdy M. Shahin, Alaa Eldeen A. Shaheen, Rama T. Rashad (2023). Macronutrients  \n\n\n\nUse Efficiency in Sandy Soil Cultivated by Magnetically Treated Seeds Pre-Sowing and Sprayed by N-Fertilizer \n Dissolvedin Magnetized Water. Malaysian Journal of Sustainable Agriculture, 7(2): 72-78. \n\n\n\n\n\n\n\n= \u201326.66 m3 mol-1, Potassium nitrate KNO3 (\u03c7m = \u2013 33.7 m3 mol-1), \nPotassium sulfate K2SO4 (\u03c7m = \u2013 67 m3 mol-1). \n\n\n\n Magnetic treatment of water is to let water flow through a MF created by \na magnet. Magnetization may affect the hydrogen bonds and Van der \nWaal\u2019s forces between the molecules, changes the size of water clusters \nthat affect some properties of water due to its magnetic sensitivity \n(Absalan et al., 2021; Karkush et al., 2019). Magnetized water (MW) may \nbreak large moieties into smaller ones and facilitate passing through the \nroots of the pores of plants and soils. It increases minerals dissolution to \nprovide sufficient nutrients for plants. Salts dissolved in water are not \nchanged, but not detrimental. The plant roots absorb soluble nutrients \nnecessary to grow, and the useless salt components are easily leached \nfrom the soil (Doklega, 2017). Interaction between bio and chemical \nfertilizers with MW have led to significant results for melon fruits \nincluding the yield (7.2 kg plant-1), weight (3.4 kg-1), and content of \nfructose sugar in fruits. Photosynthetic pigments (chlorophyll and \ncarotenoids) kinetin, potassium, GA3, nucleic acids (RNA and DNA), \nphotosynthetic activity were enhanced (Ali et al., 2019).  \n\n\n\nDirect magnetization of water containing fertilizers can increase the \nsolubility and efficiency of fertilizers (Mohamed, 2020; Mostafa, 2020). \nThe uptake of elements by hydroponically grown grapevines (with 0.1 and \n0.2 T MF intensities) was evaluated. The solutions were magnetized in two \nways: 1) solutions magnetized after preparing, and 2) salts were added to \nthe pre-magnetized waters. The results revealed that magnetic treatments \nhad effect on increasing of leaf elements uptake including N+, P+, K+, Ca2+, \nFe2+, and Zn2+. Magnetic treatments also stimulated the chlorophyll \ncontent, leaf extension and weight, carbohydrates and biomass \naccumulation (Zareei et al., 2021). \n\n\n\nEnvironmental and health problems have emerged due to the losses of \nnitrogen (N) fertilizers by leaching, nitrification-de-nitrification process or \nby ammonia NH3 emission in addition to the decreased N-use efficiency. \nThis depends on the climate, soil conditions, and management practices. A \ncalculated annual NH3 losses range is 10\u201319% has been reported, \nsometimes oxidized into nitrogen oxides (NOx), while the N2O emission \nmay be resulted from the nitrification (Ding et al., 2017). Optimized \nfertilization can decrease the nitrate loss compared to the conventional \nfertilization to improve environmental quality (Wang et al., 2019). \n\n\n\nThis study aims to enhance the nutrients use efficiency (NUE) in sandy soil \nalong with the crop yield and quality by the magnetic treatment (MT) of \nthe groundnut (Arachis hypogaea L.) seeds pre-sowing as well as the MT \nof water used to dissolve the N-fertilizer applied as a sprayed solution.   \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1   The Experimental Area Planning and Fertilizers Used \n\n\n\nThe field experiment has been carried out at the Agricultural Research \nStation, during the summer seasons of 2021 and 2022. The experiment \narea was sandy soil (Typic Torripsamment; Entisol [Arenosol AR] (FAO, \n2014)). Its analysis before planting showed a sandy textured sample with \nthe following properties: Coarse sand 70.12%, Fine sand 14.32%, Silt \n6.22%, Clay 9.34%, CaCO3 0.38%, Organic matter (OM = 0.26%), pH 7.9 \n(1:2.5 soil: water suspension), Electrical conductivity (EC = 0.4 dS m-1, in \n1:5 soil: water extract), and available N, P, and K equals 25.5, 2.2, and 55.16 \nmg kg-1, respectively. \n\n\n\nTreatments were distributed in a split-plots design in triplicates with a \nplot area 10.5 m2 (3.0 m \u00d7 3.5 m). The main factor (F1) of the study was \nthe treatments of the N-fertilizer dissolved in the magnetically treated \nwater (MTW). The sub-factor (F2) was the magnetically treated seeds \nsown in all plots in holes (20-cm apart) in lines (50-cm apart) \n\n\n\nThe applied mineral fertilizers were as follows: Before sowing, the \nphosphorous (P) fertilizer was applied as the super Calcium phosphate \nmixed with soil at application rate 476.19 kg ha-1 recommended dose (RD). \nThe nitrogen (N) in the form of urea 46% N was applied in three equal \ndoses each of which is a twenty N unit 30, 45, and 60 days after sowing to \nobtain the total RD as sixty N units per fed (142.86 N unit per hectare) for \nthe control (CL) plots. The potassium (K) fertilization was 119 kg ha-1 of \nK2SO4 (48% K2O) applied in two equal doses after planting as \nrecommended.      \n\n\n\n2.2   Magnetic Treatment (MT) of Seeds and Water  \n\n\n\nA magnetic tube (70 cm length \u00d7 1.5-inch diameter) inside which a \nmagnetic field MF 1.4 T intensity was used for the magnetic treatment \n(MT). Seeds of the groundnut (Arachis hypogaea L. cv. Giza 5) were \nexposed to the MF by placing inside the mentioned magnetic tube for 15, \n\n\n\n30, and 45 min before planting. Magnetically treated water (MTW) was \nobtained by passage of water through the MF for 15 min then used to \ndissolve the N-fertilizer. \n\n\n\n2.3   Sowing and Harvesting \n\n\n\nMagnetically treated seeds were sown for all plots in holes (20-cm apart) \nin lines (50-cm apart) on the 15th of May 2020 and May 2021. A 50% N RD \nwas applied to the surface soil for all treatments except for the CL after 30 \ndays from planting. The liquid form of the N-fertilizer was then sprayed on \nsoil at three-application rates 1000, 2000, and 3000 mg kg-1 N applied five \ntimes 45, 60, 75, 90, 105 days after planting. Plots under study have \nreceived the desired fertilization as mentioned. Other agronomic practices \nwere followed according to the recommendations of Ministry of \nAgriculture. The groundnut crop was harvested in October 2020 and/or \n2021. \n\n\n\n2.4   Soil and Plant Sampling and Analysis \n\n\n\nRepresentative soil and plant samples from all treatments\u2019 plots were \nrandomly selected after harvesting the crop and air-dried to estimate \nsome parameters according to the mentioned methods (Black, 1982). \nYield (kg ha-1) and some yield components such as seeds yield (kg ha-1), \nand 100-seeds weight (g) and the mean of the two seasons was recorded.  \n\n\n\nThe soil available N, P, and K were extracted by 1% K2SO4, 0.5 N NaHCO3, \nand 1 N NH4OAc (pH 7.0), respectively (Jackson, 1973). Groundnut seeds \nand straw were dried at 70 \u00baC for 48 h and ground. A half gram of the \nground seeds and/or straw was wet digested using the acid mixture (1:1 \nH2SO4/HClO4) (Chapman and Pratt, 1961). Total concentrations of the N, P \nand K in plant and soil extracts were estimated by distillation using \nKjeldahl apparatus, colorimetrically by the UV-Vis. Spectrophotometer \nand by flame photometer, respectively.  \n\n\n\nNutrient Use Efficiency Indices: they were calculated for different \ntreatments according to (Echeverria and Videla, 1998; Roozbeh et al., \n2011) as follows:  \n\n\n\nNutrient Use Efficiency (UE) =  \n(\ud835\udc43\ud835\udc5b\ud835\udc53\u2212\ud835\udc43\ud835\udc5b0)\n\n\n\n\ud835\udc39\ud835\udc52\ud835\udc5f\ud835\udc61\ud835\udc56\ud835\udc59\ud835\udc56\ud835\udc67\ud835\udc52\ud835\udc5f \ud835\udc5f\ud835\udc4e\ud835\udc61\ud835\udc52 (\ud835\udc41 \ud835\udc5c\ud835\udc5f \ud835\udc43 \ud835\udc5c\ud835\udc5f \ud835\udc3e,   \ud835\udc58\ud835\udc54 \u210e\ud835\udc4e-1 )\n  \u00d7 100 \n\n\n\nPnf = seeds N and/or P and/or K in fertilized plots as (g kg-1)  \n\n\n\nPn0 = seeds N and/or P and/or K in non-fertilized plots as (g kg-1) \n\n\n\n2.5   Statistical Analysis \n\n\n\nCalculations of the least significant difference (LSD) between the \ntreatments effect were  done by the two-way analysis of variance (ANOVA) \nat a significance level P \u2264 0.05 (Gomez and Gomez, 1984) using the Co-State \nsoftware Package (Ver. 6.311), a product of Cohort software Inc., Berkley, \nCalifornia. \n\n\n\n3. RESULTS \n\n\n\n3.1   Effect of the Studied Treatments on Some Properties of The \nExperiment Soil \n\n\n\nMagnetic treatment (MT) of groundnut seeds pre-sowing and/or of water \nused for preparation of the urea (CO(NH2)2) fertilization solution has \nresulted in significant variations in some of the estimated soil and plant \nparameters. As presented in Tables 1 and 2, variation in the soil pH, EC (dS \nm-1), and available K (mg kg-1) was non-significant neither due to different \nN application rates (F1) nor due to MT of seeds before cultivation (F2) \ndepending on the LSD values. The 3000 mg kg-1 of N-fertilization at 45 min \nMT resulted in a significant relative increase at P \u2264 0.05 for the soil \navailable N, P, Fe, Mn, and Zn (mg kg-1) by 34.8%, 23.0%, 9.4%, 22.2%, and \n23.2%, respectively, compared to its corresponding control (CL). The \neffects of F1 or F2 were independent of each other since the interaction of \nF1 \u00d7 F2 was non-significant for the estimated soil properties in Tables 1 \nand 2. \n\n\n\n3.2   Effect of the MT of Seeds Before Cultivation and The Studied N-\nFertilization Rates on The Groundnut Yield (Kg Ha-1) and Some Yield \nParameters \n\n\n\nTable 3 shows that pre-sowing MT of seeds for 45 min along with the 3000 \nmg kg-1 N-fertilization resulted in the most significant relative increase in \nthe plant height (cm), no. of pods/Plant, wt. pods/plant (g), and wt. \nseeds/plant (g) by 53.7, 75.3, 45.0, and 60.2%, respectively, compared to \nits corresponding CL. The effects of F1 or F2 may be complementary to \neach other in case of the wt. seeds/plant (g) as indicated by their \nsignificant interaction.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 72-78 \n\n\n\n\n\n\n\n \nCite the Article: Mohamed I. Mohaseb, Magdy M. Shahin, Alaa Eldeen A. Shaheen, Rama T. Rashad (2023). Macronutrients  \n\n\n\nUse Efficiency in Sandy Soil Cultivated by Magnetically Treated Seeds Pre-Sowing and Sprayed by N-Fertilizer \n Dissolvedin Magnetized Water. Malaysian Journal of Sustainable Agriculture, 7(2): 72-78. \n\n\n\n\n\n\n\nHowever, the effect of the studied factors F1 and F2 on the increase of the yield (kg ha-1) of groundnut pods and \nseeds was non-significant at P \u2264 0.05 as indicated by Table 4. The interaction of the F1 \u00d7 F2 was significant only \nfor the 100 seeds weight. The 100 seeds weight (g) has increased by 17.9 and 19.2% at the 3000 mg kg-1 N-\nfertilization upon MT of seeds for 30 and 45 min, respectively, compared to the CL. The relative increase in the \n\n\n\nyield (kg ha-1) of the pods and seeds due to the MT followed the order 45 min > 30 min > 15 min for the three \nN-rates 1000, 2000, and 3000 mg kg-1. At the 45 min time, the pods yield (kg ha-1) has increased relatively by \n21.1, 17.8, and 20.6%, while the seeds yield has increased by 17.5, 15.3, and 14.8% for the N-rates, respectively, \ncompared to the corresponding CL (El-Basioni et al., 2015). \n\n\n\nTable 1: Effect of the Studied N-Fertilization Rates and Magnetic Treatment of Seeds Before Cultivation on Some Properties of the Experiment Soil \n\n\n\nTreatments pH (1:2.5) EC (dS m-1) \nAvailable concentration (mg kg-1) \n\n\n\nN P K \n\n\n\nApplication \nrate of N \nfertilizer \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\n15 30 45 15 30 45 15 30 45 15 30 45 15 30 45 \n\n\n\n1000 8.00 7.96 7.95 7.93 1.07 1.04 0.97 0.94 36.2 39.4 42.3 45.6 3.89 4.15 4.55 4.93 166.3 169.0 173.2 176.0 \n\n\n\n2000 7.98 7.95 7.91 7.88 1.05 0.96 0.92 0.91 37.0 42.8 45.7 48.3 4.12 4.65 4.90 5.04 168.0 172.9 176.4 178.5 \n\n\n\n3000 7.96 7.92 7.87 7.85 1.03 1.00 0.96 0.93 39.4 45.3 49.0 53.1 4.18 4.79 4.95 5.14 172.0 174.0 176.5 178.0 \n\n\n\nF1 \nLSD 0.01 LSD 0.11 LSD 5.89 LSD 0.03 LSD 6.54 \n\n\n\nSL *** SL ns SL ns SL *** SL ns \n\n\n\nF2 \nLSD 0.50 LSD 0.10 LSD 2.43 LSD 0.02 LSD 5.72 \n\n\n\nSL ns SL ns SL *** SL *** SL * \n\n\n\nF1*F2 ns F1*F2 ns F1*F2 ns F1*F2 *** F1*F2 ns \n\n\n\nF1: main factor (application rates of the N-fertilizer), F2: sub-factor (time of magnetic treatment), LSD: least significant difference at p \u2264 0.05, SL: Significance of Level, ns: non-significant. \n \n\n\n\nTable 2: Effect of the Studied N-Fertilization Rates and Magnetic Treatment of Seeds Before Cultivation on the Available Micronutrients (Mg Kg-1) of the Experiment Soil \n\n\n\nTreatments \nAvailable concentration (mg kg-1) \n\n\n\nFe Mn Zn \n\n\n\nApplication rate of N \nfertilizer \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\n15 30 45 15 30 45 15 30 45 \n1000 1.98 2.06 2.13 2.19 1.09 1.12 1.18 1.20 0.64 0.66 0.69 0.74 \n2000 2.05 2.14 2.19 2.25 1.15 1.25 1.30 1.33 0.67 0.73 0.77 0.82 \n3000 2.13 2.18 2.28 2.33 1.17 1.29 1.35 1.43 0.69 0.75 0.79 0.85 \n\n\n\nF1 \nLSD 0.12 LSD 0.13 LSD 0.20 \nSL ns SL ns SL ns \n\n\n\nF2 \nLSD 0.17 LSD 0.11 LSD 0.06 \nSL ns SL ** SL *** \n\n\n\nF1*F2 ns F1*F2 ns F1*F2 ns \nF1: main factor (application rates of the N-fertilizer), F2: sub-factor (time of magnetic treatment), LSD: least significant difference at p \u2264 0.05, SL: Significance of Level, ns: non-significant. \n \n\n\n\nTable 3: Effect of the Studied N-Fertilization Rates and Magnetic Treatment of Seeds Before Cultivation on Some Groundnut Yield Parameters  \n\n\n\nTreatments Plant height (cm) Plant wt. (g) No. of Pods/Plant Wt. Pods/Plant (g) Wt. Seeds/Plant (g) \n\n\n\nApplication rate \nof N fertilizer \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\nCL \nTime of MT (min) \n\n\n\n15 30 45 15 30 45 15 30 45 15 30 45 15 30 45 \n\n\n\n1000 39.7 46.5 53.9 56.4 146.3 130.2 110.9 98.4 15.4 22.3 28.5 33.2 34.6 41.9 48.9 55.9 18.9 21.6 26.8 27.9 \n\n\n\n2000 41.3 47.6 56.6 61.2 129.8 132.0 134.7 121.8 19.6 25.5 31.3 35.8 37.9 46.3 55.1 57.8 21.8 25.3 27.2 31.6 \n\n\n\n3000 43.2 52.9 62.4 66.4 119.2 121.1 141.4 103.2 23.1 28.6 37.6 40.5 43.1 47.5 59.1 62.5 23.1 25.9 32.8 37.0 \n\n\n\nF1 \nLSD 3.26 LSD 0.03 LSD 1.80 LSD 1.89 LSD 0.20 \n\n\n\nSL ** SL *** SL *** SL *** SL *** \n\n\n\nF2 \nLSD 5.48 LSD - LSD 1.77 LSD 2.46 LSD 0.17 \n\n\n\nSL *** SL - SL *** SL *** SL *** \n\n\n\nF1*F2 ns F1*F2 - F1*F2 ns F1*F2 ns F1*F2 *** \n\n\n\nF1: main factor (application rates of the N-fertilizer), F2: sub-factor (time of magnetic treatment), LSD: least significant difference at p \u2264 0.05, SL: Significance of Level, ns: non-significant. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 72-78 \n\n\n\n\n\n\n\n \nCite the Article: Mohamed I. Mohaseb, Magdy M. Shahin, Alaa Eldeen A. Shaheen, Rama T. Rashad (2023). Macronutrients  \n\n\n\nUse Efficiency in Sandy Soil Cultivated by Magnetically Treated Seeds Pre-Sowing and Sprayed by N-Fertilizer \n Dissolvedin Magnetized Water. Malaysian Journal of Sustainable Agriculture, 7(2): 72-78. \n\n\n\n\n\n\n\nTable 4: Effect of the Studied N-Fertilization Rates and Magnetic Treatment of Seeds Before Cultivation on The Groundnut Pods and Seeds Yield (Kg Ha-1) \n\n\n\nTreatments 100 grain wt. (g) Pods Yield (kg ha-1) Seeds Yield (kg ha-1) \n\n\n\nApplication rate of N \n\n\n\nfertilizer \nCL \n\n\n\nTime of MT (min) \nCL \n\n\n\nTime of MT (min) \nCL \n\n\n\nTime of MT (min) \n\n\n\n15 min 30 min 45 min 15 min 30 min 45 min 15 min 30 min 45 min \n\n\n\n1000 74.30 73.16 72.79 68.67 2928.6 3214.3 3500.0 3547.6 1904.8 2023.8 2142.9 2238.1 \n\n\n\n2000 81.96 77.87 78.42 71.49 3071.4 3333.3 3523.8 3619.0 2023.8 2119.0 2214.3 2333.3 \n\n\n\n3000 65.34 57.22 77.02 77.88 3119.0 3523.8 3642.9 3761.9 2095.2 2190.5 2309.5 2404.8 \n\n\n\nF1 \nLSD 6.54 LSD 130.88 LSD 130.88 \n\n\n\nSL ns SL * SL ns \n\n\n\nF2 \nLSD 4.95 LSD 66.03 LSD 66.03 \n\n\n\nSL ns SL *** SL *** \n\n\n\nF1*F2 ** F1*F2 ns F1*F2 ns \n\n\n\nF1: main factor (application rates of the N-fertilizer), F2: sub-factor (time of magnetic treatment), LSD: least significant difference at p \u2264 0.05, SL: Significance of Level, ns: non-significant. \n\n\n\nTable 5: Effect of the Studied N-Fertilization Rates and Magnetic Treatment of Seeds Before Cultivation on the Total Concentration (G Kg-1) Of N, P, And K in the Seeds and Straw \n Total concentration (g kg-1) \n\n\n\nTreatments \nSeeds  Straw \n\n\n\nN  N \n\n\n\nApplication rate of N \nfertilizer \n\n\n\nCL \nTime of MT (min)  \n\n\n\nCL \nTime of MT (min) \n\n\n\n15 min 30 min 45 min  15 min 30 min 45 min \n1000 29.51 39.50 32.38 45.14  10.92 17.36 18.48 11.20 \n2000 32.61 38.19 33.31 43.19  15.50 19.60 20.16 19.04 \n3000 27.60 35.91 31.92 40.90  20.09 11.20 18.48 11.76 \n\n\n\nF1 \nLSD 2.27  LSD 1.31 \nSL ns  SL ** \n\n\n\nF2 \nLSD 1.75  LSD 1.14 \nSL ***  SL *** \n\n\n\nF1*F2 *  F1*F2 *** \n P  P \n\n\n\n1000 4.18 4.40 4.73 4.90  1.05 1.65 1.35 1.30 \n2000 3.73 4.84 4.54 4.39  1.25 1.60 1.65 1.40 \n3000 4.45 4.16 4.08 4.25  1.51 1.30 1.45 1.25 \n\n\n\nF1 \nLSD 0.65  LSD 0.01 \nSL ns  SL *** \n\n\n\nF2 \nLSD 0.81  LSD - \nSL ns  SL - \n\n\n\nF1*F2 ns  F1*F2 - \n K  K \n\n\n\n1000 5.86 5.51 5.69 4.92  5.86 5.65 6.28 8.03 \n2000 5.06 5.38 5.17 4.82  6.24 8.10 5.79 6.84 \n3000 4.61 5.27 5.10 5.52  6.61 7.05 8.38 6.56 \n\n\n\nF1 \nLSD 0.65  LSD  0.65  \nSL ns  SL  ns  \n\n\n\nF2 \nLSD 0.57  LSD  0.81  \nSL ns  SL  ns  \n\n\n\nF1*F2 ns  F1*F2  **  \nF1: main factor (application rates of the N-fertilizer), F2: sub-factor (time of magnetic treatment), LSD: least significant difference at p \u2264 0.05, SL: Significance of Level, ns: non-significant. \n\n\n\n3.3 Effect on the Nutrients' Total Concentrations (G Kg-1) of The N, P, K, Na, and Na/K Ratio in The Seeds \nand Straw   \n\n\n\nTable 5 shows that the range of the significant relative increase in the total concentration of N (g kg-1) was from \n2.1% (30 min, 2000 mg kg-1) to 53.0% (45 min, 1000 mg kg-1) in the seeds and from 2.6% to 69.2% at 1000 mg \n\n\n\nkg-1 for 45 and 30 min, respectively in the straw. For P (g kg-1) in the straw, the relative increase ranged from \n12.0% (45 min, 2000 mg kg-1) to 57.1% (15 min, 1000 mg kg-1). Variation in the total concentration of the P (g \nkg-1) in the seeds and the K in the seeds and straw was non-significant at P \u2264 0.05. \n\n\n\nTable 6 refers to that the Na concentration (g kg-1) in the seeds was almost decreased significantly due to the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 72-78 \n\n\n\n\n\n\n\n \nCite the Article: Mohamed I. Mohaseb, Magdy M. Shahin, Alaa Eldeen A. Shaheen, Rama T. Rashad (2023). Macronutrients  \n\n\n\nUse Efficiency in Sandy Soil Cultivated by Magnetically Treated Seeds Pre-Sowing and Sprayed by N-Fertilizer \n Dissolvedin Magnetized Water. Malaysian Journal of Sustainable Agriculture, 7(2): 72-78. \n\n\n\n\n\n\n\nN-fertilization rates compared to the corresponding CL at P \u2264 0.05. Its minimum decrease was by 10.3% at 3000 \nmg kg-1 N with 15 and 45 min of MT, while its sole maximum increase was by 24.1% at 1000 mg kg-1 N and 15 \nmin of MT. The Na/K ratio was also mainly decreased but non-significantly and only increased by 18.2% at \n\n\n\n1000 mg kg-1 N and 15 min of MT. Variation of the Na concentration (g kg-1) in the straw was non-significant, \nbut the Na/K ratio was varied significantly that may be dependent on the significant variation of K \nconcentration (g kg-1) in the straw. \n\n\n\nTable 6: Effect of the Studied N-Fertilization Rates and Magnetic Treatment of Seeds Before Cultivation on the Total Concentration (G Kg-1) of Na And Na/K Ratio in The Seeds and Straw \n\n\n\n Seeds   Straw  \n\n\n\nTreatments Total Na+ concentration (g kg-1) Na+ / K+ \n \n\n\n\nTotal Na+ concentration (g kg-1) Na+ / K+ \n \n\n\n\nApplication rate \n\n\n\nof N fertilizer \nCL \n\n\n\nTime of MT (min) \n\n\n\nCL \n\n\n\nTime of MT (min)  \n\n\n\nCL \n\n\n\nTime of MT (min) \n\n\n\nCL \n\n\n\nTime of MT (min) \n\n\n\n15 min 30 min 45 min \n15 \n\n\n\nmin \n\n\n\n30 \n\n\n\nmin \n\n\n\n45 \n\n\n\nmin \n 15 min 30 min 45 min 15 min 30 min 45 min \n\n\n\n1000 0.68 0.36 0.36 0.54 0.12 0.07 0.06 0.11  0.14 0.14 0.14 0.14 0.02 0.03 0.02 0.02 \n\n\n\n2000 0.58 0.72 0.50 0.47 0.11 0.13 0.10 0.10  0.21 0.29 0.22 0.22 0.03 0.04 0.04 0.03 \n\n\n\n3000 0.68 0.61 0.54 0.61 0.15 0.12 0.11 0.11  0.30 0.14 0.22 0.22 0.05 0.02 0.03 0.03 \n\n\n\nF1 \nLSD 0.07 LSD 0.07  LSD 0.07 LSD 0.01 \n\n\n\nSL * SL ns  SL * SL * \n\n\n\nF2 \nLSD 0.10 LSD 0.03  LSD 0.06 LSD 0.01 \n\n\n\nSL * SL ns  SL ns SL ns \n\n\n\nF1*F2 * F1*F2 ns  F1*F2 ns F1*F2 ns \n\n\n\nF1: main factor (application rates of the N-fertilizer), F2: sub-factor (time of magnetic treatment), LSD: least significant difference at p \u2264 0.05, SL: Significance of Level, ns: non-significant. \n\n\n\n3.4   The Effect of the Studied Treatments on the Chlorophyll Content (Mg G-1 F.W) In the Groundnut \nLeaves \n\n\n\nBoth of the N-fertilization rates and the MT of seeds pre-sowing as well as their interactive effect have \nsignificantly affected the chlorophyll a, b, and a/b ratio although the total chlorophyll (mg g-1 f.w) showed non-\nsignificant variations (Table 7). Increasing the N rates from 1000 to 3000 mg kg-1 with 15 and 30 min of pre-\nsowing MT of seeds have decreased the chlorophyll a, b, total chlorophyll (a + b) but increased the chlorophyll \n\n\n\na/b ratio. At 45 min of MT, the chlorophyll a, b, total chlorophyll (a + b) were decreased at 1000 and 3000 mg \nkg-1 N while increased the 2000 mg kg-1 N-fertilization rate that was opposite to the trend of the chlorophyll \na/b ratio. Pre-sowing MT of seeds for 30 min showed the maximum significant increase of the chlorophyll a (by \n35.3%), b (by 70%), total chlorophyll (a + b) (by 48.9%), and the minimum significant decrease of the \nchlorophyll a/b ratio (by \u2013 20.1%) at 1000 mg kg-1 N. The same time of MT at 3000 mg kg-1 N showed the \nminimum significant decrease the chlorophyll a (by \u2013 38.5%), b (by \u201360.7%), total chlorophyll (a + b) (by \u2013 \n47.5%), and the maximum significant increase of the chlorophyll a/b ratio (by 56.8%).  \n\n\n\nTable 7: Effect of the Studied N-Fertilization Rates and Magnetic Treatment of Seeds Before Cultivation on the Chlorophyll Content (Mg G-1 F.W) in the Groundnut Leaves \n\n\n\nTreatments Chl a (mg g-1 f.w) Chl b (mg g-1 f.w) Total Chl (mg g-1 f.w)  Chl a/b \n\n\n\nApplication rate of \n\n\n\nN fertilizer \nCL \n\n\n\nTime of MT (min) \nCL \n\n\n\nTime of MT (min) \nCL \n\n\n\nTime of MT (min) \nCL \n\n\n\nTime of MT (min) \n\n\n\n15 min 30 min 45 min 15 min 30 min 45 min 15 min 30 min 45 min 15 min 30 min 45 min \n\n\n\n1000 1.39 1.76 1.88 1.48 0.90 1.33 1.53 0.87 2.29 3.09 3.41 2.35 1.54 1.32 1.23 1.70 \n\n\n\n2000 1.48 1.22 1.61 1.58 0.99 0.80 1.11 1.12 2.46 2.02 2.72 2.70 1.50 1.53 1.45 1.41 \n\n\n\n3000 1.56 1.35 0.96 1.24 1.07 0.87 0.42 0.74 2.63 2.22 1.38 1.98 1.46 1.55 2.29 1.68 \n\n\n\nF1 \nLSD 0.13  0.06  0.65  0.13 \n\n\n\nSL **  ***  ns  ** \n\n\n\nF2 \nLSD 0.16  0.20  0.63  0.16 \n\n\n\nSL ns  ns  ns  ns \n\n\n\nF1*F2 ***  ***  ns  *** \n\n\n\nF1: main factor (application rates of the N-fertilizer), F2: sub-factor (time of magnetic treatment), LSD: least significant difference at p \u2264 0.05, SL: Significance of Level, ns: non-significant. \n\n\n\n4. DISCUSSION \n\n\n\nIn the present study, the urea fertilizer dissolved in magnetically treated water and sprayed on soil planted by \nmagnetically treated groundnut seeds significantly affected the N availability in soil and uptake by groundnut \nseeds and straw. Additionally, it affected the equilibrium concentrations of P and K nutrients along with the \nNa/K ratio. The balanced content of chlorophyll a and b in the leaves was highly disturbed. It may be due to the \nstructural difference between the two chemical forms being chlorophyll b containing an aldehyde moiety (\u2012 \nCHO) of higher susceptibility \u03c7m compared to the methyl moiety (\u2012 CH3) Scheme 1. \n\n\n\nNutrient use efficiency (%) illustrated in Fig. 1 (a-c) indicates that the 1000 mg kg-1 and the 3000 mg kg-1 N-\n\n\n\nfertilization rates shall be eliminated for the studied times of MT (15, 30, and 45 min) because they are \nnegatively affected the KUE (%) and the PUE (%), respectively. The 2000-mg kg-1 rate at 45 min MT negatively \naffected the KUE. Therefore, the 2000-mg kg-1 rate used with MT of seeds for 15 or 30 min before cultivation \nmay be suitable to obtain acceptable N, P, and KUE with the 15 min is more recommended. At the 15-min time \nand 2000-mg kg-1 rate, the yield of pods and seeds (kg ha-1) has increased relatively by 8.5% and 4.7% \nrespectively, compared to the corresponding CL. It may avoid the abnormal changes of the MT. \n\n\n\nMagnetism expressed as the magnetic susceptibility \u03c7m (10-6 cm3 mol-1) can be responsible for a matter \nbehavior when it is induced by a MF. Many studies have revealed that MT of water used for irrigation and/or \nplant seeds before cultivation effectively enhanced the plant growth and yield parameters. Although \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 72-78 \n\n\n\n\n\n\n\n \nCite the Article: Mohamed I. Mohaseb, Magdy M. Shahin, Alaa Eldeen A. Shaheen, Rama T. Rashad (2023). Macronutrients  \n\n\n\nUse Efficiency in Sandy Soil Cultivated by Magnetically Treated Seeds Pre-Sowing and Sprayed by N-Fertilizer \n Dissolvedin Magnetized Water. Malaysian Journal of Sustainable Agriculture, 7(2): 72-78. \n\n\n\n\n\n\n\nmeasurable values of the susceptibility \u03c7m were provided in the literature \nfor different elements and some of their polyatomic forms (Appendix I), it \nis extremely difficult to predict their magnetic response and behavior as \nnutrients within the soil or plant matrix. This is due to the complex \nchemical and biological reactions taking place in soil solution and inside \nthe plant cells. Positive susceptibility \u03c7m of oxygen (Appendix I) is expected \nto affect that of water so that water can be considered as a mobile phase \ncarries a magnetic force to magnetically induce matter in contact (Wang et \nal., 2018; Xiao-Feng and Bo, 2008a 2008b). They include soluble plant \nnutrients with different chemical forms and different constituents of soil. \nIn addition, ions of potassium (K), iron (Fe), manganese (Mn), and perhaps \nthe nitrate (NO3\n\n\n\n-) and phosphate (PO4\n-3) may possess positive \n\n\n\nsusceptibility \u03c7m. In the soil-plant system, the magnetic susceptibility \u03c7m of \n\n\n\ndifferent constituents, magnetic induction created by the magnetically \ntreated water or seeds, and the resulted magnetic attraction/repulsion \nforces are factors expected to control the equilibria of chemical and \nbiological reactions. It may works in the energy spectrum of plants and \naffects the balanced uptake of nutrients by plant. Some studies referred to \nthat the magnetization of water increased some ions such as Mg, K, Na, Cl, \nand SiO2 and decreases Ca and SO3. The spectral lines of an atom are \nsplitted in the presence of a MF, called the Zeeman Effect theory. Under a \nMF, the electrons in the orbitals of an atom become distorted and the gap \nbetween the different lines depends on the strength of the MF. Magnetic \nfertilizers were believed to manage and harmonize the molecular \nstructure of the soil (Li et al., 2021; Rochalska and Orzeszko-Rywka, 2005; \nVasilyeva et al., 2021).\n\n\n\n\n\n\n\nFigure 1a: Nitrogen use efficiency (NUE, %) under the effect of the \nstudied treatments \n\n\n\n\n\n\n\nFigure 1c: Potassium use efficiency (KUE, %) under the effect of the \nstudied treatments \n\n\n\n\n\n\n\nFigure 1b: Phosphorus use efficiency (PUE, %) under the effect of the \nstudied treatments \n\n\n\n\n\n\n\nScheme 1: Structural formula of chlorophyll a and b \n\n\n\nThe physicochemical properties of solvents such as water change under \nan applied MF with novel properties. Water is diamagnetic, and when \nmagnetized with a MF, the hydrogen bond in water breaks, the number of \nwater monomers increases, a reactive oxygen is formed, which increases \nsalt mobility. One of the most significant advantages of water \nmagnetization is that water retains its new properties from several \nminutes to hours and even days. It has been believed that solvents lose \nsome properties obtained by magnetization but not all properties referred \nto as \u2018\u2018solvent\u2019s memory\u201d (Absalan et al., 2021).  \n\n\n\nMulti-component magnetic fertilizer studied previously strengthens the \nmagnetic and energetic field around plants for both grain and crops. The \ngrowth parameters were also enhanced significantly in plants from \nmagnetically treated seeds. The chlorophyll contents (35.41%), fruit \nlength (18.11%), fruit weight (14.93%), yield (29.16%) and mineral \ncontents were also recorded to be higher in MF treated plants group \nversus control (Iqbal et al., 2016). \n\n\n\n5. CONCLUSION \n\n\n\nThe present study aimed to improve the sandy soil productivity via \n\n\n\nimproving the use efficiency (UE) of mineral fertilization. Magnetic \ntreatment (MT) of groundnut seeds before cultivation and/or water used \nto prepare fertilization solutions can be recommended to enhance the N, \nP, and K nutrients use efficiency under the sandy soil conditions. This can \nbe attributed to the induction effect of the magnetic force on the chemical \nand biological reactions that control the nutrients' availability and uptake \nin the soil-plant system. The 2000 mg kg-1 rate used with MT of seeds for \n15 or 30 min before cultivation may be suitable to obtain acceptable N, P, \nand KUE with the 15 min is more recommended. At the 15 min time and \n2000 mg kg-1 rate, the yield of pods and seeds (kg ha-1) has increased \nrelatively by 8.5% and 4.7% respectively, compared to the corresponding \nCL. It may avoid the abnormal changes and disturbed equilibria of the \nnutrients' availability or uptake by plant that may result from the \ninduction effect of the MT leading to a negative NUE. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThe authors thank very much the Company of Delta Water for Magnetic \nWater Treatment Technology as they provided the permanent magnet \ninstrument for research and scientific purposes.    \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 72-78 \n\n\n\n\n\n\n\n \nCite the Article: Mohamed I. Mohaseb, Magdy M. Shahin, Alaa Eldeen A. Shaheen, Rama T. Rashad (2023). Macronutrients  \n\n\n\nUse Efficiency in Sandy Soil Cultivated by Magnetically Treated Seeds Pre-Sowing and Sprayed by N-Fertilizer \n Dissolvedin Magnetized Water. Malaysian Journal of Sustainable Agriculture, 7(2): 72-78. \n\n\n\n\n\n\n\nHIGHLIGHTS  \n\n\n\n\u2022 Fertilizers dissolved in the magnetized water may increase the \nnutrients use efficiency. \n\n\n\n\u2022 Magnetic treatment of the groundnut seeds pre-sowing can affect \nthe balanced nutrient uptake. \n\n\n\n\u2022 Magnetic treatment technology can be recommended for sandy \nsoil conditions. \n\n\n\n\u2022 The magnetic induction may affect the bio/chemical reactions in \nthe soil-plant system.  \n\n\n\nSUPPLEMENTAL MATERIAL \n\n\n\nAppendix I Magnetic Susceptibility \u03c7m (10-6 cm3 mol-1) of the plant \nnutrients in their elemental form and some of their inorganic Compounds  \n\n\n\nREFERENCES \n\n\n\nAbsalan, Y., Gholizadeh, M., and Choi, H. J. 2021. Magnetized solvents: \nCharacteristics and various applications. Review. Journal of \nMolecular Liquids 335, 1-21. 116167. \n\n\n\nAli, A. F., Alsaady, M. H., and Salim, H. A. 2019. Impact of bio fertilizer and \nmagnetic irrigation water on growth and yield of melon Cucumis \nmelo L. International Conference on Agricultural Sciences, IOP Conf. \nSeries: Earth and Environmental Science 388. \n\n\n\nBlack, C. A. 1982. Methods of soil analysis. Soil Sci. Soc. Am., Inc. Pub., \nMadison, Wisconsin, USA. \n\n\n\nChapman, H. D., and Pratt, R. E. 1961. Methods of Analysis for Soil, Plants \nand Water. Department of Soil and Plant Nutrition, University of \nCalifornia, U.S.A. \n\n\n\nDe Souza, A., Garc\u00eda, D., Sueiro, L., and Gilart, F. 2014. Improvement of the \nseed germination, growth and yield of onionplants by extremely low \nfrequency non-uniform magnetic fields. Scientia Horticulturae 176, \n63\u201369. \n\n\n\nDing, H., Zheng, X., Zhang, Y., Zhang, J., and Chen, D. 2017. Gaseous losses \nof fertilizer nitrogen from a citrus orchard in the red soil hilly region \nof Southeast China. Soil Science and Plant Nutrition 63(5), 419-425. \n\n\n\nDoklega, S. M. A. 2017. Impact of Magnetized water Irrigation, Soil Mineral \nFertilization and Foliar Spraying with Nanomaterial on Potato \nPlants. J. Plant Production, Mansoura Univ. 8(11), 1113 - 1120. \n\n\n\nEcheverria, H. E., and Videla, C. C. 1998. Eficiencia fisiologica y de \nutilizacion de nitrogeno en trigo en la region Pampeana Argentina. \nCiencia del suelo 16, 83-87. \n\n\n\nEl-Basioni, S. M., Hassan, H. M., and Rashad, R. T. 2015. Effect of Magnetic \nIron Oxide Combined with some Additives on the Yield of \nGroundnut, Wheat and Nutrient Availability in Sandy Soil. Egyptian \nJournal of Soil Science 55 (4), 441- 452. \n\n\n\nGomez, K. A., and Gomez, A. A. 1984. Statistical Procedures for Agricultural \nResearch. John Wiley & Sons, New York, NY, USA, 8\u201320. \n\n\n\nHussain, M., Dastgeer, G., Afzal, A., Hussain, S., and Kanwar, R. 2020. Eco-\nfriendly magnetic field treatment to enhance wheat yield and seed \ngermination growth. Environmental Nanotechnology, Monitoring & \nManagement 14, 100299. \n\n\n\nIqbal, M., Haq, Z., Malik, A., Ayoub, C., Jamil, Y., and Nisar, J. 2016. Pre-\nsowing seed magnetic field stimulation: Agood option to enhance \nbitter gourd germination, seedling growth and yield characteristics. \nBiocatalysis and Agricultural Biotechnology 5. \n\n\n\nJackson, M. L. 1973. Soil Chemical Analysis. Prentice-Hall, Inc., Englewood \nCaliffs, New Jersy, USA, 429\u2013464. \n\n\n\nKarkush, M. O., Ahmed, M. D., and Al-Ani, S. M. A. 2019. Magnetic Field \nInfluence on The Properties of Water Treated by Reverse Osmosis. \nEngineering, Technology & Applied Science Research 9 (4), 4433-\n4439. \n\n\n\nLi, T., L\u00fc, S., Wang, Z., Huang, M., Yan, J., and Liu, M. 2021. Lignin-based \nnanoparticles for recovery and separation of phosphate and reused \nas renewable magnetic fertilizers. Science of the Total Environment \n765, 142745. \n\n\n\nLi., R., Wang, J. J., Zhou, B., Awasthi, M. K., Ali, A., Zhang, Z., Lahori, A. H., and \nMahar, A. 2016. Recovery of phosphate from aqueous solution by \nmagnesium oxide decorated magnetic biochar and its potential as \nphosphate-based fertilizer substitute. Bioresource Technology 215, \n209\u2013214. \n\n\n\nMohamed, M. S. 2020. Studying the Effect of Spraying Magnetized Fulvate \nand Humate Solutions on Phosphorus Availability in Sandy Soil \nCultivated by Faba Bean (Vicia faba L.). Egyptian Journal of Soil \nScience 60 (4), 409-423. \n\n\n\nMostafa, H. 2020. Influence of magnetised irrigation water on the \nfertigation process and potato productivity. Research in \nAgricultural Engineering 66 (2), 43\u201351. \n\n\n\nRadhakrishnan, R. 2019. Exposure of magnetic waves stimulates rapid \ngermination of soybean seeds by enzymatic regulation in cotyledons \nand embryonic axis. Biocatalysis and Agricultural Biotechnology 20, \n101273. \n\n\n\nRochalska, M., and Orzeszko-Rywka, A. 2005. Magnetic field treatment \nimproves seed performance. Seed Science and Technology 33, 669-\n674. \n\n\n\nRoozbeh, M., Sheikhdavoodi, M. J., Almassi, M., and Bahrami, H. 2011. \nEffects of tillage intensity and anionic polyacrylamide on sediment \nand nitrogen losses in irrigated wheat field. Afr. J. Agri. Res. 6(22), \n5320-5327. \n\n\n\nSamarah, N. H., Bany Hani, M. M. I., and Makhadmeh, I. M. 2021. Effect of \nMagnetic Treatment ofWater or Seeds on Germination and \nProductivity of Tomato Plants under Salinity Stress. Horticulturae 7, \n220. \n\n\n\nVashisth, A., and Nagaraja, S. 2010. Effect on germination and early growth \ncharacteristics in sunflower (Helianthus annuus) seeds exposed to \nstatic magnetic field. Journal of Plant Physiology 167, 149\u2013156. \n\n\n\nVasilyeva, M., Kovshov, S., Zambrano, J., and Zhemchuzhnikov, M. 2021. \nEffect of magnetic fields and fertilizers on grass and onion growth \non technogenic soils. Journal of Water and Land Development 49 \n(IV\u2013VI), 55\u201362. \n\n\n\nWang, L., Zhao, X., Gao, J., Butterly, C., Chen, Q., Liu, M., Yanga, Y., Xi, Y., and \nXiao, X. 2019. Effects of fertilizer types on nitrogen and phosphorous \nloss from rice-wheat rotation system in the Taihu Lake region of \nChina. Agriculture, Ecosystems and Environment 285 106605. \n\n\n\nWang, P., Wang, J., Qina, Q., and Wang, H. 2017. Life cycle assessment of \nmagnetized fly-ash compound fertilizer production: A case study in \nChina. Renewable and Sustainable Energy Reviews 73, 706\u2013713. \n\n\n\nWang, Y., Wei, H., and Li, Z. 2018. Effect of magnetic field on the physical \nproperties of water. Results in Physics 8 262\u2013267. \n\n\n\nXiao-Feng, P., and Bo, D. 2008a . Investigation of changes in properties of \nwater under the action of a magnetic field. Science in China Series G: \nPhysics, Mechanics & Astronomy 51 (11), 1621-1632. \n\n\n\nXiao-Feng, P., and Bo, D. 2008b. The changes of macroscopic features and \nmicroscopic structures of water under influence of magnetic field. \nPhysica B 403, 3571\u2013 3577. \n\n\n\nYahya Absalan, Mostafa Gholizadeh, and Choi, H. J. 2021. Magnetized \nsolvents: Characteristics and various applications: Review. Journal \nof Molecular Liquids 335, 1-21. \n\n\n\nZareei, E., Zaare-Nahandi, F., Hajilou, J., and Oustan, S. 2021. Eliciting \neffects of magnetized solution on physiological and biochemical \ncharacteristics and elemental uptake in hydroponically grown \ngrape (Vitis vinifera L. cv. Thompson Seedless) Plant Physiology and \nBiochemistry 167, 586\u2013595.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 06-09 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.06.09 \n\n\n\nCite the Article: Poonam Belbase, Archana Aryal, Ashim Aryal (2021). Evaluation Of Rice Genotype Against Leaf Folder, Case Worm And Grasshopper Desecration \nUnder Field Condition. Malaysian Journal of Sustainable Agriculture, 5(1): 06-09. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2021.06.09 \n\n\n\nEVALUATION OF RICE GENOTYPE AGAINST LEAF FOLDER, CASE WORM AND \nGRASSHOPPER DESECRATION UNDER FIELD CONDITION \n\n\n\nPoonam Belbasea, Archana Aryalb, Ashim Aryalc \n\n\n\na Midwest Academy and Research Institute, Campus of Live sciences, Dang \nb Nepal Polytechnic Institute, Bharatpur, Chitwan. \nc Agriculture and Forestry University, Rampur, Chitwan \n\n\n\n*Correspond author email: Poonam Belbase, Poonambelbase38@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 September 2020 \nAccepted 28 October 2020 \nAvailable online 20 November 2020\n\n\n\nThe research on varietal screening of rice against leaf folder, caseworm and grasshopper damage was \nconducted during 2019 in Rampur, Chitwan to study the host plant resistant of different varieties of rice \nunder field condition. The experiment was laid out in RCBD with three replications and seven treatments \nnamely i) Makawanpur -1 ii) Mansuli iii) Radha-4  iv) Ramdhan v) Sabitri vi) Sama Mansuli sub-1 and vii) \nsukkha-3. The experiment revealed that lowest population of leaf folder, caseworm and grasshopper was \nrecorded in variety Radha-4 followed by Ramdhan. The experiment showed the yield loss was significantly \nlower in Radha-4 followed by Sabitri and Ramdhan due to leaf folder, caseworm and grasshopper. So Radha-\n4 and sabitri would be good option in rice production for reducing insect pest damage.  \n\n\n\nKEYWORDS \n\n\n\nEvaluation, rice, leaf folder, case worm, grasshopper.\n\n\n\n1. INTRODUCTION \n\n\n\nRice is the major cereal crop of Nepal. Out of the total cultivated area (3.09 \n\n\n\nmillion ha) of the country, rice cultivation occupies 1.4 million ha and \n\n\n\nproductivity is 2.56-3.2 mt/ha. Rice hold a vital role in national economy, \n\n\n\nas the share of rice in AGDP is 20% and provide 50% of the total calorie \n\n\n\nrequirement of Nepalese people. Rice is the most important food in \n\n\n\nNepalese diet and plays a significant role in the economy of farmer. In \n\n\n\nNepalese diet, cereal contributes about 90% of the total calorie intake and \n\n\n\n50% of this come from rice (Government of Nepal, 1992). Rice is \n\n\n\nindigenous to humid area of tropical and sub-tropical region. Rice having \n\n\n\nwider physiological adaptability is being grown successfully in tropical, \n\n\n\nsub-tropical and temperate region; from below the sea level to 2000 \n\n\n\nmeters above the sea level. \n\n\n\nErratic rainfall, lack of irrigation, unavailability of quality seed, lack of \n\n\n\nfertilizer and incidence of insect pest are the causes of yield loss in Nepal. \n\n\n\nThe rice crop is subjected to the persistent pressure of more than 100 \n\n\n\ndifferent insect species and 20 of them are of major economic significance \n\n\n\n(Pathak, 1969; Kapur, 1967). In Asia pest alone reduce about 30% of rice \n\n\n\nproduction (Heinrichs et al., 1978). Leaf folder \n\n\n\n(CnaphalocrocismedinalisGuenee) is major pest of rice and are sporadic in \n\n\n\nnature. The larvae of leaf folder are the damaging stage. It scrapes the \n\n\n\ngreen tissue of the leaves and the leaves are folded over themselves to \n\n\n\nform rolls within which the larvae remain to pupate. In the case of severe \n\n\n\ninfestation, leaf margin and tips are dried up entirely and the crop gives a \n\n\n\nwhitish appearance. Infestation by leaf folder was also higher in high \n\n\n\nyielding rice variety with broader and dark green foliage than in tall indica \n\n\n\nrice (Kulshrestha et al., 1970). \n\n\n\nCaseworm (Nymphuladepunctalis) is a sporadic pest of rice and found \n\n\n\nwhere water remain stagnant. Case worm is important insect pest of rice \n\n\n\nwhich occurs in Australia, Africa, South America and many tropical \n\n\n\ncountries. The freshly hatched larva feed on the surface of the tender \n\n\n\nleaves, but later instars feed from within the case or on the surface of even \n\n\n\nthe older leaves. Damage is caused by larva feeding and cutting of the \n\n\n\nleaves tips for making leave cases. The young larvae feed on the epidermis \n\n\n\nof leaves. Infested plants present a frayed appearance. A group researcher \n\n\n\nreported severe infestation by the caseworm of rice in variety \u201cTaichung-\n\n\n\n65\u201d in west Bengal (Datta et al., 1967). \n\n\n\nGrasshopper (Hieroglyphus banians U.Biol) is found in most of the rice \n\n\n\ntracts where the soil is loamy or sandy loam and less rainfall. Grasshopper \n\n\n\nis important insect pest of rice which occurs in almost all type of habitat \n\n\n\nincluding the tropics, temperate grassland, rain forest, desert and \n\n\n\nmountains. Various species of grasshopper are widely distributed in \n\n\n\nNepal. They are polyphagous and feed on leaves of rice, maize, millets, \n\n\n\nsugarcane, grasses etc. The adult are 40-50 mm long and are shining \n\n\n\ngreenish yellow, having 3 black lines running across the pronotum. \n\n\n\nNymphs are yellowish with many reddish-brown spots in the early stages \n\n\n\nbut become greenish as they grow older. It is known to cause, on an \n\n\n\naverage 20% loss. Both adult and nymph of grasshopper feed on the leaves \n\n\n\nwhich appear to be partially eaten up. They chew angular holes in the \n\n\n\nleaves causing an injury similar to that caused by armyworm.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 06-09 \n\n\n\nCite the Article: Poonam Belbase, Archana Aryal, Ashim Aryal (2021). Evaluation Of Rice Genotype Against Leaf Folder, Case Worm And Grasshopper Desecration \nUnder Field Condition. Malaysian Journal of Sustainable Agriculture, 5(1): 06-09. \n\n\n\n2. MATERIALS AND METHODOLOGY\n\n\n\nThe study was conducted at Agronomy farm of Institute of Agriculture and \n\n\n\nAnimal science from June 2019 to November 2019. Chitwan district is \n\n\n\npopular for agricultural production including cereal crops. The district is \n\n\n\nsituated in middle part of Nepal covering 2118 km2 area. Geographically, \n\n\n\nit is located at 270 37\u2019 N latitude and 840 25\u2019 E longitudes with an elevation \n\n\n\nof 198 m above sea level. Basically, this farm is meant for cereal crop \n\n\n\nproduction in which wheat, barley was grown six months prior to our field \n\n\n\nresearch. At first nursery bed was prepared by ploughing the area of 8 m2. \n\n\n\nThe field was ploughed 2 times with tractor making soil finer. The \n\n\n\napplication of chemical fertilizer @ 100:50:50 kg NPK/ha from Urea, DAP \n\n\n\nand MOP and FYM @ 12.5 ton/ha. Seed @ 100 gm for each variety was \n\n\n\nsown in nursery bed by line sowing.  \n\n\n\nThe seed was brought from National Wheat Research Centre, Bhairahawa \n\n\n\nand were sown in nursery 22 June 2015. The main experimental field was \n\n\n\nthoroughly ploughed 3 times with tractor. The experimental field was laid \n\n\n\nout in randomized complete design (RCBD) with seven treatments and \n\n\n\nthree replications. The total number of plots was 21 and each plot were of \n\n\n\n2.5*2.5 m2. Spacing between the replication was 100 cm and that between \n\n\n\nthe treatments was 50 cm. Total size of whole plot was 376.25 m2 and plant \n\n\n\nto plant distance and row to row distance of 25 cm * 25 cm each with 3-5 \n\n\n\ntillers per hill. The total number of seedlings required per hill, per plot, and \n\n\n\nwhole experiment were 3,300,6300 respectively. Seedlings of 30 days \n\n\n\nwere transplanted in main field. \n\n\n\nTreatment \n\n\n\nThe treatments (T) included in this study were:  \n\n\n\nT1: Makawanpur-1    T5: Sabitri \n\n\n\nT2: Mansuli         T6: Sama mansuli sub-1 \n\n\n\nT3: Radha-4         T7: Sukkha-3 \n\n\n\nT4: Ramdhan \n\n\n\nThe field was thoroughly ploughed three times. At first plough, all the \n\n\n\nprevious crop and grasses were removed. At second plough, soil was made \n\n\n\nfine. At third plough, puddling was done using tractor for better growth of \n\n\n\nrice crop. Different varieties stated above were sown in nearby nursery \n\n\n\nfield @80 kg/ha. At the time of nursery bed preparation, 12.5 ton/ha FYM, \n\n\n\nchemical fertilizer @100:50:50 kg NPK/ha from Urea, DAP and MOP were \n\n\n\napplied. Half dose of urea with full dose of DAP and MOP was applied at \n\n\n\nthe time of field preparation and remaining half dose of urea was applied  \n\n\n\n15 days after transplanting. So, for each plot of 6.2 m2 67.93 gm DAP, \n\n\n\n52.083gm MOP, 44gm urea were applied.  \n\n\n\nWeeding was done at 15, 45 days after transplanting. After completion of \n\n\n\nlayout, 7 treatments were allocated randomly within each block. Other \n\n\n\nagronomy practices recommended for this region were followed by \n\n\n\nraising crop. Ten plants per plot were randomly selected and tagged for \n\n\n\nobservation of the insect pest damage. Damage was recorded on leaves \n\n\n\nand stem of sample plant. Data on damage pattern of insect were scored \n\n\n\non leaves and used for analysis. On the basis of insect damage intensity on \n\n\n\nrice, damage scoring was done. The first scoring was done at 35 DAT when \n\n\n\nthe insect damage started to appear. Insects occurrence and its infestation \n\n\n\nwas assessed by using 0-9 scale separately for different insects. Data were \n\n\n\ntaken for different insects at 15 days interval. Ten sample plants were \n\n\n\nselected randomly from each plot. Damage was assessed taking into \n\n\n\nconsideration the area covered by each insect damage. The scale of the \n\n\n\nassessment is as follows: \n\n\n\n\u2022 0: No damage \n\n\n\n\u2022 1: 25% damage on 25% leaves \n\n\n\n\u2022 3: 50% damage on 25% leaves \n\n\n\n\u2022 5: 50% damage on 50% leaves \n\n\n\n\u2022 7: 75% damage on 50% leaves \n\n\n\n\u2022 9: 75% damage on 75% leaves \n\n\n\nGrain from 1 m2 was harvested using quadrant in each plot and the \n\n\n\nharvested grains were weighed separately for each treatment. Yield loss \n\n\n\npercentage was calculated using potential yield and obtained yield. \n\n\n\nFormula for its calculation was: \n\n\n\nYield loss (%) = Potential yield \u2013 Obtained yield/ Potential yield * 100 \n\n\n\nMicrosoft excel was used for tabulation of data and for simple calculation. \n\n\n\nThe collected data were statistically analyzed using R-stat software \n\n\n\npackage. Means of separation was done by DMRT at 5% level of \n\n\n\nsignificance. \n\n\n\n3. RESULT\n\n\n\nThe result so obtained are assessed and interpreted with the available \n\n\n\nsupporting evidences. \n\n\n\nDAT: Days After Transplanting, CV: Coefficient of Variation, LSD: Least Significant Difference. Value with the same letter in column is not significantly \n\n\n\ndifferent at 5% by DMRT and figures after \u00b1 indicate standard error, **indicate significant, ***indicate highly significant at 0.001. \n\n\n\nAnalysis of variance (ANOVA) showed there is significant difference on \n\n\n\nleaf folder damage in 30 DAT, 45 DAT, 60 DAT and 75 DAT. At first reading, \n\n\n\nleaf folder and case worm damage is highly significant in Sabitri variety \n\n\n\n(1.72a \u00b1 0.20) which is followed by Sama Mansuli sub-1 (1.33b \u00b1 0.14), \n\n\n\nRamdhan (1.1833bc \u00b1 0.10), Sukkha-3 (0.95cd \u00b1 0.26) respectively. Leaf \n\n\n\nfolder and case worm damage is least significant in Radha-4 (0.750d \u00b1  \n\n\n\n0.05) which is statistically at par with Makawanpur-1 (0.90d \u00b1 0.1) and \n\n\n\nMansuli (0.78d \u00b1 0.10). \n\n\n\nAt second reading, leaf folder and case worm damage is highly significant \n\n\n\nin Sabitri variety ( 3.83a \u00b1 0.05) which is at par with Sama Mansuli aub-1 \n\n\n\n(3.37a \u00b1 0.35) and is followed by Ramdhan (2.50b \u00b1 0.26) which is at par \n\n\n\nTable 1: Average leaf folder and case worm damage score in different rice varieties under field condition in Chitwan, Nepal, 2019 \n\n\n\nTreatment Leaf folder and Caseworm damage Total leaf folder damage \n\n\n\n30 DAT 45 DAT 60 DAT 75 DAT \n\n\n\nMakwanpur-1 0.90d \u00b10.15 2.33b \u00b10.35 2.30b \u00b10.26 0.73b \u00b10.15 1.57b\u00b10.213 \n\n\n\nMansuli 0.78d \u00b10.10 1.17c \u00b10.30 1.27de \u00b10.15 0.23c \u00b10.32 0.86c\u00b10.168 \n\n\n\nRadha-4 0.75d \u00b10.05 2.20b \u00b10.26 2.23b \u00b10.25 0.47bc \u00b10.57 0.41b\u00b10.174 \n\n\n\nRamdhan 1.18bc \u00b10.10 2.50b \u00b10.26 1.73c \u00b10.21 0.60bc \u00b10.17 1.51b\u00b10.062 \n\n\n\nSabitri 1.72a \u00b10.20 3.83a \u00b10.05 1.67cd \u00b10.25 0.50bc\u00b10.20 1.92a\u00b10.044 \n\n\n\nSama Mansuli 1.33b \u00b10.14 3.37a \u00b10.35 2.83a \u00b10.21 1.20a \u00b10.30 2.18a\u00b10.156 \n\n\n\nSukkha-3 0.95cd \u00b10.26 2.40b \u00b10.40 0.97c \u00b10.21 0.77b \u00b10.25 1.27b\u00b10.254 \n\n\n\nP value *** *** *** ** *** \n\n\n\nLSD 0.26 0.54 0.40 0.41 0.278 \n\n\n\nCV% 13.86 11.97 12.24 35.95 10.21 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 06-09 \n\n\n\nCite the Article: Poonam Belbase, Archana Aryal, Ashim Aryal (2021). Evaluation Of Rice Genotype Against Leaf Folder, Case Worm And Grasshopper Desecration \nUnder Field Condition. Malaysian Journal of Sustainable Agriculture, 5(1): 06-09. \n\n\n\nwith Sukkha-3 (2.40b \u00b1 0.40), Makawanpur-1 (2.33b \u00b1 0.35) and Radha-4 \n\n\n\n(2.20b \u00b1 0.26). Leaf folder and case worm damage is least significant in \n\n\n\nMansuli (1.17c \u00b1 0.30). \n\n\n\nAt third reading, leaf folder and case worm damage is highly significant in \n\n\n\nSama Mansuli sub-1 (2.83a \u00b1 0.21) which is followed by Makawanpur-1 \n\n\n\n(2.30b \u00b1 0.26) and which is at par with Radha-4 (2.23b \u00b1 0.25) and is again \n\n\n\nfollowed by Ramdhan (1.73c \u00b1 0.21), Sabitri (1.67cd \u00b1 0.25), Mansuli (1.27de \n\n\n\n\u00b1 0.15) respectively. Leaf folder and case worm damage is least significant \n\n\n\nin Sukkha-3 (0.97c \u00b1 0.21). \n\n\n\nAt last reading, leaf folder and case worm damage is highly significant in \n\n\n\nSama Mansuli sub-1 (1.20a \u00b1 0.30) which is followed by Sukkha-3 (0.77b \u00b1 \n\n\n\n0.25) and which is at par with Makawanpur-1 (0.73b \u00b1 0.15), which is again \n\n\n\nfollowed by Ramdhan (0.60bc \u00b1 0.17) and is at par with Sabitri (0.50bc \u00b1 \n\n\n\n0.20) and Radha-4 (0.47bc \u00b1 0.57). Leaf folder and case worm damage is \n\n\n\nleast significant in Mansuli (0.23c \u00b1 0.320). \n\n\n\nIn total, leaf folder damage and case worm is highly significant in Sama \n\n\n\nMansuli sun-1 (2.183a \u00b1 0.156) which is at par with Sabitri (1.929a \u00b1 0.044) \n\n\n\nand is followed by Makawanpur-1 (1.5667b \u00b10.213) which is at par with \n\n\n\nRamdhan (1.504b \u00b1 0.062), Radha-4 (1.412b \u00b1 0.174) and Sukkha-3 (1.270b \n\n\n\n\u00b1 0.254). Leaf folder and case worm is least significant in Mansuli (0.863c \n\n\n\n\u00b1 0.168). \n\n\n\nFigure 1: Average leaf folder and caseworm damage in different rice \nvarieties. \n\n\n\nTable 2: Average grasshopper damage score in different rice varieties under field condition in Chitwan, Nepal, 2019 \n\n\n\nTreatment Grasshopper damage Total damage \n\n\n\n30 DAT 45 DAT 60 DAT 75 DAT \n\n\n\nMakwanpur-1 1.26bc\u00b10.11 1.43b \u00b10.15 2.06a \u00b10.25 1.20a \u00b10.17 1.49a\u00b10.09 \n\n\n\nMansuli 1.03d \u00b10.11 1.33b\u00b10.15 0.86cd \u00b10.16 0.63b \u00b10.15 0.96c \u00b10.28 \n\n\n\nRadha-4 1.43b \u00b10.11 0.90c\u00b10.20 0.73d \u00b10.15 0.56b \u00b10.15 0.90c\u00b10.08 \n\n\n\nRamdhan 1.06cd \u00b10.05 2.46a\u00b10.21 1.66ab \u00b10.46 0.53b \u00b10.15 1.43a\u00b10.15 \n\n\n\nSabitri 2.23a \u00b10.15 2.23a \u00b10.16 0.80d \u00b10.10 0.56b\u00b10.25 1.46a \u00b10.08 \n\n\n\nSama Mansuli 0.30e \u00b10.14 1.50b\u00b10.26 1.46b \u00b10.21 1.46b \u00b10.15 0.93c\u00b10.08 \n\n\n\nSukkha-3 1.10cd \u00b10.10 1.70b \u00b10.17 1.26bc \u00b10.31 0.60b \u00b10.35 1.16b \u00b10.04 \n\n\n\nP value *** *** *** * *** \n\n\n\nLSD 0.197 0.361 0.433 0.377 0.158 \n\n\n\nCV% 9.17 12.28 19.20 32.54 7.44 \n\n\n\nDAT: Days After Transplanting, CV: Coefficient of Variation, LSD: Least Significant Difference. Value with the same letter in column is not significantly \n\n\n\ndifferent at 5% by DMRT and figures after \u00b1 indicate standard error, **indicate significant, ***indicate highly significant at 0.05.\n\n\n\nAnalysis of variance (ANOVA) showed that there is significant difference \n\n\n\non grasshopper damage in 30 DAT, 45 DAT, 60 DAT and 75 DAT. At first \n\n\n\nreading, grasshopper damage was seen highly significant in Sabitri \n\n\n\n(2.233a\u00b10.152) which is followed by Radha-4 (1.433b\u00b10.115), \n\n\n\nMakawanpur-1 (1.266bc\u00b10.115), Ramdhan (1.066cd\u00b10.057), which is at \n\n\n\npar with Sukkha-3 (1.100cd\u00b10.100) and is again followed by Mansuli \n\n\n\n(1.033d\u00b10.115). Grasshopper damage is least significant in Sama Mansuli \n\n\n\nsub-1 (0.300e\u00b10.100). At second reading, grasshopper damage is highly \n\n\n\nsignificant in Ramdhan (2.466a\u00b10.208) which is at par with Sabitri \n\n\n\n(2.233a\u00b10.157), and followed by Sukkha-3 (1.700b\u00b10.173) which is at par \n\n\n\nwith Sama Mansuli (1.500b\u00b10.264), Makawanpur-1 (1.433b\u00b10.153) and \n\n\n\nMansuli (1.333b\u00b10.153). Grasshopper damage is least significant in Radha-\n\n\n\n4 (0.900c\u00b10.200). \n\n\n\nAt third reading, grasshopper damage is highly significant in \n\n\n\nMakawanpur-1 (2.067a\u00b10.252) which is followed by Ramdhan \n\n\n\n(1.66ab\u00b10.462), Sama Mansuli sub-1 (1.467b\u00b10.208), Sukkha-3 \n\n\n\n(1.267bc\u00b10.306), Mansuli (0.867cd\u00b10.156). Grasshopper damage is least \n\n\n\nsignificant in Radha-4 (0.73d\u00b10.15) which is at par with Sabitri \n\n\n\n(0.800d\u00b10.100). At fourth reading, grasshopper damage is highly \n\n\n\nsignificant in Makawanpur-1 (1.200a\u00b10.173). Grasshopper damage is least \n\n\n\nsignificant in Sama Mansuli (0.46b\u00b10.15) which is at par with Ramdhan \n\n\n\n(0.533b\u00b10.153), Radha-4 (0.566b\u00b10.153), Sabitri (0.56b\u00b10.25), Sukkha-\n\n\n\n3(0.600b\u00b10.346) and Mansuli (0.633b\u00b10.153). \n\n\n\nAt total reading, grasshopper damage is highly significant in Makawanpur-\n\n\n\n1 (1.492a\u00b10.094) which is at par with Ramdhan (1.433a\u00b10.153) and Sabitri \n\n\n\n(1.458a\u00b10.076) which are followed by Sukkha-3 (1.166b\u00b10.038).  \n\n\n\nGrasshopper damage is least significant in Radha-4 (0.908c\u00b10.076) which \n\n\n\nis at par with Sama Mansuli sub-1 (0.933c\u00b10.076) and Mansuli \n\n\n\n(0.966c\u00b10.288). \n\n\n\nFigure 2: Average grasshopper damage in different rice varieties. \n\n\n\n4. DISCUSSION\n\n\n\nAmong seven treatments, the lowest incidence of leaf folder, caseworm \n\n\n\nand grasshopper was found in Radha-4 followed by Ramdhan, Mansuli, \n\n\n\nSukkha-3, Sabitri and Sama Mansuli sub-1 respectively. The experiment \n\n\n\nshowed that the yield loss was significantly lower in Radha-4 followed by \n\n\n\nSabitri, Ramdhan, Mansuli, Makawanpur-1, Sukkha-3 and Sama Mansuli \n\n\n\nsub-1 respectively. Leaf folder infestation appeared after 4 weeks of \n\n\n\ntransplanting and reached to peak during maximum tillering stage while \n\n\n\nafter flowering it again appeared to be low and similar trend was also \n\n\n\nreported (Kraker et al., 1999). Leaf folder attack was low in early, drought \n\n\n\n1.57\n\n\n\n0.86\n\n\n\n1.41\n1.51\n\n\n\n1.92\n\n\n\n2.18\n\n\n\n1.27\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\nMakwanpur-1 Mansuli Radha-4 Ramdhan Sabitri Sama Mansuli Sukkha -3\n\n\n\nLeaf folder caseworm damage\n\n\n\nTreatment/Varieties\n\n\n\n1.49\n\n\n\n0.96\n0\n\n\n\n1.43 1.46\n\n\n\n0.93\n\n\n\n1.16\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\n1.4\n\n\n\n1.6\n\n\n\n1.8\n\n\n\nMakwanpur-1 Mansuli Radha-4 Ramdhan Sabitri Sama Mansuli Sukkha -3\n\n\n\nGrasshopper damage\n\n\n\nTreatment/Varieties\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 06-09 \n\n\n\nCite the Article: Poonam Belbase, Archana Aryal, Ashim Aryal (2021). Evaluation Of Rice Genotype Against Leaf Folder, Case Worm And Grasshopper Desecration \nUnder Field Condition. Malaysian Journal of Sustainable Agriculture, 5(1): 06-09. \n\n\n\nresistant variety Sukkha-3 in our study. In Nepal, Rice leaf folder has been \n\n\n\nnoticed as minor pest for a long time but became serious since 1978 in rice \n\n\n\nthroughout the Terai (Mallick, 1982) as higher incidence of leaf folder \n\n\n\noccurs in our research. \n\n\n\n5. SUMMARY \n\n\n\nThe study was conducted to screen different varieties of rice against leaf \n\n\n\nfolder, caseworm and grasshopper damage and to study host plant \n\n\n\nresistance of different varieties of rice namely Makawanpur-1, Mansuli, \n\n\n\nRadha-4, Ramdhan, Sabitri, Sama Mansuli sub-1 and Sukkha-3. The \n\n\n\ntreatments were replicated thrice and study was laid in RCBD design in \n\n\n\nRampur, Chitwan condition. In the field experiment, grasshopper, leaf \n\n\n\nfolder and case worm damage was found to be highly significant in Sabitri \n\n\n\nvariety followed by Makawanpur-1 and Sama Mansuli sub-1. Moderately \n\n\n\naffected rice varieties were Ramdhan, Mansuli and least significant \n\n\n\ndamage was obtained in Radha-4 and Sukkha-3. \n\n\n\n\u2022 In Sama Mansuli sub-1 with high insect preference, yield loss was \nmaximum (36%). \n\n\n\n\u2022 In sukkah-3 with moderate insect preference, yield loss was \n\n\n\nmaximum (32.43%). \n\n\n\n\u2022 In Makawanpur-1 in spite of high insect preference, yield loss was \n\n\n\nmoderate (24.3%). \n\n\n\n\u2022 In Mansuli with moderate insect preference, yield loss was moderate\n\n\n\n(21.14%). \n\n\n\n\u2022 In Ramdhan with less insect preference, yield loss was minimum \n\n\n\n(4.02%). \n\n\n\n\u2022 In Sabitri, in spite of high insect preference, yield loss was minimum \n\n\n\n(1.87%). \n\n\n\n\u2022 In Radha-4 with less insect preference, yield loss was minimum\n\n\n\n(0.937%). \n\n\n\n6. CONCLUSION \n\n\n\nEven though, insect preference was high in Sabitri, the yield loss was found \n\n\n\nto be minimum so it was concluded to be comparatively tolerant variety. \n\n\n\nSimilarly, yield loss was minimum and less preferred by insect pest in \n\n\n\nRadha-4 and Ramdhan was also found comparatively resistant variety so \n\n\n\nthese varieties could be the good option in rice production. Sama Mansuli \n\n\n\nwas found to be highly susceptible followed by Sukkha-3, Makawanpur-1 \n\n\n\nand Mansuli to grasshopper, leaf folder and caseworm. One season field \n\n\n\nresearch could be inadequate to draw conclusion about host plant \n\n\n\nresistance of different rice varieties which must be evaluated under \n\n\n\ndifferent climatic condition and different ecological zones over the years \n\n\n\nfor recommendation. \n\n\n\nREFERENCES \n\n\n\nDatta, A.R., Mozumdar, D., Gupta, D.D., 1967. Susceptibility of \u2018Taichung-\n65\u2019 variety of paddy to multiple pest infestation. Indian J. Ent, 29, Pp. \n229. \n\n\n\nGovernment of Nepal. 1992. Economics survey. Kathmandu, Ministry of \nFinance. \n\n\n\nHeinrichs, E.A., Saxena, R.C., Celliah, S., 1978. Development and \nimplementation of insect pest management system for rice in tropical \nAsia. ASPAC Food and Fert. Tech. Cent. Ext. Bull. 1270., Tokyo, Japan. \n\n\n\nKapur, A.P., 1967. Taxonomy of the rice stemborers. In the Major Insect of \nthe rice plant. Johns Hopkins Press. Baltimore, USA. Pp. 3-43. \n\n\n\nKulshrestha, J.P., Kalode, M.B., Prakasa Rao, P.S., Mishra, B.C., Verma, A., \n1970. High yielding varieties and resulting changes in the pattern of \npest in rice in India. Oryza, 7, Pp. 6164. \n\n\n\nKraker, J., Huis, A., Heong, K.L., Lentern, J.C., Rabbing, R., 1999. Population \ndynamics of rice leaffolders and their natural enemies in irrigated rice \nin Philippines. Bull. Ento. Res., 89, Pp. 411\u2013421. \n\n\n\nMallick, R.N., 1981/82. Rice in Nepal. Kala Prakashan, Kathmandu, Nepal. \n\n\n\nPathak, M.D., 1969. Stem borer and leafhopper-plant hopper resistance \nrice varieties. Entomol. Expt. Appl., 12, Pp. 789-800.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 124-130 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.124.130 \n\n\n\nCite The Article: Sheikh Rasel, Md. Hamidur Rahman, Rabeya Akter, Meherun Nisa Jinia, Md. Ahsan Bin Habib, Zannatul Ferdous (2022). \nAssessment of Growth Parameters of Spirulina (Spirulina Platensis) Using Digested Rotten Mango (Mangifera Indica) Supernatant as a Cost-\n\n\n\nEffective Culture Media. Journal of Sustainable Agricultures, 6(2): 124-130. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.124.130\n\n\n\nASSESSMENT OF GROWTH PARAMETERS OF SPIRULINA (Spirulina Platensis) \nUSING DIGESTED ROTTEN MANGO (Mangifera Indica) SUPERNATANT AS A COST-\nEFFECTIVE CULTURE MEDIA \n\n\n\nSheikh Rasela, Md. Hamidur Rahmanb, Rabeya Akterc, Meherun Nisa Jiniaa, Md. Ahsan Bin Habiba, Zannatul Ferdousa* \n\n\n\naDepartment of Department of Aquaculture, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh \nbDepartment of Aquaculture, Khulna Agricultural University, Khulna-9202, Bangladesh \ncDepartment of Fishery Biology and Genetics, Khulna Agricultural University, Khulna-9202, Bangladesh \n*Corresponding Author Email:  zferdous58@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 22 June 2022 \nAccepted 28 July 2022 \nAvailable online 08 August 2022\n\n\n\nThe culture and growth performance of Spirulina platensis in three different concentrations (25, 50, and 75 \npercent) of digested rotten mango media (DRMM) and Kosaric medium (KM) as control were investigated. \nThis study intended to examine DRMM as a low cost culture media for microalgae. For 16 days, optical \ndensity, cell weight, chlorophyll a concentration, and total biomass of S. platensis under various treatments \nwere measured in every alternate day. The growth rate of S. platensis cultured in the supernatant of DRMM \nand KM was varied and the maximum cell weight, chlorophyll a content and total biomass of S. platensis were \n0.085 mg/L, 0.08 mg/L and 4.824\u00b10.021 mg/L at 50% in DRMM of the culture. This study showed that the \ngrowth performance of S. platensis was higher in the supernatant of 50% DRMM than 25 and 75% of DRMM \nwhich resulted satisfactorily compared with standard KM. In the supernatant of 50% digested rotten mango \nmedium, large volume spirulina culture may be feasible. \n\n\n\nKEYWORDS \n\n\n\nSpirulina, Aquaculture, Growth Performance, Suspended solids, Chlorophyll a, Optical density. \n\n\n\n1. INTRODUCTION \n\n\n\nBangladesh is the largest active deltaic country of the world (Mahmuda et \nal., 2020). Bangladesh is one of the world\u2019s leading inland fish producing \ncountries, contributing about 3.50% to GDP (Gross Domestic Product), \n25.71% to agricultural production (Baroi et al., 2019). With the world's \npopulation continuously expanding, food security has become a critical \nconcern in recent years. To sustain present levels of per capita \nconsumption, the world's expanding population entails a rise in food \ndemand and aquaculture is one approach to meeting this need. \nAquaculture is the fastest-growing food production sector in the world. It \nprovides half of the global fish supply (Nasrin et al., 2021). Fish production \nthrough aquaculture is rapidly gaining importance due to increasing \nhuman population and diminishing natural fisheries resources in \nBangladesh (Mahmud et al., 2021). To sustain the current per capita \nsupply of aquatic products into the future, further elevation of aquaculture \nproduction is required as the supply of fish through capture fisheries \ncannot grow any more (Rahman et al., 2021). Furthermore, because \ntraditional agriculture is insufficient to support the world's rising \npopulation, new alternative and unconventional food sources must be \ndeveloped. With the expansion of aquaculture in Bangladesh, there has \nbeen an increasing trend in using chemicals in aquatic animal health \nmanagement (Uddin et al., 2020). Microalgae are very much helpful for \nfish health. We can use microalgae rather than using chemical in \naquaculture (Uddin et al., 2020). Increasing aquaculture practice might be \na method for producing more fish to fulfill the protein needs of the world's \nenormous population. To do this, we must produce more fish at a lower \ncost of feed. The growing use of plant protein in fish diets is thought to \nlower the cost of fish meals and feeds (Mosha, 2019). This is why using \n\n\n\ndietary spirulina in fish feed might be beneficial. Spirulina has long been \nutilized as a nutritional supplement for fish, shrimp, and poultry, and is \nincreasingly being employed as a protein and vitamin supplement in \naquafeeds. Hossain et al., explained that microalgae acts not most effective \non agro-chemical but additionally animal wastes as nicely through \nchanging them into meals substances (Hossain et al., 2021). This S. \nplatensis is very health effective for fish and shrimp production because \nmicroalgae play a critical position in oxygen in addition to carbon dioxide \nstability in the water (Rahman et al., 2021). The microalgae used for \nbiofuels production though require less land area in comparison to cereal \ncrops but the cultivation, harvesting and processing of algae is not less \ncostly (Rahman et al., 2022). So we need a less cost technique for \nproducing microalgae. As a result, this alga should be employed effectively \nand commercial spirulina production should begin to provide fish with \nimproved nutrition and an alternate protein source. This will assist \nBangladesh in becoming self-sufficient in fish production and meeting \nanimal protein needs. Spirulina may also be cultivated quickly and cheaply \nwith a low-cost culture media. It grows best in water, is readily collected \nand processed, that contains a substantial amount of macro-and \nmicronutrients (Mosha, 2019). It has been commercially farmed in several \nnations throughout the world for over ten years due to its high nutritional \ncontent, which includes protein, amino acids, vitamins, minerals, vital fatty \nacids, and -carotene (Mosha, 2019). Spirulina platensis, often known as a \n\"superfood,\" is high in protein, vitamins, and minerals. Due to its high \nquantity of nutritional elements as well as anti-viral, anti-bacterial, anti-\noxidant, anti-diabetic, anti-cancer, and anti-inflammatory characteristics, \nspirulina has a long history as a dietary supplement (Jung et al., 2019). \nSpirulina and its components, in addition to being a \"complete\" protein \nsource, have been found to have good effects on a variety of human health \nindicators, ranging from malnutrition to antioxidant characteristics (Ravi \n\n\n\n\nmailto:zferdous58@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 124-130 \n\n\n\nCite The Article: Sheikh Rasel, Md. Hamidur Rahman, Rabeya Akter, Meherun Nisa Jinia, Md. Ahsan Bin Habib, Zannatul Ferdous (2022). \nAssessment of Growth Parameters of Spirulina (Spirulina Platensis) Using Digested Rotten Mango (Mangifera Indica) Supernatant as a Cost-\n\n\n\nEffective Culture Media. Journal of Sustainable Agricultures, 6(2): 124-130. \n\n\n\net al., 2010). Bangladesh is presently producing a large volume of mango, \nwhich is being sold at a low price. Every year, between 15-25 percent of \nmangoes rot in the market while being sold, especially during peak season \n(Uddin et al., 2007). These rotten or damaged mangoes are tossed outside \nas rubbish, where they degrade and represent a threat to the environment. \nMango is one of the most popular tropical fruits enriched with potent \nantioxidants, anti-lipid peroxidation, immunomodulation, cardiotonic, \nhypotensive, wound healing, degenerative and antidiabetic properties \n(Shah et al., 2010). After aerobic or anaerobic digestion of mangoes, these \ncarbon-rich organic and inorganic elements can aid in the growth of \nspirulina in the supernatant. This study intended to examine S. platensis \nculture and growth performance in the supernatant of digested rotten \nmango. This research opened a new era for low cost live food production \nas a result fish production cost will be reduced. Fish farmers and hatchery \nowner will be benefited by this research outcome.  \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1  Collection and Maintenance of The Pure Stock Culture of \nSpirulina (S. Platensis) \n\n\n\nMicroalgae S. platensis was collected from the stock of the laboratory of \nthe Live Food Culture department of Aquaculture, BAU, Mymensingh. \nObtaining pure culture of spirulina has maintained hygiene stock (Torzillo \net al., 1986). Pure stock culture of S. platensis was maintained in the \n\n\n\nlaboratory in Kosaric Medium (KM) (Zarrouk, 1996). The growth of S. \nplatensis was monitored on every alternative day and was checked under \nthe microscope to confirm its purity following (Phang and Chu, 1999).       \n\n\n\n2.2  Experimental Environment: \n\n\n\nTemperature and light intensity of the culture media were recorded as \nfollows:  \n\n\n\nTemperature: Water temperature (\u00b0C) of the culture media was measured \nduring the time of sampling day by a celsius thermometer.  \n\n\n\nLight intensity: Light intensity (lux/m2/s) was measured during sampling \nday by using a lux-meter [digital instrument, Lutron (LX-101)].       \n\n\n\n2.3 Preparation of Supernatant of Digested Rotten Mango Media \n(DRMM)  \n\n\n\n400 g/4.0 L wet rotten mango was allowed to decompose in a 5.0 L glass \nbottle for 26 days under aerobic conditions. Then a Light reddish white-\ncolored supernatant from the bottle was screened through a net of 30 \u00b5m, \nmixed with 9.0 g/L sodium bicarbonate and 0.20 mL/L micronutrient, \nthen diluted and made in three concentrations at the rate of 25, 50 and \n75% of decomposed rotten mango (Table 1). Then the supernatant of \nthree different concentrations was taken in a 2.0 L flask with three \nreplications.  \n\n\n\nTable 1: Experimental Design for S. Platensis Culture Using Supernatant of Three Different Concentrations of Digested Rotten Mango. \n\n\n\nTypes of Medium Treatments Replications Amounts of Rotten Mango (%) Duration of Culture (days) \n\n\n\nSupernatant of DRMM \n\n\n\nT1 3(101, 102 and 103) 25 \n\n\n\n16 T2 3(201, 202, 203) 50 \n\n\n\nT3 3(301, 302, 303) 75 \n\n\n\nKosaric medium T4 3(KM-1, KM-2 and KM-3) - \n\n\n\n2.4  Culture of Spirulina (S. Platensis) in Supernatant of DRMM and \nKM  \n\n\n\nFour treatments, three from the supernatant of digested rotten mango \nfrom three different concentrations (25%, 50% and 75%) and one (KM) \nas control each with three replications were used to grow microalgae, S. \nplatensis in 1.0 L volumetric flask. Spirulina was inoculated into each \nculture flask to produce a culture containing 10% spirulina suspension \n(Optical density at 620 nm = 0.20) (Habib, 1998). All the flasks were kept \nunder fluorescent lights in light: dark (12h:12h) conditions with \ncontinuous aeration. Four sub-samplings were carried out on every \nalternative day from each flask to record dry cell weight and chlorophyll a \ncontent of spirulina, and properties of culture media. All the glassware \nused in the experiment was sterilized with dry heat at 70oC overnight. \n\n\n\n2.5   Estimation of Cell Weight (Dry Weight) of Spirulina  \n\n\n\nSample containing 20 mL spirulina suspension was filtered through a \nSartorius filter paper of mesh size 0.45 \u00b5m and diameter of 47 mm. The \nfilter papers were dried in an oven for 24 hours or overnight at 70\u00b0C and \nweighed before filtration. The filtered samples were washed three times \nto remove insoluble salts. After that, the filter papers were put in a glass \nPetri dish and kept in the oven at 70\u00b0C overnight. For cooling, petridish \nwere put into a desiccator for 20 minutes and then filter paper was \nweighed. The dry weight of algae on the filter paper was measured using \nthe following equation: \n\n\n\nDry weight (mg/L), \n100\n\n\n\n(ml)fi l trationfortakensampleof Amount\n\n\n\nIFWFFW\nW \uf0b4\n\n\n\n\u2212\n=\n\n\n\nWhere, \n\n\n\nW = Cell dry weight in mg/L;  \n\n\n\nFFW = Final filter paper weight in g; and \n\n\n\nIFW = Initial filter paper weight in g.  \n\n\n\n2.6   Estimation of Chlorophyll a of Spirulina  \n\n\n\nThe samples of S. platensis were collected at different times and \nchlorophyll a content of S. platensis was estimated. 10 mL of S. platensis \nsample was filtered with an electric filtration unit using filter papers. \nThese filtered samples together with filter paper were taken into a test \ntube and ground with a glass rod and finally mixed with 10 mL of 100% \nredistilled acetone. Each of the test tubes was wrapped with aluminum foil \npaper to inhibit the contact of light. The wrapped test tube was kept in a \n\n\n\nrefrigerator (LMS Laboratory Refrigerator) overnight and then \nhomogenized for 2 minutes followed by centrifugation at 4000 rpm for 10 \nminutes. After centrifugation, the supernatant was isolated and taken for \nchlorophyll a determination. The optical densities of the samples were \ndetermined at 664 nm, 647 nm and 630 nm by using UV. A blank with \n100% acetone was run simultaneously. Chlorophyll a content was \ncalculated by the following formula:  \n\n\n\nChlorophyll a (mg/L) = 11.85 (OD 664) \u2013 1.54 (OD 647) \u2013 0.08 (OD 630)  \n\n\n\n2.7   Total Biomass of Spirulina (S. Platensis) \n\n\n\nTotal biomass was calculated using the following formula:  \n\n\n\nTotal biomass = Chlorophyll a x 67 \n\n\n\n2.8   Specific Growth Rates (Sgrs) of S.  Platensis  \n\n\n\nSpecific growth rate (SGR) based on dry weight, chlorophyll a content and \ntotal biomass of spirulina (Clesceri et al., 1989) was calculated using the \nfollowing formulae \n\n\n\n1. Specific growth rate of cultured spirulina based on a dry weight, SGR \n(\u00b5/day) = ln(X1-X2)/t1-t2 \n\n\n\nWhere, \n\n\n\nX1 = Dry weight of biomass concentration at the end of selected time \ninterval; \n\n\n\nX2 = Dry weight biomass concentration at beginning of selected time \ninterval; and \n\n\n\nt1-t2 =Elapsed time between selected time in the day. \n\n\n\n2. Specific growth rate of cultured spirulina based on Chlorophyll a, SGR \n(\u00b5/day) = ln(X1-X2)/t1-t2 \n\n\n\nWhere, \n\n\n\nX1 = Chlorophyll a at the end of selected time interval; \n\n\n\nX2 = Chlorophyll a at the beginning of selected time interval; and \n\n\n\nt1-t2 = Elapsed time between selected time in the day. \n\n\n\n3. Specific growth rate of cultured spirulina based on total biomass SGR \n(\u00b5/day) = ln(X1-X2)/t1-t2 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 124-130 \n\n\n\nCite The Article: Sheikh Rasel, Md. Hamidur Rahman, Rabeya Akter, Meherun Nisa Jinia, Md. Ahsan Bin Habib, Zannatul Ferdous (2022). \nAssessment of Growth Parameters of Spirulina (Spirulina Platensis) Using Digested Rotten Mango (Mangifera Indica) Supernatant as a Cost-\n\n\n\nEffective Culture Media. Journal of Sustainable Agricultures, 6(2): 124-130. \n\n\n\nWhere,  \n\n\n\nX1 = Total biomass at the end of selected time interval; \n\n\n\nX2 = Total biomass at the beginning of selected time interval; and \n\n\n\nt1-t2 = Elapsed time between selected time in the day. \n\n\n\n2.9   Statistical Analysis  \n\n\n\nAnalysis of variance (ANOVA) of mean cell weight and chlorophyll a, crude \nprotein, crude lipid and ash of S. platensis cultured in different media were \ndone. To find whether there is any significant difference among treatment \nmeans was done by Duncan\u2019s Multiple Range Test (DMRT) using a \nstatistical package following Zar (1984). \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1   Growth Parameters of Spirulina (S. Platensis) \n\n\n\n3.1.1   Optical Density of Media Contained Spirulina \n\n\n\nFigure 1: Mean values of optical density of media contained S.  platensis in the supernatant of three different digested rotten mangoes, and KM. Vertical \nbars represent standard errors. \n\n\n\nOptical density (OD) of media containing spirulina was found to increase \nup to the 14th day of the culture of all the media of DRMM and KM and \nthen decreased up to the 12th day of the experiment (Figure 1). However, \nthe OD of 25% DRMM contained spirulina was 0.068\u00b10.002 g/L, whereas \nthe highest OD of 50% DRMM contained spirulina was found 0.157\u00b10.005 \ng/L (Figure 1). The OD of supernatant of 75% DRMM contained spirulina \nwas 0.114\u00b10.002 g/L and the highest optical density of Kosaric medium \ncontained spirulina was 0.426\u00b10.003 g/L (Figure 1). Soni et al. (2019) \nfound that the algal cultures generated a very small amount of biomass \nwhen grown in darkness or light intensity below 1000 lux. On the other \nhand, higher light intensities of 1500 to 3500 lux resulted in significant \nbiomass production. The best growth rates were obtained with a light \nintensity of 2500 lux.   \n\n\n\n3.1.2   Cell Weight of Spirulina \n\n\n\nCell weight (mg/L) of spirulina cultured in all the media was found higher  \n\n\n\non the 12th day of culture than on other days (Figure 2). Cell weight of \nspirulina increased from the initial day (first day) up to the 8th day \n(0.055\u00b10.002 mg/L) of the culture of 25%digested DRMM and then \ndecreased up to the 16th day (0.013\u00b10.002 mg/L) of the experiment \n(Figure 2). However, the highest cell weight of spirulina was found to be \n0.085\u00b10.001 mg/L when grown in 50% DRMM. Cell weight of spirulina \nfluctuates from the initial day (first day) up to the 16th day (0.0102\u00b10.001 \nmg/L) of the experiment (Figure 2). The highest cell weight of KM \ncontained spirulina was 0.89\u00b10.0021 mg/L on the 12th day and then \ndecreased up to the 16th day of the experiment (Figure 2). Sharker (2007) \nconducted an experiment on the cultivation of S. platensis in various \nconcentrations of papaya skin powder media (PSPM) and Kosaric medium \nfor three months, carried out for 12 days and found that on the eighth day \nof the culture period, the starting cell weight of spirulina was 0.0004 g/L, \nwith a maximum weight of 0.720 g/L concentrations of PSPM which \nsupports the current study. \n\n\n\n.\n\n\n\nFigure 2: Mean values of cell weight (mg/L) of S.  platensis grown in supernatant of three different digested rotten mangoes, and Kosaric medium. \nVertical bars represent standard errors. \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\n0.4\n\n\n\n0.45\n\n\n\n0.5\n\n\n\n2 4 6 8 10 12 14 16\n\n\n\nO\np\n\n\n\nti\nca\n\n\n\nl d\nen\n\n\n\nsi\nty\n\n\n\nDay\n\n\n\nT1(25%)DRM T2(50%)DRM\n\n\n\nT3(75%)DRM KM\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\n2 4 6 8 10 12 14 16\n\n\n\nC\ne\n\n\n\nll\n w\n\n\n\ne\nig\n\n\n\nh\nt \n\n\n\n(m\ng\n\n\n\n/\nL\n\n\n\n) \n\n\n\nDay\n\n\n\nT1(25%)DRM\n\n\n\nT2(50%)DRM\n\n\n\nT3(75%)DRM\n\n\n\nKM\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 124-130 \n\n\n\nCite The Article: Sheikh Rasel, Md. Hamidur Rahman, Rabeya Akter, Meherun Nisa Jinia, Md. Ahsan Bin Habib, Zannatul Ferdous (2022). \nAssessment of Growth Parameters of Spirulina (Spirulina Platensis) Using Digested Rotten Mango (Mangifera Indica) Supernatant as a Cost-\n\n\n\nEffective Culture Media. Journal of Sustainable Agricultures, 6(2): 124-130. \n\n\n\n3.1.3   Chlorophyll a of Spirulina \n\n\n\nChlorophyll a of spirulina was found also higher on the 12th day of culture \nthan on other days of culture of all the media (Figure 3). Chlorophyll a of \nspirulina increased from the first day up to the 4th day (0.042\u00b10.002 \nmg/L) of the culture of 25% DRMM and then decreased up to the 16th day \n(0.003\u00b10.001 mg/L) of the experiment (Figure 3). However, chlorophyll a \nof spirulina cultured in 50% DRMM was higher at 0.08\u00b10.002 mg/L on the \n10th day (Figure 3) and then decreased up to the 16th day of culture. \nChlorophyll a of spirulina grown in 75% DRMM was 0.051\u00b10.005 mg/L on \nthe 12th day and then decreased on the 16th day of the experiment (Figure \n\n\n\n3), where the highest chlorophyll a of spirulina cultured in KM was \n0.854\u00b10.003 mg/L on 12th day and decreased up to 16th day (last day) of \nthe experiment (Figure 3). Looked at the differences in Chlorophyll a \nproduction of S platensis between an open pond and a closed reactor, and \nobserved that a closed reactor with changing media (mixing with media \nand nutrient concentration modification) produces the best results (Soni \net al., 2019). Biomass was 8.568 g/l/day for OP ZM (open pond with \nZarrouk media) and 10.231 g/l/day for PBR ZM (closed reactor with \nZarrouk media), 11.34 g/l/day for OP MM (open pond with modified \nmedia), and 12.280 g/l/day for PBR MM (closed reactor with modified \nmedia). \n\n\n\nFigure 3: Mean values of chlorophyll a (mg/L) of S.  platensis grown in supernatant of three different digested rotten mangoes, and Kosaric medium. \nVertical bars represent standard errors. \n\n\n\n3.1.4   Total Biomass of Spirulina \n\n\n\nTotal biomass (mg/L) of spirulina (S. platensis) grown in all the media was \nfound to be higher on the 8th day at 25% DRMM, 6th day at 50% DRMM, \n4th day at 75% DRMM and 12th day of culture in KM than other days of \nthe experiment (Figure 4). Total biomass of spirulina was increased from \nthe initial day (first day) up to the 8th day (2.814\u00b10.001mg/L) in the \nculture of 25% DRMM and then decreased up to the 16th day (0.201\u00b10.001 \nmg/L) of the experiment (Figure 4). However, the highest total biomass of \nspirulina grown in the culture of 50% DRMM was recorded at 4.824\u00b10.021 \nmg/L on the 6th day of culture and then decreased up to the 16th day \n(1.407\u00b10.043 mg/L) during the experiment (Figure 4). Again, the total \nbiomass of spirulina cultured in the culture of 75% DRMM was increased  \n\n\n\nfrom the first day up to the 4th day (4.084\u00b10.023 mg/L) and then \ndecreased up to the 16th day (1.407\u00b10.001 mg/L) of the experiment \n(Figure 4). The highest total biomass of spirulina cultured in KM was found \nto be 56.615\u00b10.045 mg/L on the 12th day and then decreased up to the \n16th day (26.733\u00b10.023 mg/L) during the experiment (Figure 4). \nCompared the dry weight (mg/L) growth rates of S. platensis in SM \n(standard control medium) and Reduced Cost media, finding that SM gave \nthe highest biomass values (0.840 0.008 mg/L on the 15th day) (Madkour \net al., 2012). They also discovered that the growth curves for various \nconcentrations of Reduced Cost medium displayed comparable growth \nrate characteristics, with peak biomass concentrations occurring between \nthe 27th and 33rd days. \n\n\n\nFigure 4:  Mean values of total biomass (mg/L) of S.  platensis grown in supernatant of three different digested rotten mangoes, and KM. Vertical bars \nrepresent standard errors\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\n2 4 6 8 10 12 14 16\n\n\n\nC\nh\n\n\n\nlo\nro\n\n\n\np\nh\n\n\n\ny\nll\n\n\n\n a\n\n\n\nDay\n\n\n\nT1(25%)DRM\n\n\n\nT2(50%)DRM\n\n\n\nT3(75%)DRM\n\n\n\nKM\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\n2 4 6 8 10 12 14 16\n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nb\nio\n\n\n\nm\na\n\n\n\nss\n (\n\n\n\nm\ng\n\n\n\n/\nL\n\n\n\n) \n\n\n\nDay\n\n\n\nT1(25%)DRM\n\n\n\nT2(50%)DRM\n\n\n\nT3(75%)DRM\n\n\n\nKM\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 124-130 \n\n\n\nCite The Article: Sheikh Rasel, Md. Hamidur Rahman, Rabeya Akter, Meherun Nisa Jinia, Md. Ahsan Bin Habib, Zannatul Ferdous (2022). \nAssessment of Growth Parameters of Spirulina (Spirulina Platensis) Using Digested Rotten Mango (Mangifera Indica) Supernatant as a Cost-\n\n\n\nEffective Culture Media. Journal of Sustainable Agricultures, 6(2): 124-130. \n\n\n\n3.2   Comparison of Growth Parameters of Spirulina (S.  Platensis) of \n12th Day of Culture \n\n\n\nOptical density, cell weight, chlorophyll a and total biomass of supernatant \nof 50% DRMM and KM contained spirulina (S. platensis) was significantly \n\n\n\n(p < 0.01) higher than that of two other media (25% DRMM) and (75% \nDRMM) (Table 2). There was no significant (p > 0.05) difference among \noptical density, cell weight, chlorophyll a and total biomass of 25% DRMM \nand KM, and among 50% and 75% DRMM during the study. \n\n\n\nTable 2: Comparison of Optical Density, Cell Weight, Chlorophyll a and Total Biomass of S.  Platensis Grown in Supernatant of Different DRMM, and KM \non 12th Day of Culture Before Stationary Phase. \n\n\n\nParameters T1 (25% DRM) T2 (50% DRM) T3 (75% DRM) T4 (KM) \n\n\n\nOptical density 0.047\u00b10.003b 0.112\u00b10.002a 0.114\u00b10.002b 0.426\u00b10.003a \n\n\n\nCell weight (mg/L) 0.034\u00b10.002b 0.04\u00b10.003a 0.008\u00b10.002b 0.89\u00b10.0021a \n\n\n\nChlorophyll a (mg/L) 0.022\u00b10.002b 0.06\u00b10.004a 0.051\u00b10.005b 0.854\u00b10.003a \n\n\n\nTotal biomass (mg/L) * 1.474\u00b10.045c 4.02\u00b10.05b 3.417\u00b10.045c 56.615\u00b10.045a \n\n\n\n*Total biomass = Chlorophyll a x 67 (Vonshak and Richmond, 1988). Figures in common letters do not differ significantly at 5% level of probability.\n\n\n\n3.3   Correlation Among The Growth Parameters of S.  Platensis \n\n\n\nFigure 5: Correlation coefficient (r) of cell weight (mg/L) of S.  platensis with chlorophyll a (mg/L) of spirulina grown in supernatant of three DRMM and \nKM. \n\n\n\nCell weight of S. platensis had highly significant (P < 0.01) direct \ncorrelation with chlorophyll a (r=0.9924) of spirulina grown in the \nsupernatant of three different DRMM and KM during the study (Figure 5). \nSimilarly, the total biomass of S. platensis was highly (P < 0.01) and directly \ncorrelated with chlorophyll a (r = 0.999) of spirulina cultured in the \n\n\n\nsupernatant of various DRMM and KM (Figure 6). Again, the total biomass \nof spirulina was found to be highly (P < 0.01) and directly correlated with \nthe cell weight (r = 0.9941) of spirulina grown in the supernatant of \ndifferent DRMM and KM (Figure 7). \n\n\n\nFigure 6: Correlation coefficient (r) of total biomass (mg/L) of S.  platensis with chlorophyll a (mg/L) of spirulina grown in supernatant of three digested \nDRMM and KM. \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\n1\n\n\n\n0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1\n\n\n\nce\nll\n\n\n\n w\nei\n\n\n\ngh\nt \n\n\n\n(m\ng/\n\n\n\nL\n) \n\n\n\nchlorophyll a (mg/L)  \n\n\n\ny = 1.073x + 0.240\nr=0.9924,\u02c20.01\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 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9\n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nB\nio\n\n\n\nm\nas\n\n\n\ns(\nm\n\n\n\ng/\nL\n\n\n\n)\n\n\n\nChlorophyll a (mg/L) \n\n\n\ny = 67x + 1E-12\nr=0.999982,P\u02c20.01 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 124-130 \n\n\n\nCite The Article: Sheikh Rasel, Md. Hamidur Rahman, Rabeya Akter, Meherun Nisa Jinia, Md. Ahsan Bin Habib, Zannatul Ferdous (2022). \nAssessment of Growth Parameters of Spirulina (Spirulina Platensis) Using Digested Rotten Mango (Mangifera Indica) Supernatant as a Cost-\n\n\n\nEffective Culture Media. Journal of Sustainable Agricultures, 6(2): 124-130. \n\n\n\nFigure 7: Correlation coefficient (r) of total biomass (mg/L) of S.  platensis   with cell weight (mg/L) of spirulina grown in supernatant of DRMM and KM.\n\n\n\n3.4   Specific Growth Rates (Sgrs) of S.  Platensis \n\n\n\nThe SGR concerning cell weight, chlorophyll a and total biomass of \nspirulina cultured in KM and supernatant of 50% DRMM was significantly \n(P < 0.01) varied from that of spirulina grown in the supernatant of 25% \nand 75% DRMM (Table 3). It had no significant difference in SGRs based \non cell weight, chlorophyll a and total biomass when spirulina was grown \nin the supernatant of 25% and 75% DRMM, and a similar thing happened \nwhen spirulina was cultured in the supernatant of 25% and 75% DRMM, \nbut there was a remarkable significant difference between SGRs of \nChlorophyll a of KM and 50% supernatant of DRMM. Investigated the \n\n\n\ncultivation and generation of housefly larvae and spirulina from chicken \nwaste, as well as its utilization as a diet for post-larvae catfish (Satter, \n2017). He created spirulina and employed it as a major feed element to \nreplace fish meal up to 100%, but the catfish grew well after being given a \ndiet that included 25% fish meal, 50% spirulina meal, and 25% maggot \nmeal. He also had good results with post-larvae-fed diets that had 25% fish \nmeal and 75% spirulina meal, as well as a diet that contained 100% \nspirulina meal. During the culture of S. platensis in 25, 50, and 75 percent \ndigested poultry waste (DPW), the 25 percent DPW showed superior \ngrowth at the same outcome as the current investigation. \n\n\n\nTable 3: Specific Growth Rates (Sgrs) on The Basis of Cell Weight, Chlorophyll a and Total Biomass of S.  Platensis Grown in Supernatant of Different \nDRMM, and KM. \n\n\n\nParameters T1 (25%of DRMM) T2 (50% of DRMM) T3 (75% of DRMM) T4 (KM) \n\n\n\nSGR of cell weight 0.021 \u00b1 0.002c 0.085\u00b1 0.002b 0.052 \u00b1 0.003c 0.30 \u00b1 0.002a \n\n\n\nSGR of Chlorophyll a 0.032\u00b1 0.002c 0.072\u00b1 0.03b 0.061\u00b1 0.003c 0.28 \u00b1 0.004a \n\n\n\nSGR of total biomass 0.28\u00b1 0.021c 0.48 \u00b1 0.033b 0.40\u00b1 0.043c 0.81 \u00b1 0.024a \n\n\n\nN.B. Figures in common letters in the same row do not differ significantly at 5% level   of probability. \n\n\n\n3.5   Temperature  \n\n\n\nTemperature around culture media was higher than the ambient \ntemperature due to light intensity. The temperature (\u00b0C) of all the culture \nmedia was varied with slight ineffective fluctuations. However, the \ntemperature around the culture of supernatant of 25% digested rotten \nmango media (DRMM) was found 21.57 \u00b1 0.12 \u00b0C on the first day to 22.76 \n\u00b1 0.03 \u00b0C at the end (16th day) of experiment with slight up on 22.66 \u00b1 .24 \n12th day of experiment. It was also follow the similar trend of fluctuation \nfrom first to last day of experiment when spirulina cultured in supernatant \nof 50% digested rotten mango media (DRMM). and 75% DRMM. But it was \nrecorded 22.48 \u00b1 0.03 \u00b0C on the first day of experiment to 23.63\u00b1 0.03 \u00b0C \nat the end of experiment when spirulina grown in Kosaric medium.  \n\n\n\n3.6   Light Intensity \n\n\n\nIt was varied slightly in different days in all the four culture media. \nHowever, light intensity (lux/m2/s) was varied from 2725 \u00b11.85 on first \nday to 2742 \u00b1 3.21 lux/m2/s on the last day with slight variation in other \ndays when spirulina grown in supernatant of 25% digested rotten mango \nmedia (DRMM). It was varied from 2742 \u00b1 3.21 on first day to 2743\u00b1 3.92 \nlux /m2/s on the last day of experiment when spirulina cultured in \nsupernatant of 50% digested rotten mango media (DRMM). Similarly, it \nwas observed 2742\u00b1 3.8 on the first day and 2752 \u00b1 2.6 on the last day \n(12th day) of experiment when spirulina grown in supernatant of 75% \ndigested rotten mango media (DRMM) where the maximum value \n2760\u00b15.29 on 8th day.  Light intensity was found to be 2720 \u00b1 2.3 lux /m2/s \non first day when spirulina grown in Kosaric medium and 2750\u00b1 3.17 lux \n/m2/s on the last day (16th day) of experiment. \n\n\n\n4. CONCLUSION \n\n\n\nThe experiment was carried out to evaluate the growth properties of \ndigested rotten mango (Mangifera indica) employed as spirulina (Spirulina \nplatensis) production medium for sixteen days after 26 days of digestion. \n\n\n\nThree different concentrations of DRM (digested rotten mango) were \nused: 25%, 50%, and 75% and were determined every other day, the \ngrowth characteristics of the cultured media, such as optical density, cell \nweight, chlorophyll a, and total biomass. As rotten mango contains a lot of \nbroken organic and inorganic nutrients, as well as a lot of total dissolved \nsolids, total suspended solids, nitrate, phosphate, and inorganic nutrients, \nit can be utilized to produce spirulina. During the culture of S. platensis, \nspirulina grows well in the supernatant of 50% digested rotten mango due \nto suitable and favorable levels of nutrients in 50% DRMM compared to \nother concentrations of DRMM, which is fairly equal to spirulina growth in \nKosaric medium. To grow spirulina, a 50% digested rotten mango \nsupernatant must be employed. Because of the usage of rotten mango, the \nenvironment may be safe and healthy, and so there is a significant risk of \nlarge-scale rotten mango being used for commercially cultured spirulina \nand promoted as stay food for good production and fish health control. \nThis research opened a new era for low cost live food production as a \nresult fish production cost will be reduced. Fish farmers and hatchery \nowner will be benefited by this research outcome.   \n\n\n\nREFERENCES \n\n\n\nBaroi, B., Rahman, M. H., Rohani, M. F., Hossain, M. S., 2019. Effect of dietary \nvitamin C on growth and survival of GIFT Tilapia. Bangladesh Open \nUniversity Journal of Agriculture & Rural Development, 11(2), pp. 37-\n42. \n\n\n\nClesceri, L. S., Greenberg, A. E., Trussell, R. R., 1989. Standard Methods for \nthe Examination of Water and Waste water. American Public Health \nAssociation, American Water Works Association and Water Pollution \nControl Federation. 17th Edition., 2015 Washington D.C., USA, pp. 10-\n203. \n\n\n\nHabib, M. A. B., 1998. Culture of selected microalgae in rubber and palm oil \neffluents and their use in the production of enriched rotifers. Doctoral \nThesis, University of Putra.  Malaysia, pp. 532. \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\n0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1\n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nB\nio\n\n\n\nm\nas\n\n\n\ns \n(m\n\n\n\ng/\nL\n\n\n\n) \n\n\n\nCell weight (mg/L) \n\n\n\ny = 59.65x + 13.01\nr=0.994129,P\u02c20.01 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 124-130 \n\n\n\nCite The Article: Sheikh Rasel, Md. Hamidur Rahman, Rabeya Akter, Meherun Nisa Jinia, Md. Ahsan Bin Habib, Zannatul Ferdous (2022). \nAssessment of Growth Parameters of Spirulina (Spirulina Platensis) Using Digested Rotten Mango (Mangifera Indica) Supernatant as a Cost-\n\n\n\nEffective Culture Media. Journal of Sustainable Agricultures, 6(2): 124-130. \n\n\n\nHossain, M. A. A., Rahman, M. H., Hossain, M. S., Habib, M. A. B., Uddin, M. A. \n& Sarker, F., 2021. Smart production of spirulina (Spirulina platensis) \nusing supernatant of digested rotten potato (Solanum tuberosum). \nJournal of Agriculture, Food and Environment (JAFE), 2(1), pp. 62-69. \nhttp://doi.org/10.47440/JAFE.2021.2111    \n\n\n\nJung, F., Kr\u00fcger-Genge, A., Waldeck, P., K\u00fcpper, J. H., 2019. Spirulina \nplatensis, a super food? Journal of Cellular Biotechnology, 5(1), pp. 43-\n54. \n\n\n\nMadkour, F. F., Kamil, A. E. W., Nasr, H. S., 2012. Production and nutritive \nvalue of Spirulina platensis in reduced cost media. The egyptian journal \nof aquatic research, 38(1), pp. 51-57. \n\n\n\nMahmud, M. T., Rahman, M. M., Shathi, A. A., Rahman, M. H., and Islam, M. \nS., 2021. Growth variation of tilapia (Oreochromis niloticus) with \nvariation of environmental parameters. Journal of Agriculture, Food \nand Environment (JAFE), 2(2), pp. 75-79. \n\n\n\nMahmuda M., Rahman M. H., Bashar A., Rohani M. F., & Hossain M. S., 2020. \nHeavy metal contamination in tilapia, Oreochromis niloticus collected \nfrom different fish markets of Mymensingh District. Journal of \nAgriculture, Food and Environment (JAFE) 1(4), pp. 1-5. \n\n\n\nMosha, S. S., 2019. The Significance of Spirulina Meal on Fishmeal \nReplacement in Aquaculture: A Review. Journal of Fisheries and \nAquaculture Development, pp. 145. \n\n\n\nPhang, S. M., Chu, W. L., 1999. University of Malaya Algae Culture Collection \n(UMACC). Catalogue of Strain. Institute of Postgraduate Studies and \nResearch, University of Malaya, Kualalumpur, Malaysia, pp. 77. \n\n\n\nRavi, M., De, S. L., Azharuddin, S., Paul, S. F., 2010. The beneficial effects of \nSpirulina focusing on its immunomodulatory and antioxidant \nproperties. Nutrition and Dietary Supplements, 2, pp. 73-83. \n\n\n\nRahman, M. H., Mahmud, M. T., Hossain, M. S., Mou, A. T., Sarker, F., and \nRahman, U. O., 2021. Variation of production performance of Gulsha \n(Mystus cavasius) monoculture with variation of water and soil quality \nparameters. Journal of Agriculture, Food and Environment (JAFE), 2(4), \npp. 59-64. \n\n\n\nRahman, M. H., Rahman, U. O., Akter, F., Baten, M. A., Uddin, M. A.,. Bhuiyan, \nA. N. M. R. K & Mou A. T., 2021. Physico-chemical properties of digested \nrotten potato (Solanum tuberosum) used as a production medium of \nspirulina (Spirulina platensis). Journal of Agriculture, Food and \nEnvironment (JAFE), 2(4), pp. 52-58. \n\n\n\nRahman, M. H., Khan, A. A. I., Habib, M. A., Hossain, M. S., 2022. Evaluation \nof Sugar Mill By-product Molasses as a Low Cost Culture Media for \nMicroalgae. Aquaculture Studies, 22(4), AQUAST776. \nhttp://doi.org/10.4194/AQUAST776 \n\n\n\nSatter, A., 2017. Culture and production of housefly larva and Spirulina \nusing poultry waste, and their use as food for catfish post-larvae, PhD \nThesis, Department of Aquaculture, Bangladesh Agricultural University, \nMymensingh. \n\n\n\nShah, K. A., Patel, M. B., Patel, R. J., Parmar, P. K., 2010. Mangifera indica \n(mango). Pharmacognosy reviews, 4(7), pp. 42. \n\n\n\nSharker, M. G. U., Miah, M. I., Rahman, M. M., Habib, M. A. B., 2007. Culture \nof Spirulina platensis in various concentrations of papaya skin powder \nmedium. Journal of the Bangladesh Agricultural University, 5(452-\n2018-3950), pp. 117-128. \n\n\n\nSoni, R. A., Sudhakar, K., Rana, R. S., 2019. Comparative study on the growth \nperformance of Spirulina platensis on modifying culture media. Energy \nReports, 5, pp. 327-336. \n\n\n\nTorzillo, G., Pushparaj, B., 1986. A new procedure for obtaining pure \ncultures of Spirulina platensis and Spirulina maxima. Annales \nMicrobiology, 135, pp. 165-173. \n\n\n\nUddin, M. M., Nur, N.N., Parvin, M., Habib, M.A.B., 2007. Culture of Chlorella \nEllipsoidea in supernatant of different concentrations of digested \npotato powder. Pakistan Journal of Scientific and Industrial Research, \n50(3), pp.  199-203. \n\n\n\nUddin, M. A., Hassan, R., Halim, K. M. A., Aktar, M. N. A. S., Yeasmin, M. F., \nRahman, M. H., Ahmed, M. U.,  & Ahmed, G. U., 2020. Effects of aqua drugs \nand chemicals on the farmed shrimp (Penaeus monodon) in southern \ncoastal region of Bangladesh. Asian Journal of Medical and Biological \nResearch, 6(3), pp. 491-498.  \n\n\n\nVonshak, J., Richmond, A., 1988. Spirulina. In, Borowitzka, M.A., \nBorowitzka, L (editions). Microalgal Biotechnology, Cambridge U.P., \nCambridge, UK, pp. 85-121. \n\n\n\nZar, J. H., 1984. Biostatistics. Prentice-Hall Inc., Englewood Cliffs, New \nJersey, USA, pp. 718. \n\n\n\nZarrouk, C., 1996. Contribution al\u2019etuded\u2019unecyanobacterie: influence de \ndivers facteurs physiques et chimiquessur la croissance et la \nphotosynthese de Spirulina maxima (Setchell et Gardner) Geitler. PhD \nthesis, University of Paris, France. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 01-04 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.01.04 \n\n\n\n \nCite the Article: Wen Chiat Lee, Nicholas Hoe, K. Kuperan Viswanathan, Amir Hussin Baharuddin (2020). An Economic Analysis Of Anthropogenic Climate Change On \n\n\n\nRice Production In Malaysia. Malaysian Journal of Sustainable Agriculture, 4(1): 01-04. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2020.01.04 \n\n\n\n\n\n\n\n \nAN ECONOMIC ANALYSIS OF ANTHROPOGENIC CLIMATE CHANGE ON RICE \nPRODUCTION IN MALAYSIA \n \n\n\n\nWen Chiat Leea*, Nicholas Hoeb, K. Kuperan Viswanathanc, Amir Hussin Baharuddind \n \naCorresponding Author, Department of Economics and Agribusiness, School of Economics, Finance and Banking, Universiti Utara Malaysia. \nbDepartment of Economics and Agribusiness, School of Economics, Finance and Banking, Universiti Utara Malaysia. \ncOthman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia \ndDepartment of Economics and Agribusiness, School of Economics, Finance and Banking, Universiti Utara Malaysia. \n\n\n\n*Corresponding Author Email: wenchiat86@gmail.com  \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 28 November 2019 \nAccepted 30 December 2019 \nAvailable online 24 January 2020 \n\n\n\n\n\n\n\nRice is an important staple food in Malaysia and represents a substantial household expenditure. Malaysia, \n\n\n\nwhich imports about 35 percent of its rice, is the 13th largest importer of rice in the world. This makes Malaysia \n\n\n\nsusceptible to global rice crisis, similar to the one in 2008. Climate change is crucial in affecting rice \n\n\n\nproduction in tropical countries especially Malaysia as climate projections have shown that climate change \n\n\n\nwill affect countries in the tropics most negatively with increased temperature and flooding due to \n\n\n\nanthropogenic carbon dioxide emissions. This study analysed the effect of anthropogenic carbon dioxide \n\n\n\nemissions on rice production in Malaysia during the period 1970-2013. The analysis incorporated the \n\n\n\nfollowing variables: total local rice production, carbon dioxide emissions, precipitation, land used for paddy \n\n\n\nfarming, total rice imports, and global average crude oil prices. The results indicated that in the estimated \n\n\n\nmodel the level of carbon dioxide does not affect rice production in the short- run. However, increased carbon \n\n\n\ndioxide emissions can influence rice production indirectly by affecting the level of precipitation. Precipitation \n\n\n\nand area of irrigated land are significant variables in determining level of rice production. Policies for \n\n\n\nreducing carbon emissions is however crucial for ensuring long run sustainability in rice production. \n\n\n\n\n\n\n\nKEYWORDS \n\n\n\nrice production, climate change, temperature rise, precipitation, carbon dioxide emission. \n\n\n\n1. INTRODUCTION \n\n\n\nThe agriculture sector is an important component in any country as it \n\n\n\nprovides food and economic opportunities for the people. Due to its \n\n\n\nimportance, various policies and budget allocation have been \n\n\n\nimplemented to ensure its sustainability. Although this has been the case, \n\n\n\nit has been projected that in the coming two decades, developing countries \n\n\n\nwould be affected by crop production problems following changes in \n\n\n\nglobal temperatures and weather (Mekonnen, 2018). These climate \n\n\n\nchanges stemming from anthropogenic activities such as mass \n\n\n\ndeforestation, urbanization and vehicle pollution would contribute to \n\n\n\nfood and economic problems in the country. Anthropogenic climate \n\n\n\nchange is defined as \u201ca change of climate which is attributed directly or \n\n\n\nindirectly to human activity that alters the composition of the global \n\n\n\natmosphere and natural climate variability observed over comparable \n\n\n\ntime periods\u201d (Hulme, 2016). \n\n\n\nMalaysia imported RM45 billion worth of food in year 2015, thus food \n\n\n\nsecurity is an important problem to be given attention (Carvalho, 2016). \n\n\n\nAnthropogenic activities such as burning of forests for cultivation, land \n\n\n\nreclamation, carbon emission from industrial factories and housing \n\n\n\ndevelopment can cause the agriculture activities to decline. The decline in \n\n\n\nagricultural outputs may cause Malaysia to import even more food in the \n\n\n\nfuture especially rice from other nations such as Vietnam and Thailand. \n\n\n\nRice is crucial to Malaysia as rice is a staple food in Malaysia accounting \n\n\n\nfor some 25 percent of the food expenditure budget share (Sheng, 2008). \n\n\n\nAny reduction in rice production due to anthropogenic climate change will \n\n\n\nhave serious impacts on the welfare of Malaysians. This study examines \n\n\n\nthe impact of anthropogenic climate change by examining the data on rice \n\n\n\nproduction and the impact of climate change variables such as carbon \n\n\n\ndioxide emissions and the level of precipitation on rice production in \n\n\n\nMalaysia. We suggest some policy actions to manage the impact of \n\n\n\nanthropogenic activities in Malaysia that affects climate change. \n\n\n\n2. RICE IN MALAYSIA \n\n\n\nRice is the major staple food in Malaysia, making up the largest portion in \n\n\n\nfood expenditure in an average household (Ishida, 2003). Due to its \n\n\n\n\nmailto:wenchiat86@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 01-04 \n\n\n\n\n\n\n\n \nCite the Article: Wen Chiat Lee, Nicholas Hoe, K. Kuperan Viswanathan, Amir Hussin Baharuddin (2020). An Economic Analysis Of Anthropogenic Climate Change On \n\n\n\nRice Production In Malaysia. Malaysian Journal of Sustainable Agriculture, 4(1): 01-04. \n \n\n\n\n\n\n\n\nimportance, the Malaysian government allocates incentives and subsidies \n\n\n\nto local farmers to increase production. With these efforts, production has \n\n\n\nincreased from 1,318,000 tonnes in the year 1980 to 1,820,000 tonnes in \n\n\n\nthe year 2016 (Department, 2016). Although Malaysia has seen increase in \n\n\n\nrice production, demand has so far outweighed local output. In the year \n\n\n\n2017, Malaysians demanded 2.75 million tonnes of rice whereas the \n\n\n\ndomestic production was only 1.8 million tonnes (US Department, 2017). \n\n\n\nThis puts local rice supply well below sufficiency to meet the populations\u2019 \n\n\n\ndemand with a supply deficit of close to a million tonnes. To overcome this \n\n\n\ndeficit in rice supply, the Malaysian government imported rice from \n\n\n\nneighbouring countries such as Thailand, Vietnam, Cambodia, Pakistan \n\n\n\nand India. Among these countries, Thailand and Vietnam are the top \n\n\n\nexporters of rice to Malaysia (US Department, 2017). Comparatively, \n\n\n\nMalaysia is also ranked thirteen in total import of rice globally with an \n\n\n\nimport of 950,000 metric tonnes (US Department, 2017). Thus, rice is an \n\n\n\nimportant commodity for the Malaysian people, and the government relies \n\n\n\nheavily on imports to meet the high national demand. \n\n\n\nThe high dependency on rice imports has in the past, negatively affected \n\n\n\nMalaysia, such as in the case with the 2008 global rice and cereal crisis. The \n\n\n\nrice crisis, which was due to number of factors, namely constraints in trade \n\n\n\nby major exporters, the panic purchase of stock by importers, high crude \n\n\n\noil prices, and a weakening dollar, caused rice prices to increase \n\n\n\nsubstantially (US Department, 2017). Together with a weak Malaysian \n\n\n\ncurrency, meeting the country\u2019s demand for rice proved costly for the \n\n\n\nMalaysian government, as rice was a controlled commodity in the country \n\n\n\n(Arandez, 2011). Realizing that Malaysia is over dependent on rice imports, \n\n\n\nthe Malaysian Government then prompted to increase rice sufficiency level \n\n\n\nto 90 percent by the year 2010 but was later reduced to 70 percent in the \n\n\n\nTenth Malaysian Plan (2011 to 2015). Thus, being overly dependent on \n\n\n\nfood imports can be detrimental, as any regional food crisis would affect \n\n\n\nthe country\u2019s food security. \n\n\n\n3. METHODOLOGY \n\n\n\nThe framework of impacts of anthropogenic climate change on rice \n\n\n\nproduction is presented below. \n\n\n\n\n\n\n\nFigure 1: Determinants of Rice Production in Malaysia \n\n\n\nThe dependent variable is aggregated rice production in Malaysia. \n\n\n\nIndependent variables on the other hand will include carbon dioxide \n\n\n\nemissions, precipitation levels, amount of available irrigated paddy \n\n\n\nland, amount of rice imports into the country, and average crude oil \n\n\n\nprices. \n\n\n\nThe model of rice production is as follow: \n\n\n\nRice = f (CO2, rain, land, imports, oil) \n\n\n\n\u2206\ud835\udc59\ud835\udc5b\ud835\udc5f\ud835\udc56\ud835\udc50\ud835\udc52 = \ud835\udefd0 + \ud835\udefd1\u2206\ud835\udc59\ud835\udc5b\ud835\udc50\ud835\udc5c2\ud835\udc61\u22121 + \ud835\udefd2\u2206\ud835\udc59\ud835\udc5b\ud835\udc5f\ud835\udc4e\ud835\udc56\ud835\udc5b\ud835\udc61\u22121 + \ud835\udefd3\u2206\ud835\udc59\ud835\udc5b\ud835\udc59\ud835\udc4e\ud835\udc5b\ud835\udc51\ud835\udc61\u22121 + \n\n\n\n\ud835\udefd4\u2206\ud835\udc59\ud835\udc5b\ud835\udc56\ud835\udc5a\ud835\udc5d\ud835\udc5c\ud835\udc5f\ud835\udc61\ud835\udc60\ud835\udc61\u22121 + \ud835\udefd5\u2206\ud835\udc59\ud835\udc5b\ud835\udc5c\ud835\udc56\ud835\udc59\ud835\udc61\u22121 + \ud835\udefd6\ud835\udc38\ud835\udc36\ud835\udc47\ud835\udc61\u22121 + \ud835\udf00\ud835\udc61 ------------------------------ \n\n\n\n------------------------- (1) \n\n\n\nWhere: \n\n\n\nRice = total local rice production (tonnes) \n\n\n\nCO2 = concentration of carbon dioxide the atmosphere (parts per million, \n\n\n\nppm) \n\n\n\nRain = precipitation (millimetres) \n\n\n\nLand = available paddy land (% of total land) \n\n\n\nImports = total rice imports in Malaysia (tonnes) \n\n\n\nOil = average world crude oil prices (USD per barrel) \n\n\n\nECT= Error Correction Term \n\n\n\n\ud835\udf00\ud835\udc61 = error term \n\n\n\nt = time period \n\n\n\n\ud835\udefd0 = intercept \n\n\n\n\ud835\udefd1, \ud835\udefd2, \ud835\udefd3, \ud835\udefd4, \ud835\udefd5, \ud835\udefd6 = coefficient for the explanatory variables \n\n\n\nThe data collected covers a period from 1970 to year 2013. The data of \n\n\n\nrice production and area of land were obtained from Department of \n\n\n\nStatistics Malaysia. The data of precipitation was obtained from the World \n\n\n\nBank, data of carbon dioxide from United Nations Statistics Division\u2019s \n\n\n\nWorld Energy Data Set and data of petroleum price from world Brent \n\n\n\ncrude oil price. The Vector Error Correction Model was used to examine \n\n\n\nthe effect of anthropogenic climate change on rice production in Malaysia. \n\n\n\n4. RESULTS AND DISCUSSIONS \n\n\n\nThe Table 1 below shows the results of Vector Error Correction Model of \n\n\n\nRice Production. \n\n\n\nTable 1: Model of Rice Production \n\n\n\nVariables Coefficient (p-values) \n\n\n\n1. Carbon dioxide (lagged one year) \n0.045 \n\n\n\n(0.398) \n\n\n\n2. Carbon dioxide (lagged two years) \n-0.035 \n\n\n\n(0.421) \n\n\n\n3. Number of rainfall (lagged one year) 0.545 (0.002)*** \n\n\n\n4. Number of rainfall (lagged two years) 0.548 (0.0001)*** \n\n\n\n5. Area of irrigated paddy land (lagged \n\n\n\none year) \n\n\n\n-0.645 \n\n\n\n(0.296) \n\n\n\n6. Area of irrigated paddy land (lagged \n\n\n\ntwo years) \n3.00 (0.015)** \n\n\n\n7. Imported rice (lagged one year) -0.137 (0.014)** \n\n\n\n8. Imported rice (lagged two years) \n0.01 \n\n\n\n(0.410) \n\n\n\n9. Average world crude oil prices (lagged \n\n\n\none year) \n\n\n\n-0.050 \n\n\n\n(0.162) \n\n\n\n10. Average world crude oil prices \n\n\n\n(lagged two years) \n\n\n\n-0.02 \n\n\n\n(0.299) \n\n\n\n11. Error correction term -0.106 (0.004)*** \n\n\n\nNote: The values in the brackets indicate the p-values for the regression \n\n\n\nresults. * denote 10% significance level, ** denote 5 % significance level \n\n\n\nand *** denote 1% significance level. \n\n\n\nFrom Table 1, it can be observed that only rain, irrigated paddy land, \n\n\n\nimports of rice, and the error correction term are deemed significant at the \n\n\n\nleast 5% level of significance. Rain, area of irrigated paddy land and \n\n\n\nimports of rice all show significant impacts on rice production in Malaysia. \n\n\n\nFrom the regression results, every 1% increase in the change of rainfall in \n\n\n\ncountry during the previous year will result in a 0.55% increase in rice \n\n\n\noutput within the country. This is considered to be an inelastic effect as the \n\n\n\nlagged one year rain variable\u2019s coefficient is less than one unit. An increase \n\n\n\nand well distributed rain-fall throughout the year is expected to increase \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 01-04 \n\n\n\n\n\n\n\n \nCite the Article: Wen Chiat Lee, Nicholas Hoe, K. Kuperan Viswanathan, Amir Hussin Baharuddin (2020). An Economic Analysis Of Anthropogenic Climate Change On \n\n\n\nRice Production In Malaysia. Malaysian Journal of Sustainable Agriculture, 4(1): 01-04. \n \n\n\n\n\n\n\n\nrice output as farmers are not required to fully rely on the countries\u2019 \n\n\n\nirrigation system (Van, 2011). Not being dependent on irrigation results in \n\n\n\nless strain for the rice crop. Thus, allowing the aforementioned system to \n\n\n\nwater a larger area. Saseendran\u2019s results similarly found positive long-\n\n\n\nterm effects of rain on rice production in the order of below unity. \n\n\n\nFrom Table 1, besides the effect or rain, the p-value for the area of irrigated \n\n\n\npaddy land is proven to be significant at 5% level with a p-value of 0.014. \n\n\n\nFurthermore, analyzing the coefficient, available irrigated paddy land can \n\n\n\nbe observed to have an elasticity value of 3.00. In other words, a 1% \n\n\n\nincrease in area of available irrigated paddy land will result in a 3 percent \n\n\n\nincrease in total rice output in the short-run. This can be interpreted as the \n\n\n\ndemand for food increases, stress on the country\u2019s food producing sector \n\n\n\nwill prompt the opening of additional agricultural land for paddy \n\n\n\nproduction (Saseendran, 2000). Increase in the opening of irrigated paddy \n\n\n\nland may result in the increase in rice production. \n\n\n\nIncrease droughts in the country will result in low yielding crop seasons. \n\n\n\nLower yields will then prompt the need to increase food import to meet \n\n\n\nincreasing demand. Increase in food imports will hinder and reduce the \n\n\n\nrice production in Malaysia as the country will rely on food import and put \n\n\n\nless focus on developing the country\u2019s rice production to ensure self- \n\n\n\nsufficiency. This is seen from the significant negative coefficient of 0.13 \n\n\n\nbetween the imported rice and Malaysian rice production. \n\n\n\nLastly, the utilization of the vector error correction model necessitates the \n\n\n\nanalysing of the error correction term. This term allows the model\u2019s system \n\n\n\nto correct any disequilibrium that may potentially occur in the system and \n\n\n\nadds adjustments to bring the model back to equilibrium. In the model \n\n\n\nabove, the estimated coefficient for the error correction term is significant \n\n\n\nat 5% level and thus points to the validity of the existence of a long-run \n\n\n\nrelationship between the tested variables. With a coefficient of 0.106, the \n\n\n\nspeed of adjustment during events of shock can be said to be slow. In other \n\n\n\nwords, the system has a speed of adjustment of about 10.6% during shocks. \n\n\n\nThe model is also expected to be stable as any corrections made to reach \n\n\n\nthe long-run equilibrium during short-run disequilibrium will not cause \n\n\n\nthe model to explode and increase exponentially as the coefficient \n\n\n\npossesses a value smaller than one (a<1). Besides that, the value for the \n\n\n\ncoefficient of the error term is also negative, falling into the expected range \n\n\n\nof values that the error term should be, to keep the model stable. What this \n\n\n\nentails is that whenever the model encounters shocks, after a period of \n\n\n\ntime, the model will shift back to equilibrium and not grow exponentially \n\n\n\nand break down. For example, crude oil spikes, droughts, and floods are a \n\n\n\nfew commonly encountered shocks. \n\n\n\nComparing the effects of increase carbon dioxide with the other variables, \n\n\n\nit can be observed that the other variables are more significant. Carbon \n\n\n\ndioxide is found to be insignificant in affecting the rice production in \n\n\n\nMalaysia (Lambin, 2011; Garcia, 2008; Department, 2015). On the other hand, \n\n\n\nrain or precipitation has a significant impact on rice output within the \n\n\n\ncountry. The increase in carbon dioxide does not seem to have an impact \n\n\n\non rice production. This could be due to the fact that in the case of Malaysia \n\n\n\nthe increase in carbon dioxide emissions is not large enough to cause a \n\n\n\ndrop in rice production. That is, the emission of carbon dioxide is still well \n\n\n\nbelow the threshold level to cause a reduction in production of rice. Due to \n\n\n\nthis, it can be inferred that precipitation is still the major factor in affecting \n\n\n\nrice production and if emissions from carbon dioxide were to reduce \n\n\n\nprecipitation than one can expect a reduction in rice production (United \n\n\n\nNations, 2006; United Nations 2015). Thus, the impact of greenhouse \n\n\n\ngasses on precipitation would be more important in determining rice \n\n\n\nproduction. Besides that, land allocation for farming has also been found \n\n\n\nto be more significant than increase in carbon dioxide emissions. Thus it \n\n\n\ncan be said that increase in agriculture intensification may be able to \n\n\n\ncounteract the effects of increased greenhouse gasses in the atmosphere. \n\n\n\n5. CONCLUSION \n\n\n\nVarious researchers have cautioned the existence of global warming from \n\n\n\nrecent increases of greenhouse gases in the atmosphere. The rising \n\n\n\ntemperature has been found to have detrimental negative effects on plants \n\n\n\nand various food crops around the world. Malaysia is expected to \n\n\n\nexperience food production reductions in the near future. The results \n\n\n\nfrom this study however show that the impact of greenhouse gases is not \n\n\n\nsignificant in the case of rice production. However if the continued \n\n\n\nincrease in greenhouse gases were to effect temperature and rainfall than \n\n\n\nthere will be a reduction in rice production as rainfall is a significant \n\n\n\nvariable in determining production of rice. Thus, policies for monitoring \n\n\n\nthe level of greenhouse gas emissions and the control of its emissions \n\n\n\nshould be put in place to reduce the impacts of anthropogenic climate \n\n\n\nchange on rice production in Malaysia. Actions to reduce open burning, \n\n\n\nextensive forest clearing and land reclamation should be enforced to \n\n\n\nreduce future greenhouse gas emissions. \n\n\n\nAlthough it has been shown that the effects of carbon dioxide are not as \n\n\n\nprofound as compared to other factors, it is still important that efforts are \n\n\n\ntaken to manage the country\u2019s carbon emissions. This is because without \n\n\n\nany intervention, carbon dioxide emission may reach levels that would \n\n\n\ncause severe temperature rise over the coming decades. Among policies \n\n\n\nthat can be applied is the introduction of carbon taxes, providing bigger \n\n\n\nbudgets for biofuel development, improving mass public transportation, \n\n\n\nbetter urbanization management, and reducing deforestation. \n\n\n\nThe results also show that the opening of new agricultural land on the \n\n\n\nother hand, would also be able to increase rice output within the country \n\n\n\nby means of enlarging the agricultural capacity of the country. Although it \n\n\n\nis a quick way to achieve the target of self-sufficiency, the cutting down of \n\n\n\nforest may exacerbate the effects of global warming with the release of \n\n\n\nmore carbon dioxide and the loss of carbon sinks. Instead of the opening \n\n\n\nof new agriculture land, a bigger budget should be allocated to agriculture \n\n\n\nresearch and development, allowing for an increase in the yields from \n\n\n\nsmaller paddy farms. Greater yield of rice production in Malaysia would \n\n\n\nreduce the food imports from other nations. Reducing food imports from \n\n\n\nother nations are crucial for the development of local rice producers and \n\n\n\nensuring food security of the nation is sustained in the future. \n\n\n\nREFERENCES \n\n\n\nArandez- Tanchuling, H. 2011. Two years after the 2008 rice crisis. \n\n\n\nKasarinlan: Philippine, Journal of Third World Studies, 26, 295-311. \n\n\n\nCarvalho, M., Khasgar, L., Cheng, N., Kanyakumari, D. 2016. RM45 billion \n\n\n\nfor food import bill, The Star Online, Retrieved June 08, 2019, from \n\n\n\nhttps://www.thestar.com.my/news/nation/2016/03/15/rm45bil-\n\n\n\nfor- food-import-bill-amount-is-rm18bil-higher-than-export-tag-\n\n\n\nsays- shabery/ \n\n\n\nDepartment of Statistics Malaysia. 2015. Local Paddy Production 2015,\n\n\n\n Retrieved June 06, June 2016 from \n\n\n\nttps://www.dosm.gov.my/v1/index.php?r=column/ctimeseries&me\n\n\n\nnu_ id=NHJlaGc2Rlg4ZXlGTjh1SU1kaWY5UT09 \n\n\n\nDepartment of Statistics Malaysia. 2016. Total Paddy Available Paddy \n\n\n\nLand, Retrieved March 22, 2017, from \n\n\n\nhttps://www.dosm.gov.my/v1/index.php?r=column/ctimeseries&m\n\n\n\nenu_ id=NHJlaGc2Rlg4ZXlGTjh1SU1kaWY5UT09 \n\n\n\nGarcia, R.R., Randel, W.J. 2008. Acceleration of the Brewer\u2013Dobson \n\n\n\ncirculation due to increases in greenhouse gases. Journal of the \n\n\n\nAtmospheric Sciences, 65 (8), 2731-2739. \n\n\n\nHulme, M. 2016. Concept of climate change, The International \n\n\n\nEncyclopedia of Geography, John Wiley & Sons, New Jersey, United \n\n\n\nStates. \n\n\n\nIshida, A., Law, S.H., Aita, Y. 2003. Changes in food consumption \n\n\n\nexpenditure in Malaysia. Agribusiness, 19 (1), 61-76.  \n\n\n\nLambin, E.F., Meyfroidt, P. 2011. Global land use change, economic \n\n\n\nglobalization, and the looming land scarcity. Proceedings of the \n\n\n\nNational Academy of Sciences, 108 (9), 3465-3472. \n\n\n\nMekonnen, Z. 2018. Observed and Projected Reciprocate Effects of \n\n\n\nAgriculture and Climate Change: Implications on Ecosystems and \n\n\n\nHuman Livelihood. Intech Open Limited, London, United Kingdom. \n\n\n\nSaseendran, S.A., Singh, K.K., Rathore, L.S., Singh, S.V., Sinha, S.K. 2000. \n\n\n\nEffects of climate change on rice production in the tropical humid \n\n\n\nclimate of Kerala, India. Climatic Change, 44 (4), 495-514. \n\n\n\nSheng, T.Y., Shamsudin, M.N., Mohamed, Z.A., Abdullah, A.M., Radam, A. \n\n\n\n2008. Food Consumption Behavior of the Malays in Malaysia. IIUM \n\n\n\nJournal of Economics and Management, 16 (2), 209-219. \n\n\n\n\nhttps://www.thestar.com.my/news/nation/2016/03/15/rm45bil-for-food-import-bill-amount-is-rm18bil-higher-than-export-tag-says-shabery/\n\n\nhttps://www.thestar.com.my/news/nation/2016/03/15/rm45bil-for-food-import-bill-amount-is-rm18bil-higher-than-export-tag-says-shabery/\n\n\nhttps://www.thestar.com.my/news/nation/2016/03/15/rm45bil-for-food-import-bill-amount-is-rm18bil-higher-than-export-tag-says-shabery/\n\n\nhttps://www.thestar.com.my/news/nation/2016/03/15/rm45bil-for-food-import-bill-amount-is-rm18bil-higher-than-export-tag-says-shabery/\n\n\nhttps://www.thestar.com.my/news/nation/2016/03/15/rm45bil-for-food-import-bill-amount-is-rm18bil-higher-than-export-tag-says-shabery/\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 01-04 \n\n\n\n\n\n\n\n \nCite the Article: Wen Chiat Lee, Nicholas Hoe, K. Kuperan Viswanathan, Amir Hussin Baharuddin (2020). An Economic Analysis Of Anthropogenic Climate Change On \n\n\n\nRice Production In Malaysia. Malaysian Journal of Sustainable Agriculture, 4(1): 01-04. \n \n\n\n\n\n\n\n\nUnited Nations Framework Convention on Climate Change. 2006. \n\n\n\nHandbook on United Nations Framework Convention on Climate \n\n\n\nChange, Climate Change Secretariat, Bonn, Germany. \n\n\n\nUnited Nations Statistics Division\u2019s World Energy Data Set. 2015. Global \nAverage Crude Oil Price, Retrieved June 26, 2016, from \nhttps://unstats.un.org/unsd/energy/ \n\n\n\n \nUnited States Department of Agriculture. 2017. Malaysia Grain and Feed\n\n\n\n Annual 2017, Retrieved March 1, 2018,from \n\n\n\nhttps://gain.fas.usda.gov/Recent%20GAIN%20Publications/Grain\n\n\n\n%20a nd%20Feed%20Annual_Kuala%20Lumpur_Malaysia_3-27-\n\n\n\n2017.pdf \n\n\n\nVan Groenigen, K.J., Osenberg, C.W., Hungate, B.A. 2011. Increased soil \n\n\n\nemissions of potent greenhouse gases under increased atmospheric \n\n\n\nCO2. Nature, 475 (7355), 214-216. \n\n\n\n \n\n\n\n\nhttps://gain.fas.usda.gov/Recent%20GAIN%20Publications/Grain%20and%20Feed%20Annual_Kuala%20Lumpur_Malaysia_3-27-2017.pdf\n\n\nhttps://gain.fas.usda.gov/Recent%20GAIN%20Publications/Grain%20and%20Feed%20Annual_Kuala%20Lumpur_Malaysia_3-27-2017.pdf\n\n\nhttps://gain.fas.usda.gov/Recent%20GAIN%20Publications/Grain%20and%20Feed%20Annual_Kuala%20Lumpur_Malaysia_3-27-2017.pdf\n\n\nhttps://gain.fas.usda.gov/Recent%20GAIN%20Publications/Grain%20and%20Feed%20Annual_Kuala%20Lumpur_Malaysia_3-27-2017.pdf\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 49-53 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.49.53 \n\n\n\nCite the Article: Sarmila Belbase (2020). Study Of Improved Mandarin (Citrus Reticulate Blanco) Orchard Management Practices In Mid Hills Of Gandaki \nProvince, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 49-53. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.49.53 \n\n\n\nSTUDY OF IMPROVED MANDARIN (Citrus Reticulate Blanco) ORCHARD\nMANAGEMENT PRACTICES IN MID HILLS OF GANDAKI PROVINCE, NEPAL \n\n\n\nSarmila Belbase*, Anisha Tiwari, Suraksha Baral, Sarita Banjade, Divya Pandey \n\n\n\nB.Sc. Ag., Agriculture and Forestry University, Rampur, Chitwan, Nepal.\n*Corresponding Author Email: sarmila.belbase14@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 26 February 2020\n\n\n\nA survey was carried out to collect information regarding orchard management practices from mandarin \ngrowers of mid hills of Gandaki province by taking representative sample size of 80, 25 each from Beni \nMunicipality and Jaljala Rural Municipality and 30 from Kathekhola Rural Municipality of Myagdi,Parbat and \nBaglung respectively. Purposive selection of site was done, and sampling procedure was simple random \nsampling technique.  Descriptive statistical tools, chi-square test and t-test were used to analyze the data. The \nstudy was carried out to know level of adoption of improved orchard management practices, relation \nbetween socio-economic characters and adoption of technology and to identify the constraints perceived by \nfarmers during adoption. From the study it was found that majority of the respondents of mandarin growers \nwere male, middle aged, had medium sized family, had medium farm size, most of them are literate and \nagriculture was the primary source of income. Majority of farmers had taken training.  Most of the \nrespondents were in frequent contact with extension agent seeking the required information. Out of major \nten improved management practices, majority of respondents had adopted training and pruning while least \nadopted practices were micronutrient application. Majority of respondents had low adoption on \nrecommended management practices. Gender, education level, training, land holding size, contact with \nextension agent had significant association with adoption. Major problems like irrigation, insect and disease, \ntraining, lack of labor and cost of input were encountered during the adoption of improved mandarin orchard \nmanagement practices. It is recommended that, literacy program needed to be strengthened, training should \nbe based on felt need and subsidy should be given to farmers to encourage them towards mandarin \ncultivation as well as adoption of improved orchard management practices.  \n\n\n\nKEYWORDS \n\n\n\ncitrus, mandarin, orchard management practices, farmers.\n\n\n\n1. INTRODUCTION \n\n\n\nIn Nepal horticultural crops cover about 15% of total agricultural gross \n\n\n\ndomestic product (AGDP). Among horticultural crops, fruits cover 7% of \n\n\n\ntotal AGDP (MoAD, 2014; Adhikari, 2016/17). These days the \n\n\n\nconsumption of fruits is in increasing trend due to its high nutritive value.  \n\n\n\nAmong fruits, citrus is world\u2019s leading fruit crop (Bose & Mitra, Fruits: \n\n\n\ntropical and subtropical).  It is a crop adapted in subtropical region and it \n\n\n\nis considered as high value crop in mid-hills of Nepal (Gautam and \n\n\n\nBhattarai, 2006). This crop has gained a stature of huge industry which \n\n\n\nwould be a step forward towards providing a nutritional security to the \n\n\n\ngrowing population (Srivastava and Singh, 2002; Banerjee, 2008). In \n\n\n\nclimate, soil and cultivar types  It is one of the indigenous fruit contributing \n\n\n\n26.8% of total fruit production (NCRP, 2016).   \n\n\n\nIn Nepal, citrus is grown at 800- 1400 meter above mean sea level. Citrus \n\n\n\nget most favorable climatic condition in the eastern and western mid hills \n\n\n\nof Nepal with annual mean temperature 17-20 degree Celsius and annual \n\n\n\nrainfall ranging from 1000-2800 mm (Srivastava ans Singh, 2002). There \n\n\n\nare about 140 major citrus producing countries according to UNCTAD.  \n\n\n\nFAO estimated the world\u2019s citrus production to about 115.6 million tones \n\n\n\nin which Brazil, china, USA and Mexico are among the world\u2019s top citrus \n\n\n\nproducing countries while Nepal produces only 0.22 million tons (NCDP, \n\n\n\n2015/16). The productive area of citrus is 24,885 ha and production is \n\n\n\n218,447.2 Mt with productivity of 8.78 Mt/ha (Paudyal, 2002). The total \n\n\n\narea of mandarin is 16,248 ha, and production is 146,690 Mt with \n\n\n\nproductivity 9.03 Mt/ha. Mainly, three citrus species are grown \n\n\n\ncommercially in Nepal i.e. Mandarin (Citrus reticulate), sweet orange \n\n\n\n(Citrus sinensis) and lime (Citrus aurantifolia). Of the fruit area in Nepal, \n\n\n\ncitrus shares nearly 32% of the total area among which the contribution \n\n\n\nof mandarin is nearly 21%.  \n\n\n\nSimilarly, the share of citrus in total fruit production is 37%, in which \n\n\n\nmandarin is major commodity and its share in total production is nearly \n\n\n\n25% (Fao, 2011). The demand of mandarin is very high because of its \n\n\n\nnutritive value. It is rich in vitamin C (Ascorbic acid), fruit sugar and \n\n\n\nvitamin A and B.  There is high potential of mandarin cultivation in mid \n\n\n\nhills of Nepal. Major mandarin producing areas are Myagdi, Parbat, \n\n\n\nBaglung, Syangja, Gorkha, Solukhumbu, and Lamjung and so on. Orchard \n\n\n\nmanagement is the major factor to enhance the total production of \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 49-53 \n\n\n\nCite the Article: Sarmila Belbase (2020). Study Of Improved Mandarin (Citrus Reticulate Blanco) Orchard Management Practices In Mid Hills Of Gandaki \nProvince, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 49-53. \n\n\n\nmandarin (Banerjee et al., 2008). Only having favorable climatic condition \n\n\n\nand improved variety don\u2019t ensure the good production as per the \n\n\n\npotentiality (Bose, 1985). There must be adoption of proper orchard \n\n\n\nmanagement practices. Adoption of management techniques has a \n\n\n\nsignificant role in the production. There is low production due to non-\n\n\n\nadoption or poor adoption of management technology.  \n\n\n\n2. METHODOLOGY\n\n\n\n2.1 Area of study \n\n\n\nThe study was carried out in the mid hills of Gandaki province. Beni \n\n\n\nmunicipality (28.3685\u00b0 N, 83.5390\u00b0 E), Kathekhola rural municipality \n\n\n\n(28.2624\u00b0 N, 83.5393\u00b0 E), and Jaljala rural municipality (28.3351\u00b0 N, \n\n\n\n83.5768\u00b0 E) of Myagdi, Baglung and Parbat districts were chosen \n\n\n\nrespectively.  \n\n\n\n2.2 Data collection \n\n\n\n2.2.1 Pretesting \n\n\n\nThe interview for the data collection was pre-tested prior to household \n\n\n\nsurvey for checking the reliability and validity through the selection of \n\n\n\nrespondents near to the study area (Chattopadhayay, 2012). Then, \n\n\n\nnecessary adjustments were done as per the requirements in the \n\n\n\ninterview schedule. \n\n\n\n2.2.2 Interview \n\n\n\nAn interview schedule was designed for primary data collection. For the \n\n\n\nconstruction of interview schedule, a coordination schema was prepared \n\n\n\nwith the objectives of the study (Chaudhary et al., 2013). Based on co-\n\n\n\nordination scheme different variables were included in the interview \n\n\n\nschedule.  \n\n\n\n2.2.3 Key informant interview \n\n\n\nThe major key informants were farmers, stakeholders and District \n\n\n\nAgriculture Development officers. They were asked a series of question \n\n\n\nabout present scenario of mandarin cultivation and status of orchard \n\n\n\nmanagement. \n\n\n\n2.2.4 Household survey \n\n\n\nThe detail information about the socio-economic status, recommended \n\n\n\npractices adopted, household information was discussed in interview \n\n\n\nschedule (Choudhary, 2006). The interview schedule was used to collect \n\n\n\nthe information from the randomly selected farmers of mandarin zone \n\n\n\narea.  \n\n\n\n2.2.5 Focus group discussion  \n\n\n\nFocus group discussion was conducted at the study area after completing \n\n\n\ninterview schedule with the help of checklist to verify the result obtained \n\n\n\nfrom household survey and discuss about the strategies to increase \n\n\n\nadoption of improved practices in mandarin zone area (CIMMYT, 1993). \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Socio- economic characteristics of respondents \n\n\n\nThe socio- economiccharacteristics of respondents includes age, gender, \n\n\n\nethnicity, education level, religion, family size, land holding size, mandarin \n\n\n\ncultivation area, source of income, other extension related factors were \n\n\n\ntraining and contact with extension agents (Dangol, 2004). The descriptive \n\n\n\nanalysis of their characteristics is mentioned below: \n\n\n\n3.2 Age \n\n\n\nThe study revealed that the middle age group (40-60) belongs to high \n\n\n\nadopter of improved orchard management. Age was found to be \n\n\n\nstatistically insignificant (p-value 0.905) to level of adoption. Thus, we \n\n\n\nconclude that there is no relationship between age and level of adoption. \n\n\n\nSimilar result was also found conducted similar research in rice (Dangol \n\n\n\n2004).  \n\n\n\nTable 1: Distribution of respondents by their age and adoption level \n\n\n\nof farmers on improved mandarin orchard management practices \n\n\n\n(N=80) \n\n\n\nVariables \n\n\n\n(Age in \n\n\n\nyears) \n\n\n\nLow \n\n\n\nadopters \n\n\n\nHigh \n\n\n\nadopters \n\n\n\nTotal Chi-\n\n\n\nsquare \n\n\n\nvalue \n\n\n\np- \n\n\n\nvalue \n\n\n\n<40 9(20.9) 9(24.32) 18(22.5) 0.199ns 0.905 \n\n\n\nat 2df \n\n\n\n40-60 22(59.45) 19(51.35) 41(51.25) \n\n\n\n>60 12(27.9) 9(24.32) 21(26.25) \n\n\n\nFigures in parenthesis () indicate percentage \n\n\n\n3.3 Gender \n\n\n\nFrom the study, males (83.8%) were found to be higher adopter than \n\n\n\nfemale (16.2%).  Since the P-value (0.022) is less than the significance \n\n\n\nlevel (0.05), we cannot accept the null hypothesis. Thus, we conclude that \n\n\n\nthere is a relationship between gender and adoption level.  \n\n\n\nFigures in parenthesis () indicate percentage \n\n\n\nNote: ** indicates significant at 5% level \n\n\n\n3.4 Level of education \n\n\n\nThe study revealed that the farmers belonging to informal education level \n\n\n\nwere likely to be higher adopter (Dhifal, 2010).  Since the P-value (0.00) is \n\n\n\nless than the significance level (0.01), we cannot accept the null \n\n\n\nhypothesis. Thus, we conclude that there is a relationship between \n\n\n\neducation and adoption level (Feder et al., 1990; Yadav, 2006). He \n\n\n\nreported that education is one of factor determining the adoption of \n\n\n\ntechnology by farmers. \n\n\n\nTable 3: Distribution of respondents by their level of education and \n\n\n\nadoption level of farmers on improved mandarin orchard \n\n\n\nmanagement practices (N=80) \n\n\n\nVariables Low \n\n\n\nadopters \n\n\n\nHigh \n\n\n\nadopters \n\n\n\nTotal Chi-\n\n\n\nsquare \n\n\n\nvalue \n\n\n\np- \n\n\n\nvalue \n\n\n\nIlliterate 14(32.55) 5(13.51) 19(23.75) 15.701*** 0.00 \n\n\n\nat \n\n\n\n3df \n\n\n\nInformal 27(62.9) 18(48.64) 45(56.25) \n\n\n\nBelow \n\n\n\nSLC \n\n\n\n2(4.65) 6(16.21) 8(10) \n\n\n\nSLC or \n\n\n\nabove \n\n\n\n0 8(21.62) 8(10) \n\n\n\nFigures in parenthesis () indicate percentage \n\n\n\nNote:  *** Highly significant at 1% level \n\n\n\n3.5 Land holding size \n\n\n\nThe study revealed that the farmers having medium farm size were likely \n\n\n\nto be high adopters.   Since the P-value (0.03) is less than the significance \n\n\n\nlevel (0.05), we cannot accept the null hypothesis. Thus, we conclude that \n\n\n\nthere is a relationship between land holding size and adoption level \n\n\n\n(Negash, 2010). He found that land holding size is significant with \n\n\n\nadoption technology in bean production. \n\n\n\nTable 2: Distribution of respondents by their gender and adoption \n\n\n\nlevel of farmers on improved mandarin orchard management \n\n\n\npractices (N=80) \n\n\n\nVariables Low \n\n\n\nadopter \n\n\n\nHigh \n\n\n\nadopter \n\n\n\nTotal Chi-\n\n\n\nsquare \n\n\n\nvalue \n\n\n\np-\n\n\n\nvalue \n\n\n\nMale 26(60.5) 31(83.8) 57(71.3) 5.27** 0.022 \n\n\n\nat 1df \n\n\n\nFemale 1739.5) 6(16.2) 23(28.7) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 49-53 \n\n\n\nCite the Article: Sarmila Belbase (2020). Study Of Improved Mandarin (Citrus Reticulate Blanco) Orchard Management Practices In Mid Hills Of Gandaki \nProvince, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 49-53. \n\n\n\nFigures in parenthesis () indicate percentage \n\n\n\nNote: ** indicates significant at 5% level \n\n\n\n3.6 Family size \n\n\n\nThe study revealed that majority of adopters belonged to medium family \n\n\n\nsize. Since the p-value (0.16) is more than the significance level (0.1), we \n\n\n\ncan accept the null hypothesis. Thus, we conclude that there is no \n\n\n\nrelationship between family size and adoption level (Dorji et al., 2016). \n\n\n\nThe result indicated that there was no significant association between \n\n\n\nfamily size and adoption level of improved mandarin orchard \n\n\n\nmanagement practices (Pyakuryal, 1985). He reported that family size was \n\n\n\nnot associated with adoption level of recommended technology of \n\n\n\ncauliflower production. \n\n\n\nFigures in parenthesis () indicate percentage \n\n\n\n3.7 Training obtained by respondents \n\n\n\nThe farmers who received training were likely to be highly high adopters  \n\n\n\n(75.67%) than who didn\u2019t receive training (24.3). Since the p-value \n\n\n\n(0.001) is less than the significance level (0.1), we cannot accept the null \n\n\n\nhypothesis (NCRP, 2016). Thus, we conclude that there is a relationship \n\n\n\nbetween training received and adoption level (Mwangi and Kariuki, 2016). \n\n\n\nHe reported that training is one of the determinants of adoption of new \n\n\n\ntechnology by small holder farmers in developing countries. \n\n\n\nFigures in parenthesis () indicate percentage \n\n\n\nNote: *** indicates significant at 1% level \n\n\n\n3.8 Contact with extension agent \n\n\n\nThe study revealed that the farmers who were in frequent contact with \n\n\n\nextension agents were found to be highly higher adopter of improved \n\n\n\norchard management practices. Since the P-value (0.002) is less than the \n\n\n\nsignificance level (0.1), we cannot accept the null hypothesis. Thus, we \n\n\n\nconclude that there is a relationship between contact with extension agent \n\n\n\nand adoption level (Reddy and Reddy, 1998). He reported that contact \n\n\n\nwith extension agents had association with knowledge and adoption of \n\n\n\nimproved paddy cultivation practices. \n\n\n\nFigures in parenthesis () indicate percentage \n\n\n\nNote: *** indicates significant at 1% level \n\n\n\n3.9 Income from mandarin \n\n\n\nFrom the study it was found that the income from mandarin is associated \n\n\n\nwith the adoption of management practices. Since, p-value (0.002) is less \n\n\n\nthan 0.05 there is significant different between income and adoption level. \n\n\n\nNote: ** indicates significant at 5% \n\n\n\n3.10  Major problems perceived by respondents during adoption of \n\n\n\nimproved orchard management practices \n\n\n\nThe major problems perceived by respondents during adoption of \n\n\n\nimproved orchard management practices addressed in the study area \n\n\n\nwere ranked below: \n\n\n\nTable 9: Constraints regarding the adoption of improved practices of  \n\n\n\nOrchard management by farmers \n\n\n\nProblems  1 0.8 0.6 0.4 0.2 Total weight index rank \n\n\n\nLabour 2 7 13 28 30 80 32.6 0.4075 IV \n\n\n\nIrrigation 49 9 5 10 7 80 64.6 0.8075 I \n\n\n\nCost of \n\n\n\ninput \n\n\n\n1 8 12 22 37 80 30.8 0.385 V \n\n\n\nLack of  \n\n\n\ntrainings  \n\n\n\n4 26 37 13 0 80 52.2 0.6525 III \n\n\n\nInsect and  \n\n\n\nDisease \n\n\n\n25 29 13 7 6 80 60 0.75 II \n\n\n\nIt is evident from the table that the constraint \u2018irrigation\u2019 was the most \n\n\n\nperceived constraint among all the constraints faced by the farmers in \n\n\n\nadoption of recommended orchard management practices and hence it \n\n\n\nwas awarded first rank. The second most perceived constraints by the \n\n\n\nfarmers in adoption of recommended practices was insect and disease \n\n\n\nproblem followed by lack of training and shortage of labor which were \n\n\n\nranked third and fourth respectively. Similarly, the constraint \u2018cost of \n\n\n\ninput\u2019 was ranked in fifth position (Negash, 2010). The problem of \n\n\n\nirrigation might be because of the reason that the farmers were unable to \n\n\n\npull water from the river via motor as it costs much which is beyond their \n\n\n\ncapacity and lack of knowledge about the rainwater harvesting. Similarly, \n\n\n\nthe problem of insects-pest was because they lack knowledge about \n\n\n\norchard sanitation and on control measures to be taken.  \n\n\n\nThis implies that there should be proper training about the insects-pest \n\n\n\nTable 4: Distribution of respondents by their land holding size and \n\n\n\nadoption level of farmers on improved mandarin orchard \n\n\n\nmanagement practices (N=80) \n\n\n\nVariables Low \n\n\n\nadopters \n\n\n\nHigh \n\n\n\nadopters \n\n\n\nTotal Chi-square \n\n\n\nvalue \n\n\n\np- \n\n\n\nvalue \n\n\n\nSmall \n\n\n\n(<0.15 \n\n\n\nha) \n\n\n\n7(16.27) 1(2.7) 8(10) 6.98** 0.03 \n\n\n\nat 2df \n\n\n\nMedium \n\n\n\n(0.15- \n\n\n\n1.11 ha) \n\n\n\n31(72.09) 25(67.56) 56(70 \n\n\n\nLarge \n\n\n\n(>1.11 \n\n\n\nha) \n\n\n\n5(11.62) 11(29.72) 16(20) \n\n\n\nTable 5: Distribution of respondents by their family size and \n\n\n\nadoption level of farmers on improved mandarin orchard \n\n\n\nmanagement practices (N=80) \n\n\n\nVariables Low \n\n\n\nadopters \n\n\n\nHigh \n\n\n\nadopters \n\n\n\nTotal Chi-\n\n\n\nsquare \n\n\n\np-value \n\n\n\nSmall 17(39.5) 9(24.32) 26(32.5) 3.58 0.16 at 2 \n\n\n\ndf \n\n\n\nMedium 8(18.6) 15(40.54) 23(28.75) \n\n\n\nLarge 18(41.8) 13(35.13) 31(38.75) \n\n\n\nTable 6: Distribution of respondents based on training obtained and \n\n\n\nadoption level of farmers on improved mandarin orchard \n\n\n\nmanagement practices (N=80) \n\n\n\nVariables Low \n\n\n\nadopters \n\n\n\nHigh \n\n\n\nadopters \n\n\n\nOverall Chi- \n\n\n\nsquare \n\n\n\np- \n\n\n\nvalue \n\n\n\nTraining \n\n\n\nreceived \n\n\n\n15(34.9) 28(75.67) 43(53.75) 13.31*** 0.001 \n\n\n\nat \n\n\n\n2df \nTraining \n\n\n\nnot \n\n\n\nreceived \n\n\n\n28(65.1) 9(24.3) 37(46.25) \n\n\n\nTable 7: Distribution of respondents based on contact with extension \n\n\n\nagent and adoption level of farmers on improved mandarin orchard \n\n\n\nmanagement practices (N=80) \n\n\n\nVariables Low \n\n\n\nadopter \n\n\n\nHigh \n\n\n\nadopter \n\n\n\nTotal Chi-\n\n\n\nsquare \n\n\n\nvalue \n\n\n\np-\n\n\n\nvalue \n\n\n\nNever 10(23.25) 3(8.1) 13(16.25)   12.87*** 0.002 \n\n\n\nat \n\n\n\n2df \n\n\n\nSeldom 20(46.51) 8(21.62) 28(35) \n\n\n\nFrequently 13(30.23) 26(70.27) 39(48.75) \n\n\n\nTable 8: Comparison between income and adoption level \n\n\n\nVariables  Adoption level Mean \n\n\n\ndifference \n\n\n\nt- value p-\n\n\n\nvalue \nLow \n\n\n\nadopter  \n\n\n\nHigh \n\n\n\nadopter \n\n\n\nIncome \n\n\n\nfrom \n\n\n\nmandarin \n\n\n\n423756.76 114906.98 308849.78 3.131** 0.002 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 49-53 \n\n\n\nCite the Article: Sarmila Belbase (2020). Study Of Improved Mandarin (Citrus Reticulate Blanco) Orchard Management Practices In Mid Hills Of Gandaki \nProvince, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 49-53. \n\n\n\nand disease management. Likewise, the main general constraints \u201cLack of \n\n\n\nneed based training\u201d, might be due to reason that there was large gap of \n\n\n\ncommunication between extension workers and mandarin growers. The \n\n\n\nproblem of labor is due to the shortage of manual workers and brain drain \n\n\n\nwas the main reason behind this. In the study area, it was found that most \n\n\n\nof the young members of household were in foreign countries or moved in \n\n\n\nother cities in search of jobs. The constraint cost of input was because the  \n\n\n\nseedlings of mandarin, fertilizers, chemical pesticides, micronutrients etc.  \n\n\n\nrequire more cost and sinceaverage farmers cannot afford these expenses.  \n\n\n\n3.11 Problem of Disease  \n\n\n\nIn the study site, from the study it was found that there was high \n\n\n\ninfestation of powdery mildew followed by sooty mould which was ranked \n\n\n\nfirst and second respectively. \n\n\n\nTable 10: Diseases found in the study sites \n\n\n\nTypes of disease 1 0.83 0.66 0.49 0.32 0.15 Total weight Index Rank \n\n\n\nRoot rot 0 7 22 17 3 31 80 34.27 0.428 III \n\n\n\nsooty mould 2 50 8 1 1 18 80 52.29 0.653 II \n\n\n\nCitrus canker 1 2 8 15 22 32 80 27.13 0.339 V \n\n\n\nPowdery mildew 64 2 2 1 0 11 80 69.12 0.864 I \n\n\n\nFoot rot 2 3 10 15 22 28 80 29.68 0.371 IV \n\n\n\nTable 11: Insects occurrence in study sites \n\n\n\nTypes of insects 1 0.83 0.66 0.49 0.32 0.15 Total  weight index rank  \n\n\n\nLeaf miner 0 17 28 15 0 20 80 42.94 0.536 II \n\n\n\nFruit fly 16 21 14 8 1 20 80 49.91 0.623 I \n\n\n\nAphid 1 4 9 22 24 20 80 31.72 0.396 IV \n\n\n\nStem borer 0 10 7 13 32 18 80 32.23 0.402 III \n\n\n\nlemon butterfly 1 4 8 23 24 20 80 31.55 0.394 V \n\n\n\n3.12 Problem of Insects  \n\n\n\nThe study revealed that high occurrence of fruit fly in the study area and \n\n\n\nhence ranked first. Similarly, leaf miner, stem borer, aphid and lemon \n\n\n\nbutterfly were ranked second, third, fourth and fifth respectively. \n\n\n\n4. CONCLUSION \n\n\n\nMajority of the respondents of mandarin growers were male, middle aged, \n\n\n\nbelonged to Janajati ethnic group, followed Hinduism, had medium sized \n\n\n\nfamily, had medium farm size, most of them are literate and agriculture \n\n\n\nwas the primary source of income. Majority of farmers had taken training.  \n\n\n\nMost of the respondents were in frequent contact with extension agent \n\n\n\nseeking the required information. Out of major ten improved \n\n\n\nmanagement, majority of respondents had adopted training and pruning \n\n\n\nwhile least adopted practices was micronutrient application. Majority of \n\n\n\nrespondents had low adoption on recommended management practices. \n\n\n\nGender, education level, training, land holding size, contact with extension \n\n\n\nagent had significant association with adoption. Major problems like \n\n\n\nirrigation, insect and disease, training, lack of labor and cost of input were \n\n\n\nencountered during the adoption of improved mandarin orchard \n\n\n\nmanagement practices \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWith immense pleasure, I feel privileged in expressing my deep sense of \n\n\n\ngratitude and indebtedness to, Prof .Dr. Jay Prakash Dutta, Dean; Prof. Dr. \n\n\n\nKalyani Mishra Tripathi, Assistant Dean (Academic); Prof. Dr. Arjun Kumar \n\n\n\nShrestha, planning Director; Asst. Prof. Krishna Prasad Thapaliya, \n\n\n\nDepartment of Rural Sociology and Development Studies, Faculty of \n\n\n\nAgriculture, AFU, Rampur and Pashupati Pokhrel, Directorate of \n\n\n\nAgriculture, Province no. 1. I would like to acknowledge all my colleagues, \n\n\n\nsenior and juniors who assisted me during different phases of the \n\n\n\nresearch. . I extent my special thanks to my seniors Susmita Subedi, Susma \n\n\n\nBanjara, Seema Karki, Susan Poudel and friends Vijay, Anisha, Sarita, \n\n\n\nDivya, Priyanka, Simana, Suraksha, Smirti, Susmita, Saraswati, Anupa, \n\n\n\nSamata , Siddhartha  for their continuous help, untiring cooperation and \n\n\n\nencouragement. I would like to remember my family who always there to \n\n\n\npush me forward in any circumstances. I extend my heartfelt thanks to my \n\n\n\nbrother Suraj Belbase for his guidance, support and continuous \n\n\n\nencouragement. My special thanks to all the sample farmers for their \n\n\n\ncooperation which was so crucial in the accomplishment of this task. I \n\n\n\nappreciate the contribution of each and everyone who has helped me in \n\n\n\nthis endeavor.  \n\n\n\nREFERENCES \n\n\n\nAdhikari, G., 2016/17. Reason for Citrus Orchard Decline and its \nmanagement (In Nepali). National Citrus Development Program. \n\n\n\nBanerjee, B.M., 2008. A Binary logit estimation of factors affecting \nadoption of GPS guidance systems by cotton producer. A Binary logit \nestimation of factors affecting adoption of GPS guidance systems by \ncotton, 345-355. \n\n\n\nBanerjee, S., Martin, S., Roberts, R., Larkin, S., Larson, J., Paxton, K., 2008. A \nbinary logit estimation of factors affecting adoption of GPS guidance \nsystem by cotton producers. Journal of Agriculture and Applied \nEconomics, 40 (1), 345-355. \n\n\n\nBose, T. 1985. Fruits Tropical and Subtropical. New Delhi, India: Naya \nUdyog. \n\n\n\nChattopadhayay, T., 2012. Introduction to horticulture. Kalyani publisher. \n\n\n\nChaudhary, R., Saran, P., Yadav, B., 2013. Adoption of improved production \ntechnology of mandarin in Rajasthan India, A review. Indian \nAgricultural Research Institute Regional Station, Pusa Samastipur \n(Bihar) India, 848. \n\n\n\nChoudhary, H., 2006. Knowledge and adoption of recommended kinnow \nproduction technology by farmers. Rajasthan agricultural university, \n50-55. \n\n\n\nCIMMYT. 1993. The Adoption of Agricultural Technologies. A guide for \nSurvey Design CIMMYT. \n\n\n\nDangol. 2004. extension education.  \n\n\n\nDhital, P., 2010. Factors affecting adoption of recommended technology of \ncauliflower production in Kavre district of Nepal. IAAS. \n\n\n\nDorji, K., Lakey, L., Chophel, S., Dorji, S., Tamang, B., 2016. Adoption of \nImproved citrus Orchard management practices, a microstudy from \nDrujegang growers. Dugana, Bhutan. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 49-53 \n\n\n\nCite the Article: Sarmila Belbase (2020). Study Of Improved Mandarin (Citrus Reticulate Blanco) Orchard Management Practices In Mid Hills Of Gandaki \nProvince, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 49-53. \n\n\n\nFAO. 2011. Training Manual for Combating Citrus Decline in Nepal. \nDepartment of Agriculture,Ministry of Agriculture and Cooperatives. \n\n\n\nFeder, G., Jus, E., Zilberman, D., 1990. Adoption of Agricultural Innovations \nin Developing Countries:A Survey. Economic Development and Cultural \nChange, 255-298. \n\n\n\nGautam, D.M., Bhattarai, D.R., 2006. Postharvest Horticulture. kathmandu, \nNepal: Heritage publishers and Distributors pvt. Ltd. \n\n\n\nMoAD. 2014. Statistical Information on Nepalese Agriculture 2013/14. \nSingha Durbar, Kathmandu, Nepal: Agribusiness Promotion and \nStatistics Division: Ministry of Agriculture Development. \n\n\n\nMwangi, M., Kariuki, S., 2016. Factors determining adoption of new \nagricultural technology by small holder farmers in developing \ncountries.  \n\n\n\nNCDP. (2015/16). A glimpse of annual program and statistics. Department \nof Agriculture, MoAD. \n\n\n\nNCRP. 2016. annual report. dhankuta: National Citrus Research Program, \nNepal Agriculture Research Council. \n\n\n\nNegash, R., 2010. Determinants of Adoption of Improved Haricot Bean \n\n\n\nProduction Package in Alba special Woreda, southern Ethiopia. Ethopia: \nDepartment of Rural Development and Agricultural Extension, School \nof Graduate Studies. Haramaya University. \n\n\n\nPaudyal, K.M., 2002. Citrus decline and its management in Nepal. Paripatle, \nDhankuta,Nepal: National Citrus Research Program, Nepal Agriculture \nResearch Council. \n\n\n\nPyakuryal, K., 1985. Role of support agencies in Agricultural production. \nkathmandu: Agricultural extension and training division, Department of \nAgriculture. \n\n\n\nReddy, M., Reddy, S., 1998. Relationship between selected characteristics \nof contact farmers and their knowledge and adoption of improved \npaddy cultivation practices. Indian Journal Extension Education, 24 (3-\n4), 40-41. \n\n\n\nSrivastava, A., Singh, S., 2002. Citrus climate and soil. India: international \nbook distributing company. \n\n\n\nYadav, B., 2006. A study of knowledge and adoption of improved \n\n\n\nproduction technology of mandarin by the farmers in Jhalrapatan \n\n\n\nPanchayat samiti of Jhalarwar district of Rajasthan. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 38-43 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2022.38.43 \n\n\n\n \nCite the Article: Jannatul Yeasmin Joaty, Md. Mamunur Rahman, Md. Ruhul Amin, Md. Arifur Rahman Khan  (2022). Fall Armyworm Outbreaks in Asia: Analyzing the \n\n\n\nStrategies to Control. Malaysian Journal of Sustainable Agriculture, 6(1): 38-43. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2022.38.43 \n\n\n\n\n\n\n\n\n\n\n\nFALL ARMYWORM OUTBREAKS IN ASIA: ANALYZING THE STRATEGIES TO \nCONTROL \n \nJannatul Yeasmin Joatya, Md. Mamunur Rahmana*, Md. Ruhul Amina, Md. Arifur Rahman Khanb \n\n\n\n \naDepartment of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur-1706, Bangladesh \nbDepartment of Agronomy, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur-1706, Bangladesh \n\n\n\n*Corresponding Author E-mail: mamunur111@gmail.com \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 02 August 2021 \nAccepted 15 September 2021 \nAvailable online 15 October 2021 \n\n\n\n Fall armyworm (Spodoptera frugiperda) is a Lepidopteran moth of Noctuidae family. Due to its polyphagous \ncharacteristic with a large host range, strong migration ability, high fecundity (average egg production per \nfemale is about 1500) and lack of diapause has already contributed to its invasiveness in America and Africa. \nRecently it has been introduced in Asia in the year 2018. Though it has been only around three years of Fall \narmyworm (FAW) introduction, it has already spread into many Asian countries and on the way to cause \nhavoc. Though it can infest many crops, its main target and maximum yield loss has occurred in case of Maize. \nIn America and Africa, farmers are already well known to this pest and have adapted themselves to reduce \ncrop loss to some extent by undertaking several management options. As this pest is completely new to Asia, \nfarmers do not know much about its biology, nature of damage and control measures. And their misdiagnosis \nof the pest leads to panic and increased crop loss. Therefore, it is very important to increase awareness among \nthe farmers to identify its attack on the right time to take suitable control measures as well as preventive \nmeasures for upcoming cropping seasons.  Some of the cultural, mechanical, biological and chemical control \nmeasures those were effective in reducing its infestation outside Asia, has also found to be effective inside \nAsia. Collaboration of these control measures according to the field condition is main concern for the \ncultivators. But the integrated pest management option alone can also help to keep FAW population much \nbelow economic injury level and prevent its invasiveness as a tool of sustainable management for ensuring \nfood security.  \n\n\n\nKEYWORDS \n\n\n\nFall armyworm, invasiveness, yield loss, control measures, Integrated Pest Management. \n\n\n\n1. INTRODUCTION \n\n\n\nFall Armyworm (FAW) (Spodoptera frugiperda) is a Lepidopteran crop \n\n\n\npest that has more than 80 host species and causes severe damage to \n\n\n\nmaize cereals. It is native to the tropical and subtropical region of America \n\n\n\nbut has rapidly spread worldwide. The larvae and adults of FAW damage \n\n\n\nyoung leaves, leaf whorls, tassels or cobs of maize. Under heavy infestation \n\n\n\nof FAW cause 50-80% yield loss in maize crop. This pest is capable of \n\n\n\nrapidly breeding, migrating and feeding on a large variety of host plants, \n\n\n\nmaking it very difficult to monitor (Adhikari et al., 2020). \n\n\n\nIt is considered as a super pest on the basis of its host range, its inherent \n\n\n\nability to survive in a wide range of habitats, its strong migration ability, \n\n\n\nhigh fecundity, rapid resistance development to insecticides/viruses, no \n\n\n\ndiapauses stage and its gluttonous characteristics. The inherently superior \n\n\n\nbiological characteristics of FAW contribute to its invasiveness (Jing et al., \n\n\n\n2021). \n\n\n\nTwo sympatric host-plant strains of FAW including the \u201ccorn-strain\u201d (C-\n\n\n\nstrain) that feed mostly on maize, cotton and sorghum and the \u201crice-strain\u201d \n\n\n\n(R-strain) that is mostly associated with rice and various pasture \n\n\n\ngrasseshave been identified (Nagoshi& Meagher, 2004). Among these \n\n\n\nstrains, the maize strain is most widespread and causes serious damage \n\n\n\nmainly to maize (Adhikari et al., 2020). Damage by FAW was detected in \n\n\n\ncentral and western Africa in early 2016 and it spread very quickly across \n\n\n\nall over within two years to more than 40 African countries due to \n\n\n\nunscientific, uncontrolled trade and is spreading rapidly in south Asian \n\n\n\ncountries since last two years in spite of scientists deep concern (Chhetri \n\n\n\n& Acharya, 2019). After causing serious damage of crops in Africa, it was \n\n\n\nfirst spotted in Asia from Karnataka, (India) in May 2018. As of March \n\n\n\n2020, it has spread to countries beyond South Asia to South East Asia and \n\n\n\neven found in China (2019), Thailand, Myanmar, Korea, Nepal, Japan, Sri \n\n\n\nLanka, Bangladesh (Alam et al., 2018; Jing et al., 2021; Lamsal et al., 2020). \n\n\n\nMaize (Zea mays) is one of the world\u2019s major cereal crops because- it has \n\n\n\nhigh importance as a staple food as well as it is also being used as animal \n\n\n\nfeed and fuel (Abebe et al., 2017). But it was observed the productivity of \n\n\n\nmaize is getting lower than its potential in recent years due to many biotic \n\n\n\nand environmental constraints. The major constraints are pests and \n\n\n\ndisease which reduces the production and yield of the crop. Many pests \n\n\n\nare directly responsible for the damage and reduction in yield of maize \n\n\n\n(Adhikari et al., 2020). FAW is considered as the most important and \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 38-43 \n\n\n\n\n\n\n\n \nCite the Article: Jannatul Yeasmin Joaty, Md. Mamunur Rahman, Md. Ruhul Amin, Md. Arifur Rahman Khan  (2022). Fall Armyworm Outbreaks in Asia: Analyzing the \n\n\n\nStrategies to Control. Malaysian Journal of Sustainable Agriculture, 6(1): 38-43. \n \n\n\n\n\n\n\n\ndevastating insect pest of maize crops causing serious damage in many \n\n\n\ncountries (Ayala et al., 2013). FAW directly affects capital costs due to \n\n\n\nincreased need of manpower and the type of knowledge required to \n\n\n\nhandle the pest, yield losses and higher financial costs of its control (Khan \n\n\n\net al., 2018). \n\n\n\nWhile destroying the crops, exotic pest species develop some negative \n\n\n\nimpact on environment and makes the pest management under practice \n\n\n\ninvalid. The Fall armyworm is already considered a major pest of maize in \n\n\n\nwestern hemisphere and it is an invasive pest in Asia. Their potential of \n\n\n\ncompeting and hybridization with other maize pest may cause devastating \n\n\n\nconsequences disturbing available pest management strategies. Already a \n\n\n\npesticide resistant inter strain hybrid was found in China that is an \n\n\n\nalarming issue for developing successful management techniques as well \n\n\n\nas a threat to ecosystem (Ayra-Pardo et al., 2021). Since it is practically \n\n\n\nimpossible to eradicate the pest now, it is essential to work on long term \n\n\n\nmanagement to keep pest population below economically injury level. \n\n\n\nReliance on chemical pesticides is only a temporary way of dealing with \n\n\n\nthe pest because FAW is becoming tolerant to many insecticides and \n\n\n\ndifficulty is increasing in finding and surveying field infestation with \n\n\n\nsimple protocols. Educating the farmers about the pest and practicing \n\n\n\nintegrated approach of management compatible and feasible in the region \n\n\n\nwould be more sustainable. Identification and use of native species of \n\n\n\nnatural enemies, such as predators, parasites and parasitoids is the \n\n\n\ncurrent need of research (Chhetri & Acharya, 2019; Lamsal et al., 2020). \n\n\n\nThis pest is declared as invasive species in many regions of the world and \n\n\n\nfarmers are facing difficulties to control it as it is new to these regions. If \n\n\n\nnot controlled properly, it may spread all over the world causing severe \n\n\n\nfood insecurity. The objective of this review is to provide an insight of the \n\n\n\nspread of FAW referring its biology and invasiveness, and the adopted \n\n\n\nstrategies for managing the pest incidence in Asian countries leading to \n\n\n\nthe means of sustainable management. \n\n\n\n2. METHODOLOGY \n\n\n\nThis review article synthesized from secondary data of different literature \n\n\n\nrelated with Fall armyworm outbreak and its management techniques. \n\n\n\nThe information was collected from various journals, research papers, \n\n\n\nbooks, articles and the findings were summarized and arranged in texts, \n\n\n\ntable along with conclusion. \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1 Outbreak of FAW in different region \n\n\n\nFAW was first observed in tropical and subtropical America. First it was \n\n\n\nrecorded in Georgia of the United States in the 1797. Afterwards it spread \n\n\n\ninto Africa in the year 2016, followed by Asia in 2018. If not controlled \n\n\n\nproperly, the consequences can be more devastating. Its occurrence flow \n\n\n\nfrom Africa to Asia is given below (Figure 1). \n\n\n\n\n\n\n\nFigure 1: Invasion and outbreak of fall armyworm (FAW) from Africa to \n\n\n\nAsiabyJing et al., 2021). \n\n\n\nAfter being introduced in Asia, it is spreading rapidly throughout Asian \n\n\n\ncountries. Though the maximum numbers of countries are infested in \n\n\n\nAfrica, Asian countries are in second position (Table 1). \n\n\n\nTable 1: Global distribution of Fall Armyworm by Adhikary et al., \n\n\n\n2020 \n\n\n\nContinent Total number of countries per continent \n\n\n\nAfrica 54 \n\n\n\nAsia 48 \n\n\n\nEurope 44 \n\n\n\nNorth America 23 \n\n\n\nOceania 14 \n\n\n\nSouth America 13 \n\n\n\nTill now, data from papers indicate that, Pakistan and Afghanistan do not \n\n\n\nhave official report of infestation of FAW but other nearby Asian countries \n\n\n\nlike China, Myanmar, Bangladesh, Thailand, Vietnam, Malaysia, Japan and \n\n\n\nIndonesia have already confirmed cases of FAW in their country (Figure \n\n\n\n2). \n\n\n\n\n\n\n\nFigure 2: Distribution of FAW in South Asia by Lamsal et al., 2020) \n\n\n\n3.2 Biology of FAW \n\n\n\nFAW has a wide host range of more than 353 recorded plant species (Jing \n\n\n\net al., 2021). FAW has very high migratory ability, over 100 km /hr \n\n\n\n(Tendeng et al., 2019).  Average egg production per female FAW is about \n\n\n\n1500 and they don\u2019t have the diapausing ability (Prasanna et al., 2018). \n\n\n\nAccording to Ashley et al., (1980) FAW is very much similar to the true \n\n\n\narmyworm. But FAW larvae can be distinguished from armyworm by 4 \n\n\n\ndark spots in 8th abdominal segment and an inverted \u2018Y\u2019 sign on its head. \n\n\n\nIn case of adults, grayish brown forewing with light and dark splotches can \n\n\n\nbe seen in the males, whereas noticeable spot near the end of forewing and \n\n\n\niridescent silver white with thin dark border in hind wing is the identifying \n\n\n\ncharacteristic of females (Figure 3 and Figure 4). \n\n\n\n\n\n\n\nFigure 3: FAW larval identification marks (Prasanna et al., 2018) \n\n\n\n\n\n\n\nFigure 4: Adult male (left) and female (right) of FAW (Lamsal et al., \n\n\n\n2020) \n\n\n\n3.2.1 Suitable environment for FAW \n\n\n\nThe suitable environment for FAW survival and multiplication includes- \n\n\n\nwarm and humid temperature with heavy rainfall, and temperature below \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 38-43 \n\n\n\n\n\n\n\n \nCite the Article: Jannatul Yeasmin Joaty, Md. Mamunur Rahman, Md. Ruhul Amin, Md. Arifur Rahman Khan  (2022). Fall Armyworm Outbreaks in Asia: Analyzing the \n\n\n\nStrategies to Control. Malaysian Journal of Sustainable Agriculture, 6(1): 38-43. \n \n\n\n\n\n\n\n\n10\u2070C inhibits its growth and development. It completes its 4 life cycles- \n\n\n\negg, larval instars, pupae and adult stages within 1, 2 and 3 months in \n\n\n\nsummer, spring & autumn, and winter season respectively (Reinert \n\n\n\n&Engelke, 2010). Its life cycle includes egg (2\u20133 days), larvae (total six \n\n\n\ninstars, 13\u201314 days), pupae (7\u20138 days) and adults (7\u201321 days). FAW has a \n\n\n\ngeneration time of approximately 30\u201340 days during the warm summer \n\n\n\nmonths (daily temperature of ~28\u00baC), and approximately 55 days in \n\n\n\ncooler temperatures (Prasanna et al., 2018; Sharanabasappa et al., 2018). \n\n\n\nSo prolonged summer season in Asia helps FAW to develop its population \n\n\n\nmore rapidly. \n\n\n\nThe number of generations produced in an endemic area depends mainly \n\n\n\non environmental conditions, e.g., temperatures and host plants (Prasanna \n\n\n\net al., 2018). \n\n\n\n3.2.2 Nature of Damage to crops \n\n\n\nPrimary symptoms of FAW appear as small holes and window pan feeding \n\n\n\nthat is similar to other stem borers. The early stages of instars feed on \n\n\n\nleaves and late stages on tassel and ears of maize plants. Windowing and \n\n\n\nsaw dust like fecal matter on the base of upper young leaves indicate FAW \n\n\n\nlarval feeding; leaves become skeletonized during vegetative stage of \n\n\n\nmaize. Vigorous feeding by larvae in young plants can kill growing point \n\n\n\nand lead to \u201cdead heart\u201d symptom and in the reproductive stage of plant, \n\n\n\nlarval feeding cause injury to the growing cob that influences further \n\n\n\ndevelopment of plants and induce yield damage (Bateman et al., 2018; \n\n\n\nDeole & Paul, 2018; Kumela et al., 2019). \n\n\n\nThis pest can destroy the crop almost overnight, because the early stages \n\n\n\nof FAW caterpillar consume very little food, but the later stages require \n\n\n\nabout 50 times more food. As their food consumption changes rapidly, it is \n\n\n\ndifficult to notice the presence of larvae until they have destroyed almost \n\n\n\neverything within a night (Alam et al., 2018). (Figure 5). \n\n\n\n\n\n\n\nFigure 5: Windowing of leaves (left), ear feeding (middle) and moist \n\n\n\nfrass (right) in the feeding area (Lamsal et al., 2020) \n\n\n\n3.3 Control strategies for FAW \n\n\n\nWorldwide, several crops are infested by FAW, hence several control \n\n\n\nmeasure strategies have been developed in different regions. In Asia, \n\n\n\ncultural, physiological, biological, chemical control measures are followed \n\n\n\nby farmers. But approach to Integrated Pest Management (IPM) is also \n\n\n\npreferred in some countries. Some of the techniques that are used to \n\n\n\ncontrol FAW in Asia are discussed below: \n\n\n\n3.3.1 Cultural control measures against FAW \n\n\n\n3.3.1.1 Adjusting planting time \n\n\n\nSeveral studies show that, the infestation during late season can be \n\n\n\nreduced by planting early maturing variety and avoiding late season \n\n\n\nplanting with staggered planting (planting in same field at different times) \n\n\n\n(Chhetri & Acharya, 2019). \n\n\n\n3.3.1.2 Seed and variety \n\n\n\nSeeds contain genetic information of a plant. Growth and development of \n\n\n\nplant depends mainly on three factors- climate, soil condition and \n\n\n\ngenotype of seed. Some of the plants are naturally genetically resistant to \n\n\n\nsome disease and pests. Development of transgenic variety using gene of \n\n\n\nBacillus thuringiensis produce crystal like protein (Cry protein) can make \n\n\n\nthe plant resistant against some specific insect species including FAW \n\n\n\n(Chhetri & Acharya, 2019). It was found through Laboratory bioassay that, \n\n\n\ninsects invading China are resistant to organophosphate and pyrethroid \n\n\n\npesticides but are sensitive to genetically modified maize containing the \n\n\n\nBt toxin Cry1Ab in field experiments (Zhang et al., 2020). \n\n\n\n\n\n\n\n3.3.1.3 Management of crop residues \n\n\n\nCrop residues used as mulching are the clothes of soil that help in \n\n\n\nmaintaining soil temperature, soil biological activity and ultimately humus \n\n\n\nformation i.e. immune system of soil. Thus, managing the crop residues \n\n\n\npromote ultimately better plant health, and plants having better health has \n\n\n\nhigher resistibility power that can resist the adverse condition either \n\n\n\nclimatic or biological (Chhetri & Acharya, 2019). \n\n\n\n3.3.1.4 Soil health and adequate moisture \n\n\n\nDevelopment of resistance power comes genetically and from the humus \n\n\n\ncontain in soil to plant. Mulching is the process that promotes the humus \n\n\n\nformation, whereas excessive inorganic fertilizer especially nitrogen \n\n\n\ndecreases the resistibility and makes the plant susceptible to pest and \n\n\n\ndisease. Adequate moisture contains promote the physiological activity of \n\n\n\nplants ultimately plants become strong (Chhetri & Acharya, 2019). \n\n\n\nFAW doesn\u2019t damage to whole plant causes significant reduction in yield \n\n\n\nup to only 20% that can be avoided and removed if there is good plant \n\n\n\nnutrition and moisture (Baudron et al., 2019). \n\n\n\n3.3.1.5 Intercropping of maize \n\n\n\nMaize intercropping with legume was found to be more effective \n\n\n\ncompared to maize monocropping in combating FAW. If the intercrop is \n\n\n\nlegume, it advances maize by fixing nitrogen in soil thereby increasing \n\n\n\ncompensating capacity against foliar damage (Lamsal et al., 2020). The \n\n\n\nlarvae of FAW usually shift from maize to sugarcane after 40 to 50 days, so \n\n\n\nintercropping of maize with sugarcane should be avoided for blocking the \n\n\n\nmultiplication of further generations (Chormule et al., 2019). Some of the \n\n\n\nbeneficial intercropping combination of maize with legume has been \n\n\n\nfound to be effective against FAW. \n\n\n\n1. Maize + Napier (Border intercrop) (Midega et al., 2018). \n\n\n\n2. Maize + Bean (Phaseolus vulgaris L.) (Row intercrop) reduces FAW \n\n\n\nattack up to 40% due to confusion (Midega et al., 2018). \n\n\n\n3.3.2 Agroecological approach for controlling FAW \n\n\n\nAgro-ecological approaches offer culturally appropriate and low-cost pest \n\n\n\ncontrol strategies that can be readily integrated into existing efforts to \n\n\n\nimprove smallholder incomes and resilience through sustainable \n\n\n\nintensification. Three agroecological measures can be taken to reduce \n\n\n\nFAW in the long run- (i) sustainable soil fertility management to maintain \n\n\n\nor restore soil organic carbon; (ii) intercropping with appropriately \n\n\n\nselected companion plants; and (iii) diversification of farm environment \n\n\n\nthrough management of (semi)natural habitats at multiple spatial scales \n\n\n\n(Harrison et al., 2019). \n\n\n\nPush-pull is considered a more suitable and cost effective agroecological \n\n\n\ntechnology where mainly three approach is used together -1) Use of trap \n\n\n\nplants (pull) such as Napier grass or Brachiria grass for attracting pests, 2) \n\n\n\nUsing a repellent intercrop (push) such as Desmodium, to drive away the \n\n\n\npest from main crop, 3) Attracting parasitoids and predators to the field. \n\n\n\nUsing this method was found to be effective in reducing average number \n\n\n\nof larvae per plant by 82.7% and plant damage per plot by 86.7% in Africa \n\n\n\nand thus is well suited for the intensification of agro-ecosystem of \n\n\n\nsmallholder mixed farming systems (Khan et al., 2018; Midega et al., 2018) \n\n\n\n(Figure 6). \n\n\n\n\n\n\n\nFigure 6: Push-pull technology to control FAW (Khan et al., 2018) \n\n\n\n3.3.3 Mechanical control measure for FAW \n\n\n\nFor smallholder farmers it is feasible to crush young larvae and egg masses \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 38-43 \n\n\n\n\n\n\n\n \nCite the Article: Jannatul Yeasmin Joaty, Md. Mamunur Rahman, Md. Ruhul Amin, Md. Arifur Rahman Khan  (2022). Fall Armyworm Outbreaks in Asia: Analyzing the \n\n\n\nStrategies to Control. Malaysian Journal of Sustainable Agriculture, 6(1): 38-43. \n \n\n\n\n\n\n\n\nbefore they hatch. Many farmers in Africa have successfully managed FAW \n\n\n\nby using ash, sand, sawdust and even soil into whorls to desiccate young \n\n\n\nlarvae. Immature leaves are more vulnerable to early infestation and are \n\n\n\nmore likely to be seen with cluster of egg masses. Therefore, finding and \n\n\n\ndestroying these egg masses at the earliest will bring down the active pest \n\n\n\npopulation below economic injury levels (Lamsal et al., 2020) \n\n\n\n3.3.4 Use of botanicals against FAW \n\n\n\nSome botanicalscan be used as plant derived pesticides that display good \n\n\n\nperformance in insecticidal activity. These products show diverse \n\n\n\nbiological activities that result in high mortality, extended larval duration, \n\n\n\ndecreased pupal weight, insecticidal effects, growth inhibition, antifeedant \n\n\n\neffects, reduced fecundity, as well as sublethal and acute toxicity against \n\n\n\nseveral pests (Jing et al., 2021). \n\n\n\nThe opportunities and scope for botanical extracts and products for the \n\n\n\nmanagement of FAW in Africa were reviewed by Rioba & Stevenson \n\n\n\n(2020), and they summarized the efficiency and potential of 69 plant \n\n\n\nspecies from 31 families including Azadirachta indica, Phytolacca \n\n\n\ndodecandra and Schinnus molle. In China, indoor toxicity and control effect \n\n\n\nof Azadirachtin in a maize field for FAW has been estimated and \n\n\n\nAzadirachtin was found to have good toxicity and antifeedant activity on \n\n\n\nFAW, and the highest control effect was seen at seven days after treatment \n\n\n\n(Lin et al. 2020). Efficacy of Nicotiana tabacum and Lippia javanica was \n\n\n\nreported to cause up to 66% larval mortality in maize (Phambala et al., \n\n\n\n2020). \n\n\n\nBotanical extracts of pesticidal plants do not produce mortality rates as \n\n\n\nhigher as synthetic pesticides but they can be used as an independent \n\n\n\ncomponent of sustainable pest management approach.So, there is an \n\n\n\nimmense opportunity to identify and use locally available pesticidal plant \n\n\n\nspecies as an alternative to synthetic pesticides (Lamsal et al., 2020). \n\n\n\n3.3.5 Biological control \n\n\n\nBiological control can reduce environmental contamination and offer an \n\n\n\neconomically and environmentally safer alternative to synthetic \n\n\n\ninsecticides that are currently being used. Natural enemies include \n\n\n\nparasites/parasitoids, predators and entomopathogens (Jing et al., 2021). \n\n\n\n3.3.5.1 Parasitoids \n\n\n\nParasitoids are the organisms that can kill their host and are being used as \n\n\n\na natural bio control agent; they lay eggs on the egg masses, larvae or adult \n\n\n\nof FAW and destroy their host by taking nutrition and multiplying inside \n\n\n\nthem. (Lamsal et al., 2020). Recently some of the parasitoids were found \n\n\n\neffective in controlling FAW in different regions. \n\n\n\n1. Telenomus remus (Nixon)- egg parasitoid. Observed parasitism rates \n\n\n\nranged up to 69.3%. (Lamsal et al., 2020) \n\n\n\n2. Cotesia icipe -larval parasitoid. Observed parasitism rates ranged up to \n\n\n\n42% (Lamsal et al., 2020). \n\n\n\nStudies in southern India recorded five species of larval parasitoids \n\n\n\nagainst FAW: Coccygidium melleum, Campoleti schlorideae, Eriborus sp., \n\n\n\nExorista sorbillans, and Odontepyris sp. (Sharanabasappa et al., 2019). \n\n\n\n3.3.5.2 Predators \n\n\n\nPredator insects usually feed upon different stages of their hosts. (Table \n\n\n\n2). \n\n\n\nTable 2: Predators of different stages of FAW \n\n\n\nPredators Stages of FAW \n\n\n\nLadybird beetle Both larvae and adult (Chhetri & Acharya, \n\n\n\n2019). \n\n\n\nEarwig Young caterpillar (Chhetri & Acharya, \n\n\n\n2019). \n\n\n\nAnt Young caterpillar (Chhetri & Acharya, \n\n\n\n2019). \n\n\n\nCalosoma granalatum Young caterpillar (Prasanna et al., 2018). \n\n\n\nPicromerus lewisi & \n\n\n\nArma chinensis \n\n\n\n6th instar larvae of FAW (Tang et al., \n\n\n\n2019a, b). \n\n\n\n3.3.5.3 Entomopathogen \n\n\n\nPathogen like bacteria, fungi and virus affect the yield of the crop but some \n\n\n\nmicroorganisms are beneficial to farmers (Chhetri & Acharya, 2019). \n\n\n\nDifferent groups of entomopathogens have been identified by researchers \n\n\n\nthat infect FAW (Table 3). \n\n\n\nTable 3: Different groups of entomopathogens against FAW by \nChhetri & Acharya, 2019 \n\n\n\nPathogenic group Pathogens \n\n\n\nVirus Nucleo polyhidroxy virus \n\n\n\nFungi \n \n\n\n\nMetarhizium anisopilae \n\n\n\nMetarhizium rileyi \n\n\n\nBacteria Bacillus thuringiensis \n\n\n\n3.3.6 Monitoring and scouting to prevent FAW \n\n\n\nFor migratory invasive insects, monitoring and scouting are very \n\n\n\nimportant for timely responses to the pest population dynamics of pest \n\n\n\noccurrence, development and crop health. This enables the formulation of \n\n\n\ncomprehensive measures for better prevention and control. These actions \n\n\n\nmust be taken based upon cost ratios to keep the FAW population below \n\n\n\nthe economic threshold level (Jing et al., 2021). \n\n\n\nMonitoring using vertical-pointing search light traps showed that, in eight \n\n\n\nprovinces of China in 2019, the FAW population was first trapped in June \n\n\n\nand the observation peaks appeared from August to October (Jiang et al., \n\n\n\n2020). The blacklight trap and commercial male traps are recommended \n\n\n\nto farmers to monitor the field population dynamics of FAW. The \n\n\n\nrecommended height of pheromone traps is- 1.5 m above ground and the \n\n\n\ninterval between two traps should be 50 m (Malo et al., 2013). FAW \n\n\n\npheromone trap has been used for pest monitoring, mass-trapping, and \n\n\n\ninterruption in mating in different regions and was recommended in Nepal \n\n\n\n(Bhusal& Bhattarai, 2019). To record the presence of FAW in Bangladesh, \n\n\n\nmonitoring has already been started in cabbage & maize (Alam et al., \n\n\n\n2018). \n\n\n\nFarmers are recommended to scout the different plant growth stages and \n\n\n\ncrop damage to determine the optimum stages for spraying insecticides \n\n\n\nbased on action thresholds, which are expressed as percentages of plants \n\n\n\nwith typical FAW damage/injury symptoms. For the early whorl stage, \n\n\n\nfrom vegetative emergence (VE) to 6-leaf (V6) stages, the action threshold \n\n\n\nis 10\u201330% of the seedlings infested as well as the tassel and silk stages, \n\n\n\nwhile it is 30\u201350% for the late whorl stage (Prasanna et al., 2018). \n\n\n\n3.3.7 Chemical control \n\n\n\nSynthetic pesticides can only be regarded as an emergency measure to \n\n\n\ncontrol FAW outbreaks. Three groups of pesticide- emamectin benzoate, \n\n\n\nspinosad and chlorantraniliprole has been recommended by Indian \n\n\n\nInstitute of Maize Research (ICAR) against FAW. As FAW larvae stays \n\n\n\ninside the whorl of maize leaves during daytime and comes out only at \n\n\n\nnight, thus it is suggested to use the pesticide at dusk to ensure larval \n\n\n\ncontact with pesticide while coming out and make the pesticidal \n\n\n\napplication more effective. However, using pesticide in the reproductive \n\n\n\nstage of maize plants will not be effective as damage to tassel cannot affect \n\n\n\nthe yield and in that stage, damage to ear is almost inevitable as larvae \n\n\n\nkeeps hiding inside the ears. But chemical control of FAW in combination \n\n\n\nwith handpicking of larvae by close observation is said to be more \n\n\n\neffective. On the other hand, in South Asia, farmers mostly apply pesticides \n\n\n\nin the field without any personal protection, so use of synthetic pesticides \n\n\n\nagainst FAW can lead to massive health hazard in farmers. However, it is \n\n\n\nessential to train and advise farmers about rational use of pesticides to \n\n\n\nprevent any negative impacts on human health and environment. Another \n\n\n\nproblem is that majority of farmers in South Asia are small holders and \n\n\n\nchemical control of FAW might not be affordable to all unless governments \n\n\n\nsubsidy. So, it is essential that South Asian farmers do not exclusively rely \n\n\n\non synthetic chemicals for long term (Lamsal et al., 2020). \n\n\n\nSome other pesticides are also used in different regions of Asia to control \n\n\n\nFAW (Table 4). \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 38-43 \n\n\n\n\n\n\n\n \nCite the Article: Jannatul Yeasmin Joaty, Md. Mamunur Rahman, Md. Ruhul Amin, Md. Arifur Rahman Khan  (2022). Fall Armyworm Outbreaks in Asia: Analyzing the \n\n\n\nStrategies to Control. Malaysian Journal of Sustainable Agriculture, 6(1): 38-43. \n \n\n\n\n\n\n\n\nTable 4: Chemical insecticides used against the fall armyworm by Jing \n\n\n\net al., 2021 \n\n\n\nActive ingredient Active ingredient  \n\n\n\nAcephate Fenitrothion \n\n\n\nCartap Hexaflumuron  \n\n\n\nCyfluthrin Indoxacarb  \n\n\n\nCyantraniliprole Lufenuron \n\n\n\nChlorfenapyr Lambda-cyhalothrin \n\n\n\nDeltamethrin Tetrachlorantraniliprole \n\n\n\n3.3.8 Integrated Pest Management (IPM) approach to control FAW \n\n\n\nIPM involves use of several pest management strategies in a combination \n\n\n\nat a time so as to keep pest population below economic injury level without \n\n\n\ncausing any negative effect on soil health and environment. IPM practices \n\n\n\ninvolve not only curative measures, but also prophylactic measures \n\n\n\nadopted before the occurrence of infestation (Lamsal et al., 2020). For \n\n\n\nsmallholders, IPM acts as a series of low-cost agricultural control \n\n\n\nmeasures and is an optimum option to implement as part of an effective \n\n\n\ncontrol strategy against FAW. IPM approaches use the complex \n\n\n\ninteractions between organisms and their environment to develop \n\n\n\ntechniques to minimize the damage of crops by pests (Jing et al., 2021). \n\n\n\nScientists have suggested some IPM approaches to be taken in Asia for \n\n\n\nsuccessfully minimizing the FAW population. These are given below: \n\n\n\n1. Traditional pre-planting, using some measures such as deep ploughing \n\n\n\nbefore sowing can decrease the FAW population by exposing pupae to \n\n\n\nsunlight and predatory birds (Prasanna et al., 2018). \n\n\n\n2. Planting transgenic/ Bt insect-resistant maize varieties is also a very \n\n\n\neffective measure to decrease the damage by FAW and is an alternative \n\n\n\nmethod to pesticides (Jing et al., 2021). \n\n\n\n3. Use of mechanical methods like hand picking, light traps and pheromone \n\n\n\nlures could be an option for monitoring and controlling the pest for small \n\n\n\nscale farmers (Bhusal& Bhattarai, 2019). \n\n\n\n4. Use of intercropping of the legumes with maize and use of the \u201cpush and \n\n\n\npull\u201d strategy should be introduced among the farmers with awareness of \n\n\n\ncontrolling the FAW (Bhusal & Bhattarai, 2019; Jing et al., 2021). \n\n\n\n5.The synthetic chemical should be avoided as possible but should be used \n\n\n\nin severe damage more than 50% (Chhetri & Acharya, 2019). \n\n\n\n4. CONCLUSION \n\n\n\nThe invasiveness of Fall armyworm is already a matter of concern in \n\n\n\nworldwide. Spread of this pest is occurring quite fast from one country to \n\n\n\nanother despite of taking several control measures. Though it was first \n\n\n\nintroduced within Asia continent in India, it has already spread to a \n\n\n\nconsiderable number of Asian countries and FAW has the ability to spread \n\n\n\nall over the continent. It is clear from the study that the biology of FAW \n\n\n\nthat includes high fecundity, wide host range, lack of diapauses, short \n\n\n\ngeneration period and long-distance migration ability etc. are the main \n\n\n\ncontributing factors to its invasiveness. Although different control \n\n\n\nstrategies have been developed, their use should be coordinated with pest \n\n\n\noccurrence and level of damage. Instead of using cultural, mechanical, \n\n\n\nchemical or biological approaches alone, IPM approach should be followed \n\n\n\nby farmers to control FAW population in the long run as it is a complete \n\n\n\npackage for managing any pest to keep the crop loss below economic \n\n\n\nthreshold level. IPM technique will also save the cost of farmer as well as \n\n\n\nhelp to maintain sustainable agriculture. \n\n\n\nREFERENCES \n\n\n\nAbebe, Z., Dabala, C., Birhanu, T. 2017. Research Article System \n\n\n\nProductivity as Influenced by Varieties and Temporal Arrangement of \n\n\n\nBean in Maize-climbing Bean Intercropping. \n\n\n\nAdhikari, K., Bhandari, S., Dhakal, L., Shrestha, J. 2020. Fall armyworm \n\n\n\n(Spodoptera frugiperda): A threat in crop production in Africa and \n\n\n\nAsia. Peruvian Journal of Agronomy, 4(3), 121-133. \n\n\n\nAlam, S., Sarker, D., Pradhan, M., Rashid, M. 2018. First report of \n\n\n\noccurrence of Fall armyworm Spodoptera frugiperda in \n\n\n\nBangladesh. Bangladesh J. Entomol, 28(1), 97-101. \n\n\n\nAshley, T. R., Mitchell, E. R., Leppla, N. C., Grissell, E. E. 1980. Parasites \n\n\n\nattacking fall armyworm larvae, Spodoptera frugiperda, in late planted \n\n\n\nfield corn. Florida Entomologist, 136-142. \n\n\n\nAyala, O. R., Navarro, F., Virla, E. G. 2013. Evaluation of the attack rates and \n\n\n\nlevel of damages by the fall armyworm, Spodoptera frugiperda \n\n\n\n(Lepidoptera: Noctuidae), affecting corn-crops in the northeast of \n\n\n\nArgentina. Revista de la Facultad de Ciencias Agrarias, 45(2), 1-12. \n\n\n\nAyra-Pardo, C., Huang, S., Kan, Y., Wright, D. J. 2021. Impact of invasive fall \n\n\n\narmyworm on plant and arthropod communities and implications for \n\n\n\ncrop protection. International Journal of Pest Management, 1-12. \n\n\n\nBateman, M. L., Day, R. K., Luke, B., Edgington, S., Kuhlmann, U., Cock, M. J. \n\n\n\n2018. Assessment of potential biopesticide options for managing fall \n\n\n\narmyworm (Spodoptera frugiperda) in Africa. Journal of Applied \n\n\n\nEntomology, 142(9), 805-819. \n\n\n\nBaudron, F., Zaman-Allah, M. A., Chaipa, I., Chari, N., Chinwada, P. 2019. \n\n\n\nUnderstanding the factors influencing fall armyworm (Spodoptera \n\n\n\nfrugiperda JE Smith) damage in African smallholder maize fields and \n\n\n\nquantifying its impact on yield. A case study in Eastern Zimbabwe. Crop \n\n\n\nProtection, 120, 141-150. \n\n\n\nBhusal, K., Bhattarai, K. 2019. A review on fall armyworm (Spodoptera \n\n\n\nfrugiperda) and its possible management options in Nepal. Journal of \n\n\n\nEntomology and Zoology Studies, 7(4), 1289-1292. \n\n\n\nChhetri, L. B., Acharya, B. 2019. Fall armyworm (Spodoptera frugiperda): A \n\n\n\nthreat to food security for south Asian country: Control and \n\n\n\nmanagement options: A review. Farming & Management, 4(1), 38-44. \n\n\n\nChormule, A., Shejawal, N., Sharanabasappa, C. M., Asokan, R., Swamy, H. \n\n\n\nM. 2019. First report of the fall Armyworm, Spodoptera frugiperda (JE \n\n\n\nSmith) (Lepidoptera, Noctuidae) on sugarcane and other crops from \n\n\n\nMaharashtra, India. J. Entomol. Zool. Stud, 7(1), 114-117. \n\n\n\nDeole, S., Paul, N. 2018. First report of fall army worm, Spodoptera \n\n\n\nfrugiperda (JE Smith), their nature of damage and biology on maize crop \n\n\n\nat Raipur, Chhattisgarh. Journal of Entomology and Zoology \n\n\n\nStudies, 6(6), 219-221. \n\n\n\nHarrison, R. D., Thierfelder, C., Baudron, F., Chinwada, P., Midega, C., \n\n\n\nSchaffner, U., van den Berg, J. 2019. Agro-ecological options for fall \n\n\n\narmyworm (Spodoptera frugiperda JE Smith) management: Providing \n\n\n\nlow-cost, smallholder friendly solutions to an invasive pest. Journal of \n\n\n\nEnvironmental Management, 243, 318-330. \n\n\n\nJiang, Y. Y., Liu, J., Yang, J. J., Zhao, W. X., Yin, L., Liu, Y., Song, L. D. 2020. \n\n\n\nTrapping effect of searchlight-trap and light trap for the moth of \n\n\n\nSpodoptera frugiperda in 2019. Plant Protection, 46, 118-122. \n\n\n\nJing, W. A. N., Huang, C., Li, C. Y., Zhou, H. X., Ren, Y. L., Li, Z. Y., Wan, F. H. \n\n\n\n2021. Biology, invasion and management of the agricultural invader: Fall \n\n\n\narmyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae). Journal of \n\n\n\nIntegrative Agriculture, 20(3), 646-663. \n\n\n\nKhan, Z. R., Pittchar, J. O., Midega, C. A., Pickett, J. A. 2018. Push-pull farming \n\n\n\nsystem controls fall armyworm: lessons from Africa. Outlooks on Pest \n\n\n\nManagement, 29(5), 220-224. \n\n\n\nKumela, T., Simiyu, J., Sisay, B., Likhayo, P., Mendesil, E., Gohole, L., Tefera, \n\n\n\nT. 2019. Farmers' knowledge, perceptions, and management practices \n\n\n\nof the new invasive pest, fall armyworm (Spodoptera frugiperda) in \n\n\n\nEthiopia and Kenya. International Journal of Pest Management, 65(1), 1-\n\n\n\n9. \n\n\n\nLamsal, S., Sibi, S., Yadav, S. 2020. Fall Armyworm in South Asia: Threats \n\n\n\nand Management. Asian Journal of Advances in Agricultural Research, \n\n\n\n21-34. \n\n\n\nLin, S., Liu, K., Wang, R., Liu, B., Zhang, Y., Wu, J., Zhang, Z. 2020. Indoor \n\n\n\ntoxicity of azadirachtin to Spodoptera frugiperda and its control effect in \n\n\n\nfield. Journal of South China Agricultural University, 41, 22-27. \n\n\n\nMalo, E. A., Rojas, J. C., Gago, R., Guerrero, \u00c1. 2013. Inhibition of the \n\n\n\nresponses to sex pheromone of the fall armyworm, Spodoptera \n\n\n\nfrugiperda. Journal of Insect Science, 13(1), 134. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 38-43 \n\n\n\n\n\n\n\n \nCite the Article: Jannatul Yeasmin Joaty, Md. Mamunur Rahman, Md. Ruhul Amin, Md. Arifur Rahman Khan  (2022). Fall Armyworm Outbreaks in Asia: Analyzing the \n\n\n\nStrategies to Control. Malaysian Journal of Sustainable Agriculture, 6(1): 38-43. \n \n\n\n\n\n\n\n\nMidega, C. A., Pittchar, J. O., Pickett, J. A., Hailu, G. W., Khan, Z. R. 2018. A \n\n\n\nclimate-adapted push-pull system effectively controls fall armyworm, \n\n\n\nSpodoptera frugiperda (JE Smith), in maize in East Africa. Crop \n\n\n\nprotection, 105, 10-15. \n\n\n\nNagoshi, R. N., Meagher, R. L. 2004. Behavior and distribution of the two \n\n\n\nfall armyworm host strains in Florida. Florida Entomologist, 87(4), 440-\n\n\n\n449. \n\n\n\nPhambala, K., Tembo, Y., Kabambe, V. H., Stevenson, P. C., Belmain, S. R. \n\n\n\n2020. Bioactivity of common pesticidal plants on fall armyworm larvae \n\n\n\n(Spodoptera frugiperda). Plants, 9(1), 112. \n\n\n\nPrasanna, B. M., Huesing, J. E., Eddy, R., Peschke, V. M. 2018. Fall armyworm \n\n\n\nin Africa: a guide for integrated pest management. USAID CIMMYT, 7, \n\n\n\n109. \n\n\n\nReinert, J. A., Engelke, M. C. 2010. Resistance in zoysiagrass (Zoysia spp.) \n\n\n\nto the fall armyworm (Spodoptera frugiperda) (Lepidoptera: \n\n\n\nNoctuidae). Florida Entomologist, 93(2), 254-259. \n\n\n\nRioba, N. B., Stevenson, P. C. 2020. Opportunities and scope for botanical \n\n\n\nextracts and products for the management of fall armyworm \n\n\n\n(Spodoptera frugiperda) for smallholders in Africa. Plants, 9(2), 207. \n\n\n\nSharanabasappa, S., Kalleshwaraswamy, C. M., Poorani, J., Maruthi, M. S., \n\n\n\nPavithra, H. B., Diraviam, J. 2019. Natural enemies of Spodoptera \n\n\n\nfrugiperda (JE Smith) (Lepidoptera: Noctuidae), a recent invasive pest \n\n\n\non maize in South India. The Florida Entomologist, 102(3), 619-623. \n\n\n\nTang, Y. T., Li, Y. Y., Liu, C. X., Mao, J. J., Chen, H. Y., Zhang, L. S., Wang, M. Q. \n\n\n\n2019. Predation and Behavior of Armachinensis to Spodoptera \n\n\n\nfrugiperda. Plant Prot, 45, 65-68. \n\n\n\nTang, Y., Wang, M., Chen, H., Wang, Y., Zhang, H., Chen, F., Zhang, L. 2019. \n\n\n\nPredatory capacity and behavior of Picromeruslewisi Scott against \n\n\n\nSpodoptera frugiperda higher instar larvae. Chinese Journal of Biological \n\n\n\nControl, 35(5), 698-703. \n\n\n\nTendeng, E., Labou, B., Diatte, M., Djiba, S., Diarra, K. 2019. The fall \n\n\n\narmyworm Spodoptera frugiperda (JE Smith), a new pest of maize in \nAfrica: Biology and first native natural enemies detected. International \nJournal of Biological and Chemical Sciences, 13(2), 1011-1026.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 01-04 \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 15 November 2018  \nAccepted 17 December 2018 \nAvailable online 15 January 2019 \n\n\n\nABSTRACT\n\n\n\nThe effect of rooting hormones, substrate and bottom heat was studied on the rooting of the cuttings of Picea abies. \nThe treatments included bottom heat at two levels, hormones at six levels and rooting substrate at three levels. The \n\n\n\nrecorded traits included rooting percentage, the number of roots; root length and root dry weight. It was found that \n\n\n\nthe applied hormones had no considerable effect on rooting and the recorded traits, so that the application of 2000 \n\n\n\nand 4000 mg/l IBA had no significant difference with no-hormone application on all three substrates with or \n\n\n\nwithout the use of bottom heat. Cuttings treated with NAA produced no roots in any of the studied three substrates. \n\n\n\nThe highest number of roots was produced under the treatment of sand + perlite \u00d7 4000 mg/l IBA \u00d7 no-bottom heat. \n\n\n\nThe treatment of no-bottom heat \u00d7 no-hormone \u00d7 perlite produced the longest root. The highest root dry weight \n\n\n\nwas devoted the treatments of no-bottom heat \u00d7 no-hormone \u00d7 sand and no-bottom heat \u00d7 2000 mg/l IBA\u00d7 sand. \n\n\n\nKEYWORDS \n\n\n\nauxin, bottom heat, Picea abies (L.), rooting\n\n\n\n1. INTRODUCTION \n\n\n\nNorway spruce (Picea abies (L.) Karst \u2018Nidiformis\u2019) is a short conifer with \n\n\n\nneedle-like leaves similar to cushion from the family of Pinaceae which is \n\n\n\nusually grown as an ornamental plant in gardens and parks [1]. It is \n\n\n\npropagated by seeds and cuttings. But since propagation by cutting is the \n\n\n\neasiest, cheapest and best method of producing true-to-type plants, it is \n\n\n\nrecommended to propagate by cutting. It is known that semi-hardwood \n\n\n\ncuttings of spruce hardly start rooting and so, it is very important to \n\n\n\nunderstand the most appropriate conditions for the rooting of this species \n\n\n\nto maximize their rooting [2]. The rooting of cuttings, especially hardly \n\n\n\nrooting cuttings, is influenced by various environmental and internal \n\n\n\nfactors including the preparation of the cutting at the right time during \n\n\n\nplant growth cycle, the use of proper rooting substrate, the optimum \n\n\n\ntemperature and moisture in the rooting medium, and the application of \n\n\n\ngrowth regulators with correct dose; so, these factors must be taken care \n\n\n\nof to obtain acceptable rooting rate [3,4]. One of the environmental factors \n\n\n\nthat affect the rooting of the cuttings, especially hard-to-root cuttings, is to \n\n\n\nuse bottom heat. It is reported that bottom heat at rooting substrate can \n\n\n\nactivate cofactors and initiate rooting [5,6]. \n\n\n\nRooting substrate is an important factor in the rooting of hard-to- root \n\n\n\ncuttings; the selection of appropriate substrate increases the efficiency of \n\n\n\nrooting induction. The lack of appropriate planting substrate can decrease \n\n\n\nwater uptake efficiency, cause plant wilting and reduce cell size. It seems \n\n\n\nthat the use of appropriate planting substrate is an important step in the \n\n\n\npropagation of horticultural crops which can improve rooting rate and the \n\n\n\nnumber of rooted cuttings per unit area. The simplest, widely used rooting \n\n\n\nmedium for cuttings is sand. Sand is the heaviest and most common \n\n\n\nrooting substrate for cuttings. It lacks nutrients and has a high rate of \n\n\n\naeration. The rooting of leafy cuttings is reported to be increased in sandy  \n\n\n\nsubstrate [7]. A researcher reported sandy substrate as the most \n\n\n\nappropriate substrate for the rooting of semi-hardwood cutting of \n\n\n\nCallistemon viminalis [8]. Perlite is a rooting substrate that is composed of \n\n\n\ngray-while silicon with mineral and volcanic origins. Perlite can absorb \n\n\n\nand hold water 3-4 times as much as its weight. It has a great drainage, but \n\n\n\nit lacks nutrients and cation exchange property [9]. A previous researcher \n\n\n\nreported that the application of perlite mixed with other substrates like \n\n\n\npeat can improve the growth and development of the roots [10]. \n\n\n\nAnother factor that can improve the rooting of the cuttings is the rate of \n\n\n\nhormones and growth regulators. Among these factors, the highest effect \n\n\n\nis observed to be of auxins [11]. IBA and NAA are the most common auxins \n\n\n\nused for the rooting of cuttings. The application of auxins for most tree \n\n\n\nspecies increases the rooting and improves root quality. Auxins stimulate \n\n\n\nthe formation of roots on cuttings by stimulating cellular division and \n\n\n\nactivating rhizokalin [12]. Today, it is well accepted that the adventitious \n\n\n\nroots on stems are initiated by natural or application of synthetic auxins \n\n\n\nand in fact, the rate of rooting on stems is proportional to the rate of auxin \n\n\n\napplication. Based on a study, the number of roots was significantly \n\n\n\ninfluenced by the interaction between cutting and growth regulators [13]. \n\n\n\nThe rooting of the cuttings of Jojoba and Guava by the application of IBA is \n\n\n\nreported. It is argued that auxin improves cellular division, which in turn, \n\n\n\nresults in a higher number of roots [14,15]. \n\n\n\nSpruce is hard-to-root. Consequently, the objective of the present study \n\n\n\nwas to examine the effect of synthetic auxins and different rooting \n\n\n\nsubstrates as well as bottom heat on the rooting of the cuttings of Norway \n\n\n\nspruce (Picea abies (L.) Karst \u2018Nidiformi\u2019).\n\n\n\n2. MATERIALS AND METHODS \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.02.2019.01.04\n\n\n\n  RESEARCH ARTICLE \n\n\n\nSTUDY ON THE ROOTING OF PICEA ABIES CUTTINGS UNDER AUXINS, \nSUBSTRATES AND BOTTOM HEAT \n\n\n\nShahram Sedaghathoor1*, Somayeh Abdizadeh Sarem2 \n\n\n\n1Department of Horticulture, Rasht Branch, Islamic Azad University, Rasht, Iran \n2Former M.Sc. Student of Horticultural Science, Rasht Branch, Islamic Azad University, Rasht, Iran \n\n\n\n*Corresponding Author Email: sedaghathoor@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited\n\n\n\nCite The Article: Shahram Sedaghathoor, Somayeh Abdizadeh Sarem (2019). Study On The Rooting Of Picea Abies Cuttings Under Auxins, Substrates And Bottom \nHeat. Malaysian Journal of Sustainable Agriculture, 3(2): 01-04.\n\n\n\n2.1 Trial design\n\n\n\n\nmailto:sedaghathoor@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)01-04 \n\n\n\nCite The Article: Shahram Sedaghathoor, Somayeh Abdizadeh Sarem (2019). Study On The Rooting Of Picea Abies Cuttings Under Auxins, Substrates And Bottom \nHeat. Malaysian Journal of Sustainable Agriculture, 3(2): 01-04.\n\n\n\nThe study carried out as a factorial experiment based on a Randomized \n\n\n\nComplete Block Design with three factors. The first factor was bottom heat \n\n\n\n[including the lack of bottom heat (a1) and the application of bottom heat \n\n\n\n(a2)], the second factor was the application of rooting hormones [no \n\n\n\nhormone (b1), 2000 mg/l IBA (b2), 4000 mg/l IBA (b3), 2000 mg/l NAA \n\n\n\n(b4), 4000 mg/l NAA (b5), 2000 mg/l IBA + 2000 mg/l NAA (b6)] and third \n\n\n\nfactor was rooting substrate [sand (c1), perlite (c2), sand + perlite  (v/v) \n\n\n\n(c3)] at three replications in a tunnel plastic greenhouse at 18-20\u00b0C \n\n\n\nequipped with bottom heat and mist systems. \n\n\n\n2.2 Plant materials \n\n\n\nThe leafy cuttings of Norway spruce with the length of 8-12 cm and a \n\n\n\ndiameter of 1.5-2.5 mm were obtained from an ornamental propagator \n\n\n\ncompany in Ramsar, Iran in February 2014. Then, the final 2 cm of the \n\n\n\ncuttings was soaked in the hormone solution for 10 seconds. After that, \n\n\n\nthey were weathered for about 10 seconds for the alcohol of the solution \n\n\n\nto vaporize. Afterwards, they were planted in rooting substrates. Before \n\n\n\nplanting, all substrates were disinfected with copper oxychloride. The \n\n\n\ncuttings were daily irrigated with mist system. The bottom heat was \n\n\n\nadjusted at 22-24\u00b0C during winter and it was stopped in early spring. \n\n\n\nThe cuttings were taken from the substrate 140 days after planting date to \n\n\n\nrecord the studied traits. The recorded traits included rooting percentage,  \n\n\n\nthe number of roots per cutting and the length of the longest root. To \n\n\n\nmeasure the dry weight of the roots, they were parted from the cuttings \n\n\n\nand weighed. Then, they were oven-dried at 105\u00b0C for 24 hours to \n\n\n\nestimate their dry weight. The data were analyzed by the MSTATC \n\n\n\nstatistical software package and the means were compared with LSD test. \n\n\n\n3. RESULTS \n\n\n\n3.1 Rooting percentage \n\n\n\nMeans comparison of the data of rooting percentage revealed that the \n\n\n\nhighest rooting was related to the treatments of no hormone and different \n\n\n\nrates of IBA at three substrates and in both with and without bottom heat. \n\n\n\nThe highest rooting percentage (99%) was observed in the treatments of \n\n\n\nno bottom heat \u00d7 no hormone \u00d7 sand, no bottom heat \u00d7 2000 mg/l IBA \u00d7 \n\n\n\nsand, no bottom heat \u00d7 4000 mg/l IBA \u00d7 perlite, no bottom heat \u00d7 4000 \n\n\n\nmg/l IBA \u00d7 sand, bottom heat \u00d7 no hormone \u00d7 perlite, and bottom heat \u00d7 \n\n\n\nno hormone \u00d7 perlite. Different NAA concentrations had no effect on the \n\n\n\nrooting of the cuttings and in total, NAA had no specific effect on the \n\n\n\nrooting of Norway spruce (Table 1). \n\n\n\n2.3 Experimental traits \n\n\n\nTable 1: Means comparison of the effect of different treatments on the measured traits \n\n\n\nRooting \npercentage \n\n\n\nNumber \nof roots \n\n\n\nRoot length \n(cm) \n\n\n\nRoot dry \nweight (g) \n\n\n\nNo bottom heat + no hormone + sand 99 a 4.00 bcd 4.25 cde 0.036 d-h \nNo bottom heat + no hormone + perlite 88 a 2.66 def 6.19 ab 0.04 d-h \nNo bottom heat + no hormone + sand + perlite 88 a 4.66 a-d 6.14 ab 0.096 b \nNo bottom heat + 2000 mg/l IBA + sand 99 a 5.66 abc 2.04 ghi 0.023 f-i \nNo bottom heat + 2000 mg/l IBA + perlite 88 a 4.33 bcd 6.22 ab 0.063 b-e \nNo bottom heat + 2000 mg/l IBA + sand + perlite 66 a 5.33 abc 3.37 c-g 0.046 d-g \nNo bottom heat + 4000 mg/l IBA + sand 99 a 4.33 bcd 2.94 e-h 0.04 d-h \nNo bottom heat + 4000 mg/l IBA + perlite 99 a 4.3 bcd 4.70 b-e 0.043 d-h \nNo bottom heat + 4000 mg/l IBA + sand + perlite 88 a 7.00 a 4.90 bc 0.07 bcd \nNo bottom heat + 2000 mg/l NAA + sand 0 a 0.00 g 0.00 j 0.00 i \nNo bottom heat + 2000 mg/l NAA + perlite 0 a 0.00 g 0.00 j 0.00 i \nNo bottom heat + 2000 mg/l NAA + sand + perlite 0 a 0.00 g 0.00 j 0.00 i \nNo bottom heat + 4000 mg/l NAA + sand 0 a 0.00 g 0.00 j 0.00 i \nNo bottom heat + 4000 mg/l NAA + perlite 0 a 0.00 g 0.00 j 0.00 i \nNo bottom heat + 4000 mg/l NAA + sand + perlite 0 a 0.00 g 0.00 j 0.00 i \nNo bottom heat + 2000 mg/l IBA + 2000 mg/l NAA + sand 33 a 1.33 fg 1.45 hij 0.016 ghi \nNo bottom heat + 2000 mg/l IBA + 2000 mg/l NAA + perlite 0 a 0.00 g 0.00 j 0.00 i \nNo bottom heat + 2000 mg/l IBA + 2000 mg/l NAA + sand + perlite 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + no hormone + sand 88 a 4.33 cde 4.32 cde 0.136 a \nBottom heat + no hormone + perlite 99 a 5.66 abc 7.88 a 0.06 cde \nBottom heat + no hormone + sand + perlite 88 a 4.33 bcd 4.91 bc 0.093 bc \nBottom heat + 2000 mg/l IBA + sand 99 a 5.33 abc 4.11 cde 0.136 a \nBottom heat + 2000 mg/l IBA + perlite 77 a 5.66 abc 4.85 bcd 0.053 def \nBottom heat + 2000 mg/l IBA + sand + perlite 44 a 4.66 a-d 3.31 c-g 0.066 b-e \nBottom heat + 4000 mg/l IBA + sand 66 a 6.33 ab 3.95 c-f 0.063 b-e \nBottom heat + 4000 mg/l IBA + perlite 44 a 3.66 cde 2.30 f-i 0.043 b-h \nBottom heat + 4000 mg/l IBA + sand + perlite 44 a 2.66 def 3.05 d-h 0.033 e-i \nBottom heat + 2000 mg/l NAA + sand 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + 2000 mg/l NAA + perlite 22 a 0.66 fg 0.00 j 0.01 hi \nBottom heat + 2000 mg/l NAA + sand + perlite 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + 4000 mg/l NAA + sand 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + 4000 mg/l NAA + perlite 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + 4000 mg/l NAA + sand + perlite 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + 2000 mg/l IBA + 2000 mg/l NAA + sand 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + 2000 mg/l IBA + 2000 mg/l NAA + perlite 0 a 0.00 g 0.00 j 0.00 i \nBottom heat + 2000 mg/l IBA + 2000 mg/l NAA + sand + perlite 33 a 2.66 def 1.03 ij 0.036 d-h \n\n\n\nMeans followed by the same letters in each column are not significantly different in LSD test. \n\n\n\n3.2 The number of roots \n\n\n\nAccording to means comparison of root number data, the treatment of no \n\n\n\nbottom heat + 4000 mg/l IBA \u00d7 sand \u00d7 perlite produced the highest \n\n\n\nnumber of roots (7 roots) followed by the treatment of bottom heat \u00d7 4000 \n\n\n\nmg/l IBA \u00d7 sand with 6.33 roots. No roots were produced under the \n\n\n\napplication of NAA at all three substrates with and without bottom heat as \n\n\n\nwell as the treatments of no bottom heat \u00d7 2000 mg/l IBA + 2000 mg/l \n\n\n\nNAA \u00d7 perlite, no bottom heat \u00d7 2000 mg/l IBA + 2000 mg/l NAA \u00d7 sand + \n\n\n\nperlite, bottom heat \u00d7 2000 mg/l IBA + 2000 mg/l NAA \u00d7 sand and bottom \n\n\n\nheat \u00d7 2000 mg/l IBA + 2000 mg/l NAA \u00d7 perlite. Among the treatments \n\n\n\nwhich resulted in the growth of roots, the lowest number of roots was \n\n\n\nobserved in the treatment of bottom heat \u00d7 2000 mg/l NAA \u00d7 perlite \n\n\n\n(Table 1). \n\n\n\n3.3 Root length \n\n\n\nMeans comparison of root length indicated that the longest roots (7.88 cm) \n\n\n\nwere produced under the treatment of bottom heat \u00d7 no hormone \u00d7 perlite \n\n\n\nfollowed by the treatment of no bottom heat \u00d7 2000 mg/l IBA \u00d7 perlite \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)01-04 \n\n\n\nCite The Article: Shahram Sedaghathoor, Somayeh Abdizadeh Sarem (2019). Study On The Rooting Of Picea Abies Cuttings Under Auxins, Substrates And Bottom \nHeat. Malaysian Journal of Sustainable Agriculture, 3(2): 01-04.\n\n\n\nwith the root length of 6.22 cm. After the treatments in which no roots \n\n\n\nwere produced and so, the root length was zero (Table 1), the shortest \n\n\n\nroots were related to bottom heat \u00d7 2000 mg/l IBA + 2000 mg/l NAA \u00d7 \n\n\n\nsand + perlite with the length of 1.03 cm followed by the treatment of no \n\n\n\nbottom heat \u00d7 2000 mg/l IBA + 2000 mg/l NAA \u00d7 sand with the root length \n\n\n\nof 1.45 cm (Table 1). \n\n\n\n3.4 Root dry weight \n\n\n\nMeans comparison showed that the treatments of bottom heat \u00d7 no \n\n\n\nhormone \u00d7 sand and bottom heat \u00d7 2000 mg/l IBA \u00d7 sand resulted in the \n\n\n\nhighest root dry weight (0.136 g). The lowest root dry weight was related \n\n\n\nto the treatment of NAA at three substrates with and without bottom heat. \n\n\n\nAlso, the root dry weight was zero in the treatments of bottom heat \u00d7 2000 \n\n\n\nmg/l IBA + 2000 mg/l + perlite, bottom heat \u00d7 2000 mg/l IBA + 2000 mg/l \n\n\n\nNAA \u00d7 sand, no bottom heat + 2000 mg/l IBA + 2000 mg/l NAA + sand + \n\n\n\nperlite, and no bottom heat + 2000 mg/l IBA + 2000 mg/l NAA \u00d7 perlite. \n\n\n\nBesides, the treatments of bottom heat \u00d7 2000 mg/l NAA \u00d7 perlite and no \n\n\n\nbottom heat + 2000 mg/l IBA + 2000 mg/l NAA \u00d7 sand produced the \n\n\n\nlowest root dry weights, respectively (Table 1). \n\n\n\n4. DISCUSSION\n\n\n\nAs mentioned in the Results section, cuttings which were not treated with \n\n\n\nhormones had better rooting than those treated with hormones in all three \n\n\n\nsubstrates with or without bottom heat. It implies that the cuttings of \n\n\n\nNorway spruce prepared in February did not need hormone treatments \n\n\n\nfor rooting and can readily initiate rooting in sand or perlite under mist \n\n\n\nsystem. A researcher argued that semi-hardwood cuttings have an \n\n\n\noptimum auxin level and that the increase in exogenous hormone \n\n\n\nconcentration disrupts the plant hormone balance, resulting in the loss of \n\n\n\nrooting which is consistent with our results. \n\n\n\nAmong IBA and NAA treatments, the treatment of 2000 mg/l IBA had the \n\n\n\nsecond highest rooting after control. The treatments of NAA and IBA + NAA \n\n\n\nexhibited the lowest rooting. These results are in agreement with a \n\n\n\nresearcher who found that IBA was more effective than NAA in rooting and \n\n\n\nanother researcher who found that IBA was superior over other auxins in \n\n\n\nsuccessful rooting of different types of cuttings [16,17]. \n\n\n\nThe failure of hormones in inducing roots of Norway spruce cuttings as \n\n\n\ncompared to control can be related to some factors, for example \n\n\n\ninappropriate concentrations of the hormones which can be confirmed \n\n\n\nonly by an experiment on a much wider range of hormone concentrations. \n\n\n\nHowever, it should also be noted that the treatment of auxin is not effective \n\n\n\non the rooting of all species. The application of NAA alone or combined \n\n\n\nwith rooting substrate and bottom heat system had a negative effect on the \n\n\n\nrooting of Norway spruce so that the increase in its concentration not only \n\n\n\ndid not increase rooting but also decreased. This result is consistent with \n\n\n\na researcher who believe that high auxin concentrations can be harmful to \n\n\n\nthe tissue of the bottom of the cuttings and Arteca (1997) who argued that \n\n\n\nNAA is stronger and more stable than natural auxins and so, it is better to \n\n\n\nbe used in lower concentrations [18,19]. \n\n\n\nIt is known that rooting percentage and the quality of cuttings directly \n\n\n\ndepend on planting substrate in most cases [20]. Many researchers \n\n\n\nsuggest that the use of substrate with neutral pH like sand and perlite \n\n\n\nimproves rooting of the cuttings. In the present study too, sand and perlite \n\n\n\nincreased the rooting. A researcher reported that IBA and sand + perlite \n\n\n\nsubstrates were effective on the rooting of the cuttings of Thuja \n\n\n\noccidentalis [21]. Qasemi et al., (2013) also, suggested sand as the best \n\n\n\nsubstrate for the rooting of semi-hardwood cuttings of guava and stated \n\n\n\nthat a substrate with neutral pH and improved drainage increased the \n\n\n\nrooting which is in agreement with our results [22]. \n\n\n\nOne advantage of auxin is reportedly the increase in the number of roots \n\n\n\nper cutting [23]. It is believed that auxin increases the number of roots \n\n\n\nthrough stimulating the growth of adventitious roots and the development \n\n\n\nof latent and preformed root initiators [24]. However, it should be noted \n\n\n\nthat the treatment of NAA adversely impacted the number of roots in the \n\n\n\npresent study. The normal number of roots in control implies that the \n\n\n\ncontrol cuttings had indigenous auxin required for optimum production of \n\n\n\nroots. A scholar believes that 1-2% IBA increased the number of roots in \n\n\n\nmagnolias cuttings and very high concentrations of NAA decreased it, \n\n\n\nwhich is consistent with our results [25]. \n\n\n\nIn a study on rooting of guava stem cuttings, a researcher showed that the \n\n\n\nlongest roots and the highest number of roots were observed in the \n\n\n\ntreatment of 1000 mg/l IBA and NAA [26]. The highest rooting of jojoba \n\n\n\nand guava cuttings was obtained under IBA treatments. They believed that \n\n\n\nauxin improved cellular division resulting in a higher number of roots. \n\n\n\nFinally, it can be concluded that although hormone treatments had no \n\n\n\nsignificant impact on the rooting percentage of Norway spruce, they \n\n\n\nimproved the production of roots alone or in combination with other \n\n\n\ntreatments like rooting substrate. \n\n\n\nThe highest root length was obtained under the treatment of no hormone \n\n\n\non perlite with bottom heat. The treatments of IBA were more appropriate \n\n\n\nfor the increase in root length than NAA. The increase in the root length \n\n\n\nunder the application of growth regulators can be caused by higher \n\n\n\nhydrolysis of carbohydrates. By increasing metabolism in their application \n\n\n\nspot and synthesis of new proteins, auxins contribute to the development \n\n\n\nand division of cells, which in turn, results in higher numbers and length \n\n\n\nof roots [27,28]. The increase in root length under the application of 1000 \n\n\n\nmg/l IBA is reported for Poinsettia pulcherrima, too [29]. A researcher \n\n\n\nreported that the best treatment for rooting and root length was 2000 \n\n\n\nmg/l IBA + sand [30]. The highest root length of Thuja occidentalis was \n\n\n\nfound under perlite and 2000 mg/l IBA. \n\n\n\nIt was found that root dry weight was the highest under the application of \n\n\n\nbottom heat on sand substrate without the application of hormone and \n\n\n\nwith the application of 2000 mg/l IBA. According to a research, the \n\n\n\napplication of high concentrations of IBA positively influenced the quality \n\n\n\nand dry weight of roots in carnation and chyrsanthemum, which is \n\n\n\ninconsistent with our results. However, he reported that different \n\n\n\nconcentrations of NAA had no significant effect on this process which is in \n\n\n\nagreement with our results. \n\n\n\nA researcher obtained the highest root weight (1.12 g) from the treatment \n\n\n\nof NAA. In a study on the effect of auxin and different substrates on rooting \n\n\n\nof Thuja occidentalis, another researcher stated that the highest root dry\n\n\n\nweight was produced under the treatment of perlite \u00d7 4000 mg/l IBA. \n\n\n\nBased on a research, different substrates and hormone concentrations had \n\n\n\na positive effect on the dry weight of Dodoneae viscosa L. roots [31-34]. \n\n\n\n5. CONCLUSION \n\n\n\nIn the present study, it was revealed that the highest rooting of of P. abies \n(L.) Karst \u2018Nidiformis\u2019 cuttings was obtained under the treatment of no \n\n\n\nhormones and sand or perlite substrates. Therefore, it can be concluded \n\n\n\nthat the appropriate substrate and environmental factors are more \n\n\n\nimportant for the rooting of cuttings of Norway spruce than internal \n\n\n\nfactors. In fact, the hard- to- root feature of this plant cuttings is not \n\n\n\ncorrelated to the deficiency of auxin in the stem tissues; although further \n\n\n\nstudies are required to confirm this hypothesis. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nFinancial support by Rasht Branch, Islamic Azad University Grant No. \n\n\n\n4.5830 is gratefully acknowledged. \n\n\n\nREFERENCES \n\n\n\n[1] Zare, H. 2001. Native and non-native conifer species in Iran. Tehran,\n\n\n\nIran: Research Institute of Forests and Rangelands Press. \n\n\n\n[2] Sedaghathoor, S. 2012. Medicinal and aromatic trees and shrubs. \nIslamic Azad Univeristy Rasht Branch. Rasht, Iran. \n\n\n\n[3] Hartmann, H.T., Kester, D.E., Davies, Jr. F.T., Geneve, R.L. 2011. Plant \n\n\n\npropagation, principles and practices (8th ed.). New Jersey: Printice Hall, \n\n\n\nUpper Saddle River. \n\n\n\n[4] Khoshkhoy, M., Sheybani, B., Rouhani, A., Taffazoli A. 2004. Principles \n\n\n\nof horticulture. Shiraz, Iran: Shiraz University Press.\n\n\n\n[5] De Klerk, G.J., Krieken, W.D. and Jong , J. 1999. The formation of \n\n\n\nadventitious roots: New concepts, new possibilities. In Vitro Cellular and \n\n\n\nDevelopmental Biology Plant B, 35: 189-199. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)01-04 \n\n\n\nCite The Article: Shahram Sedaghathoor, Somayeh Abdizadeh Sarem (2019). Study On The Rooting Of Picea Abies Cuttings Under Auxins, Substrates And Bottom \nHeat. Malaysian Journal of Sustainable Agriculture, 3(2): 01-04.\n\n\n\n[6] Jalili Marandi, R. 2012. Fruit production. Uromia, Iran: Jahad-e \n\n\n\nDaneshghahi Press. \n\n\n\n[7] Marin, M., Koko, V., Duletic-Lausevic, S., Marin, P.D., Rancic, D., Dajic-\n\n\n\nStevanovic, Z. 2006. Glandular trichomes on the leaves of Rosmarinus\nofficinallis: Morphology, stereology and histochemistry. South African \n\n\n\nJournal of Botany, 72(3): 378-382. \n\n\n\n[8] Shokri, S., Zarei, H., Alizadeh, M. 2013. Effect of substrate on rooting of \n\n\n\nornamental shrubs of Callistemon viminalis. 7th Iranian Conference of \n\n\n\nHorticulture Science. Isfahan: Isfahan University of Technology.\n\n\n\n[9] Garillass, S., Lucas, M., Bardopoulou, E., Sarafopouios, S., Voulgari, M. \n\n\n\n2001. Perlite based soilless culture system: Current commercial \n\n\n\napplication and prospects. Acta Horticturae,  434: 103-112. \n\n\n\n[10] Smith, C.A., Hall D.A. 1994. The development of perlite as a potting \n\n\n\nsubstrate for ornamental plants. Acta Horticulturae, 361: 159-166. \n\n\n\n[11] Arteca, R.N. 1997. Plant growth substances: Principles and \n\n\n\napplications. Chapman and Hall.\n\n\n\n[12] Rugini, E. 1992. Involvement of polyamides in auxin and \n\n\n\nAgrobacterium rhizogenes induced rooting of fruit trees in vitro. Journal \n\n\n\nof American Soceity of Horticultural Science, 117: 532-536. \n\n\n\n[13] Taghvaei, M., Sadeghi, H., Bagheramiri, M. 2012. Interaction \n\n\n\nbetween the concentration of growth regulators, type of cutting and \n\n\n\nrooting medium of Cappari spinosa L. cutting. International Journal of\n\n\n\nAgriculture: Research and Review, 2(6): 783-788. \n\n\n\n[14] Bashir, M., Anjum, A., Chandhry, M.A., Rashid, H. 2009. The response \n\n\n\nof jojoba (Simmodsia chinensis) cutting to various concentrations of auxin.\n\n\n\nPakistan Journal of Botany, 41: 2831-2840. \n\n\n\n[15] Lugman, M., Ali, A., Sujid, M. 2004. Effect of different concentration \n\n\n\nof indole butyric acid (IBA) on semi hard wood guava cutting. Sarhad \n\n\n\nJournal of Agriculture, 20: 219-222. \n\n\n\n[16] Kroin, J. 1992. Advances using Indolde-3-butyric acid (IBA) \n\n\n\ndissolved in water for rooting cutting, transplanting and grafting. \n\n\n\nCombined Proceedings, International Plant Propagators Society. 42: 489-\n\n\n\n489. Washington, D.C.: Univ. Washington-Int. Plant Propagation Soc. \n\n\n\n[17] MacDonald, B. 2000. Practical woody plant propagation for nursery \n\n\n\ngrowers. Timber Press. \n\n\n\n[18] Puri, S., Verma, R. 1996. Vegetative propagation of Dalbergia sisso\nusing soft wood and hard wood stem cutting. Journal of Arid Enviroments, \n\n\n\n34: 335-345. \n\n\n\n[19] Blythe, E.K., Sibley, J.L., Ruter, J.M., Tilt, K.M. 2004. Cutting \n\n\n\npropagation of foliage crops using a foliar application of auxin. Scientia \n\n\n\nHorticulturea, 103: 31-37. \n\n\n\n[20] Sadhu, M.K. 1998. Plant propagation. New Dehli, India: Wiley\n\n\n\nEastern Limited. \n\n\n\n[21] Shafaghi, J., Sedaghathoor, S., Tabatabaei A.R. 2013. A study on the \n\n\n\neffect of IBA hormones and substrate on the rooting of Thuja occidentalis\ncuttings. 8th Iranian Conference of Horticulture Science, 2420-2423. \n\n\n\n[22] Qasemi, H., Salehi Sardoei, A., Mighani, H., Momen M.J. 2013. A study \n\n\n\non the effect of planting substrates on the rooting of semi-hardwood \n\n\n\ncuttings of guava. 8th Iranian Conference of Horticultural Science, 2451-\n\n\n\n2455. \n\n\n\n[23] Fathi, G., Ismailpour, B., Jalilvand, P. 2012. Plant growth regulators. \nMashhad, Iran: Jahad-e Daneshghahi Press. \n\n\n\n[24] Mirsoleimani, A., Rahemi M. 2007. The effects of two types of \n\n\n\nsynthetic auxin on rooting of hardwood stem  cutting of almond x peach \n\n\n\nhybrid under outdoor conditions. Pajohesh and Sazandeghi (Persian),  76: \n\n\n\n89-96. \n\n\n\n[25] Bojarczuk, K. 1985. Propagation of magnolias from green cutting \n\n\n\nusing various factors stimulation rooting and growth of plants. Acta \n\n\n\nHorticturae, 167: 423-431. \n\n\n\n[26] Rahman, N., Gholamnabi, T., Jan, T. 2004. Effect of different growth \n\n\n\nregulators, and types of cutting on rooting of guava (Psidium guajava).\n\n\n\nScience Vision, 9: 1-5. \n\n\n\n[27] Stryden, D.K., Hartman, H.T. 1960. Effect of indole butyric acid and \n\n\n\nrespiration and nitrogen metabolism in Marianna 2624 plum softwood \n\n\n\nstem cuttings. Proceeding of American Society of Horticulture, 45(1-2): \n\n\n\n81-82. \n\n\n\n[28] Susila, T., Styanarayana Reddy, G. 2013. Influence of IBA and NAA on \n\n\n\nrooting of Adathoda vaica. Academic Journal of Plant Science, 6(2): 61-63.\n\n\n\n[29] Ramtin, A., Khalighi, A., Hadavi, E., Hekmatim J. 2011. Effect of \n\n\n\ndifferent IBA concentrations and types of cuttings on rooting and \n\n\n\nflowering Poinsettia pulcherrima L. International Journal of Agricultural \n\n\n\nScience, 1(5): 303-310. \n\n\n\n[30] Galavi, M., Karimian, M.A., Mousavi, S.R. 2013. Effects of different \n\n\n\nauxin (IBA) concentrations and planting substrates on rooting grape \n\n\n\ncuttings (Vitis vinifera). Annual Review and Research in Biology, 3(4): 517-\n\n\n\n523. \n\n\n\n[31] Saffari, M., Saffari, V.R. 2012. Effects of media and indole butyric acid \n\n\n\n(IBA) concentrations on hophush (Dodoneae viscose L.) cuttings in \n\n\n\ngreenhouse. Annals of Forest Research, 55(1): 61-68. \n\n\n\n[32] Camiel, H. 1985a. The effect of NAA and IBA auxins and their mixture \n\n\n\non rooting of carnation cv. 'Scania 30'. Acta Horticturae, 167: 161-167. \n\n\n\n[33] Camiel, H. 1985b. The influence of NAA and IBA auxin and their \n\n\n\nmixture on rooting of chrysanthemum cutting cv. Super yellow. Acta \n\n\n\nHorticturae, 167: 369-378. \n\n\n\n[34] Jull, L.G., Warren S.L., Blazich, F.A. 1994. Rooting Yoshino\ncryptomeria stem cutting a influenced by growth stage, branch order and \n\n\n\nIBA treatment. Scientia Horticulturea, 29(12): 1532-1535.  \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 81-84 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.81.84 \n\n\n\nCite The Article: Donyo Ganchev (2022). Antisporulation Action of Tarbush Plant (Flourensia Cernua) Towards  \nConidiospores of Plant Pathogens. Journal of Sustainable Agricultures, 6(2): 81-84. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.81.84\n\n\n\nANTISPORULATION ACTION OF TARBUSH PLANT (FLOURENSIA CERNUA) \nTOWARDS CONIDIOSPORES OF PLANT PATHOGENS \n\n\n\nDonyo Ganchev* \n\n\n\nDepartment of Chemistry and Phytopharmacy, Faculty of Plant Protection and Agroecology, Agricultural University, Plovdiv, Bulgaria.  \n*Corresponding author email: donyo@abv.bg\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 02 December 2021 \nAccepted 06 January 2022 \nAvailable online 12 January 2022\n\n\n\nThe plant pathogens as Alternaria solani, Monilinia fructigena, Botrytis cinereae and Venturia inaequalis cause \nsignificant damages on many plants in the European region (particularly in the region of Republic of \nBulgaria), especially on orchard cultures which are very important for agricultural industry in this area. There \nis many existed commercial plant protection products towards this phytopathogens on the market and there \nis intensive pesticides treatments in order to be overtake infestations and damaging of the plants from this \npathogenic fungus.  However such king pesticides in the most cases are toxic and harmful for the humans and \nenvironment, so there is a need for development and introduction on the pesticide market of the novel \nenvironmentally friendly plant protection products against these diseases. In the present research paper an \nin vitro trials were conducted with ethanol extracts from tarbush plant (Flourensia cernua) with conidial \nsporulation of of Alternaria solani, Monilinia fructigena, Botrytis cinereae and Venturia inaequalis. The \nreceived results show the strong antisporulation action of tarbush plant ethanol extracts towards tested \npathogens. However according to the conidiospores of Alternaria solani, there was full lack of effectiveness \nand even slightly stimulation action of germination of spores. This results can be a base for development of \nthe new natural fungicides against tested plant pathogens wicth can be apllied as in the commersial \nagriculture, as in the organic or integrated pest management \n\n\n\nKEYWORDS \n\n\n\ntarbush plant, Flourensia cernua, Alternaria solani, Monilinia fructigena, Botrytis cinereae Venturia inaequalis \n\n\n\n1. INTRODUCTION\n\n\n\nFlourensia cernua is a of flowering plant in the known by the English \ncommon names American tarwort and tarbush and the Spanish common \nnames hojas\u00e9, hojas\u00e9n, and hoja ancha (Korthuis, 1988; Manhart, 2011). It \nis native to the Chihuahuan Desert of North America, where it occurs in the \nUS states of Arizona, New Mexico, and Texas, and the Mexican states of \nSonora, Chihuahua, Coahuila, Durango, San Luis Potos\u00ed, and Zacatecas \n(O'Laughlin, 1975). Flourensia cernua is a shrub growing from a network \nof roots that may extend four meters horizontally. This plant  is winter-\ndeciduous in most regions, but may retain its leaves in areas with sufficient \nmoisture. In agriculture, this shrub has been studied as a potential \nsupplemental forage for livestock such as sheep (Fredrickson et al., 1994; \nKing et al., 1996).  Flourensia cernua has medicinal uses. In Mexico it is \nsteeped to make a tea that is consumed to treat various gastrointestinal \nconditions such as indigestion and diarrhea (Lira-Saldivar et al., 2008). \n\n\n\nIt is also used for respiratory disorders; its extracts have shown the ability \nto kill multidrug-resistant Mycobacterium tuberculosis in vitro tests (de \nRodr\u00edguez et al., 2012). Compounds isolated from the plant include \nflavonoids, sesquiterpenoids, monoterpenoids, acetylenes, p-\nacetophenones, benzopyrans and benzofurans (Estell et al., 1994; Estell et \nal., 1996). Extracts of the plant have shown antifungal, anticyanobacterial, \nand antitermite effects (Tellez et al., 2001).  Some studies reveal and their \nanti-inflammatory and apoptotic effects including antioxidant activity \n(Salazar et al., 2008; de Rodr\u00edguez et al., 2019). Other studies also revel the \nantifungal activity in vitro of Flourensia spp. extracts on Alternaria sp., \nRhizoctonia solani, and Fusarium oxysporum and against Penicillium \n\n\n\nexpansum and Fusarium oxysporum and Colletotrichum gloeosporioides \n(Guerrero-Rodr\u00edguez et al., 2007; Aguilar-Alonso et al., 2013; Apaza and \nSolis, 2019; de Rodr\u00edguez et al., 2007; De Le\u00f3n et al., 2013; Prieto et al., \n2013).  \n\n\n\nOther studies show the effectiveness of plant extracts from these native \nMexican plants towards Monilinia fructigena, Botrytis cinereae, \nanthracnose and Stemphyllium solani (Linares Rivero, 2014). The \nresearches show the high potential of the extracts from tarbush to be \ndeveloped as natural plant protection way against postharvest fungi \n(Castillo et al., 2012; de Rodr\u00edguez et al., 2017; Galv\u00e1n et al., 2014; De Le\u00f3n-\nZapata et al., 2016; Espinoza Pantigozo, 2016; Rodr\u00edguez-Guadarrama et \nal., 2018; Avila-Sosa et al., 2011). The organic extracts from Flourensia \ncernua express not only antifungal but also bactericidal effects (Molina-\nSalinas et al., 2006). The plant pathogens as Alternaria so.lani, Monilinia \nfructigena, Botrytis cinereae and Venturia inaequalis cause significant \ndamages on many plants in the European region (particularly in the region \nof Republic of Bulgaria), especially on orchard cultures which are very \nimportant for agricultural industry in this area.  \n\n\n\nThere is many existed commercial plant protection products towards this \n\n\n\nphytopathogens on the market and there is intensive pesticides \n\n\n\ntreatments in order to be overtake infestations and damaging of the plants \n\n\n\nfrom this pathogenic fungus.  However such king pesticides in the most \n\n\n\ncases are toxic and harmful for the humans and environment, so there is a \n\n\n\nneed for development and introduction on the pesticide market of the \n\n\n\nnovel environmentally  friendly plant protection products against these \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 81-84 \n\n\n\nCite The Article: Donyo Ganchev (2022). Antisporulation Action of Tarbush Plant (Flourensia Cernua) Towards  \nConidiospores of Plant Pathogens. Journal of Sustainable Agricultures, 6(2): 81-84. \n\n\n\ndiseases (Badillo et al., 2008; Castillo et al., 2011; Ootani et al., 2013; \n\n\n\nAlvarez-Perez et al., 2020).  In the present study an in vitro trial of \n\n\n\ncondiospores of economic important for the region of Bulgaria, plant \n\n\n\npathogens were conducted with ethanol extracts from leaves and \n\n\n\nbranches of Flourensia cernua as Alternaria solani, Monilinia fructigena, \n\n\n\nBotrytis cinereae and Venturia inaequalis. Due to the many cited in this \n\n\n\npaper studies about fungicidal activity of tarbush plant extracts, this native \n\n\n\nMexican plant is a very promising source of such kind natural plant \n\n\n\nprotection products (Cespedes et al., 2015). \n\n\n\n2. MATERIAL AND METHODS\n\n\n\nLeaves and branches from tarbush plant (Flourensia cernua) were \n\n\n\nobtained from USDA-ARS, Jornada Experimental Range, Las Cruces, New \n\n\n\nMexico, USA. Plant extracts from leaves and branches were prepared by \n\n\n\ntreating 100 g of plant material in 1 liter ethanol in 5-liter dark-colored \n\n\n\nflask. The mixture stays at room temperature at 20\u00b0C, three days, after that \n\n\n\nshaken for 1 hour at 150 rpm. The extracts were filtered, and the solvent \n\n\n\nremoved under reduced pressure with a rotary evaporator (RVO 004, \n\n\n\nINGOS Laboratory Instruments \u201cLtd). The germ tube inhibition tests were \n\n\n\nconducted in order to be determining ability of the salts to inhibit \n\n\n\ngermination of the conidia of the plant pathogenic fungi. The microscopic \n\n\n\nslides variety \u201changing drop\u201d was sprayed with water solution of tested \n\n\n\nexctracts with desired concentration. After drying of solution, 20 \u03bcl \n\n\n\nconidial suspensions (3*104 spores/ml) was added. The slides were \n\n\n\nincubated for 24-48 h in thermostat under 22-24\u00b0C. Observations with \n\n\n\nlight microscope (10x) were conducted to be determined the germination \n\n\n\nof the spores (four observation on each slide). The percent of germination \n\n\n\nwas calculated as follows: Percent germinated conidia=number of \n\n\n\ngerminated spores*100 / (number of germinated spores + number of non-\n\n\n\ngerminated spores). According to calculated percents of germination was \n\n\n\ndetermined an effectiveness (inhibition) with formula of Abbot (Abbot, \n\n\n\n1925). Dose \u2013 Response Modeling was performed by R language of \n\n\n\nstatistical computing, drc package (R Core Team, 2020; Ritz et al., 2015). \n\n\n\n3. RESULTS\n\n\n\nThe performed test with conidiospores of Alteraria solani show that even \n\n\n\n10 % water solution of extracts from leaves and branches of tarbush plant \n\n\n\nwere no able to inhibit the germination. Even more \u2013 the extracts slightly \n\n\n\nstimulate the germination of spores (12 % stimulation for extracts from \n\n\n\nleaves and 9.4 % % stimulation for extracts from leaves). However, the \n\n\n\ntrials with conidiospores of Monilinia fructigena, Botrytis cinereae and \n\n\n\nVenturia inaequalis show the strong inhibition potential of the tarbush \n\n\n\nplant extracts. The next figures show the Dose \u2013 Response Models (Curves) \n\n\n\nof this action. \n\n\n\nFigure 1: Dose \u2013 Response Curve of Tabrbush plant ethonol leaves \nextracts towards conidiospores of Monilinia fructigena \n\n\n\n\uf0b7 NOAEL = 0.21 %\n\n\n\n\uf0b7 LOAEL = 0.29 % \n\n\n\n\uf0b7 LD50 = 0.39 %\n\n\n\n\uf0b7 LD90 = 0.84 % \n\n\n\nFigure 2: Dose \u2013 Response Curve of Tabrbush plant ethanol branches \nextracts towards conidiospores of Monilinia fructigena \n\n\n\n\uf0b7 NOAEL = 0.53 %\n\n\n\n\uf0b7 LOAEL = 1.11 % \n\n\n\n\uf0b7 LD50 = 2.14 %\n\n\n\n\uf0b7 LD90 = 2.80 %\n\n\n\nFigure 3: Dose \u2013 Response Curve of Tabrbush plant ethanol leaves \nextracts towards conidiospores of Botrytis cinereae \n\n\n\n\uf0b7 NOAEL = 0.015 %\n\n\n\n\uf0b7 LOAEL = 0.03%\n\n\n\n\uf0b7 LD50 = 0.052 %\n\n\n\n\uf0b7 LD90 = 0.24 % \n\n\n\nFigure 4: Dose \u2013 Response Curve of Tabrbush plant ethanol branches \nextracts towards conidiospores of Botrytis cinereae \n\n\n\n\uf0b7 NOAEL = 0.011 %\n\n\n\n\uf0b7 LOAEL = 0.032%\n\n\n\n\uf0b7 LD50 = 0.085 %\n\n\n\n\uf0b7 LD90 = 1.15 % \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 81-84 \n\n\n\nCite The Article: Donyo Ganchev (2022). Antisporulation Action of Tarbush Plant (Flourensia Cernua) Towards  \nConidiospores of Plant Pathogens. Journal of Sustainable Agricultures, 6(2): 81-84. \n\n\n\nFigure 5: Dose \u2013 Response Curve of Tabrbush plant ethanol leaves \nextracts towards conidiospores of Venturia inaeqialis \n\n\n\n\uf0b7 NOAEL = 0.042 %\n\n\n\n\uf0b7 LOAEL = 0.05%\n\n\n\n\uf0b7 LD50 = 0.068 %\n\n\n\n\uf0b7 LD90 = 0.087 % \n\n\n\nFigure 6: Dose \u2013 Response Curve of Tabrbush plant ethanol branches \nextracts towards conidiospores of Venturia inaeqialis \n\n\n\n\uf0b7 NOAEL = 0.049 %\n\n\n\n\uf0b7 LOAEL = 0.056%\n\n\n\n\uf0b7 LD50 = 0.078 %\n\n\n\n\uf0b7 LD90 = 0.093 % \n\n\n\nFigure 7: Summarized data received from conducted tests \n\n\n\nThe Figure 7 show summarized data from the conducted trials. In all of \ncases the extracts from branches manifest lower effect that extracts from \nleaves, most significant in the Monilinia fructigena. However, in Venturia \n\n\n\ninaequalis the difference between the effectiveness of extracts from leaves \nand branches towards germination of conidiospores is minor.  From the \nfigure above is obviously that ethanol tarbush plant extracts have the \nbiggest and extremely strong effect towards spores of Venturia inaequalis. \nThe effectiveness towards Monilinia fructigena is quite low.  \n\n\n\n4. CONCLUSIONS \n\n\n\nPresented results clearly show the strong antifungal and antisporulation \nof extracts prepared from leaves and branches of tarbush plant (Flourensia \ncernua) confirming from the cited in this paper previous researches with \nthis plant. Surprisingly, the extracts not only do not inhibit the sporulation \nof Alteraria solani but even stimulate germination of condiospores. \nTowards other tested plant pathogens however, especially Venturia \ninaeqialis, ethanol extracts from tarbush plant (Flourensia cernua) show \nstrong inhibition effect. These results can be a base for development of the \nnew natural fungicides against tested plant pathogens which can be \napplied as in the commercial agriculture, as in the organic or integrated \npest management \n\n\n\nREFERENCES \n\n\n\nAbbott, W.S., 1925.  A method of computing the effectiveness of an \ninsecticide. J. econ. Entomol., 18 (2), Pp. 265-267. \n\n\n\nAguilar-Alonso, P., Navarro-Cruz, A.R., S\u00e1nchez-Flores, A.B., \u00c1vila-Sosa, R., \nde la Cruz Meneses-S\u00e1nchez, M., 2013. Efecto antifu\u0301ngico de extractos de \nplantas originarias del estado de Puebla sobre Colletotrichum \ngloeosporioides. CienciaUAT, Pp. 06-11. \n\n\n\nAlvarez-Perez, O.B., Ventura-Sobrevilla, J.M., Ascacio-Vald\u00e9s, J.A., Rojas, R., \nVerma, D.K., Aguilar, C.N., 2020. Valorization of Flourensia cernua DC as \nsource of antioxidants and antifungal bioactives. Industrial Crops and \nProducts, 152, Pp. 112422. \n\n\n\nApaza, L.A.G., and Solis, A.L.S., 2019. Eficiencia de extractos etan\u00f3licos de \nvegetales para el control in vitro del hongo fitopat\u00f3geno (Fusarium \nverticillioides), Lima-2019. \n\n\n\nAvila-Sosa, R., Gast\u00e9lum-Reynoso, G., Garc\u00eda-Ju\u00e1rez, M., de la Cruz \nMeneses-S\u00e1nchez, M., Navarro-Cruz, A.R., D\u00e1vila-M\u00e1rquez, R.M., 2011. \nEvaluation of different Mexican plant extracts to control anthracnose. \nFood and Bioprocess Technology, 4 (4), Pp. 655-659. \n\n\n\nBadillo, L.M.D., Espinosa-Madrigal, R.M., Martinez-Mu\u00f1oz, R.E., Ron-\nEcheverr\u00eda, O.A., Salgado-Garciglia, R., Flores-Garc\u00eda, A., Pacheco, \nM.M.M., 2008. The Mexican medicinal plants with antifungal properties \nare an economic and health opportunity area. Pharmacologyonline, 3, \nPp. 61-77. \n\n\n\nCastillo, F., Aguilar, C.N., Hern\u00e1ndez, D., Gallegos, G., Rodr\u00edguez, R., 2012. \nAntifungal properties of bioactive compounds from plants. INTECH \nOpen Access Publisher. \n\n\n\nCastillo, F.D.H., Reyes, F.C., Morales, G.G., Herrera, R.R., Aguilar, C.N., 2011. \nPlant extracts from mexican native species: an alternative for control of \nplant pathogens. Research in Organic Farming. InTech, Rijeka, Pp. 139-\n156. \n\n\n\nCespedes, C.L., Alarcon, J., Aqueveque, P.M., Lobo, T., Becerra, J., Balbontin, \nC., Seigler, D. S., 2015. New environmentally-friendly antimicrobials and \nbiocides from Andean and Mexican biodiversity. Environmental \nresearch, 142, Pp. 549-562. \n\n\n\nDe Le\u00f3n, M.\u00c1., S\u00e1enz, A., Jasso-Cantu, D., Rodr\u00edguez, R., Pandey, A., Aguilar, \nC.N., 2013. Fermented Flourensia cernua extracts and their in vitro assay \nagainst Penicillium expansum and Fusarium oxysporum. Food \nTechnology and Biotechnology, 51 (2), Pp. 233-239. \n\n\n\nDe Le\u00f3n-Zapata, M.A., Pastrana-Castro, L., Rua-Rodr\u00edguez, M.L., Alvarez-\nP\u00e9rez, O.B., Rodr\u00edguez-Herrera, R., and Aguilar, C.N., 2016. Experimental \nprotocol for the recovery and evaluation of bioactive compounds of \ntarbush against postharvest fruit fungi. Food chemistry, 198, Pp. 62-67. \n\n\n\nde Rodr\u00edguez, D.J., Hern\u00e1ndez-Castillo, D., Angulo-S\u00e1nchez, J.L., Rodr\u00edguez-\nGarc\u00eda, R., Quintanilla, J.V., Lira-Saldivar, R.H., 2007. Antifungal activity \nin vitro of Flourensia spp. extracts on Alternaria sp., Rhizoctonia solani, \nand Fusarium oxysporum. Industrial Crops and Products, 25 (2), Pp. \n111-116. \n\n\n\nde Rodr\u00edguez, D.J., Hern\u00e1ndez-Castillo, F.D., Sol\u00eds-Gaona, S., Rodr\u00edguez-\nGarc\u00eda, R., and Rodr\u00edguez-Jasso, R.M., 2012. Flourensia cernua DC: a \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 81-84 \n\n\n\nCite The Article: Donyo Ganchev (2022). Antisporulation Action of Tarbush Plant (Flourensia Cernua) Towards  \nConidiospores of Plant Pathogens. Journal of Sustainable Agricultures, 6(2): 81-84. \n\n\n\nplant from Mexican semiarid regions with a broad spectrum of action for \ndisease control. Integrated Pest Management and Pest Control, Current \nand Future Tactics, InTech, Rijeka, Pp. 639-650. \n\n\n\nde Rodr\u00edguez, D.J., Puente-Romero, G.N., D\u00edaz-Jim\u00e9nez, L., Rodr\u00edguez-\nGarc\u00eda, R., Ram\u00edrez-Rodr\u00edguez, H., Villarreal-Quintanilla, J.A., Genisheva, \nZ.A., 2019. In vitro gastrointestinal digestion of microencapsulated \nextracts of Flourensia cernua, F. microphylla, and F. retinophylla. \nIndustrial Crops and Products, 138, Pp. 111444. \n\n\n\nde Rodr\u00edguez, D.J., Salas-M\u00e9ndez, E.D.J., Rodr\u00edguez-Garc\u00eda, R., Hern\u00e1ndez-\nCastillo, F.D., D\u00edaz-Jim\u00e9nez, M.L.V., S\u00e1enz-Galindo, A., Carrillo-Lomel\u00ed, \nD.A., 2017. Antifungal activity in vitro of ethanol and aqueous extracts of \nleaves and branches of Flourensia spp. against postharvest fungi. \nIndustrial Crops and Products, 107, Pp. 499-508. \n\n\n\nEspinoza Pantigozo, I.K., 2016. Efecto comparativo de aceite esencial y \nextracto acuoso de hojas de Schinus molle L.\u201cmolle\u201d sobre el crecimiento \nde Botrytis cinerea. \n\n\n\nEstell, R.E., Fredrickson, E.L., Havstad, K.M., 1996. Chemical composition of \nFlourensia cernua at four growth stages. Grass and Forage Science, 51 \n(4), Pp. 434-441. \n\n\n\nEstell, R.E., Havstad, K.M., Fredrickson, E.L., Gardea-Torresdey, J.L., 1994. \nSecondary chemistry of the leaf surface of Flourensia cernua. \nBiochemical Systematics and Ecology, 22 (1), Pp. 73-77. \n\n\n\nFredrickson, E., Thilsted, J., Estell, R., Havstad, K., 1994. Effect of chronic \ningestion of tarbush (Flourensia cernua) on ewe lambs. Veterinary and \nhuman toxicology, 36 (5), Pp. 409-415. \n\n\n\nGalv\u00e1n, J.V., D\u00edaz, C.A.G., Fern\u00e1ndez, R.G., 2014. Efecto de los extractos \nacuosos de hojas de plantas de gobernadora (Larreas tridentata), \nhojasen (Flourensia cernua) y encino (Quercus pungens), sobre el \ncrecimiento micelial in vitro de hongos fitopat\u00f3genos. Acta \nUniversitaria, 24 (5), Pp. 13-19. \n\n\n\nGuerrero-Rodr\u00edguez, E., Sol\u00eds-Gaona, S., Hern\u00e1ndez-Castillo, F.D., Flores-\nOlivas, A., Sandoval-L\u00f3pez, V., Jasso-Cant\u00fa, D., 2007. In vitro biological \nactivity of extracts of Flourensia cernua DC on postharvest pathogens: \nAlternaria alternata (Fr.: Fr.) Keissl., Colletotrichum gloeosporioides \n(Penz.) Penz. y Sacc. y Penicillium digitatum (Pers.: Fr.) Sacc. Revista \nmexicana de fitopatolog\u00eda, 25 (1), Pp. 48-53. \n\n\n\nKing, D.W., Estell, R.E., Fredrickson, E.L., Havstad, K.M., Wallace, J.D., \nMurray, L.W., 1996. Effects of Flourensia cernua ingestion on intake, \ndigesta kinetics, and ruminal fermentation of sheep consuming tobosa. \n\n\n\nKorthuis, S.L., 1988. Flourensia cernua. USDA Forest Service, Rocky \nMountain Research Station, Fire Sciences Laboratory. \n\n\n\nLinares Rivero, C., 2014. Actividad antif\u00fangica in vitro de extractos de \nhojas de Citrus spp. frente a Stemphyllium solani Webber (Doctoral \ndissertation, Universidad Central\" Marta Abreu\" de Las Villas). \n\n\n\nLira-Saldivar, R.H., Hernandez, M., Leon, E., Barajas, N., Villarreal, S., \nMedina, J.G., 2008. Antimicrobial properties of resinous plant extracts \nfrom Mexico with agrochemical and pharmaceutical potential. Planta \nMedica, 74 (09), Pp. 23. \n\n\n\nManhart, J.R., 2011. Flourensia cernua (Native) \n\n\n\nMolina-Salinas, G.M., Ramos-Guerra, M.C., Vargas-Villarreal, J., Mata-\nC\u00e1rdenas, B.D., Becerril-Montes, P., Said-Fern\u00e1ndez, S., 2006. \nBactericidal activity of organic extracts from Flourensia cernua DC \nagainst strains of Mycobacterium tuberculosis. Archives of medical \nResearch, 37 (1), Pp. 45-49. \n\n\n\nO'Laughlin, T.C., 1975. The distribution and productivity of Flourensia \ncernua DC in southern New Mexico. New Mexico State University, Las \nCruces, New Mexico, Pp. 74. \n\n\n\nOotani, M.A., Aguiar, R.W., Ramos, A.C.C., Brito, D.R., Silva, J.B.D., Cajazeira, \nJ.P., 2013. Use of essential oils in agriculture. Journal of biotechnology \nand biodiversity, 4 (2), Pp. 162-175. \n\n\n\nPrieto, J.A., Pati\u00f1o, O.J., Plazas, E.A., Pab\u00f3n, L.C., \u00c1vila, M.C., Guzm\u00e1n, J.D., \nCuca, L.E., 2013. Natural products from plants as potential source agents \nfor controlling Fusarium. Fungicides-Showcases of Integrated Plant \nDisease Management from Around the World. Croacia: Intech, Pp. 233-\n278. \n\n\n\nR Core Team. R., 2020. A language and environment for statistical \ncomputing. R Foundation for Statistical Computing, Vienna, Austria \n[Internet]; 2020. Available from: https://www.R-project.org/ \n\n\n\nRitz, C., Baty, F., Streibig, J.C., Gerhard, D., 2015. Dose-Response Analysis \nUsing R.  PLOS ONE. 10 (12) \n\n\n\nRodr\u00edguez-Guadarrama, A.H., Guevara-Gonz\u00e1lez, R.G., de Jes\u00fas, R.G.S., \nFeregrino-P\u00e9rez, A. A., 2018. Antifungal activity of Mexican endemic \nplants on agricultural phytopathogens: a review. In 2018 XIV \nInternational Engineering Congress (CONIIN), pp. 1-11. IEEE. \n\n\n\nSalazar, R., Pozos, M.E., Cordero, P., Perez, J., Salinas, M.C., Waksman, N., \n2008. Determination of the antioxidant activity of plants from Northeast \nMexico. Pharmaceutical Biology, 46 (3), Pp. 166-170. \n\n\n\nTellez, M., Estell, R., Fredrickson, E.D., Powell, J., Wedge, D., Schrader, K., \nKobaisy, M., 2001. Extracts of Flourensia cernua (L): Volatile \nconstituents and antifungal, antialgal, and antitermite bioactivities. \nJournal of Chemical Ecology, 27 (11), Pp. 2263-2273 \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 09-15 \n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ     \n\n\n\n ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nThe present study was undertaken to identify the morphological, physical and chemical characteristics of soils in \nTushka, Aswan governorate, Egypt, in order to classify and evaluate them from the agricultural use view point. \nTushka area is located in the western desert, upper Egypt. It lies between latitudes of 22\u00b0 48\u2032 00.7\" and 22\u00b0 28\u2032 \n44.2'' N and longitudes of 31\u00b0 28\u2032 07.2\" and 31\u00b0 29\u2032 08.2\" E. The soils of the study area were none to slightly \nsaline (ECe ranged from 0.53 to 6.85 dSm-1). Soil texture was mostly sand, loamy sand and sandy loam. Soil \nreaction (pH) tended to be mildly to moderately alkaline with a range of 7.6 to 8.1. Calcium carbonate and \ngypsum contents were very low. The soils were classified as Typic Torripsamments, Typic Torriorthents and \nLithic Torriorthents. Most of the soils understudy were suitable for agricultural use. The results revealed that \nthe capability of soils according to ASLE program was good (C2) and fair suitable (C3), moderate suitable (S3) \nusing MicroLEIS (Cervatana model) and good, fair and poor using Modified Storie Index. Most of the selected \ncrops were found to be the best grown ones on soils of the S2 and S3 suitability classes by ASLE program. Also, \nmost of the selected crops were moderately (S3) and marginally suitable (S4) by MicroLEIS-ALMAGRA model. \nThe main limitation factors of the study area for crop production were soil texture and soil depth.\n\n\n\n KEYWORDS \n\n\n\nTushka, ASLE program, MicroLEIS, Modified Storie Index\n\n\n\n1. INTRODUCTION\n\n\n\nEgypt has an arid land with almost 96% of uninhabited parts of its \nterritory. More than ninety million inhabitants are concentrated mainly \nin the Nile delta and valley as well as in the northern coastal zone along \nthe Mediterranean Sea and in small areas of Western desert where lands \nare suitable for agricultural production [1,2].\n\n\n\nThe main challenge facing Egypt today is the need for better \ndevelopment and management of natural resources to meet the growing \nneeds of the nation. The ratio between land and human resources is the \nmost important problem in Egypt [3]. The horizontal agricultural \nexpansion in the Western desert is one of the most important objectives \nof Egyptian agricultural policy to meet the food security needs of the \ngrowing population [4]. The agricultural expansion in new desert areas \nis also a priority to compensate the successive loss of agricultural land in \nEgypt [5].\n\n\n\nSouthwest Tushka area which lies south west of Egypt is considered as \none of the promising areas for agricultural expansion and development \n[6]. Land assessment allows lands to be evaluated for agricultural use in \naccordance with their physical and chemical capacities as well as \nlimitations to protect soil resources from degradation during \npotentialities achieving farmers' demands for optimal crop production \n[7]. Since wheat, barley, maize and sorghum are strategic crops in Egypt \nand most farmers devote high surface areas to grow wheat each year, \nthese crops were selected to be evaluated for soil adequacy assessment \nof this area.\n\n\n\nThe general view of geology and geomorphology of the western desert, \nwhich includes the area understudy [8]. Essentially it is a desertic \nplateau with a vast flat expansion of rocky ground or numerous closed \ndepressions. The greatest altitude is attained in the extreme south \nwestern corner where the general plateau character is disturbed by the \ngreat mountain of Gebel Uweinat. North of this mountain, a broad high \nterrain plateau, known as Gilf El-Kebir, extends for more than 200 km. \nThis sandstone plateau is bordered in the south by a prominent \nescarpment, that descends gradually to the north and east directions \nforming a very extensive pediment sandy plain. This sandy plain is \ndotted in several parts by many rock exposures of Tertiary volcanic \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.02.2018.09.15 \n\n\n\nSOIL CAPABILITY AND SUITABILITY ASSESSMENT OF TUSHKA AREA, EGYPT BY \nUSING DIFFERENT PROGRAMS (ASLE, MICROLEIS AND MODIFIED STORIE INDEX) \n\n\n\nSalah Hassanien Abd El-Aziz\n\n\n\nDepartment of Soils & Water, Collage of Agriculture, Assiut University, Assiut, Egypt.\n\n\n\n*Corresponding Author Email: small.hearts@yahoo.com\n\n\n\nvolcanic origin and basement complex rocks of granitites. Cretaceous \nrocks formed of what is called the Nubian formation, which is essentially \nsandstone, occupy the sand plain. In general, soil characteristics, \nclassification and evaluation of some parts in Egypt using different \nprograms (ASLE, MicroLEIS and Modified Storie Index) which were \nstudied at regional stages were investigated by many researchers [9- 22].\nThe main objective of this research is to evaluate and compare the land \nsuitability of Tushka area, Egypt for some principal crops using different \nevaluation systems. Several crops were selected to assess their \nconvenience to be grown in the studied area. This study is needed to get \nuseful information about these soils. It would help agricultural \ninvestment of various parts of Tushka area.\n\n\n\n2. MATERIAL AND METHODS\n\n\n\n2.1 Field Description and Soil Sampling\n\n\n\nFigure 1: The soil profile location map of the study area\n\n\n\nThe area under investigation is located on the east side of Abu-Simbel/ \nAswan road which is (km 50) north of Abu-Simbel city. It is a part of the \nwestern desert plateau and lies between latitudes 22\u00b048\u2032 00.7\" and 22\u00b0 \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 09-15 \n\n\n\n48\u2032 00.7\" and 22\u00b0 28\u2032 44.2'' N and longitudes 31\u00b0 28\u2032 07.2\" and 31\u00b0 29\u2032 \n08.2\" E (Figure 1). Twenty soil profiles were selected to represent the \narea under investigation according to the geological, topographic and \nrecent aerial photographic maps of the study area. The profiles were dug \ndown to parent rock and described for their morphological \ncharacteristics according to the standard procedures [23-25]. Soil \nsamples were collected from profile layers according to the vertical \nmorphological variations. The samples were air-dried, crushed, passed \nthrough a 2 mm sieve, and kept for different analysis. Soil color of both \ndry and moist samples was determined using Munsell color chart was \ndetermined [26]. The study also exploited the use of geographic \ninformation systems (GIS, ArcView, 10) for mapping the soils of the \nstudy area.\n\n\n\n2.2 Climate of the Study Area\n\n\n\nThe most important climate characteristics necessary for the suitability \ndetermination (temperature, rainfall, relative humidity, etc.) were \ncollected from Aswan metrological station. The study area has a mean \nannual rainfall of 1 mm/ year that is concentrated in the winter season, \nwith mean relative humidity of 9.4% and a mean annual temperature of \n26.3 \u00b0C (mean maximum temperature is 33.9 \u00b0C and the mean minimum \ntemperature is 18.8 \u00b0C).\n\n\n\n2.3 Laboratory Analysis\n\n\n\nThe gravels content was measured by volume according to a study [27]. \nParticle-size distribution of the studied soils was performed according to \none study by a group researcher [28]. Soil reaction (pH) of 1:1 soil to \nwater suspension was measured using a glass electrode [29]. Total \nCalcium carbonate (CaCO3) was determined by Collin\u2019s calcimeter [30, \n31]. The electrical conductivity (ECe) of the solution soil paste extract \nwas assessed by methods described in some studies [32]. Determination \nof soil gypsum content was done in using a graph showing the relation \nbetween the concentration and electrical conductivity of gypsum \nsolution [30]. The exchangeable sodium percentage (ESP) of the soil \nsamples was determined according to some research paper [32] using \nammonium acetate method. The cation exchangeable capacity was \nmeasured by sodium oxalate method [33, 34].\n\n\n\n2.4 Soil Classification\n\n\n\nThe dominant soil moisture regime is aridic (torric) with a hyperthermic \nsoil temperature regime. The soils were classified up to the sub group \naccording to Soil Taxonomy [25]. The results obtained from the visual \ninterpretation and digital elevation model as well as field data were \nincorporated using GIS in order to produce the soil map of the study \narea. \n\n\n\n2.5 Land Evaluation Methods\n\n\n\nThe studied soils were evaluated for land capability and suitability using \nseveral systems as follow:\n\n\n\na) Land capability classification\n\n\n\n\u2022 Modified Storie Index Rating, [35]: The calculation was run and marked\nusing Visual Basic for application under Microsoft Excel [36],\n\u2022 MicroLEIS [37], Internet-based program, and\n\u2022 Applied System of Land Evaluation (ASLE) program [38].\n\n\n\nb) Land suitability classification.\n\n\n\n\u2022 MicroLEIS [39], Internet-based program, and\n\u2022 Applied System of Land Evaluation (ASLE) program [38].\n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Main Morphological Aspects of the Studied Soils\n\n\n\nThe main morphological aspects of the studied soil profiles are shown in \nTable 1. The field description revealed that the topography of the \nlandscape was almost flat to gently sloping. The elevation ranged \nbetween 192 and 208 m above sea level. Most of soil profiles were fairly \nwell drained and the water table was deep (> 200 cm). Thus, the crop \ngrowth was not affected. The dominant soil color in the studied soil \nprofiles was reddish yellow (5YR 7/6, dry) to yellowish red (5YR 4/6, \nmoist) or reddish yellow (7.5YR 7/6, dry) to strong brown (7.5YR 5/6, \nmoist). However, very pale brown (10YR 8/4, dry) to yellowish brown \n(10YR 5/6, moist) colors were also detected. This could possibly be \nattributed to the heterogeneity of parent materials and/or multi-\ndepositional regime. No effervescence with dilute HCl was observed in all\n\n\n\npedons indicating absence of CaCO3.The soil structure of most soil \nprofiles was platy and subangular blocky; the consistence was slightly \nhard to extremely hard (dry) and loose to friable (moist). The area was \nvirgin without any natural vegetation. The horizon boundaries were \nabrupt in distinctness and smooth to wavy in topography.\n\n\n\nTable 1: The main morphological aspects of the studied soil profiles\n\n\n\n10\n\n\n\n Prof.\n\n\n\n No\n\n\n\nElevation \n\n\n\n A.S.L (m)\n Horizon\n\n\n\nDepth \n\n\n\n (cm)\n\n\n\n Soil Color\n Gravel\n\n\n\nTexture \n\n\n\n (I)\n\n\n\n Soil Structure (II)  Consistence (III) Boundary \n\n\n\n (IV) Hue  Dry  Moist  Grade  Size  Type  Dry  Moist\n\n\n\n 1  205\n C1\n\n\n\n 2C2\n\n\n\n 0-20\n\n\n\n 20-100\n\n\n\n 10YR\n\n\n\n 5YR\n\n\n\n 8/4\n\n\n\n 4/4\n\n\n\n 5/4\n\n\n\n 5/6\n\n\n\n few\n\n\n\n -\n\n\n\n LS\n\n\n\n SL\n\n\n\n 1\n\n\n\n 2\n\n\n\n f\n\n\n\n c\n\n\n\n pl\n\n\n\n pl\n\n\n\n sh\n\n\n\n vh\n\n\n\n loose\n\n\n\n friable\n\n\n\n as\n\n\n\n -\n\n\n\n 2  198\n\n\n\n C1\n\n\n\n 2C2\n\n\n\n 2C3\n\n\n\n 0-25\n\n\n\n 25 - 50\n\n\n\n 50 - 100\n\n\n\n 10YR\n\n\n\n 10YR\n\n\n\n 5YR\n\n\n\n 8/4\n\n\n\n 8/4\n\n\n\n 6/6\n\n\n\n 5/6\n\n\n\n 7/4\n\n\n\n 5/6\n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\n LS\n\n\n\n S\n\n\n\n S\n\n\n\n -\n\n\n\n 2\n\n\n\n 2\n\n\n\n -\n\n\n\n m\n\n\n\n m\n\n\n\n sl\n\n\n\n pl\n\n\n\n pl\n\n\n\n so\n\n\n\n h\n\n\n\n vh\n\n\n\n loose\n\n\n\n loose\n\n\n\n friable\n\n\n\n as\n\n\n\n aw\n\n\n\n -\n\n\n\n 3  195\n C1\n\n\n\n2C2 \n\n\n\n 0-15\n\n\n\n 15-70\n\n\n\n 7.5YR\n\n\n\n7.5YR \n\n\n\n7/4 \n\n\n\n6/4 \n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n -\n\n\n\n -\n\n\n\nS \n\n\n\nLS \n\n\n\n 1\n\n\n\n 1\n\n\n\n f\n\n\n\n m\n\n\n\n pl\n\n\n\nsbk \n\n\n\n sh\n\n\n\n h\n\n\n\n loose\n\n\n\n loose\n\n\n\n as\n\n\n\n -\n\n\n\n 4  193\n C1\n\n\n\n R\n\n\n\n 0-20\n\n\n\n 20-50\n\n\n\n 10YR\n\n\n\n 10YR\n\n\n\n 8/4\n\n\n\n 6/4\n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n few\n\n\n\n -\n\n\n\nSL \n\n\n\n SL\n\n\n\n -\n\n\n\n 2\n\n\n\n -\n\n\n\n m\n\n\n\n sl\n\n\n\n pl\n\n\n\n so\n\n\n\n vh\n\n\n\n loose\n\n\n\n friable\n\n\n\n as\n\n\n\n -\n\n\n\n5  195\n\n\n\n C1\n\n\n\n2C2 \n\n\n\n3C3 \n\n\n\n0-15 \n\n\n\n15-30 \n\n\n\n30 - 90 \n\n\n\n 7.5YR\n\n\n\n 10YR\n\n\n\n7.5YR \n\n\n\n 6/5\n\n\n\n 8/4\n\n\n\n 5/6\n\n\n\n 4/4\n\n\n\n 5/6\n\n\n\n 44/\n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\nSL \n\n\n\nLS \n\n\n\nSL \n\n\n\n- \n\n\n\n1 \n\n\n\n2 \n\n\n\n -\n\n\n\n f\n\n\n\n f\n\n\n\n sl\n\n\n\n pl\n\n\n\nsbk \n\n\n\n so\n\n\n\n sh\n\n\n\n h\n\n\n\n Loose\n\n\n\nv. friable\n\n\n\nfriable\n\n\n\n aw\n\n\n\n -\n\n\n\n6  192\n\n\n\nC1 \n\n\n\nC2 \n\n\n\n C3\n\n\n\n 0-20\n\n\n\n 20-50\n\n\n\n 50 - 100\n\n\n\n 5YR\n\n\n\n 5YR\n\n\n\n 5YR\n\n\n\n 7/6\n\n\n\n 7/6\n\n\n\n 7/6\n\n\n\n 4/6\n\n\n\n 4/6\n\n\n\n 4/6\n\n\n\n few\n\n\n\n -\n\n\n\n -\n\n\n\n SL\n\n\n\n SL\n\n\n\n SL\n\n\n\n -\n\n\n\n 1\n\n\n\n 2\n\n\n\n -\n\n\n\n f\n\n\n\n f\n\n\n\n sl\n\n\n\n sbk\n\n\n\n pl\n\n\n\n so\n\n\n\n h\n\n\n\n vh\n\n\n\nloose\n\n\n\nv. friable\n\n\n\nfriable\n\n\n\n as\n\n\n\n as\n\n\n\n -\n\n\n\n7  198\n\n\n\nC1 \n\n\n\n2C2 \n\n\n\n2C3 \n\n\n\n2C4 \n\n\n\n0 - 15 \n\n\n\n15 - 25 \n\n\n\n25 - 40 \n\n\n\n40 - 90 \n\n\n\n7.5YR \n\n\n\n7.5YR \n\n\n\n7.5YR \n\n\n\n10YR \n\n\n\n 5/6\n\n\n\n5/6 \n\n\n\n5/6 \n\n\n\n 8/4\n\n\n\n 4/4\n\n\n\n4/4 \n\n\n\n4/4 \n\n\n\n 6/6\n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\n SL\n\n\n\n LS\n\n\n\n LS\n\n\n\n LS\n\n\n\n -\n\n\n\n 1\n\n\n\n 2\n\n\n\n 2\n\n\n\n -\n\n\n\nf \n\n\n\nm \n\n\n\nm \n\n\n\n sl\n\n\n\n pl\n\n\n\n sbk\n\n\n\n sbk\n\n\n\n so\n\n\n\n slh\n\n\n\n h\n\n\n\n h\n\n\n\nLoose\n\n\n\nv. friable\n\n\n\nfriable\n\n\n\nfriable\n\n\n\n as\n\n\n\n as\n\n\n\naw \n\n\n\n -\n\n\n\n8  205\n C1\n\n\n\n C2\n\n\n\n 0-20\n\n\n\n 20-90\n\n\n\n 5YR\n\n\n\n 5YR\n\n\n\n 6/6\n\n\n\n 7/6\n\n\n\n 5/6\n\n\n\n 6/6\n\n\n\n -\n\n\n\n -\n\n\n\nLS \n\n\n\nLS \n\n\n\n 1\n\n\n\n 2\n\n\n\n f\n\n\n\n m\n\n\n\n pl\n\n\n\nsbk \n\n\n\n sh\n\n\n\n h\n\n\n\nloose\n\n\n\nfriable\n\n\n\n as\n\n\n\n -\n\n\n\n9  208\n\n\n\nC1 \n\n\n\n 2C2\n\n\n\n 2C3\n\n\n\n 0-20\n\n\n\n 20 - 50\n\n\n\n 50 - 80\n\n\n\n10YR \n\n\n\n 7.5YR\n\n\n\n 7.5YR\n\n\n\n 7/4\n\n\n\n 5/8\n\n\n\n 7/6\n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n 5/8\n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\nLS \n\n\n\nSL \n\n\n\nSL \n\n\n\n -\n\n\n\n 2\n\n\n\n 3\n\n\n\n -\n\n\n\n m\n\n\n\n m\n\n\n\n sl\n\n\n\n sbk\n\n\n\nsbk \n\n\n\n so\n\n\n\n h\n\n\n\n vh\n\n\n\nLoose\n\n\n\nfriable\n\n\n\nfriable\n\n\n\n as\n\n\n\n as\n\n\n\n -\n\n\n\n10  198\nC1 \n\n\n\nC2 \n\n\n\n0 - 25 \n\n\n\n25 - 60 \n\n\n\n 7.5YR\n\n\n\n 7.5YR\n\n\n\n 7/6\n\n\n\n7/6 \n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n -\n\n\n\n -\n\n\n\n SL\n\n\n\n SL\n\n\n\n 1\n\n\n\n 2\n\n\n\n f\n\n\n\n m\n\n\n\n pl\n\n\n\n sbk\n\n\n\n slh\n\n\n\n h\n\n\n\nloose\n\n\n\nfriable\n\n\n\n as\n\n\n\n -\n\n\n\n 11  195\n C1\n\n\n\nC2 \n\n\n\n0-30 \n\n\n\n 30-100\n\n\n\n 10YR\n\n\n\n7.5YR \n\n\n\n 8/4\n\n\n\n 7/6\n\n\n\n 5/4\n\n\n\n 5/6\n\n\n\n few\n\n\n\n -\n\n\n\n LS\n\n\n\nLS \n\n\n\n -\n\n\n\n 2\n\n\n\n -\n\n\n\n f\n\n\n\nsl \n\n\n\n pl\n\n\n\nso \n\n\n\n h\n\n\n\nloose\n\n\n\nfriable\n\n\n\n as\n\n\n\n -\n\n\n\n Prof.\n\n\n\n No\n\n\n\nElevation \n\n\n\n A.S.L (m)\n Horizon\n\n\n\nDepth \n\n\n\n (cm)\n\n\n\n Soil Color\n Gravel\n\n\n\nTexture \n\n\n\n (I)\n\n\n\n Soil Structure (II)  Consistence (III) Boundary \n\n\n\n (IV) Hue  Dry  Moist  Grade  Size  Type  Dry  Moist\n\n\n\n 12  193\n\n\n\n C1\n\n\n\n2C2 \n\n\n\n 3C3\n\n\n\n0-30 \n\n\n\n 30 - 70\n\n\n\n 70 - 100\n\n\n\n 7.5YR\n\n\n\n 10YR\n\n\n\n 5YR\n\n\n\n 7/4\n\n\n\n 8/4\n\n\n\n 7/6\n\n\n\n 5/6\n\n\n\n 7/4\n\n\n\n 5/6\n\n\n\n few\n\n\n\n -\n\n\n\n -\n\n\n\n SL\n\n\n\nLS \n\n\n\n SL\n\n\n\n -\n\n\n\n 2\n\n\n\n 2\n\n\n\n -\n\n\n\n f\n\n\n\n m\n\n\n\nsl \n\n\n\n sbk\n\n\n\n pl\n\n\n\nso \n\n\n\nh \n\n\n\nvh \n\n\n\n loose\n\n\n\nv. friable\n\n\n\nfriable\n\n\n\n as\n\n\n\n aw\n\n\n\n -\n\n\n\n 13  198\n C1\n\n\n\n C2\n\n\n\n0 - 30 \n\n\n\n30 - 80 \n\n\n\n 10YR\n\n\n\n 10YR\n\n\n\n 8/4\n\n\n\n 8/4\n\n\n\n 5/6\n\n\n\n 7/4\n\n\n\n -\n\n\n\n -\n\n\n\nSL \n\n\n\nSL \n\n\n\n -\n\n\n\n 2\n\n\n\n -\n\n\n\n m\n\n\n\n sl\n\n\n\n pl\n\n\n\n so\n\n\n\n h\n\n\n\nloose\n\n\n\nfriable\n\n\n\n as\n\n\n\n -\n\n\n\n 14  200\n\n\n\n C1\n\n\n\n2C2 \n\n\n\n2C3 \n\n\n\n0-30 \n\n\n\n30 - 60 \n\n\n\n 60 - 80\n\n\n\n10YR \n\n\n\n7.5YR \n\n\n\n 7.5YR\n\n\n\n 4/8\n\n\n\n6/4 \n\n\n\n6/4 \n\n\n\n4/4 \n\n\n\n4/4 \n\n\n\n4/4 \n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\nSL \n\n\n\n LS\n\n\n\n LS\n\n\n\n -\n\n\n\n 1\n\n\n\n 2\n\n\n\n -\n\n\n\n f\n\n\n\nm \n\n\n\n sl\n\n\n\npl \n\n\n\n sbk\n\n\n\n so\n\n\n\n h\n\n\n\n vh\n\n\n\nloose\n\n\n\nfriable\n\n\n\nfriable\n\n\n\nas \n\n\n\nas \n\n\n\n -\n\n\n\n15  197\n\n\n\n C1\n\n\n\nC2 \n\n\n\n2C3 \n\n\n\n 0-15\n\n\n\n15-30 \n\n\n\n30 - 70 \n\n\n\n 7.5YR\n\n\n\n7.5YR \n\n\n\n 7.5YR\n\n\n\n7/4 \n\n\n\n 6/4\n\n\n\n 6/6\n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\nLS \n\n\n\nLS \n\n\n\nSL \n\n\n\n -\n\n\n\n 1\n\n\n\n 2\n\n\n\n -\n\n\n\n f\n\n\n\n m\n\n\n\nsl \n\n\n\n pl\n\n\n\nsbk \n\n\n\n so\n\n\n\nslh \n\n\n\nh \n\n\n\nloose\n\n\n\nv. friable \n\n\n\nfriable\n\n\n\n as\n\n\n\nas \n\n\n\n -\n\n\n\n16  196\n\n\n\n C1\n\n\n\nC2 \n\n\n\n2C3 \n\n\n\n0-10 \n\n\n\n10 - 50 \n\n\n\n 50 - 100\n\n\n\n10YR \n\n\n\n10YR \n\n\n\n10YR \n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n5/3 \n\n\n\n4/4 \n\n\n\n 4/4\n\n\n\n4/3 \n\n\n\n -\n\n\n\n -\n\n\n\n -\n\n\n\nLS \n\n\n\nLS \n\n\n\nS \n\n\n\n -\n\n\n\n 1\n\n\n\n2 \n\n\n\n -\n\n\n\n f\n\n\n\nco \n\n\n\nsl \n\n\n\n pl\n\n\n\npl \n\n\n\nso \n\n\n\n slh\n\n\n\n h\n\n\n\nloose\n\n\n\nv. friable\n\n\n\nfriable\n\n\n\nas \n\n\n\naw \n\n\n\n -\n\n\n\n17  195\n\n\n\nC1 \n\n\n\nC2 \n\n\n\nC3 \n\n\n\n0-20 \n\n\n\n 20 - 40\n\n\n\n 40 -70\n\n\n\n 7.5YR\n\n\n\n 10YR\n\n\n\n10YR \n\n\n\n7/4 \n\n\n\n6/2 \n\n\n\n 8/4\n\n\n\n 5/6\n\n\n\n5/3 \n\n\n\n 5/6\n\n\n\n few\n\n\n\n -\n\n\n\n -\n\n\n\n SL\n\n\n\n SL\n\n\n\n SL\n\n\n\n -\n\n\n\n 1\n\n\n\n 2\n\n\n\n -\n\n\n\n m\n\n\n\nm \n\n\n\n sl\n\n\n\n pl\n\n\n\npl \n\n\n\n so\n\n\n\n h\n\n\n\nexh \n\n\n\nloose\n\n\n\nfriable \n\n\n\nfriable \n\n\n\n as\n\n\n\nas \n\n\n\n -\n\n\n\n18  194\n\n\n\n C1\n\n\n\n C2\n\n\n\n 2C3\n\n\n\n0-15 \n\n\n\n 15 - 35\n\n\n\n 35 - 80\n\n\n\n 5YR\n\n\n\n 5YR\n\n\n\n 7.5YR\n\n\n\n 6/6\n\n\n\n 7/6\n\n\n\n 6/6\n\n\n\n 5/6\n\n\n\n 6/6\n\n\n\n 5/6\n\n\n\n few\n\n\n\n -\n\n\n\n -\n\n\n\nLS \n\n\n\n LS\n\n\n\n SL\n\n\n\n 1\n\n\n\n 2\n\n\n\n 2\n\n\n\n f\n\n\n\n m\n\n\n\n m\n\n\n\n pl\n\n\n\n bk\n\n\n\n pl\n\n\n\n slh\n\n\n\n h\n\n\n\n vh\n\n\n\nloose\n\n\n\nloose\n\n\n\nv. friable\n\n\n\n as\n\n\n\n as\n\n\n\n -\n\n\n\n19  198\n C1\n\n\n\n 2C2\n\n\n\n0-30 \n\n\n\n30-60 \n\n\n\n 7.5YR\n\n\n\n 7.5YR\n\n\n\n 7/6\n\n\n\n 7/6\n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n -\n\n\n\n -\n\n\n\n LS\n\n\n\n SL\n\n\n\n 1\n\n\n\n 2\n\n\n\n f\n\n\n\n m\n\n\n\n pl\n\n\n\n sbk\n\n\n\n slh\n\n\n\n h\n\n\n\nloose\n\n\n\nv. friable\n\n\n\n as\n\n\n\n -\n\n\n\n20  196\n\n\n\n C1\n\n\n\n 2C2\n\n\n\n 2C3\n\n\n\n0-20 \n\n\n\n 20 - 50\n\n\n\n 50 - 80\n\n\n\n 7.5YR\n\n\n\n 7.5YR\n\n\n\n 7.5YR\n\n\n\n 7/6\n\n\n\n 6/6\n\n\n\n 7/4\n\n\n\n 5/6\n\n\n\n 5/6\n\n\n\n 6/6\n\n\n\n few\n\n\n\n -\n\n\n\n -\n\n\n\nLS \n\n\n\n SL\n\n\n\n SL\n\n\n\n -\n\n\n\n 1\n\n\n\n 2\n\n\n\n -\n\n\n\n f\n\n\n\n f\n\n\n\n sl\n\n\n\n sbk\n\n\n\n pl\n\n\n\n so\n\n\n\n slh\n\n\n\n exh\n\n\n\nloose\n\n\n\nv. friable\n\n\n\nfriable\n\n\n\n as\n\n\n\n aw\n\n\n\n -\n\n\n\nAbbreviations:\nTexture            (1): S = Sand, LS= Loamy Sand and SL= Sandy Loam\nSoil structure  (II): 1 = weak, 2= moderate, 3=strong, f=fine m=medium, \nco= coarse, sl=structureless, pl=platy and sbk= subangular blocky.\nConsistence   (III): so = soft, sh = slightly hard, h= hard, vh= very hard, \nand exh = extremely hard\nBoundary      (IV): as = abrupt smooth, and aw = abrupt wavy.\n\n\n\n3.2 Main Physical and Chemical Properties of The Studied Soils\n\n\n\nThe main physical and chemical properties are given in Tables 2 and 3, \nrespectively, and are illustrated in Figure 2. These results showed that \nthe soil profiles were generally medium deep to deep and the soil texture \nwas mainly coarse (sand, loamy sand and sandy loam). The calcium \ncarbonate content ranged from 0.92 to 12.60 % with a general trend to \ndecrease with depth. The results also displayed that the gypsum content \nwas very low (< 0.5%). Soil reaction was mildly to moderately alkaline as \nindicated by pH values, which ranged between 7.6 and 8.1. In some cases, \npH values of the surface layers were considerably higher than those of \nthe subsurface ones. This pattern was conversely correlated with the \nconcentration of total soluble salts. The soils of the study area were non-\nto slightly saline as the ECe values varied between 0.53 and 6.85 dSm-1, \nexcept in few soil samples that they were considered moderately saline \nas the ECe extended from 8.17 to 11.37 dSm-1.\n\n\n\nTable 2: Some physical properties of soils, as well as their taxa, of the \nstudied profiles\n\n\n\nClassification \nSoli Texture \n\n\n\nGrade \n\n\n\nGypsum \n\n\n\n% \n\n\n\nCaCO3\n\n\n\n% \n\n\n\nDepth of \n\n\n\nLayer (cm) \n\n\n\nProf. \n\n\n\nNo. \n\n\n\nTypic Torriorthents \nLoamy Sand 0.04 3.36 0 - 20 \n\n\n\n1 \nSandy Loam 0.06 2.94 20 - 100 \n\n\n\nTypic Torripsamments \n\n\n\nLoamy Sand 0.05 6.32 0 - 25 \n\n\n\n2 Sand 0.05 2.10 25 - 50 \n\n\n\nSand 0.05 1.76 50 - 100 \n\n\n\nTypic Torripsamments \nSand 0.10 10.5 0 - 15 \n\n\n\n3 \nLoamy Sand 0.08 1.51 15 \u2013 70 \n\n\n\nLithic Torriorthents \nSandy Loam 0.06 7.14 0 - 20 \n\n\n\n4 \nSandy Loam 0.07 2.18 20 - 50 \n\n\n\nTypic Torriorthents \n\n\n\nSandy Loam 0.36 2.02 0 - 15 \n\n\n\n5 Loamy Sand 0.13 2.18 15 - 30 \n\n\n\nSandy Loam 0.18 2.10 30 - 90 \n\n\n\nTypic Torriorthents \n\n\n\nSandy Loam 0.06 10.92 0 - 20 \n\n\n\n6 Sandy Loam 0.07 3.44 20 - 50 \n\n\n\nSandy Loam 0.02 5.54 50 - 100 \n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 09-15 11\n\n\n\nTypic Torripsamments \nSandy Loam 0.07 1.85 0 - 15 \n\n\n\n7 \nLoamy Sand 0.05 2.02 15 - 25 \n\n\n\nLoamy Sand 0.06 1.01 25 \u2013 40 \n\n\n\nLoamy Sand 0.05 9.32 40 - 90 \n\n\n\nTypic Torripsamments \nLoamy Sand 0.06 5.04 0 - 20 \n\n\n\n8 \nLoamy Sand 0.15 1.68 20 - 90 \n\n\n\nTypic Torriorthents \n\n\n\nLoamy Sand 0.07 3.11 0 - 20 \n\n\n\n9 Sandy Loam 0.11 2.52 20 - 50 \n\n\n\nSandy Loam 0.06 9.41 50 - 80 \n\n\n\nTypic Torriorthents \nSandy Loam 0.05 2.10 0 - 25 \n\n\n\n10 \nSandy Loam 0.04 2.94 25 - 60 \n\n\n\nTypic Torripsamments \nLoamy Sand 0.02 3.28 0 - 30 \n\n\n\n11 \nLoamy Sand 0.03 1.93 30 - 100 \n\n\n\nTypic Torripsamments \n\n\n\nSandy Loam 0.06 3.86  0 - 30 \n\n\n\n12 Loamy Sand 0.07 1.76 30 - 70 \n\n\n\nSandy Loam 0.07 0.92 70 - 100 \n\n\n\nTypic Torriorthents \nSandy Loam 0.12 11.0  0 - 30 \n\n\n\n13 \nSandy Loam 0.03 9.66 30 - 80 \n\n\n\nTypic Torripsamments \n\n\n\nSandy Loam 0.06 3.78  0 - 30 \n\n\n\n14 Loamy Sand 0.04 1.26 30 - 60 \n\n\n\nLoamy Sand 0.01 1.60 60 - 80 \n\n\n\nTypic Torripsamments \n\n\n\nLoamy Sand 0.04 7.14  0 - 15 \n\n\n\n15 Loamy Sand 0.05 4.37 15 - 30 \n\n\n\nSandy Loam 0.04 4.79 30 - 70 \n\n\n\nTypic Torripsamments \n\n\n\nLoamy Sand 0.06 3.53  0 - 10 \n\n\n\n16 Loamy Sand 0.06 1.26 10 - 50 \n\n\n\nSand 0.03 1.09 50 - 100 \n\n\n\nTypic Torriorthents \n\n\n\nSandy Loam 0.09 9.66  0 - 20 \n\n\n\n17 Sandy Loam 0.19 3.36 20 - 40 \n\n\n\nSandy Loam 0.08 1.26 40 - 70 \n\n\n\nTypic Torriorthents \n\n\n\nLoamy Sand 0.10 9.24  0 - 15 \n\n\n\n18 Loamy Sand .040 5.46 15 - 35 \n\n\n\nSandy Loam 0.09 10.16 35 - 80 \n\n\n\nTypic Torripsamments \nLoamy Sand 0.03 5.12  0 - 30 \n\n\n\n19 \nSandy Loam 0.18 3.44 30 - 60 \n\n\n\nTypic Torriorthents \n\n\n\nSandy Loam 0.09 4.20  0 - 20 \n\n\n\n20 Sandy Loam 0.05 5.46 20 - 50 \n\n\n\nSandy Loam 0.06 12.60 50 - 80 \n\n\n\nSoil salinity (ECe) map Soil depth map Soil texture map \n\n\n\nThe low values of electrical conductivity (ECe) may be due to free drainage \nconditions. Most of soil profiles showed a clear increase in the soluble salts \nwith depth. Moreover, these soils exhibited no sodicity as they had \nexchangeable sodium percentage (ESP) values that were less than 15 % and \nsodium adsorption ratio (SAR) values which were less than 13, except the \nsubsurface layer of profile 18 that showed a SAR value that was higher than \n13 (18.11). The examined soil samples displayed that the cation exchange \ncapacity (CEC) was very low (5.11 \u2013 14.65 cmol (+)/kg) due to their coarse \ntexture and their extremely low content of organic matter due to the \nprevailing arid climate and barren nature of the soils [6]. The predominant \nclimate of the study area was extremely arid, and the dominant soil moisture \nregime was aridic (torric) with a hyperthermic soil temperature regime. The \ninvestigated soils are classified according to Soil Survey Staff (2014) as \nTypic Torripsamments, Typic Torriorthents and Lithic Torriorthents (Table \n2 and Figure 3).\n\n\n\nTable 3: Some chemical properties of studied soil profiles\n\n\n\nCEC \n\n\n\n cmol (+)/kg \nSAR \n\n\n\nESP \n\n\n\n% \n\n\n\nECe \n\n\n\n(dSm-1) \n\n\n\nPH \n\n\n\n(1:1) \n\n\n\nDepth of \n\n\n\nLayer (cm) \n\n\n\nProf. \n\n\n\nNo. \n\n\n\n9.22 4.56 2.98 3.70 7.9 0 - 20 \n1 \n\n\n\n8.51 3.93 3.97 3.33 8.0 20 - 100 \n\n\n\n9.21 3.33 2.01 1.58 7.7 0 - 25 \n\n\n\n2 7.11 1.20 0.86 0.66 7.6 25 - 50 \n\n\n\n7.35 1.38 0.54 0.53 7.9 50 - 100 \n\n\n\n6.42 3.99 2.47 1.42 7.7 0 - 15 \n3 \n\n\n\n5.21 9.99 11.99 3.50 7.7 15 \u2013 70 \n\n\n\n13.12 4.38 5.09 2.94 7.9 0 - 20 \n4 \n\n\n\n10.24 5.21 4.17 6.12 7.7 20 - 50 \n\n\n\n6.61 8.09 7.43 3.73 7.67 0 - 15 \n\n\n\n5 8.93 8.71 5.60 5.10 7.6 15 - 30 \n\n\n\n9.32 4.61 4.11 2.13 8.1 30 - 90 \n\n\n\n12.51 3.0 3.72 1.46 7.8 0 - 20 \n\n\n\n6 13.22 11.26 6.76 4.74 7.6 20 - 50 \n\n\n\n14.65 2.32 2.72 3.95 7.6 50 - 100 \n\n\n\n10.52 5.78 6.67 5.01 8.0 0 - 15 \n\n\n\n7 \n7.34 4.13 6.38 1.72 7.9 15 - 25 \n\n\n\n6.44 9.14 3.66 2.85 7.8 25 \u2013 40 \n\n\n\n5.82 2.52 1.38 1.50 7.8 40 - 90 \n\n\n\n8.21 3.24 1.56 2.22 .81 0 - 20 \n8 \n\n\n\n7.95 2.87 3.70 1.12 7.7 20 - 90 \n\n\n\n6.44 2.20 1.81 0.90 8.0 0 - 20 \n\n\n\n9 13.47 2.31 6.26 0.75 8.1 20 - 50 \n\n\n\n12.67 4.82 4.16 1.32 8.0 50 - 80 \n\n\n\n12.78 3.34 2.33 2.09 8.0 0 - 25 \n10 \n\n\n\n10.36 5.90 5.21 2.24 7.9 25 - 60 \n\n\n\n7.41 5.05 3.50 2.49 7.9 0 - 30 \n11 \n\n\n\n8.53 9.10 4.97 4.80 .78 30 - 100 \n\n\n\n9.64 4.45 1.66 1.81 7.9  0 - 30 \n\n\n\n12 5.23 3.05 1.33 0.70 8.0 30 - 70 \n\n\n\n10.42 1.80 1.64 0.77 7.8 70 - 100 \n\n\n\n5.38 3.37 1.98 1.78 7.7  0 - 30 \n13 \n\n\n\n7.22 6.00 6.23 1.52 7.8 30 - 80 \n\n\n\n7.64 7.66 3.21 3.57 7.9  0 - 30 \n\n\n\n14 5.11 10.45 4.96 6.77 7.9 30 - 60 \n\n\n\n8.46 6.86 2.72 5.94 7.9 60 - 80 \n\n\n\n8.59 3.05 2.53 1.96 7.7  0 - 15 \n\n\n\n15 6.37 2.46 1.39 1.26 7.8 15 - 30 \n\n\n\n10.65 2.69 6.25 9.50 .78 30 - 70 \n\n\n\n8.11 3.78 1.99 3.34 7.8  0 - 10 \n\n\n\n16 6.41 8.86 2.51 5.78 7.8 10 - 50 \n\n\n\n5.24 5.61 2.20 3.37 8.0 50 - 100 \n\n\n\n9.12 4.22 3.09 2.57 7.9  0 - 20 \n\n\n\n17 9.34 9.46 5.07 6.85 7.9 20 - 40 \n\n\n\n7.58 2.29 6.51 11.00 8.0 40 - 70 \n\n\n\n6.22 3.17 1.93 1.40 8.1  0 - 15 \n\n\n\n18 8.41 7.15 3.90 2.55 7.9 15 - 35 \n\n\n\n8.67 18.11 10.48 6.75 .79 35 - 80 \n\n\n\n5.33 5.11 1.73 2.41 .77  0 - 30 \n19 \n\n\n\n12.55 2.86 4.97 0.69 7.7 30 - 60 \n\n\n\n8.23 4.47 2.32 3.12 7.9  0 - 20 \n\n\n\n20 7.95 11.05 7.03 8.17 7.9 20 - 50 \n\n\n\n10.69 7.21 4.17 11.37 7.8 50 - 80 \n\n\n\nWhere:\nESP = Exchangeable Sodium Percent\nSAR = Sodium Adsorption Ratio\n\n\n\nFigure 3: Soil classification map (subgroup level) of the study area\n\n\n\n3.3 Land Evaluation\n\n\n\n3.3.1 Land Capability Classification\n\n\n\nMost of the land characteristics that were considered in the evaluation of \nthe current land units under irrigation, ranged from very favorable to \nfavorable for agricultural purposes. Qualitative land suitability studies \nwere conducted using Modified Storie Index, MicroLEIS (Cervatana model) \nand Applied System of Land Evaluation (ASLE) program. Other information \nconcerning climatic conditions and agricultural products were also used to \npredict the general land capability. From the agriculture point of  view,\n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 09-15 12\n\n\n\nsoils of the study area are considered as promising soils. Evaluating their \ncapability is an essential stage for the future practical use. Quantitative \nestimation of soil characteristics such as slope, drainage conditions \n(wetness), soil depth, texture, calcium carbonate content, gypsum status, \nsalinity and sodicity were used in the land evaluation. The rating capability \nvalues and kinds of limitation condition types of the studied soils are \npresent in Tables 4, 5 and 6 and illustrated in Figures 4. It is clear that none \nof the soil profiles was observed to be highly suitable (S1). It may be \nattributed to the slight or moderate limitations that are present in the study \narea. Accordingly, the study area could be classified into three classes as \nfollow:\n\n\n\nClass 2: This class includes the soils which are moderately suitable with a \ncapability index (Ci) that varies between 60.85 and 63.68 % (ASLE \nprogram) and good (Modified Storie Index). However, it disappears using \nMicroLEIS- CERVATANA model. It occupies 20 and 5 % of the total area \nusing ASLE program and Modified Storie Index, respectively. The soils of \nthis class have slight limitations.\n\n\n\nClass 3: This class contains the soils which have marginally suitable \ncapability class C3 and capability index (Ci) that varies between 45.07 and \n59.05% (ASLE program), fair (Modified Storie Index) and moderate \n(MicroLEIS- CERVATANA model). It occupies 80, 50 and 100 % of the study \narea using these respective land capability systems. The soils of this class \nare affected by moderate limitations.\n\n\n\nClass 4: According to ASLE program this class comprises the soils which are \nnot suitable for agricultural use, but they are suitable for pasture, have \nsevere limitations that can be corrected and cover 45% of the study area. \nNone of these land units was observed to be not suitable using Modified \nStorie Index and MicroLEIS- CERVATANA model [2, 22].\n\n\n\nIt could be concluded that the applied system of land evaluation (ASLE) is \nthe most suitable program. It is preferable to be used as a qualitative land \ncapability system for agricultural purposes. Compared to the other two \nprograms, it is compatible with the Egyptian conditions. ASLE program can \nbe also used by decision makers when they plan for future land utilization. \nThe results of the current study indicated that the most limiting factors \nwere soil texture followed by soil depth. Under good conditions of water \navailability for agricultural purposes, the moderately and marginally \nsuitable soils (S2 and S3) could be safely used for agriculture.\n\n\n\nTable 4: Land capability classes, grades and rating using ASLE program, \nMicroLEIS (Cervatana model) and Modified Storie Index\n\n\n\nASLE Program \n\n\n\nMicroLEIS \n\n\n\n(Cervatana \n\n\n\nmodel) \n\n\n\nModified Storie Index \n\n\n\nClass Grade \nRating \n\n\n\n(%) \nClass Grade Class\n\n\n\nGrade Rating \n\n\n\n(%) \n\n\n\nC1 Excellent 80- 100 S1 Excellent Grade1 Excellent 80- 100 \n\n\n\nC2 Good 60 - 79 S2 Good Grade 2 Good 60 - 79 \n\n\n\nC3 Fair 40 - 59 S3 Moderate Grade 3 Fair 40 - 59 \n\n\n\nC4 Poor 20 - 39 N \nMarginal \n\n\n\nor Nil \nGrade 4 Poor 20 - 39 \n\n\n\nC5 Very Poor 10 - 19 -- -- Grade 5\nNon \n\n\n\nagricultural \n>  20 \n\n\n\nC6 \nNon \n\n\n\nagricultural \n>  10 -- -- -- -- -- \n\n\n\nTable 5: Land capability classification of the studied soil profiles using \nASLE, MicroLEIS and Modified Storie Index \n\n\n\nProfile No. \nASLE Program MicroLEIS (Cervatana model) Modified Storie Index \n\n\n\nClass % Grade Class Class \n\n\n\n1 C3 55.38 Fair S3r Grade 3 \n\n\n\n2 C3 57.22 Fair S3r Grade 3 \n\n\n\n3 C3 45.07 Fair S3r Grade 4 \n\n\n\n4 C3 52.84 Fair S3r Grade 4 \n\n\n\n5 C3 59.05 Fair S3r Grade 3 \n\n\n\n6 C2 63.68 Good S3r Grade 3 \n\n\n\n7 C3 57.01 Fair S3r Grade 3 \n\n\n\n8 C3 58.71 Fair S3r Grade 3 \n\n\n\n9 C2 60.85 Good S3r Grade 3 \n\n\n\n10 C2 61.55 Good S3r Grade 4 \n\n\n\n11 C3 54.25 Fair S3r Grade 3 \n\n\n\n12 C2 61.89 Good S3r Grade 2 \n\n\n\n13 C3 52.65 Fair S3r Grade 3 \n\n\n\n14 C3 52.71 Fair S3r Grade 4 \n\n\n\n15 C3 49.23 Fair S3r Grade 4 \n\n\n\n16 C3 55.2 Fair S3r Grade 4 \n\n\n\n17 C3 51.22 Fair S3r Grade 4 \n\n\n\n18 C3 55.01 Fair S3r Grade 4 \n\n\n\n19 C3 50.27 Fair S3r Grade 3 \n\n\n\n20 C3 50.14 Fair S3lr Grade 4 \n\n\n\nl: Soil limitations (mainly salinity) \n\n\n\nr: Erosion risk (mainly no vegetation cover) \n\n\n\nTable 6: Land capability classification of the study area according to ASLE \nProgram, MicroLEIS (Cervatana model) and Modified Storie Indexf the \nstudied soil profiles using ASLE, MicroLEIS and Modified Storie Index \n\n\n\nClass Area (%) Class Area (%) Class Area (%) \n\n\n\nC2 20 -- -- S2 5 \n\n\n\nC3 80 S3 100 S3 50 \n\n\n\nC4 -- -- -- S4 45 \n\n\n\nFigure 4: Land capability maps of the study area using different evaluation \nprograms\n\n\n\nThe results of this research showed that 90% of total area was suitable for \nagricultural use. The area currently lacks soils of high capability for \nagricultural use. However, improving the soil properties and applying \nmodern irrigation systems, the soil could be improved to be highly suitable \nfor agricultural use. One of the best ways to improve such light soils (sandy \nsoils) is through additions of organic materials. Good sources of organic \nmatter include manures, leaf mold, sawdust, and straw. Many farmers enrich \nsoils with natural fertilizers, such as animal manure, green manure, and \ncompost. Continuous agriculture use of these soils will upgrade their \nsuitability in the future.\n\n\n\n3.3.2 Soil Suitability Classification\n\n\n\nLand suitability assessment for agriculture is means to evaluate the ability of \na piece of land to provide the optimal ecological requirements for a certain \ncrop variety. In other words, it evaluates the capability of land in enabling \noptimum crop development and maximum productivity. This evaluation \nneeds a specification of the respective crop requirements and calibrating \nthem with the nature of the land and soil parameters. The current study used \ntwo programs, namely applied system of land evaluation (ASLE) and \nMicroLEIS (ALMAGRA model) which were used in the quantitative \nparameters of the agro-ecological evaluation in the study area for the land \nuse types of different field crops. The studied soil profiles were evaluated to \ndetermine their suitability for growing different crops according to these two \nprograms. The soil parameters used for estimating the suitability index for \ndifferent crops were, climate, slope, drainage, texture, soil profile depth, \ncalcium carbonate, gypsum status, pH, salinity and sodicity.\n\n\n\nThe results indicate that the area under consideration has a good potential to \nproduce field crops under irrigation, provided that the water requirements \nare met. Eleven crops were elected to assess their suitability for agriculture, \nnamely alfalfa, wheat, maize, cotton, soybean, sunflower, sugar beet, \nwatermelon, potato, citrus and olive. These crops are most suitable for arid \nand semi-arid soils (Tables 7, 8 and 9) and are illustrated in Figures 5, 6 and \n7.\n\n\n\nTable 7: Land suitability classes of the study area for different crops using \nthe ASLE program  \n\n\n\nProfile \n\n\n\nNo. \n\n\n\nSoil Suitability (ASLE Program) \n\n\n\nWheat Maize Watermelon Potato \nSoya \n\n\n\nbean \nCotton Sunflower \n\n\n\nSugar \n\n\n\nbeet \nAlfalfa Citrus Olive \n\n\n\n1 S2 S2 S2 S2 S3 S2 S2 S2 S2 S2 S2 \n\n\n\n2 S2 S2 S2 S2 S2 S2 S2 S2 S2 S2 S2 \n\n\n\n3 S2 S3 S2 S2 S3 S3 S2 S2 S2 NS1 S4 \n\n\n\n4 S2 S2 S2 S2 NS1 S4 S4 S2 S2 S4 S4 \n\n\n\n5 S2 S2 S2 S2 S3 S2 S2 S2 S2 S3 S2 \n\n\n\n6 S2 S2 S2 S2 S2 S2 S2 S2 S2 S2 S1 \n\n\n\n7 S2 S2 S2 S2 S3 S2 S2 S2 S2 S2 S2 \n\n\n\n8 S2 S2 S2 S2 S3 S2 S2 S2 S2 S2 S2 \n\n\n\n9 S2 S2 S2 S2 S3 S2 S2 S2 S2 S4 S4 \n\n\n\n10 S2 S2 S2 S2 S3 S2 S2 S2 S2 S4 S4 \n\n\n\n11 S2 S2 S2 S2 S3 S2 S2 S2 S2 S2 S2 \n\n\n\n12 S2 S2 S1 S2 S2 S2 S2 S2 S2 S2 S2 \n\n\n\n13 S2 S2 S2 S2 S3 S3 S2 S2 S2 NS1 S4 \n\n\n\n14 S2 S3 S2 S2 S3 S2 S2 S2 S2 NS1 S4 \n\n\n\n15 S2 S2 S2 S2 S3 S2 S2 S2 S2 S4 S4 \n\n\n\n16 S2 S2 S2 S2 S3 S2 S2 S2 S2 S2 S2 \n\n\n\n17 S2 S2 S2 S2 S3 S2 S2 S2 S2 NS1 S4 \n\n\n\n18 S2 S2 S2 S2 S3 S2 S2 S2 \nS2 S4 S4 \n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 09-15 13\n\n\n\n19 S2 S2 S2 S2 S3 S3 S2 S2 S2 NS1 S4 \n\n\n\n20 S2 S2 S2 S2 S3 S2 S2 S2 S2 NS1 S4 \n\n\n\n S2 = suitable     \n\n\n\n       NS1 = currently not suitable    \n\n\n\n   S3 = moderately suitable \n\n\n\n  NS2 permanent not \n\n\n\nS1 = highly suitable        \n\n\n\nS4 = marginally suitable  \n\n\n\nsuitable \n\n\n\nTable 8: Land suitability classes of the study area for different crops using \nMicroLEIS-Almagra model\n\n\n\nProfile \n\n\n\nNo. \n\n\n\nSoil Suitability (MicroLEIS-ALMAGRA Model) \n\n\n\nWheat Maize \nWater \n\n\n\nmelon \n\n\n\nSoya \n\n\n\nbean \nPotato Cotton Sunflower \n\n\n\nSugar \n\n\n\nbeet \nAlfalfa Citrus Olive \n\n\n\n1 S3 S3 S3 S3 S3 S3 S3 S3 S3 S2 S2 \n\n\n\n2 S4 S4 S4 S4 S4 S4 S4 S4 S4 S3 S2 \n\n\n\n3 S4 S4 S4 S4 S4 S4 S4 S4 S4 S3 S2 \n\n\n\n4 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 \n\n\n\n5 S3 S3 S3 S3 S3 S3 S3 S3 S3 S2 S2 \n\n\n\n6 S3 S3 S3 S3 S3 S3 S3 S3 S3 S2 S2 \n\n\n\n7 S4 S4 S4 S4 S4 S4 S4 S4 S4 S3 S2 \n\n\n\n8 S4 S4 S4 S4 S4 S4 S4 S4 S4 S3 S2 \n\n\n\n9 S3 S3 S3 S3 S3 S3 S3 S3 S3 S2 S2 \n\n\n\n10 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 \n\n\n\n11 S4 S4 S4 S4 S4 S4 S4 S4 S4 S3 S2 \n\n\n\n12 S3 S3 S3 S3 S3 S3 S3 S3 S3 S2 S2 \n\n\n\n13 S3 S3 S3 S3 S3 S3 S3 S3 S3 S2 S2 \n\n\n\n14 S4 S4 S4 S4 S4 S4 S4 S4 S4 S3 S3 \n\n\n\n15 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 \n\n\n\n16 S4 S4 S4 S4 S4 S4 S4 S4 S4 S3 S2 \n\n\n\n17 S3 S3 S3 S3 S3 S3 S3 S3 S3 S4 S3 \n\n\n\n18 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 \n\n\n\n19 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 S3 \n\n\n\n20 S3 S3 S3 S3 S3 S3 S3 S3 S3 S4 S3 \n\n\n\nS1 = highly suitable        \n\n\n\nS4 = marginally suitable  \n\n\n\n S2 = suitable     \n\n\n\n       NS1 = currently not suitable    \n\n\n\n   S3 = moderately suitable \n\n\n\n  NS2 permanent not suitable \n\n\n\nTable 9: Soil suitability rating and percentage for growing some crops \naccording to ASLE program and MicroLEIS-Almagra model.\n\n\n\nASLE program \n\n\n\nR\nat\n\n\n\nin\ng\n \n\n\n\nsu\nit\n\n\n\nab\nil\n\n\n\nit\ny\n \n\n\n\nW\nat\n\n\n\ner\nm\n\n\n\nel\no\n\n\n\nn\n \n\n\n\nA\nlf\n\n\n\nal\nfa\n\n\n\n\n\n\n\nW\nh\n\n\n\nea\nt \n\n\n\nS\nu\n\n\n\ng\nar\n\n\n\n b\nee\n\n\n\nt \n\n\n\nP\no\n\n\n\nta\nto\n\n\n\n\n\n\n\nM\nai\n\n\n\nze\n \n\n\n\nS\nu\n\n\n\nn\nfl\n\n\n\no\nw\n\n\n\ner\n \n\n\n\nC\no\n\n\n\ntt\no\n\n\n\nn\n \n\n\n\nO\nli\n\n\n\nv\nes\n\n\n\n\n\n\n\nS\no\n\n\n\ny\na \n\n\n\nb\nea\n\n\n\nn\n \n\n\n\nC\nit\n\n\n\nru\ns \n\n\n\nS1 5 -- -- -- -- -- -- -- 5 -- -- \n\n\n\nS2 95 100 100 100 100 90 95 80 40 15 40 \n\n\n\nS3 -- -- -- -- -- 10 -- 15 -- 80 5 \n\n\n\nS4 -- -- -- -- -- -- 5 5 55 -- 25 \nNS1 -- -- -- -- -- -- -- -- -- 5 30 \nNS2 -- -- -- -- -- -- -- -- -- -- -- \n\n\n\nMicroLEIS-Almagra model \n\n\n\nR\nat\n\n\n\nin\ng\n\n\n\n\n\n\n\nsu\nit\n\n\n\nab\nil\n\n\n\nit\ny\n \n\n\n\nO\nli\n\n\n\nv\nes\n\n\n\n\n\n\n\nW\nat\n\n\n\ner\nm\n\n\n\nel\no\n\n\n\nn\n \n\n\n\nA\nlf\n\n\n\nal\nfa\n\n\n\n\n\n\n\nW\nh\n\n\n\nea\nt \n\n\n\nS\nu\n\n\n\ng\nar\n\n\n\n b\nee\n\n\n\nt \n\n\n\nP\no\n\n\n\nta\nto\n\n\n\n\n\n\n\nM\nai\n\n\n\nze\n \n\n\n\nS\nu\n\n\n\nn\nfl\n\n\n\no\nw\n\n\n\ner\n \n\n\n\nC\no\n\n\n\ntt\no\n\n\n\nn\n \n\n\n\nS\no\n\n\n\ny\na \n\n\n\nb\nea\n\n\n\nn\n \n\n\n\nC\nit\n\n\n\nru\ns \n\n\n\nS1 -- -- -- -- -- -- -- -- -- -- -- \n\n\n\nS2 60 -- -- -- -- -- -- -- -- -- 30 \n\n\n\nS3 40 65 65 65 65 65 65 65 65 65 60 \n\n\n\nS4 -- 35 35 35 35 35 35 35 35 35 10 \nNS1 -- -- -- -- -- -- -- -- -- -- -- \nNS2 -- -- -- -- -- -- -- -- -- -- -- \nS1 = highly suitable        \n\n\n\nS4 = marginally suitable  \n\n\n\n S2 = suitable     \n\n\n\n       NS1 = currently not suitable    \n\n\n\n   S3 = moderately suitable \n\n\n\n  NS2 = permanent not suitable \n\n\n\nFigure 5: Land suitability maps for wheat, maize, watermelon and potato \nusing ASLE and MicroLEIS (Almagr Model) programs\n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 09-15 14\n\n\n\nFigure 6: Land suitability maps for soybean, cotton, sunflower and sugar \nbeet using ASLE and MicroLEIS (Almagr Model) programs\n\n\n\nFigure 7: Land suitability maps for alfalfa, olive and citrus using ASLE and \nMicroLEIS (Almagr Model) programs \n\n\n\n3.3.3 Applied System of Land Evaluation (ASLE program)\n\n\n\nAccording to the applied system of land evaluation (ASLE program), the \nresults indicated that 5% of the total study area are highly suitable (S1) and \n95% are suitable (S2) for watermelon. All the study area (100 %) is suitable \n(S2) for alfalfa, wheat, sugar beet and potato. About 90% of the agricultural \narea are suitable and 10% are moderately suitable (S2) for maize. Most of the \narea (95%) is suitable (S2) and 5% are marginally suitable (S4) for \nsunflower. For cotton cropping, 80% of the area are suitable, while 15% and \n5% are moderately and marginally suitable, respectively. A small area (5%) \nis highly suitable (S1), 40% are suitable and 55% are marginally suitable (S4) \nfor growing olive. About 15 and 40% are suitable (S2), 80 and 5% are \nmoderately suitable (S3), and 5 and 30% of the study area are not currently \nsuitable (NS1) for soybean and citrus, respectively. Moreover, 25% of the \ntotal study area are marginally suitable (S4) for citrus cropping.\n\n\n\n3.3.4 MicroLEIS, ALMAGRA Model\n\n\n\nThe current land suitability for different crops produced by MicroLEIS, \nALMAGRA model showed that about 60 % of the studied area are suitable \n(S2) and 40% are moderately suitable (S3) for olive. Crops such as: \nwatermelon, alfalfa, wheat, sugar beet, potato maize, sunflower, cotton and \nsoybean are moderately suitable (65%) and marginally suitable (35%) to be \ngrown in this area (Tables 8 and 9 and Figures 5, 6 and 7). For growing \ncitrus, about 30% of area are suitable, while 60% and 10% are moderately \nand marginally suitable, respectively [5, 14, 20, 22].\n \nSome crops are considered unsuitable (NS1) due to the moderate to severe \nfertility limitations of the study area, soil depth and coarse texture. The \ncoarse texture, shallow depth, and salinity of the soils in some soil profiles \nare the main limiting factors for growing crops especially fruit trees. Proper \nfertilization and management associated with intensive leaching can improve \nthe soil suitability for growing various crops under consideration. Many \noptions such as, use of crops which are categorized as suitable to the area can \nbe raised for the sustainable use of the land for producing different crops. \nCorrecting some limiting factors, such as pH through the application of \norganic fertilizers which can reduce the alkalinity of the soil and increase the \nsoil organic matter through crop residue management are also options to \nincrease the suitability of these soils for crop production.\n\n\n\n4. CONCLUSIONS\n\n\n\nThe purpose of this study was to evaluate the soil capability and suitability of \nTushka area for crop production and identify the factors that hinder the \ncultivation process. Agricultural land identification, according to its own \necological potentialities and limitations is a major objective of land use \nplanning. This study implies a qualitative evaluation for the actual soil \nparameters to realize a precise and objective interpretation for the area \nunder consideration and its suitability for a wide range of crops. The most \neffective soil parameters that influenced the land suitability of the study area \nwere texture, soil depth and salinity. From applying different systems used \nfor capability assessment (ASLE program, MicroLEIS and Modified Storie \nIndex), most of the studied soils are good and moderately suitable for \nagriculture. The ASLE program was found to be suitable for the land \nsuitability assessment for agricultural proposes of the study area. It is \nconvenient to be used under Egyptian conditions. Also, it is more realistic for \nthe application in arid and semi-arid areas. From the agriculture point of \nview, soils of the study area are considered as promising ones. Applying \nsome corrections on the limiting soil factors, the potential capability of the \nsoils will be improved. Some selected crops such as watermelon, alfalfa, \nwheat, sugar beet, potato maize, olive and sunflower are recommended to be \ngrown in the study area. On the other hand, the soil maps produced for \nagricultural land suitability in this research can be helpful in carrying out the \nmanagement processes.\n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 09-15 15\n\n\n\nREFERENCES \n\n\n\n[1] Bakeer, I.H.I. 2008. Using Geographic Information System (GIS) in \nReassessment of Soil and Groundwater Salinity of Sohag Soils. M.Sc. Thesis, \nFac. of Agric. Soil & water Dept., Assiut Univ., Egypt.\n\n\n\n[2] Sayed, A.S.A. 2013. Evaluation of the Land Resources for Agricultural \nDevelopment - case study: El-Hammam Canal and its Extension, NW Coast \nof Egypt. Ph.D Thesis, Department Geowissenschaften, Universit\u00e4t \nHamburg. Germany.\n\n\n\n[3] Abdel-Hamid, M.A., Ismail, M., Nasr, Y.A., Kotb, Y. 2010. Assessment of \nSoils of Wadi El-Natrun Area, Egypt Using Remote Sensing and GIS \nTechniques. Journal of American Science, 10 (6),195 -206.\n\n\n\n[4] Ismail, M., Nasr, Y.A., Kotb, Y. 2010. Assessment of Soils of Wadi El-\nNatrun Area, Egypt Using Remote Sensing and GIS Techniques. Journal of \nAmerican Science, 6 (10), 195- 206.\n\n\n\n[5] Aldabaa, A.A., Zhang, H., Shata, A., El-Sawey, S., Abdel-Hameed, A., \nSchroder, J.L. 2010. Land Suitability Classification of a Desert Area in Egypt \nfor Some Crops Using Microleis Program. American-Eurasian Journal of \nAgriculture and Environment Science, 8 (1), 80-94.\n\n\n\n[6] Abd El-Aziz, S.H. 2004. Characterization, Classification and Evaluation of \nSome Soils of Tushka, South Valley, Egypt. The Sec. Int. Conf. for Develop \nand the Environment in the Arab world, March 23-25. Assiut University \nCentre for Environmental Studies-Egypt.\n\n\n\n[7] Sharififar, A. 2012. Assessment of different methods of soil suitability \nclassification for wheat cultivation. Journal of Agrobiology, 29 (2), 47-54.\n\n\n\n[8] Said, R. 1961. The Geology of Egypt. Elsevier Publishing Company, \nAmsterdam, New York, London.\n\n\n\n[9] Khalifa, A.M. 2001. Chemical and mineralogical properties of some \nToshka soils. M. Sc. Thesis, Cairo University, Egypt.\n\n\n\n[10] Ismail, H.A., Morsy, I., El-Zahaby, E.M., El-Nagar, F.S. 2001. A developed \nexpert system for land use planning by coupling land information system \nand modelling. Alexandria Journal of Agriculture Research, 46 (3), 141-154.\n\n\n\n[11] Ageeb, G.W., Rahim, I.S., Wahba, M.M., Awad, F. 2004. Soil Potentialities \nas Useful Mean for Planning Agricultural Development in Sahl Baraka, \nFarafra Oasis, New Valley. II. Land Suitability Assessment for Some Crops. \nEgyptian Journal of Soil Science, 44 (2), 171-183.\n\n\n\n[12] Higab, I.A. 2005. An evaluation of some soils of south El- Borolous lake \nArea. Ph. D. thesis, Fac. Agric Al-Azhar University, Egypt.\n\n\n\n[13] Ismail, H.A., Bahnassy, M.H., Abd El-Kawy, O.R. 2005. Integrating GIS \nand modelling for agricultural land suitability evaluation at East Wadi El- \nNatroun Egypt. Egyptian Journal of Soil Science, 45 (3), 297-322.\n\n\n\n[14] Darwish, Kh. M, Wahba, M.M., Awad, F. 2006. Agricultural Soil \nSuitability of Haplo-soils for Some Crops in Newly Reclamid Areas of Egypt. \nJournal of Applied Sciences Research, 2, 1235-1243.\n\n\n\n[15] Shendi, M.M., Khater, E.A., Gomaa, O.R. 2006. Land Evaluation of Some \nSoils East of El-Fayoum Governorate, Adjacent to Assuit Desert Road. \nAgriculture and Food in Middle East, 3rd Egyptian-Syrian Conference 2006, \nEl-Minia University, Egypt.\n\n\n\n[16] Wahba, M.M., Darwish, Kh. M., Award, F. 2007. Suitability of Specific \nCrops Using MicroLEIS Program in Sahal Baraka, Farafra Oasis, Egypt. \nJournal of Applied Sciences Research, 3 (7), 531\u2013539.\n\n\n\n[17] Zamil, B.A., Abbd Allah, M.A.A., Abd El- Salam, G.M., El-Shahawy, M.I. \n2009. Land evaluation of old irrigated soils in north delta region (Rewena \narea) at Kafr El Sheikh Governorate. Alexandria Science Exchange Journal, \n30 (2), 214-228.\n\n\n\n[18] Belal, A.B.A., Al-Ashri, K.M.A. 2011. GIS Based Land Evaluation in \nBaharyia Oasis, Western Desert, Egypt. Journal of Soil Science and \nAgriculture Engineering, Mansoura University, 2 (1), 11 -24.\n\n\n\n[19] Ismail, M., Yacoub, R.K. 2012. Digital soil map using the capability of \nnew technology in Sugar Beet area, Nubariya, Egypt. The Egyptian Journal \nof Remote Sensing and Space Sciences, 15, 113\u2013124.\n\n\n\n[20] Elsheikh, R., Abdul Rashid, B., Shariff, M., Amiri, F., Noordin, B.A., \nBalasundram, S. K., Mohd Soom, M.A. 2013. Agriculture Land Suitability  \n\n\n\nEvaluator (ALSE): A decision and planning support tool for tropical and \nsubtropical crops. Computers and Electronics in Agriculture, 93, 98\u2013110.\n\n\n\n[21] Yousif, I.A.H. 2014. Land Evaluation and Sustainable Development of \nSome Areas of Dakhla Oasis, Egypt. Ph.D. Thesis, Faculty of Agriculture, Cairo \nUniversity, Egypt.\n\n\n\n[22] Ahmed, M.A.E. 2016. Land Evaluation of Gharb El-Mawhob Area, El \nDakhla Oasis, New Valley, Egypt. M.Sc. Thesis, Faculty of Agriculture, Assiut \nUniversity, Assiut, Egypt.\n\n\n\n[23] Fanning, D.S., Fanning, M.C.B. 1989. Soil morphology, genesis and \nclassification. John Wiley & Sons. Inc., New York, USA.\n\n\n\n[24] FAO. 2006. Guideline for soil description, fourth edition. Rome.\n\n\n\n[25] Soil Survey Staff. 2014. Keys to Soil Taxonomy. Natural Resources \nConservation Service, U.S. Dept. Agric., Twelfth Edition, U.S. Government \nprinting office, Washington, D.C., USA.\n\n\n\n[26] Soil Survey Staff. 1975. Soil Munsell Color Charts, USDA, Soil Conserve, \nWashington, DC.\n\n\n\n[27] Schoenberger, P.J., Wysocki, D.A., Benham, E.C., Soil Survey Staff. 2012.  \nField Book for Describing and Sampling Soils, Version 3.0.  Natural \nResources Conservation Service, National Soil Survey Center, Lincoln, NE.\n\n\n\n[28] Gavlak, R., Horneck, D., Miller, R.O. 2005. Soil, Plant and Water \nReference Methods for the Western Region. Oregon State University \nCorvallis, OR. USA.\n\n\n\n[29] Alvarenga, P., Palma, P., De Varennes, A., Cunha-Queda, A.C. 2012. A \ncontribution towards the risk assessment of soils from the S\u00e3o Domingos \nMine (Portugal): Chemical, microbial and ecotoxicological indicators. \nEnvironment Pollution, 161, 50- 56.\n\n\n\n[30] Nelson, R.E. 1982. Carbonates and Gypsum.  pp: 181\u2013197. In: A. L. Page, \nR. H. Miller and D. R. Keeney (eds). Methods of Soil Analysis, Part 2. \nAmerican Society of Agronomy, Inc., Madison, Wisconsin, USA.\n\n\n\n[31] Houba, V.J.G., Van der Lee, J.J., Novozamsky, I. 1995. Soil analysis \nprocedures: soil and plant analysis, part 5B. Department of Soil Science and \nPlant Nutrition, Wageningen Agriculture University, Wageningen.\n\n\n\n[32] Bashour, I.I., Sayegh, A.H. 2007. Methods of Analysis for Soils of Arid \nand Semi-Arid Region, American University of Beirut, Lebanon. FAO, Rome.\n\n\n\n[33] Rhoades, J.D. 1982. Cation exchange capacity. pp: 149-157. In: A. L. \nPage, R. H. Miller and D. R. Keeney (eds) Methods of Soil Analysis. Part 2. \nAmerican Society of Agronomy, Inc., Madison, Wisconsin, USA.\n\n\n\n[34] Lu, R. 2000. Soil and Agricultural Chemistry Analysis. Science and \nTechnology of China Agriculture Press, Beijing, 1-336.\n\n\n\n[35] O\u2019Geen, A.T., Southard, S.B., Southard, R.J. 2008. A Revised Storie Index \nfor Use with Digital Soils Information. University of California, Division of \nAgriculture and Natural Resources, USA.\n\n\n\n[36] Aldbaa, A.A.A. 2012. Potential Land Use in the New Reclamation Areas \nat Wadi El-Rayan Depression and its Environs, EL-Fayum Governorate, \nEgypt. Ph.D. Thesis. Department of Soil Science, Faculty of Agriculture, Cairo \nUniversity, Egypt.\n\n\n\n[37] De la Rosa, D. 2000.  MicroLEIS 2000: Conceptual Framework \nAgroecological land evaluation. Instituto de Recursos Naturalesy \nAgrobiologia, CSIC, Avda. Reina Mercedes 10, 41010 Sevilla, Spain, 267.\n\n\n\n[38] Ismail, H., Morsi, I. 2001. Applied System of Land Evaluation (ASLE) in \narid zones (software). Soil and water Science Department, Faculty of \nAgriculture, Alexandria University, Egypt.\n\n\n\n[39] De la Rosa D., Mayol, F., Diaz-Pereira, E., Fernandez, M., De la Rosa, D. Jr. \n2004.  A land evaluation decision support system (MicroLEIS DSS) for \nagricultural soil protection with special reference to the Mediterranean \nregion. Environmental Modelling and Software, 19, 929-942.\n\n\n\nCite the article: Salah Hassanien Abd El-Aziz (2018). Soil Capability And Suitability Assessment Of  Tushka Area, Egypt  By Using \nDifferent  Programs (Asle, Microleis And Modified Storie Index). Malaysian Journal of Sustainable Agriculture, 2(2) : 09-15. \n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 19-22 \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nJournal Homepage: http://www.razipublishing.com/journals/malaysian- journal- \n\n\n\nof-sustainable-agriculture-mjsa/ \n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.19.22\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \n\n\n\nPARTICIPATIVE ANALYSIS OF SOCIO-ECOLOGICAL DYNAMICS AND  INTERACTIONS. \nA CASE STUDY OF THE MANGLARALTO COASTAL AQUIFER, SANTA ELENA-ECUADOR \n\n\n\nG. Herrera-Francoa; T. Gav\u00edn-Quinchuelaab; N. Alvarado-Macancelaab; P. Carri\u00f3n-Merob \n\n\n\na) Universidad Estatal Pen\u00ednsula de Santa Elena, UPSE, Facultad de Ciencias de la Ingenier\u00eda, Av. Principal Santa Elena-La Libertad, \nLa Libertad, Ecuador. \n\n\n\nb) ESPOL Polytechnic University, Escuela Superior Polit\u00e9cnica del Litoral, ESPOL, Centro de Investigaci\u00f3n y Proyectos Aplicados a \nlas Ciencias de la Tierra, CIPAT-ESPOL, Campus Gustavo Galindo Km. 30.5 V\u00eda Perimetral, P.O. Box 09-01-5863, Guayaquil, \nEcuador. Correspondence author: niucalva@espol.edu.ec \n\n\n\nARTICLE DETAILS \n\n\n\nArticle history: \n\n\n\nReceived 12 May 2017 \n\n\n\nAccepted 18 May 2017 \n\n\n\nAvailable online 20 June 2017 \n\n\n\nKeywords: \n\n\n\nSocio-ecological dynamics, \ncoastal aquifer, \nparticipatory method, \nheritage. \n\n\n\nABSTRACT \n\n\n\nSocio-ecological dynamics describe forms of interaction between society and ecosystems, through social, economic \nand ecological processes that influence the state of natural resources. The aim of this paper is to understand the \nfunctioning of the Manglaralto Socio-Ecological System through a participatory modeling method called PARDI \n(Problem, Actors, Resources, Dynamics and Interactions), in order to determine possible solutions for sustainability \nof groundwater resources. The sustainable management of the Manglaralto Coastal Aquifer has been identified as the \nproblem. The actors involved in the socio-ecological dynamics of the Manglaralto Coastal Aquifer are the Manglaralto \nRegional Drinking Water Management Board as manager; as well as users, such as the population of rural \ncommunities and economic activities, highlighting the tourism activity. The key resources identified were rainfalls, \nforested areas and surface waters, which are considered the most relevant recharge sources of the coastal aquifer. \nThe main dynamics and interactions that have directly intervened in the operation of the aquifer are: the growing \nwater demand of 1,179.30% during the period 2005-2015 and the increase of 80.85% in the construction  of \nurbanized areas for housing and tourist activities during the period 2006-2013. Currently, there are thirteen water \nwells; considered as the limit for the coastal aquifer. The Manglaralto Coastal Aquifer has dropped to 32.30% of its \ncapacity, so there are schedules of water supply as a regulatory measure for the sustainability of the aquifer. Through \nin a participatory process between the actors involved, researchers and universities, possible collectively acceptable \nsolutions have been identified for a first stage. These solutions are: the water repression through the construction of \nriver tapes, the incentive for reforestation in livestock areas, a desalination plant and the nomination of the \nManglaralto Coastal Aquifer as Heritage of Ecuador. \n\n\n\n1. INTRODUCTION \n\n\n\nHumans depend on biodiversity and ecosystems for the goods and \nservices it provides, so biodiversity plays a very important role  for \nhuman well-being and subsistence (Bill\u00e9 et al., 2012). Water is one of the \nmain services provided by ecosystems (Mulligan et al., 2015); \nindispensable resource for the social and economic development of \nsocieties (Pe\u00f1a, 2016). \n\n\n\nIt is estimated that only 2.53% of the total water on the planet is fresh \nwater and the rest is salt water. Surface water accounts for less than 3% \nof fresh water, the remaining 97% is found in groundwater (World Water \nAssessment Programme, 2003). By the year 2030, the world could face a \nwater deficit of 40% (2030 Water Resources Group, 2009); so the water \nscarcity is one of the main global environmental concerns (Hoekstra, \n2016). \n\n\n\nIn most regions of the world the problem is not the lack of fresh drinking \nwater, but the poor management and distribution of water resources \n(United Nations General Assembly, 2002). It is estimated that 20% of the \nworld's aquifers are being overexploited, considering that there are 2.5 \nbillion people who rely exclusively on groundwater resources in the \nworld (Gleeson et al., 2012; United Nations World Water Assessment \nProgramme, 2015). \n\n\n\n23,586 inhabitants of the Manglaralto parish are supplied with \ngroundwater from the Manglaralto coastal aquifer. The management of \nthe Manglaralto coastal aquifer is in charge of the Manglaralto Regional \nDrinking Water Management Board (JAAPMAN, for its acronyms in \nSpanish) through the management of 13 water wells. The management of \nthe coastal aquifer began in 1980 and currently the aquifer capacity has \ndropped by 32.30%; this is due to several factors, being the most relevant \nthe increase in the local and the tourism (Herrera et al., 2010; Herrera, \n2016). \n\n\n\nThe objective of this article is to understand the  socio-ecological \ndynamics of the Manglaralto coastal aquifer, through the participatory \nmethodology called Problem-Actors-Resources-Dynamics and \nInteractions (PARDI) of Fallot (2013), in order to describe the \ninteractions between ecosystems-society and to determine collectively \nacceptable solutions for the sustainability of the coastal aquifer. \n\n\n\n2. Materials and Methods \n\n\n\nThe PARDI participatory methodology of Fallot (2013) is focused on \nimproving the knowledge those actors have about the socio-ecological \nsystem in which they act, through the communication between the \ndifferent actors and the exchange of points of view, in order to achieve a \nsocial and collaborative learning and to determine collectively acceptable \nsolutions to problems of natural resources. This study is divided  into \nthree phases as shown in figure 1. \n\n\n\nFigure 1. Phases of the study \n\n\n\nCite this article Herrera-Franco; T. Gav\u00edn-Quinchuelaa; N. Alvarado-Macancelaa; P. Carri\u00f3n-Mero Participative Analysis of Socio- \nEcological Dynamics and Interactions. A Case Study of The Manglaralto Coastal Aquifer, Santa Elena-Ecuador  Malaysian Journal of \n\n\n\nSustainable Agriculture (MJSA) 1(1) (2017) 19-22 \n\n\n\nStart \n\n\n\n1\ner  \n\n\n\nPhase: Information \n\n\n\ngathering \nPreliminary data and \n\n\n\nliterature review \n\n\n\n2\nnd  \n\n\n\nPhase: \n\n\n\nParticipatory action \n\n\n\nresearch \n\n\n\nPARDI Method \n\n\n\nSurveys Interviews Workshops \n\n\n\n3\nrd  \n\n\n\nPhase: Analysis Socioecological dynamics \n\n\n\nand interactions \n\n\n\nCollectively acceptable \n\n\n\nsolutions \n\n\n\nEnd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nmailto:niucalva@espol.edu.ec\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.19.22\n\n\n\n\n\n\nCite this article Herrera-Franco; T. Gav\u00edn-Quinchuelaa; N. Alvarado-Macancelaa; P. Carri\u00f3n-Mero Participative Analysis of Socio- \nEcological Dynamics and Interactions. A Case Study of The Manglaralto Coastal Aquifer, Santa Elena-Ecuador  Malaysian Journal of \n\n\n\nSustainable Agriculture (MJSA) 1(1) (2017) 19-22 20 \n\n\n\nCite this article Herrera-Franco; T. Gav\u00edn-Quinchuelaa; N. Alvarado-Macancelaa; P. Carri\u00f3n-Mero Participative Analysis of Socio- \nEcological Dynamics and Interactions. A Case Study of The Manglaralto Coastal Aquifer, Santa Elena-Ecuador  Malaysian Journal of \n\n\n\nSustainable Agriculture (MJSA) 1(1) (2017) 19-22 \n\n\n\nThe 1st Phase is based on the collection of information through \ndocument analysis, being the basis of the study. The 2nd Phase is \nparticipatory action research through the PARDI method. This phase \nwas developed from January to April 2017, through a participatory \nprocess among actors involved, such as the inhabitants of rural \ncommunities, JAAPMAN, parish representatives, researchers and \nuniversities as shown in figure 2; where 60 surveys, 8 interviews  and \n2 workshops were conducted. \n\n\n\nFigure 2. Participatory action research \n\n\n\nThe 3rd Phase is based on the analysis of the information, where the \ndesign of the socio-ecological dynamics and interactions of the \nManglaralto coastal aquifer was made, through the integrated \nrepresentation of the reality perceived by the actors involved. In \naddition, through this participatory modeling, collectively acceptable \nsolutions were identified. \n\n\n\n3. Results and Discussion \n\n\n\n3.1 Brief description of study area \nManglaralto Parish is located in the province of Santa Elena in the \nCoasts of the Republic of Ecuador. It is constituted by the rural parish \nheader called Manglaralto and 17 rural communities in an extension of \n497.40 km2. Its population is 35,289 inhabitants until 2016 according \nto projections of the National Census. The main economic activities are \ntourism, retail trade, agriculture, livestock and fishing. The \neconomically active population is 34.15%; while poverty  by \nunsatisfied basic needs is 88.20% (Instituto Nacional de Estad\u00edstica y \nCensos, 2010) \n\n\n\nThere are three water boards in the parish: the Manglaralto Regional \nDrinking Water Management Board, created in 1980, the  Ol\u00f3n \nRegional Drinking Water Management Board created in 1982, and   the \n\n\n\nFigure 3. Environment of Manglaralto coastal aquifer \n\n\n\n3.2 Problem, Actors and Resources \nIn the Manglaralto parish the problems of water scarcity are evident, \nmainly in the dry season (June-November), where there are no \nprecipitations. So, during the dry season the recharge of aquifer is low. \nThe problematic formulated based on the interests and needs of the \nactors involved in the subject of the sustainable management of the \ncoastal aquifer in face of risks of scarcity, quality problems and the \noverexploitation poses the need to manage in a sustainable way the \nwater of the Manglaralto coastal aquifer in a short (2 years), medium \n(9 years) and long term (20 years). \n\n\n\nIn the territory of Manglaralto six actors that affect the use of water \nresources and resources linked to water were identified. These actors \nintervene in the problem by their activities and functions, such as the \nJAAPMAN, communities, touristic and economic activities; as explained \nin table 1. Meanwhile, the main resources identified were \nprecipitations, wooded areas, and rivers. \n\n\n\nTable 1. Problem, Actors and Resources \n\n\n\nHow to sustainably manage the water of the Manglaralto coastal aquifer in a short \n\n\n\nValdivia Regional Drinking Water Management Board created in 1987. \nThe Manglaralto Regional Drinking Water Management Board is in \ncharge of managing the Manglaralto coastal aquifer and supplies water \nto a total of 23,586 inhabitants of the rural parish header and 5 rural \ncommunities such as: Monta\u00f1ita, Rio Chico, Cadeate, San Antonio and \nLibertador Bol\u00edvar (Herrera et al., 2017). \n\n\n\nIn 1980 started the exploitation of the Manglaralto Coastal Aquifer \nwith a total of two water wells. During the period 2005-2011, seven \nnew  water  wells  were  constructed.  The  Manglaralto  coastal aquifer \n\n\n\nProblem \n\n\n\nActors \n\n\n\n(2 years), medium (9 years) and long term (20 years)? \n\n\n\n\u25aa JAAPMAN: Autonomous community entity. It manages the Manglaralto\n\n\n\ncoastal aquifer. The directive is composed by six members of the rural\n\n\n\ncommunities of the Manglaralto parish, democratically elected. \n\n\n\n\u25aa Communities: 23.586 users.\n\n\n\n\u25aa Touristic activities: Tourism is one of the main sources of economic income for\n\n\n\nthe population. However, the disorderly and unplanned growth of tourism has\n\n\n\ngenerated excessive demand for water. \n\n\n\n\u25aa Economic activities: Construction, livestock, agriculture, larvicultura, fishing,\n\n\n\ncommerce, bakery. \n\n\n\n\u25aa Representatives of the parish: Parish authorities, who are in charge of the\n\n\n\nplanning and management of the parish according to the budget granted by the\n\n\n\nPresidency of the Republic of Ecuador. \n\n\n\n\u25aa Universities: Researchers interested in solving problems of the communities in\n\n\n\nhas worked at 100% capacity, providing water all hours in one day order to improve the quality of life of the country.  \nPrecipitations, wooded areas, rivers, construction of urbanized areas for housing and \n\n\n\nthrough pipes to homes. So community management of the coastal \naquifer was recognized as a world example by the United Nations in \n\n\n\nResources \ntourist activities on the banks of rivers, livestock and agricultural areas. \n\n\n\n2011 (Macneill, 2011). During the period 2012-2014, four new water \nwells were constructed (Herrera, 2016). \n\n\n\nCurrently, there are thirteen water wells; considered as the limit for \nthe coastal aquifer. The Manglaralto Coastal Aquifer has dropped to \n32.30% of its capacity (Herrera, 2016), so there are schedules of water \nsupply as a regulatory measure for the sustainability of the aquifer. In \nthe dry season of Ecuador, when there is no rain, recharge of the \naquifer is difficult. As a result, there have been demonstrations such as \nprotests by villagers in rural communities over the lack of fresh water \n(Rosales, 2015; Cazar et al., 2016), because the JAAPMAN has been \nforced to close the keys that provide water through pipes to homes, so \nthe water board have supplied water through water tanks in each \nhouse, limiting the amount of water for each household. \n\n\n\nThe Manglaralto basin has 13,238 ha of extension, while the coastal \naquifer has an area of 508 ha (Herrera 2016). There are four wooded \nareas that directly influence the aquifer: Chong\u00f3n-Colonche,  Loma \nAlta, Cangrejal de Ol\u00f3n and Esterillo Oloncito, and six agricultural and \nlivestock areas along the surface of the aquifer; such as:  Dos \nHermanos, Mar y Fr\u00edo, Siempre Verde, Soledad Mar\u00eda, El Refugio and \nLas Uvas, as shown in Figure 3. \n\n\n\n3.3 Dynamics and Interactions \nThe actors involved and researchers constructed through a \nparticipatory process a conceptual model focused on socio-ecological \ndynamics and interactions, as a tool to establish strategies for the \nsustainable management of the Manglaralto coastal aquifer. The main \ndynamics and interactions that have intervened directly in the \noperation of the aquifer have been the following: \n\n\n\n\u25aa The growing water demand of 1,179.30% during the period \n2005-2015. The aquifer is overexploited to meet the needs of \nwater demand; this reduces the fresh water table and promotes \nsaltwater intrusion into the coastal aquifer. The population has \ngrown from 2,000 inhabitants in 2005 to 23,586 in 2015. \n\n\n\n\u25aa The increase of 80.86% in the construction of urbanized areas \nfor housing and tourist activities during the period 2006-2013. \nThe increase of urbanized areas for housing and tourist activities \non the banks of the river- Manglaralto aquifer system is a \nrelevant factor in the sustainability of the coastal aquifer. By \n2013 the expansion of urban areas was 196.39 ha; all \ncommunities underwent drastic changes in the coastal profile. \n\n\n\n\u25aa The increase in tourist services, mainly the increase in \naccommodation by 1,775.00% during the period 2006-2014. In \n2006, there were 4 official hotel establishments located in the \ncommunities of Manglaralto. By 2014 there were 117 tourist \nbusinesses in the study communities according to the Ministerio \nde Turismo del Ecuador (2014), such as hostels, hotels, \n\n\n\n\n\n\n\n\nCite this article Herrera-Franco; T. Gav\u00edn-Quinchuelaa; N. Alvarado-Macancelaa; P. Carri\u00f3n-Mero Participative Analysis of Socio- \nEcological Dynamics and Interactions. A Case Study of The Manglaralto Coastal Aquifer, Santa Elena-Ecuador  Malaysian Journal of \n\n\n\nSustainable Agriculture (MJSA) 1(1) (2017) 19-22 21 \n\n\n\nCite this article Herrera-Franco; T. Gav\u00edn-Quinchuelaa; N. Alvarado-Macancelaa; P. Carri\u00f3n-Mero Participative Analysis of Socio- \nEcological Dynamics and Interactions. A Case Study of The Manglaralto Coastal Aquifer, Santa Elena-Ecuador  Malaysian Journal of \n\n\n\nSustainable Agriculture (MJSA) 1(1) (2017) 19-22 \n\n\n\n\u25aa restaurants and bars; of which 71 are hotels and hostels. In \naddition, it is known that in many cases there are \naccommodation services in housing of the inhabitants of the \nsector, because of the high demand of domestic and foreign \ntourists in the months of January to April and from July to \nSeptember. \n\n\n\n\u25aa The advance of the agricultural frontier has generated the felling \nof the humid forest located in the foothills of the Chong\u00f3n- \nColonche mountain range. Wooded areas have been reduced, so \nthere is a shortage of rain and the level of water infiltration in \nthe soil has decreased. \n\n\n\n\u25aa Outstanding activities in the parish use water from the \nManglaralto coastal aquifer. Baking is one of the main economic \nactivities of the Cadeate community, with 48 businesses \n(bakeries) distributed in the commune of 2,000 inhabitants. The \nlarviculture is another outstanding economic activity of the \nparish. There are five laboratories of shrimp and  fish \nlarviculture: Two in Cadeate, two in Manglaralto and one in \nMonta\u00f1ita. \n\n\n\n\u25aa The results of the participatory process reflect the main effects \nthat affect the recharge of the aquifer, which are: the scarce \nprecipitations and the felling of the wooded areas for \nurbanizations or agricultural and/or livestock areas; which \ninfluence the drought of rivers and the lowering of groundwater \nlevel. \n\n\n\n\u25aa The lack of regulation for the construction of urbanized areas on \nthe banks of the river was identified. In addition, there was a lack \nof measures and policies that regulate the practices of activities \nthat directly influence the exploitation of the coastal aquifer \nManglaralto, considering that affect in the sustainability of the \naquifer; such as the cementation of areas on and near to the \ncoastal aquifer, which closes the entrance of rainwater to the \ndeposit of groundwater. \n\n\n\nFigure 4 shows the representation of the reality perceived by  the \nactors involved, where the socio-ecological dynamics and interactions \nof the Manglaralto coastal aquifer resource can be observed. \n\n\n\nFigure 4. Socio-ecological dynamics and interactions of the Manglaralto \ncoastal aquifer \n\n\n\n3.4 Collectively acceptable solutions \nThrough the modeling of the socio-ecological  dynamics and \ninteractions of the Manglaralto coastal aquifer, collectively acceptable \nsolutions were visualized in the short, medium and long term, as shown \nin figure 5. \n\n\n\nProblem \nHow to sustainably manage the water of the \n\n\n\nManglaralto coastal aquifer? \n\n\n\nCollectively acceptable solutions \n\n\n\no Water repression through the construction of river\n\n\n\nFigure 5. Collectively acceptable solutions for sustainability of the \nManglaralto coastal aquifer \n\n\n\nThe construction of tapes for water repression is the earliest \nalternative according to participants in the participatory process. The \ntapes are structures similar to dykes, they are constructed \nperpendicular to the direction of the river channel, using backhoe \nloaders that transport the material of the channel of the river to block \ntheir course. In this way the water accumulates and facilitates the \ninfiltration of the water for the recharge of the aquifer. The tapes \nshould be prepared during the dry season between the months of July \nand October, being useful between February and April when the \nchannel is full and its functionality is confirmed. \n\n\n\nAbout 14,800 hectares of wooded areas in the Chong\u00f3n-Colonche \nmountain range are conserved (Ministerio del Ambiente, 2015). \nAgricultural and livestock practices and the logging to take advantage \nof timber trees have reduced wooded areas. So the incentive of \nreforestation in livestock areas is important, because they have the \nmost surface of territory of the parish. In addition, workshops with the \nlocal people are important to maintain and reforest the wooded areas; \nwith the purpose of contributing to the mitigation of climate change \nand the maintenance of water resources and environmental services. \n\n\n\nA desalination plant is another collectively acceptable solution. This \nalternative can mitigate water scarcity under extreme conditions. \nHowever, it is a process that is expensive and requires large amounts \nof energy. Therefore this possible solution is contemplated through \nalliances with the municipality and economic associations like the \nAssociation of Cattlemen and of Tourism. On the other hand, the \nnomination of the Manglaralto Coastal Aquifer as Heritage of Ecuador \nis envisaged in order to regulate and maintain the environmental \nservices and water supply to rural communities through the National \nSystem of Protected Areas, Ecuadorian institution responsible for \nguaranteeing conservation of biodiversity and the maintenance of \necological functions that allow the sustainable use of  natural \nresources. \n\n\n\n4. Conclusion \nThe analysis of the dynamics and interactions was carried out through \nthe PARDI participatory modeling (Problem, Actors, Resources, \nDynamics and Interactions) in order to understand the functioning of \nthe Socio-Ecological System of Manglaralto around a shared problem \nsuch as the sustainability of the Manglaralto coastal aquifer. Actors \ninvolved, such as the local water board, inhabitants of rural \ncommunities, economic and tourist activities, parish representatives \nand researchers, made a conceptual model focused on socio-ecological \ndynamics and interactions through a participatory process; as a tool to \nestablish collectively acceptable solutions for the sustainable \nmanagement of the coastal aquifer in a short (2 years), medium (9 \nyears) and long term (20 years). \n\n\n\nThe study has been a key to collectively identify the dynamics and \ninteractions that affect the functioning of the aquifer. Uncertainties \narise in the dynamics of resources, considering the low rainfall as a \ndetermining factor for the recharge of the aquifer.  The interactions \nthat have intervened in the overexploitation of the aquifer are the \nincreasing water demand of 1,179.30% during the period 2005-2015, \nthe increase of 80.86% of urban areas during the period 2006-2013, \nthe increase in tourist accommodations by 1,775.00% during the \nperiod 2006-2014, the clearing of forested areas for urbanization or \nagricultural and/or livestock areas and the cementing of areas on and \nnear to the coastal aquifer; so the aquifer has dropped to 32.30% of its \ncapacity. \n\n\n\nIt is evident the need to coordinate actions and establish  public \npolicies that allow the regulation and use of space, in order to mitigate \nthe negative impacts to the Manglaralto coastal aquifer; in  other \nwords, to avoid its disappearance, contamination, diminution of the \nquality and quantity of the water. In a process a participatory process \nbetween the involved actors were established possible solutions \ncollectively acceptable for a first stage. These solutions are: the water \nrepression through the construction of river tapes, the incentive for \nreforestation    in    livestock    areas,    a    desalination    plant    and the \n\n\n\ntapes \n\n\n\no Incentives for reforestation in livestock areas\n\n\n\no A desalination plant\n\n\n\no Nomination of the Manglaralto Coastal \n\n\n\nAquifer as Heritage of Ecuador \n\n\n\nShort term (2 \n\n\n\nyears) \n\n\n\nMedium term \n\n\n\n(9 years) \n\n\n\nLong term \n\n\n\n(20 years) \n\n\n\nnomination of the Manglaralto Coastal Aquifer as Heritage of Ecuador. \n\n\n\nThere are still several solutions to be discussed. This study is a first \nstep in the long way for the sustainability of the Manglaralto Coastal \nAquifer through a joint effort of stakeholders in the management of \nthe resource. This is expected to be a starting point for the \nimplementation of projects for aquifer valorization; considering that it \n\n\n\n\n\n\n\n\nCite this article Herrera-Franco; T. Gav\u00edn-Quinchuelaa; N. Alvarado-Macancelaa; P. Carri\u00f3n-Mero Participative Analysis of Socio- \nEcological Dynamics and Interactions. A Case Study of The Manglaralto Coastal Aquifer, Santa Elena-Ecuador  Malaysian Journal of \n\n\n\nSustainable Agriculture (MJSA) 1(1) (2017) 19-22 22 \n\n\n\nis a resource with an important role in the social, economic, \nenvironmental and cultural development of the rural parish, and \npromotes the creation of a long-term integrity of natural resources and \nhuman well-being. \n\n\n\n5. Acknowledgement\nThanks to the rural communities of Monta\u00f1ita, Cadeate, Libertador \nBol\u00edvar, R\u00edo Chico, San Antonio and the Manglaralto rural parish header \nfor their active participation in surveys and workshops. To the \nrepresentatives of the parish for their collaboration in the interviews \nand workshops, and a special thanks to the JAAPMAN for their \nunconditional support in the study. \n\n\n\n6. References \n[1] Davos-Klosters: Cougar Paper. 2030 Water Resources Group. \n(2009). Charting our water future: Economic frameworks to inform \ndecision-making. \n\n\n\n[2] Bill\u00e9,   R.,   Lapeyre,   R.,   &   Pirard,   R.   (2012). Biodiversity \nconservation and poverty alleviation: a way out \nof the deadlock? (I. V. Environnement, Ed.) S.A.P.I.EN.S Surveys and \nPerspectives Integrating Environment and, 5(1), 1-15. \n\n\n\n[3] Cazar,  D.,  Acu\u00f1a,  C.,  Ortiz,  F.,  Orejuela,  D.,  Madrid,  A.,     & \nTrujillo, J. (2016). Y la herencia cultural, \u00bfa qui\u00e9n le importa? \nRecuperado el 2017, de Revista Digital La Barra Espaciadora: \nhttp://www.labarraespaciadora.com/aqui-y-ahora/y-la-herencia- \ncultural-a-quien-le-importa/ \n\n\n\n[4] Fallot, A. (2013). Gu\u00eda metodol\u00f3gica PARDI - Problematica    - \nActores - Recursos - Dinamicas - Interacciones: Para el an\u00e1lisis de las \ndin\u00e1micas socio-ecol\u00f3gicas. Par\u00eds: Archive Ouverte HAL. \n\n\n\n[5] Gleeson, T., Wada, Y., Bierkens, M., & van Beek, L. (2012). \nWater balance of global aquifers revealed by groundwater footprint. \nNature, 197-200. \n\n\n\n[6] Herrera, G. (2016). Estudio para un Modelo de Gesti\u00f3n de  un \nAcu\u00edfero Costero, mediante Metodolog\u00edas Participativas y An\u00e1lisis \nGeoestad\u00edstico en el marco del Desarrollo Local. Manglaralto, Ecuador. \nDepartamento de Ingenier\u00eda Topogr\u00e1fica y Cartograf\u00eda. Madrid: \nUniversidad Polit\u00e9cnica de Madrid. \n\n\n\n[7] Herrera, G., Carri\u00f3n , P., Berrezueta , E., & Flores, D. (2010). \nValoraci\u00f3n de Impactos Ambientales Relacionados con las Aguas \nSubterr\u00e1neas y Turismo en las Comunas de Manglaralto y Ol\u00f3n; \nEcuador. En T\u00e9cnicas Aplicadas a la  Caracterizaci\u00f3n  y \nAprovechamiento de recursos Geol\u00f3gico-Mineros. (p\u00e1gs. 116-126). \nOviedo: Instituto Geol\u00f3gico y Minero de Espa\u00f1a. \n\n\n\n[8] Herrera,  G.,  Carri\u00f3n,  P.,  &  Alvarado-Macancela,  N.  (2017). \nParticipatory process for local development: Sustainability of water \nresources in rural communities. Case  Manglaralto-Santa  Elena, \nEcuador. En Handbook of Sustainability Science  and Research. \nSpringer. \n\n\n\n[9] Hoekstra,  A.  Y.  (2016).  A  critique  on  the     water-scarcity \nweighted water footprint in LCA. (J. Marques, & F. M\u00fcller, Edits.) \nEcological Indicators, 66, 564-573. http://dx.doi.org/10.1016/ \nj.ecolind.2016.02.026. \n\n\n\n[10] Instituto Nacional de Estad\u00edstica y Censos. (2010). Sistema \nIntegrado de Consultas. (D. d. Centro Latinoamericano y Caribe\u00f1o de \nDemograf\u00eda  (CELADE),  Editor)  Recuperado  el  22  de  August   de \n2016, de VII Censo de Poblaci\u00f3n y VI de Vivienda: http:// \nredatam.inec.gob.ec/ \n\n\n\n[11] Macneill, M. (2011). IAEA Helps Parched Santa Elena Find \nWater. Recuperado el 2017, de International Atomic Energy Agency : \nhttps://www.iaea.org/newscenter/news/iaea-helps-parched-santa- \nelena-find-water \n\n\n\n[12] Ministerio    de    Turismo    del    Ecuador.    (2014). Catastro \nTur\u00edstico de la Provincia de Santa Elena. Santa Elena: Rep\u00fablica del \nEcuador. \n\n\n\n[13]  Ministerio   del   Ambiente.   (2015).   Beneficiarios   de Socio \nBosque se capacitan en Santa Elena. Recuperado el 2017, de Programa \nde Protecci\u00f3n de Bosques Socio Bosque: \nhttp://sociobosque.ambiente.gob.ec/node/682 \n\n\n\n[14] Mulligan,   M.,   Ben\u00edtez-Ponce,   S.,   Lozano-V.,   J.,   &   Leon \nSarmiento, J. (2015). Policy support systems for the development of \nbenefit-sharing mechanisms for water-related ecosystem services. En \nJ. Martin-Ortega, R. Ferrier, I. Gordon, & S. Khan, Water Ecosystem \nServices. A Global Perspective (p\u00e1gs. 99-109). Cambridge: \nInternational Hydrology Series and Cambridge University Press. \n\n\n\n[15] Pe\u00f1a, H. (2016). Desaf\u00edos de la seguridad h\u00eddrica en Am\u00e9rica \nLatina y el Caribe. (Comisi\u00f3n Econ\u00f3mica para Am\u00e9rica Latina y El \nCaribe-CEPAL, Ed.) Serie Recursos Naturales e Infraestructura(178), \n1-57. \n\n\n\n[16] Rosales, E. (2015). Problemas en Manglaralto.    Recuperado \nel 2017, de Diario El Universo: http://www.eluniverso.com/ \nopinion/2015/10/19/nota/5192870/problemas-manglaralto \n\n\n\n[17] United  Nations  General  Assembly.  (2002).  Report  of  the \nWorld Summit on Sustainable Development (WSSD). Johannesburg: \nUnited Nations. \n\n\n\n[18] United   Nations   World   Water   Assessment   Programme. \n(2015). The United Nations World Water Development Report 2015: \nWater for a Sustainable World. Paris: UNESCO. \n\n\n\n[19] World  Water  Assessment  Programme.  (2003).  Water  for \nPeople, Water for Life. The United Nations World Water Development \nReport. Paris: United Nations. \n\n\n\n7. About the Authors \nProfessor Gricelda Herrera Franco has a doctorate in Geographic \nEngineering (2016). She is professor and researcher at Universidad \nEstatal de la Pen\u00ednsula de Santa Elena (UPSE, for its acronyms in \nSpanish) in Ecuador. Her current research interests include \ngeography, participative methodologies, territorial and local \ndevelopment. \n\n\n\nResearcher Tatiana Gav\u00edn Quinchuela has a degree in Ecotourism \n(2013). She is researcher at Center for Research and Projects applied \nto Earth Sciences at Escuela Superior Polit\u00e9cnica del Litoral (CIPAT- \nESPOL, for its acronyms in Spanish) in Ecuador. Her current research \ninterests include environmental sciences and sustainability. \n\n\n\nResearcher Niurka Alvarado Macancela has a degree in International \nBusiness (2015). She is researcher of the area of Project Economics \nand Technology Transfer at CIPAT-ESPOL and of Anc\u00f3n-Santa Elena \nGeopark Research Project at UPSE in Ecuador. Her current research \ninterests include development and geopark. \n\n\n\nProfessor Pa\u00fal Carri\u00f3n Mero has a doctorate in Mining Engineering \n(2005). He is professor at Escuela Superior Polit\u00e9cnica del Litoral and \nDirector of CIPAT-ESPOL in Ecuador. His current research interests \ninclude earth sciences, environmental sciences and sustainability. \n\n\n\nCite this article Herrera-Franco; T. Gav\u00edn-Quinchuelaa; N. Alvarado-Macancelaa; P. Carri\u00f3n-Mero Participative Analysis of Socio- \nEcological Dynamics and Interactions. A Case Study of The Manglaralto Coastal Aquifer, Santa Elena-Ecuador  Malaysian Journal \n\n\n\nof Sustainable Agriculture (MJSA) 1(1) (2017) 19-22 \n\n\n\n\nhttp://www.labarraespaciadora.com/aqui-y-ahora/y-la-herencia-\n\n\nhttp://www.labarraespaciadora.com/aqui-y-ahora/y-la-herencia-\n\n\nhttp://dx.doi.org/10.1016/\n\n\nhttp://www.iaea.org/newscenter/news/iaea-helps-parched-santa-\n\n\nhttp://www.iaea.org/newscenter/news/iaea-helps-parched-santa-\n\n\nhttp://sociobosque.ambiente.gob.ec/node/682\n\n\nhttp://www.eluniverso.com/\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 16-18  \n \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\n \nDOI : http://doi.org/10.26480/mjsa.02.2018.16.18 \n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online)   \nCODEN : MJSAEJ  \n\n\n\nAGRONOMIC PERFORMANCE AND CORRELATION ANALYSIS OF FINGER \n\n\n\nMILLET (Elusine corocana L.) GENOTYPES  \n \nNarayan Bahadur Dhami1*, Manoj Kandel1, Suk Bahadur Gurung1, Jiban Shrestha2 \n \n1Nepal Agricultural Research Council, Hill Crop Research Program (HCRP), Kabre, Dolakha \n\n\n\n2National Commercial Agriculture Research Program,Pakhribas, Dhankuta,Nepal  \n\n\n\n*Corresponding Author Email: nbdhami@gmail.com \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and \n\n\n\nreproduction in any medium, provided the original work is properly cited. \n \nARTICLE DETAILS ABSTRACT  \n\n\n\n \nArticle History: \n\n\n\n \nReceived 12 November 2017 \n\n\n\nAccepted 12 December 2017 \n\n\n\nAvailable online 1 January 2018 \n\n\n\n \n \nConsidering the context of climate change and food security issues of the poor, marginalized and vulnerable \nfarmers; there is urgent need of characterization of the traits and its correlation in the different genotypes of \nfinger millet for development of elite variety in Nepal. A field research was carried out at agronomy field at hill \ncrop research program (HCRP), kabre, Dolakha from June to November, 2017in order to identify the phenotypic \nvariability of the trait in different Nepalese landraces and create to promote the production and stability of \nneglected crops, finger millet. The field experiment was conducted in random complete block design with two \nreplications. The result revealed that the finger millet genotypes showed the significant differences for days to \n50 % heading, plant height, plant stand per square meter, bearing head per square meter, number of finger per \nhead, thousand grain weight and grain yield. The genotypes ACC#513 (3.68 t/ha) fallowed by ACC#2303(3.65 t/ \nha), ACC#2275 (3.57 t/ha) and ACC#5434 (3.39 t/ha) produces highest grain yield. Correlation analysis \nrevealed that plant height fallowed by plant stand per square meter, bearing head, number of finger per head \nand straw yield with minimum lodging percentage were most yield determinative traits and simultaneous \nselection for these traits might brining an improvement in finger millet grain yield. \n \nKEYWORDS \n \nFinger millet, Characterization, Correlation, Grain yield  \n  \n\n\n\n1. INTRODUCTION \n \nFinger millet is thought to be have originated from Uganda or \nneighboring Ethiopian highlands where wide diversity of the genus \nEleusineexists [1]. There was confliction about origin of the \nEleusinecoracana either from the E. indica or from the E. Africana or from \nthe cross of two diploid species. Nepal has diverse climate, topography \nand altitude which make diverse in crops. Finger millet (Elusinecorocana \n(L.) Gaertn.) ranks 4th in the world among mostly grown cereals after the \nsorghum, pearl millet and foxtail millet and 4th in Nepal in case of area of \nproduction (2,66,799 hectare), total production (3,02,397M tons) and \nproductivity after paddy, maize and wheat [2]. It is an annual hardy \ncereal crop grown in the tropical, subtropical areas of world. Nepal is rich \nin finger millet genotypes, grown upto 3150 m [3]. About 790 accessions \nhave been collected from various parts of Nepal [4]. Large diversity \nwithin Eleusinecoracana, two wild species\u2014E. indicaand E. aegyptica\u2014\nwas found [5]. It\u2019s productivity has depend on wide range of \nenvironments and growing conditions, from southindia to the foothills of \nthe Himalayas in Nepal and throughout the middle-elevation areas of \nEastern and Southern Africa [6,7]. Gandaki, Bagmati, Sagarmatha and \nLumbini zones are major finger millet producing zones in Nepal [8]. Thus, \nfinger millet diversity in Nepal is rich at both varietal and population \nlevels and this diversity could be used for variety improvement [9]. \n\n\n\n \nFinger millet can be ground and cooked into cakes, puddings or porridge. \nThe grain is made into fermented drink or beer in Nepal and in many \nparts of Africa. In central terai of Nepal, it is used as tiffin, for making \nhaluwa, roti and chokha. It is also assumed to be a good diet for pregnant \nwomen and for treatment of animal diarrhea [10]. Finger millet protein \nhas a favorable amino acid spectrum that includes cystine, tyrosine, \ntryptophan and methionine [11]. The increase global temperature leads \nclimate change directly effects in the production of the crops and \nincrease in hunger and malnutrition in world people, but finger millet can \nminimize the hunger and malnutrition because it can withstand in the \ndrought condition and have number of macro and micro nutrients. \nNutritional values of finger millet contain, moisture 13.24%, protein \n7.6%, carbohydrate 74.36%, fiber 1.52%, minerals 2.35%, fat 1.35%, \nenergy 341.6 cal/100g [12]. \n\n\n\n \nTherefore, diversity in inter population and intra population in the finger \nmillet could save the world from the scarcity of food. Mostly people are \nconcerning to develop only handful cereals crops like maize, wheat, rice \netc. in these days may cause food insecurity within few years because of \nincrease in population of world and depletion of area of production. \nDevelopment of elite variety and hybrid variety can be produced once the \ngenetic and its phenotypic variability of character are assessed in land \nraces. The research has main focused on the characterization of the trait \nand its correlation in the different traits. The research will identify the \nphenotypic variability of the trait in different Nepalese landraces and \ncreate to promote the production and stability of neglected crops, finger \nmillet. \n \n2. MATERIALS AND METHODS \n \n2.1 Research site, soil properties and Agro-meteorological condition \n \nThe field experiment was conducted on fingermillet research field at \nNARC, Hill Crop Research Program (HCRP) Dolakha, Nepal, from June to \nNovember 2017. The precise location of experimental site was 27\u00b0 39' \n59.99\" N latitude, 86 \u00b0 01\u2019 60.00\u201d E longitudes and at an altitude of 1700 \nmeters above mean sea level. The soil of the experimental plot was acidic \nsandy to silty loam with pH 4.5 to 6.2 and poor in organic carbon and total \nN content but medium in soil available P2O5 and K2O.The field were \nupland from where the water drained easily during rainfall because the \nfinger millet needs the dry but slight moisture condition in soil. The \nclimate of research location is temperate. The maximum temperature \nrecorded was 26.60C in the month of September while the minimum \ntemperature recorded was 8.60C during November. Average rainfall of \nregion is 200-400 mm per annum and relative humidity ranges from 20-\n60 %. During the experiment period, the field received total 1445 mm of \nrainfall with maximum rainfall of 2.9 mm in November. The \nmeteorological data were obtained from meteorological station, HCRP \nDolakha. \n \n2.2 Plant materials, raising of seedlings, field layout and \n\n\n\ntransplanting \n \nThe plant materials tested in the research were 16 elite finger millet \ngenotypes for development of climate resilience high yielding biotic and   \n\n\n\nCite the article: Narayan Bahadur Dhami, Manoj Kandel, Suk Bahadur Gurung, Jiban Shrestha (2018). Agronomic Performance And Correlation Analysis \nOf Finger Millet (Elusine corocana L.) Genotypes. Malaysian Journal of Sustainable Agriculture, 2(2) : 16-18. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 16-18 17  \n\n\n\n \nabiotic stress tolerance genotypes suitable for hill growing condition for \nsole cropping system. Dry nursery beds were established for each \ngenotype on 1th July 2017. Each nursery row was 1 m in length and \nsupplied with equal amount of farmyard manure. No chemical fertilizers \nwere applied on nursery beds. The seed rate applied was 8 kg ha-1. The \nage of seedlings was 27 days during transplanting. The field experiment \nwas conducted in Random complete block design (RCBD). The trial was \nreplicated twice. Each replication comprised sixteen blocks/plots. \nRandomization of experimental material will be done with the software \nCROPSTAT. Each plot contains same levels of fertilizers i.e \u2013 50 :30:00 \nNPK kg ha-1. Half dose of urea and full dose of DAP and applied as basal \nwhile remaining half dose of urea was top dressed in split at 30 DAT. \nEach plot was 2m in length and 2 m in width. Transplanting of 2-3 \nseedlings per hill was done on 28th November with a spacing of 10cm \nbetween rows and 10 cm between hills in each plot. There was a gap of \n0.5m between plots and 2 m between within a replication. There was 2.0 \nm gap between the replication. Bunds were constructed in between plots \nand replications. Weeds are the major problem in finger millet, especially \nduring 2-3 weeks after sowing. Therefore, weeding or hoeing was done at \nevery 15 days after the 25 days of sowing. And other management \npractices were done as like those for recommended varieties. Plants was \nprotected against any source of variations other than those included in \nthe treatments. There is no irrigation is required but left to receive \nnatural precipitation. The list of genotypes of finger millet included in the \nstudy is presented in Table 1. \n\n\n\n \nTable 1: Names genotypes of finger millets used for research at HCRP \n\n\n\n(2017)  \nS. N   Name of genotypes S.N. Name of genotypes S.N. Name of genotypes \n\n\n\n\n\n\n\n1 ACC#2275 7 ACC#512 13 ACC#6542 \n2 ACC#2286-1 8 ACC#2844 14 GE-0012 \n3 ACC#2301 9 ACC#513 15 GE-0480 \n4 ACC#2303 10 ACC#5434 16 Kabre Kodo-1 \n5 ACC#2400 11 ACC#6308   \n\n\n\n6 Local Variety 12 ACC#6369   \n       \n\n\n\n \n2.3 Data collection \n \nObservation will be taken for the following parameters as such as DTH \n(50%)= Days to 50 % heading, PH= plant height (cm), PS= Plant stand \nper square meter, BH= Baring heads per square meter, NF/H= Number of \nfinger per head, LS%= lodging percentage, TGW= thousand grain weight \n(gram), GY= grain yield (t/ha) and SY= straw yield (t/ha) per the \ndescriptors for finger millet [13]. Data on days to 50% heading, plant \nstand and baring head and straw yield were recorded on plot basis. \nWhereas, plant height (cm) and number of finger per head was recorded \non five selected representative plants. Thousand kernel weights were \nmeasured by counting 1000 grains from the bulk of each plot after \nshelling and weighed in grams after the moisture was adjusted to 15%. \nLodging percentage was measured in scale 1 to 5. Lodging should be \nrecorded on the scale of 1 to 5 indicating 1= 0-20%, 2=20 -40 %, 3= 40-\n60%,4= 60-80% and 5=80-100%. Grain yield per plot adjusted to 12  \n% grain moisture and converted to kilogram per hectare on basis by \nusing following formula. \n\n\n\n\n\n\n\nGrain yield (kg/ha) =  GYPP \u00d7 10000 \u00d7 (100- GMC)  \nGrain                            NHA \u00d7 88 \n NHA X 88                                    \n\n\n\nWhere, GYPP = Grain Yield per plot (kg), GMC = grain moisture content at \nharvest (%), NHA = Net harvested area (m2). \n \n3. STATISTICAL ANALYSIS \n \nThe data recorded on different parameters from field were first tabulated \nand processing in Microsoft excel (MS- Excel, 2010), then subjected \nGenStat to obtain ANOVA and all values were expressed as mean values. \nCorrelation coefficients of different traits using SPSS program were \ncarried out using the formula given by researchers through a study [14]. \nP values less than 0.05 and 0.01 were considered statistically significant \nand statistically highly significant, respectively. \n \n4. RESULTS AND DISCUSSION \n \nThe present study genetic diversity among 16 finger millet genotypes was \nanalyzed on the basis of eight agro morphological traits. The result of \ndescriptive analysis (Table 2) showed that lodging had highest variation \n(32.9) fallowed by straw yield (31.9%), grain yield (20.4) and number of \nfinger per head (11.1). Among traits days to 50 % heading showed the \nlowest (2.2) fallowed by thousand grain weight (4.8), plant height (8.1). \nSignificant variation among finger millet genotypes for grain yield, \nthousand grain weight, number of finger per head, baring head per \nsquare meter, plant stand per square meter, plant height, days to 50 % \nheading and lodging percentage. The mean value of observed traits day \n\n\n\n\n\n\n\n \nto 50 % heading (65), plant height(86 cm), plant stand per meter square  \n(87) Baring Head (132), number of finger per head (6) thousand grain \nweight (1.13), grain yield (2.85 t/ha) and straw yield (8.38 t/ha) as \npresented in Table 2.The genotypes ACC#513 (3.68 t/ha) fallowed by \nACC#2303 (3.65t/ha), ACC#2275 (3.57t/ha) and ACC#5434 (3.39) \nproduces maximum yield whereas genotypes ACC#6308 (1.5 t/ha) and \nGE-002 (1.6 t/ha) produces minimum yield under study condition. \n\n\n\n \nTable 2: Descriptive statistics of agro morphological traits of 16 finger \n\n\n\nmillet genotypes at HCRP, Kabre, Dolakha (2018) \n \n DTH PH        \n\n\n\n\n\n\n\nName of genotypes (50%) (cm) PS BH NF/H lodging% TGW GY(t/ha) SY (t /ha) \n \n\n\n\nACC#2275 67.5 83.5 66.5 163.5 6.5 1 0.875 3.57 8.55 \n \n\n\n\nACC#2286-1 72.5 84.9 71 110 6.2 1.5 1.04 2.38 6.86 \n \n\n\n\nACC#2301 64 91 89 150.5 6.4 1 1.01 2.83 8.81 \n \n\n\n\nACC#2303 63 90.9 89 118 5.5 1 1.31 3.66 10.74 \n \n\n\n\nACC#2400 66 83.5 74.5 130 5.3 1 1.14 2.93 8.27 \n \n\n\n\nACC#512 64 91.7 79.5 130 5.8 1 1.16 3.14 9.22 \n \n\n\n\nACC#2844 67.5 88.5 85.5 127 5 1 1.25 3.31 8.95 \n \n\n\n\nACC#513 64.5 87.8 87 181.5 5.4 1 1.16 3.68 11.34 \n \n\n\n\nACC#5434 65 95.1 61.5 113 4.8 1 1.305 3.39 10.22  \n\n\n\n\n\n\n\nACC#6308 63 65 68.5 139.5 6 3 1.05 1.5 3.41 \n \n\n\n\nACC#6369 72.5 79.7 75.5 122 6.6 1.5 0.975 2.2 6.24 \n \n\n\n\nACC#6542 67.5 84.5 63.5 107.5 4.95 1 1.335 2.94 8.69 \n \n\n\n\nGE-0012 62 75.5 69 133 6.4 3 0.985 1.68 5.7 \n \n\n\n\nGE-0480 63 82.9 75 147.5 5.4 2.5 0.97 2.48 5.81 \n \n\n\n\nKabre Kodo-1 64.5 90.9 82 123.5 4.6 1 1.2 2.48 12.55  \n\n\n\n\n\n\n\nLocal variety 63.5 100.5 72.5 121 4.8 1 1.38 2.7 8.75 \n \n\n\n\nGrand Mean 65.62 86 87 132.3 5.6 1.406 1.1325 2.85 8.38 \n \n\n\n\nF \u2013test <.001 0.028 0.206 0.006 0.053 0.002 <.001 0.037 0.211 \n \n\n\n\nC.V.(%) 2.2 8.1 13.2 10.8 11.1 32.9 4.8 20.4 31.9 \n \n\n\n\nLSD (0.05) 1.09 5.3 7.52 10.7 0.48 0.35 0.06 0.44 2.02 \n  \n\n\n\n \n* and **, significant at 5 % and 1 % probability level. DTH (50%)= Days to \n50 % heading, PH= Plant height (cm), PS= Plant stand square meter-1, \nBH= Bearing head square meter-1,NF/H= Number of finger head-1, TGW= \nThousand grain weight, GY= Grain yield (t/ha) and SY= Straw yield (t/ha). \n\n\n\n \n4.1 Correlation analysis \n\n\n\n \nAnalysis of variance exhibited significant difference among genotypes for \ndifferent traits. The grain yield had positive significant correlation with \nplant height fallowed by plant stand per square meter, baring head per \nsquare meter, number of finger per head and straw yield and significant \nnegative correlation with lodging percentage. The plant height was found \nsignificant positive association with straw yield fallowed by thousand \nkernel weight where as significant negative association with number \nfinger per head. Thousand grain weight were found significant negative \ncorrelation baring head per square meter and number finger per head. \nSignificant positive association between lodging percentage and straw \nyield and significant negative association between plant height and \nthousand kernel weight. \n\n\n\n \nTable 3: Pearson\u2019s Correlation coefficient among different traits of finger \n\n\n\nmillet under at HCRP, Dolakha (2018). \n \n\n\n\nDTH (50%) PH PS BH NF/H TGW GY SY \n \n\n\n\n\n\n\n\nPH -0.070  1      \n \n\n\n\nPS -0.182 0.292 1     \n \n\n\n\nBH -0.294 -0.181 0.322 1    \n \n\n\n\nNF/H 0.278  -0.531* -0.017  0.312 1   \n \n\n\n\nTGW -0.201 0.602*0.075  -0.506* -0.825** 1  \n \n\n\n\nGY 0.012 0.651* 0.219* 0.108* 0.225*0.385 1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSY -0.107 0.769*   0.435  0.022  -0.559*0.548*0.746*   1 \n \n\n\n\nLodging% -0.273 -0.782** -0.311  0.099   0.385 -0.496*-0.803** 0.814** \n \n\n\n\n\n\n\n\nValues are significant difference at 5 % level of significance (*) and highly \nsignificant at 1 % level of significant (**), DTH (50%)= Days to 50 % \nheading, PH= Plant height (cm), PS= Plant stand per square meter,BH= \nBearing head per square meter,NF/H= Number of finger per head, TGW= \nThousand grain weight, GY= Grain yield (t/ha) and SY= Straw yield (t/ha). \n\n\n\n \nIn present study there is sustainable genetic variability in different morph-\nphysiological and yield attributing traits of finger millet genotypes. A \nresearcher reported that significance amount of variability in finger millet \ngenotypes for different morph- physiological and yield attributing traits in \nthree environmental conditions over three years [15]. The genotypic \nvariability among finger millet genotypes was not influenced by  \n\n\n\n \nCite the article: Narayan Bahadur Dhami, Manoj Kandel, Suk Bahadur Gurung, Jiban Shrestha (2018). Agronomic Performance And Correlation Analysis \n\n\n\nOf Finger Millet (Elusine corocana L.) Genotypes. Malaysian Journal of Sustainable Agriculture, 2(2) : 16-18. \n\n\n\n\n\n\n\n\n \nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 16-18 18  \n\n\n\n \nenvironmental condition in characters such as days to heading, finger \nwidth, finger length, and days to maturity, culm thickness and grain yield \nper plant similar result was reported [16]. A group researcher also found \nvariation in the morphological characters such as 63.5% were green plant \ntype accessions from 909 accessions: 92.8% types are erect in growth \nhabits: varies in mean plant height, mean time to 50% flowering, mean \ninflorescence length and width, mean of panicle exertion ranges from 90-\n104cm, 65-81 days, 88-104mm, 55-103mm and 75-110mm respectively \nthat showed exist of diversity and which directly affects in yield of finger \nmillet [17]. In a study, reported similar result of variability at intra \npopulation level in all quantitative traits and similarly, higher \npolymorphism observed in 11 qualitative characters (ear shape followed \nby grain number) and plant stand, finger length, number of finger per \nhead, baring head, finger width and grain yield were distributed in \nsignificant skewness [18]. In other study also the researchers observed \nthat grain yield was positively correlated with, finger width baring head, \nand number of finger per head and he also notified that variation in these \ntraits cause diversity in Intra- and inter population of finger millet [18]. \nSome researchers also reported that grain yield was positively correlated \nwith plant height [19]. The similar result of positive correlation between \nplant height number of finger per head with grain yield also reported in a \nstudy [20]. In variability analysis among 400 finger millet germplasms \nwas found positive association between grain yield with productive \ntillers, fingers per ear and finger length and plant height [21]. In other \nstudy also, researcher reported similar result as grain yield was positive \nnumber of finger per head and baring head [18]. Others, there a \nresearchers also reported that the correlation of grain yield with straw \nyield and harvest index at genotypic and phenotypic level [22]. \n \n5. CONCLUSION \n \nThe genetic diversity was observed in different genotypes of finger millet \nfor traits such as grain yield fallowed by days to 50 % heading, plant \nheight, plant stand per square meter, bearing head per square meter, \nnumber of finger per head, and thousand grain weight under study \ncondition. Plant height fallowed by plant stand per square meter, bearing \nhead, number of finger per head and straw yield with minimum lodging \npercentage were most yield determinative traits as revealed from \ncorrelation analysis and hence simultaneous selection for these traits \nmight brining an improvement in finger millet grain yield under \ncondition. Thus, presence of high level of diversity among the genotypes \nof finger millet for grain yield indicated their superior trait value for study \ncondition; these genotypes may be of interest to researcher for further \nbreeding purpose. \n \nACKNOWLEDGEMENTS \n \nThe author would be highly indebted to Damali Sherpa and Dinesh Kumar \nNepali technical assistance (T-4) atHill Crop Research Program (HCRP) \nfor providing valuable support during data taken in research. He would \nalso like to thank to Hill Crop Research Program (HCRP),Dolakha for the \nprovision of reseach materials and field. \n \nREFERENCES \n \n[1] Werth, C.R., Hilu, K.W., Langner, C.A. 1994. Isozymes of Eleusine \n(Gramineae) and the origin of finger millet. American Journal of Botany, \n81, 1186-1197. \n\n\n\n \n[2] MoAD. 2016. Statistical Information on Nepalese Agriculture, 2016/17 \n(2072/2073). Agri business promotion and statistical division, \nAgristatistic section, Singhdurbar, Kathmandu,Nepal. \n\n\n\n \n[3] Upreti, R.P. 1999. Status of millet genetic resources in Nepal: Wild \nrelatives of cultivated plants in Nepal. In: R. Shrestha and B. Shrestha, \n(eds.). Wild relatives of cultivated plants in Nepal. Proceedings of National \nconference on wild relatives of cultivated plants in Nepal Kathmandu. \nGreen Energy Mission (GEM), Kathmandu, 2 (4), 78-82. \n\n\n\n \n[4] Upadhyay, M.P., Joshi, B.K. 2003. Plant genetic resources in SAARC \ncountries: Their conservation and management. Nepal chapter. SAARC \nAgriculture Information Center, 297-422. \n\n\n\n \n[5] Hunsigi, G., Krishna, K.R. 1998. Science of field crop production; Finger \nmillet. New Delhi Oxford and IBH publishing Co pvt.ltd.132 \n\n\n\n \n[6] ICRISAT. 2004. Finger millet. http://www.icrisat.org/web/ASP/ \nmainsection.asp (Retrieved on 15 June, 2011). \n\n\n\n \n[7] Adhikari, R.K. 2005. Economics of underutilized crop: A case of finger \nmillet from Peri urban area of Pokhara valley, Thesis, M.Sc. Ag., Tribhuvan \nUniversity, IAAS, Rampur, Chitwan, Nepal. \n\n\n\n \n \n[8] Baniya, B.K., Riley, K.W., Dongol, D.M.S., Sherchand, K.K. 1992. \nCharacterization of Nepalese hill crops landraces (Barley, Buckwheat, \nFinger millet, Grain Amaranth, Foxtail, Proso and Barnyard millets), \nNARC-IBPGR, Kabre, Dolakha, Nepal. \n\n\n\n \n[9] Rana, R.B., Rijal, D.K., Gauchan, D., Sthapit, B.R., Subedi, A., Upadhyay, \nM.P., Pandey, Y.R., Jarvis, D.I. 2000c. In situ crop conservation: Findings of \nagro-ecological, crop diversity and socio-economic baseline survey of \nBegnasecosite, 2. Kaski, Nepal. NARC, LI-BIRD and IPGRI. \n\n\n\n \n[10] Rachine, K.O. 1975. The Millets: Importance, Utilization and Outlook. \nInternational Crops Research Institute for the Semi-Arid Tropics, \nHyderabad, India, 63. \n\n\n\n \n[11] Vadivoo, A.S., Joseph, R. 1998. Genetic variability and diversity for \nprotein and calcium contents in fingermillet (Elusinecoracona (L.) \nGaertn) in relation to grain color. Plant Foods for Human Nutrition \nDordrecht. Department of Botany, Avinashilingam Institute for Home \nScience and Higher Education for Women, Deemed University, \nCoimbatore, TN, 641 043, India, 52 (4), 353-364. \n\n\n\n \n[12] IBPGR. 1985. Descriptors for finger millet (Elusine coracana (L.) \nGaertn). Rome, Italy: International Board for Plant Genetic Resources.20. \nhttp://www2.bioversityinternational.org/publications/ \nWeb_version/417 (Retrieved on 20 Feb, 2018). \n\n\n\n \n[13] Steel, R.G.D., Torrie, J.H. 1980. Principles and procedures of \nstatistics, a biochemical approach. McGraw Hill, Inc. New York. \n\n\n\n \n[14] Mishra, R.C., Das, S., Patnaik, M.C. 2009. AMMI Model Analysis of \nStability and Adaptability of Late Duration Finger Millet (Eleusine \ncoracana) Genotypes. World Applied Sciences Journal, 6 (12), 1650-1654. \n\n\n\n \n[15] Bezaweletaw, K., Sripichit, P., Wongyai, W., Hongtrakul, V. 2006. \nGenetic variation, heritability and path-analysis in Ethiopian finger millet \n(Eleusine coracana(L.) Gaertn) landraces. Kasetsart Journal, Natural \nSciences, 40, 322-334. \n\n\n\n \n[16] Upadhyaya, H.D., Gowda, C.L.L., Reddy, V.G. 2007. Morphological \ndiversity in finger millet germplasm introduced from Southern and \nEastern Africa. Journal of SAT Agriculture Research, 3 (1), 78-82. \n\n\n\n \n[17] Amgain, R.B., Joshi, B.K., Shrestha, P., Chaudahry, B., Adhikari, N.P., \nBaniya, B.K. 2004. In: B.R. Sthapit, B.R., M.P. Upadhyay, P.K. Shrestha and \nD.I. Jarvis (eds.). Intra- and interpopulation variation in finger millet ( \nEleusine coracana (L.) Gaertn) landraces grown in Kachorwa, \nBara.Conference paper: On-farm conservation of agricultural biodiversity \nin Nepal. Volume 1: assessing the amount and distribution of  \ngenetic diversity on-farm. Proceedings of the second national workshop  \nAugust 25-27, 84-95. Nagarkot,Nepal. http:// \nwww.bioversityinternational.org/Publication/Pdf/1083.pdf Retrieved on \n20 Feb,2018. \n \n[18] Dhagat, N.K., Pathak, G.L., Srivastava, P.S., Joshi, R.C. 1972. \nCorrelation and genetic variability in ragi (Eleusinecoracana G.). \nJawaharlal Nehru KrishiVishwa Vidyalay Research Journal, 6, 121\u2013 124. \n\n\n\n \n[19] Harinarayana, C., Shewall, T.T., Gaikwad, A.C., Haeper, P.N. 1989. \nVariability for components of yield in Maharashtra, Sikkim and Malawi \ncollections of finger millet. In: Finger millet Genetics and Breeding in \nIndia. Proceedings of National Seminar, University of Agricultural \nSciences, Bangalore, 119- 123. \n\n\n\n \n[20] K.1996. Evaluation of finger millet germplasm. Germplasm \nCatalogue 1. Small Millets Project Co-ordination Unit, UAS-KAR, \nBangalore,33-35. \n\n\n\n \n[21] Ramakrishna, M.B., Gowda, B.T.S., Katti, M., Seetharam, A., Mantur, \nS.G., Viswanath, S., Channamma, K.A.L., Krishnappa, M., Vasanth, K.R., \nKrishnamurthi, Jagadeeswara, B. 1996. Evaluation of finger millet \ngermplasms, Germplasm Catalogue 1. Small. Millets Project Co-ordination \nUnit, UAS-KAR, Bangalore, 33-35. \n\n\n\n \n[22] Cauvery, M.B. 1993. Variability for fodder yield, it\u2019s components and \ngrain yield in Indian and African collection of finger millet. M. Sc. (Agri.) \nThesis, University of Agricultural Sciences, Bangalore, India.  \n\n\n\n\n\n\n\n \nCite the article: Narayan Bahadur Dhami, Manoj Kandel, Suk Bahadur Gurung, Jiban Shrestha (2018). Agronomic Performance And Correlation Analysis \n\n\n\nOf Finger Millet (Elusine corocana L.) Genotypes. Malaysian Journal of Sustainable Agriculture, 2(2) : 16-18. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 12-16 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2022.12.16 \n\n\n\n \nCite The Article: Abdel Aziz Hassane Sidikou, Saad Drissi, Ahmed Bouaziz, Ahmed Bamouh, Khalid Dhassi, Yousra El -Mejjaouy, Hicham El Hajli, Abdelhadi Ait Houssa \n\n\n\n(2022). Responses of Corn Silage To Sowing Pattern Under Subsurface Drip Irrigation in A Sandy Soil. Malaysian Journal of Sustainable Agricultures, 6(1): 12-16. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2022.12.16 \n\n\n\n\n\n\n\nRESPONSES OF CORN SILAGE TO SOWING PATTERN UNDER SUBSURFACE DRIP \nIRRIGATION IN A SANDY SOIL \n \nAbdel Aziz Hassane Sidikoua, Saad Drissib*, Ahmed Bouaziza, Ahmed Bamouha, Khalid Dhassic, Yousra El-Mejjaouyd, Hicham El Hajlie, Abdelhadi \nAit Houssac \n \n a Department of Plant Production, Protection, and Biotechnology, Hassan II Institute of Agronomy and Veterinary Medicine, Rabat, Morocco. \n b Department of Agronomy and Plant Breeding, National School of Agriculture, Meknes, Morocco. \nc Agricultural Training and Research Center, Providence Verte company, Rabat, Morocco. \nd AgroBiosciences program, University Mohamed VI Polytechnic.  \ne Moroccan agricultural cooperative, Taroudant, Morocco \n*Corresponding author email: sdrissi@enameknes.ac.ma \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 16 July 2021 \nAccepted 19 August 2021 \nAvailable online 24 August 2021 \n\n\n\n\n\n\n\nThe subsurface drip irrigation technique is introduced in many agricultural areas of Morocco, particularly in \nthe forage production systems. This study aims to determine the optimal sowing pattern of forage corn \nequipped with a subsurface drip irrigation system. A field experiment was carried out on sandy soil. Five \nrows spacing were evaluated: 40 cm, 55 cm, 70 cm, 85 cm, and 100 cm. The sowing rate was around 120000 \nplants ha-1. The subsurface irrigation system consisted of drip lines buried at 30 cm and separated by 100 cm \nwith 1 L h-1 emitters and 50 cm as emitters spacing. The results revealed that the fraction of PARi and the \naccumulated PARi were not influenced by the sowing pattern. The highest corn dry biomass was recorded at \n40 cm, 70 cm, and 85 cm row spacing. The biomass increase was mainly attributed to grain yield. The lowest \nirrigation water use efficiency was recorded at 100 cm row spacing (4.3 kg m-3). Concerning the forage \nquality, the sowing pattern did not influence the net energy for lactation and other forage quality parameters. \n\n\n\nKEYWORDS \n\n\n\ncorn, sowing pattern, subsurface drip irrigation, productivity, forage quality  \n\n\n\n1. INTRODUCTION \n\n\n\nCorn is one of the most produced cereal species in the world (Wrigley, \n2017). It is a major forage for ruminants due to its high dry matter yields \nand low-cost production (Allen et al., 2003). The optimization of corn \nyielding was related to reasonable irrigation using an efficient technic \n(Henry and Krutz, 2016). In Morocco, the program of economy and \nvalorization of irrigation water launched in 2009 resulted in large-scale \nuse of the drip irrigation techniques. Therefore, surface drip irrigation \nbecame commonly used for corn production. Also, some producers are \nintroducing subsurface drip irrigation for corn silage. Many authors \nreported that subsurface water supply enhances crop yield and water use \nefficiency (Miguel et al., 2003; Najafi and Tabatabaei, 2007; Vories et al., \n2009). The yield increase due to subsurface drip irrigation was mainly \nrelated to the adequate nutrient supply around the root system (Badr, \n2007; Elhindi et al., 2016). Also, the in-depth development of roots limits \ncrop exposure to water and mineral stresses (Bar-Yosef, 1999).  \n\n\n\nHowever, subsurface drip irrigation requires optimization of specific \nagronomic factors such as sowing rate and sowing pattern according to \ndriplines spacing (Lamm, 2016). Indeed, the effects of row spacing on corn \nyield, silage quality, and radiation use efficiency have been reported by \nmany authors (Cox and Cherney, 2001; Gobeze et al., 2012; Sharratt and \nMcWilliams, 2005). The efficiency of the light conversion into biomass can \nbe improved by the canopy architecture (Long et al., 2006). The optimal \nsowing pattern for subsurface drip irrigation was mainly related to the soil \n\n\n\ntype. On clay loam soil, 76 cm between planting rows for subsurface drip \nlines of 152 cm was reported as an adequate sowing pattern for corn \n(Murley et al., 2018). However, sowing rows of 38 cm for drip lines spaced \n100 or 200 cm was suggested for corn grown on a loam sandy soil (Stone \net al., 2008). \n\n\n\nIn sandy soil of the Loukkos area (Northern Morocco), corn is usually sown \nin twin lines (90*45 cm) at a density of 120000 plants ha-1 (A\u00eft Houssa et \nal., 2008). For the subsurface drip irrigation, the driplines are buried at 30 \ncm as suggested by Douh et al. (2013) with a lateral spacing of 100 cm. \nHowever, the adequate sowing pattern is still not yet determined for this \nsouthern Mediterranean area. The objective of this study was to determine \nthe appropriate planting row spacing for corn silage equipped with \nsubsurface drip irrigation. This study will investigate the effect of the \nsowing structure on the forage quality and the uses efficiencies of \nradiation and irrigation water in the sandy soil. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Experimental Site \n\n\n\nField experiment of corn silage was conducted in 2017 (July to October). \nThe experimental site was located in the Loukkos area (Northern Morocco, \n35\u00b000'N, 6\u00b012'W, 30 m from the sea level). The soil was sandy (86.4% \nsand). It contains a low organic matter level (1.1%). The other soil \nproperties are reported in Table 1.  \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 12-16 \n\n\n\n\n\n\n\n \nCite The Article: Abdel Aziz Hassane Sidikou, Saad Drissi, Ahmed Bouaziz, Ahmed Bamouh, Khalid Dhassi, Yousra El -Mejjaouy, Hicham El Hajli, Abdelhadi Ait Houssa \n\n\n\n(2022). Responses of Corn Silage To Sowing Pattern Under Subsurface Drip Irrigation in A Sandy Soil. Malaysian Journal of Sustainable Agricultures, 6(1): 12-16. \n\n\n\n\n\n\n\nTable 1: Soil properties (0-30 cm) \n\n\n\nSoil properties \n\n\n\nSand (%) 86.40 \n\n\n\nSilt (%) 4.20 \n\n\n\nClay (%) 9.20 \n\n\n\npHa 8.00 \n\n\n\nCation exchangeable capacity (meq 100 g-1)b 8.30 \n\n\n\nElectrical conductivity (ds m-1)a 0.25 \n\n\n\nOrganic matter (%)c 1.10 \n\n\n\nP2O5 (mg kg-1)d 186.00 \n\n\n\nK2O (mg kg-1)e 405.00 \n\n\n\nZinc (mg kg-1)f 2.13 \n\n\n\nCopper (mg kg-1)f 0.64 \n\n\n\nManganese (mg kg-1)f 3.69 \n\n\n\nIron (mg kg-1)f 18.16 \n\n\n\n \na. Determined in a soil: water ratio of 1/5. \nb. Determined using Cobaltihexamine Chloride method. \nc. Determined using Walkey-Black method. \nd. Olsen extraction method. \ne. Ammonium acetate extraction. \nf. DTPA extraction. \n \n2.2 Crop management and Experimental Design \n\n\n\nThe land was prepared for planting by cultivator tillage. A subsurface drip \nirrigation system was installed before sowing. The driplines were \nseparated by 100 cm with 1 L h-1 emitters and 50 cm as emitters spacing. \nThe drip lines were buried at 30 cm depth. The sprinklers were installed \nto ensure an adequate plant emergence. The sowing was done manually. \nThe common sowing rate was 120000 seeds ha-1. Five rows spacing were \ntested: 40 cm, 55 cm, 70 cm, 85 cm, and 100 cm. The experimental design \nwas a randomized complete block with five replications. The experimental \nplot size was 56 m2 (8 m*7 m). The number of sowing rows was 18 for the \n40 cm row spacing and 8 for the 100 cm row spacing. For each plot, the \nfirst sowing line was close to the subsurface drip line (Figure 1). The \ndistribution of crop rows and subsurface drip lines according to different \nsowing patterns was reported in Figure 1. \n\n\n\nFigure 1: Studied sowing patterns on the experimental plot (7 m*8 m). \nFor all the sowing patterns, the subsurface driplines are separated by \n\n\n\n100 cm. \n\n\n\nThe soil was supplied with 220 kg ha-1 of N, 60 kg ha-1 of P2O5, and \n240 kg ha-1 of K2O. Ammonitrate, diammonium phosphorus, and sulfate of \npotassium were used as sources of nutrients. 34% of nutrients were \napplied at sowing and 66% was applied by fertigation using the subsurface \ndrip irrigation system. Concerning micronutrients, 35 kg ha-1 of zinc \nsulfate, 5 kg ha-1 manganese sulfate, and 1 kg ha-1 copper sulfate were \napplied. Weeds were controlled using a mixture of pre-emergence \nherbicides. Also, the fungal disease (Setosphaeria turcica) was controlled \nwith Epoxiconazole. During the growing season (July to October), the \naverage minimum and maximum temperatures were around 16.7 \u00b0C and \n28.9 \u00b0C. From emergence to harvest, the accumulated global radiation was \n1874 MJ m-2. The rainfall amount was around 62.2 mm (Figure 2) and the \nirrigation amount was 423 mm. \n\n\n\nFigure 2: Temperatures, rainfall, and accumulated global radiation of the \nstudied area. \n\n\n\n2.3 Measurements \n\n\n\n2.3.1 Solar Radiation \n\n\n\nGlobal radiation (Rg) was measured each 10 days using a pyranometer \n(Solar Radiation Sensor, SN. 8519). Measurements were made between \n12h and 14h GMT at the ground level. On each plot, solar radiation was \nrecorded outside canopy and under canopy between 3 successive crop \nrows spacing. Thereafter, photosynthetically active radiation (PAR) was \ncalculated using formula (1) (Szeicz, 1974). \n\n\n\n\ud835\udc39\ud835\udc43\ud835\udc34\ud835\udc45\ud835\udc56 (%)  = (\ud835\udc43\ud835\udc34\ud835\udc45 \u2212 \ud835\udc43\ud835\udc34\ud835\udc45\ud835\udc60)/\ud835\udc43\ud835\udc34\ud835\udc45                                                                   \n(1) \n\n\n\nThe fraction of PARi was calculated using formula (2) (Monteith, 1981). \n\n\n\n\ud835\udc39\ud835\udc5f\ud835\udc4e\ud835\udc50\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc5c\ud835\udc53 \ud835\udc43\ud835\udc34\ud835\udc45\ud835\udc56 (%)  = (\ud835\udc43\ud835\udc34\ud835\udc45 \u2212 \ud835\udc43\ud835\udc34\ud835\udc45\ud835\udc60)/\ud835\udc43\ud835\udc34\ud835\udc45                                                     \n(2) \n\n\n\nWhere PARs is the photosynthetically active radiation at the ground level \nof canopy. \nThe accumulated PARi was calculated using formula (3).  \n\n\n\n\ud835\udc34\ud835\udc50\ud835\udc50\ud835\udc62\ud835\udc5a\ud835\udc62\ud835\udc59\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc51 \ud835\udc43\ud835\udc34\ud835\udc45\ud835\udc56 (\ud835\udc40\ud835\udc3d \ud835\udc5a\u22122) =  \ud835\udc39\ud835\udc43\ud835\udc34\ud835\udc45\ud835\udc56 \u2217 (\ud835\udc4e\ud835\udc50\ud835\udc50\ud835\udc62\ud835\udc5a\ud835\udc62\ud835\udc59\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc51 \ud835\udc43\ud835\udc34\ud835\udc45) \n                (3) \n\n\n\nWhere the accumulated PAR was recorded from a weather station (iMetos \n3.3). \n\n\n\n2.3.2 Growth Parameters and Forage Biomass Production \n\n\n\nThe harvest was done at 37% of dry biomass. 10 plants per experimental \nplot were cut to determine biomass production. These plants were \nseparated into leaves, stem, and ears to determine the biomass allocation. \nAt harvest, the stem height, the stem diameter, and leaf area were \ndetermined on 10 plants for each plot.  The leaf area was determined using \nformula (4) (Mokhtarpour et al., 2010). \n\n\n\n\ud835\udc3f\ud835\udc52\ud835\udc4e\ud835\udc53 \ud835\udc4e\ud835\udc5f\ud835\udc52\ud835\udc4e (\ud835\udc5a2 \ud835\udc5d\ud835\udc59\ud835\udc4e\ud835\udc5b\ud835\udc61\u22121) = \u2211 (\ud835\udc59\ud835\udc56 \u2217 \ud835\udc64\ud835\udc56 \u2217 0.75)\ud835\udc5b\n\ud835\udc56=1                                                      \n\n\n\n(4) \n\n\n\nWhere l, w, and i are, respectively, leaf length, leaf greatest width, and leaf \nnumber. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 12-16 \n\n\n\n\n\n\n\n \nCite The Article: Abdel Aziz Hassane Sidikou, Saad Drissi, Ahmed Bouaziz, Ahmed Bamouh, Khalid Dhassi, Yousra El -Mejjaouy, Hicham El Hajli, Abdelhadi Ait Houssa \n\n\n\n(2022). Responses of Corn Silage To Sowing Pattern Under Subsurface Drip Irrigation in A Sandy Soil. Malaysian Journal of Sustainable Agricultures, 6(1): 12-16. \n\n\n\n\n\n\n\n2.3.3 Forage Quality Analysis \n\n\n\nAt harvest, plants were cut and shopped to determine forage quality for \neach experimental plot. Then, the samples were chemically analyzed for \nmineral matter and fat content. The mineral matter was determined after \ncalcination at 550 \u00b0C of the dry sample. The fat content was determined \nusing a Soxhlet extractor. Crude protein, cellulose, starch, dry matter \ndigestibility (DMD), neutral detergent fiber (NDF), acid detergent fiber \n(ADF), and acid detergent lignin (ADL) were analyzed using a near-\ninfrared reflectance spectrophotometer (NIRS). Thereafter, the net energy \nlactation was determined using formula (5) (Harlan et al., 1991). \n\n\n\n\ud835\udc41\ud835\udc38\ud835\udc3f(\ud835\udc40\ud835\udc50\ud835\udc4e\ud835\udc59 \ud835\udc58\ud835\udc54\u22121 \ud835\udc37\ud835\udc40) =  2.196 \u2212 0.0278 \u2217 \ud835\udc34\ud835\udc37\ud835\udc39 (% \ud835\udc37\ud835\udc40) \n                            (5) \n\n\n\n2.3.4 Uses Efficiencies of Radiation and Irrigation Water \n\n\n\nThe radiation use efficiency (RUE) was calculated for each plot as the slope \nof the linear regression between the accumulated biomass and the \naccumulated PARi determined using formula (3). Irrigation water use \nefficiency (IWUE) was calculated using formula (6). \n\n\n\n\ud835\udc3c\ud835\udc4a\ud835\udc48\ud835\udc38 (\ud835\udc58\ud835\udc54 \ud835\udc5a\u22123) =\n \ud835\udc34\ud835\udc52\ud835\udc5f\ud835\udc56\ud835\udc4e\ud835\udc59 \ud835\udc51\ud835\udc5f\ud835\udc66 \ud835\udc4f\ud835\udc56\ud835\udc5c\ud835\udc5a\ud835\udc4e\ud835\udc60\ud835\udc60 (\ud835\udc58\ud835\udc54) \ud835\udc3c\ud835\udc5f\ud835\udc5f\ud835\udc56\ud835\udc54\ud835\udc4e\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc64\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc5f \ud835\udc4e\ud835\udc5a\ud835\udc5c\ud835\udc62\ud835\udc5b\ud835\udc61 (\ud835\udc5a3)\u2044      (6) \n\n\n\n2.4 Statistical Analysis \n\n\n\nThe experimental data were subjected to analysis of variance (ANOVA). \nThe comparison of means was carried out at a 5 % level of significance \nusing the Student-Newman-Keuls test. The statistical analyses were \nperformed using the program SPSS (Version 20.0). \n\n\n\n3. RESULTS AND DISCUSSION  \n\n\n\n3.1 Solar Radiation Interception and Growth Parameters \n\n\n\nThe plant emergence rate (88%) was similar for all tested sowing patterns. \nThe final population was around 107000 plants ha-1. The fraction of PARi \nwas not significantly affected by the sowing pattern (Figure 3a). Similarly, \nthe accumulated PARi was not affected by the cropping architecture \n(Figure 3b). Therefore, the light interception does not seem to be \nimproved by reducing row spacing. In contrast, Sharratt and McWilliams \n(2005) reported more light interception for reduced row spacing (38 cm) \nthan 76 cm at a plant density of 75000 plant ha-1. At harvest, the \naccumulated PARi intercepted by corn was around 1703 MJ.  \n\n\n\nFigure 3: Evolution of the fraction of intercepted PAR and accumulated \nPARi at harvest. Values with the same letters are not significantly \n\n\n\ndifferent.  Vertical bars are standard deviation (n=15) \n\n\n\nTable 2: Plant height, stem diameter, and leaf area at harvest for \ndifferent row spacing. \n\n\n\nRow spacing \n(cm) \n\n\n\nStem height \n(cm) \n\n\n\nStem diameter \n(cm) \n\n\n\nLeaf area (m\u00b2 \nplant-1) \n\n\n\n40   266.8 \u00b1 16.3 a 1.98 \u00b1 0.24 a 0.35 \u00b1 0.07 a \n\n\n\n55 262.3 \u00b1 10.2 ab 1.96 \u00b1 0.20 a 0.34 \u00b1 0.05 a \n\n\n\n70   258.2 \u00b1 10.9 b 1.91 \u00b1 0.19 a 0.36 \u00b1 0.08 a \n\n\n\n85 263.1 \u00b1 10.5 ab 1.94 \u00b1 0.19 a 0.35 \u00b1 0.08 a \n\n\n\n100 261.1 \u00b1 11.8 ab 1.82 \u00b1 0.17 b 0.31 \u00b1 0.07 a \n\n\n\nValues are mean \u00b1 standard deviation. For each column and studied \nseason, values with the same letters are not significantly different (n=50) \n\n\n\nThe stem height and the stem diameter were significantly influenced by \nthe sowing pattern (Table 2). Overall, the height and diameter of the stem \nat harvest were enhanced by reducing crop row spacing. The highest levels \nwere recorded at 40 cm row spacing. A similar result was reported for corn \n\n\n\nat a row spacing of 50 cm compared to 75 cm (G\u00f6z\u00fcbenli, 2010). In \ncontrast, the leaf area at harvest was not significantly affected by the \ncropping arrangement. The leaf area was around 0.34 m\u00b2 plant-1 for all \ntested sowing patterns.  \n\n\n\n3.2 Biomass Production and Forage Quality \n\n\n\nThe biomass yield was significantly influenced by the sowing pattern \n(Figure 4a). 40 cm, 70 cm, and 85 cm as row spacing resulted in the highest \ndry biomass production (around 24 T ha-1). At 100 cm, the dry biomass \nshowed a decline of 24% compared to the other crop rows spacing. The \nbiomass increase at reduced row spacing was attributed to the ear \nbiomass enhancement, particularly the kernels\u2019 dry weight \n(Figure 4b and Figure 5a). Indeed, the kernels biomass was significantly \nincreased by 27% for 40 cm and 70 cm row spacing.  This kernels biomass \nincrease was mainly attributed to the enhancement of the kernel number \nper ear (Figure 5b). The low kernel number per ear at 100 cm can be \nexplained by the negative effect of competition, between plants of a crop \nrow, in the pollination rate (Zhang et al., 2018). Concerning the 1000-\nkernel weight, it was around 240 g for all tested sowing patterns (Figure \n5c).  The positive impact of narrow row spacing in grain yield was reported \nby many authors (Baron et al., 2006; Cox and Cherney, 2001; Shapiro and \nWortmann, 2006). This advantage can be explained by the high root \ndensity and limited soil evaporation at narrow row spacing                              \n(Sharratt and McWilliams, 2005). Moreover, the results revealed that the \nsubsurface driplines, separated by 100 cm, did not limit forage corn \nproduction, particularly at not adjusted crop rows to driplines (Figure 1). \nIndeed, for 100 cm row spacing, the reduced distance between plants \n(8 cm) may increase the intra-row competition, thereby reducing the \nsilage yield even the crop rows followed the driplines. \n\n\n\nFigure 4: Dry aerial biomass yield and its allocation on stem, leaves, and \near at different row spacing. Values with the same letters are not \n\n\n\nsignificantly different. Vertical bars are standard deviation (n=50). \n\n\n\nFigure 5: Kernels dry weight, number of kernels per ear and 1000-\nkernels dry weight at different row spacing. Values with the same letters \n\n\n\nare not significantly different. Vertical bars are standard deviation \n(n=50). \n\n\n\nOn the other side, the radiation use efficiency was not affected by the \nsowing pattern (Table 3). It was around 2.1 g MJ-1. Such a result can be \nexplained by the absence of a sowing pattern effect on the fraction of \nintercepted PAR and accumulated PAR as reported above (Figure 3). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 12-16 \n\n\n\n\n\n\n\n \nCite The Article: Abdel Aziz Hassane Sidikou, Saad Drissi, Ahmed Bouaziz, Ahmed Bamouh, Khalid Dhassi, Yousra El -Mejjaouy, Hicham El Hajli, Abdelhadi Ait Houssa \n\n\n\n(2022). Responses of Corn Silage To Sowing Pattern Under Subsurface Drip Irrigation in A Sandy Soil. Malaysian Journal of Sustainable Agricultures, 6(1): 12-16. \n\n\n\n\n\n\n\nTable 3: Uses efficiencies of radiation and irrigation water at different \nrow. \n\n\n\nRow spacing (cm) \nRadiation use \n\n\n\nefficiency (g MJ-1) \nIrrigation water \n\n\n\nuse efficiency (kg m-3) \n\n\n\n40 2.3 \u00b1 0.48 a 5.7 \u00b1 1.06 a \n\n\n\n55 2.0 \u00b1 0.19 a 4.7 \u00b1 0.57 ab \n\n\n\n70 2.0 \u00b1 0.49 a 5.6 \u00b1 0.40 a \n\n\n\n85 2.1 \u00b1 0.47 a 5.5 \u00b1 0.63 a \n\n\n\n100 2.0 \u00b1 0.15 a 4.3 \u00b1 0.51 b \n\n\n\nValues are mean \u00b1 standard deviation. For each column and studied \ngrowing season, values with the same letters are not significantly different \n(n=5) \n\n\n\nConcerning the irrigation water use efficiency, it was enhanced by around \n20% at 40 cm, 70 cm, and 85 cm row spacing compared to 100 cm. For the \nspring corn (2018), no significant enhancement was observed. The \nincrease of the irrigation water use efficiency was related to the high aerial \ndry biomass yield recorded at narrow crop row spacing. \n\n\n\nConcerning the response of forage quality to sowing patterns, the net \nenergy for lactation was similar for all tested sowing patterns. It was \naround 6.6 MJ kg-1 DM-1. The mineral matter, the fat content, and the crude \nprotein were, respectively, around 3.7 %, 2.9 %, and 6.7 % of dry biomass \n(Figure 6). On the contrary, other studies reported a decrease of crude \nprotein with reducing row spacing (Baron et al., 2006; Iptas and Acar, \n2011). The content of cellulose and starch in the forage were, respectively, \n18.9 % and 36.4 %. Similarly, Skonieski et al. (2014) reported no \nsignificant effect of row spacing on the mineral matter, the cellulose, the \nstarch, and the fat content of corn forage. Concerning the dry matter \ndigestibility, it was around 67.1 %. The neutral detergent fiber (NDF), the \nacid detergent fiber (ADF), and the acid detergent lignin (ADL) were, \nrespectively, around 42.4 %, 22.5 %, and 2.1 %. Likewise, no significant \neffect of row spacing on NDF and ADF of corn forage was reported by \nBaron et al. (2006).  From these results, the forage quality of corn, \nequipped with a subsurface drip irrigation system, does not seem to be \ninfluenced by the sowing pattern. \n\n\n\nFigure 6: Forage quality parameters at different row. Vertical bars are \nstandard deviation (n = 5) \n\n\n\n4. CONCLUSION  \n\n\n\nThe results of this study have revealed that the production of corn silage, \ngrown in the sandy Mediterranean soil and equipped with 100 cm \nsubsurface driplines spacing, requires an appropriate sowing pattern. The \nhighest biomass yield was achieved at crop row spacing lower than or \nequal to 85 cm. However, the forage quality parameters were not affected \nby the sowing pattern. It would be interesting to confirm these results for \nthe spring corn in this region. Also, more research is needed to identify the \nappropriate sowing pattern for other crops commonly sown in rotation \nwith forage corn, particularly soybean and fodder beet. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThe authors would like to thank Mr. Loultiti M. A.  for his financial \ncontribution. We wish to thank Mr. Belbesri M. and his managing team for \ntheir support and cooperation during the experimentations. We are \ngrateful to Msr. Alouatir Z. for her support during samples analysis. We \nalso acknowledge Dr. Philippe Debaeke and Mr. Amlal F. for their valuable \nhelp during manuscript preparation.   \n\n\n\nREFERENCES \n\n\n\nA\u00eft Houssa, A., Moutia, S., Belbasri, M., Hsayni, M., Loultiti, M.R., 2008. \nProductivit\u00e9 et rentabilit\u00e9 du ma\u00efs ensilage conduit au goutte \u00e0 goutte \nsur les sols sableux de Larache. H. T. E. 139, 65\u201372. \n\n\n\nAllen, M.S., Coors, J.G., Roth, G.W., 2003. Corn Silage, in: Buxton, D.R., Muck, \nR.E., Harrison, J.H. (Eds.), Silage science and technology. American \nSociety of Agronomy, Inc., Crop Science Society of America, Inc., Soil \nScience Society of America, Madison, Inc., pp. 547-608. \nhttps://doi.org/10.2134/agronmonogr42.c12. \n\n\n\nBadr, M.A., 2007. Spatial distribution of water and nutrients in root zone \nunder surface and subsurface drip irrigation and cantaloupe yield. \nWorld J. Agric. Res. 3 (6), 747\u2013756. \n\n\n\nBaron, V.S., Najda, H.G., Stevenson, F.C., 2006. Influence of population \ndensity, row spacing and hybrid on forage corn yield and nutritive \nvalue in a cool-season environment. Can. J. Plant Sc. 86 (4), 1131\u2013\n1138. https://doi.org/10.4141/P05-136. \n\n\n\nBar-Yosef, B., 1999. Advances in fertigation, in: Sparks, D.L. (Ed.), Advances \nin Agronomy. Academic Press, San Diego, pp. 1\u201377. \nhttps://doi.org/10.1016/S0065-2113(08)60910-4. \n\n\n\nCox, W.J., Cherney, D.J.R., 2001. Row spacing, plant density, and nitrogen \neffects on corn silage. Agron. J. 93 (3), 597. \nhttps://doi.org/10.2134/agronj2001.933597x. \n\n\n\nDouh, B., Boujelben, A., Khila, S., Mguidiche, A., 2013. Effect of subsurface \ndrip irrigation system depth on soil water content distribution at \ndifferent depths and different times after irrigation. Larhyss J. 13, 7\u2013\n16. \n\n\n\nElhindi, K., El-Hendawy, S., Abdel-Salam, E., Elgorban, A., Ahmed, M., 2016. \nImpacts of fertigation via surface and subsurface drip irrigation on \ngrowth rate, yield and flower quality of Zinnia elegans. Bragantia 75, \n96\u2013107. https://doi.org/10.1590/1678-4499.176. \n\n\n\nGobeze, Y.L., Ceronio, G.M., Rensburg, L.D.V., 2012. Effect of row spacing \nand plant density on yield and yield component of maize (Zea mays L.) \nunder irrigation. J. Agric. Sci. Technol. B 2 (2), 263\u2013271. \n\n\n\nG\u00f6z\u00fcbenli, H., 2010. Influence of planting patterns and plant density on the \nperformance of maize hybrids in the eastern mediterranean \nconditions. Int. J. Agric. Biol. 12 (4), 556\u2013560. \n\n\n\nHarlan, D.W., Holter, J.B., Hayes, H.H., 1991. Detergent fiber traits to predict \nproductive energy of forages fed free choice to nonlactating dairy \ncattle. J. Dairy. Sci. 74 (4), 1337\u20131353. \nhttps://doi.org/10.3168/jds.S0022-0302(91)78289-1. \n\n\n\nHenry, W.B., Krutz, L.J., 2016. Water in agriculture: improving corn \nproduction practices to minimize climate risk and optimize \nprofitability. Curr. Clim. Change Rep. 2 (2), 49\u201354. \nhttps://doi.org/10.1007/s40641-016-0035-9. \n\n\n\nIptas, S., Acar, A.A., 2011. Effects of hybrid and row spacing on maize forage \nyield and quality. Plant Soil Environ. 52 (11), 515\u2013522. \nhttps://doi.org/10.17221/3543-PSE. \n\n\n\nLamm, F.R., 2016. Cotton, tomato, corn, and onion production with \nsubsurface drip irrigation: A review. T. ASABE. 59 (1), 263\u2013278. \nhttps://doi.org/10.13031/trans.59.11231. \n\n\n\nLong, S.P., Zhu, X.-G., Naidu, S.L., Ort, D.R., 2006. Can improvement in \nphotosynthesis increase crop yields? Plant Cell Environ. 29 (3), 315\u2013\n330. https://doi.org/10.1111/j.1365-3040.2005.01493.x. \n\n\n\nMiguel, R.G., Ignacio, S.C., Juan, E.A., 2003. Alfalfa production using \nsubsurface drip irrigation. \nhttp://irrigationtoolbox.com/ReferenceDocuments/TechnicalPapers\n/IA/2003/IA03-0419.pdf (accessed 05 July 2021).  \n\n\n\nMokhtarpour, H., Teh, C., Saleh, G., Selamat, A., Asadi, M., Kamkar, B., 2010. \nNon-destructive estimation of maize leaf area, fresh weight, and dry \nweight using leaf length and leaf width. Commun. Biometry Crop. Sci. \n5, 19\u201326.  \n\n\n\nMonteith, J.L., 1981. Climatic variation and the growth of crops. Q. J. R. \nMeteorol. Soc. 107 (454), 749\u2013774. \nhttps://doi.org/10.1002/qj.49710745402. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 12-16 \n\n\n\n\n\n\n\n \nCite The Article: Abdel Aziz Hassane Sidikou, Saad Drissi, Ahmed Bouaziz, Ahmed Bamouh, Khalid Dhassi, Yousra El -Mejjaouy, Hicham El Hajli, Abdelhadi Ait Houssa \n\n\n\n(2022). Responses of Corn Silage To Sowing Pattern Under Subsurface Drip Irrigation in A Sandy Soil. Malaysian Journal of Sustainable Agricultures, 6(1): 12-16. \n\n\n\n\n\n\n\nMurley, C.B., Sharma, S., Warren, J.G., Arnall, D.B., Raun, W.R., 2018. Yield \nresponse of corn and grain sorghum to row offsets on subsurface drip \nlaterals. Agr. Water Manage. 208, 357\u2013362. \nhttps://doi.org/10.1016/j.agwat.2018.06.038. \n\n\n\nNajafi, P., Tabatabaei, S.-H., 2007. Effect of using subsurface drip irrigation \nand ET\u2010HS model to increase WUE in irrigation of some crops. Irrig. \nDrain. 56, 477\u2013486. https://doi.org/10.1002/ird.322. \n\n\n\nShapiro, C.A., Wortmann, C.S., 2006. Corn response to nitrogen rate, row \nspacing, and plant density in Eastern Nebraska. Agron. J. 98 (3), 529. \nhttps://doi.org/10.2134/agronj2005.0137. \n\n\n\nSharratt, B.S., McWilliams, D.A., 2005. Microclimatic and rooting \ncharacteristics of narrow-row versus conventional-row corn. Agron. J. \n97 (4), 1129. https://doi.org/10.2134/agronj2004.0292. \n\n\n\nSkonieski, F.R., N\u00f6rnberg, J.L., Kessler, J.D., David, D.B., Azevedo, E.B., \nBr\u00fcning, G., Pimentel, C.M.M., 2014. Corn plant arrangement and its \neffect on silage quality. Rev. Bras. Zootecn. 43 (3), 114\u2013119. \nhttps://doi.org/10.1590/S1516-35982014000300002. \n\n\n\nStone, K.C., Bauer, P.J., Busscher, W.J., Millen, J.A., 2008. Narrow row corn \nproduction with subsurface drip irrigation. Appl. Eng. Agric. 24 (4), \n455\u2013464. https://doi.org/10.13031/2013.25146. \n\n\n\nSzeicz, G., 1974. Solar radiation for plant growth. J. Appl. Ecol. 11 (2), 617. \nhttps://doi.org/10.2307/2402214. \n\n\n\nVories, E.D., Tacker, P.L., Lancaster, S.W., Glover, R.E., 2009. Subsurface \ndrip irrigation of corn in the United States Mid-South. Agr. Water \nManage. 96 (6), 912\u2013916. \nhttps://doi.org/10.1016/j.agwat.2008.12.004. \n\n\n\nWrigley, C., 2017. The cereal grains: providing our food, feed and fuel \nneeds, in: Wrigley, C., Batey, I., Miskelly, D. (Eds.), Cereal Grains. \nWoodhead Publishing, Duxford, pp. 27\u201340. \nhttps://doi.org/10.1016/B978-0-08-100719-8.00002-4. \n\n\n\nZhang, M., Chen, T., Latifmanesh, H., Feng, X., Cao, T., Qian, C., Deng, A., Song, \nZ., Zhang, W., 2018. How plant density affects maize spike \ndifferentiation, kernel set, and grain yield formation in Northeast \nChina? J. Integr. Agric. 17, 1745\u20131757. \nhttps://doi.org/10.1016/S2095-3119(17)61877-X. \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1016/S2095-3119(17)61877-X\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\nCite this article  Dayanand Sharmaa*, Kunwar D. Yadavb Vermicomposting of Flower Waste: Optimization of Maturity \nParameter by Response Surface Methodology  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 August 2016 \n\n\n\nAccepted 12 December 2016 \n\n\n\nAvailable online 20 January 2017 \n\n\n\nKeywords: \n\n\n\nVermicomposting, Flower waste, \nResponse surface methodology, \nCentral composite design, \nNutrient  \n\n\n\nABSTRACT\n\n\n\nIn the present study, response surface methodology (RSM) was used to develop an approach for the optimization \nof quantity of flower waste and cow dung to determine maturity during the vermicomposting of flower waste. The \neffect of maturity parameters such as C:N ratio, Germination index and CO2 evolution rate were studied using \ncentral composite design (CCD). Eisenia foetida was used in different combination of flower waste and cow dung \nduring the vermicomposting of flower waste. Results of study showed significant effect of both variables and their \ninteractions with process parameters during vermicomposting process. The optimum results obtained from \nresponse surface methodology was nearly equal between predicted and experimental analysis. The optimum \nvariation of process parameter was pH 7.07-7.12, electrical conductivity 3.28 -3.42 mS/cm, total organic carbon \n33.72-34.06%, C: N ratio 14-15, phosphorous 4.95-5.21 g/kg and potassium 13.99-14.31 g/kg. The results suggest \nthat compost obtained from the vermicomposting of flower waste and cow dung contains sodium, potassium and \nphosphorous which are beneficial for the plant growth. Flower waste compost is suitable for organic manure which \nreduces the quantity of waste by converting into valuable products. \n\n\n\n1. INTRODUCTION \n\n\n\nReduce, recycle and reuse of the organic waste is big challenges for \nmunicipal authorities in developing countries. The generation of waste is \nincreasing in faster rate due to urbanization, industrialization, rapid \nexpansion of cities and migration of people from rural area to urban area \nand living standard of peoples. In India per capita waste generation is \n0.17 to 0.8 per capita per day. Total waste generation in India is 1, 27,486 \nTPD in 34 states of municipality of this 89,334 TPD (70%) collected. \nAmong these waste 48% are biodegradable waste and only 12.5% was \nused for processing such as composting and vermicomposting, \nproduction of gas etc [1]. Biodegradable waste contains fruits, flower, \nfood waste etc. The quantity of flower waste in India is 300 MT/day [2]. \nMostly flower waste mixed with municipal waste and used for landfilling. \nFlower waste contains useful micro and macronutrients which are \nbeneficial for the growth of plants can be converted into nutrient \nenriched products.  \n\n\n\nVermicomposting is a biotechnical process for the treatment of flower \nwaste which adopts modern concept of ecological design by introducing \nearthworm [3]. It is a bio-oxidative process in which earthworm and \nmicroorganism play joint role to convert organic waste into matured and \nstabilized vermicompost. Although microbes are responsible for \nbiochemical degradation of organic matter, earthworms are important \ndriver of the process by conditioning the substrate and altering the \nbiological activity [4]. However the quality and time required for the \nvermicomposting depends on the composition of initial waste mixture \nbeing processed. The various organic waste which have been \nvermicompost and turned into nutrient enriched manure include \nvegetable waste [5] (Suthar, 2009), cow dung [6] (Lazcano et al., \n2008),water hyacinth [7] (Gajalakshmi et al., 2001), municipal waste [8] \n(Sharma, 2003).  \n\n\n\nThe aim of present study was to study the effect of physicochemical \nand maturity parameters on the flower waste and cow dung during \nvermicomposting process and optimization using response surface \nmethodology on the basis of maturity indices such as C:N ratio, CO2 \nevolution and germination index and physicochemical process.\n\n\n\n2.0 Material and Methods \n\n\n\n2.1 Feedstock \nFlower waste was collected from temple in Surat city, India. Manual \nsegregation of flower waste was carried to remove the debris (plastic, \nthreads, incense sticks, coconut, etc.). Large quantity of marigold \n(Tagetus erecta) was observed along with rose (Rosa), lotus \n(Nelumbo nucifera), and siroi lily (Lilium macklinaie). Fresh Cattle \ndung was collected from dairy farm in nearby village, Surat, India. \nTable 1 shows the initial characteristics of feedstock \n\n\n\nTable 1. Initial physicochemical characteristics of waste material\n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nJournal Homepage: http://www.razipublishing.com/journals/malaysian- journal-\n\n\n\nof-sustainable-agriculture-mjsa/ \n\n\n\nISSN: 2521-2931  (Print) \nISSN: 2521-294X  (Online)\n\n\n\nVermicomposting of Flower Waste: Optimization of Maturity Parameter by \nResponse Surface Methodology \n\n\n\nDayanand Sharma a*, Kunwar D. Yadav b\n\n\n\na,b\nDepartment of Civil Engineering, SV National Institute of Technology, Surat, Gujarat, India\n\n\n\nParameters Flowers waste Cow dung \n\n\n\n5.28 \u00b1 0.02 7.31 \u00b1 0.02 \n\n\n\n4.20 \u00b1 0.04 3.10\u00b10.01 \n\n\n\n45.58 \u00b1 1.67 33.21\u00b11.67 \n\n\n\n2.08 \u00b1 0.07 1.5 \u00b1 0.14 \n\n\n\n1.69 \u00b1 0.04 0.34 \u00b1 0.03 \n\n\n\n22.86 \u00b1 0.34 22.84 \u00b1 0.40 \n\n\n\n3.28 \u00b1 0.01 2.79 \u00b1 0.03 \n\n\n\npH \n\n\n\nElectrical conductivity (ms.cm\n-1\n\n\n\n) \n\n\n\nTotal organic carbon (%) \n\n\n\nTotal nitrogen (%) \n\n\n\nNH4-N (%) \n\n\n\nC/N ratio  \n\n\n\nTotal Phosphorous (g/kg) \n\n\n\nNa (g/kg) \n0.89 \u00b1 0.07 2.55 \u00b1 0.03 \n\n\n\n2.2 Vermireactor \nFlower waste and cow dung with appropriate proportion were \ndecomposed for seven days for semi-decomposition and stabilization \nto have optimum action of earthworms and microorganisms. The \nPrecompost was transferred into Vermireactor after 07 days and \nmatured earthworm of E. Fetida, randomly picked from stock culture, \nintroduced into each Vermireactor. The size of Vermireactor was   \n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.15.18\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.14.15\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.15.18\n\n\n\n\n\n\nCite this article  Dayanand Sharmaa*, Kunwar D. Yadavb Vermicomposting of Flower Waste: Optimization of Maturity \nParameter by Response Surface Methodology  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\nCite this article  Dayanand Sharmaa*, Kunwar D. Yadavb Vermicomposting of Flower Waste: Optimization of Maturity \nParameter by Response Surface Methodology  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\n16 \n\n\n\nlength, width and height. Each Vermireactor was prepared in duplicate \nand the average value was reported. All Vermireactor Waste was kept in \ndark at controlled laboratory temperature 22 \u00b1 4oC. The mix proportions \nof each Vermireactor are shown in Table 3 as per design expert software. \nThe biomass gain by the earthworms in each vermicomposting units was \nrecorded weekly and only the data of optimum proportions of waste \nmixtures obtained from response surface methodology has shown. The \nwaste in the container was turned out, then earthworms and cocoons \nwere separated from the waste by hand sorting, counted and weighed \nafter washing with water. Then all earthworms and the feed waste (but \nnot cocoons) were returned to their respective container. Moisture \ncontent (60-80%) was maintained throughout the study period. About \n100 g samples were collected from each 07 days interval. Number of \nearthworm has counted after adding the earthworm into reactor on 07 \ndays dried samples were grinded into fine powder and sieved through 0.2 \nmm sieve and used for further analysis. \n\n\n\n2.3 Analysis of physico-chemical parameters \nFor determining the pH and conductivity; 10 g dried sieved sample was \ndiluted by 100 ml distilled water (1:10 w/v) and kept for shaking in \nrotary shaker for two hours. Then sample was kept for half an hour for \nsettling, lastly filtered through whatman filter paper no 42 [9]. Pelican \nkelplus distyl ems instrument was used for total nitrogen determination. \nSample was digested before analysis; by heating 0.2 gram sample with \nratio of 1:5 Cupric sulphate and potassium sulphate, then 10 ml H2SO4 \nwas added. Ammonical nitrogen was performed by KCl extraction \nmethods followed by phenate methods [10]. Total organic carbon was \ncalculated by dividing the volatile solids by 1.83 [9]. Sample was digested \nbefore analysis; by heating 0.2 gram sample with ratio of 5:1 of 10 ml \nH2SO4 and HClO4 at 300oC for two hours. The digested sample were \nused for determined the total phosphorous using Stannous chloride \nmethods [15]. The concentration of Na and K were determined by using \nphlame photometer (Systronics 128\u03bc). CO2 evolution rate was \ndetermined as described by (Singh et al., 2014). Germination index test \nwas performed as described by (Zucconi et al., 1981). \n\n\n\n2.4 Response surface methodology (RSM) \nTwo independent, controllable and important variables i.e., flower waste \n(gram) and cow dung (gram) were used to model maturity of the \ncompost.  \nThirteen different combinations of flower waste and cow dung were \nmade using central composite designs (CCD) which consisted of 8 surface \npoints and 5 centre points.\n\n\n\n3.0 Results and Discussions\n\n\n\n3.1 RSM modelling  \nRSM was used and second order polynomial model [as given in Eq. (2)] \nwas developed using the experimental process the parameters data of \nwhich has been shown in Tables 3. The model was also developed for the \nprocess parameter and quality of compost such as pH, electrical \nconductivity, total organic carbon, total nitrogen, sodium, potassium and \nphosphorous (data not shown). The following model C:N ratio, \ngermination index, CO2 evolution were involved for the maturity of the \nflower waste compost  and is presented in terms of coded factors.\n\n\n\nR\n\n\n\n C:N ratio = +14.01+1.72x10-3*FW-3.66x10-3*CD ...........................................(6)\nG I = +52.17+0.002*FW+0.109*CD-1.50*10-5FW*CD-7.34*10-6*FW2\n+1.71*10-5*CD2....... (7)\nCO2 evolution = +0.93-5.65*FW-6.44*10-4*CD+2.15*10-7*FW*CD\n+2.42*10-7*FW2+1.71*CD2.... (8)\n\n\n\nMultiple linear regressions were used to compute the regression \ncoefficients of the quadratic model to minimize the sum of squares of the \nprocess parameter [11]. Tables 2 show the results of Anova analysis \ncalculated from equation 6 to 8. It shows that the model was significant \nand can be used to traverse the design domain. The F value, P value \n(P>F) and adequate precision shows the efficiency and significance of \nthe model.  \n\n\n\nTable 2. Anova for regression model and respective model terms for C:N \nratio, germination index, CO2 evolution \n\n\n\nResponse Source Sum of \n\n\n\nsquares \n\n\n\nDF Mean \n\n\n\nsquare \n\n\n\nF-value P-value \n\n\n\nprob>F \n\n\n\nModel 16.58 8.29C:N ratio   2 31.23 <0.0001 significant \n\n\n\nResidual 2.65 10 0.24 \n\n\n\nLack of fit 1.45 0.246 0.81 0.6126 Not significant \n\n\n\nPure error 1.20 4 0.30 \n\n\n\nStd. Dev = 0.52, C.V. % = 3.56  , R\n2\n= 0.8620, R\n\n\n\n2 \nadjusted= 0.8344, R\n\n\n\n2 \npredicted= 0.7618 Adeq.Precision= 15.259\n\n\n\nindex \n\n\n\nModel 258.93 5 51.79Germination     134.62 <0.0001 significant \n\n\n\nResidual 2.69 7 0.38 \n\n\n\n1.38 3 0.46Lack of fit 1.40 0.3652 Not significant \n\n\n\nPure error 1.31 4 0.33 \n\n\n\nStd. Dev = 0.62, C.V. % = 0.66  ,  R\n2\n= 0.9897 , R\n\n\n\n2 \nadjusted= 0.9824, R\n\n\n\n2 \npredicted= 0.9547 Adeq.Precision= 30.604\n\n\n\nevolution \n\n\n\nModelCO2   0.31 5 65.95 <0.0001 significant \n\n\n\nResidual 6.56E-003 7 \n\n\n\n0.062 \n\n\n\n9.382E-004 \n\n\n\nLack of fit 2.84E-003 3 0.4720 Not significant \n\n\n\nPure error 3.72E-003 4 \n\n\n\n9.491E-004 1.02 \n\n\n\n9.300E-004 \n\n\n\nStd. Dev = 0.031, C.V. % = 5.42  ,  R\n2\n= 0.9792 , R\n\n\n\n2 \nadjusted= 0.9644, R\n\n\n\n2 \npredicted= 0.9187 Adeq.Precision= \n\n\n\n25.231 \n\n\n\nA model will be significant at 95% confidence interval if F test \nhaving P value is less than 0.005. Table 4 shows the P value of all \nparameters which are less than 0.005. In the present study each \nparameter was statistically significant as per P value.  Equation \nnumbers 2 to 5 show the only statically significant model in terms \nof (P<0.005). In case of lack of fit (P>F) the P value greater than \n0.005 is considered, it shows the failure of the model in \nrepresenting data points in the experimental domain [11]. In this \nstudy the value of lack of fit was 1.45 for C:N ratio, 1.31 for \ngermination index, 2.84E-003for CO2 evolution which indicate the \nlack of fit of the model is insignificant.\n\n\n\n3.3 Maturity and quality of vermicompost produced from \nvermireactor units\nThe pH of the waste mixture significantly affects the process of \nvermicomposting. The changes in pH from initial to final compost \nare shown in table 5. Initially the pH in all reactors was acidic (5.1 \nto 5.89) and at the end of vermicomposting the pH was changed to \nbasic (7.1 to 7.78). The electrical conductivity shows the salinity of \nthe compost. Initially electrical conductivity was (2.01 to 3.8) into \nall vermireactor and it was increased to (3.3 to 4.11 mS/cm). The \nincrease in EC was due to loss of organic matter and release of \nmineral salts in available forms such as phosphate, ammonium, \npotassium etc [4]. Decreasing trends of TOC was observed in each \nreactor during the vermicomposting process shows the \nstabilization of organic matter substrate due to combined actions of \nearthworms and microorganisms. It has been reported that \nearthworms modify substrate conditions, which subsequently \nenhance the carbon losses from the substrate through microbial \nrespiration in the form of CO2 [4]. TOC reduction of 22% to 31% \nwas observed in each vermireactor (refer table 3). The final total \nnitrogen depends on the initial content of nitrogen into waste \nmixture. Increase in total nitrogen contents was observed in each \nvermireactor. The total nitrogen contents in each reactor were \nvaried from 1.7 to 2.85 (Table 3). \n\n\n\nTable 3. Physicochemical changes during initial and final day into \neach reactor \n\n\n\nTotal phosphorous was increased to (38 % to 50%) in each \nvermireactor (refer table 5). The available content of total \nphosphorous in each reactor was initially ranges from 2.1 to 2.8 g/\nkg which increased to 4.2 to 5.2 g/kg. The total available \nphosphorous was significantly higher in final day of vermicompost \nthan the initial. The increase in phosphorous was due to the direct \naction of worms gut enzymes and indirectly by stimulation of the \nmicro flora. Initial K contents in waste mixture were 7.32 to 10.32 g/\nkg while it was in the range of 11.02 to 15.02 into the vermicompost. \nThe activities of earthworms lead to an increase in Na which ranged \nfrom 2.62 to 3.85 g/kg into vermicompost, whereas it was 1.49 to \n1.89 g/kg in initial waste mixture. Sharma (2003) [8] have reported \nthat higher Na concentration (30.53-92.80%) in the vermicompost \nprepered from municipal solid waste. Ammoniacal nitrogen was \ninitially high in each vermireactor whereas it was decreased into the \nfinal vermicompost. The ammoniacal nitrogen concentration \ndecreased due to the volatilization of NH4+-N by the microbes and \nthe nitrification during the composting process [2].  \nGermination index shows the phytotoxicity of organic waste and it is \ngood indicator of maturity of \n\n\n\n\n\n\n\n\nCite this article  Dayanand Sharmaa*, Kunwar D. Yadavb Vermicomposting of Flower Waste: Optimization of Maturity \nParameter by Response Surface Methodology  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\nCite this article  Dayanand Sharmaa*, Kunwar D. Yadavb Vermicomposting of Flower Waste: Optimization of Maturity \nParameter by Response Surface Methodology  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\n17 \n\n\n\nConclusions \nThis study concludes that flower waste mixed with cow dung into \nappropriate proportion then it can be vermicompost by E.fetida. \nVermicompost obtained by flower waste was rich in sodium, \npotassium and phosphorus which is the essential for the plant growth \nand low electrical conductivity, higher reduction of total organic \ncarbon, increase rate of total nitrogen, less C:N ratio, higher \ngermination index and low CO2 evolution shows the maturity and \nstability of vermicompost. The growth of earthworm biomass into \nflower waste was is in considerable amount. RSM technique used for \noptimizing the waste combinations validated the process efficacy and \nshowed the fitness of the model. Among all the parameters value of R2 \nwas closer to 1. In the present study R2 value was closer to R2 adj \nrepresenting the higher significance of the model. Therefore the \ncoefficient of variance (CV) was not greater than 10% for all process \nefficacies which validated the fitness of the model. Hence, it can be \nconcluded that RSM technique provided the appropriate combinations \nfor performing composting with optimum ratio of 2.67:1 of flower \nwaste and cow dung. \n\n\n\nReferences \n[1] Singh, A., Jain, A., Sarma, B.K., Abhilash, P., Singh, H.B., 2013. \nSolid waste management of temple floral offerings by \nvermicomposting using Eisenia fetida. Waste management, 33, \n1113-1118. 10.1016/j.wasman.2013.01.022.\n\n\n\n[2] Sharma, D., Yadav, K.D., 2017. Bioconversion of flowers \nwaste: Composing using dry leaves as bulking agent. Environmental \nEngineering Research (Acceted manuscript).\n\n\n\n[3] Arora, S., Rajpal, A., Bhargava, R., Pruthi, V., Bhatia, A., \nKazmi, A., 2014. Antibacterial and enzymatic activity of microbial \ncommunity during wastewater treatment by pilot scale vermifiltration \nsystem. Bioresource technology, 166, 132-141. 10.1016/\nj.biortech.2014.05.041.\n\n\n\n[4] Vig, A.P., Singh, J., Wani, S.H., Singh Dhaliwal, S., 2011. \nVermicomposting of tannery sludge mixed with cattle dung into \nvaluable manure using earthworm Eisenia fetida (Savigny). \nBioresource Technology, 102, 7941-7945. 10.1016/\nj.biortech.2011.05.056.\n\n\n\n[5] Suthar, S., 2009. Vermicomposting of vegetable-market \nsolid waste using Eisenia fetida: Impact of bulking material on \nearthworm growth and decomposition rate. Ecological Engineering, \n35, 914-920. 10.1016/j.ecoleng.2008.12.019.\n\n\n\n[6] Lazcano, C., G\u00f3mez-Brand\u00f3n, M., Dom\u00ednguez, J., 2008. \nComparison of the effectiveness of composting and vermicomposting \nfor the biological stabilization of cattle manure. Chemosphere, 72, \n1013-1019. 10.1016/j.chemosphere.2008.04.016.\n\n\n\n[7] Gajalakshmi, S., Ramasamy, E., Abbasi, S., 2001. Potential of \ntwo epigeic and two anecic earthworm species in vermicomposting of \nwater hyacinth. Bioresource technology, 76, 177-181. 10.1016/\nS0960-8524(00)00133-4.\n\n\n\n[8] Sharma, S., 2003. Municipal solid waste management \nthrough vermicomposting employing exotic and local species of \nearthworms. Bioresource technology, 90, 169-173. 10.1016/\nS0960-8524(03)00123-8.\n\n\n\n[9] Singh, J., Kalamdhad, A.S., 2014. Effects of natural zeolite on \nspeciation of heavy metals during agitated pile composting of water \nhyacinth. International Journal of Recycling of Organic Waste in \nAgriculture, 3, 1-17. 10.1007/s40093-014-0055-1.\n\n\n\n[10] APHA2005, APHA (2005) Standard methods for the \nexamination of water and wastewater. Am. Publ. Health Assoc. CD-\nROM.\n\n\n\n[11] Pi, K.-W., Xiao, Q., Zhang, H.-Q., Xia, M., Gerson, A.R., 2014. \nDecolorization of synthetic methyl orange wastewater by \nelectrocoagulation with periodic reversal of electrodes and \noptimization by RSM. Process Safety and Environmental Protection, \n92, 796-806. 10.1016/j.psep.2014.02.008.\n\n\n\n[12] Zucconi, F., Pera, A., Forte, M., De Bertoldi, M., 1981. \nEvaluating toxicity of immature compost. Biocycle, 22, 54-57.\n\n\n\na b\n\n\n\nc\n\n\n\nFigure 1. Contour plots of (a) C:N ratio (b) Germination index and (c)     \nCO2  evolution vermicompost.  \nFigure 1 (a) shows the contour plots of decreasing value of C:N ratio \nduring the vermicomposting process mixed with flower waste and cow \ndung. Initial range of C:N ratio was 22 to 26 whereas at the final \nvermicompost was (15-12). \n\n\n\nFigure 1 (b) shows the contour plots of germination index which \nindicate that flower waste is the organic in nature and good for the plant \ngrowth because in each vermireactor the germination index is more \nthan 50% at the end of vermicomposting process. CO2 evolution shows \nthe maturity of the vermicompost and it is one of the best methods to \ndetermine the stability of the compost because it measures the carbon \nderived directly from decomposition or degradation of the organic \nmatter.\n\n\n\n3.4 Optimization and verification of model \nThe optimum proportionate weight of flower waste (FW) and cow dung \n(CD) for the vermicomposting was obtained from the various responses, \nusing design Expert 8.0. Table 4 shows the experimental and predicted \nvalue for the optimum combinations of waste mixtures. The \nexperimental and predicted values obtained from RSM model are nearly \nequal in optimum ratio (1280 g flower waste, 480 g cow dung). These \nresults also confirm that RSM model was appropriate for optimizing the \nproportionate weight, maturity and quality of compost \n\n\n\nTable 4. Analysis of final compost at optimum combinations (1280 g \nFlower waste: 480 g Cow dung) \n\n\n\nName of parameter Predicted Experimental Name of \n\n\n\nparameter \n\n\n\npredicted Experimental \n\n\n\npH 7.07 7.12 C:N ratio 14.46 15 \n\n\n\nElectrical conductivity 3.28 3.42 Phosphorous \n\n\n\n(g/kg) \n\n\n\n4.95 5.21 \n\n\n\n33.72 34.06 Potassium \n\n\n\n(g/kg) \n\n\n\n13.99 14.31 \n\n\n\n97.46 98.02 CO2 \n\n\n\nevolution \n\n\n\n(%) \n\n\n\n0.470 0.39 \n\n\n\n(mS/cm) \n\n\n\nTotal organic carbon (%) \n\n\n\nGermination Index (%) \n\n\n\nNH4-N (mg/kg) 92.27 91.21 Na (%) 3.29 3.42 \n\n\n\nThe variation of pH which was acidic (5.2) in initial day and it was \nincreased to 7.12 at the end of vermicomposting process. Table 4 shows \nthe predicted value of pH was 7.07 and experimental value was 7.12 in \nwhich the value of PH was slightly difference. The experimental and \npredicted value shows that the optimum combination of waste mixture \nwas sufficient for the vermicomposting process. Electrical conductivity \nwas 2.31 mS/cm at the initial day which was increased to 3.42 mS/cm \ninto the final vermicompost. The total organic carbon was reduced \n29.11% during the vermicomposting process which shows the \ndegradation of organic matter and utilization of carbon as source of \nenergy by microbes and earthworms. Total nitrogen was used by \nmicrobes for building the cell structure and nitrogen was increased \nfrom 1.83% to 2.27%. Due to decrement of total organic carbon and \nincrement of total nitrogen C:N ratio was decreased. Initial C:N ratio \ninto optimum mixture of waste was 26 whereas it was decreased to 15 \ninto final vermicompost. The presence of ammoniacal nitrogen into \ninitial vermicompost was 139.21 mg/kg which was decreased to 91.21 \nmg/kg. The experimental and predicted value was nearly equal into the \noptimum waste mixture. \n\n\n\n\n\n\n\n\nCite this article  Dayanand Sharmaa*, Kunwar D. Yadavb Vermicomposting of Flower Waste: Optimization of Maturity \nParameter by Response Surface Methodology  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\nCite this article  Dayanand Sharmaa*, Kunwar D. Yadavb Vermicomposting of Flower Waste: Optimization of Maturity \nParameter by Response Surface Methodology  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 15-18 \n\n\n\n18 \n\n\n\nAbout the Author \nDayanand Sharma is pursuing his Doctor of Philosophy degree at the \nDepartment of Civil Engineering, SVNIT, Surat, India, under the \nguidance of Kunwar D. Yadav, Assistant Professor in the same \ndepartment. He received his Bachelor in Civil Engineering from Nagpur \nUniversity in 2009. He has international as well as national \npublications in reputed journals/ conferences \n\n\n\nDr. Kunwar D. Yadav is presently an Assistant Professor in the \nDepartment of Civil Engineering, SVNIT, Surat, India. He holds the Ph.D \ndegree in Environmental Engineering from IIT Kanpur. He has \npublished 54 international as well as national publications in reputed \njournals/conferences. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 94-98 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.94.98 \n\n\n\n \nCite the Article: Om Kala Ruchal, Subodh Raj Pandey, Reja Regmi, Rajendra Regmi, Bishnu Bahadur Magrati (2020). Effect Of Foliar Application Of Micronutrient (Zinc \n\n\n\nAnd Boron) In Flowering And Fruit Setting Of Mandarin (Citrus Reticulata Blanco) In Dailekh, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 94-98. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.02.2020.94.98 \n\n\n\n\n\n\n\n \nEFFECT OF FOLIAR APPLICATION OF MICRONUTRIENT (ZINC AND BORON) IN \nFLOWERING AND FRUIT SETTING OF MANDARIN (CITRUS RETICULATA BLANCO) \nIN DAILEKH, NEPAL \n \nOm Kala Ruchala, Subodh Raj Pandeya*, Reja Regmia, Rajendra Regmib, Bishnu Bahadur Magratic  \n \n\n\n\na Faculty of Agriculture, Agriculture and Forestry University, Chitwan, Nepal \nb Asst. Professor, Department of Entomology, Agriculture and Forestry University, Chitwan, Nepal \nc Agriculture Planning Officer, Ministry of Agriculture and Livestock Development, Government of Nepal \n*Corresponding author email: timsubodh13@gmail.com \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 01 March 2020 \n\n\n\n A research was conducted in PM-AMP, Project Implementation Unit, Citrus Zone, Dailekh during the spring \nseason 2019 to find out the effect of foliar application of micronutrient in flowering and fruit setting of \nmandarin (Citrus reticulata Blanco) in Dailekh, Nepal. The experiment was laid out in one factor randomized \ncomplete block design (RCBD) with five treatments and four replications. The treatments consist of foliar \napplication of sole and combined application of Zinc and Boron namely: control (water spray), 0.15% Zn, \n0.04% B, 0.1% Zn + 0.02% B and 0.05% Zn + 0.04% B. Twenty trees of same age and height were chosen from \nthe north-facing slope. The soil of research orchard was sandy loamy. The solution for spray was made using \nstandard procedures. Foliar application of the micronutrients was done twice, the first application was done \nbefore 45 days of the flowering and second application was done after 2 days of full bloom. The data was first \nentered in MS excel and R-stat was used for further analysis of the parameters. The results revealed that the \ndifferent combination of micronutrient significantly influenced flowering, fruit setting percentage and the \nfruit drop percentage. The application of either Boron or Zinc or the combination of both were effective for \nenhancing flowering and fruit set as well as reducing the fruit drop in mandarin.  \n\n\n\nKEYWORDS \n\n\n\nfruit drop, yield, citrus, soil nutritional status. \n\n\n\n1. INTRODUCTION \n\n\n\nCitrus is one of the important fruit crops of hilly areas of Nepal. Citrus \nfruits are cultivated in the tropical and sub-tropical region having suitable \nsoil, geographical and climatic condition. The climatic conditions of mid-\nhills having altitude 800 to 1500 masl are considered favourable for all \ntypes of citrus cultivation. The three most important species of citrus \nculture in Nepal are mandarin (Citrus reticulata), sweet orange (Citrus \nsinensis) and acid lime (Citrus aurantifolia). Among them, mandarin takes \nthe first position in terms of area coverage and production. Fruit \ncontributes about 7% of total agriculture gross domestic product. Among \nthem, citrus contributes 23.34% in total fruit production whereas \nmandarin shows 68.64% of the total citrus production.  \n\n\n\nThe total production of the citrus plant in the FY 2072/73 was 2,313,838 \nfrom 197 nurseries (NCRP, 2016). The average productivity of citrus fruit \nis 8.8 Mt/ha (NCDP, 2016). The average productivity of mandarin in Nepal \nand Dailekh district is 9.42 Mt/ha and 10.06 Mt/ha respectively (MoALD, \n2017).  Dailekh district experienced the rise in the cultivation area of \nmandarin by 5.08% in the fiscal year 2016/17 as compared to FY 2015/16. \nBut the productivity has been decreased from 3.72 Mt/ha in FY 2015/16 \nto 3.55 Mt/ha in FY 2016/17 (DADO, 2017). The productivity of mandarin \nin the global scenario is 13.06 Mt/ha (FAOSTAT, 2017).  \n\n\n\nThe productivity of the citrus trees depends upon many abiotic (climate, \nsoil, nutrition and irrigation management) and biotic factor (rootstock, \ncultivar, insect, pest and disease management) (Albrigo, 1999). Among \nthem an adequate supply of micronutrients like zinc, boron, iron etc. are \nmost important to produce good quality fruits (Babu and Yadav, 2005). \nBoron has an efficient role in the growth and production which increases \nthe pollen germination and pollen tube elongation as well as increases the \nfruit set, seeds, fruit development and ultimately the production. Boron \nincreased and changed sugars composition that exists in the nectar where \nthe flowers attract more insects and it influences the pollen production \nand their viability (Mohammad et al., 2018). \n\n\n\nGrowth and development of citrus fruit follow a typical sigmoid growth \ncurve, divided into three clear-cut stages (Bain, 1958). Nutrient \nmanagement is a major problem of citrus production in Nepal (Chhetri et \nal., 2019). Likewise, in Dailekh, nutrient management is also the main \nproblem in citrus production. The soil of this region has low organic \nmatter content. Also, the limited amount of micronutrient present in soil \ncan\u2019t get absorbed due to irrigation problem as well as dry soil condition. \nNutrient management is a major problem of citrus production in Nepal \n(Chhetri et al., 2019). High flower drop, fruit drop and low productivity of \nmandarin is due to the deficiency of Zinc and Boron in the soil (Soni et al., \n2017). \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 94-98 \n\n\n\n\n\n\n\n \nCite the Article: Om Kala Ruchal, Subodh Raj Pandey, Reja Regmi, Rajendra Regmi, Bishnu Bahadur Magrati (2020). Effect Of Foliar Application Of Micronutrient (Zinc \n\n\n\nAnd Boron) In Flowering And Fruit Setting Of Mandarin (Citrus Reticulata Blanco) In Dailekh, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 94-98. \n \n\n\n\n\n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Research site \n\n\n\nThe research was conducted in the Ranukhana, Dailekh which is 6 km far \nfrom Dullu Bazaar. The research site is at 1300 masl. The mean maximum \nand minimum temperatures are 25 and 5 degrees respectively and annual \nrainfall is 1200mm. Its geographical coordinate is 28.86\u00b0N and 81.62\u00b0E. \n\n\n\n2.2 Weather condition of the site \n\n\n\nThe research site was situated in the sub temperate climate of hill \necological zone.  It is characterized by three distinct seasons; spring, \nsummer and monsoon. Monthly data on different parameters such as \nminimum, maximum and average temperature and relative humidity \nrecorded from Dailekh meteorological station are presented (Figure 1). \n\n\n\n\n\n\n\nFigure 1: Meteorological data during the investigation period \n\n\n\n2.3 Soil sample analysis \n\n\n\nThe soil of the research site was sandy loam. The soil sample was taken \nfrom a depth of 1m from three randomly selected spots at the \nexperimental site. The soil sample was tested in the Central Agricultural \nLaboratory, Hariharbhawan, Kathmandu. The results indicate that the soil \nwas slightly acidic, medium organic matter content, medium nitrogen, low \nphosphorus, medium potassium, low zinc and low boron (Table 1). \n \n\n\n\nTable1: Characteristics of the soil of the research site \n\n\n\n        Soil Properties Available Nutrient Rating \nPH 6.1 Slightly Acidic \nOrganic matter (%) 2.87 Medium \nNitrogen (%) 0.14 Medium \nPhosphorous (%) 0.19 Low \nPotassium (%) 1.641 Medium \nZinc (ppm) 0.207 Low \nBoron (ppm) 0.255 Low \n\n\n\nRating source: (Central Agricultural Laboratory, Hariharbhawan, \nKathmandu, 2019) \n\n\n\n2.4 Selection of tree \n\n\n\nFive years old twenty trees of similar age, size, height and vigour were \nselected and each tree was tagged according to the treatment. \n\n\n\n2.5 Experimental details \n\n\n\nThe experiment was laid out in Randomized Complete Block Design \n(RCBD) with five treatments and four replications consisting of 20 trees \naltogether. Each tree is considered as one replication.  The research was \nconducted at the farmer\u2019s mandarin orchard. \n\n\n\n2.6 Treatment details \n\n\n\nT\u2081: Control (Water spray) \nT\u2082: 0.15% Zn \nT\u2083: 0.04% B \nT\u2084: 0.1% Zn + 0.02% B \nT\u2085:  0.05% Zn + 0.04% B \n\n\n\n2.7     Preparation of Zinc and Boron Spray Solution \n\n\n\nFollowing formula were used to calculate the borax and zinc spray \nsolution \n\n\n\n\u2022 Amount of borax = \n% of boron required \n\n\n\n% of boron in borax\n X 1000 \n\n\n\n \n\u2022 According to Boyle\u2019s law, the required volume of zinc was \n\n\n\ncalculated as: \n \n\n\n\n                     C\u2081V \u2081 = C\u2082V \u2082 \n \nWhere, \nC \u2081 is the initial concentration \nV\u2081 is the initial volume \nC\u2082 is the final concentration \nV\u2082 is the final volume \n \nDouble chelates double-action zinc contains 10% zinc and Di-Sodium \nTetra Borate Penta Hydrate (borax) contains 14.6% boron. So, zinc (Zn) \nand boron (B) were applied in the form of liquid zinc and borax through \nfoliar spray technique. Ordinary tap water was sprayed in treatment T\u2081. \nTreatment T\u2082 and T\u2083 were sprayed at a single rate and T\u2084 and T\u2085 at \ndifferent combination. 0.15% Zinc spray was prepared by dissolving 15ml \nof liquid zinc in 1 litre of water. 0.1% zinc spray was prepared by \ndissolving 10ml of zinc in 1 litre of water. 0.05% Zinc spray was prepared \nby dissolving 5ml of zinc in 1 litre of water. 0.04% boron spray was \nprepared by dissolving 2.74gram of borax in 1 litre of water. 0.02% boron \nspray was prepared by dissolving 1.37 gram of borax in 1 litre of tap water. \nThe Sticker was used in the spray solution to prevent the solution from \nbeing washed off. 3ml sticker was used in a litre of water. \n\n\n\n2.8 Method of micronutrient application  \n\n\n\nThe mandarin trees were of medium height. Foliar application of the \nmicronutrient was done in such a way that every leaf of the tree was \nwetted with the required solution with the help of foot sprayer. The \nvolume of spray solution was determined according to the age of the tree. \nTherefore, 2 litres of spray solution was sprayed for 5-years old plant.  \n\n\n\n2.9 Time of application \n\n\n\nFlowering generally occurred in the second week of April. Foliar \napplication of the micronutrients was done twice, the first application was \ndone on 16 February 2019 before 45 days of the flowering and second \napplication was done on 12 April 2019 after 2 days of full bloom (Sajid et \nal., 2010). \n\n\n\n2.10 Observation Parameter \n\n\n\n2.10.1 Total number of flowers per branch \n\n\n\nThe five branches were tagged and the total numbers of flowers were \nmanually counted by using the ladder. The average total number of flower \nper branch was calculated. \n\n\n\n2.10.2 Number of male flowers per branch \n\n\n\nThe total number of male flowers in the tagged branches was manually \ncounted and the average number of male flower per branch was \ncalculated. \n\n\n\n2.10.3 Number of hermaphrodite flowers per branch \n\n\n\nThe total numbers of hermaphrodite flowers on the tagged branches were \ncounted manually and the average number of hermaphrodite flower per \nbranch was calculated. \n\n\n\n2.10.4 Fruit set per branch \n\n\n\nThe fruit set on the tagged branches was manually counted. The number \nof flowers per branch was also counted. Then, the fruit set per branch is \nexpressed in the form of percentage. \n \n\n\n\nFruit set percentage =\nTotal number of developing fruit set per branch \n\n\n\nTotal number of flowers per branch\n\u02df 100 \n\n\n\n2.10.5 Flower drop per branch \n\n\n\nFlower drop per branch was calculated in the form of a percentage. \n\n\n\n2.10.6 Days to first flowering \n\n\n\nThe time required for the first flowering from first spraying was recorded. \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\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\nFebuary March April May June\n\n\n\nR\nel\n\n\n\nat\niv\n\n\n\ne \nH\n\n\n\nu\nm\n\n\n\nid\nit\n\n\n\ny\n(R\n\n\n\nH\n)%\n\n\n\n\n\n\n\n&\n R\n\n\n\nai\nn\nfa\n\n\n\nll\n(m\n\n\n\nm\n)\n\n\n\nM\nax\n\n\n\n &\n M\n\n\n\nin\n t\n\n\n\nem\np\n\n\n\n(\u00b0\nC\n\n\n\n)\n\n\n\nMonth\n\n\n\nMax\u00b0C Min\u00b0C RH % Rainfall(mm)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 94-98 \n\n\n\n\n\n\n\n \nCite the Article: Om Kala Ruchal, Subodh Raj Pandey, Reja Regmi, Rajendra Regmi, Bishnu Bahadur Magrati (2020). Effect Of Foliar Application Of Micronutrient (Zinc \n\n\n\nAnd Boron) In Flowering And Fruit Setting Of Mandarin (Citrus Reticulata Blanco) In Dailekh, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 94-98. \n \n\n\n\n\n\n\n\n2.10.7 Days to first fruit set \n\n\n\nThe time required for the first fruit setting from second spraying was \nrecorded. \n\n\n\n2.10.8 Fruit drop per branch \n\n\n\nFruit drop was calculated and it was also expressed in percentage. \n\n\n\n2.11 Data analysis \n\n\n\nData were collected at 10 days interval which was started from 1st April \n2019. The data was first recorded in MS Excel and analyzed by using \nAnalysis of variance (ANOVA) using R-stat. Means of treatment were \ncompared by Duncan's Multiple Range Test (DMRT) at 5% level of \nsignificance. \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1 Days to flowering from first spraying and days to fruit set from \n\n\n\nsecond spraying \n\n\n\n Days to flowering and days to fruit set were statistically similar among \ndifferent treatments. The mean value for days to first flowering from first \nspraying was found 45.05 days and varied from 43.50 days (0.04%B) to \n46 days (control). The mean value for days to first fruit set from second \nspraying was found 7.55 days and varied from 6.50 days (0.04%B) to 9 \ndays (control) (Table 2). The characteristics of the spring flush are largely \ndetermined by the combined effect of climate and flower induction and \nbud sprouting. Deviations from these relationships may come from the \neffect of additional environmental factors and through the effect of \ncultural practices (Jahn, 1973). \n \n\n\n\nTable 2: Effect of different micro-nutrient on days to first flowering \nand first fruit set \n\n\n\nTreatments Days to first \nflowering from \nfirst spraying \n\n\n\nDays to first fruit set \nfrom second spraying \n\n\n\nControl 46.00 9.00 \n0.15% Zn 44.50 7.25 \n0.04% B 43.50 6.50 \n0.05% Zn + 0.02%B 45.50 7.00 \n0.1% Zn + 0.04% B 45.75 8.00 \nF-test NS NS \nCV (%) 9.9 15.1 \nLSD 6.87 1.76 \nSEM 0.88 0.31 \nGrand Mean 45.05 7.55 \n\n\n\nMeans followed by a common letter(s) within a column are non \u2013 \nsignificantly different based on DMRT at P = 0.05, NS: Non-significant, CV: \nCoefficient of variation, LSD: Least significant differences, SEM: Standard \nerror of mean \n\n\n\n3.2 Effect on flowering \n\n\n\nFlowering of the mandarin was found to be significantly influenced with \nfoliar application of the micronutrient at 44 days from first spraying. At 44 \nDAS, the mean value for the total number of flowers per branch was 155.7. \nThe highest number of flower per branch was observed with spray of \n0.15% Zn (252.50) which was statistically similar with 0.05% Zn + 0.02% \nB (182.50) and 0.1% Zn + 0.04% B (158.75).The lowest total number of \nflower per branch was observed in control (46.25) (Table 3). Significant \nvariation was found for the total number of flowers at 54days of first \nspraying. At 54 DAS, the mean value for the total number of flowers per \nbranch was 224.47. The highest total number of flower per branch was \nobserved with spray of 0.04% B (332.25) which was statistically similar \nwith 0.15%Zn(222), 0.05% Zn + 0.02% B, 0.1% Zn + 0.04% B. The lowest \ntotal number of flower per branch was observed in control (79) (Table 3).  \n \nAt 54 DAS, the foliar application of Zn and B had a significant effect on the \ntotal number of hermaphrodite flower per branch. The mean value of the \ntotal number of the hermaphrodite flower was 215.44. The highest total \nnumber of hermaphrodite flower per branch was observed with foliar \nspray of 0.04%B(323.10) which was statistically at par with 0.15%Zn \n(212.75), 0.05% Zn + 0.02% B (230.75), 0.1% Zn + 0.04% B (234.80). The \nlowest total number of hermaphrodite flower per branch was observed in \ncontrol (75.80) (Table 3). At 54 DAS, the foliar application of Zn and B had \na significant effect on the total number of male flower per branch. The \nmean value of the total number of the male flower was 128.7. The highest \ntotal number of male flower per branch was observed with foliar spray of \n\n\n\n0.05% Zn + 0.02% B (164.55) which is statistically similar with 0.15% \nZn(144.10), 0.04% B (146.15) and 0.1% Zn + 0.04% B (139.4). The lowest \ntotal number of flower per branch was observed in control (79) (Table 3). \nThe effective role of adding zinc contributes to pollination through its \ninfluence on the formation of the pollen tube. Boron promotes the pollen \ngermination as well as the pollen tube growth as found which is in line \nwith the result (Mohammed et al., 2018). The high concentration of Zn and \nlow concentration of B reduced the percent of rosette per plant, closely \nfollowed by the plants received low concentration of Zn and high \nconcentration of B contradicts with our result (Ibrahim et al., 2007). \n\n\n\n \nTable 3: Effect of different micronutrients in the flowering of \n\n\n\nmandarin \n\n\n\nTreatment Total flower at Hermaphrodite \nflower \n\n\n\nMale \nflower \n\n\n\n 44 DAS 54 DAS 54 DAS 54 DAS \n\n\n\nControl 46.25c 79b 75.80b 49.30b \n0.15% Zn 252.50a 222a 212.75a 144.10a \n0.04% B 138.50bc 332.25a 323.10a 146.15a \n0.05% Zn + \n0.02% B \n\n\n\n182.50ab 242.15a 230.75a 164.55a \n\n\n\n0.1% Zn + \n0.04% B \n\n\n\n158.75ab 246.75a 234.80a 139.40a \n\n\n\nF-test * * * ** \nCV (%) 43.5 35.4 37.5 23.6 \nLSD 104 122 124 46.8 \nSEM 22.07 25.42 25.06 11.12 \nGrand Mean 155.7 224.47 215.44 128.7 \n\n\n\nMeans followed by a common letter(s) within a column are non \u2013 \nsignificantly different based on DMRT at P = 0.05, NS: Non-significant, CV: \nCoefficient of variation, LSD: Least significant differences, SEM: Standard \nerror of mean, DAS: Days of first spraying \n\n\n\n3.3 Effect on flower drop \n\n\n\nNon-significant variation was found for flower drop percentage per \nbranch. The mean value for flower drop was 29.41% and varied from \n19.17% (0.1% Zn + 0.04%) to 53.13% (control). ANOVA showed no \ndifferences among treatments indicating no variation (Table 4).  \n \n\n\n\nTable 4: Effect of different micronutrients on flower drop of \nmandarin \n\n\n\nTreatment Percentage of flower drop \ncontrol 53.13 \n0.15% Zn 27 \n0.04%Zn 26.66 \n0.05% Zn + 0.02% B 21.08 \n0.1% Zn + 0.04% B 19.17 \nF-test Ns \nCV (%) 59.1 \nLSD 26.8 \nSEM 4.2 \nGrand mean 29.41 \n\n\n\nMeans followed by a common letter(s) within a column are non \u2013 \nsignificantly different based on DMRT at P = 0.05, NS: Non-significant, CV: \nCoefficient of variation, LSD: Least significant differences, SEM: Standard \nerror of mean \n\n\n\n3.4 Effect on fruit set \n\n\n\nThe fruit setting of the mandarin was influenced significantly due to the \neffect of Zn and B.  At 66 DAS, the mean value of fruit setting per branch \nwas 65.96%. The highest fruit setting per branch was observed with foliar \napplication of 0.04% B (82.09%) and 0.1% Zn + 0.04% B (78.19%) which \nwas statistically at similar with 0.05% Zn + 0.02% B(76.55%). The lowest \nfruit setting per branch was observed in control (42.17%) which was \nsimilar to that of 0.15% Zn (50.77%) (Table 5). At 76 DAS, the mean value \nof fruit setting per branch was 60.09%. The highest fruit setting per branch \nwas seen in 0.04% B (74.66%) and 0.05% Zn + 0.02% B(73.90%) which \nwas statistically similar with 0.15% Zn (67.28%).The lowest fruit setting \nper branch was observed in control (35.52%). However, it was statistically \nsimilar with 0.1% Zn + 0.04% B (49.10%) (Table 5). At 86 DAS, the mean \nvalue of fruit set per branch was 56.8%. The highest fruit set per branch \nwas obtained with the foliar spray of 0.04 % B (71.32%), which was \nsimilar with 0.05% Zn + 0.02% B (64.06%)  and 0.1% Zn + 0.04% B \n(63.24%). The lowest fruit set was observed in control (32.80%) (Table \n5). At 96 DAS, the mean value of fruit set was 50.19%. The highest fruit set \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 94-98 \n\n\n\n\n\n\n\n \nCite the Article: Om Kala Ruchal, Subodh Raj Pandey, Reja Regmi, Rajendra Regmi, Bishnu Bahadur Magrati (2020). Effect Of Foliar Application Of Micronutrient (Zinc \n\n\n\nAnd Boron) In Flowering And Fruit Setting Of Mandarin (Citrus Reticulata Blanco) In Dailekh, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 94-98. \n \n\n\n\n\n\n\n\nper branch was observed in 0.04% B (67.52) which was similar with \n0.05% Zn + 0.02% B(55.82%) and 0.1% Zn + 0.04% B (55.58%). The \nlowest fruit set per branch was obtained in control (23.55%) which was at \npar with 0.15% Zn (48.58%) (Table 5). At 106 DAS, the mean value of fruit \nset per branch was 40.01%. The highest fruit set per branch was obtained \nwith foliar spray of 0.04% B (55.27%) which was similar with that of foliar \nspray of 0.15% Zn (42.58%), 0.05% Zn + 0.02% B(47.32%) and 0.1% Zn + \n0.04% B (47.61%). As usual, the lowest fruit set was obtained in control \n(11.28%) (Table 5). \n \n\n\n\nTable 5:  Effect of different micronutrients in fruit set from the first \nspray \n\n\n\nTreatment                        Percentage of fruit set at \n\n\n\n66 DAS 76 DAS 86 DAS 96 DAS 106 \nDAS \n\n\n\ncontrol 42.17c 35.52c 32.80c 23.55c 11.28b \n0.15% Zn 50.77bc 67.28ab 52.55b 48.44b 42.58a \n0.04% B 82.09a 74.66a 71.32a 67.52a 51.27a \n0.05% Zn + \n0.02% B \n\n\n\n76.55ab 73.90a 64.06ab 55.82ab 47.32a \n\n\n\n0.1% Zn + \n0.04% B \n\n\n\n78.19a 49.10bc 63.24ab 55.58ab 47.61a \n\n\n\nF-test * * ** ** * \nCV% 26.4 26.6 21 21.3 38.3 \nLSD 26.8 24.6 18.4 16.4 23.6 \nSEM 4.96 4.75 3.8 4.14 4.39 \nGrand mean 65.96 60.09 56.8 50.19 40.01 \n\n\n\nMeans followed by a common letter(s) within a column are non \u2013 \nsignificantly different based on DMRT at P = 0.05, NS: Non-significant, CV: \nCoefficient of variation, LSD: Least significant differences, SEM: Standard \nerror of mean \n \n\n\n\nThe higher fruit set in response to the higher concentration of \nmicronutrients application is probably due to translocation of hormones, \nfood substances and other factors which stimulates fruit formation to the \ntissue of ovary in greater amount. The possible reason may be due to \nmicro-element ascribed to better photosynthesis, lesser fruit drop, \nimprove fruit size and quality characters. The beneficial role of boron is in \npollination and zinc in growth-promoting substance. Similar results were \nobserved which reported that fruit set % in the trees treated with 0.04% \nB was significantly higher than control which is in line with the result \n(Kumar et al., 2017). \n\n\n\n3.5 Effect on fruit drop \n\n\n\nSignificant variation was found for the final fruit drop percentage. The \nmean fruit drop was 49.52 and varied from 38.18% (0.04% B) and (0.1% \nZn + 0.04% B) to 77.11 % (control) among many treatments. The \nmaximum fruit drop percentage was 77.11 obtained in untreated \ntreatment (control) whereas minimum fruit drop percentage was 38.19 \nobtained in treated treatment 0.04% B and 0.1% Zn + 0.04% B. However, \nit was statistically similar with 0.15% Zn and 0.05% Zn + 0.02% B (Table \n6). Zn is a part of enzymes system which regulates the plant growth (Sajid \net al., 2010). It is essential for the formation of chlorophyll and function of \nnormal photosynthesis (Papadakis et al., 2005). The higher fruit retention \nin Zn treated trees may be ascribed to an increase in the synthesis of IAA \nwhich consequently improves the endogenous level of auxin at abscission \nzone to avoid fruit drop (Razzaq et al., 2005). \n \n\n\n\nTable 6: Effect of different micronutrients in fruit drop of mandarin \nTreatment Percentage of fruit drop \nControl 77.11a \n0.15% Zn 49.32b \n0.04% B 38.19b \n0.05% Zn + 0.02% B 41.42b \n0.1% Zn + 0.04% B 38.19 b \nF-test ** \nCV (%) 24.8 \nLSD 18.9 \nSEM 4.15 \nGrand mean 49.52 \n\n\n\nMeans followed by a common letter(s) within a column are non \u2013 \nsignificantly different based on DMRT at P = 0.05, NS: Non-significant, CV: \nCoefficient of variation, LSD: Least significant differences, SEM: Standard \nerror of mean \n \nThe minimum fruit drop was due to the positive response of zinc and \n\n\n\nboron in fruit setting of mandarin trees. Zinc enhances the synthesis of IAA \nwhich consequently improves the endogenous levels of auxin at abscission \nzone to avoid fruit drop. Stabilization, on the other hand, fulfils the \nrequirement of carbohydrates. By the foliar application of boron, the fruit \ndrop was reduced because boron plays an important role in the \ntranslocation of carbohydrate and auxin synthesis to sink and increased \npollen viability and fertilization. These all pieces of evidence clearly show \nthat Zinc and boron reduce the fruit drop in fruit trees  which is in line with \nthe result. Similarly, the foliar application of Zn and B significantly \ndecrease the fruit drop percentage than untreated (Nijjar, 1998; Ahmad et \nal., 2012). \n \nIt is now common knowledge in agriculture that properly nourished crops \nmay tolerate insect pests and diseases (Ashraf et al., 2013). Although field \nresearch has shown that supplemental foliar feeding can increase yield by \n10 to 25 percent compared with conventional soil fertilization, foliar \nfertilization should not be considered a substitute for a sound soil-fertility \nprogram (Boaretto et al., 2001). Foliar fertilizer application also provides \na more timely and immediate method for delivery of specific nutrients at \ncritical stages of plant growth. Foliar nutrition programs are therefore \nvaluable supplements to soil applications reducing the flower and fruit \ndrop (Zerki, 2004). \n\n\n\n4. CONCLUSION \n\n\n\nZinc and boron have an efficient role in the growth and production which \nincreases the flowering and fruit set as well as reduce the problem of \nflower drop and fruit drop. Thus, it can be concluded that mandarin \norchard at Dailekh is in big need of supplemental foliar application of \nmicronutrient for the optimum yield of mandarin. As the micronutrient \ncontent in soil is very low, the application of the micronutrient was found \nto increase the flowering and fruit set. The spray of either zinc or boron or \nthe combinations of both is effective for flowering and fruit set as well as \nreducing the fruit drop. \n\n\n\nRECOMMENDATION \n\n\n\nThis study will help the students, researchers, farmers, PM-AMP and \nconcerned stakeholders about the need for micronutrient and gives \ntentative dose to increase the yield in micronutrient deficient soil. Further \nresearch should be carried out to find a suitable dose of boron and zinc \neffective for reducing the flower and fruit drop.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWe would like to acknowledge the Agriculture and Forestry University for \nproviding financial support to complete this research. We would also like \nto acknowledge Prime Minister Agriculture Modernization Project, Project \nImplementation Unit, Citrus Zone, Dailekh for their constant support \nduring the entire research. \n\n\n\nREFERENCES \n\n\n\nAhmad, S.K., Ullah, W., Ahmad, R., Saleena, B.A., Rajwana, 2012. Foliar \napplication of boron and zinc influence in tree growth and fruit yield in \ncitrus reticulata. J. Agri. Sci., 113-119. \n\n\n\nAlbrigo, L.G., 1999. Effect of foliar application of urea or nutriphite on \nflowering and yields of Valencia orange trees, Proceedings of the Florida \nState Horticultural Society (pp. 1-4). 700 Experimental Station Road, \nLake Alfred, FL 33850-2299: University of Florida, IFAS. \n\n\n\nAshraf, M.Y., Akhtar, M., Mahmood, K., Saleem, R., 2013. Improvement in \nyield, quality and reduction in fruit drop in kinnow (Citrus reticulata \nBlanco) by exogenous application of plant growth regulators, potassium \nand zinc. J. Bot., 433-440. \n\n\n\nBabu, K.D., Yadav, D.S., 2005. Foliar spray of micronutrient for yield and \nquality improvement in Khasi mandarin (Citrus reticulata Blanco.). \nIndian Journal of Horticulture, 62 (3), 280-281. \n\n\n\nBain, J., 1958. Morphological, anatomical and physiological changes in the \ndeveloping fruit of the Valencia orange, Citrus sinesis (L) Osbeck. \nAustralian Journal of Botany, 6 (1), 1-23. \n\n\n\nBoaretto, A.E., Boaretto, R.M., Muraoka, T., Nascismentofilho, V.F., Tiritan, \nC.S., Mouraofiho, F.A., 2001. Foliar micronutrient application effects on \ncitrus fruit yield, soil and leaf Zn concentrations and 65Zn mobilization \nwithin the plant, International Symposium on Foliar Nutrition of \nPerennial Fruit Plants. ISHS Acta Horticulturae, 594. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 94-98 \n\n\n\n\n\n\n\n \nCite the Article: Om Kala Ruchal, Subodh Raj Pandey, Reja Regmi, Rajendra Regmi, Bishnu Bahadur Magrati (2020). Effect Of Foliar Application Of Micronutrient (Zinc \n\n\n\nAnd Boron) In Flowering And Fruit Setting Of Mandarin (Citrus Reticulata Blanco) In Dailekh, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 94-98. \n \n\n\n\n\n\n\n\nChhetri, L.B., Poudel, P.R., Khanal, A., Kandel, B.P., 2019. Effect of integrated \nplant nutrient management system in quality of mandarin orange \n(Citrus reticulata Blanco). Jurnal Agercolere, 1(1), 19-24. \n\n\n\nDADO. 2017. Statistical Annual Agriculture Report. Dailekh, District \nAgriculture Development Office, Dailekh, Nepal. \n\n\n\nFAOSTAT., 2017. FAO statistical yearbook. Rome, Food and Agriculture \nOrganization. \n\n\n\nIbrahim, M., Ahmad, N., Anwar, S., Majeed, T., 2007. Effect of \nmicronutrients on citrus fruit yield growing on calcareous soils. \nAdvances in plant zinc and boron nutrition, 179-182. \n\n\n\nJahn, O.L., 1973. Inflorescence types and fruiting patterns in Hamlin and \nValencia oranges and Marsh grapefruit. American Journal of Botany, \n663-670. \n\n\n\nKumar, N.C., Rajangam, J., Balakrishnan, K., Kavya, M.V., 2017. Influence of \nFoliar Application of Micronutrients on Tree Growth and Chlorophyll \nStatus of Mandarin Orange (Citrus reticulata Blanco.) Under Lower \nPulney Hills. Int. J. Pure App. Biosci., 5 (2), 1100-1104. \n\n\n\nMoALD., 2017. Agricultural statistical yearbook. Singhadurbar, \nKathmandu, Ministry of Agriculture and Livestock Development, \nGovernment of Nepal. \n\n\n\nMohammad, N., Malchoul, G., Aziz Bousissa, A., 2018. Effect of foliar \nspraying with B, Zn and Fe on flowering, fruit set and physical traits of \nthe lemon fruits (Citrus Meyeri). SSRG International Journal of \nAgriculture & Environmental Science (SSRG - IJAES), 5 (2), 50-57. \n\n\n\nMohammed, N., Georges, M., Bouissa, A.A., 2018. Effect of Foliar Spraying \nwith Zn and B on flowering, fruit set and physical traits of the lemon \n\n\n\nfruits (Citrus Meyeri). SSRG International Journal of Agriculture & \nEnvironment Science. \n\n\n\nNCDP., Annual Report 2072/73 (2015/16). Kathmandu, National Citrus \nDevelopment Program. \n\n\n\nNCRP., Annual Program and Statistics. Kirtipur, Kathmandu, Nepal, \nNational Citrus Research Program. \n\n\n\nNijjar, G.S., 1998. Nutrition of fruit trees. New Delhi, India, Kalyani \nPublishers. \n\n\n\nPapadakis, I.E., Protopadadaki, E., Dimassi, K.N., Therios, I.N., 2005. \nNutritional status, yield, and fruit quality of \u201cEncore\u201d Mandarin trees \ngrown in two sites of an orchard with different soil properties. Journal \nof plant nutrition, 1505-1515. \n\n\n\nRazzaq, K., Khan, A.S., Malik, A.U., Shahid, M., Ullah, S., 2013. Foliar \napplication of zinc influences the leaf mineral status, vegetative and \nreproductive growth, yield and fruit quality of \u2018kinnow\u2019 mandarin. \nJournal of Plant Nutrition, 1479-1495. \n\n\n\nSajid, M., Rab, A., Ali, N., Arif, M., Louise, F., Ahmed, M., 2010. Effect of foliar \napplication of Zn and B on fruit production and physiological disorders \nin sweet orange cv. blood orange. Sharad J. Agric., 26 (3), 355-360. \n\n\n\nSoni, U., Thakre, B., Verma, O.N., 2017. Effect of micronutrients on growth, \nvigour and fruit weight of Nagpur mandarin in Satpura Plateau Region, \nIndia. International Journal of Current Microbiology and Applied \nSciences, 435-440. \n\n\n\nZerki, M., 2004. Foliar fertilization in citriculture. Gainesville, IFAS \n\n\n\nExtension, University of Florida (IFAS).\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 11-14 \n\n\n\nCite this article  Bajracharya Daya1, Krishna Pant2  Biomonitoring of Wetland Using Macrophytes and Macroinvertebrates  \nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 11-14 \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 August 2016 \n\n\n\nAccepted 12 December 2016 \n\n\n\nAvailable online 20 January 2017 \n\n\n\nKeywords: \n\n\n\nBio-monitoring, \nmacroinvertebrates, Rampur \nGhol, Saprobic Water Quality \nClassification Approach Biotic \nIndex, macrophytes \n\n\n\nABSTRACT\n\n\n\nBio-monitoring is the use of biological responses to assess changes in the environment. Biological monitoring is \nconsidered to provide an integrated approach to assess water and overall environmental quality. The ultimate objective \nof bio-monitoring in the Rampur Ghol is to ensure that local resource users in the water sector to apply biodiversity \nfriendly management practices within their day to day activities. In this research macro invertebrates are used to \nclassify the Rampur Ghol into different Water Quality Classes based on Saprobic Water Quality Classification (SWQC) \napproach. Rampur Ghol was selected as research site for biological monitoring, situated in Chitwan district, Mangalpur \nVDC Ward No. 2. Macrophytes were collected from both the aquatic habitat and buffer zone of the Rampur Ghol in \nseasonal basis using fixed quadrate of 1\u00d71m2 . Benthic macro-invertebrates were sampled by using bin sampler and \ngrab sampler and then analysed. During study period altogether 14 families belonging to 10 orders of aquatic macro-\ninvertebrate were found in dry season and 18 families belonging to 12 orders of aquatic macro-invertebrates were \nfound in rainy season. Accessing the Biotic Index of macro-invertebrates, it was found that eight sites fall in water quality \nclass III and site 7 and site 10 were rated class II-III and class III-IV respectively in dry season. Similarly, seven sites \nwere rated water quality class III and three sites were rated water quality class II-III in rainy season. Study of the \nmacrophytes in site 7, 8 and 10 concluded that the macrophytes from sites 8 and 10 showed high degree of organic \npollution and showed the dominance of Pistia stratiotes throughout the study, which is considered to be indicator of \norganic pollution. High anthropogenic activities show fluctuation of water quality in Rampur Ghol. It can be concluded \nthat humans are the key factor for degrading the Ghol. \n\n\n\n1. INTRODUCTION \n\n\n\nWetland was a nascent term for common people until recently. The same \nholds true for Nepal too. It is said that only in the 1970\u2019s it appeared in the \nOxford Dictionary. Before that wetlands were known by different names \nsuch as lake, pond, marsh, swamp, bog, fen etc. Wetlands were named \naccording to the landscape in which they were found.  \n\n\n\nTherefore, even today, the term \u201cwetland\u201d does not have even a universally \naccepted definition because of the plurality of users, regional variations, \nbiological diversities and richness in cultural values. The meaning vary \nfrom place to place and person to person. It has many forms but the \ncommon content, i.e. water, which is the bloodstream of wetland. National \nWetlands Policy of Nepal (2003) defines wetlands as follows:  \n\n\n\n\u201cWetlands denote perennial water bodies that originate from underground \nsources of water or rains. It means swampy areas with flowing or stagnant \nfresh or salt water that are natural or man-made, or permanent or \ntemporary.  \n\n\n\nWetlands also mean marshy lands, riverine floodplains, lakes, ponds, water \nstorage areas and agricultural lands.\u201d  \nThe Convention on Wetlands of International Importance (Ramsar, Iran, \n2012) has defined wetlands in a broader sense as \u201cWetlands are areas of \nmarsh, fen, pet lands or water, whether natural or artificial, permanent or \ntemporary, with water that is static or flowing, fresh,brackish or salt, \nincluding areas of marine water, the depth of which at low tide does not \nexceeds six meters.\u201d  \nOn the basis of ecological and geographical characteristics, wetlands are \nclassified into five major types (CSUWN, 2009):   \n\n\n\n\u2022 Shallow lakes: areas of permanent or semi-permanent water \nwith little flow (e.g. Ghodaghodi Lake Area in Kailali, Kamaldaha \nin KoshiTappu Wildlife Reserve). \n\n\n\n\u2022 Marshes/Swamps: area where water is more or less \npermanently at the surface or causing saturation of the soil (e.g. \n\n\n\n\u2022 Rani Tal in Kanchanpur and Nakrodital in Kailali). These are also\ncalled Ghol. \n\n\n\n\u2022 Floodplain: areas next to the permanent course of a river that\nextends to the edge of the valley (e.g. KoshiTappu in Koshi River \nand Bandarjhula in Narayani River). \n\n\n\n\u2022 Estuaries: areas where rivers meet the sea and water changes \nfrom fresh to salt as it meet the sea (e.g. Sundarbans in India and \nBangladesh). \n\n\n\n\u2022 Coasts: areas between land and open sea that are not influenced \nby rivers (e.g. coral reefs in Australia). \n\n\n\nBio-monitoring is the use of biological responses to assess changes in the \nenvironment. Biological monitoring is considered to provide an integrated \napproach to assess water and overall environmental quality (Hynes, 1979). \nHowever, the assessment of water bodies in the Himalayan Region and in \nNepal is mostly based on the analysis of physical and chemical parameters \n(Sharma et al., 2009).   \nThe ultimate objective of bio-monitoring in the Rampur Ghol is to ensure \nthat local resource users in the water sector to apply biodiversity friendly \nmanagement practices within their day to day activities. While only one \nstudy (Baseline study by SEN) has been conducted in the Rampur Ghol, \nthese studies are common in the rest of the world. Aquatic macrophytes, \nmacroinvertebrates and vertebrates have been widely used to measure \nbiological integrity of aquatic systems, particularly rivers. \nMacroinvertebrates are largely dependent on the aquatic environment in \nwhich they live, and the presence or absence of certain macroinvertebrates \ncan therefore give an indication of the quality of the water and general \necological condition, also referred to as ecosystem health. They are \nsensitive to factors such as water quality, water quantity, habitat \navailability and food availability (Dallas and Mosepele 2007). In this \nresearch macro invertebrates are used to classify the Rampur Ghol into \ndifferent Water Quality Classes based on Saprobic Water Quality \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nJournal Homepage: http://www.razipublishing.com/journals/malaysian- journal-\n\n\n\nof-sustainable-agriculture-mjsa/ \n\n\n\nISSN: 2521-2931  (Print) \nISSN: 2521-294X  (Online) \n\n\n\nBiomonitoring of Wetland Using Macrophytes and Macroinvertebrates \n\n\n\nBajracharya Daya1, Krishna Pant2\n\n\n\n1Ministry of Agriculture Development, Nepal \n2Tribhuwan University, Nepal Corresponding Author Email: dayabajracharya@gmail.com \n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.11.14\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.11.13\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.11.14\n\n\n\n\n\n\nBajracharya Daya1, Krishna Pant2  Biomonitoring of Wetland Using Macrophytes and Macroinvertebrates  \n\n\n\nMalaysian Journal of Sust ainable Agricul ture (MJSA) 1(1) (2017) 11-14\n\n\n\nCite this article  Bajracharya Daya1, Krishna Pant2  Biomonitoring of Wetland Using Macrophytes and Macroinvertebrates  \nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 11-14\n\n\n\n12 \n\n\n\nClassification (SWQC) approach.  \nUnder SWQC system, the quality is measured in terms of presence or \nabsence of indicator biota. Basically this approach is used to measure the \ndegree of organic pollution in river based on the assemblage of organisms \npresent (Moog 1991; Onorm 1995). There are four main standard saprobic \nwater quality classes such as Class I-non-polluted (Oligosaprobic), Class II-\nmoderately polluted (Beta-mesosaprobic), Class III-heavily polluted \n(Alpha-mesosaprobic) and Class IV-extremely polluted (polysaprobic). \nThree transitional water quality classes are also identified: Class I-II-\nslightly polluted (oligosaprobic to beta-mesosaprobic), Class II-III-\ncritically polluted (beta-mesosaprobic to alpha-mesosaprobic) and Class \nIII-IV-very heavily polluted (alpha-mesosaprobic to polysaprobic).\n\n\n\n2. Methodology\n\n\n\nRampur Ghol was selected as research site for biological monitoring. It is \nsituated in Chitwan district, Mangalpur VDC Ward No.2 inside the \ncompound of the Institute of Agriculture and Animal Science (IAAS),  \nTribhuvan University. It is located at a latitude of 27\u00ba38\u00b414.1\"N and \nlongitude 84\u00ba21\u00b425.2\" E and at an altitude of 257 m. It is 9 km south-west \nfrom the Narayangarh Bazar and the climate over there is tropical. Ghol \nwetland area covers an area of 15 ha and the climate is of tropical type.  \n\n\n\nPrimary data collection  \n\n\n\nMacrophytes, fishes and macro invertebrates were identified, counted and \nanalysed to assess the biodiversity health condition of the Rampur Ghol. \nThis verifies the result from chemical parameters of the previous studies. \nFixed quadrate and grab sampler were taken as the sampling unit for \nbiodiversity analysis in and around the Rampur Ghol. The major fauna and \nflora recorded were identified in IAAS (Departments: Environmental \nScience, Aquaculture), National Herbarium at Godavari and Natural History \nMuseum at Kathmandu and analysed.   \n\n\n\nSampling of macrophytes  \n\n\n\nMacrophytes were collected from both the aquatic habitat and \nbuffer zone of the Rampur Ghol in seasonal basis. Fixed quadrate of 1\u00d71m2 \n\n\n\nwere used to collect the macrophytes from different spots in random \ncovering about 1% of the total area of the Ghol. The collected specimens \nwere tagged and pressed to prepare herbaria and then identified in IAAS, \n(Department of Environmental Science), National Herbarium at Godavari \nand Natural History Museum at Kathmandu.  \n\n\n\nSampling of macro invertebrates  \n\n\n\nBenthic macro-invertebrates were sampled by using bin \nsampler and Grab sampler. The macro-invertebrates feature were analysed \nby using the following indices of species structures in communities. \nDensity of macro-invertebrates (D) was calculated by following equation \n(Yadavet al. 1987).  \n\n\n\na. Shannon index of general diversity\nShannon index formula to find the level of species diversity in \nan area (Odum, 1996)   \n\n\n\nWhere, ni = Importance value for each species  \nN = Total of importance values   \nb. Species richness and evenness index \n\n\n\nSpecies richness is simply the number of species per unit area \n(Pielou, 1975). Evenness index stated by Maguran (1988) as another \ncomponent of diversity is calculated by using diversity index:  \n\n\n\nWhere, S = No. of species  \nN = No. of individuals   \n\n\n\nWhere, S = No. of species  \n0 < e < 1   \nc. Index of dominance\n\n\n\nWhere, ni = Importance value of each species  \nN = Total of importance value   \n\n\n\nH values behave inversely with the index of dominance. Higher the \nvalue of 'H' indicates a low concentration of dominance.  \n\n\n\nWater quality classification  \n\n\n\nWater Quality Classification was done using Saprobic Water Quality \nClass (SWQC) approach. In Saprobic system, diversity and abundance of \nbenthic macro invertebrates are used to classify wetland water quality, \nsince they represent the specific characteristic features of the different \nsites of a wetland and include pronounced response to pollution, and a \nsessile-attached mode of life that reduces the influence of neighbouring \nwater conditions on the organism. On top of all, the size of benthic \nmacro invertebrates can be seen without aid. Most of them are sensitive \nto pollution. Their abundance and diversity are subject to change due to \nhuman interventions.  \nCalculation:  \n\n\n\nBiotic Index \n\n\n\nWhere,  \nWi = Tolerance Score of ith Taxon  \n\n\n\n hi = No. of ith Taxon \n\n\n\nH = Total No. of Taxon \n\n\n\nThis BI Value was then compared with weight assigned to \neach SWQC (Table 3) and water quality class of each site were \ndetermined.  \n\n\n\nR\nesult and Discussion \n\n\n\nMacro invertebrates  \n\n\n\nDuring rainy season altogether 18 macro-invertebrate taxa (families) \nbelonging to 13 orders were recorded in the Ghol whereas in the dry \nseason 14 taxa belonging to 10 orders were recorded in the Ghol area. \nThe higher taxa were documented in rainy season while the lower in \ndry season as presented in Appendix 1. Family Thiaridae (70 \nindividuals) followed bySphaeridae (45 individuals) was found in \nhigher number in dry season while Gerridae (1 individual) and \nGomphdae (2 individuals) were found in lower number. Family \nDitiscidae (56 individuals) followed by Lymnaeidae (52 individuals) \nwere found higher in rainy season where as family Gerridae (2 \nindividuals), Unionidae (2 individuals) and Chironomidae (2 \nindividuals) were found in fewer number. Family Salifidae, \nProtoneurodae, Potamidae and Mycidae were absent in dry season as \nshown in Figure 1.  \n\n\n\n\n\n\n\n\nBajracharya Daya1, Krishna Pant2  Biomonitoring of Wetland Using Macrophytes and Macroinvertebrates  \n\n\n\nMalaysian Journal of Sust ainable Agricul ture (MJSA) 1(1) (2017) 11-14\n\n\n\nCite this article  Bajracharya Daya1, Krishna Pant2  Biomonitoring of Wetland Using Macrophytes and Macroinvertebrates  \nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 11-14\n\n\n\n13 \n\n\n\nFigure 1. Number of taxa found in different season  \n\n\n\nThe species diversity of Rampur Ghol was found to be 2.25 and \n2.43 in dry season and rainy season respectively. The species diversity \nvalue of rainy season was higher than dry season. The evenness index of \nRampur Ghol was found to be 0.85 and 0.84 in dry season and rainy season \nrespectively. The seasonal variation of diversity index and evenness index \nof macro-invertebrates in the Rampur Ghol is shown in Figure 2.  \n\n\n\nFigure 2. Diversity index and evenness index value in different seasons  \nThe density of families Salifidae, Gomphidae, Lymnaedae, \n\n\n\nDytiscidae, Viviparidae, Unionidae, Ampullariidae, Baetidae, Gerridae, \nThiaridae, Tubificidae, Chironomidae,Assiminadae, Planorbidae and \nSphaeriidae was recorded 7.81 ind/m2, 54.69ind/m2, 70.31ind/m2, 50.78 \nind/m2, 62.50ind/m2, 97.66ind/m2, 7.81 ind/m2, 3.91 ind/m2, \n273.44ind/m2, 39.06ind/m2, 27.34ind/m2, 136.72ind/m2, 85.94ind/m2 \nand 175.78ind/m2 respectively in dry season.   \nFamilies Salifidae, Protoneurodae, PotamidaeandMycidae were not found \nin dry season. Similarly, density of family Salifidae, Gomphidae, \nLymnaedae, Dytiscidae, Viviparidae, Unionidae, Ampullariidae, Baetidae, \nProtoneurodae, Gerridae, Potamidae, Thiaridae, Mycidae, Tubificidae, \nChironomidae, Assiminadae, Planorbidae and  \nSphaeriidae was recorded 11.72 ind/m2, 78.13 ind/m2, 203.13ind/m2, \n218.75ind/m2, 46.88 ind/m2, 7.81ind/m2, 132.81ind/m2, 191.41ind/m2, \n50.78 ind/m2, 7.81ind/m2, 11.72 ind/m2, 105.47ind/m2, 15.63 ind/m2, \n15.63 ind/m2, 7.81 ind/m2, 31.25 ind/m2, 70.31 ind/m2 and 54.69ind/m2 \nrespectively in rainy season as shown in figure 4.8. The average highest \ndensity value was recorded family Thiaridae (273.44 ind/m2) followed by \nSphaeridae (175.78 ind/m2) in dry season. Similarly, the highest density \nwas recorded 218.75ind/m2 of family Dytiscidae followed by Lymnaedae \n(203.13ind/m2) and Baetidae (191.41ind/m2) in rainy season as shown in \nFigure 3.   \n\n\n\nFigure 3. Density of macroinvertebrates taxa in different seasons  \n\n\n\n 4.2 Biological water quality index  \n\n\n\nBiological water quality index of Rampur Ghol area was calculated by using \nSWQC Approach (Coring and  \n\n\n\nKuchenhoff, 1994). The seasonal trend of Biotic Water Quality Index \n(BWQI) value for all the sites is given in Figure 6.According to the value, \neight sites were rated water quality class III i.e. heavily polluted and \nremaining two sites site 7 and site 10 were rated class II-III i.e. critically \npolluted and class III-IV i.e. very heavily polluted respectively in dry \nseason. Similarly, seven sites were rated water quality class III i.e. heavily \npolluted and three sites were rated water quality class II-III i.e. critically \npolluted in rainy season. In general, the low water pollution was observed \nin rainy season whereas high water pollution was observed in dry season \n\n\n\nas shown in the Table 2.  \n\n\n\nFigure 4. Seasonal biotic water quality index value  \n\n\n\nDuring dry season the Biotic Water Quality Index value was fluctuated \nin all sites as shown in the figure 4.4. Highest value of biotic index was \nrecorded 5.4 at site 7 in dry season. However, lowest index value was \nrecorded 3.09 at site 10 in dry season. Similarly, highest value of biotic \nindex was recorded 5 at site 2 and lowest value was recorded 4.16 at \nsite 10 in rainy season as shown in figure 4. Higher value of index \nindicates better water quality and vice versa.  \nFishes  \n\n\n\nDuring Monsoon and Post-Monsoon season 19 species of fishes \nbelonging to 10 families were found and in Pre-Monsoon season 16 \nspecies of fishes belonging to 8 families were found.  \n\n\n\nFigure 5.  Fish diversity of Rampur Ghol in different seasons  \n\n\n\nAltogether 22 species of fishes belonging to 12 different families were \nfound annually. Cyprinidae (147 individuals) was found highest in \nnumber while Belonidae and Calridae (1 individual each) were found \nlowest in number during Monsoon season. In Post- Monsoon season \nagain Cyprinidae (208 individuals) was found highest in number and \nMastacembelidae and Amblycioitidae (1 individual each) were found \nlowest in number. Similarly, in Pre-Monsoon Season Cyprinidae (91 \nindividuals) was found highest in number while Belonidae and \nAmphipnoidae (1 individual each) were found lowest in number as \nshown in Figure 5.  \n\n\n\nThe Shannon Weaver Diversity Index value of fishes of \nRampur Ghol was found to be 2.48, 2.38 and 2.39 in Monsoon, Post-\nMonsoon and Pre- Monsoon season respectively.Greater diversity was \nfound in Monsoon season than in dry seasons as shown in Figure 6.  \n\n\n\nFigure 6. Diversity index of fishes in Rampur Ghol in different seasons  \n\n\n\nMacrophytes  \n\n\n\nDuring SWQC approach classification of the wetland in dry season, \nthree different classes of water quality were found, i.e. Class II-III, \nClass III and Class III-IV. So, macrophytes were studied on three sites, \nSite 7, 8 and 10 of these classes.More than 45 species of macrophytes \nfound during the study at these three different sites of Rampur Ghol, \n\n\n\n\n\n\n\n\nBajracharya Daya1, Krishna Pant2  Biomonitoring of Wetland Using Macrophytes and Macroinvertebrates  \n\n\n\nMalaysian Journal of Sust ainable Agricul ture (MJSA) 1(1) (2017) 11-14\n\n\n\nCite this article  Bajracharya Daya1, Krishna Pant2  Biomonitoring of Wetland Using Macrophytes and \nMacroinvertebrates  Malaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 11-14\n\n\n\n14 \n\n\n\nfrequently recorded species from sampling stations wereFlascopa \nscandens, Erasgrostis gagentica, Persicaria barbata, Phyllanthus \nurinaria, Gonostegia pentandra, Vallisneria spirallis, Ageratum \nhoustonianum, Alternanthera sessilis, Commelina benghalensis, Lemna \nminor, Pistia stratiotes,  etc.  \n\n\n\nSite 7 showed weeds like Commelina forsskalaei and Ammannia baccifera \nis commonly growing plants on the bank of flowing water.As the water \nenters into urban influence, inflow of sewage helps to increase plant \nnutrients, particularly phosphate and nitrates, thereby increasing \ngrowth of plants. Species among plant, indicative of organic enrichment \nare Pistia stratiotes andLemna minor. These species are also found in \nlarge population in downstream sites8 and 10.The macrophytes from \nsites 8 and 10 showed high degree of organic pollution and showed the \ndominance of Pistia stratiotes throughout the study, which are \nconsidered to be indicators of organic pollution.   \nOn the basis of quantitative estimate, overall species number rank order \nis site-7>site-8>site 10. The Shannon-Weaver diversity index was \ncalculated for all the three sampling sites. Based on the Shannon-Weaver \nindex the sequence among the stations from highest to lowest diversity, \nsite7>site8>site10 (Figure 9). Site7 represented as most diverse, it has \nhighest species richness due to relatively less pollution, whereas station \n8 and 10 were having the least species Shannon diversity index as a \nresult of higher pollution.Low species diversity is correlated with due to \nchange in water level during summer months. The species having wide \nrange of distribution and abundant in occurrence include Alternanthera \nsessilis, Ludwigia hyssopifolia, Pistia stratiotes, Lemna minor,etc were \nspread all over downstream sitesof the Ghol. \n\n\n\nFigure 7. Diversity Index of macrophytes in different sampling sites of \nRampur Ghol  \n\n\n\n4. Conclusion \n\n\n\nThe Rampur Ghol is rich in terms of aquatic macro-invertebrates, \nmacrophytes and fishes taxa composition and its biodiversity. During \nstudy period altogether 14 families belonging to 10 orders of aquatic \nmacro-invertebrate were found in dry season and 18 families belonging \nto 12 orders of aquatic macro-invertebrates were found in rainy season. \nThe total density, Shannon Weiner diversity index and evenness index of \naquatic macro-invertebrates of Rampur Ghol were found to be 1094 \nind/m2, 2.25 and 0.85 in dry season and 1262 ind/m2, 2.43 and 0.84 in \nrainy season respectively.  \n\n\n\nAccessing the Biotic Index of macro-invertebrates, it was found that eight \nsites fall in water quality class III i.e. heavily polluted and remaining two \nsites site 7 and site 10 were rated class II-III i.e. critically polluted and \nclass III-IV i.e. very heavily polluted respectively in dry season. Similarly, \nseven sites were rated water quality class III i.e. heavily polluted and \nthree sites were rated water quality class II-III i.e. critically polluted in \nrainy season.  \n\n\n\nStudy of the macrophytes in site 7, 8 and 10 concluded that the \nmacrophytes from sites 8 and 10 showed high degree of organic \npollution and showed the dominance of Eichhornia crassipes, Pistia \nstratiotes throughout the study, which are considered to be indicators of \norganic pollution.On the basis of quantitative estimate, overall species \nnumber rank order is site-7>site-8>site 10. Based on the Shannon-\nWeaver index the sequence among the stations from highest to lowest \ndiversity, site 7>site 8>site 10. Site 7 represented as most diverse, it has \nhighest species richness due to relatively less pollution, whereas station \n8 and 10 were having the least species Shannon diversity index as a \nresult of higher pollution.  \n\n\n\nHigh anthropogenic activities show fluctuation of water quality in \nRampur Ghol. It can be concluded that humans are the key factor for \ndegrading the Ghol. The over harvesting of wetland resources only \naggravate deteriorating ecological condition of Rampur Ghol.  \n\n\n\nReferences  \n[1] Brehm, 1953.Some Aquatic Fauna from Kalipokhari Eastern \n\n\n\nNepal, Journal of wetland ecology.  \n\n\n\n[2] Cowardin, L. M., V. Carter, F. C. Golet, and E. T. Laroe, 1979. \nClassification of Wetlands and Deepwater Habitats of the United \nStates.U.S. Department of the Interior, U.S. Fish and Wildlife Service, \nOffice of Biological Services, Washington, DC.FWS/OBS-79/31. \n\n\n\n[3] CSUWN, 2009.Simsar Varnamala. Conservation and \nSustainable Use of Wetlands in Nepal. Kathmandu, Nepal. \n\n\n\n[4] Dangol, D. R., 1998. An Inventory of Plant Biodiversity of \nRampur, Chitwan, Nepal.Journal of Institute of Agriculture and Animal \nSciences.20:27-40.\n\n\n\n[5] Hynes, H.B.N, 1979. Ecology of Running Waters, Liverpool \nUniversity Press, Liverpool \nIUCN, 1996.An Inventory of Nepal\u2019s Wetlands. World Conservation \nUnion Kathmandu,Nepal. \nMagurran, A.E.1988. Ecological Diversity and its Measurement. \nPrinceton University Press, Princeton. \n\n\n\n[6] Odum, E.P., 1996. Fundamentals of Ecology.Third Edition \n1971 and First Indian Edition 1996. Natraj Publishers, Dehra Dun, India, \npp 148-154. \n\n\n\n[7] Ramsar-Nepal, 2012.The Annotated Ramsar list of Wetlands \nof International \nSEN, 2010.Baseline Study of Rampur Ghol, Chitwan.The Small earth \nNepal, Kathmandu, Nepal. \n\n\n\n[8] Sharma, S., Moog, O., Nesemann, H. and Pradhan, B., \n2009.Application of Nepalese Biotic Score and its Extension for River \nWater Quality Management in the Central Himalaya, Paper presented at \nThe International Symposium on Environment, Energy and Water in \nNepal: Resent Researches and Direction for Future, Kathmandu, Nepal. \n\n\n\n0 \n0.5 \n\n\n\n1 \n1.5 \n\n\n\n2 \n2.5 \n\n\n\nSite 7 Site 8 Site 10 \nSampling Sites   \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 25-31 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2023.25.31 \n\n\n\n \nCite The Article: Deginet Berhanu (2023). Labor as A Payment Vehicle for the Rangeland Improvement: An Application of Contingent \n\n\n\n Valuation Method in Yabello District, Southern Ethiopia. Malaysian Journal of Sustainable Agricultures, 7(1): 25-31. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.25.31 \n\n\n\n\n\n\n\nLABOR AS A PAYMENT VEHICLE FOR THE RANGELAND IMPROVEMENT: AN \nAPPLICATION OF CONTINGENT VALUATION METHOD IN YABELLO DISTRICT, \nSOUTHERN ETHIOPIA \n\n\n\nDeginet Berhanu* \n\n\n\nEthiopian Forestry Development, P.O. Box: 24536 (Code 1000), Addis Ababa, Ethiopia. \n*Corresponding Author Email: berhanudeginet9@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 06 December 2022 \nRevised 11 January 2023 \nAccepted 23 February 2023 \nAvailable online 27 February 2023 \n\n\n\n Studies conducted regarding goods and services exhibit a low willingness to pay (WTP) in developing \ncountries. However, many scholars found that this may not be the preference for the good but the result of \nthe choice of payment vehicle. Thus, low WTP for ecosystem conservation may not indicate a low welfare for \nthe ecosystem service. There\u2019s the argument that the choice of the payment vehicles may be needed to obtain \nexact welfare estimates where there is imperfect substitutability between money and labor. Otherwise, there \nmight occur underestimating of the welfare benefit of ecosystem services. Thus, the ultimate objective of this \nstudy is to investigate the labor-as a payment-vehicle by using a CV method and estimating the factors \ninfluencing pastoralists\u2019 decision to contribute labor for the rangeland improvement. This study \ndemonstrates it through a rural pastoralists\u2019 choice to elicit their willingness to contribute a labor for the \nrangeland improvement in Yabello District, southern Ethiopia. A total of 228 sample respondents were \nselected randomly from the two adjacent Kebeles. Logit model was used to identify the factors that influence \npastoralists\u2019 willingness to contribute the labor for the rangeland improvement. The result shows that the \nendowment of household\u2019s active labor highly influence respondents\u2019 willingness to contribute. In addition \nto that, variables like sex, age, dominant livelihood activities, livestock holding, perception towards the \nrangeland improvement, dependency ratio, and initial bid value were significantly influence pastoralists\u2019 \nwillingness to contribute the labor for the rangeland improvement. To sum up, the findings of this study \nsuggest that, just like the monetary value, the labor value can also be used to evaluate the demand of \ncommunity for the ecosystem services improvement. Thus, employing the labor as a means of payment \nvehicle for accurate welfare estimations might be a practical alternative, and also giving a chance for the \nrespondents to indicate their willingness to contribute for rehabilitation of degraded ecosystem in \ndeveloping countries.  \n\n\n\nKEYWORDS \n\n\n\nContingent Valuation, Ecosystem Service, Payment Vehicle, Welfare, Willingness to Contribute      \n\n\n\n1.   INTRODUCTION \n\n\n\nIn recent years, the application of contingent valuation (CV) and choice \nexperiments (CE) methods for the valuation of ecosystem goods and \nservices are more commonly applied in developing countries (Kassahun \net al., 2020; Meginnis et al., 2020). Many stated preference studies \nconducted in developing countries provide low willingness to pay (WTP) \nfor a wide range of goods and services in comparison to the cost of \nprovision (Whittington, 2010). For the external examiners the low WTP \nmight seem less consideration of that goods and services under valuation \nin developing country settings. However, there might need for a more \ncareful interpretation of low WTP estimates in developing countries \n(Abramson et al., 2011). It should be known that low WTP may not be the \nindicator of a low demand for public projects in developing countries.  \n\n\n\nThe public distrust in the implementation of environmental goods and \nservices projects; and the form in which payments for goods and services \nmade matter the respondents\u2019 decision for willingness to pay in \ndeveloping countries (Birol and Das, 2012; Oh and Hong, 2012). Thus, in \norder to obtain the exact welfare estimate through a stated preference \nstudy, choosing a widely used payment vehicle is required. In rural areas \n\n\n\nof developing countries where the cash economy is of limited importance, \nthe estimated value of WTP based on monetary contributions alone may \nresult in  understated welfare effects for environmental goods and \nservices (Abramson et al., 2011; Gibson et al., 2016). As a result, many \nstudies found that considering alternative payment vehicle systems for \nwelfare measurement in valuation studies, specially labor contributions is \nvery important for valuing ecosystem services.  \n\n\n\nRangelands are one of the dominant ecosystem goods and service which \nprovide the biggest bulk and least costly feed resources in arid and semi-\narid parts of the world (Zerga, 2015). The ecological features of the \nrangelands are characterized by high temperatures, low and high \nvariables rainfall regimes, low vegetation cover density and fragile soil and \nare found in several parts of the world (Charles, 2010). The area coverage \nof Ethiopian rangelands are estimated to be 78 million ha and most of \nthem located at the border of the country (Abebe, 2000). Rangelands \ncontribute a lot for the provision of ecosystem services such as: fodder, \nfuel wood, resin, construction materials, and traditional medicines \n(Berhanu et al., 2022). In Ethiopia; several studies revealed that rangeland \ndegradation have been increasing at an alarming rate, thus, proper \nmanagement is needed to optimize the aggregate benefit of the society \n\n\n\n\nmailto:berhanudeginet9@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 25-31 \n\n\n\n\n\n\n\n \nCite The Article: Deginet Berhanu (2023). Labor as A Payment Vehicle for the Rangeland Improvement: An Application of Contingent \n\n\n\n Valuation Method in Yabello District, Southern Ethiopia. Malaysian Journal of Sustainable Agricultures, 7(1): 25-31. \n\n\n\n\n\n\n\n(Berry et al., 2009).   \n\n\n\nMany efforts were done by different organizations to reverse the effects of \ndegradation. Ethiopia\u2019s Climate-Resilient Green Economy (CRGE) \ninitiative aimed to increase the productivity and resource-efficiency of the \nlivestock planned to manage rangeland, so that the ecosystem services of \nrangelands in terms of carbon storage could be realized and productivity \nof the land improved. Country\u2019s Programming Paper (CPP) for the \u2018Ending \nDrought in the Horn of Africa Initiative\u2019 has been prepared under the \nleadership of the Ministry of Agriculture to serve as a framework for long-\nterm investment intervention in pastoral and agro-pastoral communities \nin a more coordinated manner. AFR100 initiative has pledged to support \nfor rehabilitation of 15 million hectares of Ethiopia through the Bonn \nchallenge and the New York Declaration on Forest.  \n\n\n\nHowever, the degradation of forest and rangeland resources continues to \nincrease due to natural and anthropogenic factors (Arnalds and \nBarkarson, 2003). The Borana rangeland which is located in the southern \npart of Ethiopia faced various natural and man-made problems, such as \nrecurrent drought, floods, bush encroachment and conflict of resource \ncompetition (Berhanu et al., 2022). Thus, local communities\u2019 involvement \nin decision making process to extend the research findings through \nsocietal demand and rangeland improvement through pastoralists\u2019 \ncommunity participation is required. Thus, the primary focus is to \ncompare preferences across payment vehicles with the goal of helping \npolicymakers. Study conducted in various developing countries used \ncombined payment vehicles both money and labor to value environmental \ngoods and services (Asrat et al., 2004; Kassahun and Jacobsen, 2015; \nAmare et al., 2016; Kassahun et al., 2020).  \n\n\n\nHowever, the use of labor contribution for ecosystem conservation is often \nmotivated by the potentially more lasting benefits as cash payments are \nmore vulnerable to rapid spending (Wunder and Wertz-Kanounnikoff, \n2009). Based on the work of a labor contribution has received increasing \nattention in the valuation of ecosystem services in developing countries \n(Swallow and Woudyalew, 1994). With empirical evidence, using labor as \n\n\n\npayment vehicle in valuation studies can capture the communities\u2019 \ndemand for the environmental good in a more flexible and accurate \nmanner. Hence, giving opportunity to choose payment vehicle is an \nimportant thing for the valuation of ecosystem services in the developing \ncountries and thus help to avoid the rejection of socially desirable projects \ndue to inappropriate payment vehicle.  \n\n\n\nThe significance of this study is thus, taking into consideration labor \ncontribution as an alternative payment vehicle to estimate the welfare \nmeasures could come up with obtaining the right value of ecosystem \nservices and thus avoid the underestimation of the ecosystem benefits \nwhich otherwise may cause the rejection of projects that would be socially \ndesirable. Thus, the current study focuses on the importance of accounting \nfor labor-payment-vehicle using a CV survey and the factors influencing \npastoralists\u2019 decision to realize rangeland rehabilitation. The results of \nthis study contribute a lot to narrowing zthe knowledge gap regarding the \npayment vehicles that are designed for valuation study to provide a \ncontext for the methods.  \n\n\n\n2.   MATERIALS AND METHODS  \n\n\n\n2.1   The Study Area Description  \n\n\n\nThe study was conducted on the two kebeles of Yabello district in Borana \nzone, southern Ethiopia.  Yabello is located around 600 km to the south of \nthe capital city Addis Ababa. Borana zone shares a regional boundary with \nSomali regional state to the East and SNNPR in the North while it shares \nzonal boundary with Guji zone in the NE (Berhanu et al., 2022). The Borana \nrangelands cover a total area of 95,000 km2 (Coppock, 1994). The area \nextends from 4.600 N to 4.900 N latitude 37.900 E 38.400 E longitudes \n(Figure 1). The region is characterized by a semi-arid climate where the \nannual mean temperatures vary from 19 to 24\u00b0C with bimodal rainfall \npattern. The dominant vegetation species in the region are the savannah \ncommunities containing mixtures of perennial herbaceous and woody \nvegetation.  \n\n\n\n\n\n\n\nFigure 1: Location map of the study Kebeles \n\n\n\n2.2   Sampling Design and Sample Size \n\n\n\nMultistage sampling techniques were used for this study. The first was the \npurposive selection of the study district based on the potential existence \nof the rangeland resources and its degradation extent. The second, the \nrandom selection of the study kebeles namely Harewoyu and Utalo, from \nthe Yabello district, as the representative because of their large area \ncoverage of degraded rangelands. Then the sample size was determined \nusing the formula n = N/1+N (e) 2 (Israel, 1992). Finally, 227 sample \nrespondents (from a total of 1240 households of the two kebeles) were \nselected randomly following Probability Proportional to sampling size \nprocedure.  \n\n\n\nn = N/1+N (e) 2                                                                                                                       (1)  \n\n\n\n2.3   Data Collection Techniques and Experimental Design  \n\n\n\nContingent valuation method (CVM) in the form of double-bounded \ndichotomous choice elicitation method was employed to elicit households\u2019 \nWTC for the rangeland improvements. The double-bounded dichotomous \nchoice format (yes-no, no-yes responses) helps to make a clear bound on \nunobservable true WTC. Prior to conduct final survey, a pre-test survey \nwas held with 20 randomly selected households for the focus group \ndiscussion to determine initial bids in terms of both cash and labor \nthrough open-ended contingent valuation format.  The purpose of cash \nand labor contributions was clearly explained to the households before \nthey were asked to contribute.  However, all communities participated in \nthe pre-test survey were agreed only labor contribution for the rangeland \nimprovement. Pastoralists were well known about the severity of the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 25-31 \n\n\n\n\n\n\n\n \nCite The Article: Deginet Berhanu (2023). Labor as A Payment Vehicle for the Rangeland Improvement: An Application of Contingent \n\n\n\n Valuation Method in Yabello District, Southern Ethiopia. Malaysian Journal of Sustainable Agricultures, 7(1): 25-31. \n\n\n\n\n\n\n\nproblem related with the rangeland degradation which in turn leads them \nto food insecurity, expansion of aridity and the need for alternative \nlivelihood. \n\n\n\nMoreover, it has increasingly become a threat to the pastoral production \n\n\n\nsystems. To solve these problems, there might need a mega project to \n\n\n\nrehabilitate the degraded rangelands in the area. Pastoralists have been \n\n\n\ninformed and aware about the purpose of the labor contribution. The man \n\n\n\npower they contribute helps to remove bushes, to replant grass and \n\n\n\npermanent trees to serve as a shade and fodder for their animals. The kind \n\n\n\nof an improvement like building ponds and other permanent water \n\n\n\nsources so that they will no longer need to move long distances to water \n\n\n\ntheir animals, become part of the project. However, when these all things \n\n\n\nare put in place, they need proper maintenance to sustain for the long time. \n\n\n\nThus, community involvement is critical for successful interventions \n\n\n\nbecause government lonely could not achieve all these labor intensive \n\n\n\nwork without community participation.  \n\n\n\nThus, every person in the community required to contribute manpower so \n\n\n\nthat this infrastructure belongs to them. As individuals are often asked to \n\n\n\nmake contributions in terms of labor for such projects, CV surveys that \n\n\n\naddress payments in terms of labor often seem more plausible than those \n\n\n\nthat address cash payment. Then, four starting point bids (frequent \n\n\n\nresponses) determined in terms of labor days were 5, 10, 15 and 20 labor \n\n\n\ndays per month and the group participants agreed for these initial labor \n\n\n\nbids (labor days) per month. The national index of the conversion of labor \n\n\n\nwork to ETB might be lower than the labor rate of the pastoralists\u2019 area \n\n\n\nbecause of its arid weather condition. The average wage rate for a farm \n\n\n\nlaborer in the study area was 75 ETB per day which I used to convert labor \n\n\n\ndays to cash for this study.   \n\n\n\nHence, the climatic condition of the study area compels the contribution of \n\n\n\nlabor to work for only one working season (winter) per year, the time \n\n\n\nschedule for working this project become three consecutive years to \n\n\n\naccomplish the specified 1340 hectare of degraded rangeland. Thus, every \n\n\n\nwilling person was agreed to contribute predetermined labor days per \n\n\n\nmonth only for three months per year (Berhanu et al., 2022). Therefore, \n\n\n\nthe total sampled households were divided randomly into four groups \n\n\n\nrelative proportion to those initial labor bids and these sets of bids were \n\n\n\nselected for the final survey. The respondents were asked a yes/no \n\n\n\nquestions to elicit their willingness to contribute. If the respondent\u2019s \n\n\n\nanswer was yes for the first labor bid, the next higher amount of labor \n\n\n\ncontribution was asked. Finally, the survey was successfully completed \n\n\n\nwithout protest zero bidders. \n\n\n\n2.4   Contingent Valuation Method  \n\n\n\nContingent Valuation Method (CVM) and choice modeling are among the \nfrequently used methods of stated preferences. Contingent valuation \nmethod (CVM) is the method in which we construct a hypothetical market \nwhile users are asked to express their willingness to pay (WTP) for gaining \nthe benefits or willingness to accept (WTA) compensation for losing them. \nAlthough there are a number of valuation methods in environmental \neconomics, many of them are not an appropriate method to derive the \nvalues. Thus, in this case CV study involves directly asking pastoralists \nhow much they would be willing to contribute or work in exchange for \nreliable improvement service with a capacity to feed their livestock during \ndrought season. In the valuation scenario, respondents were informed \nabout the potential threat of labor contribution for rehabilitation of \ndegraded rangeland. The purpose of labor contributions was explained to \nthe respondents before they were asked to contribute. The fundamental \nreasons for incorporation of labor contribution as payment vehicle for the \nvaluation of environmental services in developing countries is that \nscarcity of cash exchanges may lead to underestimation of the value of \necosystem services. Therefore, it is hypothesized that a using labor \ncontribution as a payment vehicle gives more flexibility for the majority of \nthe poor rural households to reveal their preferences (Schiappacasse et al., \n2013). Since the rangeland rehabilitation works are labor intensive, \npastoralists\u2019 communities are asked to contribute their productive labor \ntime per year.  \n\n\n\n2.5   Data Analysis \n\n\n\nThe STATA software was used to analyze collected data and binary logistic \nmodel was employed to identify the factors that influence pastoralists\u2019 \ndecision on WTC labor. Pastoralists\u2019 willingness to contribute labor for the \nrangeland improvement in the study area is the dependent variable. \nWTP/WTC is a powerful tool used for assessing the perception and \nacceptability of the ecosystem service. In discrete choice analysis with \nrepeated responses, the correlation between observable and \nunobservable components of utility is a well-documented. According to \nTrain, accounting for this effect is a routine procedure in double-bounded \ndichotomous choice format (Train, 2009). The function of dependent and \nindependent variables were set below as follow: \n\n\n\nWTC =f (age, sex, marital status, education level, household\u2019s active labor, \ndominant livelihood Activities, cultivated land size, Total livestock owned, \nsatisfaction with status quo, perception towards rangeland rehabilitation, \ntype of housing, dependency ratio and initial labor bid value). Table 1 \nsummarizes the hypothesized effect of the independent variables on the \ndependent variable. \n\n\n\nTable 1: Explanation, Type, and Expected Sign of The Independent Variables \n\n\n\nIndependent variables Explanation Types of variable Expected sign \n\n\n\nsex Sex of the respondent Dummy variable Positive \n\n\n\nage Age of the respondent Continuous variable Positive/negative \n\n\n\nmarital Marital status of the respondent Categorical variable Positive \n\n\n\neduc \n\n\n\nlabor \n\n\n\nEducation of the respondent \n\n\n\nhousehold\u2019s active labor \n\n\n\nDummy variable \n\n\n\nContinuous variable \n\n\n\nPositive \n\n\n\npositive \n\n\n\ndominant Dominant livelihood activities Categorical variable Positive \n\n\n\ntot land Total cultivated land size Continuous variable Negative \n\n\n\nTLU \n\n\n\nsatisfaction \n\n\n\nPerception \n\n\n\nTotal livestock in tropical livestock unit \n\n\n\nSatisfaction at existing rangeland resource \n\n\n\nPerception towards rangeland \nrehabilitation \n\n\n\nContinuous variable \n\n\n\nDummy variable \n\n\n\nDummy variable \n\n\n\nPositive \n\n\n\nnegative \n\n\n\npositive \n\n\n\nhousing \n\n\n\ndependency \n\n\n\nType of housing \n\n\n\nDependency ratio \n\n\n\nDummy variable \n\n\n\nDummy variable \n\n\n\nPositive \n\n\n\nNegative \n\n\n\nbid1 Initial bid value Continuous variable Negative \n\n\n\n_cons Constant  - \n\n\n\n2.6   Empirical Model Specification \n\n\n\nThe Logit and probit models are the popular statistical techniques in \nwhich the probability of a dichotomous outcome is related to a set of \nexplanatory variables (Neupane et al, 2010). However, logistic probability \nfunction is acknowledged as computationally easier to use than the probit \nmodel (Pindyck and Rubinfeld, 1981). The logistic regression analysis \nhelps to estimate the probability of an event whether or not will occur, \nthrough the prediction of a binary dependent outcome from a set of \nindependent variables (Ayenew et al., 2019). Thus, logistic regression \n\n\n\nmodel was employed for this study. The pastoralists\u2019 responses to the \nwillingness to contribute questions were regressed against the labor bids \nthat they are willing to contribute for the rangeland improvement and \nother socioeconomic characteristics of the individual households. The \nregression logit model is specified as:  \n\n\n\nPi= E(Y=1/Xi) = 1/ (1+ e \u03b20 + \u03b21 X1)                                                                                    (2) \n\n\n\nWhere Y = pastoralists\u2019 response, either Yes or No, \u03b20 = constant, \u03b21 = \ncoefficient of the labor bid, X1 = the labor bid that the households are \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 25-31 \n\n\n\n\n\n\n\n \nCite The Article: Deginet Berhanu (2023). Labor as A Payment Vehicle for the Rangeland Improvement: An Application of Contingent \n\n\n\n Valuation Method in Yabello District, Southern Ethiopia. Malaysian Journal of Sustainable Agricultures, 7(1): 25-31. \n\n\n\n\n\n\n\nwilling to contribute for the improvement. \n\n\n\nY=1/1 + expz                                                                                                                           (3) \n\n\n\nWhere Y = responses of household WTC which is either 1 for Yes and 0 for \nNo  \n\n\n\nZ = \u03b20 + \u03b21 X1 + \u03b22 X2 +\u2026\u2026\u2026\u2026\u2026+ \u03b2n Xn                                                                      (4) \n\n\n\nX1, X2, X3 = Explanatory variables and \u03b20, \u03b21, \u03b22 = coefficient of \nexplanatory variables.   \n\n\n\nMean WTC= 1/ \u03b2*ln (1+exp \u03b1) where \u03b1 is a coefficient for the constant \nterm, and \u03b2 is a coefficient for offered bids to the respondents.  \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1   Socio Demographic Characteristics of Respondents  \n\n\n\nThe main livelihood strategy of the community of the study area is \nlivestock production. Household members typically consist of a male \n\n\n\nhousehold head, wife and children dependent upon the livestock. Men are \nlargely involved on livestock rearing, while women carry out day-to-day \nmanagement. The overall labor budget indicate that, labor is likely to be a \ncommon constraint in dry seasons. The summary statistics was computed \nfor the total sample and compared respondents willing and with the non-\nwilling respondents. The average age of the respondents was 45 years. The \nmean age of willing households was lower than the mean age of the total \nrespondents.  This show, the willing households were younger than non-\nwilling households. The average number of active labor of the willing \nrespondents was higher in comparison to the average number of active \nlabor of non-willing respondents. Respondents with large number of \nactive labor would contribute more for the improvement which is in line \nwith (Ayenew and Meride, 2015). The household\u2019s active labor is different \nfrom total family size of the respondents. The average numbers of \nlivestock of the willing respondents were 11 while non-willing \nrespondents were 4. The more the number of livestock, the higher the \npossibility of willingness to contribute labor for rangeland improvement, \nthe result is in lined with the finding of (Belay et al., 2020). Error! Not a \nvalid bookmark self-reference. summarizes the above-mentioned \ncontinuous variables.  \n\n\n\n\n\n\n\nTable 2: Definition, Expected Sign and Summary of The Continuous Variables \n\n\n\nVariable name Definition of variables Measurement Expected sign \n\n\n\nDescriptive statistics (mean) \n\n\n\nWilling  \n\n\n\nn=192 \n\n\n\nNon willing \n\n\n\nn= 35 \n\n\n\nTotal \n\n\n\nn=227 \n\n\n\nAge Respondent\u2019s age Continuous - 44 51 45 \n\n\n\nTot land Total cultivated land Continuous + 0.5 1 0.75 \n\n\n\nTLU \n\n\n\nlabor \n\n\n\nLivestock number in TLU \n\n\n\nhousehold\u2019s active labor \n\n\n\nContinuous \n\n\n\nContinuous \n\n\n\n+ \n\n\n\n+ \n\n\n\n13 \n\n\n\n4.8 \n\n\n\n4 \n\n\n\n2 \n\n\n\n11 \n\n\n\n3.3 \n\n\n\nMoreover, the result revealed that 90% of the respondents were male-\n\n\n\nheaded households.  82% of them were willing contribute labor and 18% \n\n\n\nnot willing to contribute labor for rangeland improvement. Majority of \n\n\n\nrespondents were illiterate, the only literate households were 19.7%. \n\n\n\nRegardless of willingness to contribute, no respondent was satisfied by the \n\n\n\nstatus quo level of rangeland resources in the area.  \n\n\n\n3.2   Results of the Bivariate Probit Model \n\n\n\nThe results showed that about 84.6% of the total sample households were \nwilling to contribute their active labor for the rangeland improvement. \nDouble bounded dichotomous choice format was used to estimate the \nMWTC. The result of bivariate probit model is summarized in Table 3 \nbelow. \n\n\n\nTable 3: Result of Bivariate probit model \n\n\n\nVariables Coef. Std. Err. z P>|z| [95% Conf. Interval] \n\n\n\nbid1 -.131981 .0235495 -4.31 0.000 -.1828785 -.0684646 \n\n\n\n_cons 2.38511 .3313067 5.90 0.000 1.349218 2.708436 \n\n\n\nbid2 -.132231 .04067 -3.58 0.000 -.2318123 -.0634578 \n\n\n\n_cons 1.20115 .4654487 2.23 0.026 .1164584 1.876921 \n\n\n\nLog likelihood = -116.922, No. of obs = 227 Wald chi2 (1) =.010672, chi2 (2) = 30.13, Prob > chi2 = 0.000, LR test of rho=0: Mean WTP = 13.57 (at 95% CI, \n18.06 to 9.08 man-days per household per month). \n\n\n\n3.3   Aggregate Willingness to Contribute Labor for the Rangeland \nImprovement \n\n\n\nThe mean WTC were estimated from the responses of the first and the \nsecond labor bids using double bounded dichotomous choice format. The \nnegative result of the correlation coefficient of the error term shows that \nthe random component of WTC for the first question is not perfectly \ncorrelated with the follow-up questions.  At 95% confidence interval the \naverage WTC from the double bounded question varies between 18.06 to \n\n\n\n9.08 man-days per month for the first and second bids respectively. The \naverage WTC labor is around 14 man-days per month. The aggregate WTC \nlabor was the product of average willingness to contribute and the total \nnumber of households who have a valid response in the study area. No \nprotest zero was expected from the population because there was no \nprotest zero in the sampled households. Based on the double bounded \ndichotomous questionnaires, the aggregate WTC for rangeland \nimprovement was computed at 50,480.4 labor days per year which is \nequivalent to 3,786,030 Birr (72,896.98 USD; Table: 4)  \n\n\n\nTable 4: Summary of aggregate benefit \n\n\n\nMethod \nTotal \n\n\n\nhouseholds (X) \nExpected    households to \n\n\n\nhave a protest zero (Y) \nExpected households with \n\n\n\nvalid responses (Z) \nMean WTC \n\n\n\nAggregate Benefit \n(in labor days) \n\n\n\nAggregate \nBenefit (in Birr) \n\n\n\nDouble bounded \nquestions \n\n\n\n1240 0 1240 13.57 50,480.4 3,786,030 \n\n\n\nAggregate labor days contribution = MWTC*1240*3, where MWTC is \nmean willingness to contribute obtained from bivariate probit model, \n1240 is total households of the two kebeles (Harewoyu & Utalo) and 3 \nindicates the total number of months pastoralists are willing to contribute \nper year. \n\n\n\n3.4   Determinants of Pastoralists\u2019 Willingness to Contribute Labor \n\n\n\nTable 5 below presented the result of factors affecting the households\u2019 \nWTC for rangeland improvement. Twelve independent variables were \nincluded in the model to predict willingness to contribute labor for the \n\n\n\nimprovement. Out of the total variables hypothesized to influence \nwillingness to contribute labor, seven variables were statistically \nsignificant at less than 1% (p-value <0.01). These variables are sex (X1), \nage (X2), dominant livelihood activity (X6), livestock holding in tropical \nlivestock unit (X8), active labor (X5), perception towards rangeland \nrehabilitation(X10), high dependency ratio(X12) and initial bid value \n(X13). The coefficients associated with sex, dominant livelihood activity, \nactive working labor, perception towards rangeland rehabilitation and \nlivestock holding are positive, while the coefficients associated with the \nage and initial bid value are negative. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 25-31 \n\n\n\n\n\n\n\n \nCite The Article: Deginet Berhanu (2023). Labor as A Payment Vehicle for the Rangeland Improvement: An Application of Contingent \n\n\n\n Valuation Method in Yabello District, Southern Ethiopia. Malaysian Journal of Sustainable Agricultures, 7(1): 25-31. \n\n\n\n\n\n\n\nTable 5: Determinants for Pastoralists\u2019 Willingness to Contribute \n\n\n\nVariables Coefficient Std. Err. z P>|z| odds ratio \n\n\n\nsex 2.987368 .9560154 3.12 0.002 19.23341 \n\n\n\nage -.0953504 .0273129 -3.49 0.000 .9090544 \n\n\n\nmarital 1.330628 2.048796 0.65 0.516 3.783422 \n\n\n\nedu -1.190441 .8575196 -1.39 0.165 .3040871 \n\n\n\nactive labor .62189642 .2124863 2.93 0.003 1.812846 \n\n\n\ndomin_liv 3.596297 .9508979 3.78 0.006 36.46296 \n\n\n\ntot land -.13349 .6374518 -0.21 0.834 .87503622 \n\n\n\nTLU 3557544 .1176131 3.02 0.002 1.427257 \n\n\n\nsatisfaction -.3536541 .1274132 2.87 0.003 1.631257 \n\n\n\nperception 2.289968 .9650154 3.31 0.009 21.82314 \n\n\n\nhousing -1.565673 1.435637 -1.09 0.275 .2089474 \n\n\n\ndependency -.5879213 .3254168 2.34 .0071 .9564123 \n\n\n\nbid1 -.2808196 .0924830 -3.04 0.002 .7551646 \n\n\n\n_cons -5.170255 4.586105 -1.14 0.261 .0056831 \n\n\n\nLog likelihood = -35.345769, Obs = 227, Pseudo R2 = 0.749, Prob > chi2 = 0.0000 \n\n\n\nThe results show that a variable sex had significant and positive effect on \nWTC labor. This means that male households are more likely willing to \ncontribute their active working time for the rangeland improvement than \nthe females. Female-headed households had less time as they are fully \nresponsible for more jobs in pastoralists\u2019 area. Result revealed that being \nmale increases the probability of one\u2019s willingness to contribute labor by \n19.2 times than female and is significant at (p-value 0.002). In other words, \nmen were 92% more likely to be willing to contribute in labor for the \nrangeland improvements than women. The other findings in this area are \nhouseholds\u2019 income and wealth are mainly controlled and dominated by \nmen. This implies that the opportunity cost of obtaining the fodder for \ntheir livestock is high for the male, thus, the prospects of obtaining \nwillingness to contribute responses from the male respondents for the \nrangeland improvement are high.  \n\n\n\nAge of the household head negatively and significantly affect households\u2019 \nWTC in man-days contribution at less than 1% (p-value=0.000). This may \nbe due to the older aged people fail to have capacity to spend their working \npower on labor work and tend to refrain from labor intensive activities. \nKeeping the influence of other factors constant, an increase in household \nhead age by one year reduces the odds of willingness to contribute labor \ndays by 10%. The negative relationship between WTC and age is \nconsistent with the finding of (Ayenew and Meride, 2015; Belay et al., \n2020). \n\n\n\nThe results also show that household\u2019s active labor was statistically \nsignificant with the expected positive sign (p<0.01). Under the \nhypothetical market scenario, the probability of pastoralists\u2019 WTC for the \nrangeland improvement increases as the number of active labor increases. \nKeeping other factors constant, an increase in the number of household\u2019s \nactive labor by one unit, the odds of willingness to contribute labor \nincreases by 87%. This is the fact that, rangeland improvement practices \nlike clearing of the bush, building local water storing ponds bring good \nbenefit for the community; hence, households with large active labor \npower are willing to contribute more in these practices. This result is \nconsistent with the findings of (Gebremariam, 2012; Ayenew and Meride, \n2015).  \n\n\n\nLivestock holding in tropical livestock unit has positive and significant \ninfluence on the probability of WTC for the rangeland improvement at 1%. \nIn other words, as the number of livestock increases, the probability of \nWTC will also increase. This is because the improving rangeland was the \nmajor source of their livestock. The odds ratio shows the citrus paribus \neffect of TLU variable, on which keeping the other variables constant, each \nadditional increment of livestock, will increase the odds of the households\u2019 \nwillingness to contribute labor for the improvement by 43%.  This is \nconsistent with the findings of (Gebremariam, 2012; Mezgebo et al., 2013; \nAyenew and Meride, 2015). \n\n\n\nThe variable (dominant livelihood activity) also determines respondents\u2019 \ndecision on their WTC for the rangeland improvement. As the livestock is \nthe major source of livelihood for pastoralists\u2019 community, it is expected \nto be significantly affecting pastoralists\u2019 decision on their WTC for \nrangeland improvement. The result shows that the dominant livelihood \n\n\n\nactivities had positive and significant influence on pastoralists\u2019 WTC at p-\nvalue 0.000.  Respondents whose major livelihood activity was livestock \nhave high chance to contribute labor for the rangeland improvement than \nthose whose livelihood depends on crop production and safety net.  On the \nother hand, the high the dependency ratio of the household on daily \nlaborer, the low the probability of the pastoralists\u2019 community to willingly \ncontribute their labor to improve degraded rangelands. \n\n\n\nPastoralists\u2019 perception on the rangeland rehabilitation: could be \nexplained by the level of WTC labor; when an individual has a significant \ncommitment to rangeland rehabilitation and understands its importance \nto ecosystem services provision, he/she will be willing to contribute a \nhigher amount of labor for its improvement (Schiappacasse et al., 2013). \nTherefore, payments in terms of labor could effectively be providing a \nmore flexible framework for respondents to state their \u201ctrue\u201d value for the \nrehabilitation, which would be underestimated if cash payment was asked. \nThus, in this case, it determines respondents\u2019 decision on their WTC for \nthe rangeland improvement. Result reveals that the perception on \nrangeland rehabilitation had positive and significant effect on pastoralists\u2019 \nWTC at p-value 0.009.   \n\n\n\nThe finding of the study revealed that the coefficient of labor bid has \nnegative sign and significant at p<0.01. The negative sign and the \nsignificance of this coefficient showed that, as the starting bid value \nincreases by one unit, the log odds of household\u2018s willingness to contribute \nlabor will be reduced by 75.5%. This is consistent with the findings of \n(Carlsson et al., 2004; Mousavi et al., 2011). To sum up, the goodness of fit, \nR2 =0.748, this means, the dependent variable (WTC) is explained by the \nexplanatory variables by 74.8%, and the remaining 25.2% of the WTC \nvariation is not explained. Thus, based on the results, there are a lot of \nfactors that can contribute to pastoralists\u2019 decision on WTC. \n\n\n\n4.   CONCLUSIONS \n\n\n\nEast African counties in general and Ethiopia in particular, where the \npopulation grows rapidly, rangelands degraded progressively over time \nmajorly due to natural and anthropogenic factors. Land became \nfragmented and over utilized to meet the demand of pastoral communities. \nThe Borana pastoralists have developed an exceptionally efficient system \nof range land management strategies to respond for the rangeland \ndegradation to rehabilitate the areas. However, only indigenous \nmanagement technique is not sufficient to solve the problem regarding the \nimpacts of rangeland degradation continuously. Thus, the area needs \nfurther improvements with the help of government intervention and \ncommunity participation in terms of their WTP. Considering the limited \naccess of the cash economy in rural areas of the developing countries, \nmany researchers proposed WTC as a means of welfare measurement over \nthe WTP in monetary value. The issue of considering labor-time \nelicitations for the estimation of welfare measures in CV is central to \necosystem management. Thus, for further demonstration, the use of labor \ncontribution as a payment vehicle is needed in developing countries to \ngive a valid welfare estimate where the cash payment vehicle is not \nsmooth.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 25-31 \n\n\n\n\n\n\n\n \nCite The Article: Deginet Berhanu (2023). Labor as A Payment Vehicle for the Rangeland Improvement: An Application of Contingent \n\n\n\n Valuation Method in Yabello District, Southern Ethiopia. Malaysian Journal of Sustainable Agricultures, 7(1): 25-31. \n\n\n\n\n\n\n\nThe paper demonstrated this through a rural household contingent \nvaluation survey designed to elicit pastoralists\u2019 willingness to contribute \nlabor at Yabello district of Southern Ethiopia. The overall objective of this \nstudy is to estimate pastoralists\u2019 average willingness to contribute labor \n(AWTCL) for the rangeland improvement and factors influencing their \ndecision on WTC using binary logistic model.  Indeed, the results of the \ncontingent valuation survey showed that the pastoralists of the area were \npreferred willingness to contribute labor over willingness to pay cash for \nthe rangeland improvement.  The main caveat with this study is that the \nresults may be context dependent in the sense that the option to use \nlabor/man-days as a payment vehicle was available as a credible option. \n\n\n\nHence, the households\u2019 mean WTC for the rangeland improvement was \ncomputed at 40.71 labor day per household per year. This is to say that, \nthe opportunity cost of time that one individual\u2019s willingness to contribute \nhis/her working labor for the rehabilitation project is 3,053.25 ETB \n(US$58.78) per year. The aggregate benefit or aggregate WTC for \nrehabilitation was found to be 50,480.4 labor days per year, which is \nequivalent to 3,786,030 ETB (US$72,890.16). This implies that policy \ninstruments designed for rangeland instruments could thus harness \npastoralists\u2019 labor availability and pastoralists could play a bigger role in \ncontributing to rangeland rehabilitation efforts if supported by relevant \npolicies. Here, considering the lower-income respondents who are not \nable to contribute in cash, the total value of reliable contribution for the \nrehabilitation of degraded rangeland could be underestimated if the \ncontribution would have been estimated using WTP cash. Furthermore, \namong all the factors influencing pastoralist\u2019s decision on WTC labor, \nbeing male household heads, younger age of household head, large \nproductive labor, livestock farming as dominant livelihood, total livestock \nin TLU, positive perception towards rangeland improvement, satisfaction \nat existing rangeland resource, dependency ratio on daily laborer and \ninitial labor bid found to be significant factors. Thus, the rangeland \nrehabilitation efforts could purposefully address pastoralists with the \nabove-mentioned characteristics. \n\n\n\nIn fact, we believe that this finding has important practical implications, \nespecially for the application of environmental valuation methods in \nemerging and developing economies. However, the value estimation that \nincorporates labor contribution for cost-benefit analysis should consider \nestimation of the shadow wage rate for the conversion of labor into a \nmonetary unit. In addition to that, decision makers often request a cost\u2013\nbenefit analysis as part of their deliberations, and CV is increasingly being \nused to measure the economic benefits of environmental goods. Thus, if \nwe do not take labor contribution as a payment vehicle to estimate the \nwelfare measures, we could come up with underestimating the benefits \nestimated when applying CV and may cause the rejection of projects that \nwould be socially desirable. Perhaps, this study can inspire further efforts \nto test this payment vehicle against others in relevant contexts and \nhopefully enable applied stated preference studies in developing \ncountries. Thus, labor payment vehicles are potentially useful in the \ncontext of developing countries and in settings where the ecosystem \nservices to be valued require the participation from the intended \nbeneficiaries. Policymakers can use this result as an input to help design \nsuch kind of effective payment vehicle for valuing ecosystem services in \nthe rural area of developing countries to obtain appropriate value of \necosystem services.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nFirst and foremost, I\u2019d like to extend my heart-felt thanks to the Almighty \nGod for all things done. I\u2019m very grateful to the Borana pastoralists. \n\n\n\nAUTHORS' CONTRIBUTIONS \n\n\n\nDB (corresponding author) performed all tasks starting from study design \nto the manuscript writing.  \n\n\n\nCOMPETING INTERESTS \n\n\n\nThe author declares that no one has competing interests for this study. \n\n\n\nREFERENCES \n\n\n\nAbebe, D., 2000. Pastoralism and pastoral production system. \nIn Conference of Ethiopian Society of Animal Production, 8, Addis \nAbeba (Ethiopia), Pp. 24-26. Ethiopian Society of Animal \nProduction.  \n\n\n\nAbramson, A., Becker, N., Garb, Y., and Lazarovitch, N., 2011. Willingness \nto pay, borrow, and work for rural water service improvements \nin developing countries. Water Resources Research, 47 (11). \n\n\n\nAmare, D., Mekuria, W., Twold, T., Belay, B., Teshome, A., Yitaferu, B., and \nTegegn, B., 2016. Perception of local community and the \nwillingness to pay to restore church forests: the case of Dera \ndistrict, northwestern Ethiopia. Forests, Trees and \nLivelihoods, 25 (3), Pp. 173-186. \n\n\n\nArnalds, O., and Barkarson, B.H., 2003. Soil erosion and land use policy in \nIceland in relation to sheep grazing and government \nsubsidies. Environmental Science and Policy, 6 (1), Pp. 105-113. \n\n\n\nAsrat, P., Belay, K., and Hamito, D., 2004. Determinants of farmers' \nwillingness to pay for soil conservation practices in the \nsoutheastern highlands of Ethiopia. Land Degradation and \nDevelopment, 15 (4), Pp. 423-438. \n\n\n\nAyenew, B., and Meride, Y., 2015. Labor as a payment vehicle for valuing \nsoil and water conservation practice: An application of \ncontingent valuation method in Abaro-Toga Watershed, \nSouthern Ethiopia. Journal of Economics and Sustainable \nDevelopment, 6 (15), Pp. 17-26. \n\n\n\nAyenew, B., Tilahun, A., Erifo, S., and Tesfaye, P., 2019. Household \nWillingness to Pay for Improved Solid Waste Management in \nShashemene Town, Ethiopia. African Journal of Environmental \nScience and Technology, 13 (4), Pp. 162-171. \n\n\n\nBelay, G., Ketema, M., and Hasen, M., 2020. Households\u2019 willingness to pay \nfor soil conservation on communal lands: application of the \ncontingent valuation method in north eastern Ethiopia\u2019\u2019. Journal \nof Environmental Planning and Management, 63 (12), Pp. 2227-\n2245. \n\n\n\nBerhanu, D., Anjulo, A., and Hintz, K.S., 2022. Pastoralists\u2019 willingness-to-\npay for rangeland improvement: A case of Yabello District, \nSouthern Ethiopia. Journal of Forestry and Natural Resources, 1 \n(1), Pp. 42-56. \n\n\n\nBerry, L., Olson, J., Campbell, D., 2009. Land degradation in Ethiopia. Its \nextent and impacts. Commissioned by Global Mechanism with \nsupport from the World Bank. \n\n\n\nBikila, N.G., Tessema, Z.K., and Abule, E.G., 2016. Carbon sequestration \npotentials of semi-arid rangelands under traditional \nmanagement practices in Borana, Southern \nEthiopia. Agriculture, Ecosystems & Environment, 223, Pp. 108-\n114. \n\n\n\nBirol, E., and Das, S., 2012. Valuing the environment in developing \ncountries: modelling the impact of distrust in public authorities' \nability to deliver public services on the citizens' willingness to \npay for improved environmental quality. Urban water journal, 9 \n(4), Pp. 249-258. \n\n\n\nCarlsson, F., K\u00f6hlin, G., and Mekonnen, A., 2004. Contingent valuation of \ncommunity plantations in Ethiopia: a look into value elicitation \nformats and intra-household preference variations. \n\n\n\nCharles, M.P., 2010. Economic Benefit and willingness to pay for improved \nRangelands in Uganda. VM (MUK), Editor. \n\n\n\nCoppock, D.L., 1994. The Borana plateau of southern Ethiopia: Synthesis of \npastoral research, development, and change. International \nLivestock Centre for Africa, Addis Ababa, Pp. 374. \n\n\n\nGebremariam, G.G., 2012. Households\u2019 willingness to pay for soil \nconservation practices in Adwa woreda, Ethiopia: a contingent \nvaluation study (No. 634-2016-41483). \n\n\n\nGibson, J.M., Rigby, D., Polya, D.A., and Russell, N., 2016. Discrete choice \nexperiments in developing countries: willingness to pay versus \nwillingness to work. Environmental and Resource Economics, 65 \n(4), Pp. 697. \n\n\n\nHassan, S., Olsen, S.B., and Thorsen, B.J., 2018. Appropriate payment \nvehicles in stated preference studies in developing \neconomies. Environmental and Resource Economics, 71 (4), Pp. \n1053-1075721. \n\n\n\nHoyos, D., and Mariel, P., 2010. Contingent valuation: Past, present and \nfuture. Prague economic papers, 4, Pp. 329-343. \n\n\n\nIsrael, G.D., 1992. Determining sample size. \n\n\n\nKassahun, H.T., and Jacobsen, J.B., 2015. Economic and institutional \nincentives for managing the Ethiopian highlands of the Upper \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 25-31 \n\n\n\n\n\n\n\n \nCite The Article: Deginet Berhanu (2023). Labor as A Payment Vehicle for the Rangeland Improvement: An Application of Contingent \n\n\n\n Valuation Method in Yabello District, Southern Ethiopia. Malaysian Journal of Sustainable Agricultures, 7(1): 25-31. \n\n\n\n\n\n\n\nBlue Nile Basin: A latent class analysis. Land Use Policy, 44, Pp. \n76-89. \n\n\n\nKassahun, H.T., Jacobsen, J.B., and Nicholson, C.F., 2020. Revisiting money \nand labor for valuing environmental goods and services in \ndeveloping countries. Ecological Economics, 177, Pp. 106771. \n\n\n\nK\u00f6hlin, G., 2001. Contingent valuation in project planning and evaluation: \nthe case of social forestry in Orissa, India. Environment and \nDevelopment Economics, Pp. 237-258. \n\n\n\nMeginnis, K., Hanley, N., Mujumbusi, L., and Lamberton, P.H., 2020. Non-\nmonetary numeraires: Varying the payment vehicle in a choice \nexperiment for health interventions in Uganda. Ecological \nEconomics, 170, Pp. 106569. \n\n\n\nMekonnen, A., 2000. Valuation of community forestry in Ethiopia: a \ncontingent valuation study of rural households. Environment \nand Development Economics, 5 (3), Pp. 289-308. \n\n\n\nMezgebo, A., Tessema, W., and Asfaw, Z.,  2013. Economic values of \nirrigation water in Wondo Genet District, Ethiopia: An \napplication of contingent valuation method. Journal of \nEconomics and Sustainable Development, 4 (2), Pp. 23-36. \n\n\n\nMorrison, M.D., Blamey, R.K., and Bennett, J.W., 2000. Minimizing payment \nvehicle bias in contingent valuation studies. Environmental and \nResource Economics, 16 (4), Pp. 407-422. \n\n\n\nMousavi, S.N., and Akbari, S.M.R., 2011. Estimated value of forest \nconservation in Iran: A case study of Fars Province. African \nJournal of Agricultural Research, 6 (30), Pp. 6407-6411 \n\n\n\nNegasa, B., Eba, B., Tuffa, S., Bayissa, B., Doyo, J., and Husen, N., 2014. \nControl of bush encroachment in Borana zone of southern \n\n\n\nEthiopia: effects of different control techniques on rangeland \nvegetation and tick populations. Pastoralism, 4 (1), Pp. 18. \n\n\n\nNeupane, R.P., Sharma, K.R., and Thapa, G.B., 2002. Adoption of \nagroforestry in the hills of Nepal: a logistic regression \nanalysis. Agricultural systems, 72 (3), Pp. 177-196. \n\n\n\nOh, H., and Hong, J.H., 2012. Citizens\u2019 trust in government and their \nwillingness-to-pay. Economics Letters, 115 (3), Pp. 345-347. \n\n\n\nPindyck, R.S., and Rubinfeld, D.L., 1981. Econometric Model and Economic \nForecasts. 2nd edition; London: McGraw-Hill. \n\n\n\nSchiappacasse, I., V\u00e1squez Lav\u00edn, F., Nahuelhual, L., and Echeverr\u00eda \nValenzuela, C., 2013. Labor as a welfare measure in contingent \nvaluation: the value of a forest restoration project. \n\n\n\nSwallow, B.M., and Woudyalew, M., 1994. Evaluating willingness to \ncontribute to a local public good: application of contingent \nvaluation to tsetse control in Ethiopia. Ecological Economics, 11 \n(2), Pp. 153-161. \n\n\n\nTrain, K.E., 2009. Discrete choice methods with simulation. Cambridge \nUniversity Press. \n\n\n\nWhittington, D., 2010. What have we learned from 20 years of stated \npreference research in less-developed countries?. Annu. Rev. \nResour. Econ., 2 (1), Pp. 209-236. \n\n\n\nWunder, S., and Wertz-Kanounnikoff, S., 2009. Payments for ecosystem \nservices: a new way of conserving biodiversity in forests. Journal \nof Sustainable Forestry, 28 (3-5), Pp. 576-596. \n\n\n\nZerga, B., 2015. Rangeland degradation and restoration: a global \nperspective. Point Journal of Agriculture and Biotechnology \nResearch, 1 (2), Pp. 037-054.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 65-71 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2022.65.71 \n\n\n\n \nCite The Article: Saifatul Hossain Rano, Md. Mamunur Rahman, Habibur Rahman, Totan Kumar Ghosh, Jahidul Hassan (2022). Molecular Confirmation of Two Honeybee \n\n\n\nSpecies (Apis Mellifera L. and A. Cerana F.) in Apiary and Their Foraging Behavior in Litchi Orchard. Malaysian Journal of Sustainable Agricultures, 6(1): 65-71. \n \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2022.65.71 \n\n\n\n\n\n\n\nMOLECULAR CONFIRMATION OF TWO HONEYBEE SPECIES (Apis mellifera L. \nand A. cerana F.) IN APIARY AND THEIR FORAGING BEHAVIOR IN LITCHI \nORCHARD \n\n\n\nSaifatul Hossain Ranoa, Md. Mamunur Rahmana*, Habibur Rahmana, Totan Kumar Ghoshb, Jahidul Hassanc \n\n\n\na Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh.  \nb Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh. \nc Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh. \n*Corresponding Author E-mail: mamun@bsmrau.edu.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 19 July 2022 \nAccepted 30 August 2022 \nAvailable online 06 September 2022 \n\n\n\n The foraging of honeybees is one of the most well-organized and admirable behaviors that exists among social \ninsects and being greatly influenced by nectarine sources and habitat adaptability. In Bangladesh, apiculture \nis mostly confined to rearing of European honeybee Apis mellifera L. despite of having the native A. cerana F. \ndue to lack of information about comparative foraging efficiency and productibility of two species in Asian \ncropland ecosystem. The present study aimed to molecular characterization of two honeybee species in \napiary and their foraging performance on litchi orchard. The genetic identity was revealed thorough \nphylogenetic analysis with >90% bootstrap value using mitochondrial cytochrome oxidase sub unit- 1 (CO1) \ngene and nucleotide sequence data deposited to NCBI GenBank with accession number ON680900- \nON680902 for A. mellifera and ON703291-ON703293 of A. cerana. Upon placing the identified bee hives in \nlitchi orchard, the foraging efficiency were studied based on egression and ingression rate, number of bees \nvisited flowers per minutes, and nectar and pollen collection efficiency in varied time series of the day. \nPrincipal component analysis (PCA) for measuring the contribution of different foraging parameters and the \nspecies wise PCA biplot revealed the better foraging efficiency by A. mellifera L. compared to A. cerana F. in \nlitchi blooms. However, foraging efficiency of other nectarine sources should be analyzed for suggesting best \nperforming bee species in apiculture. \n\n\n\nKEYWORDS \n\n\n\nForaging, European and Asian Honeybee, MtDNA, Honeybee Phylogeny  \n\n\n\n1.   INTRODUCTION \n\n\n\nHoneybees are an important part of the natural ecosystem since its \ncontribution to biodiversity and agricultural output by providing vital \npollination services, which are based on the ecological principle of mutual \ninteractions between fertilized plants and pollinators (Vaziritabar et al., \n2015). The role of bees as pollinators and honey production, in turn, \ndepends on their foraging ecology, including foraging range, daily patterns \nof activity, and exploratory behavior. Their close relation to a lot of \nimperative crops and its foraging behavior makes them successful insect \npollinators (Said et al., 2015). An understanding of different bee foraging \nbiology is especially important in tropical ecosystems, where the vast \nmajority of agricultural crops depend upon bee pollinators and where \necosystems are currently under threat from human actions such as land \nuse change, pesticide use, and pollution. (Brown et al., 2016; \nThimmegowda et al., 2020; Donaldson-Matasci and Dornhaus, 2012; \nDainese et al., 2019). The knowledge on bee behavior and foraging activity \nand their interactions with different plant species are pre-requisite to \nframe on strategy for effective crop pollination and beehive productions \nfor different agro-ecological regions (Pudasaini and Thapa, 2014). \n\n\n\nHoneybees, (genus Apis), are important pollinators in both agricultural \nand natural ecosystems. Although there are more than 3000 pollinators \nother than honeybees, but among them honeybees are ranked first \n(Vaziritabar et al., 2015). In Bangladesh, Apiculture has huge impacts on \n\n\n\nagricultural, ecological and socio economical aspects (Rumman et al., \n2021). Beekeeping in Bangladesh mostly meant by rearing of the \nEuropean honeybee, A. mellifera for production of honey, with rare \nexceptions using A. cerana. About 25000 people involved with beekeeping \nthat collect honey from mustard, coriander and black cumin fields apart \nfrom litchi garden and the Sundarbans in Bangladesh and the country \nproduces nearly 10,000 tons of honey annually (Topu and Parvez, 2021). \nThe European honeybee, Apis mellifera was introduced into Bangladesh on \nan experimental basis in 1992 (Sivaram, 2012).  However, the two species, \nApis mellifera and Apis cerana are mostly used by the beekeepers \nworldwide for production of honey including Bangladesh (Hung et al., \n2018; Requier et al., 2019).  \n\n\n\nAs a managed agricultural species worldwide, recent studies have \nhighlighted only the role of the western honeybee A. mellifera, as a \npotential threat to wild pollinators that are in danger of extinction \n(Requier et al., 2019). Numerous studies have also demonstrated that, this \nintroduced honeybee can decrease the survival, growth, reproduction, and \ndietary behaviors of native pollinators because of their aggressive \ndominance (Stanley et al., 2015; Liu, 2016). On the other hand, due to \nnative origin of Apis cerana bee colonies, it is easy to find and cultivate \n(Islam, 2016). In many countries of the world, A. cerana is considered one \nof the desired bee species in beekeeping because of the nutritional quality \nand price of A. cerana honey. Like other Asian countries, in Bangladesh, the \nprice of A. cerana honey is usually three to five times higher than that of A. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Saifatul Hossain Rano, Md. Mamunur Rahman, Habibur Rahman, Totan Kumar Ghosh, Jahidul  Hassan (2022). Molecular Confirmation of Two Honeybee \n\n\n\nSpecies (Apis Mellifera L. and A. Cerana F.) in Apiary and Their Foraging Behavior in Litchi Orchard. Malaysian Journal of Sustainable Agricultures, 6(1): 65-71. \n \n \n\n\n\nmellifera honey due to limited productivity and local consumer \npreferences. A. cerana is originated from Asia and thus, it may have better \nsurvival capacity against insect pest and also can play more efficient role \nin foraging and honey production in Asian cropland ecosystem however, \nthis species has been underrated. \n\n\n\nTo reveal the role of the two bee species in their foraging perspective in \nAsian croplands, it is necessary to confirm their genetic identity in the \nhives placed in the apiary. There are few literatures describing about the \nforaging parameters of the honeybee, but a systematic analysis is lacking \nprior to those study. For the identification of bee species reared in the \napiary, the most relevant instruments for species differentiating proof in \nanimal products and processed food are DNA-based entomological \nauthentication procedures (Amaral et al., 2016). Mitochondrial \ncytochrome oxidase subunit I (COI) gene region is one of the gene markers \nused to classify and identify honeybee species now a days (Zhao et al., \n2014). DNA-based approaches are thought to be faster and more exact \nthan other methods, with soundness and ubiquity in all cell types. The \ncorrelations between DNA characteristics and cytochrome oxidase \nsubunit -1 amino acid content in Apis reflecting an opportunity to evaluate \nif there is any hint of such a relationship for this mitochondrial gene, at \nleast on a general level (Crozier and Crozier, 1992). As well as there is no \ncomparative study on foraging behaviors of A. cerana and A. mellifera is \navailable. \n\n\n\nSince the beekeepers of Bangladesh has mostly shifted to mono-floral \nhoney production strategies, therefore, determining the best foraging bee \nspecies with its associated crops will boost up the honey production and \npollination scenario. Despite of the importance of Asian honeybee species \nin Asian tropical ecosystems, their foraging ecology remains poorly \nstudied as compared to the Western honeybees (A. mellifera) (Kohl et al., \n2020). Although investigations have been made on species specific \nbehavior, efforts comprising both the species in the same cropland \necosystem should certainly provide a valid information regarding the \nefficiency of foraging behavior of those species. Thus, the findings can be \ntransfer to the beekeepers for choosing the best fitted and suited bee \nspecies for smart apiculture in Bangladesh. The molecular \ncharacterization of both the species prior to analyzing the foraging ability \nwill provide authentic data for further study of ecosystem restoration \nusing these bee species. Conceiving all these thoughts and ideas, the \npresent study was undertaken with the objectives to reveal and confirm \nthe genetic identity of the honeybee species in the hives using \nmitochondrial cytochrome oxidase subunit- 1 (CO1) gene for characterizing \nthe bee colony in apiary and to determine the foraging behavior of A. \nmellifera and A. cerana in litchi orchard as swarmed from the identified \nbee colony \n\n\n\n2.   MATERIALS AND METHODS \n\n\n\nThe present research included two experiments where the first part \nfocused on molecular characterization for revealing the taxonomic \nidentity of the honeybee species in the apiary while the 2nd part \nemphasized on determination of foraging performance of A. cerana F. and \nA. mellifera L. in litchi orchard. The experiment was conducted from \nFebruary 2021 to March 2022. The detailed methodology of these two \nexperiments were described under the following subheadings: \n\n\n\n2.1   Sample Collection and Specimen Preservation \n\n\n\nFor taxonomic identification of the honeybees from apiary, honeybee \nsamples were collected from the two distinct apiaries managed by the \nbeekeepers.  Both the apiary was placed in the litchi orchard of village \nPazulia, Joydebpur, Gazipur district. The experimental area was belonging \nto latitude 24\u00b002\u02ca57\u02ca\u02caN and longitude 90\u00b026\u02ca30\u02ca\u02caE. From the litchi orchard, \na total of 8 hives from each apiary were selected based on beekeeper\u2019s \nspecies-specific recommendation for molecular study for revealing their \ntaxonomic identity. Three adult honeybee workers from each hive of a \nsingle apiary have been taken for molecular characterization. The \ncollected specimens were preserved in 99.9% Ethanol prior to DNA \nextraction. \n\n\n\n2.2   Characterization of The Beehives by Revealing its Taxonomic \nIdentity \n\n\n\nThe molecular analyses were conducted in the Advanced Entomology \nLaboratory of Bangabandhu Sheikh Mujibur Rahman Agricultural \nUniversity, Bangladesh. The molecular identification was done by the \ncharacterization of honeybee samples by sequencing mtDNA. Genomic \nDNA was extracted from alcoholic preserved specimens by using QAGEN \nDNeasy Blood and Tissue kit, following manufacturer\u2019s instruction (Zhao et \nal., 2014). Amplification of DNA was done by polymerase chain reaction \n\n\n\n(PCR) TaKaRa Ex Taq PCR kit, according to the manufacturer\u2019s \ninstructions. The target site was detected through mitochondrial DNA of \nCytochrome b region. For, mitochondrial DNA analysis, primers for COI \ngene fragment, forward and reverse primers were used as COI 1\u20133 (5\u2032 \nATAATTTTTTTTATAGTTATACC\u20193) and COI 2\u20134 (5\u2032 \nTCCTAAAAAATGTTGAGGAAA\u20193) as cited (Crozier and Crozier, 1993). \n\n\n\nThe thermal cycling parameters for COI basically followed the protocols \nestablished, including 95 0C for 5 min for initial denaturation, 35 cycles of \ndissociation (92 0C, 1 min), annealing (54 0C, 1min), and extension (70 0C, \n2 min) (Sameshima et al., 1999). The purified PCR product was sequenced \nwith the results derived from nucleotide sequencing company and the \nsequenced data of mtDNA derived from mitochondrial cytochrome oxidase \nsubunit 1 gene was submitted to GenBank of NCBI, for accession number. \nUpon receiving the accession numbers from NCBI of all the collected bee \nsamples, the nucleotide sequence data had been processed for further \nanalysis. The retrieved nucleotide sequences were aligned using the MEGA \n11 software's Clustal X. For A. cerana F. and A. mellifera L., a total of 627 bp \nand 798 bp of nucleotide sequences were used in the analysis, \nrespectively. The phylogenetic study revealed the nucleotide diversity of \ncollected honeybee samples and thus, their taxonomic identity is retrieved \nthrough boots strap consensus. The hives with identified bee species were \nplaced in the litchi orchard for studying the foraging performance.  \n\n\n\n2.3   Study of The Foraging Behavior of Two Types of Honeybees \n\n\n\nAfter confirming the honeybee species, 4 hives from each apiary were used \nto analyze the foraging attributes in litchi field. The data based on foraging \ntime, number of worker bees egressing from the colony per minute, \nnumber of worker bees ingressing into the colony per minute, number of \nflowers visited per minute and number of worker bees entering with \npollen and nectar into the hives per minute were taken. All the data were \ntaken three times with four days intervals (17 March\u201922-27 March\u201922) as \nthe litchi flower blooming is varied. The details of data analysis are \ndescribed below: \n\n\n\n2.3.1 Foraging Time \n\n\n\nForaging time of both the species was assessed in terms of timings of \ncommencement and cessation of flight activity by noting the time when \nfirst bee started its flight in the morning and the last bee ceased its flight \nin the evening (Joshi and Joshi, 2010). \n\n\n\n2.3.2 Number of Worker Bees Egressing from and Ingressing into The \nColony Per Minute \n\n\n\nTotal number of worker bees egressed from and ingressed into the colony \nper minute was counted by digital watch. The egressing record of worker \nbees was taken for 3 weeks. In each week each existing data was recorded \non hourly basis started from 9.00 to 14.00 hrs. The average number of \nworker bees egressing in each week at each hour of the day was calculated.  \n\n\n\n2.3.3 Number of Flowers Visited by Bees Per Minutes \n\n\n\nForaging of bees started from morning and ended in the afternoon. \nForaging behavior of bees in respect to number of litchi flowers visited per \nminute was determined by tracking 5 bees at an hourly interval from 9.00 \nto 14.00 hour of the day. Hourly intervals were 9.00-10.00 hrs, 10.00-\n11.00 hrs, 11.00-12.00 hrs, 12.00-13.00 hrs, 13.00-14.00 hrs. At each \nhourly interval a foraging worker bee (irrespective of pollen or nectar \ncollector) was tracked and recorded the time (second) spent on a flower. \nThe same bee was tracked when it flown to another flower. The bee was \nfollowed until it gone beyond the sight and recorded the total number of \nflowers visited. In this way 5 bees were tracked at each hourly interval \nfrom morning to afternoon. This tracking was done for 3 weeks beginning \nfrom flower initiation. \n\n\n\n2.3.4 Number of Worker Bees Entering The Colony with Pollen and \nNectar Per Minute \n\n\n\nThe number of worker bees entering the colony with pollen and nectar per \nminute were recorded separately on four-day interval (17 March\u201922-27 \nMarch\u201922) of the season. The number of bees carrying pollen and nectar \nduring 9.00 to 14.00 hrs of each day of the week were recorded. Pollen \ncollectors were identified by the presence of pollen load on their hind legs. \nNectar collectors do not bear such load on their legs. Data was collected by \nobserving workers bees carrying pollen or nectar from litchi field and \nlanding on the entrance. \n\n\n\n2.4 Data Analysis \n\n\n\nThe molecular nucleotide sequence was analyzed using MEGA 11 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Saifatul Hossain Rano, Md. Mamunur Rahman, Habibur Rahman, Totan Kumar Ghosh, Jahidul  Hassan (2022). Molecular Confirmation of Two Honeybee \n\n\n\nSpecies (Apis Mellifera L. and A. Cerana F.) in Apiary and Their Foraging Behavior in Litchi Orchard. Malaysian Journal of Sustainable Agricultures, 6(1): 65-71. \n \n \n\n\n\n(Molecular Evolutionary Genetic Analysis) software and the reference \ndata was retrieved from National Center for Biotechnology Information, \nNCBI to construct the phylogenetic tree. Principal component analysis \n(PCA) was done to determine the contribution of different parameters in \nforaging efficiency. All the statistical analysis to determine the foraging \nefficiency of the two types of honeybees were performed through \nstatistical package \u201cR\u201d.   \n\n\n\n3.   RESULTS  \n\n\n\nThe results of the current study were described in this chapter with two \nsections. The first part of the results focused on revealing the genetic \nidentity of the honeybees in hive while the second section described the \nforaging efficiency of those identified beehives in the apiary. \n\n\n\n3.1   Molecular Identification of Honeybee Species from Beehives \nPlaced in Apiary \n\n\n\nThe identification of honeybee species in apiary was done by \nmitochondrial DNA analysis through cytochrome oxidase subunit-1 gene. \nThe nucleotide sequences were submitted to NCBI GenBank and received \nthe accession number (ON703291-ON703293 & ON680900-ON680902) \nrespectively for A. cerana and A. mellifera.  \n\n\n\n3.1.1   Molecular Identification of A. Mellifera L. and A. Cerana F. From \nBee Hives \n\n\n\nThe molecular identification of A. mellifera L. and A. cerana F. from \nbeehives was done by phylogenic analysis. The analysis was done using \nthe 3-nucleotide sequence of A. mellifera L. samples collected from \ndifferent hives, with 3 reference data of A. mellifera from GenBank and 2 \noutgroup of A. cerana cerana and A. dorsata in case of A. melliferea. While \n3-nucleotide sequence of A. cerana F. samples collected from different \nhives, with 3 reference data from GenBank and 2 outgroup of A. mellifera \nand A. dorsata were used in case of A. cerana. \n\n\n\nFor the phylogenetic analysis of A. mellifera L. and A. cerana F from bee \nhives, the evolutionary history was inferred using Neighbor- joining tree \ngenerated by MEGA 11 software (Figure 1). Each of the analysis involved \n8 nucleotide sequences. The evolutionary distances were computed using \nthe Maximum Composite Likelihood method and are in the units of the \nnumber of base substitutions per site (Tamura et al., 2004). All ambiguous \npositions were removed for each sequence pair (pairwise deletion option). \nThere was a total of 798 positions and 627 positions in the final dataset \nfor A. mellifera and A. cerana respectively.  \n\n\n\n\n\n\n\n(Green circles indicates A. mellifera L. species of beehives, red rectangle \nindicates standard A. mellifera for reference from GenBank and A. cerana \n\n\n\ncerana and A. dorsata used as outgroup (blue triangle)). \n\n\n\nFigure 1: Neighbor-joining tree of A. mellifera and A. cerana \n\n\n\nResult retrieved through the molecular study of collected honeybee \nsamples confirmed the genetic identity of two honeybee species as of A. \nmellifera L. and A. cerana F. The foraging efficiency of the honeybee on \nlitchi orchard are described under the following subheadings. \n\n\n\n3.2   Foraging Pattern of A. Mellifera L. and A. Cerana F. on Litchi \n\n\n\nTo determine the foraging behavior of two types of honeybee species in \nlitchi bloom, the rate of ingression and egression per minute were \nmeasured along with their spent time per flower with nectar and pollen \ncollection efficiency. All the parameters taken in this analysis are strongly \ncorrelated with significant contribution towards determining the foraging \nefficiency. Performance of different variables for aforementioned \npurposed are showed in Figure 2.  \n\n\n\nThe correlation among the variables was analyzed using the correlation \nmatrixes, where the red to white colors indicated positive correlation and \n\n\n\nthe white to blue color indicated negative correlation. Deeper the red color \nindicated the strong positive (Figure 2). In this figure, PC had positive \ncorrelation with TF (64%). EG had strong positive correlation with PC \n(88%) than TF (64%) but didn\u2019t have any correlation with the other \nvariables. IG had strong positive correlation (97%) with EG than PC (91%) \nand had lowest positive correlation (72%) with TF but didn\u2019t have any \ncorrelation with the variable of NC. NC had strong positive correlation \n(98% & 96% respectively) with both IG and EG than PC (90%) and had \nlowest positive correlation (74%) with TF (Figure 2). \n\n\n\n\n\n\n\n(PC- Pollen collectors entering into hive with pollen load per minute; EG- \nEgression of worker bees from hive per minute; IG- Ingression of worker \n\n\n\nbees into hive per minute; NC- Nectar collectors entering into hive per \nminute with nectar TF- Time spent per flower) \n\n\n\nFigure 2: Correlation matrix of different parameters for determining \nforaging efficiency. \n\n\n\nHeatmap dendrogram was used to visualize the result of a hierarchical \nclustering calculation of the variables. The result of a clustering was \npresented as the distance or the similarity between the clustered rows or \ncolumns depending on the selected distance measure (Figure 3). In this \nheat map, NC was closely associated with IG rate as they offered the short \ndistance followed by the rate of EG. TF showed in distant clades with other \nvariables and provided a significant importance in determining the \nforaging performance (Figure 3). \n\n\n\n\n\n\n\nFigure 3: Heatmap dendrogram to visualize the result of a hierarchical \nclustering calculation of the analyzed parameters \n\n\n\n\n\n\n\nFigure 4: Principal Component analysis (PCA) among the foraging \nvariables of two honeybees \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Saifatul Hossain Rano, Md. Mamunur Rahman, Habibur Rahman, Totan Kumar Ghosh, Jahidul  Hassan (2022). Molecular Confirmation of Two Honeybee \n\n\n\nSpecies (Apis Mellifera L. and A. Cerana F.) in Apiary and Their Foraging Behavior in Litchi Orchard. Malaysian Journal of Sustainable Agricultures, 6(1): 65-71. \n \n \n\n\n\nPrincipal component analysis (PCA) was done with variables of data \ncollections of two honeybee species and it was found that, the first two \ncomponents could explain more than 96% of the variation presented in \nFigure 4. Hence, in the PCA biplot analysis, two dimensions were \nconsidered with refereeing to variance 1 and 2. \n\n\n\n\n\n\n\nFigure 5: Principle component analysis representing different variables \nfor both A. cerana F. and A. mellifera L. honeybees. \n\n\n\nAmong the 5 parameters of which the data have been collected to evaluate \nthe comparative behavior of both species (Figure 5), TF exposed strong \npositive correlation within other variables by contributing 14.21% in \ndimension 1 and 83.50% in dimension 2; while PC exposed weak \ncontribution of 19.90% in dimension 1 and 6.41% in dimension 2 among \nall other variables. These correlations among the variables may differ due \nto change in the presence of flower, colony needs, day temperature, \nhumidity, wind speed and other situation. \n\n\n\nThe contribution of different variables in PCA with its corresponding \nhoneybee species is presented in Figure 6. TF played the most positive \ncorrelation for determining the signature pattern of the bee species. In S2, \nNC and IG had high correlation between them as they almost overlapped \neach other; as well PC and EG also had high correlation between them as \nthey also overlapped each other. On the other hand, TF had lower \ncorrelation with other variables as it was distant from the others (Figure \n6). \n\n\n\n\n\n\n\n(S1= Apis cerana F.; S2= Apis mellifera L.) \n\n\n\nFigure 6: Biplot generated through principal component analysis \ncorresponding with honeybee species cluster. \n\n\n\nTime series ranging from 9:00 AM to 3:00 PM provided marked impact on \nthe foraging efficiency as it had a strong correlation in different foraging \nactivities of the bees. The contribution of different variables in PCA with \n\n\n\ntime is presented in this figure 7. T5 showed distinct relation among \nothers crossing both dimension 1 and dimension 2 with completely \npositive relation with dimension 1 and completely negative relation with \ndimension 2. T3 and T4 overlapped each other having positive relation \nwith dimension 1 and negative relation with dimension 2. On the other \nhand, T1 and T2 overlapped each other having both positive relation in \ndimension 1 and dimension 2. \n\n\n\n\n\n\n\n(T1= 9:00-10:00; T2= 10:00-11:00; T3=11:00-12:00; T4= 13:00-14:00; \nT5= 14:00-15:00) \n\n\n\nFigure 7: Biplot generated through principal component analysis \ncorresponding with different time \n\n\n\n3.3   Comparative Foraging Efficiency of Apis Mellifera L. and Apis \nCerana F. in Litchi Orchard \n\n\n\nThe foraging behavior of both the species were influenced with several \nfactors namely, total foraging time, time spent per flower, number of \nflowers visited per minute, ingression and egression rate. The \ncomparative foraging efficiency of two honeybee in several parameters \nare shown in table 1. Between the two species, A. mellifera L. and A. cerana \nF. the European bees, A. mellifera L. showed significant values of egression \nand ingression of worker bees per minute, number of pollen and nectar \ncollectors entering into hive per minute and time spent per flower on a \nforaging trip than A. cerana F. \n\n\n\nTable 1: Comparative Foraging Behaviors of Two Honeybee Species \nin Different Parameters \n\n\n\nForaging Parameters A. Mellifera L. A. Cerana F. \n\n\n\nEgression of Worker Bees Per \nMinute \n\n\n\n76.02 a 34.92 b \n\n\n\nIngression of Worker Bees \nPer Minute \n\n\n\n85.18 a 35.04 b \n\n\n\nPollen Collectors Entering \ninto Hive with Pollen Load \n\n\n\nPer Minute \n9.60 a 0.66 b \n\n\n\nNectar Collectors Entering into \nHive Per Minute with Nectar \n\n\n\n76.06 a 30.18 b \n\n\n\nTime Spent Per Flower 8.040 a 4.703 b \n\n\n\nForaging time was significantly higher in case of A. cerana F. than A. \nmellifera L. and A. cerana F. as mentioned in Table 1. A. cerana F. had \nstarted foraging early in the morning (6:10\u00b10.5hrs) than A. mellifera L. \n(6:30\u00b10.5hrs). Similarly in the evening A. cerana F. ceased its flight \n(18:45\u00b10.10hrs) later than A. mellifera L. (18:30\u00b10.10hrs). The flight \nactivity of A. cerana F. lasted for 12:35\u00b10.10 hrs while in A. mellifera L. it \nlasted for 12:00\u00b10.10 hrs. Therefore, these results suggested that A. \nmellifera visit considerably a less amount time span than that of A. cerana \n(Table 1). \n\n\n\nEgression and ingression of worker bees per minute was statistically \nsignificant during full blooming of litchi flower for A. mellifera L. and A. \ncerana F. at different time of the day (Table. 2). Egression of worker bees \nwas maximum for both of the species from 10:00am-11:00 am. In case of \ningression, A. mellifera L. showed non-significant value from 9:00 am to \n12am and A. cerana F. showed maximum ingression from 10:00am- 11:00 \nam. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Saifatul Hossain Rano, Md. Mamunur Rahman, Habibur Rahman, Totan Kumar Ghosh, Jahidul  Hassan (2022). Molecular Confirmation of Two Honeybee \n\n\n\nSpecies (Apis Mellifera L. and A. Cerana F.) in Apiary and Their Foraging Behavior in Litchi Orchard. Malaysian Journal of Sustainable Agricultures, 6(1): 65-71. \n \n \n\n\n\nTable 2: Number of Worker Bees Exiting from (EG) and Entering Into (IG) Hive Per Minute of Two Species at Different Time of The Day \n\n\n\nEgression of Honeybee/ Minute Ingression of Honeybee/ Minute \n\n\n\nA. mellifera L. 9:00-10:00 81.6 b A. mellifera L. 9:00-10:00 92.3 a \n\n\n\nA. mellifera L. 10:00-11:00 100.8 a A. mellifera L. 10:00-11:00 105.7 a \n\n\n\nA. mellifera L. 11:00-12:00 82.7 b A. mellifera L. 11:00-12:00 94.6 a \n\n\n\nA. mellifera L. 12:00-13:00 70.1 bc A. mellifera L. 12:00-13:00 77.9 ab \n\n\n\nA. mellifera L. 13:00-14:00 44.9 de A. mellifera L. 13:00-14:00 55.4 bc \n\n\n\nA. cerana F. 9:00-10:00 37.3 ef A. cerana F. 9:00-10:00 39.7 c \n\n\n\nA. cerana F. 10:00-11:00 55.1 cd A. cerana F. 10:00-11:00 51.4 bc \n\n\n\nA. cerana F. 11:00-12:00 33.6 ef A. cerana F. 11:00-12:00 NS \n\n\n\nA. cerana F. 12:00-13:00 24.9 f A. cerana F. 12:00-13:00 NS \n\n\n\nA. cerana F. 13:00-14:00 NS A. cerana F. 13:00-14:00 NS \n\n\n\n(*NS= non-significant)  \n\n\n\nThe nectar and pollen collection efficiency for both the species are \npresented in Figure 8. The number of nectar and pollen collectors entered \ninto hive per minute was significantly different in case of A. cerana F. and \nA. mellifera L while it was also different at different time of the day (hour). \n\n\n\nThe highest number of nectar collectors\u2019 entry for both the species was at \nT2 and it was lowest for both at T5. That indicated similar trend of nectar \ncollection efficiency in case of both the species. \n\n\n\n\n\n\n\na) Number of nectar collectors entering into hive per minute at different time of the day (hour) \n\n\n\n\n\n\n\nb) Number of pollen collectors entering into hive per minute at different time of the day (hour) \n\n\n\n(T1= 9:00-10:00am; T2= 10:00- 11:00am; T3=11:00-12:00am; T4=12:00-13:00pm; T5= 13:00-14:00pm) \n\n\n\nFigure 8: Relationship between numbers of nectar collectors (a) and pollen collectors (b) entering into hive with nectar and pollen per minute at \ndifferent time of the day (hour) \n\n\n\nOn the other hand, maximum number of pollen collectors were seen \nentering into the hive with pollen load at T1 in case of A. cerana F. which \nwas lowest at T5; while, maximum number of pollen collectors were seen \nentered into hive at T2 in case of A. mellifera L. which was lowest at T5 \n(Figure 8).  \n\n\n\n4.   DISCUSSION \n\n\n\nMolecular characterization of different honeybee species using \nmitochondrial DNA is one of the most reliable methods for measuring the \nparental inheritance. Apart, of using mtDNA, some approaches based on \nnuclear DNA has been also made however, using mtDNA is found more \ncongenial as it contains the maternal traits where, nDNA is used to detect \nthe heterozygosity in the bee colony (Chalapathy, 2014). The results \n\n\n\ntoward molecular detection of two bee species reflected a comprehensive \nphylogenetic tree based on mtDNA of COI gene where the analyzed \nhoneybee species were being supported with higher rate of bootstrap \nvalue and that identical with the findings of Genchi (Garc\u00eda et al., 2018). \n\n\n\nBehavior of A. mellifera L. and A. cerana F. was studied in this research \nusing number of worker bees exiting from and entering into the hive per \nminute, pollen and nectar collectors entering into hive per minute with \npollen load and nectar and time spent per flower by the bees. This study \nrevealed that, foraging activities including ingression of worker bees into \nhive and egression of workers from hive per minute were higher at the \nmorning time. Bee forging activity varies every hour depending on the \navailability of floral supplies, the needs of their colony, and the weather \nconditions (Hemalatha et al., 2018). Foraging activity was found to be at \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Saifatul Hossain Rano, Md. Mamunur Rahman, Habibur Rahman, Totan Kumar Ghosh, Jahidul  Hassan (2022). Molecular Confirmation of Two Honeybee \n\n\n\nSpecies (Apis Mellifera L. and A. Cerana F.) in Apiary and Their Foraging Behavior in Litchi Orchard. Malaysian Journal of Sustainable Agricultures, 6(1): 65-71. \n \n \n\n\n\nits peak in the morning due to ideal air temperature, relative humidity, \nwind speed, and floral availability; on the other hand, foraging activity \ndecreases with higher temperature and higher wind speed (Young et al., \n2021; Hemalatha et al., 2018). The activities of the workers were greatly \ninfluenced by time (Chen, 2016). \n\n\n\nAmong the two honeybee species, A. cerana F. foraged for longer time than \nA. mellfiera L., and A. cerana F. started foraging early in the morning \n(6:10\u00b10.5hrs) and ceased later than A. mellifera L. According to a study, \nApis cerana bees began foraging early in the morning (06.14 \u00b1 0.004) and \nended late night (Singh, 2008). Similar results of foraging time were also \nseen in the observations of many authors where they mentioned A. cerana \nstarted foraging earlier and ceased later than A. mellifera and foraged for \nlonger time (Joshi and Joshi, 2010; Said et al., 2015; Vaziritabar et al., 2015; \nAryal et al., 2016; Mattu and Bhagat, 2016). \n\n\n\nEgression was maximum for both the species at 10:00-11:00 am and \ningression was maximum at 9:00-12:00 am for A. mellifera L. and at 10:00-\n11:00 am for A. cerana F. According to a study, maximum number of A. \nmellifera and A. cerana entering into hive as well as leaving the hive per \nfive minute was highest at noon and lowest at 5pm (Aryal et al., 2016). In \ncase of time spent per flower, similar result was found by other authors \nalso. A. cerana visited more flowers per minute and took much longer to \ncomplete a single forging trip than A. mellifera (Joshi and Joshi, 2010; \nPudasaini and Thapa 2014; Mattu and Bhagat 2016; Ahmad et al., 2017). \n\n\n\nIn this study, nectar collectors of both A. cerana F. and A. mellifeara L. \nforaged maximum at 10:00-11:00 am at the morning and foraging was \nlowest at 13:00-14:00 pm. According to the greatest nectar collection \nactivity of the Indian bee was seen in the morning between 1000 and 1200 \nhours and nectar harvesting process came to an end around 1400 hours \n(Anandhabhairavi et al., 2020). Among the species, nectar collection was \nhigher in A. mellifera L. than A. cerana F. Since A. mellifera has a larger body \nsize, a greater flying range, and greater defensiveness when compared to \nA. cerana, it is the greatest advantage of A. mellifera when thieving nectar \n(Ernesto and Vergara, 2011). As well as A. cerana honeybees cover less \nground in search of nectar and collects less nectar than A. mellifera \n(Mamatha et al., 2018).  \n\n\n\nPollen collection is seen to be maximum at early morning due to ambient \ntemperature for both the species. A. cerana F. collected maximum pollen \nat 9:00-10:00 am and lowest was seen at 13:00-14:00 pm and in case of A. \nmellifera L. it was highest at 10:00-11:00 am and lowest at 13:00-14:00pm. \nAccording to foraging activities of A. cerana were slowly turned down to \nits minimum level during late hours of the day (Said et al., 2015). A much \nhigher number of flowers are visited around 0800-1000h than during the \nother time intervals (Anandhabhairavi et al., 2020). Other authors \nmentioned similar result in case of A. cerana pollen collection, where the \nmaximum pollen load was found early in the morning which is to be \nbetween 0900 and 1000 hours (Partap et al., 2000; Singh, 2008; Bhagawati \net al., 2016).  \n\n\n\nA study conducted in India revealed that, the greatest pollen collection \nactivity of the Indian bee was found between 0800 and 1000 hours of the \nday (Anandhabhairavi et al., 2020). According to bees caried highest \npollen load during 1000\u20131100 hours (Rajkhowa and Deka, 2013). \nBetween 9.00 and 10:00 am, there was more activity and pollen gathering \nwere higher for A. mellifera (Layek et al., 2020). Also in comparison to A. \ncerana, A. mellifera transports much higher pollen loads, a higher number \nof pollen grains (Joshi & Joshi 2010; Said et al., 2015; Ahmad et al., 2017) \nbecause A.cerana honeybees cover less ground in search of pollen due to \ntheir smaller size and smaller foraging area (Mamatah et al., 2018). \nForagers activity for pollen collections varied due to season, resource \navailability, time of the day and weather conditions.  \n\n\n\n5.   CONCLUSION  \n\n\n\nIn this study, the presence of both the honeybees; A. mellifera L and Apis \ncerana F in the hives were revealed by analyzing the mitochondrial DNA \nof COI gene with accession number (ON680900- ON680902 & ON703291-\nON703293). A. mellifera found to be superior over A. cerana in nectar and \npollen collection, egression and ingression efficiency despite of spending \nmore foraging time by A. cerana. However, in some parameters, A. cerana \nshowed almost similar trend like A. mellifera. The time series data revealed \nthat, in the morning, between 9:00 AM to 11:00 AM was found effective for \nthe foraging activities for both the bee species while it decreased with the \nincreasing temperature and light intensity. The foraging behavior were \nstudied in litchi orchard where the litchi bloom was the only source of \nnectar and pollen. However, the foraging efficiency can be influenced by \nother corresponding factors like types of nectarine sources, flower \norientation, floral types and weather condition. Therefore, before \n\n\n\nrecommending the most efficient bee species, similar experiment with \ndiversified flowering sources is suggested. \n\n\n\nREFERENCES \n\n\n\nAhmad, S.B., Dar, S.A., Pandith, B.A., 2017. Comparative foraging behaviour \nof honeybees, Apis cerana f. and Apis mellifera l. (Hym: Apidae) on \napple bloom. Journal of Entomology and Zoology Studies, 5 (1), Pp. \n474-482. \n\n\n\nAmaral, J., Meira, L., Oliveira, M.B.P.P., Mafra, I., 2016. Advances in \nauthenticity testing for meat speciation. In Advances in Food \nAuthenticity Testing, Woodhead Publishing, Pp. 369-414. \n\n\n\nAnandhabhairavi, N., Ambethgar, V., Philip Sridhar, R., 2020. Foraging \nbehavior of Apis cerana indica Fab (Apidae Hymenoptera) on \nCucumber. Journal of Entomology and Zoology Studies, 8 (6), Pp. 189-\n192.  \n\n\n\nAryal, L.N., Thapa, R.B., Tiwari, S., Chaudhary, N.K., 2016. Foraging \nbehavior of native honeybee (Apis cerana F.) and European honeybee \n(Apis mellifera L.) on flowers of common buckwheat (Fagopyrum \nesculentum M.) in Chitwan, Nepal. International Journal of Applied \nSciences and Biotechnology, 4 (2), Pp. 236-239. \n\n\n\nBhagawati, S., Rahman, A., Deka, M.K., 2016. Diversity of insect foragers \nwith reference to foraging behaviour of Asian honeybee, Apis cerana \nF. on sesamum, Sesamum indicum L. Journal of Entomological \nResearch, 40 (3), Pp. 213-216. \n\n\n\nBrown, M.J., Dicks, L.V., Paxton, R.J., Baldock, K.C., Barron, A.B., Chauzat, \nM.P., Stout, J.C., 2016. A horizon scan of future threats and \nopportunities for pollinators and pollination. PeerJ., 4, Pp. e2249.  \n\n\n\nChalapathy, C., 2014. Mitochondrial DNA Diversity Studies in Apis cerana \npopulations of Nilgiri Biosphere Reserve. Biomirror, 5 (5). \n\n\n\nChen, F.J., Yang, Q.Q., Long, L., Hu, H.M., Duan, B., Chen, W.N., 2016. Activity \npatterns and foraging behavior of Apis cerana cerana in the urban \ngardens in winter. Ying Yong Sheng tai xue bao: The Journal of Applied \nEcology, 27 (1), Pp. 275-281. \n\n\n\nCrozier, R.H., Crozier, Y.C., 1992. The cytochrome b and ATPase genes of \nhoneybee mitochondrial DNA. Molecular Biology and Evolution, 9 (3), \nPp. 474-482. \n\n\n\nCrozier, R.H., Crozier, Y.C., 1993. The mitochondrial genome of the \nhoneybee Apis mellifera: complete sequence and genome \norganization. Genetics, 133 (1), Pp. 97-117. \n\n\n\nDainese, M., Martin, E.A., Aizen, M.A., Albrecht, M., Bartomeus, I., \nBommarco, R., Steffan-Dewenter, I., 2019. A global synthesis reveals \nbiodiversity-mediated benefits for crop production. Science \nadvances, 5 (10), Pp. 0121.  \n\n\n\nDalio, J.S., 2015. Foraging behaviour of Apis mellifera on Trianthema \nportulacastrum. Journal of Entomology and Zoology studies, 3 (2), Pp. \n105-108. \n\n\n\nDonaldson-Matasci, M.C., Dornhaus, A., 2012. How habitat affects the \nbenefits of communication in collectively foraging \nhoneybees. Behavioral Ecology and Sociobiology, 66 (4), Pp. 583-592. \n\n\n\nErnesto, I.B., Carlos, H.V., 2011. Potential negative effects of exotic \nhoneybees on the diversity of native pollinators and yield of highland \ncoffee plantations, 13 (4), Pp. 365\u2013372. doi:10.1111/j.1461-\n9563.2011.00527.x  \n\n\n\nGenchi, G.M.L., Reynaldi, F.J., Bravi, C.M., 2018. An update of Africanization \nin honeybee (Apis mellifera) populations in Buenos Aires, \nArgentina. Journal of Apicultural Research, 57 (5), Pp. 611-614. \n\n\n\nHemalatha, D., Jayaraj, J., Murugan, M., Balamohan, T.N., Senthil, N., \nChinniah, C., Suresh, K., 2018. Foraging behaviour of asiatic honeybee \nApis cerana indica (F.) during summer in Madurai, Tamil Nadu. Indian \nJournal of Entomology, 80 (4), Pp. 1389-1394. \n\n\n\nHung, K.L.J., Kingston, J.M., Albrecht, M., Holway, D.A., Kohn, J.R., 2018. The \nworldwide importance of honeybees as pollinators in natural \nhabitats. Proceedings of the Royal Society B: Biological Sciences, 285 \n(1870), Pp. 20172140. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Saifatul Hossain Rano, Md. Mamunur Rahman, Habibur Rahman, Totan Kumar Ghosh, Jahidul  Hassan (2022). Molecular Confirmation of Two Honeybee \n\n\n\nSpecies (Apis Mellifera L. and A. Cerana F.) in Apiary and Their Foraging Behavior in Litchi Orchard. Malaysian Journal of Sustainable Agricultures, 6(1): 65-71. \n \n \n\n\n\nIslam, M.R., Chhay, L., Mian, M.M., Nasry, A.A.N.B., 2016. The financial \nanalysis of apiculture profitability in Bangladesh. Asian Journal of \nAgricultural Extension, Economics & Sociology, 9 (2), Pp. 1-8. \n\n\n\nJoshi, N.C., Joshi, P.C., 2010. Foraging behaviour of Apis spp. on apple \nflowers in a subtropical environment. New York Science Journal, 3 (3), \nPp. 71-76. \n\n\n\nKohl, P.L., Thulasi, N., Rutschmann, B., George, E.A., Steffan-Dewenter, I., \nBrockmann, A., 2020. Adaptive evolution of honeybee dance \ndialects. Proceedings of the Royal Society B, 287 (1922), Pp. \n20200190. \n\n\n\nLayek, U., Manna, S.S., Karmakar, P., 2020. Pollen foraging behaviour of \nhoneybee (Apis mellifera L.) in southern West Bengal, \nIndia. Palynology, 44 (1), Pp. 114-126. \n\n\n\nLiu, Z.G., Chen, C., Guo, H.K., Lyu, L.P., Shi, W., 2016. Mortality in Apis \nmellifera L. colonies in China from 2009 to 2013. Acta Ecologica \nSinica, 36 (10), Pp. 3005-3012. \n\n\n\nMamatha, K., Devender, R., Kailash, J.G., Niranja, S., Ramakrishna, H., 2018. \nComparative Pollen Analysis of Apis Cerana Fabricius and A. Mellifera \nL. Honey Samples from Guntur, Sothern India. Mellifera, 18 (1), Pp. 1-\n14. \n\n\n\nMattu, V.K., Bhagat, T., 2016. Foraging strategies of honeybees in \npollinating apple flowers and its variation with altitude in Kullu hills \nof western Himalaya, India. Journal of Entomology and Zoology \nStudies, 4 (1), Pp. 164-169. \n\n\n\nPartap, U., Shukla, A.N., Verma, L.R., 2000. Comparative foraging behavior \nof Apis cerana and Apis mellifera in pollinating peach and plum \nflowers in Kathmandu Valley Nepal. In Proceedings of the 4th Asian \napicultural association conference, Kathmandu, Pp. 193-198. \n\n\n\nPudasaini, R., Thapa, R.B., 2014. Foraging behavior of different honeybee \nspecies under natural condition in Chitwan, Nepal. European Journal \nof Academic Essays, 1 (9), Pp. 39-41. \n\n\n\nRajkhowa, D., Deka, M.K., 2013. Insect foragers and foraging behaviour of \nhoneybee, Apis cerana on pigeon pea. Indian Journal of \nEntomology, 75 (3), Pp. 232-235. \n\n\n\nRequier, F., Garnery, L., Kohl, P.L., Njovu, H.K., Pirk, C.W., Crewe, R.M., \nSteffan-Dewenter, I., 2019. The conservation of native honeybees is \ncrucial. Trends in ecology & evolution, 34 (9), Pp. 789-798. \n\n\n\nRumman, S., Reybroeck, W., Islam, T., 2021. Precision apiculture in \nBangladesh: opportunities and challenges. Ecology Journal, 3 (2), Pp. \n193. \n\n\n\nSaid, F., Inayatullah, M., Ahmad, S., Iqbal, T., Shah, R.A., 2015. Foraging \nbehavior of the Himalayan Honeybee, Apis cerana (Hymenoptera: \nApidae) associated with sunflower (Helianthus annuus L.) at \nPeshawar District of Khyber Pakhtunkhwa (KP). Journal of \nEntomology and Zoology Studies, 3 (3), Pp. 203-207. \n\n\n\nSameshima, S., Hasegawa, E., Kitade, O., Minaka, N., Matsumoto, T., 1999. \nPhylogenetic comparison of endosymbionts with their host ants based \non molecular evidence. Zoological Science, 16 (6), Pp. 993-1000. \n\n\n\nSingh, M.M., 2008. Foraging behaviour of the Himalayan honeybee (Apis \ncerana F.) on flowers of Fagopyrum esculentum M. and its impact on \ngrain quality and yield. Ecoprint: An International Journal of \nEcology, 15, Pp. 37-46. \n\n\n\nSivaram, V., 2012. Status, prospects, and strategies for development of \norganic beekeeping in the South Asian Countries. Division of \nApiculture and Biodiversity, Department of Botany, Bangalore \nUniversity. \n\n\n\nStanley, J., Sah, K., Jain, S.K., Bhatt, J.C., Sushil, S.N., 2015. Evaluation of \npesticide toxicity at their field recommended doses to honeybees, Apis \ncerana and A. mellifera through laboratory, semi-field and field \nstudies. Chemosphere, 119, Pp. 668-674. \n\n\n\nTamura, K., Nei, M., Kumar, S., 2004. Prospects for inferring very large \nphylogenies by using the neighbor-joining method. Proceedings of the \nNational Academy of Sciences, 101 (30), Pp. 11030-11035. \n\n\n\nThimmegowda, G.G., Mullen, S., Sottilare, K., Sharma, A., Mohanta, S.S., \nBrockmann, A., Olsson, S.B., 2020. A field-based quantitative analysis \nof sublethal effects of air pollution on pollinators. Proceedings of the \nNational Academy of Sciences, 117 (34), Pp. 20653-20661.  \n\n\n\nTopu, A.H.K., Parvez, S., 2021. Time for honey harvest. The Daily Star, 08 \nJanuary 2021. https://www.thedailystar.net/business/news/bees-\nboon-mustard-plantations-2024213 \n\n\n\nVaziritabar, S., Oshidari, S., Esmaeilzade, S.M., 2015. Comparative foraging \nbehavior of eastern honeybee (Apis cerana F.) and western honeybee \n(Apis mellifera carnica) in pollinating pear and apricot flowers in \nTaleghan, Iran. Iran. J. Biodivers. Environ. Sci, 7, Pp. 136-149. \n\n\n\nYoung, A.M., Kohl, P.L., Rutschmann, B., Steffan-Dewenter, I., Brockmann, \nA., Dyer, F.C., 2021. Temporal and spatial foraging patterns of three \nAsian honeybee species in Bangalore, India. Apidologie, 52 (2), Pp. \n503-523. \n\n\n\nZhao, W., Tan, K., Zhou, D., Wang, M., Cheng, C., Yu, Z., He, S., 2014. \nPhylogeographic analysis of Apis cerana populations on Hainan Island \nand southern mainland China, based on mitochondrial DNA \nsequences. Apidologie, 45 (1), Pp. 21-33.\n\n\n\n \n\n\n\n\nhttps://www.thedailystar.net/business/news/bees-boon-mustard-plantations-2024213\n\n\nhttps://www.thedailystar.net/business/news/bees-boon-mustard-plantations-2024213\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 34-35 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.34.35 \n\n\n\n \nCite the Article: Jubaidur Rahman, Mukaddasul Islam Riad (2020). Response Of Growth Regulator To Groundnut In Charland Area.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 34-35. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2020.34.35 \n\n\n\n\n\n\n\n \nRESPONSE OF GROWTH REGULATOR TO GROUNDNUT IN CHARLAND AREA  \n\n\n\n \nJubaidur Rahman*a, Mukaddasul Islam Riadb \n \na Scientific Officer, Agronomy Division, Bangladesh Agricultural Research Institute, Jamalpur-2000, Bangladesh  \nb Scientific Officer, Plant Genetic Resources Centre, Bangladesh Agricultural Research Institute, Jamalpur-2000, Bangladesh  \n*Corresponding Author Email: jubaidurjp@gmail.com \n    \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 01 December 2019 \nAccepted 06 January 2020 \nAvailable online 05 February 2020 \n\n\n\n The experiment was conducted at the charland area of Jamalpur during rabi 2017-18 and 2018-19 to find out \nthe suitable growth regulator for groundnut in char land. Treatments included in the experiment were: Flora \n(Nitrobenzene, ACI), Nafa (Entefa), Maxsulphar (Sulfer-80%, Mcdonald), Alba (Avamectin-1.8 EC, SAMP \nLimited), Calsol and Control (without growth regulator). Growth regulator were applied Flora (2 ml/L), Nafa \n(2.5 ml/L), Maxsulphar (2 ml/L), Alba (0.5 ml/L), Calsol (3 ml/L) as foliar spray at 35 and 45 days after \nsowing (DAS). Several yield parameters e.g. plant height, number of pod/plant, number of effective pod/plant, \nnumber of uneffective pod/plant, root length, 100 seed wt. and yield were analyzed. Growth regulators \neffective to groundnut in charland area from Flora, Nafa, Maxsulphar and Alba application due to formation \nof nodulation, chlorophyll synthesis and supply of plant growth agent. Control treatment performs better \nthan some growth regulator treatments.  \n\n\n\nKEYWORDS \n\n\n\nGrowth regulator, groundnut, charland area. \n\n\n\n1. INTRODUCTION \n\n\n\nGroundnut (Arachis hypogaea L.) is an important leguminous oilseed crop \nwhich is commonly known as poor man\u2019s nut as it is a cheaper source of \nprotein when comparable to other nuts like cashew nut. It is also called as \npeanut, monkey nut and goober nut. Groundnut seed contains 44 to 56% \noil and 22 to 30% protein on dry seed basis and is a rich wellspring of \nminerals (Phosphorus, Calcium, Magnesium and Potassium) and vitamins \n(Kaba et al., 2014). In Bangladesh, there are about 0.82 million hectares of \nchar land (Ahmed et al., 1987). \u201cCharland\u2019\u2019 is the Bengali term, its English \nmeaning is \u201cRiverine Island\u201d for mid-channel island that emerges \nperiodically from riverbed as a consequence of accretion (Elahi, 1991). In \nBangladesh the char lands can be divided into five sub areas which has \nhighly potential for groundnut production (The Jamuna, the Ganges, The \nPadma, The upper Meghna and the lower Meghna River) where Tista and \nold Brahmaputra also constitute some char land areas (Islam et al., 2012).  \n \nThe major char inhabited districts of Bangladesh are Jamalpur, Sirajgonj, \nNoakhali, Bogra, Rangpur and Mymensingh. Plant growth regulators are \nknown to enhance the yield, oil and fatty acids content in peanut (Malik et \nal., 1993). Groundnut is the most important oil crop in area and production \nin Bangladesh especially in char land areas. Jamalpur district which most \ngroundnut production area of Bangladesh 1387 acres area produced 1314 \nMT (BBS, 2016). Farmers of charland area of Jamalpur district applied \nplant growth regulators which locally named vitamins. They used different \nvitamins in different companies for increasing yield of groundnut. Just the \nfarmers of charland area foliar spray of plant growth regulators to get \nhealthy and high yielding plant. But they do not know main activities of \nplant growth regulators. In this case the experiment is undertaken to find \nout suitable growth regulator locally named Vitamins for groundnut in \ncharland. \n \n\n\n\n2. METHODS AND MATERIALS  \n\n\n\nThe district lies between 24\u00b034\u00b4 and 25\u00b026\u00b4 north latitudes and between \n89\u00b040\u00b4 and 90\u00b012\u00b4 east longitudes and it is situated at elevation 23 meters \nabove sea level (Pal, 2012). The annual average temperature of this district \nvaries from maximum 36.63\u00b0C to minimum 9.4\u00b0C. Annual average rainfall \nis 933.7 mm (Regional Research Report, 2018-19). The experimental site \nwas of medium high land belonging to the agro-ecological zone Old \nBrahmaputra Floodplain under Agro-Ecological Zone 9 (UNDP-FAO, \n1988). The experiment was conducted at the charland area of Jamalpur \nduring rabi 2017-18 and 2018-19 to find out the suitable growth regulator \nfor groundnut in char land. Design of the experiment was RCB with 3 \nreplications. Each treatment was sown in unit plot having 5m \u00d7 3m with \nthe spacing of 15 \u00d7 30 cm. Spacing between two plots and replications \nwere 1m and 1m respectively.  \n \nBARI Badam-8 was used as a check variety in the experiment. Treatments \nincluded in the experiment were: Flora (Nitrobenzene, ACI), Nafa (Entefa), \nMaxsulphar (Sulfer-80%, Mcdonald), Alba (Avamectin-1.8 EC, SAMP \nLimited), Calsol and Control (without growth regulator). Growth regulator \nwere applied Flora (2 ml/L), Nafa (2.5 ml/L), Maxsulphar (2 ml/L), Alba \n(0.5 ml/L), Calsol (3 ml/L) as foliar spray at 35 and 45 days after sowing \n(DAS). Fertilizers were applied at the rate of 25-160-85-300-10 kg/ha \nNPKSB (FRG, 2012) as urea, triple super phosphate (TSP), muriate of \npotash (MOP), gypsum, Boron. Seeds were sown on November 16, 2017 \nand November 08, 2018 in rows. Weeding was done at 20 days after \nemergence of the crop. Grain yield was calculated from the whole plot. \nYield contributing characters were taken from 05 randomly selected \nplants from the middle rows of each plot. Data were analyzed with the help \nof a computer package program STAR and means were separated \nfollowing LSD test at 5% level of significance. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 34-35 \n\n\n\n\n\n\n\n \nCite the Article: Jubaidur Rahman, Mukaddasul Islam Riad (2020). Response Of Growth Regulator To Groundnut In Charland Area.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 05-06. \n \n\n\n\n\n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\nYield and yield components like plant height, number of pod per plant, \nnumber of effective pod per plant, number of uneffective pod per plant and \nyield differed significantly influenced by growth regulator. The highest \nplant height was found in application of Alba Y1 (39.2 cm) containing \navamectin which good performance against mites because groundnut field \nattacked by mites and in Y2 highest performed by Nafa (48.1cm) and \nlowest (35.9cm) from control treatment (Ministry of Water resources, \n2004). Highest pod per plant was found Y1 and Y2 from Nafa and lowest \nfrom control treatment. Number of effective pod per plant was found Y1 \n\n\n\nfrom Maxsulphar (21.07) Y2 from Nafa (24) due to formation of nodulation \nand chlorophyll synthesis and lowest from control treatment. Number of \nuneffective pod per plant lowest Y1 from Maxsulphar, Y2 from Nafa because \nof maximum effective pod per plant and lowest calsol both the year. \nHighest root length was found Y1 from Nafa (12.72cm), Y2 from Flora (18.2 \ncm) and lowest from Y1 Calsol (11.71 cm), Y2 control (14.4 cm) (Nickell, \n1982). 100 seed weight was highest Y1 from Alba (41.76 gm), Y2 from \ncalsol (55 gm) due to supply of plant growth agent. Highest yield was \nfound Y1 from Nafa (2.45 t/ha), Y2 from Flora (2.69 t/ha) may be cause of \nsupply to plant growth agent and management (Rahman et al., 2015). \n\n\n\n\n\n\n\nTable 1: Yield and yield components of growth regulator to groundnut in charland area (Jamalpur 2017-18 and 2018-19) \n\n\n\nTreatment  Plant height(cm) No.of  pod/ \n\n\n\nplant \n\n\n\nNo.of effective \n\n\n\npod/plant \n\n\n\nNo.of \n\n\n\nuneffective \n\n\n\npod/plant \n\n\n\nRoot \n\n\n\nlength(cm) \n\n\n\n100 seed \n\n\n\nweight(gm) \n\n\n\nYield \n\n\n\n(t/ha) \n\n\n\nY1 Y2 Y1 Y2 Y1 Y2 Y1 Y2 Y1 Y2 Y1 Y2 Y1 Y2 \n\n\n\nFlora 28.6 43.2 20.93 23 18.87 22 3.1 0.9 12.09 18.2 37.56 45.2 1.87 2.69 \n\n\n\nNafa 33.73 48.1 20.4 26 17.27 24 3.6 1.9 12.72 16.9 39.89 54.2 2.45 1.93 \n\n\n\n Maxsulphar 35.87 44.2 22.8 24 21.07 23 2.47 1.5 12.41 17 37.65 50 2.42 2.16 \n\n\n\nAlba 39.2 42.2 21.47 23 17.47 21 4.2 1.5 11.82 16 41.76 53 2.13 2.32 \n\n\n\nCalsol 30.93 43.9 20.87 22 18 21 6.6 0.8 11.71 16.2 37.96 55 2.17 2.56 \n\n\n\nControl 32.6 35.9 23.87 13 17.07 12 5.13 1.2 12.03 14.4 37.96 54 2.25 2.18 \n\n\n\nLSD0.05 7.38 8.5 - 11 - 4.9 2.0 - - - - - 0.14 2.2 \n\n\n\nCV (%) 7.77 6.99 9.43 10.2 10.6 11.2 16.95 18 3.24 11 11.54 15 2.2 5 \n\n\n\n \nY1= 2017-18 and Y2= 2018-19 \n \n\n\n\n4. CONCLUSION \n\n\n\nApplication of growth regulator to groundnut in charland area effective by \nFlora, Nafa, Maxsulphar and Alba application due to formation of \nnodulation, chlorophyll synthesis and supply of plant growth agent. \nControl treatment performs better against some growth regulator \ntreatments. \n\n\n\nREFERENCES \n\n\n\nAhmed, M.M., Alam, N., Kar, N.K., Maniruzzaman, A.F.M., Abedin, Z., \n\n\n\nJasimuddin, G., 1987. Crop production in saline and charlands-existing \n\n\n\nsituation and potentials. Advances in Agronomic Research in \n\n\n\nBangladesh, 2, 1-27. \n\n\n\nand time of flowering on pod yield of peanut (Arachis hypogaea L) \n\n\n\ngenotypes. Journal of Agriculture and Veterinary Science, 7(4), 44-9. \n\n\n\nBangladesh Bureau of Statistics (BBS). 2016. Yearbook of Agricultural \n\n\n\nStatistics.  \n\n\n\nElahi, K.M., 1991. Impacts of riverbank erosion and flood in Bangladesh: \n\n\n\nAn introduction. Riverbank Erosion Impact Study. \n\n\n\nIslam, M.S., Hasan, T., Chowdhury, M.S.I.R., Rahaman, M.H., Tusher, T.R., \n\n\n\n2012. Coping Techniques of Local People to Flood and River Erosion in \n\n\n\nChar Areas of Bangladesh. J. Environ. Sci. & Natural Resources, 5 (2), 251 \n\n\n\n\u2013 261. Jahangirnagar University, Dhaka, p.11-67. \n\n\n\nKaba, J.S., Kumaga, F.K., Ofori, K., 2014. Effect of flower production  \n\n\n\nMalik, C.P., Setia, R.C., Setia, N., 1993. Role of plant growth regulators in \n\n\n\nincreasing crop productivity and the Indian Scenario: In Hormonal \n\n\n\nRegulation of Plant Growth and Development, Eds. IV, Purohit, S.S., \n\n\n\nAgro-Botanical Publishers, Bikaner, India, Pp. 161-167. \n\n\n\nMinistry of Water resources. 2004. Bangladesh Floods, Govt. of \n\n\n\nBangladesh. \n\n\n\nNickell, L.G., 1982. Plant Growth Regulators: Agricultural Uses. Springer-\n\n\n\nVerlag Berlin Heidelberg, New York. Pp. 173. \n\n\n\nPal, S., 2012. \"Jamalpur District\". In Sirajul Islam and Ahmed A. Jamal. \n\n\n\nBanglapedia: National Encyclopedia of Bangladesh (Second ed.). Asiatic \n\n\n\nSociety of Bangladesh. \n\n\n\nRahman, J., Talukder, A.H.M.M.R., Nahar, L., Rahman, M.M., Kayser, N., \n\n\n\n2015. International Journal of Applied Research, 1 (2), 79-82. \n\n\n\nRegional Research Report. 2018-19. Regional Agricultural Research \n\n\n\nStation, BARI, Jamalpur-2000. \n\n\n\nUNDP \u2013 FAO. 1988. Land resources appraisal of Bangladesh for agricultural \ndevelopment. Report to Agro-ecological regions of Bangladesh. UNDP- \nFAO, BGD/81/ 035 Technical Report 2, 570 pp.\n\n\n\n \n\n\n\n\nhttp://en.banglapedia.org/index.php?title=Jamalpur_District\n\n\nhttps://en.wikipedia.org/wiki/Asiatic_Society_of_Bangladesh\n\n\nhttps://en.wikipedia.org/wiki/Asiatic_Society_of_Bangladesh\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 38-44 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2023.38.44 \n\n\n\n \nCite The Article: Norah Chisha, Muchaiteyi Togo (2023). A Review of The National Agricultural Policy of Zambia and Its Alignment with Sustainable  \n\n\n\nAgricultural Practices: A Case Study of Kasisi Agricultural Training Centre. Malaysian Journal of Sustainable Agricultures, 7(1): 38-44. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.38.44 \n\n\n\n\n\n\n\nA REVIEW OF THE NATIONAL AGRICULTURAL POLICY OF ZAMBIA AND ITS \nALIGNMENT WITH SUSTAINABLE AGRICULTURAL PRACTICES: A CASE STUDY OF \nKASISI AGRICULTURAL TRAINING CENTRE \n\n\n\nNorah Chisha*, Muchaiteyi Togo \n\n\n\nDepartment of Environmental Sciences, University of South Africa, Pretoria- South Africa \n*Corresponding author email: 48922536@mylife.unisa.ac.za/mwanbanorth@gmail.com   \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 11 December 2022 \nRevised 17 January 2023 \nAccepted 27 February 2023 \nAvailable online 08 March 2023 \n\n\n\n Kasisi Agricultural Training Centre (KATC) in association with small-scale farmers, has a vital role in fostering \n\n\n\nsustainable organic practices for sustainable agricultural development. A study was conducted to establish \nthe alignment of sustainable agriculture practices at KATC, with that of the National agricultural policy and, \n\n\n\nhow sustainable practices are implemented by the training institution. The qualitative case study involved \n\n\n\nseventeen (17) participants who were purposively selected from the target population. Data was collected \nusing semi-structured interviews, observations, and a focus group discussion. A document review on SNAP\u2019s \n\n\n\nsustainable agricultural practices (SAPs) was conducted, and data were analysed using thematic content \n\n\n\nanalysis. The study showed that KATC\u2019s sustainable organic practices such as soil fertility management, soil \n\n\n\nand water conservation, and pest and disease management were aligned with policy objectives of, increasing \ncrop production, food security, and nutrition, promoting sustainable management and use of natural \n\n\n\nresources. An integrated approach is essential in implementing SAPs aligned with policy objectives, to enable \n\n\n\nthe effective adoption of SAPs by small-scale farmers and agricultural development. The study recommended \nspecific policies and legislation on sustainable agriculture to influence the implementation of sustainability \n\n\n\npractices. \n\n\n\nKEYWORDS \n\n\n\nSustainable Agricultural Practice, Small-Scale Farmers, Food Security, Climate Change, Policy. \n\n\n\n1.   INTRODUCTION \n\n\n\nClimate change and its associated negative impacts have been identified as \na major threat to global food security, economies, human health, and many \nother sectors of life (Engelbrecht et al., 2015). The situation has \naccelerated efforts to respond to climate change challenges, through the \ndevelopment of global treaties such as the United Nations Framework \nConvention on Climate change (UNFCCC), whose primary aim is to \nstabilise the greenhouse gas levels in the atmosphere to mitigate \ndangerous human-induced elements within the climate system (MNDP, \n2016). Lupele suggested that, where policies are concerned, climate \nchange education should be provided by governments and should entail \nempowering all stakeholders and major groups at local and international \nlevels (Lupele, 2020). \n\n\n\nThe agricultural sector in Africa, which is mainly dependent on rainfall, \nbears the most impacts from environmental variabilities. Zambia has not \nbeen spared from the negative consequences of climate change. The \nproductivity of crops, livestock, fisheries, and overall national food \nsecurity has been adversely affected (Ministry of Agriculture, 2016). The \ncountry identified four main sectors that are most vulnerable to climate \nchange: agriculture, water and energy, natural resources and human \nhealth (Shitima, 2015). The Zambian agricultural sector constitutes about \n67% of the workforce and contributes 20 % to the national gross domestic \nproduct (Moonga and Chileshe, 2019). It is also a source of livelihood for \nhalf of the country\u2019s population (Wineman and Crawford, 2017).  \n\n\n\nThe country\u2019s agricultural sector is guided by national agricultural policies \nwhich undergo periodic reviews, depending on prevailing conditions, for \ninstance, climatic conditions and/or the social and economic well-being of \nthe country (MA, 2016). The policies are implemented through established \nstructures for national resources management, agricultural production, \nfood and nutrition security, and institutional strengthening.  Despite these \nefforts, food security remains a priority to Zambia and the Southern \nAfrican Development Community (SADC). The agricultural sector still \nrequires urgent intervention in terms of raising awareness around \ngreening/sustainable ways of farming and practices by education and \ntraining institutions. Crush and Frayne noted that food insecurities in \nsouthern Africa are mainly caused by the inability to produce enough food \ndue to a lack of agricultural technical support, unfavourable \nenvironmental conditions, land pollution, unsustainable farming \npractices, and the inability to adapt to climate change (Crush and Frayne, \n2010). \n\n\n\nAgricultural training institutions play a significant role in executing \npolicies through initiatives based on identified priority sectors \n(agriculture, water, energy, natural resources, and health). The training \ncentres provide training and support to farmers. However, many farmers, \nespecially small-scale farmers, lack the appropriate skills, awareness, and \ntechnical knowledge to adapt to, and mitigate the impacts of climate \nchange. Adenle, Wedig and Azadi suggested that advanced technology \nadoption by small-scale farmers can be considered as an alternative \nstrategy to addressing food insecurities (Adenle et al., 2019). Sustainable \nagricultural practices could also assist in mitigating the negative impacts \n\n\n\n\nmailto:mwanbanorth@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 38-44 \n\n\n\n\n\n\n\n \nCite The Article: Norah Chisha, Muchaiteyi Togo (2023). A Review of The National Agricultural Policy of Zambia and Its Alignment with Sustainable  \n\n\n\nAgricultural Practices: A Case Study of Kasisi Agricultural Training Centre. Malaysian Journal of Sustainable Agricultures, 7(1): 38-44. \n\n\n\n\n\n\n\nfrom climate change and, at the same time, promote social-economic \ndevelopment in the country. Developing appropriate agricultural policies \nthat align with sustainable agricultural practices is crucial to effectively \naddress challenges identified within the agricultural sector. The current \nSecond National Agricultural Policy (SNAP) objectives address several \naspects, including food and nutrition security, natural resource use and \nmanagement, the mainstreaming of environment and climate, and the \nproduction of crops, livestock, and fisheries, which could be regarded as \ndirectly speaking to sustainability practices in agriculture.  \n\n\n\nAt an international level, the Intergovernmental Panel on Climate Change \n(IPCC) plays a vital role in shaping policies and influences the development \nof appropriate climate change adaptation strategies to address climate \nchallenges faced by member counties (IPCC, 2022). The Economic \nCommunity of West African States (ECOWAS), through the Economic \nCommunity of West African Agricultural Policy (ECOWAP), addresses \npriority areas based on sustainable agriculture, with an emphasis on the \nmanagement of water and soil in agriculture (ECOWAS Commission, \n2008). In this policy, irrigation and integrated water resources \nmanagement, integrated soil management, and the use of improved \ntechnologies are promoted. \n\n\n\nWhere sustainable agricultural policies are concerned, there are \ndifferences among Southern African countries. Some do not have \napproved policies and do not practice any sustainable agricultural \ninitiatives, some have the policies but are not plasticising the policy \nobjectives, and yet some are involved in sustainable agricultural practices \neven though they do not have policies in place. In countries like Malawi, \nBotswana, and Namibia, national agricultural policies do not clearly define \nissues around sustainable agricultural practices. As for South Africa, the \ncountry has no approved policy on sustainable agriculture but there are \nseveral drafted policy guidelines such as a white paper on sustainable \nagriculture and a policy on agriculture which indicate the intention \ntowards effecting sustainable agricultural development (Khwidzhili and \nWorth, 2017).  \n\n\n\nThe two South African policy documents are also closely related to the \npillars of sustainable agriculture (Khwidzhili and Worth, 2017).  The \n\n\n\nchallenge for South Africa, is aligning these policies to government \nprogrammes, and farmers\u2019 activities, so that practices in the sector can be \nimplemented without undermining the core values of sustainable \nagriculture. Like in the SNAP, the use of organic and chemical fertilizers \nand the use of improved seeds and hybrids are highlighted as means of \nincreasing productivity in agriculture. However, this begs the question of \nhow sustainable agricultural practices and the underlying principles are \nunderstood by policymakers. Ghana does not have a specific policy on \nsustainable agriculture but was found to have, in implementation, several \nsustainable agricultural practices, even though the rate of adoption of such \npractices is low (Agula et al., 2018). \n\n\n\nClimate change challenges associated with agricultural production and \nfood insecurities remain a primary concern for Africa, especially because \nmany people depend on agriculture. Literature revealed the non-\nalignment of national agricultural policies with agricultural practices \nwhere addressing climate change challenges in agricultural development \nis concerned (Mdee et al., 2021; Midgley, 2015). This paper is based on a \nbroader study that aimed at understanding sustainable agricultural \npractices and the role of policy in promoting sustainable agriculture in \nZambia. It draws insights from a case study of the Kasisi Agricultural \nTraining Centre. This paper analyses the objectives of the second \nagricultural policy and then reviews its alignment with sustainable \nagricultural practices. \n\n\n\n2.   RESEARCH DESIGN AND METHODS \n\n\n\nThe research was conducted at Kasisi Agricultural Training Centre \n(KATC). The training centre is in the Chongwe district, which is situated \n30km northeast of Lusaka, Zambia, as shown in figure 1. A qualitative case \nstudy approach was adopted based on social constructivism, which \nemphasises the subjectivity of interrelations between the researcher and \nthe participant and epistemologically assumes that reality could be a \nresult of a co-construction between the researcher, what is being \nresearched and individual experiences (Maguire and Delahunt, 2017; \nCreswell, 2013). Data collection was conducted from May to August 2021. \n\n\n\n\n\n\n\nFigure 1: Study area map, Kasisi Agricultural Training Centre- Chongwe, Zambia. \n\n\n\nInterviews (semi-structured interviews and key informant interviews), \nobservations, and a documentary review were used to collect data on \nsustainable agriculture and practices that are implemented by KATC and \ntheir alignment with the Second National Agricultural Policy. Participants \nfor the study comprised key informants from KATC staff (director/head of \ndepartments, extension workers), KATC stakeholders such as trainee \nfarmers, and individuals working in collaboration with the centre (KATC) \nin initiatives relevant to the study focus. The participants were \npurposively selected from the target population (KATC). The sample \nconsisted of twelve (12) participants for interviews and five (5) \nparticipants for key informant interviews. Table 1 is a summary of \nmethods, the respondents and the information collected each respondent \ngroup. Each interview took a duration of between thirty (30) to sixty (60) \nminutes.  \n\n\n\nThe English language was used as the main media of communication; \nhowever, the local languages such as Nyanja/Bemba (conversant with the \nresearcher and participant) were used in instances when certain words \ncould not be well expressed in the primary language (English). A digital \naudio recorder was used to record the proceedings of the interviews. In \naddition, field observations were conducted to record information on \nsustainable agricultural practices implemented or being practised at the \ntraining institution. Angrosino describes observation as an act of using \none\u2019s senses to note an observed phenomenon in the field setting by use \nof instruments and recording for scientific study (Angrosino, 2007). The \nmethod was employed to take note of practices in their physical settings \nat the institution, as well as to note activities by the KATC partners. \nMethodological triangulation helped to provide an in-depth \nunderstanding of sustainable agricultural practices at KATC.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 38-44 \n\n\n\n\n\n\n\n \nCite The Article: Norah Chisha, Muchaiteyi Togo (2023). A Review of The National Agricultural Policy of Zambia and Its Alignment with Sustainable  \n\n\n\nAgricultural Practices: A Case Study of Kasisi Agricultural Training Centre. Malaysian Journal of Sustainable Agricultures, 7(1): 38-44. \n\n\n\n\n\n\n\nTable 1: A Summary of The Employed Research Methods. \n\n\n\nMethod Participants \nNumber of \n\n\n\nrespondents \nSolicited information Data analysis method \n\n\n\nSemi-structured \ninterviews \n\n\n\nKATC staff 12 \n\n\n\nEstablishing sustainable agricultural \npractices at KATC and the alignment \nof KATC practices with the National \n\n\n\npolicy on sustainable agriculture. Thematic content analysis \nof interview data \n\n\n\nKey informant \ninterviews \n\n\n\nKey informants \u2013 KATC Director, \nhead of departments, extension \n\n\n\nworkers, KATC stakeholders \n5 \n\n\n\nEstablishing the role of KATC in \nsustainable agriculture \n\n\n\nMethod Documents \nNumber of \ndocuments \n\n\n\nSolicited information Data analysis method \n\n\n\nDocument review Second National Agricultural Policy 1 \nEstablishing policy objectives and \n\n\n\nmajor sustainable agricultural \nthemes in the policies \n\n\n\nThematic content analysis \nof policy documents \n\n\n\nMethod Purpose Solicited information Data analysis method \n\n\n\nObservations \nTo take note of and photograph visible sustainable \n\n\n\nagricultural practices \nAny noticeable sustainability \n\n\n\npractices \n\n\n\nThematic content analysis \nof filed notes and \n\n\n\nphotographs \n\n\n\nThe Second National Agricultural Policy document was reviewed to \nestablish sustainable agriculture themes. Four main themes emerged from \nthe policy document that is, agricultural production, food and nutrition \nsecurity, sustainable management and use of natural resources, and \nclimate change adaptation and mitigation. The data recordings obtained \nfrom the interviews and group discussions were transcribed by the \nresearcher to create a narrative. Data collection notes were pre-analysed \nby reviewing what transpired from each discussion at the end of the day, \n\n\n\nidentifying what went well and what did not go well, and identifying new \nideas which could have surfaced during the day. Transcribed data were \nsorted based on thematic content analysis using a deductive approach. The \ntranscript was carefully read and the data was sorted into relevant \ncategories and themes, by copying and pasting into a word file using \nMicrosoft Word (Anderson, 2007; Maguire and Delahunt, 2017). Data \naligning with the four main themes established from the policy document \nwere also organised accordingly for the purpose of this paper.  \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1   SNAP Policy Objectives and Sustainable Agricultural Practices at KATC \n\n\n\nTable 2: National Agricultural Policy Objectives and Sustainable Agricultural Practices Implemented by KATC \n\n\n\nPolicy Objectives on \nSustainability Practices \n\n\n\nMeasures in Policy Sustainable practices at KATC \n\n\n\nAgricultural Production \n\n\n\n\n\n\n\n\u2022 Promote the efficient use of available water \nresources \n\n\n\n\u2022 Encourage effective use of agrochemicals and \nfertilisers \n\n\n\n\u2022 Improve the use of crop varieties and certified seeds \n\n\n\n\u2022 Establish livestock breeding centres and improve \npastures \n\n\n\n\u2022 Encourage the production of high-value irrigable \ncrops \n\n\n\n\u2022 Promote the conservation of fodder \n\n\n\n\u2022 Promote production of farmed-fish species \n\n\n\n\u2022 Implements soil fertility management practices: \nproduction of on-farm fertiliser (compost/ bio-\nfertilizer), intercropping, crop rotation, planting of \ncover crops \n\n\n\n\u2022 Use of biological control measures in diseases and \npest management. \n\n\n\n\u2022 Promotes and implement soil and water \nconservation practices such as minimum tillage, \nmulching, use of conservation basins, erosion control \n\n\n\n\u2022 Use of farmer-saved seeds/indigenous species \n\n\n\nFood and Nutrition \nSecurity \n\n\n\n\n\n\n\n\u2022 Promote diversified agricultural production \n\n\n\n\u2022 Promote accessibility to bio-fortified seeds/ vines \n\n\n\n\u2022 Encourage on-farm agro-processing \n\n\n\nPromote Value addition, preservation, on-farm \nstorage \n\n\n\n\u2022 Encourage cultivation/consumption of indigenous \ncrop varieties \n\n\n\n\u2022 Provide nutrition education \n\n\n\n\u2022 Promote crop diversification \n\n\n\n\u2022 Use of indigenous crop varieties \n\n\n\n\u2022 Promote the production of food organic crops \n\n\n\n\u2022 Value-adding to organic products \n\n\n\n\u2022 Establish community seed banks \n\n\n\n\u2022 Provide training and community education on \nsustainable agriculture \n\n\n\n\n\n\n\nSustainable \nManagement and Use of \n\n\n\nNatural Resources \n\n\n\n\n\n\n\n\u2022 Promote sustainable land management technologies \n\n\n\n\u2022 Promote agroforestry \n\n\n\n\u2022 Foster utilisation of renewable energy resources \n\n\n\n\u2022 Develop water harvesting and storage infrastructure \n\n\n\n\u2022 Promote Integrated agriculture \n\n\n\n\u2022 Promote sustainable use of farm-based resources \n\n\n\n\u2022 Promotes agroforestry. \n\n\n\n\u2022 Establish tree/agroforestry nurseries \n\n\n\n\u2022 Practice beekeeping \n\n\n\n\u2022 Establish woodlot \n\n\n\n\u2022 Advance soil-water conservation activities \n\n\n\nClimate Change \nAdaption and Mitigation \n\n\n\n\u2022 Promote resilient agricultural methods \n\n\n\n\u2022 Develop integrated plans/programs on climate \nchange adaptation measures \n\n\n\n\u2022 Conducts climate change risk assessments \n\n\n\n\u2022 Promote Indigenous plant/crop species \n\n\n\n\u2022 Promote early maturing plant varieties \n\n\n\n\u2022 Conduct soil and water conservation practices: Basin \nplanting, minimum tillage, mulching \n\n\n\n\u2022 Conduct weather observations/monitoring and \ndevelop plans for the farming season \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 38-44 \n\n\n\n\n\n\n\n \nCite The Article: Norah Chisha, Muchaiteyi Togo (2023). A Review of The National Agricultural Policy of Zambia and Its Alignment with Sustainable  \n\n\n\nAgricultural Practices: A Case Study of Kasisi Agricultural Training Centre. Malaysian Journal of Sustainable Agricultures, 7(1): 38-44. \n\n\n\n\n\n\n\nThe SNAP\u2019s primary objectives are to speed up the reduction of food and \nnutrition insecurity, reduce poverty and increase employment in the \nagriculture sector as well as increase the growth of the sector. The policy \nis facilitated, monitored, regulated, and evaluated by the Ministry of \nAgriculture and Ministry of Fisheries and Livestock. The implementation \nof the policy is strongly dependent on partnerships with the farming \ncommunities and other stakeholders. Four policy areas were identified as \nthe areas that speak to sustainable agricultural practices. These are \nagricultural production, food and nutrition security, natural resources use \nand management, and climate change. Table 2 outlines these four areas. It \nalso states the measures identified in the policy under each of the areas. \nBased on thematic analysis, the last column identifies the sustainability \npractices at KATC which align with each of the four policy areas. \n\n\n\n3.1.1   Agricultural Production \n\n\n\nThis policy objective describes production and productivity where crops, \nlivestock, and fisheries are concerned. To increase productivity, measures \noutlined in Table 2 were recommended for implementation by the policy. \nThe measures include efficient use of available water resources, use of \nhigh-value irrigable crops, improved pastures, conservation of fodder in \nanimal production, and increased production of farmed-fish species. \nHowever, practices such as the use of fertilisers and agrochemicals raise \nquestions about the subsequent impacts of such measures on the crops, \nsoils, and biodiversity in farmlands (Savci, 2012). Further, the SNAP \nencourages promoting conservation agriculture by incorporating the use \nof chemicals (fertilisers) in crop and animal production is contradictory to \nthe principles of sustainable agriculture. A practical example on the \nground is the implementation of the Farmer Input Support Programme \n(FISP) by the department of agriculture. Inputs (seeds and chemical \nfertilisers) are handed out to small farmers every farming season.  \n\n\n\nThe programme may be viewed by others as the government\u2019s effort in \nassisting small-scale farmers that are unable to afford the purchase of \ninputs for crop production. A review by Mason, Jayne and Mofya-mukuka \nof Zambia\u2019s agricultural input subsidy program, showed that the use of \nfertiliser increases soil acidity and that 98% of small-scale farmers\u2019 maize \nis growing on acidic soils of a PH of less than 5.5 (Mason et al., 2013). This \ncompromises the growth and performance of crops and organisms which \nare found in such soil environments.  Further, some researchers noted that \nby promoting agricultural productivity using inorganic fertilisers, the FISP \ncontradicts the core values of sustainable crop production for \nenvironmental sustainability (Mason et al., 2013). This research revealed \nthat 12 142 out of the 26 000 farmers under the department of agriculture \nin the Rufunsa district in the year 2021, were on the FISP program but only \n1000 were using sustainable farming practices (in partnership with \nKATC). This shows that the SNAP has not been very effective in terms of \ninfluencing agricultural production where ecosystem protection and \nenvironmental sustainability are concerned. \n\n\n\n3.1.2   Sustainable Management of Natural Resources \n\n\n\n\n\n\n\nFigure 2: A beehive at a farmer\u2019s apiary in Nkondola village in Chongwe \ndistrict. \n\n\n\n\n\n\n\nFigure 3: A KATC linked farmer\u2019s agroforestry nursery in Chongwe. \n\n\n\nTo ensure the sustainable use and management of natural resources, the \nSNAP outlines measures such as the use of sustainable land management \ntechnologies which include conservation agriculture, agroforestry, the use \nof renewable energy resources, and the development of an integrated \nagricultural approach. Other measures for ensuring the sustainable use of \nwater resources mentioned in the policy are water conservation and the \nestablishment of water infrastructure. KATC has significantly \nimplemented initiatives and practices to respond to these policy \nobjectives. These practices include among others; the use of farm-based \nresources in land production, the establishment of tree nurseries and \nwoodlots for reforestation, fruit, medicinal and energy source, beekeeping, \nand agroforestry (Kralik et al., 2020; Campanhola and Pandey, 2019). \nThese were also noted during observations. Figure 2 shows a beehive in a \nfarmer\u2019s apiary at Nkondola village in Chongwe district while figure 3 \nshows an agroforestry nursery in Chongwe district. Other authors also \nnoted that sustainable practices such as the management of water \nresources and conservation of forests, and reafforestation of catchment \nareas are prioritised in the sustainable management of resources (Fon et \nal., 2020; Nkiakia and Lovett, 2018). In agreement, Adnele et al. (2019) \nalso argued that the adoption of such technologies was crucial for the \nadvancement of environmental sustainability.  \n\n\n\n3.1.3   Food and Nutrition Security \n\n\n\nThe SNAP of 2016 noted that national production for most crops in Zambia \nincreased in the years around the 2000s while average yields changed \nsignificantly due to changes in weather conditions. Diversified agricultural \nproduction, consumption of indigenous crop varieties, and nutrition \neducation were identified in the SNAP as measures to increase food \nproduction, reduce food insecurity, and enhance nutrition among farmers \nand communities. Other ways of enhancing food and nutrition security \nwere identified in the policy as access to biofortified seeds/vines, \npromotion of on-farm agro-processing, value-adding, preservation and \nestablishment of the on-farm storage systems, and cultivation of \nindigenous crop varieties. In practice, KATC and its farmers\u2019 accent to crop \ndiversification enhances food sustainability, because a variety of crops \nrespond differently to environmental variabilities. Diversification enables \nthe farmer to produce and contribute to the household food basket despite \nenvironmental variabilities (Nkomoki et al., 2018). Figure 4. Shows a \nvariety of harvested crops that are grown organically by a group of \nfarmers Kansonkomona Village in Chongwe. \n\n\n\n\n\n\n\nFigure 4: Crops that were grown organically by a group of farmers, \nKansonkomona Village in Chongwe. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 38-44 \n\n\n\n\n\n\n\n \nCite The Article: Norah Chisha, Muchaiteyi Togo (2023). A Review of The National Agricultural Policy of Zambia and Its Alignment with Sustainable  \n\n\n\nAgricultural Practices: A Case Study of Kasisi Agricultural Training Centre. Malaysian Journal of Sustainable Agricultures, 7(1): 38-44. \n\n\n\n\n\n\n\nOther practices such as intercropping and crop rotation contribute to food \nsecurity among farmers (Kinkese, 2017; Zulu et al., 2017). Value adding \nbased on sustainable processing methods, packaging, and storage \nprovides a longer storage lifespan for harvested crops and preserves the \nnutritional value of the crops/products. KATC, through its production and \nmarketing division, processes, packages, and sells its organic crops such \nas wheat and wheat products. For example, wheat is processed into oats \nand flour to generate income. The KATC-linked farmers established \ncommunity seed banks, where harvested seeds are selected, sorted out for \nquality control, and packaged. They are clearly labeled, stored in an \nidentified secured facility and later shared for re-planting amongst \nfarmers and communities. Figure 5 shows a seed storage system for a \ngroup of farmers in Kansonkomona Village in Chongwe. \n\n\n\n\n\n\n\nFigure 5: Seed storage system or seed bank for farmers in \nKansonkomona Village in Chongwe \n\n\n\nThe policy also emphasises accessibility to bio-fortified seeds to produce \nnutrient-enhanced varieties (NAP, 2016). Despite the policy recognising \nthe existence of indigenous (local) and hybrid seeds, the issue of seed \nownership has recently become contentious in the agricultural sector. The \nPlant Breeders Rights Act of 2007 does not make provisions for farmers\u2019 \nsaved seed varieties, therefore small-scale farmers who are custodians of \nindigenous seeds are prone to market exploitation and are not protected \nby SNAP and the law in this regard (Mwila, 2016). However, KATC makes \na significant effort; beyond what is in the policy to sensitive, educate and \npractically involve local small-scale farmers in promoting crop \ndiversification and local seed preservation for the enhancement of food \nand nutrition security for farmers and for future generations (figure 5). \n\n\n\n3.1.4   Climate Change \n\n\n\nThe National Agricultural Policy (2016, pp vi) described climate change as \n\u2018significant changes in weather patterns or change in the distribution of \nweather parameters around the average condition over specified time\u2019. \nThe SNAP aims at promoting sustainable production methods that are \nresilient to climate change and raising awareness of adaptation and \nmitigation measures that should be incorporated into government \nprogrammes. The SNAP also makes provision for integrating environment \nand climate change in the agricultural sector by strengthening production \nmethods that are resilient, and adaptative to climate change, and \nconducting climate risk assessments. On the ground, KATC and its farmers \npromote sustainable organic agricultural systems which enhance \nproductivity and are not harmful to the environment.  \n\n\n\nThese agricultural methods promote the use of compost, green manure, \nbio-fertilisers and natural pesticides and forbid the use of chemical \nfertilisers and pesticides (Becker et al., 1995). Other practices include soil \nand water conservation management methods such as the use of planting \nbasins, minimum tillage (Pang et al., 2020), mulching, zero burning in \nagricultural fields, use of indigenous plant/crop species, and growing early \nmaturing plant varieties (Barreiro and D\u00edaz-Ravina, 2021). A group \nresearchers\u2019 recent study on the impact of SAPs on production among \nsmall-scale farmers in Cameron revealed that long crop rotational \nschemes are beneficial to agricultural production (Fon et al., 2020).   \n\n\n\nAs highlighted earlier in this discussion, crop diversification, especially the \nuse of indigenous seeds locally known as orphan seeds or Gankata and \ndisease-resistant crop varieties, is encouraged by KATC (see figure 4). \n\n\n\nThese methods are used as part of climate adaptation responses in \nagricultural crop production. KATC and its farming community conduct \ntheir own weather pattern observations and monitoring and obtain data \non annual weather predictions from a local meteorological centre, which \nhelps them in making informed decisions on what crops to grow in a \nparticular season. This is like the situation with ECOWAP where aspects of \nnatural disaster management and food crisis prevention are emphasised \nthrough the development of strategies for early warning and crisis \nmanagement systems and mitigation (ECOWAS Commission, 2020).  \n\n\n\nHowever, this differs from the situation in Kenya where there are \ninadequate early warning systems and increased agricultural practices \nthat conflict with environmental sustainability practices within the food \nand agricultural policy framework (Alila and Atieno, 2006). Farming \npractices that are destructive to the environment are said to be \ncontributors to environmental degradation and food insecurities in the \ncountry (Alila and Atieno, 2006). The coverage of sustainable agricultural \npractices in the NAP is limited. Some of the practices it suggests are not \nwell-aligned with sustainability practices, especially on chemical fertiliser \nand pesticide use and the promotion of hybrids. KATC has more SAPs than \nthose covered by the policy. This could have been influenced by factors \nsuch as the institutional objectives and principles on which the institution \nwas founded, and other local and international policy imperatives. The \nnext section discusses some of the factors that could have influenced the \nalignment of KATC\u2019s work with SAPs.  \n\n\n\n3.2   Other Factors Influencing KATC Work \n\n\n\nOther than the SNAP, several other factors influence the work of KATC. The \ninstitution was established by a religious organisation (Society for Jesuits) \nbased on the view that it was God\u2019s instruction to humans to be \n\u2018caretakers\u2019 of the environment, not destroyers. This underlying principle \ninfluenced its adoption of sustainable organic and environmentally \nfriendly practices. At the social-economic level, health concerns explain \nwhy KATC took responsibility to produce and provide healthy food to \nsociety. Through its marketing and sales department, the institution \npromotes crop diversification to enhance income security within the \ninstitution as well as among the households of KATC-linked small-scale \nfarmers. At a policy level, national policies such as the Zambian 7th National \nDevelopment Plan recognises organic sustainable agriculture as a niche \nthat could increase the country\u2019s income through organic crop exports \n(MNDP, 2017).  \n\n\n\nKATC is one of the main advocates against the use of GMOs by farmers in \nthe country and works in collaboration with the University of Zambia to \nfind ways of empowering rural people where issues of land ownership are \nconcerned. A group researcher in their study on the adoption of \nsustainable agricultural practices, food securities and land tenure noted \nthat land tenure insecurities could hinder the adoption of sustainable \nagricultural practices for small-scale farmers who are mainly on \ncustomary land (Nkomaki et al., 2016). Some of the policies that influence \nKATC\u2019s work include the Child protection policy (safeguarding the rights \nof children against abuse, during the implementation of its programmes); \nthe Gender policy (women\u2019s representation in promoting sustainable \nagriculture); the National policy on climate change of 2016 and the \nNational Adaptation Plan of Action (NAPA).  \n\n\n\nInternational policies or regional agreements which KATC\u2019s practices align \nwith, and which may have influenced its work include the Sustainable \nDevelopment Goals (SDGs), the United Nations Framework Convention on \nClimate Change (UNFCCC), the IPCC guideline on Agriculture, Forestry and \nOther Land Use which has a focus on the restoration of organic soils, the \nComprehensive Africa Agriculture Development Programme (CAADP) for \nthe Framework for African Food Security (FAFS), and the Alliance for a \nGreen Revolution in Africa (AGRA) (IPCC, 2014; Lokosang et al., 2016). \nKATC\u2019s sustainable agricultural practices also relate to imperatives in the \nAgenda 2063 on modern Agriculture for increased productivity and \nproduction, environmentally sustainable and climate resilient economies \nand communities, the AU Agriculture and Food Security on initiatives such \nas Africa Seed and Biotechnology Programme (AFSB) and the Ecological \nOrganic Agriculture Initiative (EOAI) and the African regional nutritional \nstrategy 2016 \u2013 2025\u2019s agenda. \n\n\n\n4. CONCLUSION \n\n\n\nThe study\u2019s main objective was to review the National Agricultural Policy \nfor Zambia and establish alignment with sustainable agricultural \npractices, using case study insights from KATC. The study showed that \nKATC and its associated farmers implemented sustainable practices, such \nas the production of organic manures and fertilisers from locally based \nmaterials, which are used for soil fertility management. Soil, and water \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 38-44 \n\n\n\n\n\n\n\n \nCite The Article: Norah Chisha, Muchaiteyi Togo (2023). A Review of The National Agricultural Policy of Zambia and Its Alignment with Sustainable  \n\n\n\nAgricultural Practices: A Case Study of Kasisi Agricultural Training Centre. Malaysian Journal of Sustainable Agricultures, 7(1): 38-44. \n\n\n\n\n\n\n\nconservation practices included minimum tillage, mulching, and \nconservation basins. Farmer-saved seeds are used in crop diversification \nfor crop production. The study also showed that these sustainability \npractices are aligned with the SNAP policy objectives of crop production, \nfood security, sustainable use and management of natural resources and \nclimate change adaptation and mitigation.  \n\n\n\nHowever, the policy recommendations on promoting the use of inorganic \nchemicals for crop and animal production, and the use of hybrid seed \nvarieties and species do not align with the values of sustainable organic \nagriculture. This study, therefore, recommends policy reforms to \nrecognise local and indigenous seeds produced and preserved by small \nfarmers to help attain the full potential for food and agricultural \ndiversification; sensitisation of the public and policymakers on the \nimportance of sustainable organic agricultural practices development in \nthe country; further research into sector-specific responses to challenges \nrelating to climate change and linking such studies with sustainable \nagricultural productivity; the formulation of legislation to address \nsustainable organic agricultural practices, and the integration of such into \nthe national agricultural policy. \n\n\n\nCONFLICT OF INTEREST  \n\n\n\nThe authors declare that there are no known conflicting interests that \ncould influence the outcome of this study.  \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThank you to Kasisi Agricultural Training Centre, its partners (small-scale \nfarmers and NGOs) in Chongwe and Rufunsa Districts, the Jesuits society, \nthe Ministry of Agriculture and Livestock, for their participation in the \nstudy, and the academic supervisor for the support. This study was \nfinanced by the University of South Africa- Postgraduate bursaries \nthrough the College of Agriculture and Environmental Science (CAES). \n\n\n\nREFERENCES \n\n\n\nAdenle, A.A., Wedig, K., Azadi, K.W., 2019. Sustainable agriculture and food \nsecurity in Africa: The role of innovative technologies and \ninternational organizations. Technology in Society, 58, \nPp. 101143. https://doi.org/10.1016/j.techsoc.2019.05.007. \n\n\n\nAgula, C., Akudugu, M.A., Dittoh, S., Mabe, F.N., 2018. Promoting \nsustainable agriculture in Africa through ecosystem-based farm \nmanagement practices: evidence from Ghana. Agriculture & \nFood Security, 7 (1), Pp. 1-11. https://doi.org/10.1186/s40066-\n018-0157-5 \n\n\n\nAlila, P.O., Atieno, R., 2006. Agricultural policy in Kenya: Issues and \nprocesses. Nairobi: Institute of Development Studies. \n\n\n\nAnderson, R., 2007. Thematic Content Analysis (TCA) 1 Descriptive \nPresentation of Qualitative Data Using Microsoft Word.  \n\n\n\nAngrosino, M.V., 2007. Doing ethnographic and observational research. \nThousand Oaks, CA: Sage. \n\n\n\nBarreiro, A., D\u00edaz-Ravina M., 2021. Fire impacts on soil microorganisms: \nMass, activity, and diversity. Current Opinion in Environmental \nScience and Health, 22, Pp. 100264. \nhttps://doi.org/10.1016/j.coesh.2021.100264. \n\n\n\nBecker, M., Ladha, J.K., Ali, M., 1995. Green manure technology: Potential, \nusage, and limitations. A case study for lowland rice. In: Ladha, \nJ.K., Peoples, M.B. (eds) Management of Biological Nitrogen \nFixation for the Development of More Productive and \nSustainable Agricultural Systems. Developments in Plant and Soil \nSciences, 174, Pp. 181-194. https://doi.org/10.1007/978-94-\n011-0053-3_8. \n\n\n\nCampanhola, C., Pandey, S., 2019. Chapter 24 \u2013 Agroforestry: Sustainable \nFood and Agriculture. Academic Press, Pp. 237-240. \nhttps://doi.org/10.1016/B978-0-12-812134-4.00024-8. \n\n\n\nCreswell, J.W., 2012. Educational research: Planning, conducting, and \nevaluating quantitative and qualitative research (4th ed.). Upper \nSaddle River, NJ: Pearson Education.  \n\n\n\nCrush, J., Frayne, B., 2010. The Invisible Crisis:  Urban Food Security in \nSouthern Africa. Urban food security series 1. Urban press. Cape \nTown. \n\n\n\nECOWAS Commission, 2008. Regional Agricultural Policy for West Africa: \nECOWAP.  Paris conference on the Regional Agricultural Policy \nfor West Africa, 9th December 2008.  Corlet Imprimeur, Cond\u00e9-\nsur Noireau. \n\n\n\nEngelbrecht, F.J., Adegoke, M.J., Bopape, Naidoo M., Thatcher, G.M., \nMcGregor, J., Katzfey, M., Werner, M., Ichoku., Gatebe, C., 2015. \nProjections of rapidly rising surface temperatures over Africa \nunder low mitigation. Environmental Research Letters, 10 (8), \nPp. 085004. \n\n\n\nFon, D.E., Marthe, T., Chotangui, A.H., 2020. Impact of sustainable \nagricultural practices on farmers\u2019 production cost in the Noun \nDivision, West Region of Cameroon: policy \nrecommendation. International Journal of Agricultural Policy \nand Research, 8, Pp. 48-5. \n\n\n\nIPCC. 2014. Summary for Policymakers. In: Climate Change 2014: \nMitigation of Climate Change. Contribution of Working Group III \nto the Fifth Assessment Report of the Intergovernmental Panel \non Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, \nE. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, \nP. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von \nStechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University \nPress, Cambridge, United Kingdom and New York, NY, USA. \n\n\n\nIPCC. 2022. Climate Change 2022. Impacts, Adaptation, and Vulnerability. \nSummary for Policymakers. IPCC WGII Sixth Assessment Report.  \n\n\n\nKhwidzhili, R.H., Worth, S.H., 2017. Evaluation of Policies Promoting \nSustainable Agriculture in South Africa. South African Journal of \nAgricultural Extension. 2, Pp.  73 \u2013 85.  \n\n\n\nKinkese, T., 2017. Climate change impacts and farmers' responses in \nChilanga District, Zambia, Master's thesis, University of Cape \nTown. \n\n\n\nKralik, T., Vavrova, K., Knapek, J., Weger, J., 2022. Agroforestry systems as \nnew strategy for bioenergy \u2014 Case example of Czech Republic. \nEnergy Reports, 8, Pp. 519-525. \nhttp://dx.doi.org/10.17159/2413-3221/2017/v45n2a443. \n\n\n\nLokosang, L., Osei, A., Covic, N., 2016. The African Union policy \nenvironment toward enabling action for nutrition in \nAfrica. Achieving A Nutrition Revolution for Africa: The Road to \nHealthier Diets and Optimal Nutrition. Annual Trends and \nOutlook Report, Pp. 5-11. \n\n\n\nLupele, J., 2020. National Climate Change Learning Strategy: Background \nReport. Ministry of Lands and National Resources. Zambia. \n\n\n\nMaguire, M., Delahunt, B., 2017. Doing a Thematic Analysis: A Practical, \nStep-by-Step Guide for Learning and Teaching Scholars. All \nIreland Journal of Teaching and Learning in Higher Education \n(AISHE-J). 8(3), Pp. 335.  http://ojs.aishe.org/index.php/aishe-\nj/article/view/335. \n\n\n\nMason, N.M., Jayne, T.D., Mofya-mukuka R., 2013. A Review of Zambia\u2019s \nAgricultural Input Subsidy Programs: Targeting, Impacts, and the \nway forward. Working Paper No. 77. Indaba Agricultural Policy \nResearch Institute (IAPRI). Lusaka, Zambia.  \nhttp://www.iapri.org.zm. \n\n\n\nMdee, A., Ofori, A., Chasukwa, M., Manda, S., 2021. Neither sustainable nor \ninclusive: a political economy of agricultural policy and \nlivelihoods in Malawi, Tanzania, and Zambia. The Journal of \nPeasant Studies, 48 (6), Pp. 1260-1283. DOI: \n10.1080/03066150.2019.1708724. \n\n\n\nMidgley, S., Drimie, S., von Loeper, W.J., Mudavanhu, S., Blignaut, J., Smith, \nH., 2015. Implications for a Future Agrarian Structure in South \nAfrica based on Conservation and Sustainable Agriculture: \nAlignment of a Multi-institutional and Multi-policy \nLandscape. Pretoria. Green Fund, Pp. 1-21. \n\n\n\nMinistry of Agriculture., 2016. The National Agricultural Extension & \nAdvisory Services Strategy (Naess). Government Republic of \nZambia.  \nhttps://www.agextonline.com/uploads/3/2/4/3/3243215/fin\nal_validated_naess.pdf. [Accessed; March 2021].  \n\n\n\n\nhttps://doi.org/10.1016/j.techsoc.2019.05.007\n\n\nhttps://doi.org/10.1186/s40066-018-0157-5\n\n\nhttps://doi.org/10.1186/s40066-018-0157-5\n\n\nhttps://doi.org/10.1016/j.coesh.2021.100264\n\n\nhttps://doi.org/10.1007/978-94-011-0053-3_8\n\n\nhttps://doi.org/10.1007/978-94-011-0053-3_8\n\n\nhttps://doi.org/10.1016/B978-0-12-812134-4.00024-8\n\n\nhttp://dx.doi.org/10.17159/2413-3221/2017/v45n2a443\n\n\nhttp://ojs.aishe.org/index.php/aishe-j/article/view/335\n\n\nhttp://ojs.aishe.org/index.php/aishe-j/article/view/335\n\n\nhttp://www.iapri.org.zm/\n\n\nhttps://www.agextonline.com/uploads/3/2/4/3/3243215/final_validated_naess.pdf.%20%5bAccessed\n\n\nhttps://www.agextonline.com/uploads/3/2/4/3/3243215/final_validated_naess.pdf.%20%5bAccessed\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 38-44 \n\n\n\n\n\n\n\n \nCite The Article: Norah Chisha, Muchaiteyi Togo (2023). A Review of The National Agricultural Policy of Zambia and Its Alignment with Sustainable  \n\n\n\nAgricultural Practices: A Case Study of Kasisi Agricultural Training Centre. Malaysian Journal of Sustainable Agricultures, 7(1): 38-44. \n\n\n\n\n\n\n\nMinistry of National Development Planning, 2017. Seventh National \nDevelopment Plan 2017-2021. Lusaka, Zambia. \n\n\n\nMinistry of National Planning and Development., 2016. National Policy on \nClimate Change. Republic of Zambia. \nhttp://www.lse.ac.uk/GranthamInstitute/wp-\ncontent/uploads/laws/8142.pdf. (Accessed; 30 March 2020). \n\n\n\nMoonga, M.S., Chileshe, B., 2019. Zambia\u2019s transition to a green \neconomy. Multidisciplinary Journal of Language and Social \nSciences Education (2664-083X, Online ISSN: Print ISSN: 2616-\n4736), 2 (1), Pp. 96-122. \n\n\n\nMwila, G., 2016. Commentary on the Zambian Plant Breeder\u2019s Rights Act. \nIn Farmers' Crop Varieties and Farmers' Rights. Routledge. Pp. \n333-337. \n\n\n\nNkiaka, E., Lovett, J.C., 2018. Mainstreaming climate adaptation into \nsectoral policies in Central Africa: Insights from Cameroun. \nEnvironmental Science and Policy, 89, Pp. 49\u201358. \n\n\n\nNkomoki, W., Bavorov\u00e1, M., Banouta, J., 2018. Adoption of sustainable \nagricultural practices and food security threats: Effects of land \ntenure in Zambia. Land Use Policy, 78, Pp. 532\u2013538. \n\n\n\nPang, J., Liu, X., Huang, Q., 2020. A new quality evaluation system of soil \nand water conservation for sustainable agricultural \ndevelopment. Agricultural water management, 240, Pp. 106235. \n\n\n\nSavci, S., 2012. Investigation of effect of chemical fertilizers on \nenvironment. Apcbee Procedia, 1, Pp. 287-292. \n\n\n\nShitima, E., 2015. Climate Change Policy Implementation for Zambia \nClimate Vulnerable Forum - Regional Workshop for Africa, \nUnited Nations Conference Centre, Addis Ababa, Ethiopia, 7 - 8 \nMay 2015. https://www.thecvf.org/wp-\ncontent/uploads/2015/07/Zambia.pdf. (Accessed; 30 March \n2021). \n\n\n\nWineman, A., Crawford, E.W., 2017. Climate change and crop choice in \nZambia: A mathematical programming approach. NJAS-\nWageningen Journal of Life Sciences, 81, Pp. 19-31. \n\n\n\nZulu, D., Phiri, D., Chisha\u2013Kasumu, E., 2016. Sustainable Agricultural \nSystems in Zambia: A Case of Conservation Agriculture with \nTrees, in: Ng\u2019andwe, P., Saram\u00e4ki, K., Simfukwe, P., Chisha-\nKasumu, E., Korhonen, K. (Eds.) Sustainable Agriculture and \nLivelihood. Sustainable Livelihoods in the Green Economy, \nKarelia University of Applied Sciences Publications B, Article \ncollections, 45, Pp. 41-55. \n\n\n\n \n\n\n\n\nhttp://www.lse.ac.uk/GranthamInstitute/wp-content/uploads/laws/8142.pdf\n\n\nhttp://www.lse.ac.uk/GranthamInstitute/wp-content/uploads/laws/8142.pdf\n\n\nhttps://www.thecvf.org/wp-content/uploads/2015/07/Zambia.pdf.%20(Accessed\n\n\nhttps://www.thecvf.org/wp-content/uploads/2015/07/Zambia.pdf.%20(Accessed\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 04-07 \n\n\n\n\n\n\n\nCite this article Dr. Md. Kumruzzaman a,  Anirban Sarker b  Water requirements for various crops and impact of irrigation in barind area  Malaysian Journal of \n\n\n\nSustainable Agriculture (MJSA) 1(1) (2017) 04-07 \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 August 2016 \n\n\n\nAccepted 12 December 2016 \n\n\n\nAvailable online 20 January 2017 \n\n\n\nKeywords: \n\n\n\nConsumptive use, consumptive \n\n\n\nirrigation requirement, Blaney-\n\n\n\nCriddle Method.  \n\n\n\nABSTRACT\n\n\n\nIn this paper successive depletion of groundwater level with expansion of ground water irrigation in Barind Tract \n\n\n\nhas been discussed from 1985 to 2015. Rajshahi is a city in western Bangladesh. It is located in the north-west part \n\n\n\nof the country and situated on the northern banks of the river Padma. After starting of groundwater irrigation in \n\n\n\nBangladesh, it spread rapidly all over the country, and about 80% of agricultural land is now supplied irrigation \n\n\n\nfrom groundwater. This study was conducted to estimate the Consumptive use and Crop Irrigation Requirement for \n\n\n\nvarious crops like Amon(rice), Boro(rice), Wheat and Potato. Blaney-Criddle Method was used in this study. In this \n\n\n\nstudy, data was collected from the zonal office of Barind Multipurpose Development Authority (BMDA), Rajshahi \n\n\n\nand Bangladesh Meteorological Department, Meteorological complex, Green Road, Dhaka. From the data analysis, \n\n\n\nthe maximum value of consumptive use is 27.88 cm in the month of April, 1989 and minimum value of consumptive \n\n\n\nuse is 2.02 cm in the month of February, 2011. The maximum value of Crop Irrigation Requirement is 21.66 cm in \n\n\n\n1987.  \n\n\n\n1. INTRODUCTION \n\n\n\nIn order to achieve sustainable agricultural growth and to maintain \necological balance under the Barind Multipurpose Development \nAuthority (BMDA) was launched during late eighties century Chapai \nNawabgang, Noagan and Rajshahi districts which includes 25 \nUpazillas (sub-district) in the \u2018Barind Area\u2019 at the north western part \nof Bangladesh covering an area of 7500 km2.[1] In this area there is \nlimited scope to conserve rain water for irrigation and lack of modern \nagricultural technology resulted in agricultural and socio-economic \nbackwardness.  \n\n\n\nUnder K\u04e7ppen climate classification [2] Rajshahi has a tropical wet \nand dry climate. The climate of Rajshahi is generally marked with \nmonsoons, high temperature, considerable humidity and moderate \nrainfall. The hot season commences early in March and continues till \nthe middle of July. The maximum mean temperature observed is \nabout 32 to 36\u00b0C (90 to 97\u00b0F) during the months of April, May, June \nand July and the minimum temperature recorded in January is about \n7 to 16\u00b0C (45 to 61\u00b0F). the highest rainfall is observed during the \nmonths of monsoon. The annual rainfall in the district is about 1,448 \nmillimeters (57.0in).  \n\n\n\nIrrigation is the artificial application of water to the land or soil. It is \nused to assist in the growing of agricultural crops, maintenance of \nlandscapes and re-vegetation of disturbed soils in dry areas and \nduring periods of inadequate rainfall. Additionally, irrigation also has \na few other uses in crop production, which include protecting plants \nagainst frost, suppressing weed growing in grain fields and helping in \npreventing soil consolidation.[3] In contrast, agriculture that relies \nonly on direct rainfall is referred to as rain-fed or dry land farming. \nIrrigation systems are also used for dust suppression, disposal of \nsewage and in mining. Irrigation is often  \n\n\n\nstudied together with drainage, which is the natural or artificial  \n\n\n\nremoval of surface and sub-surface water from a given area. [4]  \n\n\n\nClimate is the most important to decide the rate of \nevapotranspiration. Several empirical formulas are available to \nestimate evapotranspiration from climate data. FAO expert group of \nscientists has recommended four methods for adoption of different \nregions of world. Among all of these methods Penman Method \nprovides more accurate result.[5]  One of the practical application of \nestimation of evapotranspiration is in the design of irrigation system \nto meet the water demand of plant growth during the period of \nsufficient water.  \n\n\n\nWater is needed mainly to meet the demand of evaporation, \ntranspiration and metabolic needs of the planets, all together is \nknown as consumptive use. . Since water used in the metabolic \nactivities of plant is negligible, being only less than one percent of \nquantity of water passing through the plant, evaporation and \ntranspiration, i.e. evaporation is directly considered as equal to \nconsumptive use.[6] In addition to evapotranspiration, water \nrequirement includes losses during the application of irrigation \nwater to field (percolation, seepage and run off) and water required \nfor special operation such as land preparation, transplanting, \nleaching etc. [7]  \nMacDonald (1978) studied that that transmissivity values of the \naquifer ranges from 1000 m2/day to 2000 m2 /day but it less than \n1000 m2/day in Paba upazilla and average storage coefficient value \nwas estimated 0.01. [8]  Consequently ground water is being \nwithdrawn from storage and water levels are declining resulting in \ncrop failure adverse salt balance, sea waters intrusion in costal \naquifers and subsidence in areas where draft resulting compaction of \nsediment. Even in high rainfall areas of the state, water scarcity is \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nISSN: 2521-2931  (Print)\nISSN: 2521-294X  (Online) \n\n\n\nWATER REQUIREMENTS FOR VARIOUS CROPS AND IMPACT OF IRRIGATION IN \nBARIND AREA \n\n\n\nMd. Kumruzzaman a,  Anirban Sarker b  \n\n\n\na Professor, b Undergraduate Research Scholar, Department of Civil Engineering, Rajshahi University of Engineering and Technology, Rajshahi-6204, \nBangladesh.   \n\n\n\nJournal Homepage: http://www.razipublishing.com/journals/malaysian- journal-\nof-sustainable-agriculture-mjsa/ \n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.04.07\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.07.08\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.04.07\n\n\n\n\n\n\nDr. Md. Kumruzzaman a,  Anirban Sarker b  Water requirements for various crops and impact of irrigation in barind area  Malaysian Journal of Sustainable \n\n\n\nAgriculture (MJSA) 1(1) (2017) 04-07   \n\n\n\nCite this article Dr. Md. Kumruzzaman a,  Anirban Sarker b  Water requirements for various crops and impact of irrigation in barind area  Malaysian Journal of Sustainable \n\n\n\nAgriculture (MJSA) 1(1) (2017) 04-07 \n\n\n\n5 \n\n\n\nexperienced in summer months [9].  \n\n\n\nThe proper management of ground water resources requires an \nadequate knowledge of the extent of the storage, the rate of discharge, \nthe rate of recharge to ground water body and the use of economical \nof extraction.[10] Ground water has been the source of irrigation in \nthe agro based Barind area by installation of Deep Tube wells (DTWs) \nand Shallow Tube wells (STWs). At present the total cultivable area is \nabout 0.58 m-ha of which only 26% (0.58mha) have been brought \nunder irrigation by both surface and groundwater. Study also \nincluded analysis of available metrological data. [11]  \nThe objectives of this study is to determine the consumptive use and \ncrop irrigation requirement of the Barind area. The consumptive use \nand crop irrigation requirement is to be compared. Total irrigation \nwater requirement is to be calculated by using Blaney-Criddle \nMethod. The study helps to know the total amount of water that is \nrequired for crops during its base period, the maximum amount of \nirrigation water required for growing crops, the Fluctuation of \nground water level in Barind area.  \n\n\n\n2. STUDY AREA \nRajshahi is a city in western Bangladesh and the divisional \nheadquarters of Rajshahi Division as well as the administrative \ndistrict that bears its name and is one of the seven metropolitan cities \nof Bangladesh. Silk of Rajshahi was of great quality once upon a time, \nso this city is often referred to as Silk City and Education City for its \ncalm environment. Rajshahi is located in the north-west part of the \ncounty and has an estimated population of 853,000 people. Its total \narea is 96.69 km2 (37.33 mile2) and it\u2019s situated on the northern \nbanks of the river Padma (or Ganges which is one of the major rivers \nof the Indian subcontinent). \n\n\n\nFIGURE 2.1: PHYSIOGRAPHIC MAP OF STUDY AREA \n\n\n\nThe hard red soil of this area is very significant in comparison to the \nother part of the county (BMDA). The Rajshahi district is located in \nbetween 24 degree 23 minute to 25 degree 15 minute north latitude \nand 88 degree 2 minute to 88 degree 57 minute east longitude.  \n\n\n\n3. RESEARCH METHODOLOGY \n\n\n\n3.1 Blaney-Criddle Method  \nBlaney and Criddle (1950) observed that the amount of water \n\n\n\nconsumptive use by  \n\n\n\ncrops during their growing seasons was closely correlated with mean \nmonthly temperature and day light hours and developed an empirical \nrelationship stated as follows:  \n\n\n\nCu= [1.8t+32]\u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026. \u2026. \u2026 \u2026. \u2026 (4.1)  \n\n\n\nWhere, Cu= Monthly consumptive use in cm  \n\n\n\n      K = Crop factor, determined from experiment \n\n\n\nfor each crop and depends  \n\n\n\nupon the environmental condition of the area.  \n\n\n\n      P = Monthly percentage of annual day light \n\n\n\nhours   T = Mean monthly temperature in \u00b0C  \n\n\n\nLet, f = \n\n\n\nSo, Cu= K\u00d7\ud835\udc53 \u2026 .\u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026. \u2026. \u2026 \u2026. \u2026 \u2026(4.2) Where, f = \nSummation of \u2018f\u2019 values during the crop season.  \n\n\n\nThe above formula involves the use of crop factor, the value of which is \n\n\n\nto be  \n\n\n\ndetermined for each crop and for different places and now this \ninformation is not available in our country for different places and \ndifferent crops. Moreover, this formula does not take into consideration \nthe factors such as elevation wind velocity and humidity etc. on which \nconsumptive use depends.  \n\n\n\nIt is the amount of irrigation water required in \n\n\n\norder to meet the evapotranspiration  \n\n\n\nneeds of the crop during its full growth. It is, therefore, nothing but the \nconsumptive use itself, but exclusive of effective precipitation, stored \nsoil moisture, or ground water. When the last two are ignored, then we \ncan write,  \n\n\n\nC.I.R = Cu-Re\u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026 \u2026. \u2026. \u2026 \u2026.(4.3)  \n\n\n\nTotal water requirement = C.I.R\u00d7Area\u2026 \u2026 \u2026 \u2026 \u2026 \u2026. \u2026. \u2026 \u2026.(4.4)  \n\n\n\n4. DATA COLLECTION \n\n\n\nRainfall and static water level data collect from the zonal office of \n\n\n\nBarind Multipurpose  \n\n\n\nDevelopment Authority (BMDA), Rajshahi. Temperature and monthly \nsun shine hour\u2019s data has been collected from Bangladesh \nMeteorological Department, climate division, Dhaka  \n\n\n\n5. GRAPHICAL REPRESENTATION\n\n\n\nMonthly variation of Crop Irrigation Requirement(C.I.R) and \n\n\n\nConsumptive use(Cu) for Amon  \n\n\n\n(rice) crops for maximum, minimum temperature are given from figure \n\n\n\n5.1-5.4  \n\n\n\nFigure 5.1: Monthly Variation of C.I.R for minimum Figure 5.2: Monthly Variation of C.I.R for maximum  \n\n\n\n  temperature for Amon (rice)    temperature for Amon (RICE)  \n\n\n\nFigure 5.3: Monthly Variation of Cu for maximum    Figure 5.4: Monthly Variation of Cu for minimum  \n\n\n\ntemperature for Amon(rice)    temperature for Amon(RICE)  \n\n\n\nMonthly variation of Crop Irrigation Requirement (C.I.R) and \n\n\n\n\n\n\n\n\nDr. Md. Kumruzzaman a,  Anirban Sarker b  Water requirements for various crops and impact of irrigation in barind area  Malaysian Journal of Sustainable \n\n\n\nAgriculture (MJSA) 1(1) (2017) 04-07   \n\n\n\nCite this article Dr. Md. Kumruzzaman a,  Anirban Sarker b  Water requirements for various crops and impact of irrigation in barind area  Malaysian Journal of Sustainable \n\n\n\nAgriculture (MJSA) 1(1) (2017) 04-07 \n\n\n\n3\n\n\n\n6 \n\n\n\nConsumptive use(Cu) for Boro  \n\n\n\n(rice) crops for maximum, minimum temperature are given from figure \n\n\n\n5.5-5.8  \n\n\n\nFigure 5.5: Monthly Variation of C.I.R for minimum    Figure 5.6: Monthly Variation of C.I.R for maximum  \n\n\n\n  temperature for Boro (rice)    temperature for Boro (RICE)  \n\n\n\nFigure 5.7: Monthly Variation of Cu for maximum    Figure 5.8: Monthly Variation of Cu for minimum  \n\n\n\n  temperature for Boro (rice)    temperature for Boro (RICE)  \n\n\n\nMonthly variation of Crop Irrigation Requirement (C.I.R) and Consumptive \nuse(Cu) for Wheat crops for maximum, minimum temperature are given \nfrom figure 5.9-5.12  \n\n\n\nFigure 5.9: Monthly Variation of C.I.R for maximum    Figure 5.10: Monthly Variation of C.I.R for minimum  \n\n\n\n  temperature for Wheat    temperature for Wheat  \n\n\n\nFigure 5.11: Monthly Variation of Cu for maximum    Figure 5.12: Monthly Variation of Cu for minimum  \n\n\n\n  temperature for Wheat    temperature for Wheat  \n\n\n\nFigure 5.13: Monthly Variation of C.I.R for maximum    Figure 5.14: Monthly Variation of C.I.R for minimum  \n\n\n\n  temperature for Potato    temperature for Potato  \n\n\n\n    Figure 5.15: Monthly Variation of Cu for maximum    Figure 5.16: Monthly Variation of Cu for minimum  \n\n\n\n  temperature for Potato    temperature for Potato  \n\n\n\nFigure 5.17: Yearly minimum groundwater depletion    Figure 5.18: Yearly maximum groundwater depletion  \n\n\n\n    rate of Boalia & Tanore    rate of Boalia & Tanore  \n\n\n\n6. RESULTS AND DISCUSSION \nCrop water requirement from 1985 to 2015 in Barind area Rajshahi \nfor Amon (rice) for average temperature is 558.8cm, for maximum \ntemperature is 683.2cm and for minimum temperature is 684.5cm. \nMaximum water requirement for Amon (rice) is in november. No \nadditional water is required for Amon (rice) is in july. \n\n\n\nCrop water requirement from 1985 to 2015 in Barind area Rajshahi \nfor Boro (rice) for average temperature is 423.6cm, for maximum \ntemperature is 540.4cm and for minimum temperature is 571.7cm. \nMaximum water requirement for Boro (rice) is in february. No \nadditional water is required for Boro (rice) is in june.   \n\n\n\nCrop water requirement from 1985 to 2015 in Barind area Rajshahi \nfor Wheat for average temperature is 164.9cm, for maximum \ntemperature is 165.3cm and for minimum temperature is 166.8cm. \nMaximum water requirement for Wheat is in November.  \n\n\n\nCrop water requirement from 1985 to 2015 in Barind area Rajshahi \nfor Potato for average temperature is 167.7cm, for maximum \ntemperature is 168cm and for minimum temperature is 168.9cm. \nMaximum water requirement for Potato is in November.  \n\n\n\nTotal Consumptive use from 1985 to 2015 in Barind area Rajshahi for \naverage temperature for Amon(rice), Boro(rice), Wheat, Potato are \n312.9cm, 609.6cm, 232.5cm, 138.4cm respectively. Total \nConsumptive use from 1985 to 2015 in Barind area Rajshahi for \nmaximum temperature for Amon(rice), Boro(rice), Wheat, Potato are \n1345.8cm, 2868.1cm, 1276cm, 810.5cm respectively.  \nTotal Consumptive use from 1985 to 2015 in Barind area Rajshahi for \n\n\n\nminimum temperature for  \n\n\n\nAmon(rice), Boro(rice), Wheat, Potato are 1123.2cm, 2095.5cm, \n\n\n\n871.2cm, 553.6cm respectively.  \n\n\n\n7. CONCLUSION \nTotal irrigation requirement for the crops Amon, Boro, Potato and \nWheat were determined. From calculation it is seen that maximum \nirrigation water is required in the month of April and groundwater \nextraction is more due to less rainfall and no additional water is \nrequired in the month of June and groundwater recharge is more due \nto excessive rainfall for rice. The maximum irrigation water is \nrequired for Potato crops in the month of January and groundwater \nextraction is more due to less rainfall and continuous water is to be \nsupplied for the base period. For Wheat crops the maximum \nirrigation water required in the month of December to January and \ngroundwater extraction is more due to less rainfall and continuous \nwater has to be supplied for its base period. This conclusion can be \ndrawn for aa crops that are included in this study. \n\n\n\nREFERENCES  \n\n\n\n[1] DAS KUMAR SUMON (2007)- M.Sc thesis on \u201cstudy of irrigation \nefficiencies and crop water requirement in various crops under barind area, \nBUET, DHAKA 1000 \n\n\n\n[2] SARMA, S.K.(1984)- \u201cPrinciple and practice of irrigation \nengineering, S. Chand And Company Ltd. Ram Nagar, New Delhi-110055. \n\n\n\n[3] Dhamge, M.N.R, Badar, A.M and Baiswarey, N.Z (2008)- crop \nwater requirement by modified penman method using Hymos. The Indian \nSociety for Hydraulics (ISH), Volume-14, Issue-3, pp. 28-42. \n\n\n\n[4] Israelsen, W. O. and others (1962)-Irrigation Principles and \npractice. John Weilly and Sons, Inc, New York, London, Sydney.\n\n\n\n[5] Rahman Azizur Md (2006)-M.Sc. thesis on estimation of \npotential recharge and ground water resources. A case study in low Barind \narea, Bangladesh University Applied Sciences Cologne, Germany.\n\n\n\n\n\n\n\n\nDr. Md. Kumruzzaman a,  Anirban Sarker b  Water requirements for various crops and impact of irrigation in barind area  Malaysian Journal of Sustainable \n\n\n\nAgriculture (MJSA) 1(1) (2017) 04-07   \n\n\n\nCite this article Dr. Md. Kumruzzaman a,  Anirban Sarker b  Water requirements for various crops and impact of irrigation in barind area  Malaysian Journal of Sustainable \n\n\n\nAgriculture (MJSA) 1(1) (2017) 04-07 \n\n\n\n7 \n\n\n\n[6] Michael, A.M. (1978)-Irrigation Theory and Practice, vikas \npublishing house (P) Ltd 57G, Masjid road, Jangpura, New Delhi-110014. \n\n\n\n[7] Raghunath, H.M. (1985) \u2013 Hydrology, principle, analysis, design. \nNew age international Ltd. New Delhi-110002. \n\n\n\n[8] Modi, P.N. (1988)-Irrigation Water Resources and Water Power \nEngineering, Standard Book House, 1705-4 Nai sarak, Delhi-110006 \n\n\n\n[9] Mahmud, N.N. (1996)-M.Sc. thesis on irrigation requirement of \nvarious crops under G-K project. \n\n\n\n[10] Shiferaw, B.A, Wani, S.P and Rao, G.D.N (2003)- Irrigation \ninvestments and groundwater depletion in Indian semi-arid villages: the \neffect of alternaive water. \n\n\n\n[11] Terrell. B.L and Johnson, P.N (1994) worked on- Economics \nimpact of the Ogallala aquifer: a case study of the southern plains of Texas\n\n\n\n[12] Liamas, M.R and Santos, P.M (2003) worked on- Intensive \nGround water use: Salient Revolution and potential source of social conflict \n\n\n\n[13] Garg, S.K. (1976)-Irrigation Engineering and Hydraulic \nStructure, Khanna publishers, 28, Nath Market, Nai Sarak, Delhi-110006 \n\n\n\n[14] Rahman, M.s(2004) worked on \u2013study of irrigation by groung \nwater in barind area, Rajshahi Barind Department, Rajshahi \n\n\n\n[15] Khan, M.A.A and Pramanik,M.M.I (1995) worked on- Study of \ndifferent water distribution system in irrigation project. \n[16] Habib, M.A(2001) worked on \u2013 Evaluation of ground water \npotentially in Barind area (Godagari thana) Rajshahi. \n\n\n\n[17] Reddi, P.J. (1986)-A text book of hydrology, Laxmi publications \n(P) Ltd. New Delhi11002\n\n\n\nWebsite: en.wikipedia.org  \n\n\n\n[18]  Lambers H 2003. Dry land salinity: A key environmental issue in \nsouthern AustraliaIntroduction. Plant Soil 257: VVii \n\n\n\n[19] Munns R., 2005. Genes and salt tolerance: bringing them \ntogether. New Phytol 167: 645\u201363 \n\n\n\n[20] Qadir M, A Tubeileh, J Akhtar,ALarbi, PS Minhas, MA Khan \n2008.Productivity enhancement of salt-affected environments through \ncrop diversification. Land Degradation Development. 19:429\u2013453 \n\n\n\n[21] Rozema J and TJ Flowers, 2008.Crops for a salinized world. \nScience 322:1478\u20131480 \n\n\n\n[22] Munns, R., 2009: Strategies for crop improvement in saline soils. \nIn: M. Ashraf, M. Ozturk, and H. R. Athar, eds. Salinity and Water Stress: \nImproving Crop Efficiency, pp. 99\u2013110. Springer, The Netherlands. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 10-15 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.10.15 \n\n\n\nCite the Article: Santosh Bhandari, Saroj Sapkota, Chetan Gyawali (2020). Effect Of Different Methods Of Crop Establishment On Growth And Yield Of A Spring Rice At \nJanakpurdham-17, Dhanusha. Malaysian Journal of Sustainable Agriculture, 4(1): 10-15. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2020.10.15 \n\n\n\nEFFECT OF DIFFERENT METHODS OF CROP ESTABLISHMENT ON GROWTH AND \nYIELD OF A SPRING RICE AT JANAKPURDHAM-17, DHANUSHA \n\n\n\nSantosh Bhandaria, Saroj Sapkotab, Chetan Gyawalic  \n\n\n\na Faculty of Agriculture, Agriculture and Forestry University, Chitwan, Nepal \nb Assistant Professor, Department of Biochemistry and Crop Physiology, Rampur, Chitwan, Nepal \nc Scientist, National Rice Research Institute, National Agriculture Research Council, Nepal \n\n\n\n*Corresponding Author Email: santoshbhandari4556@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 02 October 2019 \nAccepted 15 December 2019 \nAvailable Online  31 January 2020\n\n\n\nThe study was conducted to know and evaluate the performance of different methods of crop establishment \nof Hardinath-1, spring rice, under RCBD with 5 treatments and 4 replications; treatments used were- Open, \nstraight row, SRI, random and dry bed method of transplanting. The parameters like number of tillers per \nsquare meter, plant height, panicle length, effective number of tillers per square meter, thousand grain \nweight, grain yield in MT/ha and sterility percentage were accounted for the study. The findings suggest \nstatistical similarity in grain yield for SRI (4.475 Mt/ha), straight row (4.45 Mt/ha) and open method of \ntransplanting (4.45 Mt/ha), although the former, literally, being a slight superior among three. Random \n(20.00a) and dry bed (19.64a) method of transplanting were statistically at par and highest in value for \nsterility percentage followed by open (17.68b), SRI (16.73bc) and straight row (16.12c) method, the least of \nall. SRI method of transplanting exhibited highest mean value for number of tillers/m2 (294.4), thousand \ngrain weight (22.87a), effective number of tillers/m2 (254.8a), grain yield (4.475 t/ha) and second lowest \nsterility percentage and plant height after straight row method of transplanting. Straight row method of \ntransplanting exhibited highest mean value for plant height (39.36 cm, 43.02 cm, 43.91 cm and 102.28 cm) \nand lowest sterility percentage (16.12c) but, showed comparatively poor performance in other parameters \nin respect to SRI method of transplanting. Dry bed method, as a whole, comparatively exhibited the worst \nperformance of all and thus, categorized as control treatment. This study suggests that SRI method of crop \nestablishment is an easy and effective technique for improving physiological and yield attributing characters \nof spring rice.  \n\n\n\nKEYWORDS \n\n\n\nSRI, Crop establishment techniques, Spring rice, Hardinath-1. \n\n\n\n1. INTRODUCTION\n\n\n\nRice (Oryzae sativa L.) is a perennial grass belonging to family \n\n\n\nPoaceae/Graminae. There are about 23 species of rice out of which only \n\n\n\ntwo species have been known for their wide domestication and \n\n\n\ncommercial value. These two species are Oryzae sativa (Asian rice) and \n\n\n\nOryzae glaberrima (African rice) (Ajaib, 2014). Globally, rice ranks second \n\n\n\nto wheat in terms of area harvested, but in terms of importance as a food \n\n\n\ncrop, rice provides more calories per hectare than any other cereal crops. \n\n\n\nRice is the staple food of more than 60% of the world population. Rice is \n\n\n\ngrown from 50 \u1d52 N latitude to 40 \u1d52S latitude from equator. In Nepal, rice \n\n\n\nranks first both in terms of area cultivated, production and livelihood of \n\n\n\npeople (Ajaib, 2014). More than 1, 700 rice landraces are reported in Nepal \n\n\n\ngrowing from 60 to 3,050 m altitude (Amod, 2011). The total area, \n\n\n\nproduction and yield of rice in Nepal are 14,69,545 ha, 51,51,925 MT, and \n\n\n\n3506 kg/ha respectively (Anas et al., 2011). Rice is a widely cultivated crop \n\n\n\nof Nepal with about 51.45% of total edible cereal production in the country \n\n\n\n(Anas et al., 2011). Cereals provide 65 percent of the total Dietary Energy \n\n\n\nsupplies to Nepalese people and out of which, 30 percent is contributed by \n\n\n\nrice alone (Awan, 2011). \n\n\n\nIn Nepal, rice is grown in three agro-ecological regions (Terai and inner \n\n\n\nterai- 67 to 900 masl, Mid hills- 1000 to 1500 masl, and High hills- 1500 to \n\n\n\n3050 masl) and three major production environments; irrigated, rain-fed \n\n\n\nlowland and upland (Ajaib, 2014). The Terai region, considered the \n\n\n\ngranary of the country, accounts for about 70 percent of the country's rice \n\n\n\noutput, while the hill produces 26 percent and the mountain produces \n\n\n\nabout 4 percent (Ajaib, 2014). Dhanusha district lies between latitude 25\u00b0 \n\n\n\n35' to 27\u00b0 50' due North and longitude 85\u00b050' to 86\u00b020' due East. The \n\n\n\ndistrict is located in the elevation range of 60.89 to 609.76 metres above \n\n\n\nthe sea level. The total area of the district is 1180 square kilometer. Rice is \n\n\n\nthe major crop grown in almost all areas of Dhanusha. The area, \n\n\n\nproduction and yield in the fiscal year 2072/73 in Dhanusha district was \n\n\n\n35,200ha, 1,21,100 Mt and 3.44 Mt/ha respectively (Awan, 2011). Also, \n\n\n\nthe productivity of irrigated land in 2063 B.S was 3.2Mt per ha and in 2072 \n\n\n\nB.S it was 4.1 Mt per ha (Awan, 2011). The different source of irrigation in \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 10-15 \n\n\n\nCite the Article: Santosh Bhandari, Saroj Sapkota, Chetan Gyawali (2020). Effect Of Different Methods Of Crop Establishment On Growth And Yield Of A Spring Rice At \nJanakpurdham-17, Dhanusha. Malaysian Journal of Sustainable Agriculture, 4(1): 10-15. \n\n\n\nDhanusha district consist of  kamala irrigation ha project, the first \n\n\n\nirrigation ha project, second irrigation sector plan and many more sub-\n\n\n\nirrigation projects (Awan, 2011; Balachandra, 2007; Barison, 2003; Bedi, \n\n\n\n2013; Cao, 2002; CDD, 2015; Ceesay, 2006). Main varieties of rice \n\n\n\ncultivated in this region are Lalka basmati, Sabitri, Hardinath-1, Ram dhan, \n\n\n\nSwarna sub-1, Sukkha-1, Sukkha-2 and Sukkha-3. Maximum land areas are \n\n\n\nunder rain-fed condition.  \n\n\n\n1.1 OBJECTIVES \n\n\n\n1.1.1 General objective \n\n\n\n\u2022 To study and assess the feasibility of spring season rice \n\n\n\ncultivation practices in the Dhanusha district. \n\n\n\n1.1.2 Specific objectives \n\n\n\n\u2022 To be familiar with the various methods of rice crop \n\n\n\nestablishment \n\n\n\n\u2022 To assess the comparative advantage of one method of rice \n\n\n\ncrop establishment over the other \n\n\n\n\u2022 To suggest farmers to adopt the best crop establishment \n\n\n\nmethod depicted by the research \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Site selection and research design \n\n\n\nField experiment was carried out in the farmer\u2019s field at Tetariya Bazaar, \n\n\n\nJanakpurdham-17, located at Province No. 2 of Nepal. Seed rate of 40 \n\n\n\nkg/ha (Hardinath-1) was used for this purpose. Five treatments and four \n\n\n\nreplications were taken. 100 grams of healthy, bold and pure seeds of \n\n\n\nHardinath-1 variety of Spring season rice were allocated for each plot \n\n\n\nunder various methods of crop establishment in single factorial RCBD \n\n\n\ndesign. Fertilizers were applied at 100:30:30 NPK kg/ha (Chapagain and \n\n\n\nYamaji, 2010; Chaudhary, 2007; Chen et al., 2013; DADO, 2072/73; DADO, \n\n\n\n2072/73). The net plot size was allocated as 9 square metre each. 100 cm \n\n\n\nwas left between the plots and 1m in between the replication plots. FYM \n\n\n\nwas applied 2 weeks before sowing date. Irrigation was given at 15 days' \n\n\n\ninterval. Observation and measurement were recorded for number of \n\n\n\ntillers emerged, plant height, days of heading, days of floral initiation etc. \n\n\n\nT1R1 T2R2 T5R3 T3R4 \n\n\n\nT3R1 T4R2 T4R3 T2R4 \n\n\n\nT5R1 T1R2 T2R3 T5R4 \n\n\n\nT2R1 T3R2 T1R3 T1R4 \n\n\n\nT4R1 T5R2 T3R3 T4R4 \n\n\n\nLayout of the Field \n\n\n\nInter-plot distance in between a replication: 100 cm \n\n\n\nInter-replication distance: 100 cm (1 Metre) \n\n\n\nTreatments: A total of 4 replications each was constructed. The \n\n\n\nexperiment was done in single factorial RCBD design. \n\n\n\nThe treatments are:  \n\n\n\nT1 \u2013 Open transplanting   \n\n\n\nT2- Straight row transplanting \n\n\n\nT3-  SRI system of rice cultivation \n\n\n\nT4 \u2013 Random transplanting (Farmer's transplanting method) \n\n\n\nT5 \u2013 Dry bed transplanting (Unpuddled transplanting) \n\n\n\n3. OBSERVATIONS \n\n\n\n3.1.1 Morphological observations \n\n\n\nRandom sampling was followed for sampling plants on parameters like \n\n\n\nnumber of tillers and plant height in cm. For sampling on further \n\n\n\nparameters, ten plants were randomly selected and tagged and data \n\n\n\ncollection were made on the basis of morphological observation. \n\n\n\n3.1.1.1 Plant Height (cm) \n\n\n\nThe height of the main shoot of randomly selected sample plants was \n\n\n\nmeasured from the base of the plant to the tip of the longest leaf. After \n\n\n\npanicle emergence, height was measured from the base of the plant to the \n\n\n\ntip of the panicle or leaf, which ever was longest. The observations were \n\n\n\nrecorded at 27, 41, 56 DAT and at harvest. \n\n\n\n3.1.1.2 Number of tillers/m2 \n\n\n\nThe counting of tillers/m2 was taken at each plot at 27, 41 and 56 DAT \n\n\n\nthen, average was worked out. \n\n\n\n3.1.2 Phenological studies \n\n\n\n3.1.2.1 Days to tillering \n\n\n\nNumber of days from transplanting to 50% tillering in the field. It was \n\n\n\nrecorded as 23 DAT on 21st April. \n\n\n\n3.1.2.2 Days to Panicle initiation \n\n\n\nNumber of days from transplanting to 50% panicle initiation stage in the \n\n\n\nfield. It was recorded as 54 DAT on 23rd May. \n\n\n\n3.1.2.3 Days to heading and flowering \n\n\n\nNumber of days from transplanting to 50% heading and flowering in the \n\n\n\nfield. It was recorded as 64 DAT on 1st June. \n\n\n\n3.1.2.4 Days to maturity \n\n\n\nNumber of days from transplanting to complete maturity. \n\n\n\n3.1.3 Yield attributing characters \n\n\n\n3.1.3.1 Plant population/m2 \n\n\n\nTotal number of plant/m row length was counted from selected randomly \n\n\n\nfour rows in one metre row length in each plot. \n\n\n\n3.1.3.2 Panicle length (cm) \n\n\n\nFive panicles from each plot were selected randomly at the time of \n\n\n\nharvesting and length of each panicle was measured from the base to tip \n\n\n\nof panicle with the help of scale. Thereafter, mean length of panicle was \n\n\n\ncalculated. \n\n\n\n3.1.3.3 Number of grains/panicle \n\n\n\nTotal number of grains from five randomly selected panicles was counted. \n\n\n\nThereafter, mean number of grains per panicle was computed. \n\n\n\n3.1.3.4 Number of healthy (Filled) grains/panicle \n\n\n\nTotal number of grains from five randomly selected panicles were \n\n\n\nseparated and counted. Later filled grains were counted manually. \n\n\n\nThereafter mean filled grains per panicle were computed. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 10-15 \n\n\n\nCite the Article: Santosh Bhandari, Saroj Sapkota, Chetan Gyawali (2020). Effect Of Different Methods Of Crop Establishment On Growth And Yield Of A Spring Rice At \nJanakpurdham-17, Dhanusha. Malaysian Journal of Sustainable Agriculture, 4(1): 10-15. \n\n\n\n3.1.3.5 Number of chaffy grains/panicle \n\n\n\nChaffy grains were worked out by subtracting the filled grains from the \n\n\n\ntotal grains obtained from five randomly selected panicles as done for \n\n\n\nabove observation. Then mean chaffy grains per panicle were worked out. \n\n\n\n3.1.3.6 Test weight of grains (1000-grain weight) \n\n\n\nGrain samples were drawn randomly from the total produce of each net \n\n\n\nplot at the time of weighing and 5000 grains were counted manually. \n\n\n\nThereafter, the weight of these grains was recorded on electronic balance \n\n\n\nand mean test weight was computed by dividing the total weight of 5000 \n\n\n\ngrains with 5. \n\n\n\n3.1.4 Yields \n\n\n\n3.1.4.1 Grain yield q/ha \n\n\n\nAfter winnowing and cleaning, the grain yield obtained from each plot, \n\n\n\nwere weighed on double pan balance. Later it was converted into grain \n\n\n\nyield q/ha by multiplying with appropriate factor. \n\n\n\n3.2 Statistical analysis \n\n\n\nThe data recorded on different observations were tabulated and analyzed \n\n\n\nstatistically by using the techniques of analysis of variance (ANOVA) \n\n\n\nthrough statistical package- Genestat, 15th edition. Critical difference at \n\n\n\n0.05 probability level was worked out to compare the treatments when \u2018F\u2019 \n\n\n\ntest was found significant and the means were compared using Duncan's \n\n\n\nMultiple Range Test. (DMRT) \n\n\n\n4. RESULTS AND DISCUSSION\n\n\n\nThe analysis of data provided various results which are presented and \n\n\n\ndiscussed below. \n\n\n\n4.1 Number of tillers per square meter \n\n\n\nTable 1: No. of tillers per square meter as influenced by crop \n\n\n\nestablishment techniques \n\n\n\nTreatments No. of tillers per square metre \n\n\n\nOpen 219.8c \n\n\n\nStraight row 256.0b \n\n\n\nSRI 294.4a \n\n\n\nRandom 202.0cd \n\n\n\nDry bed 185.5d \n\n\n\nGrand mean 231.5 \n\n\n\nCV(%) 6.8 \n\n\n\nSEM(\u00b1) 7.82 \n\n\n\nLSD0.05 24.09 \n\n\n\nNote: CV, Coefficient of variation; LSD, Least Significant Difference; SEM \n\n\n\n(\u00b1), Standard error of mean. Letters a, b, c, d represents the ranking of \n\n\n\ntreatments according to DMRT at 0.05 level of significance. Number of \n\n\n\ntillers/m2 was affected significantly by different methods of crop \n\n\n\nestablishment. (Table 1). Analysis of data revealed that SRI method of \n\n\n\ntransplanting produced maximum average number of tillers per square \n\n\n\nmetre (294.4) as compared to straight row/line (256), open (219.8), \n\n\n\nrandom (202) and dry bed method of transplanting (Farooq et al., 2009; \n\n\n\nGayathry, 2002; Guohua, 2003; Janarthanan, 2008; Joshy, 1997). Dry bed \n\n\n\nmethod produced significantly least average number of tillers per square \n\n\n\nmetre (185.5). All the treatments responded differently to the parameter. \n\n\n\nThe SRI planting with two-way rotary weeder weeding thrice at weekly \n\n\n\ninterval starting from 15 DAT in transplanted rice produced more number \n\n\n\nof tillers per m2 (Balachandra, 2007). Wider the spacing provided, the \n\n\n\ntillers per hill produced are higher (44) than closer spacing. In cluster \n\n\n\nplanting (two or four seedlings together), there were initially more \n\n\n\nprimary tillers and ultimately lesser tillers (40.0-42.2) per hill due to \n\n\n\nmutual competition (Barison, 2003; Kavitha, 2011; Krishi Diary, 2005; \n\n\n\nKrishna, 2008; Kumar, 2007; Ladha, 2009; Mallick, 1981; Manjunatha et \n\n\n\nal., 2010; MoAD,CBS and FAO, 2016; MoALD, 2018/19; MoALD, 2072/73; \n\n\n\nMof, 2018; Mura, 2013-2015; Nissanka and Bandara, 2004; Pandey and \n\n\n\nVelasco, 2005). It was observed that the roots of rice plants have least \n\n\n\ncompetition under wider spacing so that growth is stimulated by sunlight \n\n\n\nand space for the canopy expansion (Bedi, 2013).  \n\n\n\n4.2 Panicle length in cm \n\n\n\nTable 2: Panicle length as influenced by crop establishment \n\n\n\ntechniques \n\n\n\nTreatments Panicle length in cm \n\n\n\nOpen 21.40b \n\n\n\nStraight row 23.38b \n\n\n\nSRI 25.53a \n\n\n\nRandom 23.32b \n\n\n\nDry bed 23.22b \n\n\n\nGrand mean 23.37 \n\n\n\nCV(%) 5.3 \n\n\n\nSEM(\u00b1) 0.618 \n\n\n\nLSD0.05 1.904 \n\n\n\nNote: CV, Coefficient of variation; LSD, Least Significant Difference; SEM \n\n\n\n(\u00b1), Standard error of mean. Letters a, b, c, d represents the ranking of \n\n\n\ntreatments according to DMRT at 0.05 level of significance \n\n\n\nPanicle length was significantly affected by different method of crop \n\n\n\nestablishment. (Table 2) Data analysis through Genestat and comparison \n\n\n\nusing DMRT revealed that SRI method of transplanting produced highest \n\n\n\naverage panicle length/cm i.e, 25.53 cm as compared to straight row \n\n\n\n(23.38 cm), open (21.40 cm), random (23.32 cm) and dry bed (23.22 cm) \n\n\n\nmethod of transplanting. When all the four components of SRI viz., young \n\n\n\nseedling, one seedling, square planting and conoweeding were combined, \n\n\n\nit gave  more number of panicles per hill (17), panicle length (23 cm), \n\n\n\npanicle weight (2.13 g), number of filled grains per panicle (101) which \n\n\n\nultimately resulted in higher grain (3682 kg ha-1) and straw yield (5010 kg \n\n\n\nha-1) and improved harvest index (0.424) in short duration rice variety \n\n\n\nADT 43 during kharif season (Balachandra, 2007; Prasad, 2004; Rajesh \n\n\n\nand Thanunathan, 2003; Raju, 2008; Rao et al., 2007; Reddy, 2005; Saha, \n\n\n\n2010; Sanchez, 1973; Shao-hua, 2002; Sharma, 2003; Sheehy, 2004). \n\n\n\n4.3 Plant Height \n\n\n\nTable 3: Plant height as influenced by crop establishment techniques \n\n\n\nTreatments Plant height (cm) \n\n\n\n25th April  10th May 25th May At harvesting \n\n\n\nOpen 33.31b 39.36b 40.62b 98.5bc \n\n\n\nStraight row 39.36a 43.02a 43.91a 102.28a \n\n\n\nSRI 35.15b 37.35b 39.01b 100.18ab \n\n\n\nRandom  33.96b 40.23ab 41.93ab 99.70ab \n\n\n\nDry bed 34.33b 38.72b 40.40b 96.08c \n\n\n\nMean 35.222 39.736 41.174 99.348 \n\n\n\nCV (%) 7.6 5.2 4.4 2.0 \n\n\n\nSEM (\u00b1) 1.895 1.450 1.293 0.980 \n\n\n\nLSD0.05 4.128. 3.159* 2.818* 3.020** \n\n\n\nNote: CV, Coefficient of variation; LSD, Least Significant Difference; SEM \n\n\n\n(\u00b1), Standard error of mean. Letters a, b, c, d represents the ranking of \n\n\n\ntreatments according to DMRT at 0.05 level of significance \n\n\n\nPlant Height was affected significantly by the method of crop \n\n\n\nestablishment. (Table 3) It is evident from the above table that the height \n\n\n\nof sample plants gradually increased in rate over successive growth stage \n\n\n\ntill May and decreased afterwards (Singh, 1999; Solanki, 2011; Sridevi and \n\n\n\nChellamuthu, 2007; Sridevi, 2011; Surendra, 2001). The highest recorded \n\n\n\nheight of 39.36 cm, 43.02 cm, 43.91 cm and 102.28 cm was found under \n\n\n\nstraight row method of transplanting. No statistical difference was found \n\n\n\nin between open, SRI, Random and Dry bed method of transplanting at 27 \n\n\n\nDAT however, a significant difference in height was exhibited at the time \n\n\n\nof harvesting. In an experiment conducted to test the productivity of \n\n\n\nSystem of Rice Intensification (SRI) method over the conventional rice \n\n\n\nfarming systems in Sri Lanka, average plant height growth and leaf \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 10-15 \n\n\n\nCite the Article: Santosh Bhandari, Saroj Sapkota, Chetan Gyawali (2020). Effect Of Different Methods Of Crop Establishment On Growth And Yield Of A Spring Rice At \nJanakpurdham-17, Dhanusha. Malaysian Journal of Sustainable Agriculture, 4(1): 10-15 .\n\n\n\nchlorophyll content during the growing stages were also similar among \n\n\n\nthe treatments (Cao, 2002). The slight inferior response of SRI in respect \n\n\n\nto conventional method of transplanting may be due to detrimental \n\n\n\nweather condition and water stress during the mid-period of crop. \n\n\n\n4.4 Sterility Percentage \n\n\n\nTable 4: Sterility percentage as influenced by crop establishment \n\n\n\ntechniques \n\n\n\nTreatments Sterility % \n\n\n\nOpen 17.68b \n\n\n\nStraight row 16.12c \n\n\n\nSRI 16.73bc \n\n\n\nRandom 20.00a \n\n\n\nDry bed 19.64a \n\n\n\nGrand mean 18.034 \n\n\n\nCV(%) 3.9 \n\n\n\nSEM(\u00b1) 0.347 \n\n\n\nLSD0.05 1.071 \n\n\n\nNote: CV, Coefficient of variation; LSD, Least Significant Difference; SEM \n\n\n\n(\u00b1), Standard error of mean. Letters a, b, c, d represents the ranking of \n\n\n\ntreatments according to DMRT at 0.05 level of significance \n\n\n\nSterility percentage was significantly affected by different methods of crop \n\n\n\nestablishment as depicted by Table 4. Data analysis revealed that random \n\n\n\nmethod of crop establishment has significantly highest average sterility \n\n\n\npercentage (20%). However, there is no statistical difference between \n\n\n\nrandom and dry bed method of transplanting. Straight row/ line method \n\n\n\nof transplanting has least average sterility percentage (16.12%). In an \n\n\n\nexperiment conducted at rice zone of Punjab, sterility percentage was \n\n\n\nmaximum (16.46) in farmer\u2019s practice of random transplanting and was \n\n\n\nsignificantly different from line transplanting (10.34) and open \n\n\n\ntransplanting (10.09) (CDD, 2015). In farmer's practice of random \n\n\n\ntransplanting, the tiller mortality rate and sterility percentage is higher as \n\n\n\ncompared to other methods since, there is greater competition for \n\n\n\nnutrient, space, water and light (Thakur, 2010; Thiyagarajan, 2002; \n\n\n\nThiyagarajan, 2006; Tripathi, 2005a; Triveni, 2006). \n\n\n\n4.5 Effective number of tillers per square meter \n\n\n\nTable 5: Number of effective tillers per square meter as influenced by \n\n\n\ncrop establishment techniques \n\n\n\nTreatments Effective tillers per square meter\n\n\n\nOpen 179.0c \n\n\n\nStraight row 214.8b \n\n\n\nSRI 254.8a \n\n\n\nRandom 170.8cd \n\n\n\nDry bed 146.2d \n\n\n\nGrand mean 193.12 \n\n\n\nCV(%) 10.2 \n\n\n\nSEM(\u00b1) 9.81 \n\n\n\nLSD0.05 30.23*** \n\n\n\nNote: CV, Coefficient of variation; LSD, Least Significant Difference; SEM \n\n\n\n(\u00b1), Standard error of mean. Letters a, b, c, d represents the ranking of \n\n\n\ntreatments according to DMRT at 0.05 level of significance \n\n\n\nDifferent methods of crop establishment had significant effect on effective \n\n\n\nnumber of tillers per square method as depicted by table 5. SRI method of \n\n\n\ncrop establishment exhibited significantly highest average effective \n\n\n\nnumber of tillers per square meter as compared to other methods followed \n\n\n\nby straight row method of transplanting. Dry bed method of transplanting \n\n\n\nexhibited the worst performance of all (Uphoff, 2009). The percentage of \n\n\n\neffective tiller production was highest in SRI method in both the seasons \n\n\n\nfollowed by seedling throwing method due to less number of tiller \n\n\n\nabortion. In other methods tiller abortion started 20 days earlier than SRI \n\n\n\nmethod and it continued till panicle initiation stage. A group researchers \n\n\n\nfound in an experiment the increase in the productive tillers with SRI \n\n\n\nmethod was to the extent of 217% over traditional method (Ceesay, 2006; \n\n\n\nChapagain and Yamaji, 2010). The increase in the effective tillers per plant \n\n\n\nmight be due to the better spacing provided to the plants by planting in \n\n\n\nsquare method (Upphoff, 2002; Uprety, 2005; Wang, 2003; Yasmeen, \n\n\n\n2010; Zhang, 2009). Profused tillering due to lower plant density was \n\n\n\nnoticed under wider spacing compared to closer spacing. A group \n\n\n\nresearchers stated that conversion of the majority of the tillers into \n\n\n\nproductive tillers have facilitated better utilization of resource by the plant \n\n\n\nin SRI system (Chaudhary, 2007; Chen et al., 2013; DADO, 2072/73; \n\n\n\nFarooq et al., 2009; Gayathry, 2002; Guohua, 2003; Janarthanan, 2008; \n\n\n\nJoshy, 1997). \n\n\n\n4.6 Thousand grain weight \n\n\n\nTable 6: Thousand grain weight as influenced by crop establishment \n\n\n\ntechniques \n\n\n\nTreatments Thousand grain weight \n\n\n\nOpen 21.37c \n\n\n\nStraight row 22.07b \n\n\n\nSRI 22.87a \n\n\n\nRandom 20.82c \n\n\n\nDry bed 21.47bc \n\n\n\nGrand mean 21.72 \n\n\n\nCV(%) 1.9 \n\n\n\nSEM(\u00b1) 0.2098 \n\n\n\nLSD0.05 1.23*** \n\n\n\nNote: CV, Coefficient of variation; LSD, Least Significant Difference; SEM \n\n\n\n(\u00b1), Standard error of mean. Letters a, b, c, d represents the ranking of \n\n\n\ntreatments according to DMRT at 0.05 level of significance \n\n\n\nDifferent methods of crop establishment significantly affected thousand \n\n\n\ngrain weight as depicted by table 6. The statistical analysis suggests that \n\n\n\nSRI method of crop establishment exhibited highest mean thousand grain \n\n\n\nweight (22.87a) followed by straight row method of transplanting \n\n\n\n(22.07b). However, there was no statistical difference in thousand grain \n\n\n\nweight among open (21.37c) and random (20.82c) method of transplanting \n\n\n\nand both exhibited the lowest mean value of thousand grain weight. Some \n\n\n\nresearchers observed the individual grain weight of rice slightly varied \n\n\n\ndue to planting of rice on different dates. Heavier grain weight was found \n\n\n\nin early planted crop and grain weight decreased with the delay \n\n\n\ntransplanting. Probably the grain filling was hampered due to late planting \n\n\n\nand decreased individual seed weight. \n\n\n\n4.7 Grain yield \n\n\n\nTable 7: Grain yield as influenced by crop establishment techniques \n\n\n\nTreatments Grain yield t/ha \n\n\n\nOpen 4.45a \n\n\n\nStraight row 4.45a \n\n\n\nSRI 4.475a \n\n\n\nRandom 4.1b \n\n\n\nDry bed 4.25b \n\n\n\nGrand mean 4.345 \n\n\n\nCV(%) 2.3 \n\n\n\nSEM(\u00b1) 0.05 \n\n\n\nLSD0.05 0.154 \n\n\n\nNote: CV, Coefficient of variation; LSD, Least Significant Difference; SEM \n\n\n\n(\u00b1), Standard error of mean. Letters a, b, c, d represents the ranking of \n\n\n\ntreatments according to DMRT at 0.05 level of significance \n\n\n\nGrain yield was significantly affected by different methods of crop \n\n\n\nestablishment as exhibited in table 7. From the analysis, it was evident that \n\n\n\nthere was no statistical difference in grain yield between open (4.45 t/ha), \n\n\n\nstraight row (4.45 t/ha) and SRI (4.75 t/ha) method of transplanting \n\n\n\nalthough, SRI produced highest average grain yield. Also, there was no \n\n\n\nstatistical difference in grain yield for random (4.1 t/ha) and dry bed (4.25 \n\n\n\nt/ha) method of transplanting. A research insisted no significant yield \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 10-15 \n\n\n\nCite the Article: Santosh Bhandari, Saroj Sapkota, Chetan Gyawali (2020). Effect Of Different Methods Of Crop Establishment On Growth And Yield Of A Spring Rice At \nJanakpurdham-17, Dhanusha. Malaysian Journal of Sustainable Agriculture, 4(1): 10-15. \n\n\n\nadvantages for SRI over \u2018best management practices\u2019 (BMPs) documented \n\n\n\nexperimentally except from Madagascar trials. Grain yield was found \n\n\n\nsignificantly higher in case of SRI method of crop establishment technique \n\n\n\nthan that of conventional technique in both the years. The pooled mean \n\n\n\nvalue was also found to be higher in case of SRI technique than that of \n\n\n\nconventional method (Chen et al., 2013). In an experiment conducted to \n\n\n\ntest the productivity of System of Rice Intensification (SRI) method with \n\n\n\nconventional rice farming systems in Sri Lanka, dry weight of stems, \n\n\n\nleaves, and roots and the total dry weights, leaf area and total root length \n\n\n\nper hill during the growing period and the tiller number per plant at \n\n\n\nheading were significantly higher in SRI compared to other treatments. \n\n\n\nHowever, all these parameters, when expressed per unit area basis, were \n\n\n\nnot significantly different (Cao, 2002). \n\n\n\n5. CONCLUSION \n\n\n\nThe effect of different crop establishment techniques in growth and yield \n\n\n\nof spring rice (Hardinath-1) was studied and the effect of crop \n\n\n\nestablishment techniques on number of tillers/m2, effective number of \n\n\n\ntillers/m2, plant height, panicle length, thousand grain yield, grain yield \n\n\n\nand sterility was assessed. Three averaged data across different dates of \n\n\n\n15 days' interval suggested that SRI method of crop establishment \n\n\n\nexhibited highest average number of tillers/m2 (294.4), highest average \n\n\n\neffective number of tillers/m2 (254.8), highest average thousand grain \n\n\n\nweight (22.87 gm), highest average panicle length (25.53 cm), highest \n\n\n\ngrain yield (4.475 t/ha) and low sterility percentage (16.73%). Though, \n\n\n\nstraight row method of transplanting exhibited lowest possible sterility \n\n\n\npercentage (16.12%) and highest mean values of plant height (39.36 cm, \n\n\n\n43.02 cm, 43.91 cm and 101.98 cm), this method is not superior to SRI in \n\n\n\nother context of more significant differential parameters contributing to \n\n\n\nphysiology and yield of spring rice. Dry bed method was evaluated as least \n\n\n\nproductive method or control plot/treatment. All other treatments of crop \n\n\n\nestablishment except dry bed method of transplanting performed better \n\n\n\nresult than control treatment. Concluding at a point, SRI method of crop \n\n\n\nestablishment proved to be the best alternative for crop establishment \n\n\n\ntechnique. The better performance of SRI method of transplanting may be \n\n\n\ndue to increased microbial activity led by facilitation of aeration which \n\n\n\ncaused increased enzymatic activities (amylase, catalase and \n\n\n\ndehydrogenase) and higher net photosynthesis in SRI rice. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors are thankful to Prime Minister Agriculture Modernization \n\n\n\nProject (PMAMP), National Rice Research Institute (NRRI), Agriculture \n\n\n\nand Forestry University (AFU) and Rice Zone, Dhanusha for the support \n\n\n\nprovided for carrying out this research and for their co-operation. \n\n\n\nREFERENCES \n\n\n\nAjaib, S., 2014. Comparitive advantage of direct sown paddy against \nconventional puddled transplanted rice. Indian Agricultural \nResearch Institute (IARI). \n\n\n\nAmod., 2011. Impact of water management on yied and water productivity \nwith system of rice intensification (SRI) and conventional \ntransplanting system in rice. India: Paddy and water environment. \n\n\n\nAnas, I., Rupela, O.P., Thiyagarajan, T.M., Uphoff, N., 2011. A review of \nstudies on SRI effects on beneficial organisms in rice soil \nrhizospheres. Paddy and Water Environment, 9 (1), 53-64. \n\n\n\nAwan, T.H., 2011. Effect of different transplanting methods on paddy yield \nand its components at farmer's field in rice zone of Punjab. Punjab: \nJournal of Animal and Plant Sciences. \n\n\n\nBalachandra, L., 2007. Evaluation of the effect of different planting \nmethods on growth and yield of rice. ARS, Pattambi. \n\n\n\nBarison., 2003. Nutrient-use efficiency and nutrient uptake in \nconventional and intensive (SRI) cultivation systems in Madagascar. \nIthaca, New York: Cornell University. \n\n\n\nBedi, N., 2013. Rice transplantation. Evaluation of rice varieties under \ndifferent sowing methods. \n\n\n\nCao, E., 2002. Water and Nitrogen use efficiency. A knowledge-based \nmodel for Nitrogen Management in Rice and Wheat. \n\n\n\nCDD., 2015. Rice Varietal Mapping in Nepal; Implication for Development \nand Adoption. Hariharbhawan: Government of Nepal. \n\n\n\nCeesay, E.M., 2006. Effect of repeated soil wetting and drying on lowland \nrice yield with SRI methods. \n\n\n\nChapagain, Yamaji., 2010. The effects of irrigation method, age of seedling \nand spacing on crop performance, productivity and water wise rice \nproduction in Japan (Vol. 8). Japan: Paddy and water environment. \n\n\n\nChaudhary, E., 2007. System of rice intensification and irrigated \ntransplanted rice: Effect on crop water productivity. Journal of the \nIndian Society of soil Science, 4. \n\n\n\nChen, S., Zheng, X., Wang, D., Xu, C., Zhang, X., 2013. Influence of the \nImproved System of Rice Intensification (SRI) on Rice Yield, Yield \nComponents and Tillering Characteristics under Different Rice \nEstablishment Methods. Plant Production Science, 16 (2), Pp. 191-\n198. \n\n\n\nDADO. (2072/73). Annual report. Dhanusha, Nepal: Government of Nepal. \n\n\n\nFarooq, M., Basra, S.M.A., Asad, S.A., 2009. Comparison of conventional \npuddling and dry tillage in rice\u2013wheat system. Paddy and Water \nEnvironment, 6 (4), pp 397\u2013404. \n\n\n\nGayathry, G., 2002. Studies on dynamics of soil microbes in rice \nrhizosphere with water saving irrigation and insitu weed \nincorporation. M.Sc. (Ag.) Thesis. Coimbatore, Tamil nadu, India: \nTamil Nadu Agricultural University. \n\n\n\nGuohua, C.L., 2003. Comparison of the yield differences between rice \nhybrid under SRI and TCM. Journal of Human Agricultural University, \n2, 89-91. \n\n\n\nJanarthanan., 2008. Impact of number of seedlings and spacing in Direct \nPlanting System and modified SRI method in rice cultivation. M. Sc. \n(Ag.) Thesis. Coimbatore, Tamil Nadu, India: Tamil Nadu Agricultural \nUniversity. \n\n\n\nJoshy, E., 1997. Rice Gene Pool for Tarai and Inner Tarai Areas of Nepal. \nNepal Agriculture Research Journal, 6, 10-22. \n\n\n\nKavitha, M.G., 2011. Effect of age of seedlings, weed management practices \nand humic acid application on system of rice intensification. Indian \nJournal Of Agricultural Research, 4, 294 \u2013 299. \n\n\n\nKrishi Diary, G.O., 2075. Krishi Diary. Hariharbhawan, Lalitpur: Ministry of \nAgriculture, land management and co-operatives. \n\n\n\nKrishna., 2008. Influence of system of rice intensification cultivation on \nseed yield and quality. Journal of Agricultural Science, 3, 369-372. \n\n\n\nKumar, A., 2007. A socioeconomic study on Pros and cons of SRI method \nof paddy cultivation in ormanjhi block of Ranchi district, Jharkhand. \nIndian Journal of Agricultural Research, 1, 74-77. doi: \n10.18805/ijare.v0i0.7008 \n\n\n\nLadha, J.K., 2009. Integrating crop and resource management technologies \nfor enhanced productivity, profitability, and sustainability of the rice-\nwheat system in South Asia. Los Banos, Phillipines: International Rice \nResearch Institute. \n\n\n\nMallick., 1981. Rice in Nepal. Kathmandu: Kala Prakasan. \n\n\n\nManjunatha, M.V., Reddy, B.G.M., Shashidhar, S.D., Joshi, V.R., 2010. Studies \non the performance of self- propelled rice transplant and its effect on \ncrop yield. Karnataka Journal of Agricultural Sciences, 2, 385-387. \n\n\n\nMoAD, CBS, & FAO. 2016. \n\n\n\nMoALD. (2018/19). AICC. Hariharbhawan: Government of Nepal. \n\n\n\nMoALD. 2072/73. Statistical information on Nepalese Agriculture. Singh \nDurbar, Kathmandu: Government of Nepal. \n\n\n\nMOF, M.O., 2018. Economic survey 2017/18. Singhdurbar, Kathmandu: \nGovernment of Nepal. \n\n\n\nMura, S.S., 2013-2015. Effect of different crop establishment techniques \nand varieties on growth parameters and yield of hybrid rice. West \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 10-15 \n\n\n\nCite the Article: Santosh Bhandari, Saroj Sapkota, Chetan Gyawali (2020). Effect Of Different Methods Of Crop Establishment On Growth And Yield Of A Spring Rice At \nJanakpurdham-17, Dhanusha. Malaysian Journal of Sustainable Agriculture, 4(1): 10-15. \n\n\n\nBengal: International Journal of Applied and Pure Science and \nAgriculture (IJAPSA). \n\n\n\nNissanka, Bandara., 2004. Comparison of productivity of system of rice \nintensification and conventional rice farming systems in the dry-\nzone region of Sri Lanka. New directions for a diverse planet. \nProceedings of the 4th International Crop Science Congress. \nBrisbane, Australia. Retrieved from \n[http://www.cropscience.org.au/icsc2004/poster/1/2/1177_nissa\nnkara.htm] \n\n\n\nPandey, Velasco., 2005. Trends in crop establishment method in Asia and \nresearch issues. Toriyama. \n\n\n\nPrasad, R., 2004. Current Status, Challenges, and Opportunities in Rice \nProduction. Rice Production Worldwide. \n\n\n\nRajesh, Thanunathan., 2003. Effect of seedling age, number and spacing on \nyield and nutrient uptake of traditional Kambanchamba rice. Madras \nAgricultural Journal, 90. \n\n\n\nRaju, R., 2008. Agronomic evaluation of system of rice intensification \nmethods in Godavari delta, Oryza, 4. \n\n\n\nRao, A.N., Johnson, D.E., Sivaprasad, B., Ladha, J.K., Mortimer, A.M., 2007. \nWeed Management in Direct\u2010Seeded Rice. Advances in Agronomy, \n93, Pp. 153-255. \n\n\n\nReddy, V.R., 2005. Water Use Efficiency: A Study of System of Rice \nIntensification (SRI0 Adoption in Andra Pradesh. Indian Journal of \nAgri-economics, 60. \n\n\n\nSaha, A.V.B., 2010. Effect of different establishment methods of rice on \ngrowth and economics of rice.  \n\n\n\nSanchez., 1973. Influence of different crop establishment methods on \nproductivity of rice- A Review.  \n\n\n\nShao-hua., 2002. Physiological Characteristics and High- Yield Techniques \nin SRI Rice in assessments of the System of Rice Intensification (SRI). \nProceedings of an International Conference in Sanya. Sanya, China: \nJournal of Integrative Agriculture. \n\n\n\nSharma., 2003. Productivity of rice as influenced by crop diversification in \nwheat-rice cropping system on Mollisols of foothills of Himalayas. \nPantnagar: Indian Journal of Agricultural Sciences. \n\n\n\nSheehy., 2004. Fantastic yields in the SRI: fact or fallacy. Field crop \nresearch, 1-8. \n\n\n\nSingh., 1999. Effect on grain yield due to various method of crop \nestablishment. Effect of crop establishment practices in rice varieties \nunder puddled irrigated conditions. \n\n\n\nSolanki, R., 2011. Influence of crop establishment methods on yield, \neconomics and water productivity of rice cultivars under upland and \n\n\n\nlowland production ecologies of Indo-Gangetic Plains. Paddy and \nwater environment, 15, 861-877. \n\n\n\nSridevi, Chellamuthu., 2007. Effect of system of rice intensification (SRI) \npractices on the yield and yield attributes of rice (Oryza sativa). India. \n\n\n\nSridevi, V., 2011. Studies on crop establishment techniques, weed and \nnutrient management practices on the productivity of rice. Ph.D (Ag.) \nThesis. Coimbatore, Tamil Nadu, India: Tamil Nadu Agricultural \nUniversity. \n\n\n\nSurendra., 2001. Influence of different crop establishment methods on \nproductivity of rice- A Review.  \n\n\n\nThakur., 2010. An assessment of physiological effects of System of Rice \nIntensification (SRI) practices compared with recommended rice \ncultivation practices in India. Expl. Agric., 1. \n\n\n\nThiyagarajan., 2002. Performance of rice hybrid CORH-2Bb under SRI. \nEffect of SRI practices on hybrid rice performance in Tamli Nadu, \nIndia. \n\n\n\nThiyagarajan., 2006. A comparative evaluation SRI and conventional \ncultivation in Tamil Nadu and Cauvery Delta. LEISA, 4, 9-10. \n\n\n\nTripathi., 2005a. Tillage index: An approach to optimize tillage in rice-\nwheat system. New Delhi, India: Soil Till. Res. \n\n\n\nTriveni, S.R., 2006. Influence of SRI method of cultivation on microbial \npopulation in wet lands. In: Abstracts of National Symposium on \nSystem of Rice Intensification (SRI) - Present status and Future \nProspects, Agartala, India. Agartala, India. \n\n\n\nUphoff., 2009. Rice Yield and its relation to root growth and nutrient-use \nefficiency under SRI and conventional cultivation: An evaluation in \nMadagascar. Madagascar: Paddy water environment. \n\n\n\nUpphoff, N., 2002. The System of Rice Intensification (SRI) as a System of \nAgricultural Innovation. Madagascar: Cornell university. \n\n\n\nUprety, I.R., 2005. Comparitive study of yield difference in between SRI \nand traditionally grown rice. System of Rice Intensification (SRI) \nPerformance in Morang District during 2005 main season. \n\n\n\nWang., 2003. Effect of rice physiological characteristics and population \ndevelopment on various parameter under SRI. Yield potential and \nNitrogen use efficiency of China's Super rice. \n\n\n\nYasmeen., 2010. Effect of Crop Establishment Methods on the Performance \nof Boro Rice (Oryza sativa L.) in the Cooler Region under Light \nTexture Soil Condition. Rangpur, Bangladesh: The Agriculturists. \n\n\n\nZhang., 2009. Yield potential and radiation use efficiency of super hybrid \nrice grown under sub-tropical conditions. China: Field crops \nresearch.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019)  01-04 \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.01.2019.01.04 \n\n\n\nREVIEW ARTICLE \n\n\n\nMINERAL NUTRIENT CONTENT OF BUCKWHEAT (Fagopyrum esculentum \n Moench) FOR NUTRITIONAL SECURITY IN NEPAL \n\n\n\nBikram Nepali1*, Devashish Bhandari1, Jiban Shrestha2\n\n\n\n1Agriculture and Forestry University, Rampur, Chitwan, Nepal. \n2National Commercial Agriculture Research Program, NARC, Pakhribas, Dhankuta, Nepal \n\n\n\n*Corresponding author email: bikramn25@gmail.com\n\n\n\nORCID: https://orcid.org/0000-0001-9566-291X \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\nARTICLE DETAILS \n\n\n\nArticle History: \n\n\n\nReceived 15 November 2018 \nAccepted 17 December 2018 \nAvailable online 2 January 2019 \n\n\n\nABSTRACT \n\n\n\nBuckwheat (Fagopyrum  esculentum   Moench) is grown primarily to produce grains for human consumption because of \n\n\n\nits nutritive and health promoting value. Buckwheat is the sixth staple food after rice, wheat, maize, finger millet \n\n\n\nand barley in Nepal. This study principally focuses on the mineral nutrient content of buckwheat and their role in \n\n\n\nhuman health and nutrition. Buckwheat is used as basic food, animal feed, vegetable, soup, beverage and medicine. It \n\n\n\nis rich source of proteins, starch, antioxidants, dietary fibre and trace elements. It is rich in minerals like Zn, Cu, Fe, \n\n\n\nMn, Se, K, Na, Ca and Mg. It is rich in fats, flavonoid and vitamin especially B groups. Buckwheat proteins contain amino \n\n\n\nacid which is well balanced and is of a high biological value. Buckwheat is rich source of rutin. The high level of rutin \n\n\n\nis extracted from the leaves for medicine to treat high blood pressure. This review serves as a useful tool to \n\n\n\nresearchers and nutritionist who are working in food and nutritional security in Nepal. \n\n\n\nKEYWORDS \n\n\n\nCommon Buckwheat, Nutrient value, Food security, Nepal \n\n\n\n1. INTRODUCTION\n\n\n\nIn Nepal, buckwheat is a sixth staple food crop after rice, wheat, maize, \n\n\n\nfinger millet, and barley. It is considered as poor man\u2019s crop and is an \n\n\n\nalternate cereal that represents an important food supply in remote places \n\n\n\nof Himalayas. Both species of buckwheat species namely Fagopyrum \n\n\n\nesculentum Moench and F. tataricum Geartn are grown in Nepal. It is \n\n\n\nstaple food crop in high hills where it is grown as the major summer \n\n\n\ncrop. In colder areas Tataricum type is more common where common \n\n\n\nbuckwheat cannot be cultivated [1]. Common Buckwheat (fagopyrum  \n\n\n\nesculentum) is grown throughout the country, whereas bitter \n\n\n\nBuckwheat (fagopyrum tatricum) is grown in the hilly area of Nepal. Hill \n\n\n\nCrops Research Program (HCRP), Dolakha, Nepal has 495 accessions of \n\n\n\nbuckwheat that includes common and tataricum type from local and \n\n\n\nexotic sources [1]. Relatively wide adaptability has been observed in \n\n\n\ntataricum type than in common buckwheat. \n\n\n\nIt is the best crop in higher altitude in terms of adaptation to different \n\n\n\nclimatic variables and easily fitted to different cropping patterns due to \n\n\n\nshort duration. It is cultivated on marginal land in 61 out of 75 districts of \n\n\n\nNepal from some 60 m to 4500 m above sea level, especially hilly and \n\n\n\nmountain districts like Rukum, Rolpa, Jajarkot, Dolpa, Humla, Jumla, \n\n\n\nKalikot, Kavre, Dolakha, and Okhaldhunga, Mustang, Solukhumbu, and \n\n\n\nTaplejung districts regularly since time immemorial [2]. Recently it has \n\n\n\nbeen grown in some Terai districts like Chitwan, Jhapa, and Nawalparasi \n\n\n\nfor commercial purposes especially for green vegetable which has very \n\n\n\nhigh demand due to rutin contents. Every family grows Tartary buckwheat \n\n\n\nin upper Mustang and Dolpa districts and diversity of buckwheat is very \n\n\n\nhigh in Manang, Dolpa, Mustang, Jumla, and Solukhumbu [2,3]. \n\n\n\nGrain and other tissues of buckwheat contain many nutraceutical \n\n\n\ncomponents and rich in vitamins, especially B groups [4,5]. The amino acid \n\n\n\ncomposition of buckwheat proteins is well balanced and has a high \n\n\n\nbiological value, although protein digestibility is relatively low [6]. The \n\n\n\nmicroelements, such as Zn, Cu, Mn, Se can be achieved from buckwheat \n\n\n\ngrains and and microelements: K, Na, Ca, Mg [7,8]. Rutin, catechins and \n\n\n\nother polyphenols and their significant antioxidant effects the dietary \n\n\n\nvalue [9,10]. Buckwheat grain is a rich source of TDF (total dietary fiber), \n\n\n\nsoluble dietary fiber (SDF), and is used in the prevention of obesity and \n\n\n\ndiabetes [11]. \n\n\n\nRational of study \n\n\n\nHuman daily basis consumption of food were Rice, Maize, and wheat in \n\n\n\nNepal. The ratio of cultivation and consumption of those food crop was \n\n\n\nincreasing annually, whereas these crop does not provide ample nutrition \n\n\n\nfor pregnant women and children. However, underutilized crop like: \n\n\n\nBuckwheat, finger millet, prosomillet, and amaranthus which contain \n\n\n\nhigh nutrition value are in the looming stage. If such production trend of \n\n\n\nmajor crops spike, it would threat the food basket of Nepal. This review \n\n\n\nassesses the nutrition value of Buckwheat, so that it can be an informative \n\n\n\npaper to every viewer. \n\n\n\n2. Growth value of buckwheat \n\n\n\nCite The Article:  Bikram Nepali,  Devashish  Bhandari, Jiban  Shrestha  (2019). Mineral   Nutrient Content  of  Buckwheat (Fagopyrum  esculentum Moench) For \nNutritional Security In Nepal. Malaysian Journal of Sustainable Agriculture, 3(1): 01-04. \n\n\n\n\nhttps://www.ncbi.nlm.nih.gov/pubmed/22888949\n\n\nhttps://www.ncbi.nlm.nih.gov/pubmed/22888949\n\n\nhttps://www.ncbi.nlm.nih.gov/pubmed/22888949\n\n\nmailto:bikramn25@gmail.com\n\n\nhttps://orcid.org/0000-0001-9566-291X\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019)  01-04 \n\n\n\nCite The Article: Bikram Nepali, Devashish Bhandari, Jiban Shrestha (2019). Mineral Nutrient Content Of Buckwheat (Fagopyrum esculentum Moench) For Nutritional \nSecurity In Nepal. Malaysian Journal of Sustainable Agriculture, 3(1): 01-04. \n\n\n\n\n\n\n\n\n\n\n\nBuckwheat is a multipurpose crop and is grown for use as basic food, \n\n\n\nanimal feed, vegetable, soup, beverage and medicine [2,12]. All parts of \n\n\n\nbuckwheat plants are used in various ways. The rutin produced by leaf is \n\n\n\nan important pharmaceutical product used to brew tea for the treatment \n\n\n\nof hypertonia. About a month, blooming flowers produce high-quality \n\n\n\nnectar for honey; grains are the basic food; hulls of grains are used to make \n\n\n\npillows; straw is a good source for livestock; green plants are used as green \n\n\n\nfertilizers [2, 13]. \n\n\n\n \nIn Nepal there is a list of 34 dishes prepared with buckwheat, such as such \n\n\n\nas dhindo (thick porridge), roti (bread), momo (Chinese pancake), lagar \n\n\n\n(very thick bread), dheshu (thicker than lagar), fresh vegetables, dried \n\n\n\nvegetables, Kancho pitho (raw flour), chhyang or jaand (local beer), raksi \n\n\n\n(alcohol), salad (leaves), pickle (fresh and dry leaves), soup, ryale roti, \n\n\n\nNoodle, sel roti, bhat (rice), sausage, dorpa dal, tea, vinegar, jam, macaroni, \n\n\n\nbiscuit, cakes, mithai (sweet), haluwa, puri, puwa, bhuteko Phapar \n\n\n\n(roasted grain), satu, phuraula, porridge, and pakauda. Nepalese people \n\n\n\nfrom mountain region prefer dhindo than other items due to their specific \n\n\n\ntaste [2,3]. \n\n\n\n \n3. Health benefits of buckwheat \n\n\n\n \nBuckwheat is a very nutrient-rich, gluten-free plant source for a wealth of \n\n\n\nhealth benefits, including a boost in heart health, reduction in blood \n\n\n\npressure, aid in weight loss, prevention of certain cancers, management of \n\n\n\ndiabetes, improved digestion and cholesterol levels, and stronger immune \n\n\n\nsystem. Buckwheat gives higher calories and carbohydrates than the \n\n\n\nwidely consumed wheat. It can easily serve as an excellent alternative to \n\n\n\nthe traditional wheat [14]. Buckwheat is a great source of dietary fibre, \n\n\n\nwith 10g per 100g [15]. It is another gluten-free food source. The grains \n\n\n\ncompose of several polyphenolic antioxidant compounds such as rutin, \n\n\n\ntannins, and catechin. The rutin (extracted from the buckwheat leaves) is \n\n\n\nused as medicine to treat high blood pressure. Buckwheat is a good source \n\n\n\nof protein with 13.2g per 100g [15]. The protein it contains is of a very high \n\n\n\nquality, the amino acids are well balanced. It is particularly high in lysine \n\n\n\nand arginine [16]. It is a very good source of the mineral magnesium \n\n\n\n(231mg per 100g) [15]. Buckwheat is a great source of manganese, \n\n\n\nphosphorous, copper [17]. For the production of red blood cells copper is \n\n\n\nnecessary. Magnesium make relaxes blood vessels leading to brain and \n\n\n\nfound to have healing effects on depression, and headache. Buckwheat is \n\n\n\nrich in Folate, it helps your body produce and maintain new cells, \n\n\n\nespecially red blood cells. It is particularly important for pregnant women \n\n\n\nto have enough folate. They must start having folate rich foods like \n\n\n\nBuckwheat even while they are planning to conceive. Consuming enough \n\n\n\nfolate before and during pregnancy helps to prevent major birth defects \n\n\n\nconcerning the baby's brain. Buckwheat grains have more B-complex \n\n\n\ngroup of vitamins, especially riboflavin (vitamin B2) and niacin (vitamin \n\n\n\nB3). \n\n\n\n \n4. Nutritional and chemical components of buckwheat grains \n\n\n\n \nBuckwheat protein is rich in arginine and lysine, which constituent about \n(13.36%) [10,18]. The amino acid composition of buckwheat proteins is of \na high biological value and is well balanced [19]. Buckwheat products is an \nimportant source of retrograded starch [18]. It also contains some healing \ncomponent and biologically active properties, such as flavonoids and \nflavon, condensed thanins, phenolic acid, phytosterols and fagopyrins in \ngrain and hulls. Flavonoids are phytonutrients which act as antioxidants \nand having chelating properties [20]. Flavonoid compound is effective for \nthe reduction of blood cholesterol and helping the reduction of blood \npressure. \n\n\n\n \nRutin was the essential and beneficial component from health point of \nview especially found in the Buckwheat. It was a flavonol glycoside \ncomposed of flavonol quercetin and disaccharide rutinose. It has a ability \nto generate reactive oxygen due to antioxidant power. Rutin was found to \npossess highest antioxidant activity of all the identified phenolics in \nbuckwheat. \n\n\n\n \nTable 1: Amount of proteins contents in buckwheat grains [18] \n\n\n\nN (% d.m.) \u00d7 6.5 Authors \n\n\n\n12.0 \u2013 13.0 [21] \n\n\n\n12.11 [5] \n\n\n\n13.30 \u2013 15.55 [8] \n\n\n\n8.51 \u2013 18.87 [22] \n\n\n\n12.02 [23] \n \n\n\n\nTable 2: Amino acid content of buckwheat grain (%w/w) [18] \n\n\n\n \nAminoacids [8] [24] [25] \nLysine 4.9 6.17 5.68 \nHistidine 1.4 2.44 2.52 \nArginine 5.4 8.85 11.16 \nGlutamic acid 9.7 15.37 19.38 \nAspartic acid 5.2 9.10 9.54 \nThreonine 1.9 4.04 3.5 \nSerine 2.4 4.89 4.61 \nProline 2.6 4.57 7.93 \nGlycine 4.2 6.23 5.66 \nAlanine 3.0 4.82 3.89 \nValine 3.4 4.97 4.26 \nIsoleucine 2.6 3.41 3.12 \nLeucine 2.8 6.12 5.94 \nMethionine 1.6 0.99 2.3 \nTyrosine 1.5 1.94 3.03 \nPhenylalanine 2.0 4.42 4.3 \nTryptophane 1.5 2.14 2.0 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 3: Nutrient content in Buckwheat. \n\n\n\nS.No. Component Amount Cited \n\n\n\n1. Content of phytosterols 250 mg/day [26] \n\n\n\n\nhttps://www.organicfacts.net/home-remedies/15-best-foods-for-a-healthy-heart.html?utm_source=internal&amp;utm_medium=link&amp;utm_campaign=smartlinks\n\n\nhttps://www.organicfacts.net/home-remedies/weight-loss.html?utm_source=internal&amp;utm_medium=link&amp;utm_campaign=smartlinks\n\n\nhttps://www.organicfacts.net/home-remedies/home-remedies-for-cancer.html?utm_source=internal&amp;utm_medium=link&amp;utm_campaign=smartlinks\n\n\nhttps://www.organicfacts.net/diabetes.html?utm_source=internal&amp;utm_medium=link&amp;utm_campaign=smartlinks\n\n\nhttps://www.organicfacts.net/home-remedies/20-tips-to-improve-digestive-health.html?utm_source=internal&amp;utm_medium=link&amp;utm_campaign=smartlinks\n\n\nhttps://www.organicfacts.net/health-benefits/other/cooking-methods-cholesterol.html?utm_source=internal&amp;utm_medium=link&amp;utm_campaign=smartlinks\n\n\nhttps://ascensionkitchen.com/are-you-getting-enough-magnesium-top-food-sources/\n\n\n\n\n\n\nCite The Article: Bikram Nepali, Devashish Bhandari, Jiban Shrestha (2019). Mineral Nutrient Content Of Buckwheat (Fagopyrum esculentum Moench) For Nutritional \nSecurity In Nepal. Malaysian Journal of Sustainable Agriculture, 3(1): 01-04. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 01-04 \n \n\n\n\n\n\n\n\n\n\n\n\n2. \n \n\n\n\nProtein content \n \n\n\n\n11.5g/100g \n \n\n\n\n[27] \n\n\n\n \n3. Lipid content 3.45g/100g [27] \n\n\n\n \n4. Carbohydrate content 6.4g/100g [28] \n\n\n\n \n5. Vitamin B6 content 0.61mg/100g [29] \n\n\n\n\n\n\n\nBuckwheat was used for making bread, chapati, biscuits, cakes, dhindo, \n\n\n\nwine, buckwheat tea etc. Thus, it can be used as staple diet in our country. \n\n\n\nConsumption of bread with 30% added buckwheat increases antioxidant \n\n\n\ncapacity of serum [2]. Buckwheat kernel was rich in soluble protein, but it \n\n\n\nhas leucine as a first limited amino acid [8]. Flavonoids biosynthesis in \n\n\n\nbuckwheat makes it a health promoter food [30]. Buckwheat kernel was \n\n\n\nrich in K, Fe and Zn in albumin, Ca, Mg and Mn in globulin and Na in \n\n\n\nprolamin and glutelin [8]. Buckwheat protein has one of the highest amino \n\n\n\nacid scores of protein in plant food stuffs [31]. Buckwheat products have \n\n\n\nhigh level of resistant starch [32]. Buckwheat is rich source of phytosterol \n\n\n\nmainly sitosterol and campesterol [27]. Plant sterols have positive \n\n\n\ncorrelation with lowering blood cholesterol level [22]. Cycloartanol was \n\n\n\nidentified as unique sterol in raw and roasted buckwheat products [27]. \n\n\n\n\n\n\n\n5. Role of buckwheat in nutritional security in Nepal \n\n\n\n\n\n\n\nBuckwheat can be used as a staple diet in Nepal by substituting highly \n\n\n\npolished rice. Buckwheat higher nutritional value and medicinal value acts \n\n\n\nas a food guard in the food security of Nepal. It has a multiple use thus \n\n\n\nproviding hub for agrobased industry. It is grown in marginal lands with \n\n\n\nharsh environmental conditions thus being friendlier with farmers. But its \n\n\n\ncultivation is decreasing, and its landraces are deteriorated due to various \n\n\n\nfactors. Preserving germplasm and planting local landraces helps for long \n\n\n\nterm sustainable agriculture in Nepal. It has an allelopathy effect thus we \n\n\n\ndo not have to deal with weed problems like in other crops. It can easily \n\n\n\ncope with changing climate. Buckwheat flowers are very fragrant and are \n\n\n\nattractive to bees thus they can be used to produce special, strong, dark \n\n\n\nhoney (Up2018). Buckwheat can be served as an alternative to rice \n\n\n\n(Up2018). Buckwheat should be introduced in our daily diet to overcome \n\n\n\nvarious health problems. Raw buckwheat groats are rich source of lipid, \n\n\n\nprotein and sterol in comparison to roasted buckwheat groats [27]. \n\n\n\nBuckwheat contains high level of starch similar to many cereal grains [31]. \n\n\n\nComponents responsible for technological products may be concentrated \n\n\n\nor regulated to obtain a desired product [32]. It grows well in areas with \n\n\n\nless fertile soil and little rainfall. Leaves and shoot of common buckwheat \n\n\n\nis used as leafy vegetable in Himalayan region [33]. Emphasis has to be \n\n\n\ngiven on conservation and utilization of various genetic resources of this \n\n\n\nmultipurpose crop for economic and food security [33,34]. \n\n\n\n \n6. CONCLUSION \n\n\n\n \nIncreased production of major cereals undermined the production of \n\n\n\nhighly nutritious crop like Buckwheat. Lack of Proper extension and \n\n\n\nknowledge regarding the nutrition value of underutilized crops has caused \n\n\n\nincreased obsession with major crops. Buckwheat is rich in retrograded \n\n\n\nstarch, well balanced proteins, fats and vitamin especially B groups. Rutin \n\n\n\nacts as a good antioxidant and sterols are positively associated with health \n\n\n\nbenefits. It is rich in minerals like Ca, Mg, Zn, K and Na. Buckwheat can be \n\n\n\nused as nutritious and energizing food which contribute food and \n\n\n\nnutritional security in Nepal. \n\n\n\n \nREFERENCES \n\n\n\n \n[1] Uprety, R.P. 1995. Buckwheat Overviews, in Nepal: Scope and \n\n\n\nCultivation Strategy Practices, Research. Current Advances in Buckwheat \n\n\n\nResearch, 35, 277 \u2013 284. \n\n\n\n \n[2] Luitel, D.R., Siwakoti, M., Jha, P.K., Jha, A.K., Krakauer, N. 2017. An \n\n\n\nOverview: Distribution, Production, and Diversity of Local Landraces of \n\n\n\nBuckwheat in Nepal. Advances in Agriculture, 1-6. \n\n\n\n \n[3] Joshi, B.K. 2008. Buckwheat genetic resources: status and prospects in \nNepal. Agriculture Development Journal, 5, 13\u201330. \n\n\n\n[4] Fabjan, N., Rode, J., Ko\u015dir, I. J., Zhang, Z., Kreft, I. 2003. Tatary buckwheat \n\n\n\n(Fagopyrum  tartaricum Gaertn.) as a source of dietary rutin and \n\n\n\nquercetin. Journal of Agricultural and Food Chemistry, 51, 6452\u20136455. \n\n\n\n \n[5] Li, S., Zhang, Q.H. 2001. Advances in the development of functional \n\n\n\nfoods from buckwheat. Critical Reviews in Food Science and Nutrition, 41, \n\n\n\n451\u2013464. \n\n\n\n \n[6] Liu, Z., Ishikawa, W., Huang, X., Tomotake, H., Watanabe, H., Kato, N. \n\n\n\n2001. Buckwheat protein product suppresses 1,2-dimethylohydrazine- \n\n\n\ninduced colon carcinogenesis in rats by reducing cell proliferation. \n\n\n\nNutrition and Cancer \u2013 Research Communication: 1850\u20131853. \n\n\n\n \n[7] Stibilj, V., Kreft, I., Smrkolj, P., Osvald, J. 2004. Enhanced selenium \n\n\n\ncontent in buckwheat (Fagopyrum esculentum Moench) and pumpkin \n\n\n\n(Cucurbita pepo L.) seeds by foliar fertilization. European Food Research \n\n\n\nand Technology, 219, 142\u2013144. \n\n\n\n \n[8] Wei, Y.M., Hu, X.Z., Zhang, G.Q., Ouyang, S.H. 2003. Studies on the amino \n\n\n\nacid and mineral content of buckwheat protein fractions. Nahrung - Food, \n\n\n\n47 (2), 114\u2013116. https://doi.org/10.1002/food.200390020 \n\n\n\n \n[9] Oomah, D.B., Mazza, G. 1996. Flavonoids and antioxidative activities in \n\n\n\nbuckwheat. Journal of Agricultural and Food Chemistry, 44, 1746\u20131750. \n\n\n\n \n[10] Wanatabe, M. 1998. Catechins as antioxidant from buckwheat \n\n\n\n(Fagopyrum esculentum Moench) groats. Journal of Agricultural and Food \n\n\n\nChemistry, 46, 839\u2013845. \n\n\n\n \n[11] Brennan, C.S. 2005. Dietary fibre, glycaemic response and diabetes. \n\n\n\nMolecular Nutrition and Food Research, 49, 560\u2013570. \n\n\n\n \n[12] Dhakal, S., Regmi, P., Thapa, R., Sah, S., Khatri-Chhetri, D. 2015. \n\n\n\nResource Use Efficiency of Mustard Production in Chitwan District of \n\n\n\nNepal. International Journal of Applied Sciences and Biotechnology, 3 (4), \n\n\n\n604-608. \n\n\n\n \n[13] Gondola, Papp, P.P. 2010. Origin, geographical distribution and \n\n\n\nphylogenic relationships of common buckwheat (Fagopyrum \n\n\n\nesculentum Moench.)\". The European Journal of Plant Science and \n\n\n\nBiotechnology. Global Science Book. \n\n\n\n \n[14] Kevat, D. 2018. 7 Buckwheat Health Benefits [Fitness Lovers Should \n\n\n\nNot Miss]. https://wiki-fitness.com/nutrition-facts-health-benefits-of- \n\n\n\nbuckwheat/. \n\n\n\n \n[15] USDA. 2008. United States Department of Agriculture (USDA). Basic \n\n\n\nreport: 20008, buckwheat. \n\n\n\nhttp://ndb.nal.usda.gov/ndb/foods/show/6479?fgcd=&manu=&lfacet= \n\n\n\n&format=&count=&max=35&offset=&sort=&qlookup=buckwheat \n\n\n\n \n[16] Mota, C., Santos, M., Mauro, R., Samman, N., Matos, A.S., Torres, D., \n\n\n\nCastanheira, I. 2016. Protein content and amino acids profile of pseudo \n\n\n\ncereals. Food Chemistry, 193, 55-61. doi: \n\n\n\n10.1016/j.foodchem.2014.11.043. \n\n\n\n \n[17] GMF. 2002-2016. The George Matelijan Foundation (GMF). \n\n\n\nBuckwheat. \n\n\n\nhttp://www.whfoods.com/genpage.php?tname=foodspice&dbid=11 \n\n\n\n \n[18] Christa, K., Soral-\u015amietana, M. 2008. Buckwheat grains and \n\n\n\nbuckwheat products \u2013 nutritional and prophylactic value of their \n\n\n\ncomponents \u2013 a review. Czech J. Food Sci., 26, 153\u2013162. \n\n\n\n \n[19] Kato, N., Kayashita, J., Tomotake, H. 2001. Nutritional and \n\n\n\nphysiological functions of buckwheat protein. Recent Research \n\n\n\nDevelopment Nutrition, 4, 113\u2013119. \n\n\n\n\nhttps://doi.org/10.1002/food.200390020\n\n\nhttps://wiki-fitness.com/nutrition-facts-health-benefits-of-buckwheat/\n\n\nhttps://wiki-fitness.com/nutrition-facts-health-benefits-of-buckwheat/\n\n\nhttps://wiki-fitness.com/nutrition-facts-health-benefits-of-buckwheat/\n\n\nhttp://ndb.nal.usda.gov/ndb/foods/show/6479?fgcd&amp;manu&amp;lfacet&amp;format&amp;count&amp;max=35&amp;offset&amp;sort&amp;qlookup=buckwheat\n\n\nhttp://ndb.nal.usda.gov/ndb/foods/show/6479?fgcd&amp;manu&amp;lfacet&amp;format&amp;count&amp;max=35&amp;offset&amp;sort&amp;qlookup=buckwheat\n\n\nhttp://ndb.nal.usda.gov/ndb/foods/show/6479?fgcd&amp;manu&amp;lfacet&amp;format&amp;count&amp;max=35&amp;offset&amp;sort&amp;qlookup=buckwheat\n\n\nhttp://www.sciencedirect.com/science/article/pii/S0308814614017725\n\n\nhttp://www.sciencedirect.com/science/article/pii/S0308814614017725\n\n\nhttp://www.sciencedirect.com/science/article/pii/S0308814614017725\n\n\nhttp://www.sciencedirect.com/science/article/pii/S0308814614017725\n\n\nhttp://www.whfoods.com/genpage.php?tname=foodspice&amp;dbid=11\n\n\n\n\n\n\nCite The Article: Bikram Nepali, Devashish Bhandari, Jiban Shrestha (2019). Mineral Nutrient Content Of Buckwheat (Fagopyrum esculentum Moench) For Nutritional \nSecurity In Nepal. Malaysian Journal of Sustainable Agriculture, 3(1): 01-04. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 01-04 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n[20] Boj\u0148ansk\u00e1, T., Fran\u010d\u00e5kov\u00e1, H., Chlebo, P., Vollmannov\u00e1, A. 2009. Rutin \n\n\n\ncontent in buckwheat enriched bread and influence of its consumption on \n\n\n\nplasma total antioxidant status. Czech Journal of Food Sciences, 27(SPEC. \n\n\n\nISS.). \n\n\n\n \n[21] Steadman, K.J., Burgoon, M.S., Lewis, B.A., Edwardson, S.E., Obendorf, \n\n\n\nR.L. 2001. Minerals, phytic acid, tannin and rutin in buckwheat seed \n\n\n\nmilling fractions. Journal of the Science of Food and Agriculture, 81, 1094\u2013 \n\n\n\n1100. \n\n\n\n \n[22] Krko\u0161kov\u00e1, B., Mr\u00e1zov\u00e1, Z. 2005. Prophylactic components of \nbuckwheat. Food Research International, 38, 561\u2013568. \n\n\n\n \n[23] Stempi\u0144ska, K., Soral-\u015amietana, M. 2006. Chemical components and \n\n\n\nphysicochemical evaluation of buckwheat kernels - comparison of three \n\n\n\nPolish varieties. Food Science Technology Quality,13 (2/47) Supplement: \n\n\n\n348\u2013357. \n\n\n\n \n[24] Soral-Smietana, M. 1984. Buckwheat seed protein. Progress of \n\n\n\nAgricultural Sciences, 3, 35-46. \n\n\n\n \n[25] Tomotake, H., Yamamoto, N., Yanaka, N., Ohinata, H., Yamazaki, R., \n\n\n\nKayashita, J., Kato, N. 2006. High protein flour suppresses \n\n\n\nhypercholesterolemia in rats and gallstone formation in mice by \n\n\n\nhypercholesterolemic diet and body fat in rats because of its low protein \n\n\n\ndigestibility. Nutrition, 22, 166\u2013173. \n\n\n\n \n[26] Moreau, R.A., Singh, V., Hicks, K.B. 2001. Comparison of oil and \n\n\n\nphytosterol levels in germplasm accessions of corn, teosinte, and Job's \n\n\n\ntears. Journal of Agricultural and Food Chemistry, 49 (8), 3793-3795. \n\n\n\n[27] Dziedzic, K., G\u00f3recka, D., Marques, A., Rudzi\u00f1ska, M., Podolska, G. \n\n\n\n2015. Content of phytosterols in raw and roasted buckwheat groats and \n\n\n\nby-products. Czech Journal of Food Sciences, 33 (5), 424\u2013430. \n\n\n\nhttps://doi.org/10.17221/121/2015-CJFS \n\n\n\n \n[28] Steadman, K.J., Burgoon, M.S., Schuster, R.L., Lewis, B.A., Edwardson, \n\n\n\nS.E., Obendorf, R. L. 2000. Fagopyritols, D-chiro-inositol, and other soluble \n\n\n\ncarbohydrates in buckwheat seed milling fractions. Journal of Agricultural \n\n\n\nand Food Chemistry, 48 (7), 2843\u20132847. \n\n\n\n \n[29] Bonafaccia, G., Gambelli, L., Fabjan, N., Kreft, I. 2003. Trace elements \n\n\n\nin flour and bran from common and tartary buckwheat. Food \n\n\n\nChemistry, 83 (1), 1-5. \n\n\n\n \n[30] Taguchi, G. 2016. Flavonoid Biosynthesis in Buckwheat. Molecular \n\n\n\nBreeding and Nutritional Aspects of Buckwheat. Elsevier Inc. \n\n\n\nhttps://doi.org/10.1016/B978-0-12-803692-1.00030-4 \n\n\n\n \n[31] Qin, P., Wang, Q., Shan, F., Hou, Z., Ren, G. 2010. Nutritional \n\n\n\ncomposition and flavonoids content of flour from different buckwheat \n\n\n\ncultivars. International Journal of Food Science and Technology, 45 (5), \n\n\n\n951\u2013958. https://doi.org/10.1111/j.1365-2621.2010.02231.x \n\n\n\n \n[32] Skrabanja, V., Kreft, I., Golob, T., Modic, M., Ikeda, S., Ikeda, K., Kosmelj, \n\n\n\nK. 2004. Nutrient Content in Buckwheat Milling Fractions. Cereal \n\n\n\nChemistry, 81 (2), 172\u2013176. \n\n\n\nhttps://doi.org/10.1094/CCHEM.2004.81.2.172 \n\n\n\n \n[33] Arora, R.K., Baniya, B.K., Joshi, B.D. 1995. Buckwheat genetic \n\n\n\nresources in the Himalayas: their diversity, conservation and use. In: \n\n\n\nMatano T, Ujihara A (eds) Current advances in buckwheat research. \n\n\n\nShinshu University Press, Asahi Matsumoto City, Japan, pp 39\u201346 \n\n\n\n \n[34] Zhu, X., Guo, W., Wang, S. 2013. Sensing Moisture Content of \n\n\n\nBuckwheat Seed from Dielectric Properties. Transactions of the ASABE, 56 \n\n\n\n(5), 1855\u20131862. https://doi.org/10.13031/trans.56.10220 \n\n\n\n\n\n\n\n \n\n\n\n\nhttps://doi.org/10.17221/121/2015-CJFS\n\n\nhttps://doi.org/10.1016/B978-0-12-803692-1.00030-4\n\n\nhttps://doi.org/10.1111/j.1365-2621.2010.02231.x\n\n\nhttps://doi.org/10.1094/CCHEM.2004.81.2.172\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA)  3(1) (2019) 20-22  \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Ofoegbu J, Mike-Anosike E, Nwachukwu IN, Adeleye S, Chinakwe PO (2019). Evaluation Of Plant Growth \nPromoting Potentials Exhibited By Rhizobacteria Associated With Beans Plant . Malaysian Journal of Sustainable Agriculture, 3(1) : 20-22. \n\n\n\n\n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 29 November 2018\nAccepted 30 December 2018\nAvailable online 9 January 2019\n\n\n\nABSTRACT\n\n\n\nABSTRACT \n\n\n\nPlant growth promoting rhizobacteria (PGPR) isolated from the rhizosphere of velvet Beans plant grown on the \nagricultural research farmland of Federal University of Technology, Owerri, were evaluated for their growth \npromoting potentials. The four isolates: Micrococcus sp, Bacillus sp, Corynebacterium sp, and Enterococcus sp, were \nevaluated for plant growth promoting abilities, such as phosphate solubilization, indole acetic acid (IAA), ammonia \n(NH3), and Hydrogen Cyanide (HCN) production. Micrococcus sp, Bacillus sp, and Enterococcus sp produced IAA, \nwhile Corynebacterium sp, and also Bacillus sp and Enterococus sp solubilized phosphate. All the isolates were able \nto produce HCN and NH3. Rhizobacteria associated with beans plant constitute important sources of potentially \nbeneficial microorganisms with plant growth promoting activity or antagonistic effects against phytopathogens. The \nresults obtained in this study suggests that these rhizobacteria possess multiple plant growth promoting attributes \nwhich can be applied as biofertilizers or as biocontrol agents in agriculture, to improve plant growth and \nproductivity.  \n\n\n\n KEYWORDS \n\n\n\nBiofertilizer, rhizobacteria, rhizosphere, phytopathogens.\n\n\n\n1. INTRODUCTION \n\n\n\nBased on a study, the rhizosphere is that region of the soil where plant \n\n\n\nroots and microorganisms interact with each other [1-3]. This zone \n\n\n\ncontains different plant root exudates such as amino acids and sugars, \n\n\n\nwhich serve as nutrients for the colonizing bacteria [4]. The rhizosphere \n\n\n\nsoil is richer in nutrients than the bulk soil, and the interactions between \n\n\n\nthe plants and microorganisms help to make the soil fertile and in turn \n\n\n\nimprove plant growth and yield. According to research, bean plants are \n\n\n\nleguminous plants, and are known to have symbiotic interactions with \n\n\n\nnitrogen fixing bacteria, which help to improve soil fertility [5]. \n\n\n\nRhizobacteria, also known as plant growth promoting rhizobacteria are \n\n\n\nbacteria that colonize the roots of plants, forming symbiotic associations \n\n\n\nwith them. Studies have shown that plant roots supply energy sources for \n\n\n\nthese microorganisms, which in turn reciprocate by affecting plant growth \n\n\n\nand yield in several ways [6]. The different mechanisms suggested for \n\n\n\nplant growth promotion include: nitrogen fixation, production  of \n\n\n\nphytohormones: auxins, gibberellins, cytokinins, ethylene production, \n\n\n\nsolubilization of phosphorus, oxidation of sulfur, increase in nitrate \n\n\n\navailability, extracellular production of antibiotics, lytic enzymes, \n\n\n\nhydrocyanic acid, increases in root permeability, competition for root sites \n\n\n\nand available nutrient, suppression of diseases caused by pathogenic \n\n\n\nrhizobacteria; increased nutrient availability and absorption, etc. [7]. \n\n\n\nSeveral studies have identified that plant growth promoting rhizobacteria \n\n\n\ncan affect plant growths by either direct or indirect mechanisms [8]. Direct \n\n\n\nplant growth promoting mechanisms include: atmospheric nitrogen \n\n\n\nfixation, insoluble phosphate solubilization, secretion of hormones such as \n\n\n\nindole acetic acid (IAA), gibberellins, and kinetins beside ACC (1-\n\n\n\nAminocyclopropane-1-carboxylic acid) deaminase production, which \n\n\n\nregulates ethylene. Indirect plant promotion mechanisms include: \n\n\n\ninduced systemic resistance (ISR), antibiosis, competition for nutrients, \n\n\n\nparasitism and secretion of metabolites such as hydrogen cyanide and \n\n\n\nsiderophores, that are suppressive to pathogenic rhizobacteria. \n\n\n\nThe aim of this study was to isolate possible rhizobacteria from the \n\n\n\nrhizosphere of growing velvet bean plant and evaluate them for multiple \n\n\n\nplant growth promoting attributes. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Study Area \n\n\n\nThe farmland soil of the School of Agriculture and Agricultural Technology, \n\n\n\nFederal University of Technology, Owerri, Imo State, Nigeria (5.3905oN, \n\n\n\n6,9907oE.) \n\n\n\n2.2 Collection of Samples \n\n\n\nBean plants growing on the research farmland soil of School of Agriculture \n\n\n\nand Agricultural Technology, Federal University of Technology, Owerri, \n\n\n\nImo State, Nigeria were randomly selected and gently uprooted and the \n\n\n\nrhizosphere soils (soils around the bean plants) were collected in sterile \n\n\n\npolythene bags, bulked and taken to the laboratory immediately for \n\n\n\nanalysis. \n\n\n\n2.3 Microbiological Analysis \n\n\n\nBased on a study, one (1) gram of rhizospheric soil of the bean plant was \n\n\n\nserially diluted and cultured on appropriate media [9]. Appropriate \n\n\n\ndilutions were plated on Nutrient Agar and incubated at 37oC for 24 hours \n\n\n\nto obtain discrete colonies and pure cultures of the organisms. \n\n\n\n2.4 Evaluation for Plant Growth Promoting Potentials \n\n\n\n2.4.1 Hydrogen Cyanide Production \n\n\n\nBacterial isolates grown on Nutrient Agar had the lids of the plates placed \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.01.2019.20.22 \n\n\n\nEVALUATION OF PLANT GROWTH PROMOTING POTENTIALS EXHIBITED BY \nRHIZOBACTERIA ASSOCIATED WITH BEANS PLANT \n\n\n\nChinakwe EC1, Ibekwe VI1, Nwogwugwu UN1, Ofoegbu J2, Mike-Anosike E1, Nwachukwu IN1, Adeleye S1, Chinakwe PO3 \n\n\n\n1Department of Microbiology, Federal University of Technology, Owerri, Imo State, Nigeria \n2Department of Science Laboratory Technology, Federal University of Technology, Owerri, Imo State, Nigeria \n3Department of Crop Science, Federal University of Technology, Owerri, Imo State, Nigeria \n*Corresponding Author E-mail: eti_chukwumaeze@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\n\nhttp://doi.org/10.26480/mjsa.01.2019.01.03\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)  3(1) (2019) 20-22 \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Ofoegbu J, Mike-Anosike E, Nwachukwu IN, Adeleye S, Chinakwe PO (2019). Evaluation Of Plant Growth Promoting \nPotentials Exhibited By Rhizobacteria Associated With Beans Plant. Malaysian Journal of Sustainable Agriculture, 3(1) : 20-22. \n\n\n\nwith filter paper strips previously saturated in 0.5% picric acid solution \n\n\n\nand 2% sodium carbonate solution. The culture plates were incubated in \n\n\n\ninverted positions over the lids at 37oC for 24 hours. A color change of the \n\n\n\nfilter paper from deep yellow to reddish or orange brown was noted. \n\n\n\n2.4.2 Ammonia Production \n\n\n\nProduction of ammonia by the isolates was tested in peptone medium. \n\n\n\nFreshly grown pure cultures of the isolates were grown for 48 \u2013 72 hours \n\n\n\nat 28oC, and Nesseler\u2019s (0.5ml) reagent added. Development of yellow to \n\n\n\nbrown color was noted. \n\n\n\n2.4.3 Indole Acetic Acid Production \n\n\n\nCultures of the isolates on Jones medium were incubated for 24 hours at \n\n\n\n28oC. They were centrifuged at 350 rpm for 15 minutes and 2ml of \n\n\n\nSalkowski reagent added to 2ml of the supernatant, shaken and incubated \n\n\n\nin the dark at room temperature for 30 minutes. They were observed for \n\n\n\ncolor changes and results recorded. \n\n\n\n2.4.4 Phosphate Solubilization \n\n\n\nThe isolates were grown on Pikovskya\u2019s medium for 48 \u2013 72 hours at room \n\n\n\ntemperature. They were observed for halo zones around the colonies \n\n\n\nwhich indicated phosphate solubilization and results recorded. \n\n\n\n3. RESULTS \n\n\n\n3.1 Microbiological Analysis \n\n\n\nThe bacteria isolated from the rhizospheric soil of velvet bean were \n\n\n\nidentified as Micrococcus sp, Bacillus sp, Corynebacterium sp, and \n\n\n\nEnterococcus sp based on cellular morphology, microscopic features, \n\n\n\nbiochemical characteristics and carbohydrate utilization. \n\n\n\n3.2 Evaluation of Plant Growth Promoting Potentials \n\n\n\nThe plant growth promoting potentials of the rhizobacteria isolated from \n\n\n\nvelvet bean are shown in Table 1 below \n\n\n\nTable 1: Plant Growth Promoting Potentials of the Bacterial Isolates \n\n\n\nIsolates HCN NH3 IAA PO4 \n\n\n\nMicrococcus sp ++ + + - \n\n\n\nBacillus sp ++ ++ + ++ \n\n\n\nCorynebacterium sp + ++ - + \n\n\n\nEnterococcus sp ++ + + + \n\n\n\nKey: HCN: Hydrogen Cyanide Production \n\n\n\nIAA: Indole Acetic Acid Production \n\n\n\nNH3: Ammonia Production \n\n\n\nPO4: Phosphate Solubilization \n\n\n\n+: Positive \n\n\n\n-: Negative \n\n\n\nNote: The positive reaction capacity is shown by the number of (+) \n\n\n\nsymbols, \n\n\n\n  i.e. + = positive; (++) = strongly positive. \n\n\n\n4. DISCUSSION\n\n\n\nBased on a research, the four bacteria isolated from this study had multiple \n\n\n\nplant growth promoting potentials. This is in agreement with previous \n\n\n\nreports by a researcher that plant growth promoting rhizobacteria are \n\n\n\nusually nonpathogenic to the plant, and also increase the plant\u2019s yield by \n\n\n\none or more mechanisms [10]. These mechanisms may be direct or \n\n\n\nindirect and help facilitate rooting and growth of plants generally. Direct \n\n\n\nmechanisms of plant growth promotion were observed, since all the \n\n\n\norganisms produced ammonia (NH3); and three of the four isolates \n\n\n\nproduced indole acetic acid and solubilized phosphate. Micrococcus sp, \n\n\n\nBacillus sp and Enterococcus sp produced IAA, except Corynebacterium sp. \n\n\n\nMoreso, Bacillus sp, Corynebacterium sp and Enterococcus sp were able to \n\n\n\nsolubilize phosphate (Table 1.0). Indirect plant growth promoting \n\n\n\nmechanism was also exhibited through the production of hydrogen \n\n\n\ncyanide which play important roles in the suppression of pathogenic \n\n\n\nrhizobacteria, thereby improving plant growth and health. All the isolates: \n\n\n\nMicrococcussp, Bacillus sp, Corynebacterium sp and Enterococcus sp were \n\n\n\nable to produce hydrogen cyanide. Bacillus sp and Enterococcus sp had all \n\n\n\nthe plant growth promoting attributes studied. This corroborates reports \n\n\n\nof a researcher in his list of some notable plant growth promoting \n\n\n\nrhizobacteria genera[10]. \n\n\n\nAccording to research, plant growth promoting rhizobacteria (PGPR) can \n\n\n\nbe applied as biofertilizers to improve plant yield and growth [11]. \n\n\n\nAccording to research, Bacillus sp have been used previously to improve \n\n\n\nyield in non-leguminous crops such as sugar beet, sugar cane, rice, maize \n\n\n\nand wheat [12]. The production of indole acetic acid (IAA) or auxins affect \n\n\n\nthe plant root by increasing its size, weight and number of branches, as \n\n\n\nwell as surface area in contact with the soil. This helps in nutrient uptake, \n\n\n\nimproving plant nutrition and growth capacity [13]. \n\n\n\nThe production of hydrogen cyanide by all the four isolates highlights their \n\n\n\npotential for use as biocontrol agents. Cyanide is a phytotoxic agent and is \n\n\n\na characteristic of rhizobacteria deleterious to others and helps plants in \n\n\n\ndisease suppression by pathogenic microorganisms in the rhizosphere. \n\n\n\nThis could also form control for weeds and reduce their negative effects \n\n\n\non the growth of desired plants [14-16]. \n\n\n\n5. CONCLUSION \n\n\n\nPlant growth promoting rhizobacteria (PGPR) affect plant growth and \n\n\n\nhealth through several mechanisms, directly or indirectly. The \n\n\n\nrhizosphere of beans plant is endowed, by implication, with valuable \n\n\n\nmicrobiota that have potentials as biofertilizers, as well as biocontrol \n\n\n\nagents which could be employed in sustainable agriculture, thereby \n\n\n\nimproving food security and safeguarding our environment from pollution \n\n\n\nthat may result from the use of inorganic fertilizers and chemical \n\n\n\npesticides. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWe thank Mr. Ononiwu Nnaemeka of Imo State Agricultural development \n\n\n\nprogramme office, Owerri, Nigeria for providing the bean seeds and Dr. \n\n\n\nWesley Braide of Federal University of Technology, Owerri - Nigeria, for \n\n\n\nhis valuable advice. \n\n\n\nREFERENCES \n\n\n\n[1] Huang, X., Jacqueline, M.C., Kenneth, F.R., Ruifu, Z., Qirong, S., Jorge, \n\n\n\nM.V. 2014. Rhizosphere Interactions: Root Exudates, Microbes and \n\n\n\nMicrobial Communities. NRC Research Press, Botany, 92, 267-275. \n\n\n\n[2] Chinakwe, E.C., Egbadon, E.O., Ofoh, M.C., Ojibe, O., Onyeji-Jarret, C., \n\n\n\nEmeakaroha, M.C., Nwogwugwu, N.U., Chinakwe, P.O. 2017. In Vitro \n\n\n\nEvaluation of the Effect of Inorganic Fertilizer on Rhizosphere Soil \n\n\n\nMicrobial Populations during Early Growth of Zea mays and Phaseolus \n\n\n\nvulgaris. Biotechnology Journal International, 18(1), 1-9. \n\n\n\n[3] Sushanto, G., Rout, K., Gitishree, D., Spiros, P. Han-Seung, S., Jayanta \n\n\n\nK.P. 2018. Revitalization of plant growth promoting rhizobacteria for \n\n\n\nsustainable development in agriculture Microbiological Research, \n\n\n\n206:131-140. \n\n\n\n[4] Bais, H.P., Weir, T.L., Perry, L.G., Gilroy, S., Vivanco, J.M. 2006. The role \n\n\n\nof root exudates in rhizosphere interactions with plants and other \n\n\n\norganisms. Annual Review of Plant Biology, 57, 234-266. \n\n\n\n[5] Dinesh, R., Anandaraj, M., Kumar, A., Bini, Y.K., Subila, K.P., Aravind, R. \n\n\n\n2015. Isolation, Characterization and Evaluation of Multi-Tract Plant \n\n\n\nGrowth Promoting Rhizobacteria for their Growth Promoting and Disease \n\n\n\nSuppressing Effects on Ginger. Microbiological Research, 173: 34-43. \n\n\n\n[6] Nadeem, S.M., Zahir, Z.A., Naveed, M., Ashraf, M. 2014. The Role of \n\n\n\nMycorrhizae and Plant Growth Promoting Rhizobacterial (PGPR) in \n\n\n\nImproving Crop Productivity under Stressful Environments. \n\n\n\nBiotechnology Advances, 32, 429-448. \n\n\n\n[7] Kumar, A., Maurya, B.R., Raghuwanshi, R. 2014. Isolation and \n\n\n\nCharacterization of PGPR and their Effect on Growth, Yield and Nutrient \n\n\n\nContent in Wheat. (Triticum aestivum L.) Biocatalysis and Agricultural \n\n\n\nBiotechnology, 3, 121-128. \n\n\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0944501317303415#%21\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0944501317303415#%21\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0944501317303415#%21\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0944501317303415#%21\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0944501317303415#%21\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0944501317303415#%21\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0944501317303415#%21\n\n\nhttps://www.sciencedirect.com/science/journal/09445013\n\n\nhttps://www.sciencedirect.com/science/journal/09445013/206/supp/C\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)  3(1) (2019) 20-22 \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Ofoegbu J, Mike-Anosike E, Nwachukwu IN, Adeleye S, Chinakwe PO (2019). Evaluation Of Plant Growth Promoting \nPotentials Exhibited By Rhizobacteria Associated With Beans Plant. Malaysian Journal of Sustainable Agriculture, 3(1) : 20-22. \n\n\n\n[8] Glick, B. 2014. Bacteria with ACC (1-Aminocyclopropane -1- \n\n\n\nCarboxylic Acid) Deaminase Can Promote Plant Growth and Help to Feed \n\n\n\nthe World. Microbiological Research, 169, 30-39. \n\n\n\n[9] Pochon, J., Tardieux, P. 1962. Analytical Techniques of Soil \n\n\n\nMicrobiology. St-Mande. Edition de la Tourtourella, 111. \n\n\n\n[10] Babalola, O.O. 2010. Beneficial Bacteria of Agricultural Importance. \n\n\n\nBiotechnology Letters, 32,1559-1570.  \n\n\n\n[11] Yadegari, M., Rahmani, H.A., Noormohammadi, G., Ayneband, A. \n\n\n\n2008. Evaluation of Bean (Phaseolus vulgaris) Seeds Inoculation with \n\n\n\nRhizobium phaseoli and Plant Growth Promoting Rhizobacteria on Yield \n\n\n\nand Yield Components. Pakistan Journal of Biological Sciences, 11: 1935-\n\n\n\n1939. \n\n\n\n[12] Cakmakci, U.G., Erdogan, D.M.F. 2007. The Effect of Plant Growth \n\n\n\nPromoting Rhizobacteria on Barley Seedling Growth, Nutrient Uptake, \n\n\n\nSome Soil Properties, and Bacterial Counts. Turkish Journal of Agriculture \n\n\n\nand Forestry, 31, 189-199. \n\n\n\n[13] Shi, C.L., Park, H.B., Lee, J.S., Ryu, S., Ryu, C.M. 2010. Inhibition of \n\n\n\nPrimary Roots and Stimulation of Lateral Root Development in Arabidopsis \n\n\n\nthaliana by the rhizobacterium, Serratia marcescens is through both Auxin \n\n\n\nDependent and Independent Signaling aPathways. Molecules and Cells, 29, \n\n\n\n251-258. \n\n\n\n[14] Hayat, R., Ali, S., Amara, U., Khalid, R., Ahmed, I. 2010. Soil Beneficial \n\n\n\nBacteria and their Role in Plant Growth Promotion. A Review: Annals of \n\n\n\nMicrobiology, 10, 010-017. \n\n\n\n[15] Son, J.S., Sumayo, M., Hwang, Y.J., Kim, B.S., Ghim, S.Y. 2014. \n\n\n\nScreening of plant growth-promoting rhizobacteria as elicitor of systemic \n\n\n\nresistance against gray leaf spot disease in pepper, Applied Soil Ecology, \n\n\n\n73, 1-8. \n\n\n\n[16] Ahemad, M., Khan, M.S. 2012. Evaluation of plant-growth promoting \n\n\n\nactivities of rhizobacterium Pseudomonas putida under herbicide stress. \n\n\n\nAnnals of Microbiology, 62,1531-1540. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 07-11 \n\n\n\nCite the article: Farhadullah Khan, Muhammad Irfaq Khan, Shaheedullah Khan, Muhammad Aftab Uz  Zaman, Haroon Rasheed (2018). Evaluation Of Agronomic Traits For \nYield And Yield Components In Wheat Genotypes With Respect To Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(1) : 07-11. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ    \n\n\n\n   ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nFourteen genotypes including two checks varieties were evaluated for agronomic traits and their adaptability study on \ntwo different sowing dates at the experimental farm of Nuclear Institute for Food and Agriculture, Tarnab, Peshawar, \nPakistan. The combined analysis of variance showed that there were significant variations among genotypes, dates of \nsowing and their interaction. Based on regression coefficient (bi) and mean square deviation from linear regression \n\n\n\n(\ud835\udc46 2\n\ud835\udc51\ud835\udc56\n\n\n\n) for the individual genotypes regarding the parameters Viz. plant height, spike length, spikelets spike-1, number \n\n\n\nof tillers plant-1 and grain yield (kg) plot-1 under consideration, most of the genotypes responded negatively with \nrespect to all the traits under late planting condition. However, some of the genotypes such as CT-09117, CT-09137, \nCT-09141 and SRN-09111 revealed stable performance with respect to the yield assorted traits. They have been \nrecommended for the late planting conditions where sowing is delayed due to some unavoidable circumstances than \nthe other elite wheat genotypes.  \n\n\n\nKEYWORDS \n\n\n\nNormal and late sowing, yield components, environmental effects, Triticum aestivum L.\n\n\n\n1. INTRODUCTION \n\n\n\nWheat (Triticum aestivum L.) is one of the most important grass family \n(Poaceae) cereal crop throughout the world [1]. The world\u2019s leading wheat \nproducing countries are United States, China, India, Russian Federation, \nAustralia, France, Germany, Canada, United Kingdom, Ukraine, Turkey, \nPakistan, Argentina and Kazakhstan [2]. In Pakistan wheat is grown on an \narea of about 8.3 million ha with average yield ranging from 2.7 to 4.2 tons \nha-1 and total production of 23.7 million tons [3].  \n\n\n\nStability in performance is one of the most important property of a \ngenotype for wide cultivation. That is why multi-locational trials are \nconducted for number of years to estimate the performance and \nphenotypic stability. Sometimes the uni-locational trials can also serve the \npurpose provided different environments are created by planting \nexperimental material at different sowing dates on the same location [4]. \nThis differential yield response of genotypes in different environment is \ncalled genotype \u00d7 environment (G\u00d7E) interaction [5]. Grain yield being a \npolygenic character and is greatly affected by different environmental \nconditions. Therefore, a wide research work is required to develop such \nvarieties which could give high yield across different environments [6]. \nThe demand for wheat is increasing, because of the rapid increase in the \npopulation growth rate. Because of urbanization and industrialization, \nland and water resources are being decline.  \n\n\n\nIt is therefore, a great need to increase wheat production within the \navailable resources in order to meet the increasing demand for food. \nWheat breeders are engaged to improve the yield potential by developing \nnew cultivars having desirable genetic make-up [7]. To release a new \nvariety for wide cultivation stability in performance is one of the most \ndesirable properties of genotypes. The present study was designed to \ndetermine environmental effects on yield and yield related agronomic \ntraits with respect to the performance of some elite wheat genotypes. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nThe present research work was carried out at the experimental farm of \n\n\n\nNuclear Institute for Food and Agriculture (NIFA)in 2012-2013, Peshawar, \nPakistan with two experimental sets where each set had 12 genotypes \n(WL-0916-2, CT-09065, CT-09117, CT-09137, CT-09141, CT-09149, SRN-\n09048, SRN-09063, SRN-09065, SRN-09087, SRN-09102 and SRN-\n09111and two check cultivars (Bathoor-08 and Pirsabak-08). Data on five \nrandomly selected plants from each plot were recorded and average value \nwas calculated for plant height (cm), number of productive tillers plant-1, \nspike length (cm), number of spikelets spike-1 and grain yield plot-1 (kg). \n\n\n\nThe data collected on 5 randomly selected plants from each plot were \naveraged separately for each parameter and were subjected to the analysis \nof variance using Gen Stat 12th statistical software [8]. To detect the \npresence of genotype by environment (different sowing dates) interaction \nand to partition the variation due to genotype, date and genotype by date \ninteraction, a pooled analysis of variance was computed. After \nsubstantiation a significant genotype by environment interaction through \nF-test, univariate stability parameters were performed in accordance with \nthe coefficient of regression (bi) by using Eberhart and Russell\u2019s model as \nstability test [9]. Regression coefficient (bi) was calculated as a parameter \nof measuring the response of a particular genotype on varying \nenvironments (dates) with respect to each parameter. \n\n\n\nThe environmental index (Ij) for each genotype was computed in order to \ndetermine the deviation of mean of all the genotypes at given date from \nthe overall mean. Using MS Excel program for Windows, as outlined by a \nresearchers, variance of means over different sowing dates with regard to \n\n\n\nindividual genotype (\u03c3 2\n\ud835\udc49\ud835\udc56\n) and mean square deviations (\ud835\udc46 2\n\n\n\n\ud835\udc51\ud835\udc56\n) from linear \n\n\n\nregression were also worked out as the parameter of stability [10].  \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Plant Height \n\n\n\nThe combined analysis of variance (Table1) indicated highly significant \ndifference for mean square plant height with respect to genotypes and \nsowing dates. However, mean square values for plant height with respect \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.01.2018.07.11 \n\n\n\nEVALUATION OF AGRONOMIC TRAITS FOR YIELD AND YIELD COMPONENTS IN \nWHEAT GENOTYPES WITH RESPECT TO PLANTING DATES \n\n\n\nFarhadullah Khan1*, Muhammad Irfaq Khan2, Shaheedullah Khan3, Muhammad Aftab uz Zaman3, Haroon Rasheed3 , Abdur Rahim Khan1 \n\n\n\n1 Hazara University Mansehra  \n2 Nuclear Institute for food & agriculture Tarnab Peshawar \n3 University of Science and Technology Bannu \n*Corresponding Author E-mail:khan.farhadullah@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited.\n\n\n\n\nmailto:khan.farhadullah@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 07-11 \n\n\n\nCite the article: Farhadullah Khan, Muhammad Irfaq Khan, Shaheedullah Khan, Muhammad Aftab Uz Zaman, Haroon Rasheed (2018). Evaluation Of Agronomic Traits For Yield \nAnd Yield Components In Wheat Genotypes With Respect To Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(1) : 07-11. \n\n\n\nto interaction between genotypes and dates (genotype \u00d7 date) were non-\nsignificant.  \n\n\n\nTable 2 presents mean plant height (cm) with respect to different dates, \nover all mean on the two dates, variance due to deviation from regression \n\n\n\n(\u03c3 2\nVi\n\n\n\n), regression coefficient (bi) and mean square deviation from linear \n\n\n\nregression (S 2\ndi\n\n\n\n) regarding the individual genotypes. It is evident from \n\n\n\nTable 2 that mean plant height of all the genotypes was reduced when their \n\n\n\nsowing was delayed. Based on bi andS 2\ndi\n\n\n\n values, CT-09149, CT-09065 and \n\n\n\nBathoor-08 were the most stable genotypes among all the tested \ngenotypes for plant height.  \n\n\n\nMean plant height on both the sowing dates was recorded as 94.0, 91.1 and \n89.2 cm, respectively. Both the test genotypes i. e. CT-09149 and CT-09065 \nexceeded in stability performance for plant height then the check varieties \ni.e. Bathoor-08 and Pirsabak-08. According to Table 2, the genotypes CT-\n09149, CT-09065, SRN-09087, SRN-09111 and Bathoor-08 have higher \nvalue of bi> 1 and are more responsive for medium plant height under \nhighly favorable environments. Medium plant height with stout stem is \ndesired agronomic trait in wheat because it may avoid lodging and \ncontributes to biomass.  \nHowever, wheat varieties recommended for rainfed areas must have \nconsiderable plant height to compensate under drought response. Greater \nplant height is an undesirable trait under irrigated condition because the \nvarieties with larger plant height may lodge easily due to excessive \nvegetative growth under irrigation [11].  \n\n\n\nThe present results are in close proximity with those reported by some \ngroup researchers who also found that significant reduction in plant \nheight is associated with delayed sowing on different locations [12]. \nSimilar results have also been reported by others researchers who found \nprominent as well as significant effect on plant height of different wheat \ncultivars when sown under diverse environments under different sowing \ndates [11].  \n\n\n\n3.2 Spike length \n\n\n\nCombined analysis of variance for spike length (cm) of 14 genotypes tested on \ntwo different sowing dates (Table 3) show that means square values for spike \nlength were highly significant regarding to sowing dates and genotypes. \nHowever, non-significant difference in the mean square values for the \ninteraction between genotypes and dates was observed.  \n\n\n\nComparatively larger spike length with dense spikelets plays significant role \nin yield maximization as it has significant positive genotypic correlation with \ngrain yield [13]. Very little information is available regarding the effect of \nsowing date on spike length. The data was subjected to Eberhart and Russel \nmodel in order to investigate the stability performance. Table 4 indicates that \naverage spike length of almost all the genotypes decreased when sowing was \ndelayed. Based on bi and Sd2 values, Table 4 further indicates that genotypes \nCT-09141, SRN-09048 and SRN-09063 are highly stable. \n\n\n\n3.3 Spikelets spike-1 \n\n\n\nThe analysis of variance (Table 5) reveals that the mean square values for \ndifferent sowing dates were highly significant while non-significant for \ngenotype and interaction between genotype and dates. It is evident from \n\n\n\nTable 6 that number of spikelets spike-1 was decreased when the \ngenotypes were sown under late planting condition. According to ranking \n\n\n\nbased on unit bi values (bi=1) and \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\nvalues nearly equal to zero (\ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n~ 0), \n\n\n\nthe genotypes SRN-09048 and SRN-09065 were found most stable in \ncomparison to the rest of the genotypes. These genotypes have bi values of \n\n\n\n3.6, 3.2 and \ud835\udc46 2\n\ud835\udc51\ud835\udc56\n\n\n\n values of 1.01, 0.95, respectively. No apparent reduction in \n\n\n\nthe number of spikelets spike-1 was observed under late sowing condition \n(Table 6).  \n\n\n\nHowever, some of the genotypes i.e. WL-0916-2, CT-09065, CT-09117, CT-\n09149and SRN-09048 indicated reduced number of spikelets spike-1under \n\n\n\nlate sowing. According to unit bi values and \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\nvalues, the same genotypes \n\n\n\nwere found stable with respect to the number of spikelets spike-1 under \nboth normal and late planting conditions. The check Bathoor-08 was \nstable with respect to check Pirsabak-08. The present results are in \nagreement with a scientist who found that some of the genotypes might \nrespond well even under late planting condition. This superiority of some \ngenotypes over the other may be due to their genetic adaptive background \nto late planting environment. Due to the availability of such genotypes to \nthe growers, wheat yield may be stabilized if sowing is delayed due to \nsome circumstances. \n\n\n\n3.4 Number of tillers plant-1 \n\n\n\nTable 7 indicates high level of significance for mean squares regarding \ngenotypes and dates. However non-significant variation was observed due \nto the interaction between genotypes and dates (genotypes \u00d7 dates).  \n\n\n\nOn the bases of bi values (bi =1) and \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\nvalues nearly equal to zero (\ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n~ \n\n\n\n0), the genotypes SRN-09065, SRN-09102, CT-09137, SRN-09048, CT-\n09149 and SRN-09111 were found stable regarding the trait under \nconsideration. Table 8 further indicates a general trend among the tested \ngenotypes that number of tillers plant-1 for all the genotypes were \ndecreased when they were sown in late planting condition. The results \npresented in Table 8 are comparable with some studies which advocated \nthat number of tillers plant-1 have positive correlation with normal sowing \n[14,15]. The results forwarded in others studies which have also \nconfirmed the present results by suggesting that delay in sowing is \nassociated with the reduced number of productive tillers in wheat [16]. \n\n\n\n3.5 Grain yield (kg) plot-1 \n\n\n\nCombined analysis of variance for grain yield (kg) plot-1 regarding mean \nsquares for 14 wheat genotypes tested on two different sowing dates \n(Table 9) shows high level of significance for genotypes, dates and \ninteraction due to genotype and dates (genotype \u00d7 dates).  \n\n\n\nTable 10 indicates that grain yield (kg) plot-1 was decreased when the \ngenotypes were sown under late planting condition. Based on unit bi value \n\n\n\nand value of\ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n , Table 10 further indicates that genotypes CT-09137, CT-\n\n\n\n09141 and CT-09117 in comparison to check Pirsabak-08 were the most \nstable among all the genotypes. There also more studies have forwarded \nresults quite coincident to the findings of the present investigations \n[11,14,17-19]. These observers have argued that late planting affects grain \nyield plot-1 negatively. \n\n\n\nTable 1: Univariate stability parameters for plant height (cm) of 14 wheat genotypes \n\n\n\nGenotype \nMean plant height (cm) \n\n\n\n\ud835\uded4\n\ud835\udfd0\n\n\n\n\ud835\udc7d\ud835\udc8a\nbi \ud835\udc7a\n\n\n\n\ud835\udfd0\n\n\n\n\ud835\udc85\ud835\udc8a\nRanking \n\n\n\nDate-1 Date-2 Average \n\n\n\nWL-0916-2 94.0 88.7 91.3 5575.4 0.67 0.8 12 \n\n\n\nCT-09065 97.2 85.0 91.1 5604.8 1.52 0.8 2 \n\n\n\nCT-09117 95.0 89.0 92.0 5660.7 0.75 0.8 10 \n\n\n\nCT-09137 94.3 87.0 90.7 5507.2 0.92 0.8 8 \n\n\n\nCT-09141 97.3 93.0 95.2 6047.2 0.54 1.0 14 \n\n\n\nCT-09149 101.0 87.0 94.0 5988.7 1.75 0.9 1 \n\n\n\nSRN-09048 99.3 92.3 95.8 6147.2 0.88 1.0 9 \n\n\n\nSRN-09063 93.7 88.0 90.8 5516.5 0.71 0.8 11 \n\n\n\nSRN-09065 88.0 83.0 85.5 4886.0 0.63 0.6 13 \n\n\n\nSRN-09087 94.0 85.0 89.5 5380.7 1.13 0.8 4 \n\n\n\nSRN-09102 98.0 89.0 93.5 5868.7 1.13 0.9 5 \n\n\n\nSRN-09111 100.0 91.0 95.5 6120.7 1.13 1.0 6 \n\n\n\nBathoor-08 94.3 84.0 89.2 5353.9 1.29 0.7 3 \n\n\n\nPirsabak-08 90.3 82.7 86.5 5017.6 0.96 0.7 7 \n\n\n\nTotal 1336.5 1224.7 1280.6 - 14.00 - - \n\n\n\n8\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 07-11 \n\n\n\nCite the article: Farhadullah Khan, Muhammad Irfaq Khan, Shaheedullah Khan, Muhammad Aftab Uz Zaman, Haroon Rasheed (2018). Evaluation Of Agronomic Traits For Yield \nAnd Yield Components In Wheat Genotypes With Respect To Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(1) : 07-11. \n\n\n\n\ud835\uded4 \ud835\udfd0\n\ud835\udc7d\ud835\udc8a\n\n\n\n= variance due to deviation from regression, bi = coefficient of regression and  \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n = deviation from linear regression. \n\n\n\nTable 3: Combined analysis of variance for spike length (cm) of 14 wheat \ngenotypes tested on two different sowing dates \n\n\n\nSource of \nvariation \n\n\n\nd. f. S. S. M. S. v. r. F. Pr \n\n\n\nGenotype 13 77.35*** 5.95*** 11.92 0.001 \n\n\n\nReplication  2 4.8957 2.4479 4.90 - \n\n\n\nDate 1 32.04*** 32.04*** 64.18 0.001 \n\n\n\nGenotype \u00d7 Date 13 6.71 0.52 1.03 0.434 \n\n\n\nResidual 54 26.96 0.50 - - \n\n\n\nTotal 83 147.95 - - - \n\n\n\nd.f.= degrees of freedom, SS= sum of square, MS= mean square, v.r.= \nvariance ratio = F-calculated, F. Pr= F probability, *= p< 0.05, **= p< \n0.01 and ***= p< 0.001 \n\n\n\nTable 4: Univariate stability parameters for spike length (cm) of 14 wheat genotypes \n\n\n\nGenotype \nSpike length (cm) \n\n\n\n\ud835\uded4\n\ud835\udfd0\n\n\n\n\ud835\udc7d\ud835\udc8a\nbi \ud835\udc7a\n\n\n\n\ud835\udfd0\n\n\n\n\ud835\udc85\ud835\udc8a\nRanking \n\n\n\nDate-1 Date-2 Average \n\n\n\nWL-0916-2 13.10 12.07 12.58 106.06 0.83 1.00 9 \n\n\n\nCT-09065 11.11 10.05 10.58 75.17 0.86 0.71 8 \n\n\n\nCT-09117 14.03 13.30 13.67 124.75 0.59 1.18 10 \n\n\n\nCT-09137 10.75 10.05 10.40 72.31 0.57 0.68 12 \n\n\n\nCT-09141 11.55 9.92 10.74 78.18 1.32 0.73 4 \n\n\n\nCT-09149 13.98 12.37 13.17 116.96 1.30 1.10 5 \n\n\n\nSRN-09048 13.17 11.40 12.29 102.18 1.43 0.95 3 \n\n\n\nSRN-09063 12.17 10.17 11.17 85.16 1.62 0.79 2 \n\n\n\nSRN-09065 12.50 10.90 11.70 92.57 1.30 0.86 6 \n\n\n\nSRN-09087 13.12 12.42 12.77 108.93 0.56 1.03 13 \n\n\n\nSRN-09102 12.27 12.13 12.20 99.24 0.11 0.94 14 \n\n\n\nSRN-09111 13.32 11.77 12.55 106.13 1.26 0.99 7 \n\n\n\nBathoor-08 11.81 11.10 11.46 87.73 0.57 0.83 11 \n\n\n\nPirsabak-08 12.53 10.48 11.51 90.38 1.66 0.84 1 \n\n\n\nTotal 175.41 158.11 166.76 - 14.00 - - \n\n\n\n\ud835\uded4 \ud835\udfd0\n\ud835\udc7d\ud835\udc8a\n\n\n\n= variance due to deviation from regression, bi = coefficient of regression and \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n = deviation from linear regression. \n\n\n\nTable 5: Combined analysis of variance for spikelets spike-1of 14 wheat genotypes tested on two different sowing dates \n\n\n\nSource of variation d. f. S. S. M. S. v. r. F. Pr \n\n\n\nGenotype 13 47.01 3.62 1.75 0.076 \n\n\n\nReplication 2 22.858 11.429  5.54 - \n\n\n\nDate 1 20.01*** 20.01*** 9.69 0.003 \n\n\n\nGenotype \u00d7 Date 13 38.41 2.96 1.43 0.176 \n\n\n\nResidual 54 111.49 2.07 - - \n\n\n\nTotal 83 - - - \n\n\n\nd.f.= degrees of freedom, SS= sum of square, MS= mean square, v.r. =  variance ratio = F-calculated, F. Pr= F probability, *= p<0.05, **= p<0.01 and ***= \np<0.001 \n\n\n\nTable 6: Univariate stability parameters for number of spikelets spike-1of 14 wheat genotypes \n\n\n\nGenotype \nNumber of spikelets spike-1 \n\n\n\n\ud835\uded4\n\ud835\udfd0\n\n\n\n\ud835\udc7d\ud835\udc8a\nbi \ud835\udc7a\n\n\n\n\ud835\udfd0\n\n\n\n\ud835\udc85\ud835\udc8a\nRanking \n\n\n\nDate-1 Date-2 Average \n\n\n\nWL-0916-2 21.80 20.53 21.17 597.37 1.3 0.99 5 \n\n\n\nCT-09065 19.53 18.73 19.13 488.11 0.8 0.85 7 \n\n\n\nCT-09117 20.73 19.00 19.87 526.25 1.8 0.92 4 \n\n\n\nCT-09137 20.17 20.80 20.48 538.42  1.6 0.27 1 \n\n\n\nCT-09141 18.23 18.53 18.38 450.60  1.3 0.18 2 \n\n\n\nCT-09149 20.30 19.47 19.88 527.13 0.3 0.02 13 \n\n\n\nSRN-09048 22.67 19.13 20.90 582.41 3.6 1.01 6 \n\n\n\nSRN-09063 19.93 19.20 19.57 510.47 0.8 0.89 8 \n\n\n\nTable 2: Combined analysis of variance for plant height (cm)  of \n14wheatgenotypes tested on two different sowing dates \n\n\n\nSource of \nvariation \n\n\n\nd.f SS MS v.r. F. Pr \n\n\n\nGenotypes 13 1083.81*** 83.37*** 6.17 0.001 \n\n\n\nReplication 2 247.17  123.58 9.14 - \n\n\n\nDate 1 398.68*** 398.68*** 29.50 0.001 \n\n\n\nGenotypes \u00d7 \nDate \n\n\n\n13 319.57 24.58 1.82 0.064 \n\n\n\nResidual 54 729.83 13.52 - - \n\n\n\nTotal 83 2779.06 - - - \n\n\n\nd.f. = degrees of freedom, SS= sum of square, MS= mean square, v.r. = \nvariance ratio = F-calculated. Pr= F probability, *= p<0.05, **= p<0.01 \nand ***= p<0.001 \n\n\n\n9\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 07-11 \n\n\n\nCite the article: Farhadullah Khan, Muhammad Irfaq Khan, Shaheedullah Khan, Muhammad Aftab Uz Zaman, Haroon Rasheed (2018). Evaluation Of Agronomic Traits For Yield \nAnd Yield Components In Wheat Genotypes With Respect To Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(1) : 07-11. \n\n\n\nSRN-09065 21.83 18.67 20.25 546.75 3.2 0.95 12 \n\n\n\nSRN-09087 20.63 20.33 20.48 559.42 0.3 0.97 10 \n\n\n\nSRN-09102 20.27 21.53 20.90 582.41 -1.3 1.01 14 \n\n\n\nSRN-09111 20.77 20.33 20.55 563.07   0.4 0.98 9 \n\n\n\nBathoor-08 22.07 19.33 20.70 571.32   2.8 1.00 3 \n\n\n\nPirsabak-08 19.73 19.40 19.57 510.47   0.3 0.89 11 \n\n\n\nTotal 288.67 275.00 281.83 -   14.0 - - \n\n\n\n\ud835\uded4 \ud835\udfd0\n\ud835\udc7d\ud835\udc8a\n\n\n\n= variance due to deviation from regression, bi = coefficient of regression and \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n = deviation from linear regression. \n\n\n\nTable 7: Combined analysis of variance for number of tillers plant-1of \n14 wheat genotypes tested on two different sowing dates \n\n\n\nSource of variation d. f. S. S. M. S. v. r. F. Pr.\n\n\n\nGenotype 13 57.54*** 7.5*** 2.58 0.007 \n\n\n\nReplication 2 15.070  7.535 2.59 - \n\n\n\nDate 1 1246.63*** 1246.63*** 428.86 0.001 \n\n\n\nGenotype \u00d7 Date 13 52.96 4.07 1.40 0.189 \n\n\n\nResidual 54 156.97 2.907 - - \n\n\n\nTotal 83 1569.17 - - - \n\n\n\nd.f.= degrees of freedom, SS= sum of square, MS= mean square, v.r. = \nvariance ratio = F-calculated, F. Pr= F probability, *= p<0.05, **= \np<0.01 and ***= p<0.001 \n\n\n\nTable 8: Univariate stability parameters for number of tillers plant-1 \n\n\n\nof 14 wheat genotypes \n\n\n\nGenotype \nNumber of tillers plant-1 \n\n\n\n\ud835\uded4\n\ud835\udfd0\n\n\n\n\ud835\udc7d\ud835\udc8a\nbi \ud835\udc7a\n\n\n\n\ud835\udfd0\n\n\n\n\ud835\udc85\ud835\udc8a\nRanking \n\n\n\nDate-1 Date-2 Average \n\n\n\nWL-0916-2 15.90 7.20 11.55 126.78 1.13 0.82 5 \n\n\n\nCT-09065 14.40 7.40 10.90 103.71 0.91 0.73 12 \n\n\n\nCT-09117 16.60 7.33 11.97 138.40 1.20 0.88 13 \n\n\n\nCT-09137 16.53 7.80 12.17 136.82 1.13 0.91 6 \n\n\n\nCT-09141 13.60 10.07 11.83 99.59 0.46 0.86 14 \n\n\n\nCT-09149 17.67 9.00 13.33 156.07 1.12 0.10 3 \n\n\n\nSRN-09048 14.83 7.80 11.32 110.11 0.91 0.79 11 \n\n\n\nSRN-09063 16.53 7.80 12.17 136.82 1.13 0.91 8 \n\n\n\nSRN-09065 19.50 9.60 14.55 190.14 1.01 0.01 1 \n\n\n\nSRN-09087 18.83 10.47 14.65 178.08 1.09 1.34 7 \n\n\n\nSRN-09102 15.60 10.13 12.87 125.31 1.03 0.02 2 \n\n\n\nSRN-09111 16.80 9.13 12.97 141.48 1.00 0.04 4 \n\n\n\nBathoor-08 17.00 9.47 13.23 145.12 0.98 1.08 9 \n\n\n\nPirsabak-08 15.87 8.60 12.23 126.17 0.94 0.92 10 \n\n\n\nTotal 229.67 121.80 175.73 - 14.00 - - \n\n\n\n\ud835\uded4 \ud835\udfd0\n\ud835\udc7d\ud835\udc8a\n\n\n\n= variance due to deviation from regression, bi = coefficient of regression \n\n\n\nand   \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n = deviation from linear regression.\n\n\n\nTable 9: Combined analysis of variance for grain yield (kg) plot-1of 14 \nwheat genotypes tested on two different sowing dates \n\n\n\nSource of \nvariation \n\n\n\nd. f. S. S. M. S. v. r. F. Pr.\n\n\n\nGenotype 13 1292.67*** 99.44*** 2.89 0.003 \n\n\n\nReplication 2 0.26454  0.13227  2.39 - \n\n\n\nDate 1 535.05*** 5.35.05*** 256.79 0.001 \n\n\n\nGenotype \u00d7 Date 13 97.95*** 7.54*** 2.76 0.004 \n\n\n\nResidual 54 147.41 2.73 - - \n\n\n\nTotal 83 842.29 - - - \n\n\n\nd.f.= degrees of freedom, SS= sum of square, MS= mean square, v.r. = \nvariance ratio = F-calculated, F. Pr= F probability, *= p<0.05, **= \np<0.01 and ***= p<0.001 \n\n\n\nTable 10: Univariate stability parameters for grain yield (kg) plot-1of 14 \nwheat genotypes \n\n\n\nGenotype \nGrain yield (kg) plot-1 \n\n\n\n\ud835\uded4\n\ud835\udfd0\n\n\n\n\ud835\udc7d\ud835\udc8a\nbi \ud835\udc7a\n\n\n\n\ud835\udfd0\n\n\n\n\ud835\udc85\ud835\udc8a\nRanking \n\n\n\nDate-1 \nDate-\n2 \n\n\n\nAverage \n\n\n\nWL-0916-2 2.28 1.15 1.71 2.60 1.38 0.6 6 \n\n\n\nCT-09065 2.02 1.49 1.76 2.20 0.65 0.7 13 \n\n\n\nCT-09117 2.02 1.85 1.94 2.04 1.17 0.5 3 \n\n\n\nCT-09137 2.30 1.94 2.12 2.03 1.05 0.7 1 \n\n\n\nCT-09141 2.11 1.98 2.05 2.12 0.79 0.7 2 \n\n\n\nCT-09149 1.77 1.10 1.43 1.59 0.81 0.4 10 \n\n\n\nSRN-09048 1.98 1.36 1.67 2.05 0.76 0.6 12 \n\n\n\nSRN-09063 2.17 1.18 1.68 2.37 1.21 0.6 5 \n\n\n\nSRN-09065 2.25 1.35 1.80 2.57 1.09 0.7 7 \n\n\n\nSRN-09087 2.00 1.17 1.59 2.02 1.01 0.5 8 \n\n\n\nSRN-09102 1.80 1.47 1.64 1.84 0.39 0.6 14 \n\n\n\nSRN-09111 2.63 1.65 2.14 3.54 1.19 1.0 11 \n\n\n\nBathoor-08 1.99 1.29 1.64 2.03 0.84 0.6 9 \n\n\n\nPirsabak-08 2.31 1.04 1.68 2.28 1.54 0.6 4 \n\n\n\nTotal 29.63 18.10 23.87 - 14.00 - - \n\n\n\n\ud835\uded4 \ud835\udfd0\n\ud835\udc7d\ud835\udc8a\n\n\n\n= variance due to deviation from regression, bi = coefficient of \n\n\n\nregression and  \ud835\udc7a \ud835\udfd0\n\ud835\udc85\ud835\udc8a\n\n\n\n = deviation from linear regression. \n\n\n\nREFERENCES \n\n\n\n[1] Briggle, L.W., Reitz, L.P. 1963. Classification of Triticum species and of \nwheat varieties grown in the United States. U.S. Dept. of Agriculture \nBulletin 1278. \n\n\n\n[2] FAO. 2005. FAO production year book, 54, 74-75. \n\n\n\n[3] Government of Pakistan. 2010-2011. Year Book, Agricultural Statistics \nof Pakistan. Ministry of Food, Agriculture and Livestock, Islamabad, pp. 55. \n\n\n\n[4] Luthra, O.P., Singh, R.K., Kakar, S.N. 1974. Comparison of different \nstability models in wheat. Theoretical and Applied Genetics, 45, 143-149. \n\n\n\n[5] Basford, K.E., Cooper, M. 1998. Genotype environment interactions\nand some considerations of their implications for wheat breeding in \nAustralia. Australian Journal of Agricultural Research, 49 (2), 153-174. \n\n\n\n[6] Allard, R.W., Bradshaw, A.D. 1964. Implications of genotype-\nenvironmental interactions in applied plant breeding. Crop Science, 4, \n503-507. \n\n\n\n10\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 07-11 \n\n\n\nCite the article: Farhadullah Khan, Muhammad Irfaq Khan, Shaheedullah Khan, Muhammad Aftab Uz Zaman, Haroon Rasheed (2018). Evaluation Of Agronomic Traits For Yield \nAnd Yield Components In Wheat Genotypes With Respect To Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(1) : 07-11. \n\n\n\n[7] Memon, S.M., Qureshi, M.U., Ansari, B.A., Sial, M. A. 2007. Genetic \nheritability for grain yield and its related character in spring wheat. \nPakistan Journal of Botany, 39 (5), 1503-1509. \n\n\n\n[8] GenStat. 2009. GenStat for Windows (12thEdition) Introduction. VSN \nInternational,  Hemel Hempstead. \n\n\n\n[9] Eberhart, S.A., Russel, W. A. 1966. Stability parameters for comparing \nvarieties. Crop Science,  6, 36-40. \n\n\n\n[10] Singh, R.K., Chaudhaey, B. D.  1985-1980. Stability models In: \nBiometrical methods in quantitative genetic analysis. 3rdEd., Kalyani \npublishers, New Delhi, India, pp 253-268. \n\n\n\n[11] Sakin, M. A., Akinci, C., Duzdemir, O., Donmez, E. 2011. \nAssessment of genotype environment interaction on yield and yield \ncomponents of durum wheat genotypes by multivariate analyses. African \nJournal of Biotechnology, 10 (15), 2875-2885. \n\n\n\n[12] Khan, F., Inamullah, I. H., Khalil, S., Khan, Munir, I. 2012. Yield \nstability genotypic correlations among yield contributing traits in spring \nwheat under two environments. Sarhad Journal of Agriculture, 28 (1), 27-\n36. \n\n\n\n[13] Shahid, M., Fida, M., Tahir, M. 2002. Path coefficient analysis in \nwheat. Sarhad Journal of Agriculture, 18 (4), 383-388. \n\n\n\n[14] Ahmad B., Khalil, I., Iqbal, M., Rahman, H.U. 2010. Genotypic and \nphenotypic correlation among yield components in bread wheat under \nnormal and late plantings.  Sarhad Journal of Agriculture, 26 (2), 259-265.\n\n\n\n[15] Ahmad, F., Saleem, S., Khan, S. Q., Khan, H., Khan, A., Muhammad, \nF. 2011. Genetic analysis of some quantitative traits in bread wheat across\nenvironments. African Journal of Agricultural Research, 6 (3), 686-692. \n\n\n\n[16] Akhtar, M., Iqbal, R. M., Jamil, M.Z., Akhtar, L. H.  2012. Effect of \nsowing date on emergence, tillering and grain yield of different wheat \nvarieties under Bahawalpur conditions. Pakistan Journal of Agricultural \nSciences, 49 (3), 255-259. \n\n\n\n[17] Soomro, A., Oed, F.C., Bhutto, Z.A. 2002. Yield potential of wheat \n(Triticum aestivumL.) genotypes under different planting times. Journal of \nApplied Sciences, 2 (7), 713 - 714. \n\n\n\n[18] Hussain, M., Khan, A. S., Khaliq, I., Maqsood, M.  2012. \nCorrelation studies of some qualitative and quantitative traits with grain \nyield in spring wheat across two environments. Pakistan Journal of \nAgricultural Sciences, 49 (1), 1-4. \n\n\n\n[19] Baloch, M.S., Nadim, M.A., Zubair, M., Awan, I., Khan, E.A., Ali, S. \n2012. Evaluation of wheat under normal and late sowing conditions. \nPakistan Journal of Botany, 44 (5), 1727-1732. \n\n\n\n11\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2022.29.37 \n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply of \n\n\n\nPotato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2022.29.37 \n\n\n\n\n\n\n\n\n\n\n\nTREND ANALYSIS OF AREA, PRODUCTION, PRODUCTIVITY, AND SUPPLY OF \nPOTATO IN SINDHULI DISTRICT AND NEPAL: A COMPARATIVE STUDY \n \nAmrita Paudel*, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel \n\n\n\n \nAgriculture and Forestry University, Chitwan, Nepal \n\n\n\n*Corresponding Author E-Mail: Pdlamr1998@gmail.com \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 02 August 2021 \nAccepted 15 September 2021 \nAvailable online 14 October 2021 \n\n\n\n The study; conducted from January, 2020 to June, 2020; focuses on the comparative study of the area, \n\n\n\nproduction, and productivity trend of potatoes over 50 years in Sindhuli district and Nepal and a brief \n\n\n\noverview on quantity supply to the Kalimati fruits and vegetable market. The time-series data from 1968/69 \n\n\n\nto 2017/18 of Sindhuli and Nepal along with 6 years supply data (2013/14-2018/19) from different districts \n\n\n\nto Kalimati market were collected from reliable sources (Ministry of Agriculture and Livestock Development \n\n\n\nand Kalimati Fruits and Vegetable Market Development Board) and analysis was done using Microsoft Excel. \n\n\n\nBetween 1968/69 and 2017/18, the area under potato cultivation in Nepal and Sindhuli has changed by 573 \n\n\n\npercent and -46 percent respectively while production increased by 907.6 percent in Nepal and 46 percent \n\n\n\nin Sindhuli. After 1982 dramatic shift in production was observed in Nepal as there was 7 percent of growth \n\n\n\nrate while in Sindhuli, the production trend highly fluctuates throughout the period. The average yield was \n\n\n\n9.75mt/ha and 8.75mt/ha for Nepal and Sindhuli district. Sindhuli district contributes 1.16 percent of \n\n\n\nNepalese potato growing area and 0.91 percent of Nepalese potato production. The trend of quantity supply \n\n\n\nreveals that during 6 years, Indian potato contributes 58 percent of the total amount that came into Kalimati \n\n\n\nmarket, while within-country Kavre has the largest share of 19 percent followed by Kathmandu-6 percent \n\n\n\nand Dolakha-4 percent. However, the trend of quantity supply of potatoes seems highly fluctuating and the \n\n\n\nNepalese market is dominated by Indian imports.  \n\n\n\nKEYWORDS \n\n\n\nTrend analysis, Potato Production, Annual Growth Rate, Supply. \n\n\n\n1. INTRODUCTION \n\n\n\nAgriculture is an important practice in the Nepalese community as a \n\n\n\nsource of food, employment, income generation, and well-being. \n\n\n\nAgriculture occupies 65.1 percent of people, contributing 31.23 percent of \n\n\n\nGDP, with the horticulture sub-sector accounting for 21.42 percent \n\n\n\n(Ghimire et al., 2018). There is a 2.4 percent annual increase in agricultural \n\n\n\nproduction but could not maintain with a 2.6 percent annual increase in \n\n\n\npopulation (Wikipedia, 2020). \n\n\n\nPotato (Solanum tuberosum) is a major vegetable crop which is more \n\n\n\nproductive than cereal and has high economic value than cereals and it is \n\n\n\ncultivated in altitude of 100m in southern terai and up to 4000 m in the \n\n\n\nnorthern range, where it is used as subsidiary food in terai but it is used as \n\n\n\na staple food in mountain and hills. It is the fourth most important crop in \n\n\n\nNepal after cereal in terms of production area but it ranks first in terms of \n\n\n\nproductivity, which is 9.89 tons per hectare (ha) and production 2,586,287 \n\n\n\nmt in Nepal (NPDP, 2016). The major potato growing district is Kavre, \n\n\n\nBara, Jhapa, Solukhumbu, Illam, Khotang, Kailali, Bardiya, Bhojpur, \n\n\n\nMakawanpur, Nuwakot, Rupandehi, etc (MOALD, 2012). The current five-\n\n\n\nyear plan (2075/76-2080/81) aimed at increasing vegetable productivity \n\n\n\nfrom 14.1 Mt/ha (2075/66) to 20 Mt/ha (2080/81) by the end of the \n\n\n\nperiod (NPC, 2019). For the total area under potato, 20% is in the high \n\n\n\nhills, the mid-hills hold the highest 41.5% and the terai region is 38.5% \n\n\n\n(Gotame et al., 2021). \n\n\n\nFrom the past few decades, Sindhuli has also been one of the largest \n\n\n\nproducers and suppliers of potatoes. The average productivity during \n\n\n\nthree years (2014-2015, 2015-2016, 2016-2017) is 14.3 Mt/ha/year, \n\n\n\nwhereas the potential productivity is 17 Mt/ha/year (MOAD, 2015). So, \n\n\n\nthere is a productivity gap of almost 3 Mt/ha/year. In the year 2076, \n\n\n\nSindhuli has supplied 57928kg which is 0.09% of the total quantity arrival \n\n\n\n(KFVMDB, 2019). According to the Kalimati fruit and vegetables market \n\n\n\ndevelopment board, approximately 76 tons of potatoes arrived daily on \n\n\n\nthe Kalimati market, 10 percent of which are imported from India (The \n\n\n\nKathmandu Post, 2014). With exports worth Rs.3.11 billion, India is \n\n\n\nNepal's leading potato exporter (Spotlight, 2014). \n\n\n\nHorticultural crops including potatoes can contribute to food security \n\n\n\nimprove nutritional status and provide employment, increase the income, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\nand overall GDP of the country (Bhandari & Aryal, 2014). Potatoes are \n\n\n\nproduced in areas such as Africa's tropical highlands, South America's \n\n\n\nAndes, and South Asia's Indo-Gangetic basin where there is significant \n\n\n\nrates of deprivation, malnutrition, and food insecurity (FAO Perspective, \n\n\n\n2015). \n\n\n\nThe average growth rate of potato production is 214.49 kg per hectare per \n\n\n\nyear, which has risen at a compound annual growth rate of 1.76 percent \n\n\n\nper year (Timsina et al., 2019). So talking about production in Nepal seed \n\n\n\ncost occupied a major portion 33.33% of the cost of production followed \n\n\n\nby human labor (26.3%), FYM (12.3%), chemical fertilizer cost (7%) \n\n\n\nirrigation cost 1.7% micronutrient (0.8%) and pesticide (5.2%) lastly \n\n\n\npackaging storage and transportation cost jointly attribute 3.3% to total \n\n\n\nvariable costs and lack of quality seed in sowing time is one of the major \n\n\n\ncauses for higher seed cost and government subsidiary on fertilizer is \n\n\n\nmajor causes for lower fertilizer cost (Subedi et al., 2019). The national \n\n\n\npotato development program (NPDP) focuses to assist the government in \n\n\n\nmaking plans and policies for potato development, organizing training, \n\n\n\nseminar, research, and studies as well as coordinating with concerned \n\n\n\nstakeholders for import substitution and export promotion (NPDP, 2016). \n\n\n\nA study finding revealed that during the fiscal year 1977/78 to 2016/17 \n\n\n\nthe area under vegetable cultivation has sharply increased by 222.8% with \n\n\n\nits increased production of 728.2%. However, compared to an increase in \n\n\n\narea, the rate of vegetable production was not ideal due to a shortage of \n\n\n\nquality seeds and timely supply of fertilizer (Ghimire et al., 2018). \n\n\n\n1.1 Statement of the problem \n\n\n\nAgriculture generates about 54 percent of Nepal\u2019s jobs and contributes 27 \n\n\n\npercent to Gross Domestic Product (GDP), but only 2.8 percent of the \n\n\n\noverall budget was allocated to agriculture in the 2020/21 fiscal year \n\n\n\n(MOAD, 2015). In comparison, the total land ownership of people engaged \n\n\n\nin agriculture is just 0.6 ha/household (MOAD, 2015). Nepalese people's \n\n\n\nsocio-economic status is likewise critically poor. The food and nutrition \n\n\n\nsecurity situation is highly fragile since in 2015 AD, stunting was reported \n\n\n\nat about 37.4 percent, underweight at 30.1 percent, overweight at 11.3 \n\n\n\npercent, and women with low BMI at 18.1 percent (MOAD, 2015). Despite \n\n\n\nbeing the active producer, there is still a lack of awareness among farmers \n\n\n\nabout correct seed selection and proper use of chemical and organic \n\n\n\nfertilizers (Parajuli, 2005). The stored seed was very high in Sindhuli, \n\n\n\nwhile the volume of seed collected from agro-vets and government \n\n\n\nresearch and development programs was very small (Gairhe et al., 2017). \n\n\n\nCompared to other districts Sindhuli has lower supplies to Kalimati Fruits \n\n\n\nand Vegetable Market mainly because of rapid urbanization, poor tuber \n\n\n\nquality, and use of conventional technologies (KFVMDB, 2019). \n\n\n\n1.2 Rationale of the study \n\n\n\nPotato is an important sub-sector in both Nepal and the Sindhuli district. \n\n\n\nThe commercialization of potatoes not only uplifts people's economic \n\n\n\nstatus in the Sindhuli but also helps in import substitution and promotion \n\n\n\nof exports in Nepal. Potato is a high-value cash crop since can be a good \n\n\n\nsource of income for the farmers in mid-hills and high hills where there is \n\n\n\na high comparative advantage of growing potato. It can aid in food security \n\n\n\nas it has high calorific value. This study will assist stakeholders to get \n\n\n\ninsight into the marketing situation; also contribute to planning and policy \n\n\n\nmaking for the marketing of potato by increasing the area, production, and \n\n\n\nproductivity. Thus, the global reach on potato trend overs 50 years in \n\n\n\nterms of area, production and productivity aims at encouraging youths for \n\n\n\nexpansion of potato business. \n\n\n\n1.3 Objectives \n\n\n\nThe broad objective is to study the trend of potato production, area, and \n\n\n\nproductivity in the Sindhuli district and Nepal. \n\n\n\nSpecific objectives \n\n\n\n\u2022 To figure out how the area, productivity, and their interactions affect \n\n\n\npotato production. \n\n\n\n\u2022 To estimate the magnitude of variability in area, production, and \n\n\n\nproductivity of potato in Sindhuli and compare it to the national level. \n\n\n\n\u2022 To determine the average annual yield and annual growth rate. \n\n\n\n\u2022 To estimate the trend of quantity supply of potato to Kalimati Fruits \n\n\n\nand Vegetable Market. \n\n\n\n1.4 Limitation of the study \n\n\n\nThe followings are the limitation of the study. \n\n\n\n\u2022 The time-series data might have been fitted to exponential, \n\n\n\nlogarithmic, cubic, polynomial, or other non-monotonic trends but \n\n\n\nthis study only considered a monotonic trend and linear trend. \n\n\n\n\u2022 The analysis did not consider the entire commodity supply as data for \n\n\n\nall supplies were not available. \n\n\n\n2. METHODOLOGY \n\n\n\n2.1 Data and its type \n\n\n\nAll the data that has been published and available were used for this \n\n\n\nresearch. Time series data from 1968/69 to 2017/2018 on area, \n\n\n\nproduction, and productivity of potato were collected from \u201cstatistical \n\n\n\nInformation on Nepalese Agriculture: Time series Information published \n\n\n\nby MoALD (Ministry of Agriculture and Livestock Development) (MOALD, \n\n\n\n2020). To analyze supply trend data from 2070B.S to 2076B.S (2013/14-\n\n\n\n2018/19 A.D), information was also collected from the Kalimati Fruits and \n\n\n\nVegetable Market Board website (KFVMDB, 2019). \n\n\n\nThe quantitative data was gathered and entered in MS Excel. The data was \n\n\n\nanalyzed to draw meaningful inferences by using, R programming and MS-\n\n\n\nExcel. The data was analyzed by using tools like descriptive statistics for \n\n\n\nmean comparison, frequency distribution, correlation analysis, and Mann \n\n\n\nKendall test analysis, etc. The findings were represented and \n\n\n\ndemonstrated by using tables, figures, line diagrams, pie charts, etc. \n\n\n\nMann Kendall test helps to know the linear trend between time-series \n\n\n\ndata, correlation helps to know the relation between parameters like \n\n\n\nproduction and production area, mean comparison is useful in performing \n\n\n\nt-test, descriptive analysis, and frequency distribution are useful tools for \n\n\n\nunderstanding the present and past condition of potato subsector in \n\n\n\nSindhuli district and Nepal. \n\n\n\n2.2 Interpolation of data \n\n\n\nThe time-series data which was taken from MoALD (Ministry of \n\n\n\nAgriculture and Livestock Development) sites has some missing values for \n\n\n\na particular year. To fulfill that interpolation technique was used. The \n\n\n\nsimplest type of interpolation technique is linear interpolation that makes \n\n\n\na mean between the values before the missing data and the value after. \n\n\n\n\ud835\udc40\ud835\udc56\ud835\udc60\ud835\udc60\ud835\udc56\ud835\udc5b\ud835\udc54 \ud835\udc51\ud835\udc4e\ud835\udc61\ud835\udc4e =\n\ud835\udc5d\ud835\udc5f\ud835\udc52\ud835\udc63\ud835\udc56\ud835\udc5c\ud835\udc62\ud835\udc60 \ud835\udc51\ud835\udc4e\ud835\udc61\ud835\udc4e(\ud835\udc59\ud835\udc4e\ud835\udc60\ud835\udc61 \ud835\udc51\ud835\udc4e\ud835\udc61\ud835\udc4e \u2212 \ud835\udc5d\ud835\udc5f\ud835\udc52\ud835\udc63\ud835\udc56\ud835\udc5c\ud835\udc62\ud835\udc60 \ud835\udc51\ud835\udc4e\ud835\udc61\ud835\udc4e)\n\n\n\n\ud835\udc5b\ud835\udc5c \ud835\udc5c\ud835\udc53 \ud835\udc5a\ud835\udc56\ud835\udc60\ud835\udc60\ud835\udc56\ud835\udc5b\ud835\udc54 \ud835\udc5d\ud835\udc52\ud835\udc5f\ud835\udc56\ud835\udc5c\ud835\udc51\n \n\n\n\n \n2.3 Data analysis technique \n \n2.3.1 Mann-Kendall test \n \nMann-Kendall test was used in this analysis to detect the existence of a \nmonotonic pattern in time series (Hess et al., 2001). Mann-Kendall test \nanalyses the sign of the difference between later measurements with \nearlier measurements in time series data. Each later measured value is \ncompared to all values measured earlier which results in a total (\ud835\udc5b\u22121)2 \npossible pairs of data, \ud835\udc5b being the total observations. The null hypothesis \n(H0) for this test is, \u201cthere is no monotonic trend in time-series\u201d and the \nalternative hypothesis (H1) for this test is, \u201cthere is a monotonic trend in \ntime-series\u201d (Meals et al., 2021). \n \nThe measured Z-value was then compared with the Z-value from the \nstandard Z-probability table at a sensible level of 5 percent to accept or \nreject the hypothesis and to conclude whether there was a monotonous \npattern. The trend was presumed to increase if Z was favorable and if Z \nwas considered negative, the decreasing trend was concluded (Alhaji et al., \n2018). In this study, the conclusion about whether there was a monotonic \ntrend in the given time series data was drawn from the p-value obtained \nin the Mann-Kendall test. If the p-value was less than the meaning level (\u03b1 \n= 5 %), i.e. 0.05, the null hypothesis was rejected, and it was concluded that \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\nthe time series data had a monotonous trend. Failure to reject the null \nhypothesis (p > 0.05) would lead to the conclusion that there is no \nmonotonous trend in data from the time series. \n \nThe conclusion about how strongly the two variables were monotonically \nrelated was drawn based on the Kendall correlation coefficients, generally \nknown as the tau of Kendall. The coefficient of correlation for the Kendall \ntakes values from -1 to +1. The coefficient sign indicates the sign of the \nrelation slope, i.e. increasing or declining trend, and the absolute value \nindicates the relationship intensity (Helsel & Hirsch, 1993). \n\n\n\n2.3.2 Sen\u2019s slope \n\n\n\nWhere linear trends are present in time series data, the slope (change rate \n\n\n\nper unit time) or trend magnitude can be estimated using the least square \n\n\n\nor simple linear regression method. But the least square estimation of the \n\n\n\nregression coefficient (i.e. slope) is vulnerable to gross errors, and the \n\n\n\nconfidence interval obtained is sensitive to parent distribution non-\n\n\n\nnormality (Sen, 1968). Besides, the slope determined with this method \n\n\n\nwill deviate considerably from the true slope if there are gross errors or \n\n\n\noutliers in the data (Gilbert, 1987). (Sen, 1968) argued that the median is \n\n\n\nless influenced by gross errors or outliers than the weighted average and \n\n\n\nthat the median-based regression coefficient (i.e. slope) calculation is \n\n\n\nmore robust than the slope obtained from the least square method. Thus, \n\n\n\nin this analysis, the magnitude of the trend was calculated using a simple \n\n\n\nnon-parametric method developed by Sen commonly known as Sen\u2019s \n\n\n\nslope. \n\n\n\nThe linear model assumed was \n\n\n\n\ud835\udc4c = \ud835\udefc + \ud835\udefd\ud835\udc61 \n\n\n\nWhere, \n\n\n\n                   \ud835\udefd= slope \n\n\n\n                   \ud835\udefc =  Intercept \n\n\n\n                   Y= time i.e. independent variable \n\n\n\nSen\u2019s estimator of the slope is associated with the Mann Kendall test and \n\n\n\nwas derived firstly by computing slopes of all data pairs as \n\n\n\n\ud835\udefd\ud835\udc56\ud835\udc57 =\n\ud835\udc66\ud835\udc57 \u2212 \ud835\udc66\ud835\udc56\n\n\n\n\ud835\udc57 \u2212 \ud835\udc56\n \n\n\n\nWhere \n\n\n\n\ud835\udefd\ud835\udc56\ud835\udc57= all the slope of lines connecting each pair of points (ti,yi) and (tj,yj) and \n\n\n\nti  \u2260  tj  \n\n\n\nIf there are n values in the series we obtained exactly \ud835\udc41 =  \n\ud835\udc5b(\ud835\udc5b\u22121)\n\n\n\n2\n  slope \n\n\n\nestimates \u03b2ij. The N values of slopes are arranged in ascending order of \n\n\n\nmagnitude and the median was calculated which is the Sen estimator of a \n\n\n\nslope. \n\n\n\n\ud835\udefd = {\n\n\n\n1\n\n\n\n2\n (\ud835\udefd\ud835\udc41\n\n\n\n2\n+ \ud835\udefd\ud835\udc41 \n\n\n\n2 +1\n ) , \ud835\udc56\ud835\udc53 \ud835\udc41 \ud835\udc56\ud835\udc60 \ud835\udc52\ud835\udc63\ud835\udc52\ud835\udc5b\n\n\n\n\ud835\udefd\ud835\udc41+1\n2\n\n\n\n  , \ud835\udc56\ud835\udc53 \ud835\udc41 \ud835\udc56\ud835\udc60 \ud835\udc5c\ud835\udc51\ud835\udc51 \n} \n\n\n\nThe intercept was computed according to the Sens method for each time \n\n\n\nstep t as \n\n\n\n\ud835\udefc\ud835\udc61 = \ud835\udc4c\ud835\udc61 \u2212 \ud835\udefd\ud835\udc61 \n\n\n\nAnd the respective intercept value (\u03b1) is the mean of all \u03b1t values (Alhaji \n\n\n\net al., 2018). Sen\u2019s positive slope (\u03b2) value reflects an upward trend, and \n\n\n\nthe negative value represents a downward trend. The line of regression \n\n\n\nwas plotted to visualize the trend, based on the slope and intercept of the \n\n\n\nSen. \n\n\n\nThis method was used to analyze and detect the linear trend present in \n\n\n\ntime-series data for, production, area, and productivity, of potatoes in \n\n\n\nNepal, and Sindhuli. The Microsoft Excel program and the Ms. Excel \n\n\n\nprogram XLSTAT tool developed by Addinsoft (2020) have been used for \n\n\n\ndata analysis and data visualization. \n\n\n\n2.3.3 T-test \n\n\n\nH0= There is no significant difference between the two sample mean \n\n\n\nH1= There is a significant difference between the two sample mean \n\n\n\nTable 1: t-test \n\n\n\nGroup Size of sample Sample Mean \nSample (SD) \n\n\n\nStandard Deviation \n\n\n\n1 n1 x\u03051 s1 \n\n\n\n2 n2 x\u03052 s2 \n\n\n\nTo check the null hypothesis that either mean population, \u03bc1, and \u03bc2, is \n\n\n\nequal differences between two sample mean need to be calculated. \n\n\n\n                               Difference= x\u03051- x\u03052 \n\n\n\n\n\n\n\nSecondly pooled standard deviation needs to be computed as, \n\n\n\n\n\n\n\n\ud835\udc60\ud835\udc5d = \u221a\n(\ud835\udc5b1 \u2212 1)\ud835\udc601\n\n\n\n2 + (\ud835\udc5b2 \u2212 1)\ud835\udc602\n1\n\n\n\n\ud835\udc5b1 + \ud835\udc5b2 \u2212 2\n \n\n\n\n\n\n\n\nThirdly standard error of the difference between the mean must be \n\n\n\nestimated. \n\n\n\n\ud835\udc46\ud835\udc38(x\u03051 \u2212 x\u03052) = \ud835\udc60\ud835\udc5d\u221a\n1\n\n\n\n\ud835\udc5b1\n\n\n\n+\n1\n\n\n\n\ud835\udc5b2\n\n\n\n\n\n\n\n\n\n\n\nCalculation of t-statistics which is given by  \ud835\udc47 =\nx\u03051\u2212 x\u03052\n\n\n\n\ud835\udc46\ud835\udc38(x\u03051\u2212 x\u03052)\n  . Under the null \n\n\n\nhypothesis, this statistic follows a t-distribution with n1+n2-2 degree of \n\n\n\nfreedom.  \n\n\n\n\n\n\n\nAt last, we use t-distribution table to compare the value for \u201ct\u201d to the tn1+n2-\n\n\n\n2 this gives a p-value for an unpaired t-test. \n\n\n\n\n\n\n\n2.3.4 Average annual yield  \n\n\n\n\n\n\n\nThe average yield per annum is calculated by dividing the revenue earned \n\n\n\nfrom an investment by the period it has been owned (Hayes, 2021b).  \n\n\n\n\n\n\n\n\ud835\udc4e\ud835\udc63\ud835\udc52\ud835\udc5f\ud835\udc4e\ud835\udc54\ud835\udc52 \ud835\udc4e\ud835\udc5b\ud835\udc5b\ud835\udc62\ud835\udc4e\ud835\udc59 \ud835\udc4c\ud835\udc56\ud835\udc52\ud835\udc59\ud835\udc51 =\n\u2211 \ud835\udc5d\ud835\udc5f\ud835\udc5c\ud835\udc51\ud835\udc62\ud835\udc50\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc56\ud835\udc5b \ud835\udc4e\ud835\udc59\ud835\udc59 \ud835\udc66\ud835\udc52\ud835\udc4e\ud835\udc5f\n\n\n\n\u2211 \ud835\udc4e\ud835\udc5f\ud835\udc52\ud835\udc4e \ud835\udc5c\ud835\udc53 \ud835\udc5d\ud835\udc5f\ud835\udc5c\ud835\udc51\ud835\udc62\ud835\udc50\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc56\ud835\udc5b \ud835\udc4e\ud835\udc59\ud835\udc59 \ud835\udc66\ud835\udc52\ud835\udc4e\ud835\udc5f\n \n\n\n\n\n\n\n\n2.3.5 Trend line \n\n\n\n\n\n\n\nProduction, area supplies, and productivity data were illustrated in trend \n\n\n\nlines and the inference was made based on the nature of the trend line. \n\n\n\n \n2.3.6 Annual growth rate \n\n\n\n \nThe annual growth rate for each year was calculated in percentage terms \n\n\n\nand thereby doing overall average; annual growth rate was calculated \n\n\n\n(Hayes, 2021a).  \n\n\n\n\n\n\n\n\ud835\udc4e\ud835\udc5b\ud835\udc5b\ud835\udc62\ud835\udc4e\ud835\udc59 \ud835\udc54\ud835\udc5f\ud835\udc5c\ud835\udc64\ud835\udc61\u210e \ud835\udc5f\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc61 =\n\ud835\udc63\ud835\udc4e\ud835\udc59\ud835\udc62\ud835\udc52 \ud835\udc56\ud835\udc5b \ud835\udc61\ud835\udc61\u210e \ud835\udc66\ud835\udc52\ud835\udc4e\ud835\udc5f \u2212 \ud835\udc63\ud835\udc4e\ud835\udc59\ud835\udc62\ud835\udc52 \ud835\udc56\ud835\udc5b (\ud835\udc61 \u2212 1)\ud835\udc61\u210e\ud835\udc66\ud835\udc52\ud835\udc4e\ud835\udc5f\n\n\n\n\ud835\udc63\ud835\udc4e\ud835\udc59\ud835\udc62\ud835\udc52 \ud835\udc56\ud835\udc5b (\ud835\udc61 \u2212 1)\ud835\udc61\u210e\ud835\udc66\ud835\udc52\ud835\udc4e\ud835\udc5f\n\u2217 100% \n\n\n\n\n\n\n\nAlso, the compound annual growth rate (CAGR) was computed: \n\n\n\n\n\n\n\n\ud835\udc5d\ud835\udc5f\ud835\udc5c\ud835\udc51\ud835\udc62\ud835\udc50\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc4e\ud835\udc61 \ud835\udc61\ud835\udc61\u210e \ud835\udc66\ud835\udc52\ud835\udc4e\ud835\udc5f = \ud835\udc5d\ud835\udc5f\ud835\udc5c\ud835\udc51\ud835\udc62\ud835\udc50\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc4e\ud835\udc61 \ud835\udc53\ud835\udc56\ud835\udc5f\ud835\udc60\ud835\udc61 \ud835\udc66\ud835\udc52\ud835\udc4e\ud835\udc5f \u2217 (1 + \ud835\udc36\ud835\udc34\ud835\udc3a\ud835\udc45)\ud835\udc61 \n\n\n\n\n\n\n\nHere CAGR was computed by MS Excel. \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Trend of production, area, and productivity of potato in Nepal \n\n\n\n3.1.1 Trend of potato production in Nepal \n\n\n\nIt is found that there is an increase in production from 1968 to 2015, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\nproduction pattern is erratic. It is observed that the minimum production \n\n\n\nis in 1968 with 245000mt but after 1968 it started to rise steadily until \n\n\n\n1976. But for the next 5 years after 1976, it continues to drop until 1982. \n\n\n\nAfter 1982 dramatic shifts in production can be observed. By comparing \n\n\n\nthe CAGR trend from 1968 to 1982, we can observe the 1.8 percent growth \n\n\n\nrate. We witnessed maximum production in 2015; production \n\n\n\nunexpectedly increased in 2015 relative to the projected rate of growth as \n\n\n\nit grew 110 percent more than the previous year. After 2015 we observed \n\n\n\na similar trend of growth except for 2017, where the output graph fell. The \n\n\n\noverall percentage increase in production from 1965 to 2018 is about \n\n\n\n907.6 percent. \n\n\n\n\n\n\n\nFigure 1: Production vs Area graph in case of Nepal since 1968/69-\n\n\n\n2017/18 \n\n\n\nThe R square value for production and area trend line is 0.7179 and 0.9311 \n\n\n\nrespectively which are closer to 1. This indicates that the data is more \n\n\n\nprecisely represented by the trend line. The coefficient of determination \n\n\n\nR2 computed from the linear regression method was found to be 0.931 \n\n\n\nwhich reveals that 93.11% of the variation in production can be explained \n\n\n\nby the variation in the area. The remaining 6.89% variation is unexplained \n\n\n\nby variation in the area. \n\n\n\n3.1.2 Trend of potato production area in Nepal \n\n\n\nIn the past 5 decades, the production area has grown by 3.7 percent. We \n\n\n\nobserved the lowest output area in 1968 with just 29,000 ha in the country \n\n\n\nas a whole but over the next 10 years, it is continuously grown and reach \n\n\n\n522,27 ha in 1977. There is quite a fluctuation in data afterward with an \n\n\n\nerratic trend of rising and falling up to 1982. After 1983 the production \n\n\n\narea increases continuously until 2014 and became maximum with a \n\n\n\nproductive area of 205725 ha so during that time the growth rate was \n\n\n\nhighest with an increase of 4 percent per annum and the overall increase \n\n\n\nin the production area is about 573 percent. \n\n\n\n3.1.3 Trend of productivity of potato in Nepal \n\n\n\nProductivity is a factor depending on both the area and the production, the \n\n\n\ntrend curve varies from both. The compound growth rate (CAGR) was \n\n\n\nfound to be 0.8 percent from the last 5-decade and its average annual yield \n\n\n\nis 9.27mt / ha so Nepalese potato productivity was below this annual level \n\n\n\nup to 1998.  In 1965, where productivity was 9857 kg/ha, its value \n\n\n\ndecreased continuously until 1971, when it reached 5603 kg/ha, but \n\n\n\nproduction remained more or less similar. From 1971 to 1987 we \n\n\n\nobserved quite an unusual curve pattern of up and down but after 1988 \n\n\n\nthere is continuous growth in potato production due to which productivity \n\n\n\nhas also increased significantly and in 2018 it has become maximum with \n\n\n\nthe productivity of 14765 kg/ha. There are a lot of ups and downs in data \n\n\n\nin the productivity graph and the overall percentage rise is also \n\n\n\ncomparatively smaller i.e. just 49 percent. \n\n\n\n\n\n\n\nFigure 2: Productivity graph in Nepal since 1968/69-2017/18 \n\n\n\n3.1.4 Mann Kendal and Sen slope analysis for Nepalese trend of \n\n\n\npotato \n\n\n\nAll parameters had an increasing trend from 1965 to 2018 as indicated by \n\n\n\nthe positive Tau value. It is also statistically significant at a 95% confidence \n\n\n\nlevel as indicated by the p-value. Sen Slope quantified that area of potato \n\n\n\nproduction increase by the rate of 3442 ha, productivity by 194 kg/ha, and \n\n\n\nproduction by 50864 Mt per year annually. Kendall tau value which is \n\n\n\npositive in all the cases shows that there is very a strong correlation \n\n\n\nbetween production and time, area and time and it shows a strong \n\n\n\nrelationship between productivity and time.\n\n\n\nTable 2: Result of Mann Kendall and Sen slope for production, area, and productivity of potato in Nepal \n\n\n\nParameter P-value Kendall Tau (\ud835\udf0f) Sen Slope Trend Significance Alfa value \n\n\n\nProduction (mt) <0.0001 0.901 50864 Increasing significant 0.05 \n\n\n\nArea (ha) <0.0001 0.933 3442 Increasing significant 0.05 \n\n\n\nProductivity(kg/ha) <0.0001 0.719 194 Increasing Significant 0.05 \n\n\n\n3.2 Trend of production, area, and productivity of potato in Sindhuli \n\n\n\n\n\n\n\n3.2.1 Trend of potato production in the Sindhuli district \n\n\n\n\n\n\n\nThe average production of Sindhuli district from 1968 to 2018 was \n\n\n\n10846.14 Mt. The annual production of Sindhuli up to 1994 was below the \n\n\n\noverall average, i.e. 10846.14 Mt. After 1994, production increased \n\n\n\nsignificantly, besides that there was also a significant drop in a year like \n\n\n\n2009, 2017,2018. Minimum production was observed in 2009 with the \n\n\n\nproduction of only 1928Mt, and the highest production was observed in \n\n\n\n2015 with a production of 49200Mt. 2015 has also been a turning point in \n\n\n\na production trend as the production sum drops significantly after that \n\n\n\nyear. When moving from 2015 to 2016 production amount declined by 31 \n\n\n\npercent, going forward further i.e. from 2016 to 2017 there is a drop of 85 \n\n\n\npercent which is very serious and the highest percentage drop in the case \n\n\n\nof Sindhuli district. At the last from 2017 to 2018 there is a growth of 3.9 \n\n\n\npercent in production. Overall trends in the Sindhuli district are severely \n\n\n\nhindered by the output statistics of the last few years, where the \n\n\n\npercentage rise in production is also just 46 percent. \n\n\n\n\n\n\n\n3.2.2 Trend of potato production area in Sindhuli district \n\n\n\n\n\n\n\nThe production area in Sindhuli is very close to the trend graph of \n\n\n\nproduction. The average area of production over the last 5 decades is \n\n\n\n1238.5 ha where the annual productive area was below this figure up to \n\n\n\n1982. The productive area reaches the maximum in 2015 with 2900 ha \n\n\n\nand reaches the minimum point with only 323 ha just after leaving 1-year \n\n\n\ni.e.in 2017. Thus, two extremes of the production line were observed \n\n\n\nwithin 1 year because of this overall growth rate, i.e.the CAGR rate was -\n\n\n\n46 percent. Nonetheless, as we analyze until 2015, the situation is \n\n\n\nsomewhat different, a rise of approximately 383.3% and a growth of 3% \n\n\n\ncan be observed. \n\n\n\ny = 3369.8x + 9754.8\nR\u00b2 = 0.9311\n\n\n\ny = 58990x - 489822\nR\u00b2 = 0.7179\n\n\n\n-1000000\n\n\n\n0\n\n\n\n1000000\n\n\n\n2000000\n\n\n\n3000000\n\n\n\n4000000\n\n\n\n5000000\n\n\n\n6000000\n\n\n\n7000000\n\n\n\n0\n\n\n\n50000\n\n\n\n100000\n\n\n\n150000\n\n\n\n200000\n\n\n\n250000\n\n\n\n1\n9\n\n\n\n6\n5\n\n\n\n1\n9\n\n\n\n6\n8\n\n\n\n1\n9\n\n\n\n7\n1\n\n\n\n1\n9\n\n\n\n7\n4\n\n\n\n1\n9\n\n\n\n7\n7\n\n\n\n1\n9\n\n\n\n8\n0\n\n\n\n1\n9\n\n\n\n8\n3\n\n\n\n1\n9\n\n\n\n8\n6\n\n\n\n1\n9\n\n\n\n8\n9\n\n\n\n1\n9\n\n\n\n9\n2\n\n\n\n1\n9\n\n\n\n9\n5\n\n\n\n1\n9\n\n\n\n9\n8\n\n\n\n2\n0\n\n\n\n0\n1\n\n\n\n2\n0\n\n\n\n0\n4\n\n\n\n2\n0\n\n\n\n0\n7\n\n\n\n2\n0\n\n\n\n1\n0\n\n\n\n2\n0\n\n\n\n1\n3\n\n\n\n2\n0\n\n\n\n1\n6\n\n\n\np\no\n\n\n\nta\nto\n\n\n\n p\nro\n\n\n\nd\nu\n\n\n\nct\nio\n\n\n\nn\n in\n\n\n\n n\nep\n\n\n\nal\n\n\n\np\nla\n\n\n\nn\nta\n\n\n\nti\no\n\n\n\nn\n a\n\n\n\nre\na \n\n\n\nin\n n\n\n\n\nep\nal\n\n\n\nyears\n\n\n\nProduction vs Area graph of potato in Nepal\n\n\n\npotato plantation area in nepal\n\n\n\npotato production in metric ton\n\n\n\ny = 180.83x + 3919\nR\u00b2 = 0.8074\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\n16000\n\n\n\n1965 1975 1985 1995 2005 2015\n\n\n\np\no\n\n\n\nta\nto\n\n\n\n y\nie\n\n\n\nld\n in\n\n\n\n k\ng\n\n\n\n/h\na\n\n\n\nyear\n\n\n\npotato yield in kg/ha\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Production vs area graph in Sindhuli district since 1968/69-\n\n\n\n2017/18 \n\n\n\nThe R square value for the production and area trend line is 0.435 and \n\n\n\n0.4254 respectively which are not closer to 1. This indicates that the data \n\n\n\nis less precisely represented by the trend line. The coefficient of \n\n\n\ndetermination R2 computed from the linear regression method was found \n\n\n\nto be 0.425, which reveals that 42.5% of the variation in production can be \n\n\n\nexplained by the variation in the area. The remaining percentage is \n\n\n\nunexplained by variation in the area. \n\n\n\n3.2.3 Trend of productivity of potato in Sindhuli district \n\n\n\nThe highest yield was obtained in 2015(17mt / ha) from the evaluation of \n\n\n\nthe initial data set and the minimum was obtained in 2009(1.2mt / ha). \n\n\n\nOver the last 5 decades, the average annual yield was estimated as 8.75mt \n\n\n\n/ ha, and annual production was below the average annual yield up to \n\n\n\n1994. As there was a serious drop in area and production since 2015, \n\n\n\nproductivity was unlikely to be affected as the declining ratio in both cases \n\n\n\nwas more or less equal due to which there is no significant drop in \n\n\n\nproductivity like that of production and area. The productivity growth was \n\n\n\n157.3 percent. \n\n\n\n\n\n\n\nFigure 4: Productivity graph in Sindhuli since 1968/69-2017/18 \n\n\n\n3.2.4 Mann Kendal analysis for potato production in Sindhuli district \n\n\n\nIt is evident from the table that all parameters had an increasing trend \n\n\n\nduring the period 1968 to 2018 as indicated by positive tau value, all the \n\n\n\ntrends of the area, production, and productivity were statistically \n\n\n\nsignificant at a 95% confidence level as indicated by the p-value. Sen slope \n\n\n\nmethod quantifies that area of potato production increases at the rate of \n\n\n\n24.24 ha per year, and production increase at the rate of 337.87mt per \n\n\n\nyear, and productivity increase at the rate of 0.146mt/ha per year. Kendal \n\n\n\ntau is positive which represents an increasing trend. The magnitude of the \n\n\n\nKendal tau value shows that there is only a moderate correlation of \n\n\n\nproduction, area, and productivity with time. \n\n\n\n\n\n\n\nTable 3: Result of Mann Kendall and Sen slope for production, area, and productivity in Sindhuli district \nParameter  p-value Kendall tau(\ud835\udf0f) Sen slope  Trend Significance  Alfa value \nProduction(mt) <0.0001 0.680 337.87 Increasing significant  0.05 \nArea(ha) <0.0001 0.769 24.42 Increasing significant 0.05 \nProductivity(mt/ha) <0.0001 0.500 0.146 increasing significant 0.05 \n\n\n\n3.3 Comparison between Sindhuli district and Nepal in different \n\n\n\naspects \n\n\n\n3.3.1 Production Comparison between Sindhuli district and Nepal \n\n\n\nProduction amount data and its statistics were compared through an \n\n\n\nunpaired t-test with an equal variance which is presented below. \n\n\n\nTable 4: Result of t-test for production in Nepal and Sindhuli district \nParameter Production in \n\n\n\nNepal \nProduction in \nSindhuli \n\n\n\nMean (metric ton) 1182095.73 10846.14 \nStandard deviation 1107359.59 8257.33 \nCoefficient of variation 93.67% 76.13% \nDf 100  \nt-stat 7.55  \nP(T<=t) one tailed <0.001  \n\n\n\nThe production level of Nepal (M=1182095.75, SD=1107359.54, n=51) \n\n\n\nwas hypothesized to be greater than the production level of Sindhuli \n\n\n\n(M=10846.14, SD=1156.26, n=51). The difference was significant t (100) \n\n\n\n=7.55, p=<0.0001(one-tailed value). \n\n\n\n3.3.2 Productivity comparison between Sindhuli and Nepal \n\n\n\nTable 5: Result of t-test for productivity in Nepal and Sindhuli \nParameter Productivity of \n\n\n\nNepal \nProductivity of \nSindhuli \n\n\n\nMean(mt/ha) 9.27 8.27 \nStandard deviation 4.37 3.36 \nCoefficient of variation 47.14% 40.62% \nDegree of freedom 100  \nt-stat 1.30  \nP(T<=t) one-tailed 0.09  \n\n\n\nThe productivity of Nepal (M=9.27, SD=4.37, n=51) was hypothesized to \n\n\n\nbe greater than the productivity level of Sindhuli (M=8.27, SD=3.36, n=51). \n\n\n\nThe difference was significant t (100) =1.30, p=0.09(one-tailed) \n\n\n\n3.3.3 Production area comparison between Sindhuli district and Nepal \n\n\n\nTable 6: Result of t-test for production area in Nepal and Sindhuli \nParameter Production area \n\n\n\nof Nepal \nProduction area of \nSindhuli \n\n\n\nMean(ha) 106213.19 1238.50 \nStandard deviation 54159.45 468.0377 \nCoefficient of variation 51% 37.79% \nDf 100  \nt-stat 13.84  \nP(T<=t) <0.0001  \n\n\n\nStatistically, the production area of Nepal is greater than in the Sindhuli \n\n\n\ndistrict. The production area of Nepal (M=106213.19, SD=54159.45, \n\n\n\nn=51) was hypothesized to be greater than the production area of Sindhuli \n\n\n\n(M=1238.50, SD=468.0377, n=51). The difference was significant t (100) \n\n\n\n=13.84, p=<0.0001(one-tailed). \n\n\n\nFrom the t-test table, we find the difference of 1171249.59 metric tons in \n\n\n\nproduction, 104974.69ha in the production area, and 1mt/ha in \n\n\n\nproductivity. The overall contribution of the Sindhuli district to Nepal in \n\n\n\nthe case of potato production is 0.91% and in the case of potato production \n\n\n\narea is 1.16%. The production coefficient of variation of Nepal is 93.67% \n\n\n\nwherein the case of Sindhuli is 76.13% which shows Nepalese potato \n\n\n\nproduction is highly dispersed from its mean than Sindhuli. In the case of \n\n\n\nproductivity, variation is moderate but it is still higher in the case of Nepal \n\n\n\ni.e. COV of Nepal is 47.14% and COV of Sindhuli is 40.62%. Similarly in the \n\n\n\ncase of production area variation of Nepal is 51% and of Sindhuli is \n\n\n\n37.79%. \n\n\n\ny = 366.5x + 1317.1\nR\u00b2 = 0.4354\n\n\n\ny = 20.535x + 704.61\nR\u00b2 = 0.4254\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\n0\n\n\n\n10000\n\n\n\n20000\n\n\n\n30000\n\n\n\n40000\n\n\n\n50000\n\n\n\n60000\n\n\n\nYe\nar\n\n\n\n19\n70\n\n\n\n19\n73\n\n\n\n19\n76\n\n\n\n19\n79\n\n\n\n19\n82\n\n\n\n19\n85\n\n\n\n19\n88\n\n\n\n19\n91\n\n\n\n19\n94\n\n\n\n19\n97\n\n\n\n20\n00\n\n\n\n20\n03\n\n\n\n20\n06\n\n\n\n20\n09\n\n\n\n20\n12\n\n\n\n20\n15\n\n\n\nP\no\n\n\n\nta\nto\n\n\n\n p\nla\n\n\n\nn\nta\n\n\n\nti\no\n\n\n\nn\n a\n\n\n\nre\na(\n\n\n\nh\nec\n\n\n\nta\nre\n\n\n\n) \nin\n\n\n\n s\nin\n\n\n\nd\nh\n\n\n\nu\nli\n\n\n\np\nro\n\n\n\nd\nu\n\n\n\nct\nio\n\n\n\nn\n\n\n\nyears\nProduction of potato (metric ton) in sindhuli\nPotato plantation area(hectare) in sindhuli\nLinear (Production of potato (metric ton) in sindhuli)\n\n\n\ny = 0.1639x + 4.0099\nR\u00b2 = 0.5249\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\n1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016\n\n\n\np\nro\n\n\n\nd\nu\n\n\n\nc\nti\nv\nit\ny\n\n\n\nYear\n\n\n\nProductivity of potato in sindhuli\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\n3.4 Growth rate of area, production, and productivity \n\n\n\n3.4.1 Annual Growth Rate (AGR) in Sindhuli \n\n\n\nDuring the period of study from 1968/69 to 2017/018 varying growth \n\n\n\nrates for the area as was observed under potato cultivation in Sindhuli \n\n\n\ndistrict. The annual growth rate varies from 37% to -581%. The average \n\n\n\nannual growth rate was found to be -10% (which represents a decreasing \n\n\n\ntrend) and the compound growth rate is -1%. Since 2014/15 we can \n\n\n\nobserve a severe drop in growth rate as it has dropped down by -32% in \n\n\n\n2015/16 and -581% in 2016/17. The production growth rate varies from \n\n\n\n87% to -728%. The average annual growth rate is -22 % (decreasing \n\n\n\ntrend) and the compound growth rate is -1%. The maximum growth rate \n\n\n\nwas seen in the year 2009/10 and the minimum was seen in 2008/09. \n\n\n\nAfter 2015 growth of production is similar to the growth rate of the area \n\n\n\ni.e. growth in production has been drop by -47% in the year 2015/016 and \n\n\n\nby -588% in 2016/017. Productivity growth rate varies from 86% to -\n\n\n\n764%. Average annual growth for productivity is -13% (decreasing) and \n\n\n\ncompound annual growth rate is 2% similar to the production, AGR during \n\n\n\n2008/09 and 2009/10 has a minimum and maximum productivity growth \n\n\n\nrate. \n\n\n\n\n\n\n\nTable 7: Annual Growth Rate (AGR) of Area, Production, and Productivity in Sindhuli \nYear Production AGR Area AGR Productivity AGR Year Production AGR Area AGR Productivity AGR \n1968/69 -7% -9% 2% 1994/95 37% 1% 36% \n1969/70 0% 0% 0% 1995/96 21% 2% 19% \n1970/71 5% 8% -3% 1996/97 -21% -27% 5% \n1971/72 8% 20% -15% 1997/98 26% 8% 19% \n1972/73 7% 4% 3% 1998/99 1% 7% -7% \n1973/74 7% 4% 3% 1999/00 1% 13% -13% \n1974/75 6% 4% 3% 2000/01 9% 0% 8% \n1975/76 11% 7% 5% 2001/02 0% 0% -1% \n1976/77 -11% 0% -11% 2002/03 0% 0% 0% \n1977/78 18% 1% 17% 2003/04 -18% 0% -17% \n1978/79 -20% -1% -19% 2004/05 2% 3% -1% \n1979/80 -4% 5% -10% 2005/06 2% 0% 2% \n1980/81 9% 0% 9% 2006/07 2% 0% 1% \n1981/82 4% 0% 4% 2007/08 3% 0% 3% \n1982/83 21% 21% 0% 2008/09 -728% 4% -764% \n1983/84 5% -14% 17% 2009/10 87% 6% 86% \n1984/85 -10% 5% -15% 2010/11 18% 0% 18% \n1985/86 -30% 8% -42% 2011/12 -1% 2% -4% \n1986/87 6% 10% -4% 2012/13 0% 4% -4% \n1987/88 45% -2% 46% 2013/14 4% 0% 4% \n1988/89 1% 1% 0% 2014/15 63% 37% 41% \n1989/90 1% 1% 1% 2015/16 -47% -32% -11% \n1990/91 -25% 1% -26% 2016/17 -588% -581% -1% \n1991/92 -25% 1% -26% 2017/18 4% 0% 3% \n1992/93 39% 1% 39% Average AGR -22% -10% -13% \n1993/94 -50% 0% -51% Compound AGR 1% -1% 2% \n\n\n\n3.4.2 Annual Growth Rate (AGR) in Nepal \n\n\n\nIn Nepal from 1968/69 to 2017/018 growth rate from 14% to -8% was \n\n\n\nobserved for the area under cultivation of potatoes. The average annual \n\n\n\ngrowth rate for the area was found to be 3% and the compound growth \n\n\n\nrate was found to be 3.7%. In the case of production maximum annual \n\n\n\ngrowth rate varies from 52.50% to -109% where the average annual \n\n\n\ngrowth rate is 3.4% and compound annual growth rate is 4.5%. Similarly \n\n\n\nin productivity growth varies from 25% to -26% where the average annual \n\n\n\ngrowth rate is 2% and compound annual growth rate is 0.8%. \n\n\n\nTable 8: Annual Growth Rate (AGR) of Area, Production, and Productivity in Nepal \nYear Area AGR Production AGR Productivity AGR Year Area AGR Production AGR Productivity AGR \n1968/69 0% 2.00% 1% 1994/95 8% 10.70% 3% \n1969/70 7% 4.90% 0% 1995/96 8% 6.60% -1% \n1970/71 6% 3.70% -3% 1996/97 4% 9.90% 6% \n1971/72 4% 6.80% 3% 1997/98 5% -2.60% -8% \n1972/73 0% -0.30% -1% 1998/99 1% 11.00% 10% \n1973/74 4% 3.90% 0% 1999/00 4% 7.70% 4% \n1974/75 2% 1.00% 0% 2000/01 5% 10.00% 5% \n1975/76 -1% 2.10% 3% 2001/02 4% 10.80% 7% \n1976/77 -2% -16.10% -14% 2002/03 4% 3.80% 0% \n1977/78 -3% 0.70% 4% 2003/04 2% 6.80% 5% \n1978/79 0% 2.50% 2% 2004/05 3% 5.50% 3% \n1979/80 1% -0.10% -1% 2005/06 3% 11.90% 10% \n1980/81 -4% 0.80% 4% 2006/07 2% -1.60% -3% \n1981/82 5% 12.60% 8% 2007/08 2% 5.40% 3% \n1982/83 12% 13.90% 2% 2008/09 14% 15.20% 2% \n1983/84 -1% 2.60% 3% 2009/10 2% 3.70% 2% \n1984/85 10% 8.80% -1% 2010/11 -2% -0.40% 1% \n1985/86 6% -17.80% -26% 2011/12 4% 3.00% -1% \n1986/87 6% 9.70% 4% 2012/13 4% 3.00% 0% \n1987/88 7% 30.30% 25% 2013/14 4% 4.50% 0% \n1988/89 2% 11.50% 10% 2014/15 -4% 52.50% -4% \n1989/90 2% 4.60% 3% 2015/16 1% -109.1% 6% \n1990/91 1% 9.00% 8% 2016/17 -8% -8.30% -1% \n1991/92 1% -0.70% -2% 2017/18 5% 10.10% 6% \n1992/93 2% 0.10% -2% Average AGR 3% 3.40% 2% \n1993/94 3% 2.10% -1% Compound AGR 3.70% 4.50% 0.80% \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\n3.5 Supplies \n\n\n\nWhen we analyze the data from 2070B.S (2013A.D), we find that more \n\n\n\namounts of potatoes came from India. It occupies 58% of the total amount. \n\n\n\nKavre is the largest supplier of potatoes to the Kalimati market from inside \n\n\n\nthe country. It contributes 19% of the share to the total amount, followed \n\n\n\nby Kathmandu, Dolakha, Sindhupalchowk, Nuwakot, and Makwanpur with \n\n\n\n6%, 4% 3%, 2%, and 1% respectively. The amount of Indian potato is \n\n\n\nincreasing every year, its compound growth rate is about 23% in the last \n\n\n\n7 years. Every year Nepalese production is decreasing. All top producing \n\n\n\ndistricts have a negative Compound growth rate in 6 years except for \n\n\n\nMakwanpur and Dolakha. Among them, the growth rate of Dolakha is quite \n\n\n\ninsignificant and Makwapur has a very irregular trend of supply. The \n\n\n\ncompound growth rate of Kavre is -4%, similarly, Kathmandu has -7%, \n\n\n\nDolakha has 1%, Sindupalchowk has -13%, Nuwakot has -1%, and \n\n\n\nMakwanpur has 25%.  \n\n\n\n\n\n\n\nFigure 5: Pie chart for Kalimati market share \n\n\n\n4. CONCLUSION \n\n\n\nPotato is an important crop in our country but production and \n\n\n\nproductivity are very low as compared to our neighboring countries. The \n\n\n\nproductivity averaged over 3 years (2014/2015, 2015/2016, 2016/2017) \n\n\n\nis 14.3 Mt/ha/year and the production potential is 17 Mt/ha/year where \n\n\n\nwe observe a productivity gap of almost 3 Mt/ha/year. So, Nepal is facing \n\n\n\nproblems with the production of potatoes with optimum efficiency. The \n\n\n\nproductivity of vegetables is in increasing trend after 1991/92 up to \n\n\n\n2015/16. So, in the case of Nepal and Sindhuli district up to 2015, the \n\n\n\nproductivity of potato was rising, the even year 2015 was the year of \n\n\n\nmaximum productivity in both cases, (Sindhuli productivity=17mt/ha, \n\n\n\noverall Nepal productivity= 29mt/ha). But after 2015 productivity has to \n\n\n\ndrop down in both cases by 3% and 21% in Sindhuli and Nepal \n\n\n\nrespectively. There is very little difference between the average annual \n\n\n\nyield of Sindhuli and Nepal i.e of 1mt/ha. It represents a similar trend in \n\n\n\nproductivity between them. The production coefficient of variation of \n\n\n\nNepal is 93.67%, wherein the case of Sindhuli is 76.13%. The productivity \n\n\n\ncoefficient of variation of Nepal is 47.14% and Sindhuli is 40.62%. The \n\n\n\narea coefficient of variation in Nepal is 51% and in Sindhuli is 37.79% so \n\n\n\nin all the cases; variability is higher in the case of the Nepalese trend. \n\n\n\nIndian imports contribute 58% of the total arrival amount of potato in \n\n\n\nKalimati Fruits and vegetable market, while from inside country Kavre has \n\n\n\nthe largest share of 19%. The average annual growth rate for the Sindhuli \n\n\n\ndistrict was found to be negative while that for Nepal was positive. The \n\n\n\nmagnitude of variability in area, production, and productivity of Nepal was \n\n\n\nfound to be greater than the Sindhuli district. The coefficient of \n\n\n\ndetermination (R2) which determines the effect of an area in production \n\n\n\nwas found to be greater in the case of Nepalese potato trend (0.93) than \n\n\n\nSindhuli district (0.42). Thus, the utilization of the opportunities in this \n\n\n\nsector is the boon for the successful nation. \n\n\n\nAPPENDIX \n\n\n\nTable 9: Original data series (Area, production and productivity of potato in Sindhuli district and Nepal) \n\n\n\nYear \nProduction of \npotato (metric ton) \nin Sindhuli \n\n\n\nPotato plantation \narea (hectare) in \nSindhuli \n\n\n\nProductivity \n(mt/ha) \n\n\n\nPotato plantation \narea in Nepal \n\n\n\nPotato production in \nNepal (metric ton)  \n\n\n\nNepal Productivity \n(mt/ha) \n\n\n\n1968 3660 600 6.1 43000 245000 5.70 \n1969 3410 550 6.2 43000 250000 5.81 \n1970 3410 550 6.2 46000 263000 5.72 \n1971 3600 600 6 49000 273000 5.57 \n1972 3900 750 5.2 51000 293000 5.75 \n1973 4200 780 5.4 51000 292000 5.73 \n1974 4500 810 5.6 53000 304000 5.74 \n1975 4800 840 5.7 54000 307000 5.69 \n1976 5400 900 6 53280 313509 5.88 \n1977 4860 900 5.4 52287 270025 5.16 \n1978 5950 910 6.5 50650 271870 5.37 \n1979 4950 900 5.5 50700 278790 5.50 \n1980 4750 950 5 51330 278400 5.42 \n1981 5220 950 5.5 49580 280540 5.66 \n1982 5460 950 5.7 52010 321100 6.17 \n1983 6900 1200 5.75 59200 372970 6.30 \n1984 7240 1050 6.9 58880 383080 6.51 \n1985 6600 1100 6 65540 420160 6.41 \n1986 5080 1200 4.2 69960 356720 5.10 \n1987 5400 1330 4.06 74310 395110 5.32 \n1988 9750 1300 7.5 80180 566950 7.07 \n1989 9820 1310 7.50 81570 640910 7.86 \n1990 9962 1318 7.6 83350 671810 8.06 \n1991 7969 1326 6.01 84280 738030 8.76 \n1992 6375 1334 4.8 85300 732860 8.59 \n1993 10530 1350 7.8 87020 733300 8.43 \n1994 7020 1356 5.2 89664 748913 8.35 \n1995 11138 1369 8.1 97634 838932 8.59 \n1996 14020 1402 10 106000 898350 8.48 \n1997 11550 1100 10.5 110850 997400 9.00 \n1998 15518 1193 13.0 116290 971680 8.36 \n1999 15637 1287 12.1 118043 1091218 9.24 \n2000 15875 1475 10.8 122620 1182500 9.64 \n2001 17400 1482 11.7 129019 1313717 10.18 \n\n\n\n0thers\n7%\n\n\n\nkavre\n19%\n\n\n\nkathmandu\n6%\n\n\n\ndolakha\n4%\n\n\n\nsindhupalchowk\n3%\n\n\n\nnuwakot\n2%\n\n\n\nmakwanpur\n1%\n\n\n\nindia\n58%\n\n\n\nKalimati market share in potato\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\n2002 17365 1489 11.7 135093 1472757 10.90 \n2003 17400 1496 11.6 140171 1531315 10.92 \n2004 14750 1489 9.9 143027 1643357 11.49 \n2005 14990 1531 9.79 146789 1738840 11.85 \n2006 15230 1532 9.9 150864 1974755 13.09 \n2007 15470 1534 10.1 153534 1943246 12.66 \n2008 15950 1538 10.4 156737 2054817 13.11 \n2009 1926 1605 1.2 181900 2424048 13.33 \n2010 14550 1700 8.6 185342 2517696 13.58 \n2011 17700 1700 10.4 182600 2508044 13.74 \n2012 17500 1740 10.1 190250 2584301 13.58 \n2013 17560 1820 9.6 197234 2663839 13.51 \n2014 18200 1820 10 205725 2789012 13.56 \n2015 49200 2900 17.0 197037 5865914 29.77 \n2016 33556 2201 15.2 199971 2805582 14.03 \n2017 4880 323 15.1 185879 2591686 13.94 \n2018 5072 324 15.7 195173 2881829 14.77 \n\n\n\nTable 10: Supply amount to Kalimati Fruits and Vegetable Market \n\n\n\nAmount of potato supply in kg by various districts \n\n\n\nYears Kavre  Kathmandu Dolakha Makawanpur Nuwakot Sindupalchowk \n\n\n\n2070 12214802 5535428 3157136    \n\n\n\n2071 10620578 4562684 4395506  1415876  \n\n\n\n2072 10578321 3577880 1657390  1223330 2180080 \n\n\n\n2073 9011958 2554366 1608970 899530  2700058 \n\n\n\n2074 9326690 1664026  1271450 909374 2284355 \n\n\n\n2075 9432358 1454532 2307180  1367726 2797484 \n\n\n\n2076 12210275 3492003 3371450 2021160 1643109  \n\n\n\nREFERENCES \n\n\n\nAlhaji, U. U., Yusuf, A. S., Edet, C. O., Oche, C. O., Agbo, E. P. 2018. Trend \n\n\n\nAnalysis of Temperature in Gombe State Using Mann Kendall Trend \n\n\n\nTest. Journal of Scientific Research and Reports, 20(3), 1\u20139. \n\n\n\nhttps://doi.org/10.9734/JSRR/2018/42029 \n\n\n\nBhandari, N. B., Aryal, M. 2014. Average Cost of Production and Gross \n\n\n\nProfit of Fruit Farming in Nepal Average Cost of Production and Gross \n\n\n\nProfit of Fruit Farming in Nepal Ministry of Agriculture Development \n\n\n\nDepartment of Agriculture Agribusiness Promotion and Marketing \n\n\n\nDevelopment Dire. www.mrsmp.gov.np \n\n\n\nFAO Perspective. 2015. World agriculture: towards 2015/2030. \n\n\n\nwww.earthscan.co.uk \n\n\n\nGairhe, S., Gauchan, D., Timsina, K. 2017. Adoption of Improved Potato \n\n\n\nVarieties in Nepal. Journal of Nepal Agricultural Research Council, 3, 38\u2013\n\n\n\n44. https://doi.org/10.3126/JNARC.V3I1.17274 \n\n\n\nGhimire, D., Lamsal, G., Paudel, B., Khatri, S., Bhusal, B. 2018. Analysis of \n\n\n\ntrend in area, production and yield of major vegetables of Nepal. Trends \n\n\n\nin Horticulture, 1(2). https://doi.org/10.24294/TH.V1I2.914 \n\n\n\nGilbert, R. O. 1987. Statistical Methods for Environmental Pollution \n\n\n\nMonitoring | Wiley. https://www.wiley.com/en-\n\n\n\nus/Statistical+Methods+for+Environmental+Pollution+Monitoring-p-\n\n\n\n9780471288787 \n\n\n\nGotame, T. P., Poudel, S., Thapa, B., Neupane, J. D. 2021. Performance \n\n\n\nevaluation of potato clones for the central Terai Region of Nepal. Journal \n\n\n\nof Agriculture and Natural Resources, 4(2), 155\u2013166. \n\n\n\nhttps://doi.org/10.3126/janr.v4i2.33707 \n\n\n\nHayes, A. 2021a. Average Annual Growth Rate (AAGR) Definition. \n\n\n\nInvestopedia. https://www.investopedia.com/terms/a/aagr.asp \n\n\n\nHayes, A. 2021b. Average Annual Yield Definition. Investopedia. \n\n\n\nhttps://www.investopedia.com/terms/a/average-annual-yield.asp \n\n\n\nHelsel, D. R., Hirsch, R. M. 1993. Statistical Methods in Water Resources. \n\n\n\nhttps://books.google.fr/books \n\n\n\nHess, A., Iyer, H., Malm, W. 2001. Linear trend analysis: A comparison of \n\n\n\nmethods. Atmospheric Environment, 35(30), 5211\u20135222. \n\n\n\nhttps://doi.org/10.1016/S1352-2310(01)00342-9 \n\n\n\nKFVMDB. 2019. Kalimati Fruit and Vegetable Market Development \n\n\n\nCommittee: Regulating the market in Nepalese consumer interest since \n\n\n\n1995. https://kalimatimarket.gov.np/ \n\n\n\nMeals, D. W., Spooner, J., Dressing, S. A., Harcum, J. B. 2021. Statistical \n\n\n\nAnalysis for Monotonic Trends. National Nonpoint Source Monitoring \n\n\n\nProgram, 23. https://www.epa.gov/sites/production/files/2016-\n\n\n\n05/documents/tech_notes_6_dec2013_trend.pdf \n\n\n\nMOAD. 2015. Agriculture Development Strategy (ADS) 2015 to 2035. \n\n\n\nMinistry of Agricultural Development, Part 1, 20. \n\n\n\nMOALD. 2012. Ministry of Agriculture and Livestock Development. \n\n\n\nhttps://www.moald.gov.np/ \n\n\n\nMOALD. 2020. Statistical Information in Nepalese Agriculture. Ministry of \n\n\n\nAgriculture and Livestock, 290. \n\n\n\nhttps://nepalindata.com/resource/statistical-information-nepalese-\n\n\n\nagriculture-207374-201617/ \n\n\n\nNPC. 2019. The Fifteenth Plan Government of Nepal National Planning \n\n\n\nCommission. www.npc.gov.np \n\n\n\nNPDP. 2016. National Potato Development Program | Other Materials. \n\n\n\nhttp://www.npdp.gov.np/eng/publications/general.html \n\n\n\nParajuli, R. K. 2005. Kavre leads nation in potato production - The \n\n\n\nHimalayan Times - Nepal\u2019s No.1 English Daily Newspaper | Nepal News, \n\n\n\nLatest Politics, Business, World, Sports, Entertainment, Travel, Life Style \n\n\n\nNews. The Himalayan Times. \n\n\n\nhttps://thehimalayantimes.com/business/kavre-leads-nation-in-\n\n\n\npotato-production \n\n\n\nSen, P. K. 1968. Estimates of the Regression Coefficient Based on Kendall\u2019s \n\n\n\nTau. Journal of the American Statistical Association, 63(324), 1379\u2013\n\n\n\n1389. https://doi.org/10.1080/01621459.1968.10480934 \n\n\n\nSpotlight. 2014. FOOD SECURITY: Alarm Ringing | New Spotlight \n\n\n\nMagazine. https://www.spotlightnepal.com/2014/06/06/food-\n\n\n\nsecurity-alarm-ringing/ \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 29-37 \n\n\n\n\n\n\n\n \nCite the Article: Amrita Paudel, Koshis Babu Basnet, Anish Paudel, Bikash Gurung, Uttam Poudel  (2022). Trend Analysis of Area, Production, Productivity, and Supply \n\n\n\nof Potato in Sindhuli District and Nepal: A Comparative Study. Malaysian Journal of Sustainable Agriculture, 6(1): 29-37. \n \n\n\n\n\n\n\n\nSubedi, S., Ghimire, Y. N., Gautam, S., Poudel, H. K., Shrestha, J. 2019. \n\n\n\nEconomics of potato (Solanum tuberosum L.) production in terai region \n\n\n\nof Nepal. Archives of Agriculture and Environmental Science, 4(1), 57\u2013\n\n\n\n62. https://doi.org/10.26832/24566632.2019.040109 \n\n\n\nThe Kathmandu Post. 2014. Potato, onion prices jump due to slump in \n\n\n\nsupply. https://kathmandupost.com/money/2014/06/20/potato-\n\n\n\nonion-prices-jump-due-to-slump-in-supply \n\n\n\nTimsina, K. P., Gaire, S., Ghimire, Y. N., Poudel, H. K., Devkota, D., Subedi, S., \n\n\n\nAdhikari, S. P. 2019. Returns to Potato Research Investment in Nepal. \n\n\n\nJournal of Agriculture and Natural Resources, 2(1), 1\u201313. \n\n\n\nhttps://doi.org/10.3126/JANR.V2I1.26002 \n\n\n\nWikipedia. 2020. Agriculture in Nepal - Wikipedia. \n\n\n\nhttps://en.wikipedia.org/wiki/Agriculture_in_Nepal. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2023.88.97 \n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-293X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.02.2023.88.97 \n\n\n\n\n\n\n\nCHARACTERIZATION OF QUALITATIVE AND QUANTITATIVE TRAITS FOR \nDIVERSITY ASSESSMENT AND CORRELATION STUDIES IN FOXTAIL MILLET \n(SETARIA ITALICA (L.) BEAUV.) \n\n\n\nBimochana G.C.a, Ashmita Upadhyaya, Prabesh Dhakala, Saujan Bashyala, Subash Adhikaria, Prabin Kumar Poudela, Dipendra Kumar Ayerb \n\n\n\na Department of Agronomy, Plant breeding, and Agriculture Statistics, Institute of Agriculture and Animal Science, Lamjung Campus, Lamjung \n33600, Nepal. \nb Department of Genetics and Plant breeding, Institute of Agriculture and Animal Science, Gauradaha Agriculture Campus, Jhapa, Nepal. \n*Corresponding Author Email: bimochanagc@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 20 August 2023 \nRevised 21 September 2023 \nAccepted 23 October 2023 \nAvailable online 27 October 2023 \n\n\n\n A higher level of variability can be found in Nepalese foxtail millet. To assess potential traits for further \n\n\n\nbreeding activities and estimate the genetic diversity, phenotypic and genotypic coefficient of variation, \n\n\n\nheritability, genetic advance, and correlation coefficient for 15 different genotypes of foxtail millet, an \n\n\n\nexperiment was conducted in alpha lattice design with 3 replications at Agronomy farm of IAAS, Lamjung, \nNepal during March- June 2021. The analysis of variance revealed highly significant differences among the \n\n\n\naccessions for all the traits observed indicating the presence of a sufficient amount of variability. The value \n\n\n\nof the Shannon index ranged from 0.245 (plant anthocyanin coloration of the basal sheath) to 1.309(grain \n\n\n\ncolor) which indicates that the characters were more diverse for grain color while the value of evenness \nranged from 0.22 (plant: anthocyanin coloration of the basal sheath) to 0.965(panicle density) indicating that \n\n\n\nthe characters are more evenly distributed for the intensity of green leaf foliage. High PCV, high GCV, high \n\n\n\nheritability, and high genetic advance per mean were found for flag leaf length, peduncle length, no. of nodes, \nand yield per hectare. It indicates that these traits are less influenced by the environment and hence can be \n\n\n\nexploited by pure line and mass selection methods for crop improvement. Yield per hectare was positively \n\n\n\nand significantly correlated with grain per panicle (r=0.4**) and no. of nodes (r=0.39**). A strong positive \ncorrelation was observed between plant height and the number of nodes, flag leaf width, panicle length, and \n\n\n\nflag leaf length indicating their consideration for further breeding activities. The findings of the study can be \n\n\n\ncrucial in identifying better-performing genotypes that can help develop improved varieties \n\n\n\nKEYWORDS \n\n\n\nCorrelation, Diversity, Foxtail millet, Genetic Advance, Heritability \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nFoxtail millet belonging to the subfamily Panicoidea and tribe Paniceae \nincludes grain, wild, and weed species along with their different breeding \nsystems, life cycles, and ploidy levels (Lata et al., 2013). It is one of the \noldest cultivated crops in the world and was domesticated around 11000 \nyears ago from the wild species green foxtail (Setaria viridis) in northern \nChina (Doust et al., 2009; Yang et al., 2012).  Nowadays it is cultivated in \n26 countries extensively in India, Nepal, Sri Lanka, Pakistan, Russia, \nUkraine, the Middle East, Turkey, and Romania (Ravi, 2004). According to \nit ranks second in the most widely cultivated species among millets and \nthe most important millet in East Asia (Kumari et al., 2011). In Nepal, it is \ncultivated in hills and mid-hills districts, especially in the Karnali zone. It \nis also widely distributed in Mugu, Kalikot, Humla, Jumla, Bajhang, Bajura, \nDolpa, Lamjung, Gorkha, Ramechhap, Kavre, etc. where it is grown sole as \nwell as intercropped with finger millet (Eleusine coracana Gaertn.), proso \nmillet (Panicum miliaceum L.), beans (Phaseolus vulgaris), amaranths \n(Amaranthus hypochondriacus), maize (Zea mays L.), etc. (Ghimire et al., \n2017).  \n\n\n\nNutritionally, foxtail millet has a higher nutrient content compared to the \nmajor cereals such as wheat and rice (Parameswaran and Sadasivam, \n1994). Singh and Raghuvanshi have reported that it has a high content of \n\n\n\namino acids, phytochemicals, micronutrients, and antioxidants as \ncompared to non-millet cereals and hence is called \u2018Nutri cereals (Singh \nand Raghuvanshi, 2012). Similarly, because of its small diploid genomes \n(2n=18), short life cycles, self-pollination, small adult height, and prolific \nseed production, domesticated foxtail millet has been used as the novel \nmodel species for the functional genomics of the grass crop, particularly \nfor the study of the C4 photosynthesis (Muthamilarasan and Prasad, 2015). \nMoreover, foxtail millet is considered a neglected and underutilized crop \n(NUS) in Nepal that has tremendous potential to contribute to food \nsecurity, nutrition, dietary and culinary diversification, health, and income \ngeneration along with its better adaptability to marginal and harsh \nclimatic conditions. It is a traditional staple food used as rice, porridge, \nalcoholic beverage, and even as an offering to the local deities in drier \nparts of the high mountain landscape (Joshi et al., 2020). \n\n\n\nDespite its tremendous value, it is still underutilized. In Nepal, research on \nNUS had been started in 1991 but systematic conservation of these crops \nwas started in 2010 but the efforts are not still sufficient. Some research \nhas been conducted on NUS but for a short period (5 years) it could not \nsignificantly contribute to the development and utilization of these crops \n(Joshi et al., 2020). Further, a higher level of variability can be found in \nNepalese foxtail millet. Therefore, Nepal can be one of the centers of \ndiversity for foxtail millet (Nakayama et al., 1999). So, it is imperatively \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\nimportant to evaluate the local genotypes of foxtail millet to incorporate \nthem in the plant breeding and crop improvement programs. Knowledge \nof variability, genetic diversity, heritability, genetic advance, and genetic \nadvance per mean is a prerequisite for successful plant breeding \nprograms. Similarly, correlation studies help to know the association \nbetween yield and yield attributing traits for the indirect selection of traits \ncontributing to yield (Adhikari et al., 2018). \n\n\n\nTherefore, the objective of the study was to assess the different diversity \nindices, phenotypic and genotypic coefficient of variation, heritability, and \ngenetic advance per mean and correlation between different quantitative \ncharacters. The findings of the study would be crucial in identifying \npromising parental lines that would aid in future plant breeding and crop \nimprovement programs in foxtail millet. This study can be a bridge for the \nexisting research gap in foxtail millet and a comprehensive reference for \nthe development of improved foxtail millet varieties. \n\n\n\n2.   MATERIALS AND METHODS \n\n\n\n2.1   Experimental Site and Planting Materials \n\n\n\nThe study was conducted to access diversity indices (Shannon Weiner \nIndex and Evenness), phenotypic and genotypic coefficient of variation, \nheritability, genetic advance per mean, and correlation between different \nquantitative traits. The experimental material comprised fifteen \ngenotypes of foxtail millet (Table 1) collected from Ghanpokhara \ncommunity seed bank, Ghanpokhara, Lamjung, and National Genetic \nResource Centre, Khumaltar, Lalitpur, Nepal. The experiment was \nconducted from 23rd March to 29th July 2021 at the Agronomy farm of the \nInstitute of Agriculture and Animal Science (IAAS), Lamjung Campus, \nLamjung, Nepal, geographically located at the 632 masl with the sub-\ntropical type of climate.  \n\n\n\nTable 1: List of genotypes along with the place of collection \n\n\n\nS.N. Name of accessions Places of collection \n\n\n\n1. NGRCO 7417 National Genetic Resource Centre, Khumaltar, \n\n\n\n2 NGRCO 7419 National Genetic Resource Centre, Khumaltar, \n\n\n\n3 NGRCO 7420 National Genetic Resource Centre, Khumaltar, \n\n\n\n4 NGRCO 7416 National Genetic Resource Centre, Khumaltar, \n\n\n\n5 NGRCO 8391 National Genetic Resource Centre, Khumaltar, \n\n\n\n6 Pahelo Kaguno Jumla Ghanpokhara Community Seed Bank, Lamjung \n\n\n\n7 Pahelo Kaguno Humla Ghanpokhara Community Seed Bank, Lamjung \n\n\n\n8 NGRCO 7421 National Genetic Resource Centre, Khumaltar, \n\n\n\n9 NGRCO 6108 National Genetic Resource Centre, Khumaltar, \n\n\n\n10 NGRCO 7949 National Genetic Resource Centre, Khumaltar, \n\n\n\n11 NGRCO 6659 National Genetic Resource Centre, Khumaltar, \n\n\n\n12 Seto Kaguno Jumla Ghanpokhara Community Seed Bank, Lamjung \n\n\n\n13 Kalo Kaguno Jumla Ghanpokhara Community Seed Bank, Lamjung \n\n\n\n14 Bariyo Kaguno Lamjung Ghanpokhara Community Seed Bank, Lamjung \n\n\n\n15 Seto Kaguno Kaski Ghanpokhara Community Seed Bank, Lamjung \n\n\n\n2.2   Experimental design and field layout  \n\n\n\nThe field trial was conducted under rainfed conditions in an alpha lattice \ndesign with 15 foxtail millet genotypes as treatments and three \nreplications with a spacing of 1m between replications. Each replication \nhad three blocks with a block spacing of 50cm. Each block had five plots \nand each plot was 25cm apart. Each plot measured 1m2   and was cultivated \nwith five rows of the crop at a distance of 25 cm between the rows.  \n\n\n\n2.3   Cultural practices \n\n\n\nDirect and continuous row sowing of seed with the row-row spacing of 25 \ncm was done on 23rd March 2021. A complete dose of fertilizer was applied \nat the rate of 60: 30: 20 NPK at the time of field preparation (Ojha et al., \n\n\n\n2018). All the standard agronomic packages of practices were followed for \nthe healthy and proper growth of the crop. \n\n\n\n2.4   Data collection \n\n\n\n15 different qualitative traits were recorded as per the descriptor \nestablished by UPOV(2013) and IBPGR (1985) for the assessment of \ndiversity. The detailed description and evaluation stage of the traits are \ndepicted in Table 2. Similarly, ten different quantitative traits were \nassessed for the estimate of heritability, genetic advance per mean, and \ncorrelation between traits according to the descriptor established by \nUPOV (2013) and IBPGR (1985) was used. The detailed description and \nevaluation stage of the traits are presented in Table 3. \n\n\n\n\n\n\n\nTable 2: Qualitative data recording guidelines \n\n\n\nS.N. Qualitative characters Descriptor Evaluation phase \n\n\n\n1. \nPlant: anthocyanin coloration of the basal \n\n\n\nsheath \n(1) absent (2) medium (3) strong 15 DAS \n\n\n\n2. Intensity of green leaf foliage (1) light (3) medium (5) dark 35 DAS \n\n\n\n3. Plant growth habit (1) erect (2) semi-erect (3) spreading 35 DAS \n\n\n\n4. Leaf altitude of blade \n(1) erect (2) semi-erect (3) slightly drooping (4) \n\n\n\nstrongly drooping \n47DAS \n\n\n\n5. Anther color (1) white (2) orange (3) brown 65 DAS \n\n\n\n6. Panicle length of bristle (1) short (3) medium (5) long 65 DAS \n\n\n\n7. Flag leaf anthocyanin coloration of blade (1) absent (3) medium (5) very strong 71 DAS \n\n\n\n8. Glume anthocyanin color (1) absent (9) present 83 DAS \n\n\n\n9. Sheath pubescence \n(1) essentially glabrous (5) medium pubescent \n\n\n\n(9) strongly pubescent \n83 DAS \n\n\n\n10. Panicle altitude in relation to stem \n(1) erect (2) semi-erect (3) horizontal (4) \n\n\n\ndrooping \n92 DAS \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\nTable 2: Qualitative data recording guidelines \n\n\n\n11. Panicle type \n(1) conical (2) spindle (3) cylindrical (4) club (5) \n\n\n\nduck mouth (6) cat foot (7) branched \n92 DAS \n\n\n\n12. Panicle density (1) lax (3) medium (5) dense 92 DAS \n\n\n\n13. Lobe compactness \n(3) loose (5) medium (7) compact \n\n\n\n(9) spongy \n92 DAS \n\n\n\n14. Grain color \n(1) whitish (2) grey (3) yellow (4) brown (5) red \n\n\n\n(6) black \n92 DAS \n\n\n\n15. Dehusked grain color (non-polished) (1) whitish (2) grey (3) yellow 92 DAS \n\n\n\nTable 3: Quantitative data recording guidelines \n\n\n\nS.N. Quantitative characters Evaluation phase Procedure \n\n\n\n1. Flag leaf length 71 DAS Measured from ligule to tip of flag leaf \n\n\n\n2. Flag leaf width 71 DAS Measured at the widest point of the flag leaf \n\n\n\n3. Peduncle length 71 DAS Measured from the topmost node to the base of inflorescence \n\n\n\n4. Plant height 71 DAS Measured from ground level to tip of inflorescence \n\n\n\n5. Stem length 71 DAS Measured from the ground level to the base of \n\n\n\n6. No. of nodes 71 DAS Counted from the ground level to the top excluding the peduncle \n\n\n\n7. Panicle length 92 DAS Measured from the lowest branch to the tip of the last branch of the panicle \n\n\n\n8. Thousand seed weight 92 DAS \n1000 seeds from each plot were randomly taken from randomly selected \nsample panicles and weight was determined using an electronic balance. \n\n\n\n9. Grain per panicle 92 DAS The grain from randomly selected panicles was counted and recorded. \n\n\n\n10. Yield per hectare 92 DAS Recorded as kg/ha \n\n\n\n \n2.5   Statistical Analysis \n\n\n\nThe R studio software package and Microsoft Excel 2016 were used for \nstatistical analysis.  \n\n\n\n2.5.1   Shannon -Diversity Index \n\n\n\nThe Shannon index was calculated according to the formula  given by \n(Spellerberg and Fedor, 2003) \n\n\n\nShannon-Weiner index (H\u2019) =\u2212\u2211\ud835\udc79  \ud835\udc77\ud835\udc8a \ud835\udc25\ud835\udc27 \ud835\udc77\ud835\udc8a      \n\n\n\n                                                      i=1                                                                             \n\n\n\nWhere pi is the proportion of the trait i.e.,  \n\n\n\npi=\n\ud835\udc5d\ud835\udc5c\ud835\udc5d\ud835\udc62\ud835\udc59\ud835\udc4e\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b \ud835\udc4f\ud835\udc52\ud835\udc4e\ud835\udc5f\ud835\udc56\ud835\udc5b\ud835\udc54 \ud835\udc54\ud835\udc56\ud835\udc63\ud835\udc52\ud835\udc5b \ud835\udc50\u210e\ud835\udc4e\ud835\udc5f\ud835\udc4e\ud835\udc50\ud835\udc61\ud835\udc52\ud835\udc5f \ud835\udc5c\ud835\udc53 \ud835\udc4e \ud835\udc61\ud835\udc5f\ud835\udc4e\ud835\udc56\ud835\udc61\n\n\n\n\ud835\udc61\ud835\udc5c\ud835\udc61\ud835\udc4e\ud835\udc59 \ud835\udc5d\ud835\udc5c\ud835\udc5d\ud835\udc62\ud835\udc59\ud835\udc4e\ud835\udc61\ud835\udc56\ud835\udc5c\ud835\udc5b\n  \n\n\n\n2.5.2   Evenness \n\n\n\nIt is calculated according to the procedure given by (Magurran, 2004): \n\n\n\nEvenness (E)= \n\ud835\udc3b\u2032\n\n\n\nln (\ud835\udc46)\n  \n\n\n\nWhere S= total number of variation case \n\n\n\n2.5.3   Genotypic and Phenotypic Coefficient of Variation \n\n\n\nThe genotypic and phenotypic variance of various traits was calculated \naccording to the procedure introduced by  (Burton, 1951) \n\n\n\nGenotypic variance (\u03c32g) = \n\ud835\udc47\ud835\udc40\ud835\udc46\ud835\udc46\u2212\ud835\udc38\ud835\udc40\ud835\udc46\ud835\udc46\n\n\n\n\ud835\udc45\n                                                                            \n\n\n\nError variance (\u03c32e) = EMSS \n\n\n\nPhenotypic variance ((\u03c32p) = Genotypic variance + Error variance = \u03c32g + \n\u03c32e \n\n\n\nGenotypic coefficient of variation (GCV)= \n\ud835\udf0e\ud835\udc54\n\n\n\n\ud835\udc65\n * 100                                                      \n\n\n\nPhenotypic coefficient of variation (PCV) =  \n\ud835\udf0e\ud835\udc5d\n\n\n\n\ud835\udc65\n * 100                                                    \n\n\n\nWhere, \n\n\n\n\u03c3g= genotypic standard deviation \n\n\n\n\u03c3p= phenotypic standard deviation \n\n\n\nx= General mean of the trait \n\n\n\nThe GCV and PCV values were categorized as low for 0-10%, moderate for \n10-20%, and high for = >20% (Sivassubramanian and Madhavamenon, \n1973) \n\n\n\n2.5.4   Broad sense heritability (h2bs) \n\n\n\nAccording to the formula given  by (Burton and DeVane, 1953) \n\n\n\nh2bs =  \n\ud835\udc49\ud835\udc54\n\n\n\n\ud835\udc49\ud835\udc43\n *100   =  \n\n\n\n\ud835\udf0e2\ud835\udc54\n\n\n\n\ud835\udf0e2\ud835\udc5d\n  *100                                                                                  \n\n\n\nwhere \n\n\n\nVg= genotypic variance \n\n\n\nVp= phenotypic variance \n\n\n\nHeritability was categorized as low for a percentage value of 0-30%, \nmedium for 30-60%, and high for above 60% (Johnson et al., 1955) \n\n\n\n2.5.5   Genetic advances (GA) and genetic advance per mean \n\n\n\nAccording to the formula given by (Johnson et al., 1955) \n\n\n\nGA= K. \u03c3P. h2 bs \n\n\n\nWhere \n\n\n\nGA= Genetic advance \n\n\n\nK= selection differential (value of K= 2.056 at 5% selection intensity) \n\n\n\n\u03c3P = phenotypic standard deviation \n\n\n\nh2bs = broad sense heritability \n\n\n\nThe genetic advance as per mean (GAM) was calculated as  \n\n\n\nGAM =   \n\ud835\udc3a\ud835\udc34\n\n\n\n\ud835\udc3a\ud835\udc5f\ud835\udc4e\ud835\udc5b\ud835\udc51 \ud835\udc5a\ud835\udc52\ud835\udc4e\ud835\udc5b\n *100 \n\n\n\nThe range of genetic advance per mean was classified as suggested by \n(Johnson et al., 1955). \n\n\n\nLow: Less than 10% \n\n\n\nModerate: 10-20% \n\n\n\nHigh: More than 20% \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\n2.5.6   Pearson\u2019s correlation coefficient:  \n\n\n\nIt was calculated in the R studio software package. The effects were \nclassified based on the scale given by (Lenka and Misra, 1973): \n\n\n\n   Value                                           Scale \n\n\n\nMore than 1.00                              very high \n\n\n\n0.30-0.99                                        High \n\n\n\n0.20-0.29                                        Moderate \n\n\n\n0.10-0.19                                        low \n\n\n\n0.00-0.09                                        negligible \n\n\n\n3.   RESULT AND DISCUSSION \n\n\n\n3.1   Qualitative characters \n\n\n\nQualitative characters illustrate stable and discrete inheritance and are \nused as plant descriptors to identify genotypes, which are less \nenvironment-dependent (Nirubana et al., 2019). The frequency \ndistribution for 15 qualitative traits is calculated and presented in Table 4.   \nConcerning plant: anthocyanin coloration of the basal sheath, 93.33% of \nthe genotypes lack anthocyanin, for flag leaf anthocyanin coloration of the \nblade, 86.67% lack anthocyanin, and glume anthocyanin color, 66.67% \nlack anthocyanin pigmentation.  The genotypes with anthocyanin \npigmentation were more resistant to blast disease, thereby contributing \nto increasing yield (Rao, 1948). 40% of the genotypes have light green leaf \nfoliage whereas 60 % have a moderately green leaf.  Plant growth habit \n\n\n\nwith a semi-erect type (53.33%) was more frequent compared to erect \n(40%) and spreading (6.67%) types.  \n\n\n\nThe maximum number of genotypes (53.33%) exhibited semi-erect \nfollowed by erect (33.33%) and slightly drooping type (13.33%) for leaf \naltitude of the blade. The anther color showed less variability: 80% were \nbrown followed by 20% orange anther.  The panicle length of bristle was \nfound to be short for 46.67% of the genotypes, 33.33% of them were long \nand 20% of them were medium in length. The bristles with silica play an \nimportant role in insect pest resistance and therefore help to increase \nyield (Liang, 2006). Among the genotypes, essentially glabrous and \nmoderately pubescent leaves each exhibited 46.67% of the total while 6.67 \n% of leaves were strongly pubescent. The finding of the study for sheath \npubescence was similar to the finding by (Banu et al., 2018).  The \ngenotypes with drooping panicle altitude were higher in frequency (60%) \ncompared to 20% semi-erect and horizontal type each. The frequency of \nthe conical type of panicle (73.33%) was predominant followed by the \ncylindrical type (13.33%) and spindle and cat foot each (6.67%).  \n\n\n\nConsidering panicle density, the majority of the genotypes were dense \n(46.67%), while 26.67% were lax and medium each. The length of \nbranches, grain density, number, and spikelet sterility were high and the \naverage grain weight of the basal spikelet of the panicle of the main shoot \nwas low in compact panicle compared to lax type in rice (Panda et al., \n2015).  For lobe compactness, 46.67% of the genotypes were compact \nfollowed by medium (26.67%), loose (20%), and spongy (6.67%) types. \nThe finding is sympathetic to the finding reported by (Banu et al., 2018). \nThe majority of the genotypes had grain yellowish in color (40%), while \n26.67% of them were brown followed by white (20%) and black (2%). A \nsimilar result was reported by Vetriventhan  for grain color (Vetriventhan, \n2011). Regarding dehusked grain color, the frequency of white was \n66.67%, yellow (26.67% and grey (6.67%). \n\n\n\nTable 4:  Frequency distribution of 15 qualitative traits of 15 foxtail millet genotypes \n\n\n\nS.N. Qualitative characters Descriptor Frequency Frequency (%) \n\n\n\n1. \nPlant: anthocyanin coloration of the basal \n\n\n\nsheath \n\n\n\n(1) absent \n\n\n\n(2) medium \n\n\n\n(3) strong \n\n\n\n14 \n\n\n\n0 \n\n\n\n1 \n\n\n\n93.33 \n\n\n\n0.00 \n\n\n\n6.67 \n\n\n\n2. Intensity of green leaf foliage \n\n\n\n(1) light \n\n\n\n(3) medium \n\n\n\n(5) dark \n\n\n\n6 \n\n\n\n9 \n\n\n\n0 \n\n\n\n40 \n\n\n\n60 \n\n\n\n0.00 \n\n\n\n3. Plant growth habit \n\n\n\n(1) erect \n\n\n\n(2) semi-erect \n\n\n\n(3) spreading \n\n\n\n6 \n\n\n\n8 \n\n\n\n1 \n\n\n\n40 \n\n\n\n53.33 \n\n\n\n6.67 \n\n\n\n4. Leaf altitude of blade \n\n\n\n(1) erect \n\n\n\n(2) semi-erect \n\n\n\n(3) slightly drooping \n\n\n\n(4) strongly drooping \n\n\n\n5 \n\n\n\n8 \n\n\n\n2 \n\n\n\n0 \n\n\n\n33.33 \n\n\n\n53.33 \n\n\n\n13.33 \n\n\n\n0.00 \n\n\n\n5. Anther color \n\n\n\n(1) white \n\n\n\n(2) orange \n\n\n\n(3) brown \n\n\n\n0 \n\n\n\n3 \n\n\n\n12 \n\n\n\n0.00 \n\n\n\n20 \n\n\n\n80 \n\n\n\n6. Panicle length of bristle \n\n\n\n(1) short \n\n\n\n(3) medium \n\n\n\n(5) long \n\n\n\n7 \n\n\n\n3 \n\n\n\n5 \n\n\n\n46.67 \n\n\n\n20 \n\n\n\n33.33 \n\n\n\n7. Flag leaf anthocyanin coloration of blade \n\n\n\n(1) absent \n\n\n\n(3) medium \n\n\n\n(5) very strong \n\n\n\n13 \n\n\n\n1 \n\n\n\n1 \n\n\n\n86.67 \n\n\n\n6.67 \n\n\n\n6.67 \n\n\n\n8. Glume anthocyanin color \n(1) absent \n\n\n\n(9) present \n\n\n\n10 \n\n\n\n5 \n\n\n\n66.67 \n\n\n\n33.33 \n\n\n\n9. Sheath pubescence \n\n\n\n(1) essentially glabrous \n\n\n\n(5) medium pubescent \n\n\n\n(9) strongly pubescent \n\n\n\n7 \n\n\n\n7 \n\n\n\n1 \n\n\n\n46.67 \n\n\n\n46.67 \n\n\n\n6.67 \n\n\n\n10. Panicle altitude in relation to stem \n\n\n\n(1) erect \n\n\n\n(2) semi-erect \n\n\n\n(3) horizontal \n\n\n\n(4) drooping \n\n\n\n0 \n\n\n\n3 \n\n\n\n3 \n\n\n\n9 \n\n\n\n0.00 \n\n\n\n20 \n\n\n\n20 \n\n\n\n60 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\nTable 4:  Frequency distribution of 15 qualitative traits of 15 foxtail millet genotypes \n\n\n\n11. Panicle type \n\n\n\n(1) conical \n\n\n\n(2) spindle \n\n\n\n(3) cylindrical \n\n\n\n(4) club \n\n\n\n(5) duck mouth \n\n\n\n(6) cat foot \n\n\n\n(7) branched \n\n\n\n11 \n\n\n\n1 \n\n\n\n2 \n\n\n\n0 \n\n\n\n0 \n\n\n\n1 \n\n\n\n0 \n\n\n\n73.33 \n\n\n\n6.67 \n\n\n\n13.33 \n\n\n\n0.00 \n\n\n\n0.00 \n\n\n\n6.67 \n\n\n\n0.00 \n\n\n\n12. Panicle density \n\n\n\n(1) lax \n\n\n\n(3) medium \n\n\n\n(5) dense \n\n\n\n4 \n\n\n\n4 \n\n\n\n7 \n\n\n\n26.67 \n\n\n\n26.67 \n\n\n\n46.67 \n\n\n\n13. Lobe compactness \n\n\n\n(3) loose \n\n\n\n(5) medium \n\n\n\n(7) compact \n\n\n\n(9) spongy \n\n\n\n3 \n\n\n\n4 \n\n\n\n7 \n\n\n\n1 \n\n\n\n20 \n\n\n\n26.67 \n\n\n\n46.67 \n\n\n\n6.67 \n\n\n\n14. Grain color \n\n\n\n(1) whitish \n\n\n\n(2) grey \n\n\n\n(3) yellow \n\n\n\n(4) brown \n\n\n\n(5) red \n\n\n\n(6) black \n\n\n\n3 \n\n\n\n0 \n\n\n\n6 \n\n\n\n4 \n\n\n\n0 \n\n\n\n2 \n\n\n\n20 \n\n\n\n0.00 \n\n\n\n40 \n\n\n\n26.67 \n\n\n\n0.00 \n\n\n\n13.33 \n\n\n\n15. Dehusked grain color (non-polished) \n\n\n\n(1) whitish \n\n\n\n(2) grey \n\n\n\n(3) yellow \n\n\n\n10 \n\n\n\n1 \n\n\n\n4 \n\n\n\n66.67 \n\n\n\n6.67 \n\n\n\n26.67 \n\n\n\n3.2   Diversity indices \n\n\n\nDiversity indices of qualitative traits studied in foxtail millet genotypes are \n\n\n\npresented in Table 5. The Shannon- Weiner index was used to study both \n\n\n\nthe abundance and evenness of characters of the trait. The higher the value \n\n\n\nof the index, the higher the diversity in the given trait and species in a \n\n\n\nparticular community. The lower the value of the index, the lower the \n\n\n\ndiversity in the trait and species in the given community.  A value of H =0 \n\n\n\nindicates a community that has only one species. Shannon Weiner index \n\n\n\nwas found to be maximum for grain color (H\u2019 = 1.309), followed by lobe \n\n\n\ncompactness (H\u2019= 1.21), and panicle density (H\u2019= 1.06). This means that \n\n\n\nthe genotypes had shown the most diversity in these traits. Similarly, the \n\n\n\nShannon- Weiner index was found to be minimum for plant: anthocyanin \n\n\n\ncoloration of the basal sheath (H\u2019=0.245), followed by flag leaf anthocyanin \n\n\n\ncolor of the blade (H\u2019=0.49), and anther color (H\u2019=0.5). It indicates that the \n\n\n\ngenotypes were the least diverse for seedling anthocyanin. The evenness \n\n\n\nof genotypes indicated that the intensity of panicle density (E= 0.97) was \n\n\n\nmore even followed by glume anthocyanin coloration (E= 0.91), and leaf \n\n\n\nattitude of the blade (E=0.88). Similarly, the minimum value of evenness \n\n\n\nwas observed in plant anthocyanin coloration of the basal sheath (E =0.22) \n\n\n\nwhere only one accession showed anthocyanin coloration but the \n\n\n\nremaining 14 accessions showed no anthocyanin coloration. It was \n\n\n\nfollowed by flag leaf anthocyanin coloration of the blade (E= 0.44) and \n\n\n\npanicle type (E= 0.44). \n\n\n\nTable 5: Diversity indices of qualitative traits \n\n\n\nS.N. Characters Shannon Index Evenness \n\n\n\n1 Plant: anthocyanin coloration of the basal sheath 0.24493 0.22 \n\n\n\n2 Intensity of green leaf foliage 0.673012 0.61 \n\n\n\n3 Plant growth habit 0.882311 0.8 \n\n\n\n4 Leaf attitude of blade 0.970116 0.88 \n\n\n\n5 Panicle length of bristle 1.043757 0.69 \n\n\n\n6 Anther color 0.500402 0.46 \n\n\n\n7 Flag leaf anthocyanin color of the blade 0.485094 0.44 \n\n\n\n8 Glume anthocyanin coloration 0.636514 0.91 \n\n\n\n9 Sheath pubescence 0.891867 0.81 \n\n\n\n10 Panicle attitude in relation to stem 0.950271 0.69 \n\n\n\n11 Panicle type 0.857174 0.44 \n\n\n\n12 Panicle density 1.060602 0.965 \n\n\n\n13 Lobe compactness 1.210558 0.87 \n\n\n\n14 Grain color 1.309526 0.73 \n\n\n\n15 Dehusked grain color 0.803315 0.73 \n\n\n\n \n3.3   Analysis of variance (ANOVA) \n\n\n\nAnalysis of variance for different characters studied is presented in Table \n6. The results revealed highly significant differences among the accessions  \n\n\n\nfor all the traits observed indicating the presence of a sufficient amount of \nvariability. The considerable genetic variation for different traits among \nthe accessions can be valuable in the selection and screening for future \nbreeding programs. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\nTable 6: Analysis of variance for 10 traits in foxtail millet \n\n\n\nTreatment Flag leaf length Flag leaf width Peduncle length Plant height Stem length No. of nodes Panicle length 1000 grain weight Grains per panicle \nYield per \n\n\n\nhectare(kg/ha) \n\n\n\nNGRCO 7417 18.08de 2.04abc 11.64ef 145.93cd 132.5bcd 9.94ab 13.39bcd 1.53def 1484.33a 384.8def \n\n\n\nNGRCO 7419 22.99b 1.99abcd 13.67de 129.07de 117.4def 8.83bcde 11.71cd 1.51ef 1028.67abcd 741ab \n\n\n\nNGRCO 7420 20.27bcd 2.09abc 16.14cd 155.25bc 142.48abc 9.97ab 11.88cd 1.71cde 1284ab 887.6a \n\n\n\nNGRCO 7416 18.88cd 1.82bcde 11.23ef 126.13def 113.9defg 8.03efg 12.18cd 1.89bcde 1366ab 555.5bcde \n\n\n\nNGRCO8391 29.49 a 2.13 ab 21.19 ab 179.89a 160.97a 10.23a 18.91a 1.98bc 1294ab 635.33bc \n\n\n\nPAHELO KAGUNO JUMLA 20.54bcd 1.79bcde 17.82bc 116.01efgh 104.36efgh 6.2h 11.65cd 1.79bcde 415.33de 91.83h \n\n\n\nPAHELO KAGUNO HUMLA 23.07b 1.77cde 23.21 a 104.04fgh 92.2gh 4.4i 11.84cd 2.01bc 471.67de 231.4fgh \n\n\n\nNGRCO 7421 29.37 a 1.75cde 9.05 f 169.52ab 152.7ab 9.54abcd 17.364a 1.48ef 93.67e 455cde \n\n\n\nNGRCO 6108 10.51f 2.32 a 13.83de 136.5cde 121.3cde 9.16abcde 7.87e 1.73bcde 473de 136.67gh \n\n\n\nNGRCO 7949 28.15 a 2.13 ab 13.67de 144.2cd 128.2cde 9.8abc 16.04ab 1.73bcde 786bcd 208.27gh \n\n\n\nNGRCO 6659 18.09 de 1.65 de 5.27 g 95.6h 84.88h 7.52fg 10.57de 1.94bcd 891abcd 538.87cde \n\n\n\nSETO KAGUNO JUMLA 18.79cd 1.99abcd 20.55ab 129.09de 117.3def 8.53cdef 12.11cd 2.15b 978.33abcd 343.73efg \n\n\n\nKALO KAGUNO KASKI 21.71bc 1.82bcde 24.48 a 100.58gh 87.76h 4.53i 10.75de 2.63a 433.67de 213.8gh \n\n\n\nBARIYO KAGUNO 14.96 e 1.49e 11.64ef 122.57defg 108.8defgh 8.47def 14.23bc 1.27f 637.67cde 610.63bcd \n\n\n\nSETO KAGUNO KASKI 19.94bcd 1.99abcd 12.41 def 105.03fgh 94.1fgh 6.93gh 10.93d 2.15b 1135.67abc 499.1cde \n\n\n\nLSD (0.05) 3.18 0.29 3.75 21.22 21.82 1.18 2.75 0.37 568.63 203.87 \n\n\n\nF test *** ** *** *** ** *** *** *** *** *** \n\n\n\nCV, % 8.96 9.18 14.71 9.59 10.99 8.62 12.74 11.93 39.43 27.64 \n\n\n\nGrand mean 20.99 1.92 15.05 130.63 117.26 8.14 12.76 1.84 851.53 435.6 \n\n\n\n*Significant at 5 percent level, **significant at 1 percent level ***significant at 0.1 percent level \n\n\n\n3.4   Variance, coefficient of variance, heritability, and genetic advance per mean \n\n\n\nThe estimate of the coefficient of variance, heritability, and genetic advance per mean is represented in Figure \n1 and Figure 2. Flag leaf length was found to have high GCV (23.76%), high PCV (26.31%), and high heritability \n(81.54%) with high genetic advance per mean (44.20%).  Similar results were obtained by for GCV, PCV, \nheritability, and genetic advance per mean (Pallavi et al., 2020; Anuradha and Patro, 2020). The PCV and GCV \nof flag leaf width were estimated to be moderate (13.5%) and 9.79% with moderate heritability of 52.54% and \nmoderate genetic advance per mean of 14.62%. These results were accompanied by the findings of for \nmoderate PCV and for moderate genetic advance per mean (Mohan et al., 2019; Kavya et al., 2017). Similar \nfindings were reported by for moderate PCV, low GCV, moderate heritability, and moderate genetic advance \nper mean (Anuradha and Patro, 2020).  \n\n\n\nThe peduncle length was found to have high GCV (35.22%), high PCV (38.19%), and high heritability of 85.07% \nwith a high value of genetic advance per mean (64.98%). These findings were similar to for high PCV and GCV \nand for high heritability and genetic advance per mean (Pallavi et al., 2020; Bhakuni et al., 2021). Plant height \nwas estimated to have moderate GCV (18.33%), high PCV (20.78%), and high heritability of 77.85% with high \ngenetic advance per mean of 33.32%. These results are in accordance with the results reported by for high \nheritability and high genetic advance for plant height (Bhakuni et al., 2021). Similarly, Anuradha and Patro \nfound the same result for the high heritability of plant height in foxtail millet (Anuradha and Patro, 2020). The \nPCV and GCV for stem length were estimated to be 21.17 % and 18.98% and heritability of 80.41% with high \n\n\n\ngenetic advance per mean of 35.07%.  \n\n\n\nThe number of nodes was found to have high PCV (24.16%) high GCV (22.67%) and high heritability of 88.08% \nwith high genetic advance per mean of 43.84. Panicle length was found to have moderate GCV (19.24%), high \nPCV (27.27%), and moderate heritability of 49.75% with high genetic advance per mean (27.95%).  Similar \nresults were reported for high GCV, PCV, moderate heritability, and high genetic advance per mean (Pallavi et \nal., 2020). TSW was found to have moderate GCV (16.62 %), high PCV (20.41%), and high heritability (66.27%) \nwith high genetic advance per mean of 27.87%. Similar results were reported by for high PCV, high heritability, \nand high genetic advance per mean (Karvar et al., 2020; Pallavi et al., 2020). Grain per panicle was found to \nhave high GCV (43.94%), high PCV (57.54%), and moderate heritability of 58.29% with high genetic advance \nper mean of 69.11%. Yield per hectare was found to have high GCV (52.63%), high PCV (56.69%), and high \nheritability of 86.17% with high genetic advance per mean of 99.35%. Similar results were reported for high \nPCV, high GCV, high heritability, and high genetic advance per mean (Ashok et al., 2016; Mohan et al., 2019; \nBhakuni et al., 2021). \n\n\n\nMost of the traits under study exhibited slightly higher PCV than GCV which indicates that the variation is not \nonly due to genetic factors but also due to the influence of environmental factors. The lower the difference \nbetween PCV and GCV indicates the lower effect of the environment and greater influence of the genetic factors \nfor the expression of the characters thus providing a higher scope for crop improvement. So, the selection can \nbe done based on the phenotype independent of genotype for crop improvement (Dhakal et al., 2020)  Higher \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\ndifference between the PCV and GCV indicates that the expression of traits \nis more susceptible to environmental factors.  Moderate PCV and GCV \nvalues indicate the existence of moderate variability that can be exploited \nfor crop improvement through selection in advanced generations \n(Amarnath et al., 2018; Brunda et al., 2014).  \n\n\n\nThe genetic coefficient of variation alone would not indicate the \nproportion of total heritable variation. Heritability together with genetic \nadvance is essential for the study of total heritable variation. The broad \nsense of heritability includes the contribution of additive gene effects, \nallelic interactions due to dominance, and non-allelic interactions due to \nepistasis. Genetic advance provides knowledge about expected genetic \ngain for a particular trait after selection. High heritability and high genetic \nadvance per mean were found for flag leaf length, peduncle length, plant \nheight, stem length, number of nodes, thousand seed weight, and yield per \nhectare. It indicates that these traits are controlled by additive gene action \nand have less influence of the   environment. Hence, the   traits   with   high \n\n\n\n heritability and high genetic advance per mean can be exploited by pure \nline selection and mass selection methods for crop improvement (Brunda \net al., 2014; Mohan et al., 2019).  \n\n\n\nSimilarly, moderate heritability with moderate genetic gain per mean was \nfound for flag leaf width. This can be exploited by mass selection, progeny \nselection, hybridization, and simple selection. Moderate heritability with \nhigh genetic advance per mean was found for panicle length and grain per \npanicle which indicates the presence of both additive and nonadditive \ngene action. Hence the simple selection is not rewardable in this case \n(Ashok et al., 2016). Heterosis breeding has to be followed for breeding \nprograms. Hence, the traits having high heritability coupled with high \ngenetic advance per mean like flag leaf length, peduncle length, plant \nheight, stem length, number of nodes, thousand seed weight, yield per \nhectare. Thus, these can be exploited in breeding programmes based on \nsimple selection. \n\n\n\n\n\n\n\nFigure 1: PCV and GCV values for quantitative characters \n\n\n\n(GCV=genotypic coefficient of variation, PCV= phenotypic coefficient of \nvariation, FLL= flag leaf length, FLW= flag leaf width, PL=peduncle length, \nPH= plant  height, SL=  stem length, NN=  number  of  nodes, PaL = panicle \n\n\n\n length, TSW= thousand seed weight, GPP= grain per panicle, YPH=yield \nper hectare)   \n\n\n\n\n\n\n\nFigure 2: Heritability and Genetic advance per mean for quantitative characters \n\n\n\nFLL FLW PL PH SL NN PaL TSW GPP YPH\n\n\n\nHBS% 81.54502 52.54472 85.07869 77.85417 80.4175 88.08478 49.75512 66.27303 58.29757 86.17796\n\n\n\nGAM% 44.20278 14.62124 64.98817 33.32885 35.07418 43.84732 27.95825 27.87411 69.1124 99.35\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\nH\nB\n\n\n\nS/\nG\n\n\n\nA\nM\n\n\n\n)\n\n\n\nHBS% GAM%\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\n(HBS=broad sense heritability, GAM=genetic advance as a percent of \nmean, FLL= flag leaf length, FLW= flag leaf width, PL=peduncle length, PH= \nplant height, SL= stem length, NN= number of nodes, PaL= panicle length, \nTSW= thousand seed weight, GPP= grain per panicle, YPH=yield per \nhectare) \n\n\n\n3.5   Pearson\u2019s Correlation Analysis \n\n\n\nThe correlation coefficient is used to measure the statistical relationship \nbetween two variables that assist in the indirect selection of traits for crop \nimprovement programs (Adhikari et al., 2018). Yield per hectare was \nfound to have a highly significant and positive correlation with grain per \npanicle (r = 0.4**) and no. of nodes (r = 0.39**). A similar result was found \nby for no. of nodes (Kandel et al., 2020). Nodes act as the central hub for \nthe allocation of mineral nutrients regulated by node-based transporters \ncontributing to an increase in yield (Yamaji and Ma, 2017). Therefore, \nthese traits can be considered to increase yield per hectare. Thousand seed \nweight was found to have a highly significant and positive correlation with \npeduncle length (r = 0.48***). Similarly, it was found to have a negative but \nhighly significant correlation with no. of nodes (r = -0.51***), and plant \nheight (r = -0.35*). It suggests that the increase in height leads to the \nhigher accumulation of photosynthates in vegetative parts rather than \nreproduction and augments susceptibility to lodging (Roy et al., 2015).  \n\n\n\nThis  result was in accord with the finding of for plant height and for \npeduncle length (Amgai et al., 2011; Pallavi et al., 2020). In general \nnegative correlation indicates the retrogressive association between the \ntraits and simultaneous selection can be considered for a variety of \nimprovement activities (Osundare et al., 2017). Panicle length was found \nto have a highly significant and positive correlation with flag leaf length (r \n= 0.74***), plant height (r = 0.69***), and no. of nodes (r = 0.41**). Similar  \n\n\n\nresults were found by for flag leaf length and plant height (Sapkota et al., \n2016).  Number of nodes was found to have highly significant and positive \nrelationship with plant height (r = 0.76***), significant and positive \ncorrelation with flag leaf width (r = 0.39**), panicle length (r = 0.41**), \nyield per hectare (r = 0.39**), grain per panicle (r = 0.41**). Similarly, it \nwas found to have a negative and highly significant correlation with \nthousand seed weight (r = -0.51***) and peduncle length (r = -0.37*). The \nresult was sympathetic to the finding reported by for plant height, panicle \nlength, yield per hectare, and thousand seed weight (Kandel et al., 2020). \n\n\n\nPeduncle length was found to have a positive and highly significant \ncorrelation with thousand seed weight (r = 0.48***) and a negative and \nsignificant correlation with no. of nodes (r = -0.37*). A similar result was \nreported by for thousand seed weight (Pallavi et al., 2020). Plant height \nwas found to have a highly significant and positive relation with no. of \nnodes (r= 0.76***), flag leaf width (r =0.54***), panicle length (r = 0.69***), \nand flag leaf length (r = 0.49***). Similarly, plant height was found to have \na negative and a highly significant correlation with thousand seed weight \n(r= -0.35*). The finding was supported by for thousand seed weight and \nby for panicle length (Amgai et al., 2011; Anuradha and Patro (2020). The \nresult was in accord with the finding obtained by for panicle length, flag \nleaf length, and flag leaf width (Pallavi et al., 2020). Flag leaf length was \nfound to have a highly significant and positive relationship with panicle \nlength (r =0.74***), plant height, and stem length (r =0.49***). Similar \nresult was reported by (Pallavi et al., 2020). Flag leaf width was found to \nhave a highly significant and positive correlation with stem length and \nplant height (r = 0.54***) and no. of nodes (r = 0.39**). Similarly, FLW was \nfound to have a positive and significant relationship with grain per panicle \n(r = 0.32*). A similar result was reported by for plant height (Sapkota et \nal., 2016). \n\n\n\n\n\n\n\nFigure 3: Pearson\u2019s correlation matrix \n\n\n\nFlag leaf length (FLL), Flag leaf width (FLW), Peduncle length (PL), Plant \nheight (PH), Stem length (SL), Panicle length (PaL), No of nodes (NN), \nGrain per panicle (GPP), Thousand Seed weight (TSW) and Yield per \nhectare (YPH). \n\n\n\n4.   CONCLUSION \n\n\n\nNepal is one of the centers of diversity for foxtail millet with high genetic \ndiversity of this crop. However, Nepalese farmers do not have sufficient \nimproved variety officially released due to the lack of substantial research \non this crop. So, this research emphasizing the assessment of diversity \nindices, evaluation of inheritance of yield and its related traits, expected \n\n\n\ngenetic advance, and association between different traits provides a huge \nopportunity for the development of new cultivars through selection. The \nresults from the study revealed highly significant differences among the \naccessions for all the traits observed indicating the presence of a sufficient \namount of variability.  \n\n\n\nThe highest Shannon Weiner Index value for grain color indicated the \npresence of maximum diversity for these traits. Similarly, high GCV, high \nPCV, high heritability, and high genetic advance per mean were found for \nflag leaf length, peduncle length, number of nodes, and yield per hectare \nrevealing that these traits can be exploited with pureline selection and \nmass selection methods for crop improvement. Yield per hectare was \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\npositively correlated with no. of nodes and grain per panicle which \nindicates the importance of these traits for crop improvement \nprogrammes. Therefore, the considerable genetic variation for different \ntraits among the accessions can be valuable in selection and screening for \nfuture breeding programs. \n\n\n\nREFERENCES \n\n\n\nAdhikari, B.N., Joshi, B.P., Shrestha, J., Bhatta, N.R., 2018. Genetic \nvariability, heritability, genetic advance, and correlation among yield \nand yield components of rice. Journal of Agriculture and Natural \nResources, 1, Pp. 149\u2013160. \n\n\n\nAmarnath, K., Prasad, A.D., Reddy, C.C., 2018. Character association and \npath analysis in foxtail millet genetic resources. Current Advances in \nAgricultural Sciences (An International Journal), 10 (2), Pp. 89. \nhttps://doi.org/10.5958/2394-4471.2018.00016.3 \n\n\n\nAmgai, R.B., Pantha, S., Chhetri, T.B., Budhathoki, S.K., Khatiwada, S.P., \nMudwari, A., 2011. Variation on agro-morphological traits in \nNepalese foxtail millet (Setaria italica (L) P Beauv). Agronomy \nJournal of Nepal (Agron JN), 2, Pp. 133\u2013138. \n\n\n\nAnuradha, N., Patro, T., 2020. Estimates of variability, heritability, and \ngenetic advance in foxtail millet. Journal of Pharmacognosy and \nPhytochemistry, 9 (1), Pp. 1614\u20131616. \n\n\n\nAshok, S., Patro, T.S.S.K., Jyothsna, S., Divya, M., 2016.  Studies on Genetic \nparameters, correlation and path analysis for grain yield and its \ncomponents in foxtail millet (Setaria italica). Progressive Research- \nAn International Journal, 11 (3), Pp. 300\u2013303. \n\n\n\nBanu, H., Gowda, J., Gowda, M.V.C., 2018. Characterization and \nIdentification of DUS Traits in Reference Set of Foxtail Millet \nGermplasm (Setaria italica (L.) Beauv.). International Journal of \nCurrent Microbiology and Applied Sciences, 7 (12), Pp. 175\u2013183. \nhttps://doi.org/10.20546/ijcmas.2018.712.023 \n\n\n\nBhakuni, V., Prasad, R., Shukla, P.S., 2021. Genetic variability and \ncorrelation studies for morphological and seed quality parameters in \nfoxtail millet (Setaria italic (L.) Beauv.). The Pharma Innovation, 10 \n(4), Pp. 160\u2013165. \n\n\n\nBrunda, S.M., Kamatar, M.Y., Naveenkumar, K.L., Hundekar, R., 2014. Study \nof Genetic Variability, Heritability and Genetic Advance in Foxtail \nMillet in both Rainy and Post Rainy Season. IOSR Journal of \nAgriculture, 7 (11), Pp. 34-37. https://doi.org/10.9790/2380-\n071133437 \n\n\n\nBurton, G.W., 1951.  Quantitative Inheritance in Pearl Millet (Pennisetum \nglaucum). Agronomy Journal, 43 (9), Pp. 409\u2013417. \nhttps://doi.org/10.2134/agronj1951.00021962004300090001x \n\n\n\nBurton, G.W., DeVane, E.H., 1953. Estimating Heritability in Tall Fescue \n(Festuca arundinacea) from Replicated Clonal Material. Agronomy \nJournal, Pp. 478\u2013481. \n\n\n\nDhakal, A., Sharma, S., Pokhrel, A., Poudel, A., 2020.Variability and \nheritability estimate of 30 rice landraces of Lamjung and Tanahun \ndistrict, Nepal. Indonesian Journal of Agricultural Science, 21 (1), Pp. \n1\u201310. https://doi.org/10.21082/ijas.v.21.n1.2020.p.1-10 \n\n\n\nDoust, A.N., Kellogg, E.A., Devos, K.M., Bennetzen, J.L., 2009. Foxtail millet: \nA sequence-driven grass model system. Plant Physiology, 149 (1), Pp. \n137\u2013141. https://doi.org/10.1104/pp.108.129627 \n\n\n\nGhimire, K., Bhandari, B., Gurung, S.B., Nb, D., 2017. Biotechnology for \nConservation and Utilization of Agricultural Plant Genetic Resources \nin Nepal. Journal of Nepal Agricultural Research Council, 3, Pp. 49\u2013\n59. https://doi.org/10.3126/jnarc.v3i1.17276 \n\n\n\nIBPGR., 1985. Descriptors_setaria_Italica.pdf. \n\n\n\nJohnson, H.W., Robinson, H.F., Comstock, R.E., 1955. Estimates of genetic \nand environmental variability in soyabeans. Agronomy Journal, Pp. \n314. https://doi.org/10.1017/S0014479700009510 \n\n\n\nJoshi, B.K., Shrestha, R., Gauchan, D., Shrestha, A., 2020. Neglected, \nunderutilized, and future smart crop species in Nepal. Journal of Crop \nImprovement, 34 (3), Pp. 291\u2013313. \nhttps://doi.org/10.1080/15427528.2019.1703230 \n\n\n\nKandel, M., Gurung, S.B., Dhami, N.B., Shrestha, J., 2020. Assessment of \nvariability of foxtail millet (Setaria italica (L.)). Bhutan Journal of \nNatural Resources & Development, 7 (2), Pp. 51\u201361. \n\n\n\nKarvar, S., Vaidya, E., Ghorade, R., Gomashe, S., Kohakade, S., Rajane, A., \n2020. Extent of genetic variability for morpho-nutritional traits in \nfoxtail millet [ Setaria italica (L.) P. Beauv]. The Pharma Innovation, \n10 (1), Pp. 576\u2013579. \n\n\n\nKavya, P., Sujatha, M., Pandravada, S., Hymavathi, T., 2017. Variability \nStudies in Foxtail Millet [Setaria italica (L.) P. Beauv]. International \nJournal of Current Microbiology and Applied Sciences, 6, Pp. 955\u2013\n960. https://doi.org/10.20546/ijcmas.2017.609.115 \n\n\n\nKumari, R., Dikshit, N., Sharma, D., Bhat, K.V., 2011. Analysis of molecular \ngenetic diversity in a representative collection of foxtail millet \n[Setaria italica (L.) P. Beauv.] from different agro-ecological regions \nof India. Physiology and Molecular Biology of Plants, 17 (4), Pp. 363\u2013\n374. https://doi.org/10.1007/s12298-011-0085-3 \n\n\n\nLata, C., Gupta, S., Prasad, M., 2013. Foxtail millet: A model crop for genetic \nand genomic studies in bioenergy grasses. Critical Reviews in \nBiotechnology, 33 (3), Pp. 328\u2013343. \nhttps://doi.org/10.3109/07388551.2012.716809 \n\n\n\nLenka, D., Misra, B., 1973. Path coefficient analysis of yield in rice varieties. \nIndian Journal of Agricultural Science, 43, Pp. 376\u2013379. \n\n\n\nLiang, M.D., 2006. Silicon use for pest control in agriculture: A review. Proc. \nS. Afr. Sug. Technol. Ass, 80, Pp. 278\u2013286. \n\n\n\nMagurran, A., 2004. Measuring Biological Diversity - Chapter 2. Measuring \nBiological Diversity, Pp. 18\u2013215. \n\n\n\nMohan, N., CR, R., HC, L., Vijayakumar, L., HR, R., 2019. Characterisation of \nnew germplasm accessions for yield and yield related traits in Foxtail \nmillet [Setaria italica]. Journal of Pharmacognosy and \nPhytochemistry, 1, Pp. 649\u2013652. \n\n\n\nMuthamilarasan, M., Prasad, M., 2015. Advances in Setaria genomics for \ngenetic improvement of cereals and bioenergy grasses. Theoretical \nand Applied Genetics. Theoretische Und Angewandte Genetik, 128 \n(1), Pp. 1\u201314. https://doi.org/10.1007/s00122-014-2399-3 \n\n\n\nNakayama, H., Komatsu, S., Namai, H., Okuno, K., 1999. N-terminal amino \nacid sequences of prolamins encoded by the alleles at the Pro1 and \nPro2 loci in foxtail millet, Setaria italica (L.) P. Beauv. Genes and \nGenetic Systems, 74 (6), Pp. 309\u2013314. \nhttps://doi.org/10.1266/ggs.74.309 \n\n\n\nNirubana, V., Ravikesavan, R., Ganesamurthy, K., 2019. Characterization \nand clustering of kodo millet (Paspalum scrobiculatum L.) genotypes \nbased on qualitative characters. Electronic Journal of Plant Breeding, \n10 (1), Pp. 101\u2013110. https://doi.org/10.5958/0975-\n928X.2019.00012.7 \n\n\n\nOjha, E., Adhikari, B.B., Katuwal, Y., 2018. Nurient Management Trial on \nFoxtail Millet at Sundarbazar, Lamjung. Journal of the Institute of \nAgriculture and Animal Science, 35 (1), Pp. 89\u201394. \nhttps://doi.org/10.3126/jiaas.v35i1.22518 \n\n\n\nOsundare, O.T., Akinyele, B.O., Fayeun, L.S., Osekita, O.S., 2017. Evaluation \nof Qualitative and Quantitative Traits and Correlation Coefficient \nAnalysis of Six Upland Rice Varieties. Journal of Biotechnology and \nBioengineering, 1 (1), Pp. 17\u201327. \n\n\n\nPallavi, N.L., Venkatesh, R., Ram, B.J., BG, S., 2020. Studies on correlation \nand path coefficient analysis in foxtail millet [Setaria italica (L.) \nBeauv]. International Journal of Chemical Studies. \nhttps://doi.org/10.22271/chemi.2020.v8.i6ab.11048 \n\n\n\nPanda, B.B., Badoghar, A.K., Das, K., Panigrahi, R., Kariali, E., Das, S.R., Dash, \nS.K., Shaw, B.P., Mohapatra, P.K., 2015. Compact panicle architecture \nis detrimental for growth as well as sucrose synthase activity of \ndeveloping rice kernels. Functional Plant Biology, 42 (9), Pp. 875\u2013\n887. https://doi.org/10.1071/FP14363 \n\n\n\nParameswaran, K.P., Sadasivam, S., 1994. Changes in the carbohydrates \nand nitrogenous components during germination of proso millet, \nPanicum miliaceum. Plant Foods for Human Nutrition, 45 (2), Pp. 97\u2013\n102. https://doi.org/10.1007/BF01088466 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 88-97 \n\n\n\n\n\n\n\n \nCite The Article: Bimochana G.C., Ashmita Upadhyay, Prabesh Dhakal, Saujan Bashyal, Subash Adhikari, Prabin Kumar Poudel, Dipendra Kumar Ayer  (2023). \n\n\n\nCharacterization of Qualitative and Quantitative Traits for Diversity Assessment and Correlation Studies in Foxtail Millet (Setaria Italica (L.) Beauv.).  \nMalaysian Journal of Sustainable Agricultures, 7(2): 88-97. \n\n\n\n\n\n\n\nRao, C.R., 1948. The utilization of multiple measurements in problems of \nbiological classificataion. J. Roy. Sat. Soc, 10, Pp. 159\u2013203. \n\n\n\nRavi, S.B., 2004. Neglected millets that save the poor from starvation. \nLEISA India, Valuing Crop Diversity, 1, Pp. 34-36. \n\n\n\nRoy, R.K., Majumder, R.R., Sultana, S., Hoque, M., Ali, M., 2015. Analysis for \nYield and Yield Components in Transplant. Bangladesh J. Bot, 44 (4), \nPp. 529\u2013535. \n\n\n\nSapkota, M., Pandey, M.P., Thapa, D.B., 2016.  Agromorphological \ncharcaterization of Foxtail millet ( Setaria italica L. Beauv) at \nRampur, Chitwan, Nepal. International Journal of Applied Sciences \nand Biotechnology, 4 (3), Pp. 298\u2013307. \nhttps://doi.org/10.3126/ijasbt.v4i3.14545 \n\n\n\nSingh, P., Raghuvanshi, R.S., 2012. Finger millet for food and nutritional \nsecurity. African Journal of Food Science, 6 (4), Pp. 77\u201384. \nhttps://doi.org/10.5897/AJFSX11.010 \n\n\n\nSivassubramanian, S., Madhavamenon, P., 1973. Genotypic and phenotypic \nvariability in rice. Madras Agriculture Journal, 60, Pp. 1093\u20131096. \n\n\n\nSpellerberg, I.A.N.F., Fedor, P.J., 2003. Tribute To Claude Shannon (1916-\n2001) and a plea for more rigorous use of species richness species \ndiversity and the 'Shannon-Weiner Index'. Global Ecology and \nBiogeography, 12, Pp. 177\u2013179. \n\n\n\nUPOV., 2013. International union for the protection of new varieties of \nplants. Pp. 1\u201327. \n\n\n\nVetriventhan, M., 2011. Phenotypic and genetic diversity in the foxtail \nmillet (Setaria italica (L.) P. Beauv.). Core Collection. PhD Thesis. \nTamil Nadu Agricultural University. \n\n\n\nYamaji, N., Ma, J.F., 2017. Node-controlled allocation of mineral elements \nin Poaceae. Current Opinion in Plant Biology, 39, Pp. 18\u201324. \nhttps://doi.org/10.1016/j.pbi.2017.05.002 \n\n\n\nYang, X., Wan, Z., Perry, L., Lu, H., Wang, Q., Zhao, C., Li, J., Xie, F., Yu, J., Cui, \nT., Wang, T., Li, M., Ge, Q., 2012. Early millet use in northern China. \nProceedings of the National Academy of Sciences of the United States \nof America, 109 (10), Pp. 3726\u20133730. \nhttps://doi.org/10.1073/pnas.1115430109 \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 90-94 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \nwww.myjsustainagri.com \n\n\n\nDOI: \n10.26480/mjsa.02.2021.90.94 \n\n\n\nCite the Article: J.J. Gairhe, S. Khanal and S. Thapa (2021). Soil Organic Matter (SOM): Status, Target and Challenges in Nepal. \nMalaysian Journal of Sustain\n\n\n\n \nable Agriculture, 5(2): 90-94. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2021.90.94 \n\n\n\nSOIL ORGANIC MATTER (SOM): STATUS, TARGET AND CHALLENGES IN NEPAL \nJ.J. Gairhea*, S. Khanala and S. Thapab \n\n\n\naPaklihawa Campus, Institute of Agriculture and Animal Science \nbPG Program, Institute of Agriculture and Animal Science \n*Corresponding Author e-mail: janma@iaas.edu.np, janmajaya@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 05 February 2021 \nAccepted 05 March 2021 \nAvailable online 18 March 2021\n\n\n\nChanges in soil organic matter (SOM) are slow and difficult to monitor, usually apparent after few decades. \nRecent changes in the agriculture had its influence on soil, including the soil organic matter content. About \n60% of soil in Nepal now have low organic matter content. Agriculture Perspective Plan (APP) was focused \nmore on the concept of green revolution to increase the chemical fertilizer inputs, however, the scenario is \nchanging. Use of organic fertilizers is promoted extensively by government and different organizations with \na target of increasing SOM content from 1.92% in 2015 to 4% by 2035. This paper aims at analysing the \ncurrent status, targeted goal and the challenges faced in the augmentation of the soil organic matter using \ndata available. Achieving this target requires an addition of extra 2.244 ton/ha of organic matter on a yearly \nbasis for 20 years. The average amount of organic matter (2.5-3 ton/ha) applied is lesser than a single season \ngrain harvest. 4.69% of sites had high soil organic matter in fiscal year 075/76 which slipped to 2.64% in \n2076/77. The sites with low soil organic matter increased from 12.73% to 15.31%.  The causes behind the \nSOM decline varies according to different agro-ecological zones like soil erosion, residue burning, imbalanced \nfertilizer use, defective FYM production etc.  Findings suggest precise technologies required to be adopted to \ntackle with the different niche specific causes of soil fertility decline. Despite the complete nutrient content, \nbulky nature of organic fertilizers sets a major drawback regarding their transportation, distribution and \ncommercialization. Government of Nepal is promoting organic fertilizer use by subsidizing their production \ncost by 50%. Following integrated nutrient management (INM) techniques, sustainable soil management \npractices (SSMP) and promotion of use of locally available resources can play a huge role in making the \ntechnology sustainable to the farmers.  \n\n\n\nKEYWORDS \n\n\n\nSoil organic carbon, soil health, food security \n\n\n\n1. INTRODUCTION \n\n\n\nOrganic matter, the living fraction of the soil, is considered as the heart of \na healthy soil. Soil with adequate organic matter implies better soil \nstructure, aggregate stability, bulk density, water holding capacity, \nnutrient cycling, cation exchange capacity, buffering capacity and much \nmore (Murphy, 2015). The effects of soil organic matter (SOM) on the \ncomplex of soil properties is extensive and is usually subsumed under the \nterm \u2018soil fertility\u2019. Soil contains three times more carbon (C) than in \natmosphere and 3.8 times more C than in biotic pool (Shrestha et al., \n2008). Agricultural soil generally has organic matter between 3 to 6% \n(Berns and Knicker, 2014). \n\n\n\nProviding food and fibre for a growing population in 21st century is no less \nthan a challenge. Modern farming practice have a considerable impact on \nsoil. Three to four crops are harvested annually in rotation from a single \npiece of land (Deshar, 2013; Raut et al., 2010; Dahal and Bajracharya, \n2013). For centuries, subsistence form of Nepalese agriculture used \norganic fertilizer as the only source of nutrients. This scenario changed \npost the introduction of chemical fertilizers in 1960s. These fertilizers \nrapidly gained popularity accounting to their less bulky nature, \ntransportability, ease of application and most importantly, the dramatic \nincrease in yield. As the sales of chemical fertilizers escalated at the rate of \n\n\n\n882.43 MT per annum in between 1991/92 to 2015/16, the traditionally \nused organic fertilizers were given less emphasis (Pandey et al., 2017). \n\n\n\nMostly centred on the use of technology based on green revolution, the \nAgriculture perspective plan (APP) (1995-2015) was focused more on the \nuse of chemical fertilizers to rapidly increase production. It emphasized \nmore on input and output rather than outcomes and impacts. Chemical \nfertilizer and irrigation were considered to be the main source of \nagricultural growth, somewhat like green revolution (PPTA, 2012). Aimed \nto increase the chemical fertilizer use with an average increment of 26000 \nmetric ton per year, organic fertilizers seem to have received less \nattention. Changes in SOM are difficult to monitor, often becoming evident \nafter few decades (Basnet, 1999). Slow decrease in soil organic matter has \nled us to this point, where apparently 60% of the soil in Nepal have low \norganic matter content (Kharal et al., 2018; SSD, 2016). \n\n\n\nThe nutrient available to the plant depends upon the soil properties \n(MOAC, 2000). Organic matter increases the water and nutrient retention \ncapacity of soil (SoCo, 2009). However, decline in the SOM content leads \nto reduced cation exchange (CEC) and water holding capacity, resulting in \na reduction in nutrient retention and supply capacity of the soil (Kimetu et \nal., 2008). Nutrient deficiency resulting from the deficiency of the organic \nmatter cannot be restored by increased application of chemical fertilizers, \n\n\n\n\nmailto:janma@iaas.edu.np\n\n\nmailto:janmajaya@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 90-94 \n\n\n\nCite the Article: J.J. Gairhe, S. Khanal and S. Thapa (2021). Soil Organic Matter (SOM): Status, Target and Challenges in Nepal. \nMalaysian Journal of Sustain\n\n\n\n \nable Agriculture, 5(2): 90-94. \n\n\n\nespecially in the humid tropics, where low activity clay have the limited \nability to retain nutrients. The readily available nutrients applied to the \nsoil will instantly be leached, causing decrease in the crop use efficiency of \nchemical fertilizer accordingly (Parr and Colacicco, 1987). This could be \nthe one reason for the productivity of major food crop to be almost \nconstant since the last three decades (Rijal, 2000). Government has put \nforward Agriculture development strategy (ADS) which has a target to \nincrease the soil organic matter 1.96% in 2015 to 4% by 2035, which is a \nrather ambitious target. Different factors play an important role in SOM \ncontent variation throughout different parts of Nepal. As an important \ncomponent for maintaining soil health, SOM needs to be closely studied. \nThis paper attempts to analyse the past, present and future of SOM in \nNepal. Various researches in the past have mentioned different factors \ninfluencing the SOM content from their respective fields. These available \ndata are compiled and analysed with a goal of sustainable increase in SOM \ncontent. In these contexts, this paper attempts to explore the status of soil \norganic matter in Nepal along with the target set and challenges to achieve \nit. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nThis article is based on secondary information collected from the findings \nof different research articles, review papers, reports, books and \nconference proceedings. The collected information was subsequently \nreviewed and synthesized into the present form. Moreover, paired sample \nt test was used to test the significance between the longitudinal data. The \nprocessed data were presented in tables and graphs. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Status and target of soil organic matter in Nepal \n\n\n\nIn Nepalese condition, it is estimated that average quantity of nutrients \nremoved by cereal crops annually approximates to 310 kg per hectare \n(Ghimire, 2009). However, the current fertilizer use trend in Nepal is 41 \nkg per hectare (Katyal and Reddy, 2020). The huge gap between nutrient \nuptake by plant and supplied by the soil creates a negative nutrient \nbalance (MOAD, 2015). These nutrients are extracted by crops from the \nsoil indigenous nutrient supply. Rapid removal of nutrients from soil \nwithout sufficient replenishment slowly strips the soil, making it infertile \nin long run. 60% of soil in Nepal are said to have low organic matter, 23% \nhave low phosphorus, 18% have low potassium and 67% of soil are acidic \n(Mandal et al., 2004). \n\n\n\nThe forest soil had the highest SOC percentage (0.95%), followed by Bari \n(0.74%), Khet (0.65%) and degraded land (0.52%) in Chure region of \nNepal. Moreover, the total The total SOC stock in the same study followed \nthe order as Forest > Bari > Khet > Degraded land with the total SOC stock \neach land use being 110.0 t ha-1, 96.5 t ha-1, 86.8 t ha-1 and 72.0 t ha-1 \nrespectively. The lower SOC content in Khet and Bari soil probably reflects \ncontinuous cultivation with minimum addition of SOM and sandy textured \nsoil.  Improvement of SOC percentage and soil quality maintenance is an \nimportant intervention to increase SOC storage capacity. \n\n\n\nReported that out of 5718 samples tested in fiscal year 2070/71, 44.47% \ncontained low, 40.89% contained medium and 14.64% contained high \n\n\n\norganic matter (figure 1) (Dawadi and Thapa, 2015). The amount of \norganic matter differed as per different developmental regions, SOM \ncontent of eastern and far western region was found to be the lowest while \nit was highest at central. \n\n\n\nFigure 1: Status of soil organic matter in FY 2070/71 (2013/14) (Source: \nDawadi & Thapa [19]) \n\n\n\nBased on the soil samples analysed by soil laboratory located at soil \nmanagement directorate and regional soil laboratories located at various \nlocations, it was found that soil samples with low soil organic matter \ndeclined by 0.04 units per year from fiscal year 071/72 to 074/75. \nMoreover, there was decline in soil samples with medium and high organic \nmatters by 0.0228 and 0.0238 units per year during the same period \n(figure 2).  \n\n\n\nFigure 2: Status of soil organic carbon on samples studied by various soil \nlabs of Nepal \n\n\n\nSoil management directorate conducted 18 mobile soil campaign, regional \nsoil test lab (RSTL) at Jhumka, Hetauda, Pokhara and Sundarpur \nconducted each of 10 mobile soil campaign and   RSTL, Khajura conducted \n14 such campaigns.  The result of those campaigns is shown in table 1. \nTotal of 73.05% of samples showed low organic matter, 24.67% samples \ncontained medium organic matter and only 2.28% were with high organic \nmatter.\n\n\n\nTable 1: Status of soil organic matter obtained in mobile soil test campaign in Nepal conducted by various laboratories for fiscal year 074/75 \n\n\n\nSoil Management Directorate \nMobile soil test campaign by Regional laboratory \n\n\n\nTotal \nJhumka Hetauda Pokhara Sundarpur \n\n\n\nLow 597(83.61) 231(61.43) 848(81.61) 971(76.51) 474(54.24) 3121(73.05) \nMedium 79(11.06) 131(34.84) 186(17.90) 283(22.3) 375(42.9) 1054(24.67) \nHigh 38(5.32) 14(3.72) 5(0.49) 15(1.19) 25(2.86) 97(2.28) \nTotal 714 376 1039 1269 874 4272 \n\n\n\nNote: Figure in the parenthesis includes percentage (Source: Dawadi and Thapa [19])  \n\n\n\nTable 2: Status of soil organic matter obtained in soil laboratories of Nepal for fiscal year 074/75. \nSoil organic \n\n\n\nmatter \nSoil management \n\n\n\ndirectorate \nRegional Soil lab Soil lab \n\n\n\nSurunga Total \nJhumka Hetauda Pokhara Khajura Sundarpur \n\n\n\nLow 224 \n(37.96) \n\n\n\n291 \n(45.25) \n\n\n\n485 \n(71.85) \n\n\n\n167 \n(39.66) \n\n\n\n194 \n(46.63) \n\n\n\n181 \n(52.61) \n\n\n\n84 \n(55.26) \n\n\n\n1626 \n(50.17) \n\n\n\nMedium 295 \n(50) \n\n\n\n291 \n(45.25) \n\n\n\n173 \n(25.62) \n\n\n\n219 \n(52.01) \n\n\n\n212 \n(50.96) \n\n\n\n126 \n(36.63) \n\n\n\n62 \n(40.79) \n\n\n\n1378 \n(42.52) \n\n\n\nHigh 71 \n(12.34) \n\n\n\n61 \n(9.5) \n\n\n\n17 \n(2.53) \n\n\n\n35 \n(8.33) \n\n\n\n10 \n(2.41) \n\n\n\n37 \n(10.76) \n\n\n\n6 \n(3.95) \n\n\n\n237 \n(7.31) \n\n\n\n590 643 675 421 416 344 152 3241 \nNote: Figure in the parenthesis includes percentage (Source: Dawadi and Thapa [19]) \n\n\n\n44%\n\n\n\n41%\n\n\n\n15%\nLow\n\n\n\nMedium\n\n\n\nHigh\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 90-94 \n\n\n\nCite the Article: J.J. Gairhe, S. Khanal and S. Thapa (2021). Soil Organic Matter (SOM): Status, Target and Challenges in Nepal. \nMalaysian Journal of Sustain\n\n\n\n \nable Agriculture, 5(2): 90-94. \n\n\n\nVarious laboratories conducted 16316 tests from 3300 samples obtained \nfrom farmers, former DADO, students and various organizations. The \ninterested farmers were found to increase within these years though they \naccount only 10-15% of beneficiaries. Out of such analysed soil samples, \n50.17% were with low organic matter, 42.52% were with medium level of \norganic matter and 7.31% were with high organic matter. Detail is shown \nin table 2. \n\n\n\nOrganic fertilizers have an upper hand over inorganic fertilizers in terms \nof complete nutrient content and longer availability of those nutrients \n(Khadka, 2016) reported the nutrient availability to plant to be positively \ncorrelated with soil organic matter content as it improves the soil \nproperties. As reported by (Magdoff, 2000), crop yield increased by up to \n12% per increase in 1% of soil organic matter. Depletion of organic matter \nhas been recognized as one of the challenges for agriculture of Nepal in \nboth hill and terai region. At present, organic matter usage by farmer \napproximates around 2.5-3 ton/ha. This amount is relatively more in hilly \nregion as compared to terai region in spite of the higher cropping intensity \nin terai.  \n\n\n\nFigure 3: Target set by ADS for increase in soil organic matter in Nepal \n\n\n\nImportance of organic matter in sustainability of food production is \nrealized by Agriculture development strategy (ADS) which has a target to \nincrease the soil organic matter 1.96% in 2015 to 4% by 2035. If we \nconsider a hectare furrow slice of area (approximately 2,200 ton of soil), \nincreasing the organic matter content by one percent requires addition of \nan extra 22 ton of organic matter over the crops nutritional requirement. \nNow, for increasing SOM from 1.92% to 4%, we require 44.88 ton per \nhectare (2.04% of 2,200 ton) increase in the SOM. If done on a yearly basis, \nan additional 2.244 ton per hectare of organic matter needs to be added \nover the crops nutritional requirement each year. This figure nearly \napproaches the current organic fertilizer usage by farmers. A rough \nevaluation of this amount can be made by the fact that grain harvest from \nthe rice fields alone exceeds the total organic matter input (Tripathi et al., \n2018). \n\n\n\nThough, the ADS has targeted an increase of 4% in soil organic matter, but \nthe scenario of achieving it is not conducive. Upon analysis of soil organic \nmatter of Bagmati Province, significant decrease (p<0.001) was observed \nwhen comparing between fiscal year 2075/76 and 2076/77 (table 1). \n4.69% of sites had high soil organic matter in fiscal year 075/76 which \nslipped to 2.64% in 2076/77. Moreover, the sites with low soil organic \nmatter increased from 12.73% to 15.31%. Detail is shown in table 3. The \ndetail of status on soil organic matter is shown in appendix 1.  \n\n\n\nTable 3: Ratings of soil organic matter in Bagmati Province in fiscal \nyear 2075/76 and 2076/77. \n\n\n\n075/76 076/77 \n\n\n\nHigh 28(4.69) 14(2.64) \n\n\n\nMedium 169(28.31) 260(49.14) \n\n\n\nLow 324(54.27) 174(32.89) \n\n\n\nVery low 76(12.73) 81(15.31) \n\n\n\nTotal 597(100) 529(100) \n\n\n\nP value  ***(t= -9.56) \n\n\n\nNote: ***=P<0.001   \n\n\n\nSource: Annual reports (2075/76 & 2076/77, Provincial Soil & Fertilizer \nlab, Hetauda, Bagmati Province, Nepal.  \n\n\n\n3.2 Challenges in increasing soil organic matter in Nepal  \n\n\n\nOrganic matter affects soil structure (aggregation), drainage, aeration (gas \nexchange properties), water holding capacity, pH, compaction and overall \nplant growth. The maintenance of adequate supply of organic matter is \nimportant in productive agriculture. An ideal conditioned soil contains 5% \nof organic matter. The historic loss of C from the SOM pool between the \n1850s and 2000 is estimated at 78 \u00b1 12 Gt compared with the emission of \n270 \u00b1 30 Gt from fossil fuel combustion. Despite its numerous direct and \nancillary benefits, enhancing the SOM pool is a major challenge, especially \nin impoverished and depleted soils in harsh tropical climates. Increasing \nsoil organic matter in Nepal is cumbersome task. The organic matter in soil \nis comparatively low in Nepal. In our context, soil organic matter is lost \nvery quickly due to erosion, residue burning, lack of organic manure and \npoor farming techniques.   \n\n\n\nCrop residue play an important role in maintaining soil organic matter as \nwell as improving the physical, chemical and biological properties of soil. \nHuge amount of nutrient is removed from the soil with crop residue \nremoval. About 19.41 million metric tons of agriculture residue is \nproduced in Nepal annually. These residues are produced mostly from \nterai region of Nepal (60%), followed by hill (35%) and mountain region \n(5%) respectively (WECS, 2010). Rice accounts for about 58% of this \nresidue. In a rice-wheat cropping system, a total of 7-10 ton of residue is \nproduced each year (Mandal et al., 2004). This residue is used as fuel for \ncooking, ruminant fodder, bedding material and raw materials in \nindustries. About 10 ton of crop can remove 730kg NPK per hectare that \nis returned back to the soil in very little amount. In many districts of Nepal, \nthe crop residue is burnt, instead of incorporation or being removed. This \nburning can lead up to 80% loss of N, 25% of P, 21% of K and 4-60% loss \nof S (Bisen and Rahangdale, 2017; Dobermann and Fairhurst, 2002). \nReported the nutrient loss of over 35000 Mt of carbon, 571Mt of Nitrogen, \n40Mt of phosphorus, 254 Mt of Potash and 38 Mt of sulphur from three \ndistricts of western terai in year 2013/14 due to residue burning. In the \nlong run, removal of the straw causes reduction in soil nutrients like K, S, \nZn, and Si. Residue burning reduces the loss of K as compared to \ncompletely removing residue. It converts straw into a source of mineral K \n(Dobermann and Fairhurst, 2002). The concept of crop residue removal \nhas been described in terms of  biomass energy input and output by (Rijal \net al., 1991), highlighting the competing usage of animal dung between \nmanure and fuel, agricultural residue between fuel and fodder. According \nto  Sherchan and Karki even 10 ton ha-1 of farmyard manure is not enough \nto maintain nutrient balance under rice-wheat rotation system (Sherchan \nand Karki, 2006).  \n\n\n\nOrganic matter depletion not only puts question on the sustainability of \ncurrent biomass production but also the food security of increasing \npopulation. Schreier et al. attested decrease of soil organic matter to be \none of the cause for soil fertility decline (Schreier et al., 1994). The organic \nmatter decline is not merely restricted to agricultural land but also \nprevalent in forest areas where an increasing amount of litter is removed \nand applied to agricultural land.  \n\n\n\nMany studies have time and again emphasized on the fact that the farm \nyard manure is the primary source of organic matter content in Nepalese \nsoil. However, it is defectively prepared. Manure is left openly in field for \nseveral months in small heaps before finally being incorporated into the \nsoil, causing a huge amount of nutrient loss through leaching and \nvolatilization. This practice can lead to an approximate loss of 50% N and \n90% K, especially in the rainy season. Manures are not properly \ndecomposed before being incorporated leading to high C/N ratio and poor \nquality of FYM (Shrestha, 2009). Urine is another important source of \nnutrient containing about 1% N and 1.35% K, which is highly \nunderutilized, most of this N is lost through volatilization (Amgai et al., \n2018). \n\n\n\nTraditional farming included a balanced integration between crops and \nlivestock in a single management structure. Thus, a recycling of nutrient \noccurred between these two systems, providing FYM for crops and forage \nfor animals. However, the shift from traditional farming has  Pilbeam et al. \nreports that the breakdown in the linkage between forest, livestock and \ncropping system is affecting the soil fertility (Pilbeam et al., 2005).  \n\n\n\nLand abandonment has drastically increased over the past few decades. \nCurrent exodus to foreign countries and urban areas for employment has \na considerable effect in the population demographics. As a result, soil in \nless populated areas were found to be less fertile due to less livestock and \nconsequently less manure (Jaquet et al., 2015). Requiring three person \nmonths activity per year, production of FYM is quite a time-consuming and \nlaborious process; thus, lack of availability of labour can affecting the \nproduction of FYM. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 90-94 \n\n\n\nCite the Article: J.J. Gairhe, S. Khanal and S. Thapa (2021). Soil Organic Matter (SOM): Status, Target and Challenges in Nepal. \nMalaysian Journal of Sustain\n\n\n\n \nable Agriculture, 5(2): 90-94. \n\n\n\nTopographical structure of Nepal accounts for erosion of a huge amount of \ntop soil (Shrestha et al., 2008).  Soil organic matter is mostly concentrated \nin the top 30 cm of the mineral soil horizon; consequently, they are lost \nalong with the top soil (Sitaula et al., 2004). A single inch of soil requires \n500-1000 years to form but can be destroyed in a short and single rain \nevent. Every year about 336 million tons of soil are carried from the main \nriver system to India (Acharya and Kafle, 2009). Agricultural land is mainly \nprone to surface soil erosion, ranging from 2 ton/ha to 105 ton/ha of soil \neroded annually. Five ton/ha of soil loss equates to 75 kg/ha of organic \nmatter, 8 kg/ha of nitrogen, 10kg/ha of potassium and 5 kg/ha of \nphosphorus loss (Tripathi, 2009).  \n\n\n\nOrganic matter in cultivated soil is much lesser as compared to other land \nuse system. Rapid mineralization due to tillage, insufficient addition, crop \nresidue removal and lack of crop rotation might be the reason behind it \n(Chauhan et al., 2014). High temperature in terai region makes the \nsituation worse by facilitating a rapid organic matter mineralization.   \n\n\n\nOwing to the subsidies, farmers in Nepal have a general tendency of \napplying the acid forming nitrogenous fertilizers like urea only. This \nprevalence of imbalanced use of these fertilizer causes the soil \ndegradation. Organic matter can be used in reclamation of acidic soil, as it \nis considered to be as good as lime in terms of its capacity to buffer soil \nacidity. \n\n\n\n3.3 Initiatives for amelioration \n\n\n\nGovernment is promoting vermi-culture technology, cattle shed \nimprovement program, organic fertilizer production plant establishment \nprogram and subsidy on purchase of organic fertilizers. Reducing \ndeforestation, promoting integrated soil and plant nutrient management, \nintegrated crop nutrition, crop residue use etc. are some of the strategies \nmade for achieving these targets.         \n\n\n\nSubsidy on organic fertilizer was started in 2011 by Ministry of \nAgricultural Development with the promulgation of organic fertilizer \nsubsidy guideline for promoting the use of organic fertilizers and maintain \nsoil health (Bista et al., 2018). Government provided 50% subsidy on cost \nof production of 5000 Mt of organic fertilizer. Organic fertilizer grant \nprocedure (2019) subsidizes 50 percent or NRs10 per kg on organic \nfertilizer (Henderson et al., 2016; MOALD, 2019).  \n\n\n\nBishwakarma et al.  reported an increase in SOM from 3.3% to 3.8% by just \nimprovement of management and quality of FYM (Bishwakarma et al., \n2015; Shrestha et al., 2014). Improved heap or pit method of FYM \npreparation, along with improved cattle shed management helps to reduce \nthe nutrient loss from manure pits. Manures prepared with improved \nmethod provides 2-3 times more nutrients than the ordinary manure and \nsupports better crop yield. About 90 percent of the nutrient loss from the \nsoil due to crop harvest could be recovered through incorporation of 30 \npercent of crop residue back to the soil (Bista et al., 2014). \n\n\n\nEven if we have a good market, the lack of raw materials and bulky nature \ncan make the availability of commercial organic fertilizers difficult. Thus, \nit is important to promote organic fertilizers at local level. Pilbeam et al.  \nreported the farmers to prefer chemical fertilizer as to FYM due to the ease \nof transport and application, disregarding the detrimental hardening of \nsoil. \n\n\n\nFarmers have low technical knowledge and resource constraints to \nadoption of a rational management option. The application rates are more \naffected by the availability than by the clear understanding of balanced \ncrop nutrition and concept of soil fertility. Subsistence agriculture is a way \nof life in Nepal with less than 0.8 hectare of average landholding per family \n(Shrestha, 2011). Given the circumstances, high input commercial \nagriculture seems out of question. It is important to consider the usage of \nlocally available resources for SOM management. \n\n\n\nInterest in the soil organic matter has hyped due to soil organic carbon. \nThis carbon build-up in the form of CO2 is responsible for global warming. \nSoil contains more carbon stored in it than both vegetation and \natmosphere combined. Each percent of the soil organic matter in the top 6 \ninches of soil contains same amount of carbon as in the atmosphere \ndirectly over the field. Therefore, the amount of carbon-dioxide in \natmosphere doubles per percentage decrease in the soil organic matter. \nVarious human activities are responsible for converting the soil organic \ncarbon to carbon dioxide. \n\n\n\n4. CONCLUSIONS \n\n\n\nIncreasing the productivity for feeding the ever-increasing world \npopulation is a major challenge in recent times. Sustainable maintenance \n\n\n\nof productivity can be achieved only with minimal or zero adverse impact \nto the environment.  Soil organic matter plays an important role in soil \nsustainability. Sole use of organic fertilizer might be an option to solve the \norganic matter decline, however, the extent of its applicability is a \nquestion, considering the quantity of food required to be produced. An \nintegration of organic matter with inorganic nutrient is a must. Application \nof inorganic fertilizers in a nutrient deficient soil increases the plant \navailable nutrients, crop production and consequently, the plant residue.  \n\n\n\nThe major contributors of organic matter decline might vary according to \ndifferent agro-ecological zones. Thus, the management strategies must \nvary accordingly, with the niche specific cause identification. Site-specific \ntechnologies should be identified according to the soils, climate, cropping \nand farming systems to create a positive nutrient balance system.   \n\n\n\nAdoption of these technologies at farmers\u2019 level seem to be lacking. Lack \nof awareness, decreased landholding, labour shortage etc. may be amongst \nthe few causes behind it. It is necessary that the future researches be \nfocused on making these technologies more farmer-friendly and adaptable \nat local condition. Extension work should be made more effective for small \nholder farmers. Amelioration the present SOM to the targeted level \nrequires a collective effort from all the stakeholders. \n\n\n\nREFERENCES  \n\n\n\nAcharya, A.K., Kafle, N. 2009. Land Degradation Issues in Nepal and Its \nManagement Through Agroforestry. J Agric Environ, 10, 133\u201343.  \n\n\n\nAmgai, S., Paudel, S.R., Bista, D.R., Poudel, S.R. 2018. Government \nintervention on organic fertilizer promotion, a key to enhancing soil \nhealth and environment. J Agric Environ, 18, 131\u20139.  \n\n\n\nBasnet, K. 1999.  Review of APP. Contrib Nepalese Stud [Internet]. 26(2), \n323\u20136. \n\n\n\nBerns, A.E., Knicker, H. 2014. Soil organic matter. eMagRes, 3(1), 43\u201354.  \n\n\n\nBisen, N., Rahangdale, C.P. 2017. Crop residues management option for \nsustainable soil health in rice-wheat system, A review. Int J Chem Stud \n[Internet], 1038\u201342.  \n\n\n\nBishwakarma, B.K., Dahal, N.R., Allen, R., Rajbhandari, N.P., Dhital, B.K., \nGurung, D.B. 2015. Effects of improved management and quality of \nfarmyard manure on soil organic carbon contents in small-holder \nfarming systems of the Middle Hills of Nepal. Clim Dev., 7(5), 426\u201336.  \n\n\n\nBista, D.R., Dhungel, S., Adhikari, S. 2018. Status of fertilizer and seed \nsubsidy in Nepal, review and recommendation. J Agric Environ, 1\u201310.  \n\n\n\nBista, P., Ghimire, R., Shah, S.C., Pande, K.R. 2014. Assessment of Soil \nFertility Management Practices and Their Constraints in Different \nGeographic Locations of Nepal. Forum Geogr, IX (9), 41\u20138.  \n\n\n\nChauhan, R.P., Pande, K.R., Thakur, S. 2014. Soil Properties Affected by \nLand Use Systems in Western Chitwan, Nepal. Int J Appl Sci Biotechnol, \n2(3), 265\u20139.  \n\n\n\nDahal, N., Bajracharya, R.M. 2013. Effects of Sustainable Soil Management \nPractices on Distribution of Soil Organic Carbon in Upland Agricultural \nSoils of Mid-hills of Nepal. Nepal J Sci Technol, 13(1), 133\u201341.  \n\n\n\nDawadi, D.P., Thapa, M. 2015. Soil fertility status of Nepal, report from \nlaboratory analysis of soil samples of five developmental regions. Proc \nSecond Natl Soil Fertil Res Work 24-25 March, 42\u201351.  \n\n\n\nDeshar, B.D. 2013. An Overview of Agricultural Degradation in Nepal and \nits Impact on Economy and Environment. Glob J Econ Soc Dev \n[Internet], 3, 1\u201320. \n\n\n\nDobermann, A., Fairhurst, T.H. 2002. Rice straw management. Better Crop \nInt, 7\u201311.  \n\n\n\nGhimire, S.R. 2009. Environmental Concern in Nepalese Agriculture. J \nAgric Environ, 9, 41\u20135.  \n\n\n\nHenderson, C., Piya, S., Kharel, M. 2016. Market-based strategies to upscale \norganic fertilizer use in Nepal to achieve productivity, resilience, and \nthe SDGs. Food Chain, 6(2), 51\u201364.  \n\n\n\nJaquet, S., Schwilch, G., Hartung-Hofmann, F., Adhikari, A., Sudmeier-Rieux, \nK., Shrestha, G. 2015. Does outmigration lead to land degradation? \nLabour shortage and land management in a western Nepal watershed. \nAppl Geogr [Internet], 62, 157\u201370. Available from, http, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 90-94 \n\n\n\nCite the Article: J.J. Gairhe, S. Khanal and S. Thapa (2021). Soil Organic Matter (SOM): Status, Target and Challenges in Nepal. \nMalaysian Journal of Sustain\n\n\n\n \nable Agriculture, 5(2): 90-94. \n\n\n\n//dx.doi.org/10.1016/j.apgeog.2015.04.013 \n\n\n\nKatyal, J.C., Reddy, M.N. 2020.  Fertilizer Use in South Asia, II(M).  \n\n\n\nKhadka, D. 2016. Assessment of Relationship between Soil Organic Matter \nand Macronutrients, Western Nepal. J Biol Pharm Chem Res, 3(1), 4\u2013\n12.  \n\n\n\nKharal, S., Khanal, B., Panday, D. 2018. Assessment of Soil Fertility under \nDifferent Land-Use Systems in Dhading District of Nepal. Soil Syst, \n2(4), 57.  \n\n\n\nKimetu, J.M., Lehmann, J., Ngoze, S.O., Mugendi, D.N., Kinyangi, J.M., Riha, S. \n2008 Reversibility of soil productivity decline with organic matter of \ndiffering quality along a degradation gradient. Ecosystems, 11(5), \n726\u201339.  \n\n\n\nMagdoff, F, E.H. 2000. Building Soils for Better Crops [Internet], Available \nfrom, http, \n//www.nysenvirothon.net/Referencesandother/bettersoils.pdf \n\n\n\nMandal, K.G., Misra, A.K., Hati, K.M., Bandyopadhyay, K.K., Ghosh, P.K. 2004. \nRice residue- management options and effects on soil properties and \ncrop productivity, 224\u201331.  \n\n\n\nMOAC. 2000. Components of Integrated Plant Nutrient Management for \nNepal.  \n\n\n\nMOAD. 2015. Agriculture Development Strategy (ADS) 2015 to 2035.  \n\n\n\nMOALD.2019. Organic fertilizer grant procedure.pdf [Internet], Available \nfrom, https, //s3-ap-southeast-1.amazonaws.com/prod-gov-\nagriculture/server-assets/publication-1558871979916-1f3f7.pdf \n\n\n\nMurphy, B.W. 2015. Impact of soil organic matter on soil properties - A \nreview with emphasis on Australian soils, Soil Res, 53(6), 605\u201335.  \n\n\n\nPandey, G., Khanal, S., Pant, D., Chhetri, A., Basnet, S. 2017. An Overview of \nFertilizer Distribution Scenario in Nepal, a Time Series An Overview \nof Fertilizer Distribution Scenario in Nepal, a Time Series Analysis.  \n\n\n\nParr, J.F., Colacicco, D. 1987. Organic Materials as Alternative Nutrient \nSources. Energy plant Nutr pest Control, 81\u201399.  \n\n\n\nPaudel, G., Mc Donald, A., Justice, S., Adhikari, S., Devkota, M.K., Sherchan, \nD.P. 2015. Conservation Agriculture, a resilient way to exterminate \ntrade-offs in combine harvesters use and residue burning in rice-\nwheat systems of Nepal. Int coference \"Open Burn Agric Residue \nHimalayas Reg, 1\u20139.  \n\n\n\nPilbeam, C.J., Mathema, S.B., Gregory, P.J., Shakya, P.B. 2005. Soil fertility \nmanagement in the mid-hills of Nepal, Practices and perceptions. \nAgric Human Values, 22(2), 243\u201358.  \n\n\n\nPPTA. 2012. Nepal, Agricultural Development Strategy \u2014 Assessment \n\n\n\nReport.  \n\n\n\nRaut, N., Sitaula, B.K., Bajracharya, R.M., 2010. Agricultural Intensification, \nLinking with Livelihood Improvement and Environmental \nDegradation in Mid-Hills of Nepal. J Agric Environ, 11(2), 83\u201394.  \n\n\n\nRijal, K., Bansal, N.K., Grover, P.D. 1991. Energy and subsistence Nepalese \nagriculture. Bioresour Technol, 37(1), 61\u20139.  \n\n\n\nRijal, S.P. 2000. Soil Fertility Decline in Nepal, Problem and Strategy, 3, 41\u2013\n6.  \n\n\n\nSchreier, H., Shah, P.B., Lavkulich, L.M., Brown, S. 1994. Maintaining soil \nfertility under increasing land use pressure in the Middle Mountains \nof Nepal. Soil Use Manag, 10(3), 137\u201342.  \n\n\n\nSherchan, D.P., Karki, K.B. 2006. PLANT NUTRIENT MANAGEMENT FOR \nIMPROVING CROP PRODUCTIVITY IN NEPAL [Internet]. Available \nfrom, http, //www.fao.org/3/AG120E10.htm \n\n\n\nShrestha, A., Bishwakarma, B., Allen, R. 2014. Climate Smart Management \nOptions for Improving the Soil Fertility and Farm Productivity in the \nMiddle Hills of Nepal. Univers J Agric \u2026 [Internet], 2(7), 253\u201363. \nAvailable from, http,\n//www.hrpub.org/journals/article_info.php?aid=1938 \n\n\n\nShrestha, B.M., Certini, G., Forte, C., Singh, B.R. 2008. Soil Organic Matter \nQuality under Different Land Uses in a Mountain Watershed of Nepal, \nSoil Sci Soc Am J, 72(6), 1563\u20139.  \n\n\n\nShrestha, R.K. 2009. Soil Fertility under Improved and Conventional \nManagement Practices, 9(1995),  \n\n\n\nShrestha, S. 2011. Status of Agricultural Mechanization in Nepal [Internet], \n1\u20134.  \n\n\n\nSitaula, B.K., Bajracharya, R.M., Singh, B.R., Solberg, B. 2004. Factors \naffecting organic carbon dynamics in soils of Nepal/Himalayan region \n- A review and analysis. Nutr Cycl Agroecosystems, 70(2), 215\u201329.  \n\n\n\nSoCo. 2009. Organic matter decline. Sustain Agric Soil Conserv, (3).  \n\n\n\nSSD. 2016. Annual Report-Soil Science Division-Fiscal Year 2072/73 \n(2015/16).pdf.  \n\n\n\nTripathi, B. 2009. Sustainable Soil Fertility Management Practices in Nepal. \nActa Sci Agric. 3(4), 112\u20133.  \n\n\n\nTripathi, B.P., Bhandari, H.N., Ladha, J.K. 2018. Rice strategy for Nepal. Acta \nSci Agric, 3(2), 171\u201380.  \n\n\n\nWECS. 2010. Energy sector synopsis Report. In, Energy sector synopsis \nReport [Internet]. Kathmandu, Nepal, Water and Energy Commission \nSecretarait, Available from, www.wec.gov.np \n\n\n\n\nhttp://www.wec.gov.np/\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 65-71 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2023.65.71 \n\n\n\n \nCite The Article: Bhimsen Mahat, Bijaya Upadhayay, Ajay Poudel (2023). Effect Of Different Nitrogen Dose on Growth and Yield Characterestics  \n\n\n\nof Hybrid Maize (Zea Mays L.) Varieties qt Sundarbazar, Lamjung. Journal of Sustainable Agricultures, 7(2): 65-71. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.02.2023.65.71 \n\n\n\n\n\n\n\nEFFECT OF DIFFERENT NITROGEN DOSE ON GROWTH AND YIELD \nCHARACTERESTICS OF HYBRID MAIZE (Zea mays L.) VARIETIES AT \nSUNDARBAZAR, LAMJUNG \n\n\n\nBhimsen Mahata, Bijaya Upadhayay*b, Ajay Poudelb \n\n\n\naInstitute of Agriculture and Animal Science, Lamjung, Nepal \nbAgriculture and Forestry University, Rampur, Chitwan, Nepal \n*Corresponding Author Email:bijusharma616@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 23 February 2023 \nRevised 27 March 2023 \nAccepted 02 May 2023 \nAvailable online 08 June 2023 \n\n\n\n This research aimed to determine the best combination of nitrogen levels and hybrid varieties for optimal \ngrowth and yield of hybrid maize in Sundar bazar municipality, Lamjung, Nepal. The study used a two-factor \nfactorial randomized complete block design (RCBD) with 12 treatments and three replications. The \ntreatments consisted of two hybrid maize varieties (Rampur Hybrid-10 and CP 808) and six different levels \nof nitrogen doses (control, 120, 150, 180, 210, 240 kg N ha-1). The results showed that the growth and yield \nparameters of hybrid maize varieties increased significantly with increasing nitrogen levels. The application \nof nitrogen at the rate of 240 kg N ha-1 produced the highest plant height, number of leaves, leaf area index \n(LAI), stem diameter, thousand grain test weight, grain per cob, grain yield, biological yield, and harvest index. \nEven a small difference of 30 kg of nitrogen ha-1 within a treatment showed a significant effect on the growth \nand yield parameters of hybrid maize. The control plot had the least growth and yield parameters. The hybrid \nmaize variety CP 808 outperformed Rampur Hybrid-10 in terms of grain yield, thousand grain test weight, \ncob length, grain per cob, and LAI. In conclusion, this study suggests that cultivating hybrid maize variety CP-\n808 with the use of nitrogen at the rate of 240 kg ha-1 is optimal for maize production in Sundar bazar, \nLamjung, and mid-hills of Nepal with similar altitude and climatic conditions. This information can assist \nmaize farmers in achieving high yields and increasing their income. \n\n\n\nKEYWORDS \n\n\n\nGrowth, Yield, Hybrid, Maize, Nitrogen use efficiency \n\n\n\n \n1.   INTRODUCTION \n\n\n\n1.1   Background \n\n\n\nMaize is a major cereal crop that plays a significant role in the global \nagricultural economy as both food for humans and feed for animals. Maize \nis a versatile crop that is commonly cultivated for various purposes such \nas food, feed, and fodder. This crop is known for its nutritional value as it \ncomprises around 72% starch, 10% protein, 9.5% fiber, and 4% fat, and is \nhighly nourishing with an energy density of 365 Kcal per 100 grams \nfurther known as \u201cQueen of the Cereals\u201d (Begam et al., 2018; Nuss and \nTanumihardjo, 2010). It is the second most important cereal crop after rice \nin Nepal and is grown in 979,776 hectares of land with an average \nproduction of 2,997,733 tons and productivity of 3.06 tonha-1 \n\n\n\n(MoALD,2021/22). Maize occupies about 31.69 % of the total cultivated \nagricultural land and shares about 28.27 % of the total cereal production \nin Nepal. It shares about 4.63% to Agricultural Gross Domestic Product \n(MOALD, 2078). Most of the maize cultivating area (83.88%) lies in the \nmountains (10.36%) and Hills (73.52%) of Nepal, the productivity of the \nprovince-1 (3.0 ton ha-1), Madhesh (3.48 ton ha-1), Bagmati province (3.35 \nton ha-1), Gandaki province (3.02 ton ha-1), Lumbini (2.86 ton ha-1), Karnali \n(2.72 ton ha-1) and Sudurpaschim province (2.71 ton ha-1) (MoALD, 2078).  \nIn Lamjung, it is grown in 43,896 hectares of land with the productivity of \n2.35 tonha-1 (MOALD, 2078). \n\n\n\nMaize is summer (April-August) seasonal crop in Hills, grown as single \ncrop or relayed with millet later in the season. In the Terai, inner-Terai, \nvalleys, and low-lying river basin areas, it can be grown in the winter and \nspring with irrigation. Major portion of the maize produced in the mid Hills \nand high Hills is used for direct human consumption as staple food in \nhousehold level. A significant proportion of maize production in Terai is \ndirected towards the market, with less than 50% being utilized for human \nconsumption. Maize is becoming increasingly significant as a commercial \nand industrial crop, with a variety of products being made from its grains.  \n\n\n\nThe maize demand has grown at a rate of approximately 6% annually over \nthe last decade and it is estimated that over the next two decades, the \noverall demand for maize will rise by 6-8% annually, primarily due to an \nincrease in demand for food in Hill regions and feed (11% growth rate \nannually) in Terai and Inner Terai regions (Paudyal et al., 2001; Sapkota \nand Pokhrel, 2013). Over the past five years, the demand for poultry feed \nand animal feed in Nepal has risen by 13% and 8.5%, respectively \n(Timsina et al., 2016). According to a study, it was found that 60% of the \ngrain was utilized as animal feed, 25% as food, and 3% as seed in the Terai \nregion. The remaining portion of maize (12%) was sold to various buyers \n(Timsina et al., 2016). To fulfill the growing feed demand, Nepal is \nimporting about 45% of maize from India (Neupane and Subedi, 2019). \n\n\n\nTo sustain the current poultry industry in Nepal, approximately 6.46 \nmillion metric tons of feed are required annually, compared to 0.5 million \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Bhimsen Mahat, Bijaya Upadhayay, Ajay Poudel (2023). Effect Of Different Nitrogen Dose on Growth and Yield Characterestics  \n\n\n\nof Hybrid Maize (Zea Mays L.) Varieties qt Sundarbazar, Lamjung. Journal of Sustainable Agricultures, 7(2): 65-71. \n\n\n\n\n\n\n\nmetric tons produced domestically. (NFEA,2021). With the shift in demand \nfor maize from food to livestock and poultry feed, there is also a growing \nneed for new maize-based products like soups, vegetables, and edible oils. \nTo address the challenge of heavy import, it is necessary to increase the \nproductivity of maize on limited available land.  \n\n\n\nThe area, production and productivity of maize in Nepal have been \nincreasing since 1984, but the production remains low compared to \nneighboring countries (MOALD, 2078). One of the reasons for this might \nbe a stark difference between Nepalese maize varieties yield (5.49 tons h-\n\n\n\n1) and national average yield (3.06 tons h-1), creating a yield gap of 2.03 \ntons h-1 (MOALD, 2078) \n\n\n\nAlmost half of the maize cultivation area is planted with conventional \nvarieties, i.e., home-saved seeds that are prone to degeneration due to \nopen pollination, and there is a lack of sufficient application of manures \nand fertilizers (Bahadur BK and Shrestha, 2014). The maize production \nfaces several challenges such as diminishing soil fertility, limited access to \nimproved seeds and quality fertilizers, and the emergence of new pest \nspecies, all of which contribute to the low yield potential of existing \ngenotypes. To enhance maize productivity, it is essential to cultivate new \nhigh-yielding varieties using optimal cultural practices and timely \napplication of appropriate nutrients in the required amounts.  \n\n\n\n1.3   Statement of Problem  \n\n\n\nThere is wide gap between farmer\u2019s field yield and research field. The \nproductivity of maize is still very low (3.06 tonha-1 ) (MoALD, 2078). There \nis a need to improve the productivity of maize in order to maintain food \nbalance in the country and for that Hybrid varieties can play an important \nrole in increasing the present yield level.  \n\n\n\nSeveral factors contribute to low maize production in farmers\u2019 fields, \nincluding a lack of high-yielding varieties, low plant populations, and \nhaphazard use of chemical fertilizers. Furthermore, in recent years, the \nincreased use of high yielding crop varieties in intensive cropping systems \nhas led to a substantially increased demand for nutrients. The genetic \npotential of hybrid varieties may not be fully realized through the supply \nof nutrients solely from FYM/compost but, the high cost of inorganic \nfertilizer dramatically limits its use by farmers (Chapagain and Gurung, \n2010). Nitrogen is perhaps the most crucial nutrient for optimum crop \nyields and its inaccessibility is a burning problem in most of the maize \ngrowing areas of Nepal. Moreover, even the nitrogen that is accessible, is \nnot used judicially by the farmers because there is no updated \nrecommendation on the doses of fertilizer for high yielding hybrids, \nwinter, spring and summer season, rainfed and irrigated maize.  \n\n\n\nIt is common knowledge that both the over and under use of nitrogen lead \nto yield decline. In such a situation, optimum application of N fertilizer is \nnecessary to achieve optimum productivity. And there are about 7 hybrid \nmaize varieties cultivated in Sundar bazar, Lamjung, most of which are \nIndian hybrids. The recommended dose of nitrogen over Indian conditions \nmay not be best fitted in Nepalese conditions and here is hardly any \ninformation about the dose and efficient hybrid varieties. So, it is \nnecessary to access the performance of hybrid varieties in this climatic \nscenario with different nitrogen levels for higher yields. \n\n\n\nFarmers often rely on their own technology or recommendations from \nagro-vets or seed companies, as there are no agronomic management \ntechnologies recommended by the government of Nepal or the National \nMaize Research Program. Some farmers use high amounts of fertilizers, \npesticides, and closer spacing, but due to a lack of technical knowledge, \nthey may apply fertilizers haphazardly and not in sufficient quantities. \nWithout knowledge of the right time, dose, and amount of fertilizer to \napply at each stage of hybrid growth, yields can be reduced. However, by \nintervening in prevailing practices with promising technologies, yield \nlevels in the Terai and mid-hills regions can be increased. \n\n\n\nThe general recommendation of 120 kg/ha of nitrogen may not be optimal \nfor all hybrids or seasons, and different hybrids may have different \noptimal levels of nitrogen. Consequently, inappropriate selection of maize \nvarieties, poor management of nutrients, damage from pests and diseases, \nand weed infestations become major factors contributing to low maize \nyields in Nepal (Chapagain and Gurung, 2010). \n\n\n\nWhile some hybrids have been developed by the national research system \nof Nepal, hybrids from multinational seed companies are gaining \npopularity among farmers. This is because national hybrids are often \nunable to compete with multinational hybrids in terms of yield (Tripathi \net al., 2016).  \n\n\n\nAnother glaring problem is lack of quality assessment of the few \n\n\n\ndomestically developed hybrids in the desired locations. Most maize \nvarieties are only put under trial at research stations, which may or may \nnot be representative of the field condition of a normal farmer. As a result, \nsome released varieties have failed to meet farmers\u2019 production \nexpectations, leading to a preference for multinational varieties over \nlocally released ones. To address this issue, a study was conducted to \nevaluate 117 maize hybrids from 20 seed companies at three locations in \nNepal. The study aimed to identify the best maize hybrids for planting \nduring winter in these regions. But the study is only among the few that \nhave been conducted successfully. There is a need of many of these types \nof similar research studies done throughout the nooks and corners of this \ngeographically diverse country. This study is an attempt to do the same in \nthe climatic conditions of Sundar bazar, Lamjung.  \n\n\n\n1.4   Role of Nitrogen Fertilizer Use Efficiency (NUE) on Maize Crop \n\n\n\nNitrogen fertilizer plays a crucial role in crop yield, contributing to about \n50% of yield performance (Bakht et al., 2006). Adequate supply of plant \nnutrients at the right time is necessary for hybrids and composite varieties \nto exhibit their full yield potential (Singh & Kumar, 2016). Plants require \nnitrogen in larger amounts than other elements as it is a constituent \nelement of protein, nucleic acids, chlorophyll, and many enzymes, and also \nmediates the utilization of phosphorus, potassium, and other elements \n(Chapagain and Gurung, 2010). The increased use of nitrogen fertilizer \nover the past four decades has played a significant role in improving crop \nyield and increasing agricultural food production worldwide (Barbieri et \nal., 2008). \n\n\n\nHowever, excess nitrogen can lead to environmental problems such as \ngroundwater contamination through leaching, volatilization, and \ndenitrification (Tamme et al., 2010). By optimizing the application of \nnitrogen fertilizer, it is possible to reduce soil nitrate leaching (Power et \nal., 2000). Application of high nitrogen rates may result in poor nitrogen \nuptake and low nitrogen use efficiency (NUE) due to excessive nitrogen \nlosses (Hammad et al., 2011). Matching application rate and timing with \nplant demands can improve plant NUE (Liao et al.,2021). However, cereal \ncrops recover only 33% of applied nitrogen on average, with 50-70% of \nthe nitrogen provided to the soil being lost, and NUE may vary with crop \nspecies, soil type, and rate of nitrogen fertilizer application (Hawkesford \nMJ, 2019).Therefore, ideal nitrogen management should optimize yield, \nfarm profit, and NUE while minimizing potential nitrogen leaching beyond \nthe crop rooting zone (Arif et al.,2010). \n\n\n\n1.5   Characteristics of Rampur Hybrid-10 and CP 808 \n\n\n\nRampur Hybrid-10: It is a national hybrid (F1) seed produced by \nNational Maize Research Program, Rampur, Chitwan registered and \ncertified in 2076 BS. This hybrid maize can be grown with in the altitude \nof 800-1800 masl and potential yield is around 8-8.5-ton ha-1. It takes 120 \ndays in spring and 150 days in winter for maturity. The special features of \nRampur hybrid-10 are: Tolerance to high temp. plant remain green after \nmaturity so maturity for fodder. \n\n\n\nCP 808: It is multinational hybrid (F1) seed imported from India \nregistered in 2068 BS and has the average productivity of 9-10 ton ha-1 \nwith the average maturity of 120-150 days. Special features: big ear, high \nyield etc. it can be cultivated in the terai to mid hills of Nepal. \n\n\n\n1.6  Effect of Different Nitrogen Levels on Growth and Yield \nPerformance of Hybrid Maize \n\n\n\nAccording to a study nitrogen rates have the most significant impact on \nthe variability of maize yield, accounting for 72.03% of the variability. \nNitrogen deficiency can result in a more significant reduction in corm yield \ncompared to the deficiency of other elements by (Nemati and Seyed, \n2012). Nitrogen-limiting conditions can hinder plant development by \ndelaying silking, decreasing pre-anthesis crop growth rate, reducing leaf \narea index during flowering, and accelerating leaf senescence rates \nthroughout the plant's life cycle (Ye, 2022).On the other hand, optimum \nnitrogen levels can increase growth rate, delay senescence, promote \nhigher leaf expansion rate and duration, and result in higher dry matter \nproduction in maize, as observed by (Yue,  2021;  Anas et al., 2020) .Maize \ngrain yield is linked to both higher nitrogen uptake and higher ability to \nutilize nitrogen accumulated in the plant in maize production (Adhikary et \nal., 2020). The application of nitrogen at higher rates resulted in an \nincrease in both the yield and yield components of maize (Hussain et al., \n2007, Bakht et al., 2006 and Khaliq et al., 2009). According to studies \nconducted by there was a significant increase in the number of grains per \ncob, 100 seed weight, and grain weight per cob when the nitrogen \napplication rate was increased up to 180 kg N-1 ha. Moreover, the grain \nyield per plant increased significantly up to 240 kg N ha. In studies \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Bhimsen Mahat, Bijaya Upadhayay, Ajay Poudel (2023). Effect Of Different Nitrogen Dose on Growth and Yield Characterestics  \n\n\n\nof Hybrid Maize (Zea Mays L.) Varieties qt Sundarbazar, Lamjung. Journal of Sustainable Agricultures, 7(2): 65-71. \n\n\n\n\n\n\n\nconducted by it was found that the highest values for plant height, leaf area \nindex (LAI), and dry matter accumulation were observed when 120 kg N \nha-1 was applied (Sagwal and Scholar, 2020;  Amanullah et al., 2016;  \nSharma et al., 2019; Adhikari et al., 2021). The application of 200 kg N ha-\n1 resulted in the highest recorded values for maximum days to 50% \ntasseling and 50% silking, number of leaves per plant, number of cobs per \nplant, number of grains per cob, plant height, as well as grain and \nbiological yield (Bakht et al., 2006). According to the nitrogen requirement \nfor maize can range from 150-240 kg N depending on the variety \n(Adhikary et al., 2020). The study also found that the highest grain yield of \n9352 kg/ha was achieved with the application of 120 kg N ha-1. On the \nother hand, Ma B, 2022 reported significantly higher grain yield with the \napplication of 180 kg N ha-1. also reported that application of 200 Kg N ha-\n\n\n\n1 increased grain yield of maize (Sharma et al., 2019). The research \nconducted by Sharma et al., 2019 found that highest 1000 grain weight \n(482.16g) was obtained from application of 180 kg N and the plant \npopulation of 66,666 plants ha. In the same study highest grain yield of \nl1.10-ton ha was recorded with l180 kg N ha and the plant population of \n83,383 plants ha-1 . Increasing the nitrogen levels resulted in an increase \nin the number of cobs per plant, cob length, cob diameter, number of grain \nrows per cob, number of grains per grain row, number of grains per cob, \nand thousand grain weight (Adhikary et al., 2020). \n\n\n\n1.7   Objective of the Study \n\n\n\nThe broad objective of this study is to increase the yield of maize in \nLamjung, Sundar bazar and regions with similar altitude and climate, by \nidentifying the optimal combination of nitrogen level and maize hybrid \nvariety for maximum growth, yield, and nitrogen use efficiency. The \nspecific objectives of this study are to: \n\n\n\nI. Evaluate the impact of varying nitrogen levels on growth and \nyield parameters of hybrid maize varieties. \n\n\n\nII. Determine the optimal nitrogen level for achieving maximum \ngrowth and yield of hybrid maize varieties. \n\n\n\nIII. Compare the performance of different hybrid maize varieties \nunder different levels of nitrogen application. \n\n\n\nIV. Provide evidence-based recommendations to maize farmers in \nSundar bazar, Lamjung, and mid-hills of Nepal with similar \naltitude and climatic conditions on the most effective \ncombination of hybrid maize variety and nitrogen level for \noptimal maize production. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1   Description of Research Site \n\n\n\nThe experiment was conducted in agronomy farm of Lamjung campus, \nSundar bazar, western mid-hills of Nepal lie at the Northern longitude of \n28.130 Eastern latitude of 84.420 with an altitude of 800 masl with \nsubtropical humid climate. The experiment was conducted from 26th \nMarch 2022.The average annual rainfall of experimental site is 2000mm. \nThe maximum temperature of experimental site up to 390 c at the month \nof April-June and minimum temperature of 6-100c at month of December-\nJanuary. The characteristics of soil used for experiment were: \n\n\n\nTable 1: Edaphic Condition of Soil Before the Research Conducted \n\n\n\nCoordinates: 84.417 0N, 28.128 0 E \n\n\n\nParent Soil: Fluvial non calcareous \n\n\n\npH value: 5.88 \n\n\n\nOrganic Matter: 2.00% \n\n\n\nTotal Nitrogen: 0.10% \n\n\n\nAvailable Potassium (K2O): 152.04 kg/ha \n\n\n\nAvailable Phosphorus (P2O5): 8.051 kg/ha \n\n\n\n \n2.2   Experimental Details \n\n\n\nExperiment was conducted in factorial RCBD design. Two mostly \ncultivated hybrid maize varieties (Rampur Hybrid-10 and CP-808) were \nused with six different doses of nitrogen levels. There were altogether 36 \nplots each of 3.6 m2. Spacing between the adjacent plot was 0.5 m with \nplant to plant-to-plant distance of 20 cm and row to row of 60 cm. so, that \naltogether there were 30 plants in each plot. 5 sample plants were taken \n\n\n\nrandomly from each plot excluding the border plants.  \n\n\n\nTable 2: Table Showing Experimental Details \n\n\n\nExperimental detail Descriptions \n\n\n\nCrop Maize \n\n\n\nVariety \nRampur Hybrid-10(V1) and CP-\n\n\n\n808(V2) \n\n\n\nNumber of treatments 12 \n\n\n\nNumber of replications 3 \n\n\n\nDesign Factorial RCBD \n\n\n\nGross plot size 3.6 m2 \n\n\n\nNet plot size 270m2 \n\n\n\nSpacing (r*p) 60cm*20cm \n\n\n\nDoses of fertilizer \nN (0, 120, 150, 180, 210, 240 kg \nh-1), P (60 kg h-1), and K (40 kg \n\n\n\nh-1), FYM (10ton ha-1) \n\n\n\n\n\n\n\nTable 3: Table Showing Treatment Details \n\n\n\nTreatment combinations \nTreatments (NPK kg h-1 + \n\n\n\nVarieties) \n\n\n\nV1D0 Rampur Hybrid-10 + 0:60:40 \n\n\n\nV1D1 Rampur Hybrid-10 + 120:60:40 \n\n\n\nV1D2 Rampur Hybrid-10 + 150:60:40 \n\n\n\nV1D3 Rampur Hybrid-10 + 180:60:40 \n\n\n\nV1D4 Rampur Hybrid-10 + 210:60:40 \n\n\n\nV1D5 Rampur Hybrid-10 + 240:60:40 \n\n\n\nV2D0 CP-808 + 0:60:40 \n\n\n\nV2D1 CP-808 + 120:60:40 \n\n\n\nV2D2 CP-808 + 150:60:40 \n\n\n\nV2D3 CP-808 + 180:60:40 \n\n\n\nV2D4 CP-808 + 210:60:40 \n\n\n\nV2D5 CP-808 + 240:60:40 \n\n\n\n \n2.3   Cultivation Practices \n\n\n\nThe land was ploughed thoroughly using power tiller 2-3 times to make \nsoil pulverized and leveled. And well decomposed FYM incorporated \nthoroughly. After 15 days, field layout was done making 36 plots using \ntape, pegs, rope prior to sowing. Field was treated with Cartap \nhydrochloride 4% granules, as the field around were infected with \ncutworm. The recommended dose of FYM @ 10 ton/ ha was applied 15 \ndays before sowing of seeds. The recommended dose of 60 kg P2O5/ha \nand 40 kg K2O/ha was applied as basal in all plots at the time of seed \nsowing. 1/2 dose of N (120/150/180/210/240 kg ha-1) was used at the \ntime of seed sowing as basal dose. The remaining 1/2 dose of N was side-\ndressed at knee high stage and tasseling stage. Bold and disease-free seeds \nof Rampur Hybrid-10 and CP. The depth of sowing was 3-5 cm. Thinning \nwas done 2 weeks after sowing. At knee high stage some of the plants were \ndamaged by fall army worm, Emmamectin benzoate 5% soluble granule \nwas used to control their damage. First hand weeding was done at the time \nof application of first split dose of nitrogen and second at the time of \ntasseling. Along with weeding, soil loosening and earthing up was also \ndone at tasseling stage. When the plants turned yellow and ear husk \nturned brownish black maize was harvested manually. The harvesting was \ndone from very bottom of plants with sickles. Plants were allowed for sun \ndrying for 2-3 days.    \n\n\n\n2.4   Data Collection \n\n\n\nRequired data were taken from tagged plants 5 form each plot. Days to \ntasseling and silking were recorded from the date of sowing till when 80% \nplants in each plot produce fully opened tassels and silking start. Data of \nnumber of leaves, plant height, stem diameter was co from sampled plant \nof each plot at maturity stage. And average was calculated from 5 plants. \nLeaf area was calculated by multiplying leaf length, maximum leaf width \nand with a correction factor of 0.75 as suggested by Francis et al., 1969. It \nwas taken from 5 tagged plant from each plot at the time of maturity. \nLength of cob of tagged plants were measured from base to top grain \nbearing portion of each ear. The average of 5 ears was calculated and \nexpressed as ear length (cm). Diameter of cob was measured with the help \nof vernier caliper from three parts as top, mid and base and average \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Bhimsen Mahat, Bijaya Upadhayay, Ajay Poudel (2023). Effect Of Different Nitrogen Dose on Growth and Yield Characterestics  \n\n\n\nof Hybrid Maize (Zea Mays L.) Varieties qt Sundarbazar, Lamjung. Journal of Sustainable Agricultures, 7(2): 65-71. \n\n\n\n\n\n\n\ndiameter was calculated for each cob (mm). Weight of cob was measured \nwith the help of weighing machine (gm). After few days of sun drying, the \nweight of plants with cob were weighed as net plot area (kg/ha) and later \nconverted into ton/ha. Total number of grains per cob was counted from \ncobs of selected plants of each plot mean was analyzed. A total of 1000 \ngrains were counted and weighed with the help of portable electronic \nbalance. The moisture percentage of grain was determined with the help \nof gravimetric method. The grain yield per hectare was computed for each \ntreatment from the net plot yields. The moisture percentage of the grain \nwas determined by using gravimetric method. The final grain yield was \n\n\n\nadjusted to 15% moisture level. Stover yield was calculated by subtracting \nthe cob weight from the total biomass yield. Harvest index (HI) was \ncomputed as suggested by Stern,1986.  \n\n\n\n 2.5 Data Analysis \n\n\n\nAfter completion of data collection, data were tabulated according to \nreplications and treatments. Data entry and tabulation was done by using \nMS-Excel-2019-word processing by MS-Word-2019. Statistical analysis \nwas done by using R-studio version 4.2.2. \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1   Growth Parameters \n\n\n\nTable 4: Effect of Different Nitrogen Levels on Growth Parameters of Hybrid Maize Varieties \n\n\n\nTreatment Plant height (cm) No. of Leaves Leaf Area Index Stem diameter (mm) \n\n\n\nVarieties     \n\n\n\nRampur Hybrid-10 152.05a 12.616a 7.256b 22.33a \n\n\n\nCP-808 146.71a 12.467a 7.977a 22.76a \n\n\n\nLSD (0.05) 9.32 0.37 0.700 1.01 \n\n\n\nF test (\u03b1=0.05) NS NS * NS \n\n\n\nN levels (kg ha-1) \n\n\n\n240 163.71a 12.96a 8.83a 24.60a \n\n\n\n210 150.90ab 12.73ab 7.76ab 22.87ab \n\n\n\n180 156.75ab 12.67ab 8.46a 23.54a \n\n\n\n150 152.07ab 12.80ab 7.89ab 23.26a \n\n\n\n120 142.31bc 12.16bc 6.89bc 21.18bc \n\n\n\n0 130.53c 11.91c 5.83c 19.83c \n\n\n\nLSD(\u03b1=0.05) 16.15 0.64 1.21 1.75 \n\n\n\nCV 9.03 4.28 13.3 6.49 \n\n\n\nF test ** * *** *** \n\n\n\nGrand mean 149.38 12.54 7.61 22.55 \n\n\n\nNote: CV: coefficient of variation, LSD: Least significant differences, \u2018***\u2019 significant at 0.1%, \u2018**\u2019 significant at 1%, \u2018*\u2019 significant at 5%, \u2018NS\u2019 non-significant, N: \nNitrogen levels. \n\n\n\n3.1.1   Plant Height \n\n\n\nPlant height at 90 DAS was found statistically significant due to various \nlevels of nitrogen. While there were no significant changes observed due \nto different varieties on plant height at 90 DAS. The maximum plant height \nwas found in the plot with 240 kg N ha1(163.72 cm) followed by 180 kg N \nha-1 (156.76 cm). whereas the minimum plant height was found in control \nplot (130.54 cm) and the plot with 120 kg N ha-1(142.32cm). plant height \nat 150 kg N ha-1 was statistically at par with 180 and 210 kg N ha-1. \n\n\n\nIncreasing the amount of N application leads to several positive effects on \nplant growth, such as enhanced cell division, elongation, and nucleus \nformation, which promote the development of green foliage and an \nincrease in chlorophyll content. As a result, the rate of photosynthesis is \nboosted, and the stem grows longer, ultimately leading to an overall \nincrease in plant height (Diallo et al.,1996, Thakur et al., 1998). These \nfindings were in similar to the finding of Adhikary et al., 2020 , the \nmaximum plant height (199.92 cm) with the application of 220 kg N ha-1 \n. Studies conducted by Dawadi and Sah (2012), Bakht et al. (2006), Khan \net al. (2014), and Sagwal and Scholar (2020) all showed that an increase \nin nitrogen level led to an increase in plant height of hybrid maize \nvarieties. This relationship between nitrogen and plant height can be \nexplained by improved vegetative growth, which promotes mutual \nshading and internodal extension. \n\n\n\n3.1.2   Number of Leaves \n\n\n\nThere was no significant difference in number of leaves due to different \nvarieties, but with increasing level of nitrogen there is increasing in \nnumber of leaves. Highest number of leaves were obtained at 240 kg N ha-\n\n\n\n1 (12.96) which was at statistically at par with Number of leaves at 180, \n\n\n\n210, 150 kg N ha-1 respectively and lowest leaves number were obtained \nin control plot. \n\n\n\n3.1.3   Stem Diameter \n\n\n\nThere was no significant difference was obtained due to different varieties. \nStem diameter was found to be influenced by varying level nitrogen. Stem \ndiameter at 240 kg N ha-1 (24.60 mm) was statistically at par with 180 and \n150 kg N ha-1 and lowest cob diameter was found in control plot (19.83 \nmm). (Hassan et al., 2010) also found similar type of result. \n\n\n\n3.1.4   Leaf Area Index (LAI) \n\n\n\nLAI was found statistically significant on both the varieties and nitrogen \nlevel. The effect of different varieties and nitrogen levels on LAI was highly \nsignificant. CP-808 produced the higher LAI (7.977) as compared to \nRampur hybrid-10 LAI (7.256). The highest nitrogen level (240 kg N ha-1) \nproduced maximum LAI (8.838) which was statistically at par with 180 kg \nN ha-1 (8.464) followed by 210 kg N ha-1 (7.769) and 150 kg N ha-1 (7.899) \nrespectively. The lowest LAI was found in control plot (5.834) and plot \nwith 120 kg N ha-1 (7.899). There was no significant effect of varieties and \nnitrogen levels interaction on LAI of maize. \n\n\n\nHighest LAI in CP-808 as compared to Rampur hybrid-10 may be due to \ndifference in leaf arrangement, chlorophyll content and activities of \nphotosynthetic enzymes as affected by genetic characteristics of \nindividual maize varieties. plant height reflects the canopy of plant. \nIncrease in leaf area index may be due to rapid cell division and active cell \nmultiplication \n\n\n\nbrought up by nitrogen. (Hammad et al., 2011) found the highest LAI at \n250 kg N ha-1 (5.06) followed by 200 kg ha-1 (4.74) respectively. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Bhimsen Mahat, Bijaya Upadhayay, Ajay Poudel (2023). Effect Of Different Nitrogen Dose on Growth and Yield Characterestics  \n\n\n\nof Hybrid Maize (Zea Mays L.) Varieties qt Sundarbazar, Lamjung. Journal of Sustainable Agricultures, 7(2): 65-71. \n\n\n\n\n\n\n\n3.2   Yield parameters \n\n\n\nTable 5: Effect of Different Nitrogen Levels on Yield Parameters of Hybrid Maize Varieties. \n\n\n\nTreatment Cob length (cm) Cob diameter (mm) Cob Weight Grain/cob \n\n\n\nVarieties     \n\n\n\nRampur Hybrid-10 15.82b 40.61b 11.09a 322.16b \n\n\n\nCP-808 16.59a 42.02a 11.35a 396.92a \n\n\n\nLSD (0.05) 0.72 1.39 0.69 58.24 \n\n\n\nF test * * NS * \n\n\n\nN levels (kg ha-1) \n\n\n\n240 18.05a 45.59a 14.84a 451.60a \n\n\n\n210 16.61bc 42.84bc 11.78ab 378.90ab \n\n\n\n180 17.09ab 44.63ab 13.10ab 415.60ab \n\n\n\n150 16.05bc 41.52c 10.65b 341.26bc \n\n\n\n120 15.41c 38.28d 10.42b 331.16bc \n\n\n\n0 14.03d 35.05e 6.54c 238.73c \n\n\n\nLSD(\u03b1=0.05) 1.25 2.42 3.41 1.25 \n\n\n\nCV 6.44 4.89 24.05 23.43 \n\n\n\nF test (\u03b1=0.001) *** *** ** ** \n\n\n\nGrand mean 16.21 41.32 11.2 359.54 \n\n\n\nNote: CV: coefficient of variation, LSD: Least significant differences, \u2018***\u2019 significant at 0.1%, \u2018**\u2019 significant at 1%, \u2018*\u2019 significant at 5%, \u2018NS\u2019 non-significant, \nN: Nitrogen levels. \n\n\n\n\n\n\n\nTable 5: Effect of Different Nitrogen Levels on Yield Parameters of Hybrid Maize Varieties. \n\n\n\nTreatment Test weight (gm) Biological yield Grain yield Harvest index Stover yield \n\n\n\nVarieties      \n\n\n\nRampur Hybrid-10 228.41b 36.45a 7.46a 14.50b 25.36a \n\n\n\nCP-808 249.77a 36.46a 7.99a 15.68a 25.10a \n\n\n\nLSD (0.05) 18.922 4.282 0.89 0.69 2.59 \n\n\n\nF test * NS NS ** NS \n\n\n\nN levels (kg ha-1) \n\n\n\n240 269.30a 43.96a 10.40a 17.80a 29.12a \n\n\n\n210 252.01ab 38.26ab 7.93bc 15.02b 26.47a \n\n\n\n180 252.18ab 41.60ab 9.29 ab 16.62a 28.50a \n\n\n\n150 233.11bc 35.59b 7.16c 14.17b 24.94a \n\n\n\n120 221.69bc 35.47b 6.56c 12.77c 25.05a \n\n\n\n0 206.25c 23.86c 5.01d 14.11b 17.31b \n\n\n\nLSD(\u03b1=0.05) 32.77 7.41 1.55  4.42 \n\n\n\nCV 11.44 16.99 16.75 6.69 14.89 \n\n\n\nF test (\u03b1=0.001) ** *** *** *** *** \n\n\n\nGrand mean 239.09 36.45 7.73 15.09 25.23 \n\n\n\nNote: CV: coefficient of variation, LSD: Least significant differences, \u2018***\u2019 significant at 0.1%, \u2018**\u2019 significant at 1%, \u2018*\u2019 significant at 5%, \u2018NS\u2019 non-significant, \nN: Nitrogen levels. \n\n\n\n3.2.1   Cob Length \n\n\n\nCP-808 produced the highest cob length of 16.598 cm and shortest by \nRampur hybrid-10 of 15.821cm. The length of cob was found to be \ninfluenced by both the varieties and nitrogen levels. Statistical analysis of \nthe data showed that the various levels of nitrogen had significant effect \non cob length. The plot with highest level of nitrogen 240 kg N ha-1 \nproduced 18.05 cm of cob followed by 180 kg N ha-1 (17.09 cm). cob length \nat 210 kg N ha- 1 (16.61 cm) was statistically at par with 150 kg N ha-1 \n(16.05 cm). control plot had the least cob length of 14.03 cm. \n\n\n\nDerby et al., 2004 reported that the increased length of the ear could be \nattributed to the favorable solar light environment, which led to higher \nassimilation and subsequent conversion to starches. The results are also \nin agreement with (Turgut, 2000), who reported increase in nitrogen \nlevels positively influences the cob length of maize. Similar type of result \nwas found by (Ahmad et al., 2018), in which cob length at 180 kg N ha-1 \n(17.18 cm) followed by 150 kg N ha-1 (16.52 cm). \n\n\n\n3.2.2   Cob Diameter \n\n\n\nBoth varieties and nitrogen levels showed the statistically significant \nresult for cob diameter. CP 808 had highest cob diameter 42.022 mm as \ncompared to Rampur hybrid-10 (40.61 mm). plot with nitrogen level 240 \nkg ha-1 produced the highest cob diameter of 45.59 mm followed by 44.63 \nmm (180 kg N ha-1) and 42.84 mm (210 kg N ha-1) respectively. Control \n\n\n\nplot had the least cob diameter of 35.05 mm. \n\n\n\nA similar type of result was obtained by (Adhikari et al., 2021), who \nobserved the highest cob diameter with the application of 220 kg N ha-1 \n(45.40 mm). The increment of cob diameter could be due to the supply of \nsufficient nitrogen. A higher cob diameter was obtained from higher dose \nof Nitrogen application due to sufficient availability of Nitrogen which is \nresponsible for cell division and cell elongation. \n\n\n\n3.2.3   Cob Weight \n\n\n\nThere was no significant difference in cob weight due to different varieties \nbut at different nitrogen levels there was variance in cob weight was \nfound. Highest cob weight was found at 240 kg N ha-1 (14.84-ton ha-1) and \nwas statistically at par with 180 and 210 kg N ha-1. The lowest cob weight \nper hectare was found in control plot (6.54-ton ha-1).     \n\n\n\n3.2.4 Grain Per Cob \n\n\n\nAnalysis of data revealed that the different varieties and various levels of \nnitrogen had significantly (p \u2264 0.5 and p \u2264 0.01 respectively) affected the \nnumber of grains per cob. CP 808 produced the maximum grains per cob \ni.e., 396.92 as compared to Rampur hybrid-10 (322.16). Maximum number \nof grains per cob was obtained from the plot with 240 kg N ha-1 (451.60) \nand 180 kg N ha-1 (415.60) which was statistically at par with 210 kg N ha-\n\n\n\n1 (378.90). And the plot with 150 kg N ha-1 produced the grains number \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Bhimsen Mahat, Bijaya Upadhayay, Ajay Poudel (2023). Effect Of Different Nitrogen Dose on Growth and Yield Characterestics  \n\n\n\nof Hybrid Maize (Zea Mays L.) Varieties qt Sundarbazar, Lamjung. Journal of Sustainable Agricultures, 7(2): 65-71. \n\n\n\n\n\n\n\nwhich were statistically at par with 180 kg N ha-1 as shown in table. The \ncontrol plot produced the cob with least cob grain number (238.73). \n\n\n\nThe findings align with those reported indicating that increasing nitrogen \nlevels can result in higher grain yields per cob by (Adhikari et al., 2021; \nDawadi and Sah, 2012). The increase in the number of grains per cob \nunder higher nitrogen levels can be attributed to reduced competition for \nnutrients, which enables plants to accumulate more biomass, have a \nhigher capacity to convert more photosynthesis into sink, and ultimately \nproduce more grains per cob. The optimal nitrogen fertilizer availability \nhelped the plant to utilize nutrients to their maximum potential, resulting \nin a higher number of grains per cob. The number of grains per cob is a \ncritical factor in determining the final grain yield. \n\n\n\n3.2.5   Test Weight \n\n\n\nStatistical analysis of the data revealed that, both the different varieties \nand various level       nitrogen levels significantly affected the test weight \n(1000 grains weight). CP 808 had the highest test weight (249.77 gm) as \ncompared to Rampur hybrid-10. And highest test weight was found in the \nplot with 240 kg N ha-1 (269.30 gm) and was statistically at par with 180 \n(252.18 gm) and 210 kg N ha-1 (252.01 gm) respectively and lowest test \nweight was found in control plot (206.25 gm). \n\n\n\nHighest 1000 grains test was also obtained in where 254.1 gm of 1000 \ngrains at 160 kg N ha-1. In (Adhikari et al., 2021), 220 kg N ha-1 yield \nhighest test weight (276.77gm). also found similar result in higher dose of \nnitrogen (Arif et al., 2010; Hussain et al., 2007; Ahmad et al., \n\n\n\n2018). \n\n\n\n3.2.6   Stover Yield \n\n\n\nStatistical analysis of the data revealed that various levels of nitrogen had \nsignificantly affected on stover yield (p\u2264 0.001). Maximum stover yield \nwas found in 240 kg N ha-1 (29.12-ton ha-1) which was statistically at par \nwith 180 kg (28.50-ton ha-1) and 210 kg (26.47-ton ha-1) and lowest stover \nyield was found in control one (17.31-ton ha-1). \n\n\n\nSimilar type of result was obtained who found highest stover yield in \nhighest dose 220 kg N ha-1 (12.91-ton ha-1). (Bhandari et al., 2019) also \nfound highest stover yield in 200 kg N ha-1(10.61-ton ha-1) by (Adhikari et \nal., 2021). As a also found the highest stover yield in highest dose of \nnitrogen fertilizer (Krishnamurthy et al., 1974; Bhatti and Gurmani, 2007). \n\n\n\n3.2.7   Biological yield \n\n\n\nSignificant difference (p\u2264 0.001) in biological yield was found due to \nvarying levels of nitrogen but not due to different varieties. Maximum \nvalue was obtained in the plot with 240 kg N ha-1 (43.96-ton ha-1) followed \nby 180 kg N ha-1 (41.60-ton ha-1) and this was statistically at par with 210 \nkg N ha-1 (38.26-ton ha-1) and lowest biological yield was found in control \nplot (23.86-ton ha-1). Hammad et al., 2011 found 16.56-ton ha-1 of \nbiological yield in 300 kg N ha-1 and also found highest biological yield in \nhigher dose of nitrogen (Bahadar Marwat et al., 2009). Nitrogen \napplication rate showed highly significant effect on biological yield as well \nas its response was linear and highly significant. \n\n\n\n3.2.8   Grain Yield \n\n\n\nStatistical analysis of data uncovered that the effect of varieties had no \nsignificant effect on grain yield but different levels of Nitrogen had a \nsignificant effect on grain yield. Various levels of nitrogen had significantly \n(p\u2264 0.001) affected the grain yield. Highest grain yield was obtained from \nthe plot with 240 kg N ha-1 (10.40 ton ha-1) followed by 180 kg N ha-1 \n(9.295 ton ha-1) and 210 kg N ha-1 (7.93 ton ha-1) respectively. The control \nplot produced the least grain yield of 5.01 ton ha-1. \n\n\n\nSimilar type of result was obtained who found that application of high \ndoses of nitrogen at 220 kg ha-1 produced highest yield of 10.07 ton ha-1. \nHigher levels of nitrogen may result in increased grain yield due to \nreduced competition for  nutrients by (Adhikari et al., 2021; Sharma et al., \n2019). This, in turn, leads to a larger plant canopy and increased \nphotosynthetic activity, resulting in the accumulation of more biomass and \nultimately yielding bold grains. \n\n\n\n3.2.9   Harvest Index \n\n\n\nHighest HI was found at 240 kg N ha-1 (17.80%) which was statistically \nsimilar to that plot with 180 kg N ha-1 (16.62%). HI at 210 kg N ha-1 was \nstatistically at par with 150 kg N ha- 1, and 120 kg N ha-1. And CP 808 \n(15.68%) had the highest HI as compared to Rampur hybrid-10 (14.50%). \n\n\n\nOur result was satisfied with those of in which highest HI was found in 220 \nkg N ha-1 (43.38-ton ha-1) (Adhikari et al., 2021). At low N supply, crop \ngrowth rate slows down causing reproductive structures to decline, as a \nresult lower maize grain yield (and its components) as well as lesser \nharvest index (Hammad et al., 2011). \n\n\n\n4. SUMMARY AND CONLUSION \n\n\n\nIn conclusion, this study aimed to determine the best combination of \nnitrogen levels and hybrid maize varieties for optimal growth and yield in \nSundar bazar municipality, Lamjung, Nepal. It was concluded that \nincreasing nitrogen levels significantly enhanced the growth and yield \nparameters of hybrid maize varieties. The application of 240 kg N ha-1 \nresulted in the highest values for plant height, number of leaves, leaf area \nindex (LAI), stem diameter, thousand grain test weight, grain per cob, \ngrain yield, biological yield, and harvest index. Furthermore, the hybrid \nmaize variety CP 808 exhibited superior performance compared to \nRampur Hybrid-10 in terms of grain yield, thousand grain test weight, cob \nlength, grain per cob, and LAI. These findings have important implications \nfor maize farmers in Sundar bazar, Lamjung, and mid-hills of Nepal with \nsimilar altitude and climatic conditions. By cultivating hybrid maize \nvariety CP-808 with the application of 240 kg N ha-1, farmers can increase \ntheir yields and income and contribute to the ever-increasing demand of \nmaize feed (poulry and cattle feed) and food. However, further research \non multilocation and multi-season basis is needed to consolidate the \nfindings retrieved from the study.  \n\n\n\nThis research also presents opportunities for academia and policy makers \nto address the challenges facing maize production in Nepal based on the \nresearch gaps and opportunities pointed out by this study. The low yield \npotential of existing genotypes can be addressed by cultivating new high-\nyielding varieties using optimal cultural practices and timely application \nof appropriate nutrients in the required amounts. Furthermore, this study \nunderscores the importance of improving access to improved seeds and \nquality fertilizers to enhance maize productivity. By addressing the \nchallenges and seizing the opportunities presented by this research, it is \npossible to improve maize production and contribute to food security in \nhilly Nepal.  \n\n\n\nREFERENCES \n\n\n\nAdhikari, K., Bhandari, S., Aryal, K., Mahato, M., and Shrestha, J., 2021. Effect \nof different levels of nitrogen on growth and yield of hybrid maize \n(Zea mays L.) varieties. Journal of Agriculture and Natural \nResources, 4(2), Pp. 48\u201362. \nhttps://doi.org/10.3126/janr.v4i2.33656  \n\n\n\nAdhikary, B. H., Baral, B. R., and Shrestha, J., 2020. Productivity of winter \nmaize as affected by varieties and fertilizer levels. International \nJournal of Applied Biology, 4(1), Pp. 85\u201393. \nhttps://doi.org/10.20956/ijab.v4i1.10192  \n\n\n\nAhmad, S., Khan, A. A., Kamran, M., Ahmad, I., Ali, S., and Fahad, S., 2018. \nResponse of maize cultivars to various nitrogen levels. European \nJournal of Experimental Biology, 08(01). \nhttps://doi.org/10.21767/2248-9215.100043  \n\n\n\nAmanullah*, D., Zahid, A., Iqbal, A., and Ikramullah, M., 2016. Phosphorus \nand tillage management for maize under irrigated and dryland \nconditions. Annals of Plant Sciences, 5(03), Pp. 1304. \nhttps://doi.org/10.21746/aps.2016.03.004  \n\n\n\nAnas, M., Liao, F., Verma, K.K., Sarwar, M. A., Aamir, M., Chen, Z-L., Li, Q., \nZeng, X-P., Liu, Y., Li, Y-R.,  2020. Fate of nitrogen in agriculture and \nenvironment: agronomic, eco-physiological and molecular \napproaches to improve nitrogen use efficiency. Biol Res 53, 47 \n(2020). https://doi.org/10.1186/s40659-020-00312-4 \n\n\n\nBahadur, B.K. S., and Shrestha, J., 2014. Effect of conservation agriculture \non growth and productivity of maize (zea mays L.) in terai region of \nNepal. World Journal of Agricultural Research, 2(4),Pp.  168\u2013175. \nhttps://doi.org/10.12691/wjar-2-4-6  \n\n\n\nBaral, B. R., Adhikari, P., and Shrestha, J., 2015. Growth and yield response \nof hybrid maize (Zea mays L.) to phosphorus levels in sandy loam \nsoil of Chitwan Valley, Nepal. International Journal of Environment, \n4(2), Pp. 147\u2013156. https://doi.org/10.3126/ije.v4i2.12634  \n\n\n\nBarbieri, P. A., Echeverr\u00eda, H. E., Sa\u00ednz Rozas, H. R., and Andrade, F. H., 2008. \nNitrogen use efficiency in maize as affected by nitrogen availability \nand row spacing. Agronomy Journal, 100(4), Pp. 1094\u20131100. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(2) (2023) 65-71 \n\n\n\n\n\n\n\n \nCite The Article: Bhimsen Mahat, Bijaya Upadhayay, Ajay Poudel (2023). Effect Of Different Nitrogen Dose on Growth and Yield Characterestics  \n\n\n\nof Hybrid Maize (Zea Mays L.) Varieties qt Sundarbazar, Lamjung. Journal of Sustainable Agricultures, 7(2): 65-71. \n\n\n\n\n\n\n\nhttps://doi.org/10.2134/agronj2006.0057  \n\n\n\nBegam, A., Ray, M., Roy, D. C., and  Adhikary, S., 2018. Performance of \nhybrid maize (Zea mays L.) in different levels and time of nitrogen \napplication in Indo-Gangetic plains of Eastern India. Journal of \nExperimental Biology and Agricultural Sciences, 6(6), Pp.929\u2013935. \nhttps://doi.org/10.18006/2018.6(6).929.935  \n\n\n\nBhandari, D., Shrestha, R., and Joshi, B. K., 2019. National Winter Crops \nWorkshop. November, Pp. 672-686.  \n\n\n\nBk, A., Shrestha, J., and Subedi, R., 2018. Grain yield and yield attributing \ntraits of maize genotypes under different planting dates. Malaysian \nJournal of Sustainable Agriculture, 2(2), Pp. 06\u201308. \nhttps://doi.org/10.26480/mjsa.02.2018.06.08  \n\n\n\nChapagain, T., and Gurung, G. B., 2010. Effects of integrated plant nutrient \nmanagement (IPNM) practices on the sustainability of maize-based \nfarming systems in Nepal. Journal of Agricultural Science, 2(3). \nhttps://doi.org/10.5539/jas.v2n3p26  \n\n\n\nDawadi, D. R., and Sah, S. K., 2012. Growth and Yield of Hybrid Maize (Zea \nmays L.) in Relation to Planting Density and Nitrogen Levels during \nWinter Season in Nepal. Tropical Agricultural Research, 23(3), Pp. \n218\u2013227.   \n\n\n\nHawkesford, M.J., Griffiths S., 2019. Exploiting genetic variation in nitrogen \nuse efficiency for cereal crop improvement. Curr Opin Plant Biol. \nJun; 49:35-42. doi: 10.1016/j.pbi.2019.05.003.  \n\n\n\nIqbal, M. A., Lone, A. A., Rather, M. A., and Ahmad, S., 2016. Water \nproductivity and profitability of different rice (Oryza sativa L.) \nestablishment methods under varying irrigation regimes in \ntemperate Kashmir. Indian Journal of Agricultural Sciences, 86(12), \n1576-1581. https://doi.org/10.21088/ijas.2016.0122  \n\n\n\nKC, G., Karki, T. B., Shrestha, J., and Achhami, B. B., 2015. Status and \n\n\n\nprospects of maize research in Nepal. Journal of Maize Research and \nDevelopment, 1(1), Pp. 1\u20139. \nhttps://doi.org/10.3126/jmrd.v1i1.14239  \n\n\n\nKhan, A., Munsif, F., Akhtar, K., Afridi, M. Z., Z., Ahmad, Z., Fahad, S., Ullah, \nR., Khan, F. A., and Din, M., 2014. Response of fodder maize to \nvarious levels of nitrogen and phosphorus. American Journal of Plant \nSciences, 05(15), Pp. 2323\u20132329. \nhttps://doi.org/10.4236/ajps.2014.515246  \n\n\n\nLiao, J., Li, Z., Wang, J., Tian, D., Tian, D., Niu, S., 2021. Nitrogen use \nefficiency of terrestrial plants in China: geographic patterns, \nevolution, and determinants. Ecol Process 10, Pp. 69 \nhttps://doi.org/10.1186/s13717-021-00338-w  \n\n\n\nMa B, Wang J, Han Y, Zhou C, Xu T, Qu Z, Wang L, Ma B, Yuan M, Wang L, \nDing X, Qian C.2022. The response of grain yield and ear \ndifferentiation related traits to nitrogen levels in maize varieties \nwith different nitrogen efficiency. Sci Rep. Aug 26;12(1):14620. doi: \n10.1038/s41598-022-18835  \n\n\n\nMinistry of Agriculture and Livestock Development, Government of Nepal.  \n2022. Statistical information on Nepalese agriculture 2077/78. \nRetrieved from https://moald.gov.np/wp-\nContent/Uploads/2022/07/Statistical-Information-On-Nepalese-\nAgriculture-2077-78.Pdf  \n\n\n\nNemati, A., and  Seyed, R. 2012. Effects of rates and nitrogen application \ntiming on yield, agronomic characteristics and nitrogen use \nefficiency in corn. Annals of Biological Research, 3(6), Pp. 2976-\n2983.  \n\n\n\nNeupane, R. P., and  Thapa, G. B., 2001. Impact of agroforestry intervention \non soil fertility and farm income under the subsistence farming \nsystem of the Middle Hills, Nepal.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 13-19 \n\n\n\nCite The Article: Aimi Fadzirul Kamarubahrin, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul Aini \nMuhamed And Abu Hassan Makmun Abdul Qadir (2019). An Overview Malaysia As A Hub Of Planting Prophetic Fruits .  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 3(1) : 13-19. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 29 November 2018  \nAccepted 30 December 2018 \nAvailable online 7 January 2019 \n\n\n\nABSTRACT\n\n\n\nOnly a small number of farmers involve in planting prophetic fruits such as dates palm, figs, pumpkin and \nwatermelon in Malaysia. If look at Malaysia, Muslim is the majority population and practices Prophet Muhammad \n(pbuh) is a sunnah in order to gain benefit at the end of the day as a Muslim. There is a potential for Malaysian to \nplant and produce these prophetic fruits due to it availability in small scale. The main purpose of this study is to \nprovide an overview of Malaysia as a hub of planting the prophetic fruits as well as known as sunnah consumption. \nMethodology is based on review of previous literatures and interview conducted. As recommendation and findings, \nthere is huge potential for planting these prophetic fruits as shown in findings dates palm, figs, pumpkin and \nwatermelon. Economic and religious information will lead to the success of planting these types of fruits. The \ninvolvement of government agencies and private sectors is essential in the promotion of planting prophetic fruits to \nfarmers. This study contributes to the literature of planting several prophetic fruits which is available to cultivate, \nplant and harvest in Malaysia.  \n\n\n\n KEYWORDS \n\n\n\nPlanting prophetic fruits, Dates palm, Figs, Pumpkin, Watermelon, Review.\n\n\n\n1. INTRODUCTION \n\n\n\nMalaysia has a rich growth of agriculture cash crops which are mainly \ngrown in the small-scale farming holdings ranging from one to ten acres. \nAccording to a study, despite of fast developing into an industrial country \nMalaysia are still basically an agricultural country [1]. It has 4.06 million \nhectares of agricultural land distributed throughout 13 states. Eighty \npercent of this land is cultivated with industrial crops such as oil palm, \nrubber, cocoa, coconut, pepper and rice. In 2016, agriculture remains an \nimportant sector of Malaysia economy, contributing at 8.1 per cent or \nRM89.5 billion to the Malaysian Gross Domestic Product (GDP) and \nproviding employment for 16 percent of the population [2]. Malaysia is a \nnet importer for fruit industry, contributing sector in the economy as it \nsupplies fresh fruit to the population. The trade performance of fresh \nfruits, however has not improved much despite the various incentive \nprogrammes implemented by the Malaysian government through its Third \nNational Agricultural Policy. Study showed fruits industry in Malaysia \nbased on some production is only meets the domestic consumption [3]. In \nMalaysia, fruits are steadily becoming an important component of the \nagricultural production [4]. Moreover, Muslim in Malaysia are luckily in \nterms of diversity of fruit plant and harvest because the availability on the \nfruit that consumed by the Prophet Muhammad (pbuh) which called as \nprophetic fruit. \n\n\n\nProphetic fruits are referred to the fruit consumed by the Prophet \nMuhammad (pbuh) regularly and mentioned in the Quran about its benefit \nto the earth and human. Moreover, prophetic fruits such as dates, figs, \npumpkin, watermelon and so on are types of fruit that consumed by the \nProphet Muhammad (pbuh) high nutritious and benefit to the human \nbody. Demand toward consumption of prophetic foods including fruit are \nincrease due to increase of awareness about its health benefit. However, \nMalaysia struggling in import these types of fruit this due to only a small \nnumber of farmers have interest in planting these types of prophetic fruits. \nIn addition, Malaysia has been burdened with hefty bills on the \nimportation of food, worth about US$3.5 billion each year. The current \ndeficit in agriculture, especially food, is about US$1.35 billion. Thus, the \n\n\n\nMalaysian government is trying to encourage production in the fruit sector \nfor export in order to balance the trade, especially in agriculture by 2020 \namong others the demand side offers good prospects for Malaysian \nagriculture [5]. Analyst forecast that food needs will rise at extraordinary \nrates due to rapid increase in population, rising level of nutrition as \nincome levels will increase and changing in consumption [6]. According to \nDepartment of Statistics Malaysia, total imported of fruit in Malaysia for \nthe year 2013 is around 2,149.74 million Ringgit Malaysia [7]. Thus, this \nstudy aims to provide an overview Malaysian as a hub to plant prophetic \nfruits. This study will provide economics data to present the current \nMalaysia productivity and findings from Quran and hadith based on these \nprophetic fruits which available planting and harvest in Malaysia. \nMoreover, this was in line with the government's policy to promote \ncommodities which have high added values and good export potential. \nThis paper begins with an introduction and literature review. The third \nsection explains the methodology of the research. The fourth section \nexplains the research findings. The final section concludes the study. \n\n\n\n2. LITERATURE REVIEW\n\n\n\n2.1 Fruit Plants \n\n\n\nBased on a study, cultivated fruit plants represent an essential resource to \nimprove human nutrition, health, and well\u2010being [8]. Malaysian tropical \nfruits have many health benefits. Fruits in general are high in fibre which \nis necessary to improve digestion and prevent constipation and has no \ncholesterol. Malaysian tropical fruits contain a variety of micronutrients, \nvitamins, minerals, carotenoids, riboflavin, niacin and other \nphytochemicals. Some of these fruits are reputed to be able to prevent \ncertain non-communicable diseases such as blood pressure, diabetes, \nlower cholesterol or even cancer.  In another word, healthy diet of fruits \nsince young can build a strong immune system and maintain a healthy \nbody. Islam also suggest consuming good things as mentioned in Quran: \n\u201cEat from the good things which we have provided for you and be grateful to \nAllah\u201d \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.01.2019.13.19\n\n\n\nREVIEW ARTICLE \n\n\n\nAN OVERVIEW MALAYSIA AS A HUB OF PLANTING PROPHETIC FRUITS \nAimi Fadzirul Kamarubahrin*, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul \nAini Muhamed and Abu Hassan Makmun Abdul Qadir \n\n\n\nFaculty of Economics and Muamalat, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan Darul Khusus. \n*Corresponding Author E-mail: aimi_fadzirul4@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\n\nmailto:aimi_fadzirul4@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 13-19 \n\n\n\nCite The Article: Aimi Fadzirul Kamarubahrin, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul Aini Muhamed \nAnd Abu Hassan Makmun Abdul Qadir (2019). An Overview Malaysia As A Hub Of Planting Prophetic Fruits .  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 3(1) : 13-19. \n\n\n\n[Quran: Al-Baqarah 2: 172] \nEight major fruits given more emphasised for domestic as well as for \nexport markets are pineapple, papaya, watermelon, starfruit, banana, \ncitrus, mangosteen and durian. In addition to these products, Malaysian \nfarmers produce a number of fruits for the domestic market, including \nbananas, coconuts, durian, pineapples and others. Despite of Malaysian \nfarmer produce a number of tropical fruits as mentioned, Malaysia survive \nand highly dependable on imported sunnah food or know as prophetic \nfruits. Among others prophetic fruits such as dates, figs, pumpkin, \nwatermelon and others. This due to unavailability in mass productivity for \nlocal production to these types of fruits. However, due to research findings \nand scientific proven dates palm, figs, pumpkin and watermelon can be \nplant in Malaysia. Thus, this study aims to provide an overview Malaysian \nas a hub to plant prophetic fruits. Malaysian government is trying to \nencourage production in the fruit sector for export in order to balance the \ntrade, especially in agriculture by 2020, among others the demand side \noffers good prospects for Malaysian agriculture. \n\n\n\n3. METHODOLOGY\n\n\n\nMethodology used by two technique which is first is interview with \npractitioners and expert. Meanwhile, second technique is through \ngathering the previous articles, Ph.D. and Master thesis, company report, \nweb site and private blog. First technique which is through interview with \nthe local farmers in Malaysia who are involve in this planting these types \nof sunnah fruit in Malaysia. According to several info gather from website \nand contact get from government agencies about availability of farmer \ninvolves in planting these types of sunnah fruit were find. Interviews \nsessions were conduct during mid of January 2018 up to end of April 2018. \nInterview session were conduct in two places in Malaysia which based on \navailability in planting this type of sunnah fruits such as in Kelantan were \nfound farmer cultivate and planting dates palm. Meanwhile, in Negeri \nSembilan were found farmer planting watermelon. \n\n\n\nFor the second technique which is adopts document and literature review \nmethods in order to gather the data. For document reviews, a number of \nreports and statistics from the Department of Agriculture of Malaysia \n(DOA) and the Food and Agriculture Organization (FAO) especially on the \ndemand and supply on pumpkin in Malaysia. This include such as \nimported, exported, harvested, production and even distribution channel \nprice of pumpkin in Malaysia. Meanwhile, for literature reviews, a \nsignificant number of articles published in websites, journals, book \nchapters, theses and review manuscript were obtained using different \nsearch engines, namely: Google Scholar, Science Direct, EBSCO, and library \nsearch engines. The keywords included in the search were \u2018dates fruit, figs \nfruit, pumpkin and watermelon\u2019 \u2018fruit planting in Malaysia; Dates Palm, Figs \nPlanting, Pumpkin Farm and Watermelon Farm\u2019 which had to appear in the \ntitle or abstract and somewhere within the text of the publication. \n\n\n\n4. RESULTS \n\n\n\n4.1 Dates Palm \n\n\n\nDates fruit (Pheonix Dactylifera L.) belongs to family Arecaceae (syn. \nPalmaceae), and the genus Phoenix contains 12 species. Dactylifera is the \nmost important species in terms of commercial value and human food use. \nWhile the date palm tree is called \u201cnakhl\u201d the fruit is called \u201ctamr\u201d in \nArabic. Dates fruit (Phoenix Dactylifera L.) is among the main top fruit crop \nof the Middle East [9]. Dates palm or known in as Phoenix Dactylifera L. is \n\n\n\na plant that growth in temperate climate region. This plant origin from \nPersian Gulf countries and has long been used for basic food by the people \nin the Middle East. In Malaysia, date palm production in Malaysia context \nis still extremely rare [10]. But, increased popularity of date palm tree \namongst Muslim in Malaysia due to it can be plant in Malaysia climate. \n\n\n\nBased on a study, dates cultivation was introduced into Malaysia in late \n2010 [11]. Several privately-owned farms have been established in East-\nCoast (Terengganu and Kelantan) and the Northern Territory (Johor) of \nMalaysia [12]. However, the date farming sector in Malaysia is still in the \nprimary stages and operated as fundamental industries. Currently, based \non preliminary (observation) and secondary (search engine and previous \nliterature) data findings, there are still not proven Malaysia have a \ncommercial dates palm farm. Most of the farmers are still in the early \nstage, which is in the process of cultivation date palm seed. \n\n\n\nIn Malaysia, recognizing the potential of dates as commercial crops, the \ngovernment initiate to introduce this plant as a new economy source for \nlocal farmers. The initial project was planted on an 8,000-square-meter \nland in Limbongan, Pasir Puteh, Kelantan. Seed of dates palm were taken \nfrom Thailand. Based on interview conducted with dates palm farmer: \n\u201cI am started plants date palm in 2010 at Kelantan. As started a total of 50 \ndates palm trees were plant and found have produced fruit\u201d \n(Dates Palm Farmer: Mr. Zain) \n\n\n\nAccording to Mr. Zain, dates palm is reported to be successful cultivated in \nThailand and it may be the result of plant breeding work and the \nadaptation of the ongoing agronomic practice in the country with a new \nvariety of dates that fit with the hot and humid climate such as in Southeast \nAsia. However, increased popularity of date palm tree amongst Muslim in \nMalaysia due to it can be plant in Malaysia climate. Date palm can be grown \nin a wide range of soil types. According to research, deep sandy soils with \na good moisture supply are best [13]. Good drainage and aeration are the \nmain soil requirements for ideal production. Date palm tree will grow in \nheavier soils, but care must be taken not to waterlog these soils. It will \ngrow in soils that are high in alkali and salt content, but growth and \nproductivity will be affected. More sandy soils with their great drainage \nrequire more fertilization, as fertilizers are more easily leached out by \nirrigation. \n\n\n\nThey are many farmers and nursery start to cultivate date seed for \ncommercial purpose without promising it can fruitful and sell it as a gift \ndue to small size of the date tree. The price of date palm tree or seed bag \nis depending on the dates palm types, approximately from RM65 to RM100 \n[14]. From a marketing perspective, the key challenge for Malaysian date \npalm farmers is being able to supply dates palm production based on \nplanting in Malaysia. Limited to none of commercial local production of \ndates had made Malaysia survive on imported this type of fruit. Malaysia \nis the major countries importing both fresh and dried dates from Pakistan \nand Middle East countries. \n\n\n\nThe annual dates palm imports to Malaysia are estimated at 19,000 to \n20,000 thousand tons, and almost 75% is Iranian dates [15]. Meanwhile, \nMalaysia also had export 12,258 thousand tons of date palm from 2010 to \n2013 [16,17]. Malaysia generally not produce local dates palm; however, \nMalaysia import and generate additional value for commercial benefits by \nconsidering the utilization of date industry by-products. Total import and \nexport of Malaysia dates is shown in Table 1. \n\n\n\nTable 1: Total Import and Export Dates. \n\n\n\nImport and Export (Thousand tons) 2010 2011 2012 2013 Total \nImport 17,980 16,236 20,394 19,421 74,031 \nExport 4,268 3,906 2,430 1,654 12,258 \n\n\n\nSource: DOS (2014); FAO (2015). \n\n\n\nDates, or its scientific name is Phoenix Dactylifera, is among the main top \nfruit crop of the Middle East. Today, large quantities of many dates \nvarieties are commercially produced in countries such as Algeria, China, \nEgypt, Iran, Iraq, Pakistan, Saudi Arabia, Sudan and the United Arab \nEmirates [18]. Dates are rich in vitamins, minerals and fibre. It contains \noil, calcium, sulphur, iron, potassium, phosphorus, manganese, copper and \nmagnesium. Dates consumption can also provide relief from constipation, \nintestinal disorders, heart problems, anaemia, diarrhoea, abdominal \ncancer and many other conditions. It is also identified as having \nantioxidant and anti-mutagenic properties and reduces heart disease. The \nFood and Agriculture Organization (FAO) estimated the daily per capita \nsupplies of dates are around 50g. A more recent study by a scholar \n\n\n\nsuggested that average daily consumption per capita is 114g. Thus, the \nMalaysian government is trying to encourage production in the dates palm \ndue to its \u2018baraqah\u2019 to earth and human as mentioned in Quran. \nDate fruit or palm cultivation are encouraged in Quran and Prophet \nMuhammad (pbuh) due to its benefit for human and earth. The date palm \nis mentioned more than any other fruit-bearing plant in Quran. One of the \nverses is: \n\u201cIn the earth there are diverse regions side by side and gardens of grapes and \ncultivated fields, and date-palms sharing one root and others with individual \nroots, all watered with the same water. And, we make some things better to \neat than others. There are signs in that for people who use their intellect\u201d. \n(Quran: Surah Ar-Ra\u2019d 13:4) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 13-19 \n\n\n\nCite The Article: Aimi Fadzirul Kamarubahrin, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul Aini Muhamed \nAnd Abu Hassan Makmun Abdul Qadir (2019). An Overview Malaysia As A Hub Of Planting Prophetic Fruits .  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 3(1) : 13-19. \n\n\n\nMoreover, there is also a hadith regarding dates palm narrated by Ibnu \nUmar (RA) which is: \n\n\n\nI was with the Prophet (PBUH) while he was eating spadix. He said, \u201cFrom \nthe trees there is a tree which resembles a faithful believer.\u201d I wanted to say \n\n\n\nthat it was the date palm, but I was the youngest among them (so I kept \nquiet). He added, \u201cIt is the date palm.\u201d \n(Hadith No.411, Volume 3, Book 34, Sahih Bukhari) \nThus, increased popularity of date palm tree amongst farmers in Malaysia \ndue to it can be plant in Malaysia climate is in line with the government's \npolicy to promote commodities which have high added values and good \nexport potential. In addition, this will decrease Malaysia dependency on \nimported in future and perhaps the county able to be a hub in producing \ndates in Southeast Asia. \n\n\n\n4.2 Fig \n\n\n\nFig (Ficus carrica L) is the most common fruit worldwide and is traded \ninternationally. Based on a study, dry figs are more common in Asia [19]. \nCommon fig tree, originated in the Middle East, is one of the first plants \nthat was cultivated by humans and is an important crop worldwide for dry \nand fresh consumption [20]. Most of the world\u2019s fig production occurs \nnowadays in the Mediterranean basin [21]. Figs have been an important \npart of the traditional Mediterranean diet with origins in the eastern \nMediterranean region and southern Arabia [22,23]. Since early in the man \n\n\n\nhistory, fig fruit was appreciated as food and for its medicinal properties \n[24]. Ficus carica L is an amazing and ancient source of medicines and food.  \nThe fig (Ficus carica) was domesticated during the early period of human \ncivilization. It originated from Asia Minor and spread throughout \nMediterranean area of the world. Most of Malaysia current production is \nof dried figs; fresh production is limited by the high perishability of the \nfruit and the lack of techniques and facilities to allow sustainable \ndistribution to local and global markets. \n\n\n\nIn Malaysia, commercial crops are being carried out with 16,000 fig trees \nare planted in the area of 10 hectares at the project site namely Indonesia, \nMalaysia and Thailand Growth Triangle (IMT-GT) which is located at \nChuping, Perlis. In addition, fig fruit getting high demand among Asian \ncountries. This made Perlis become as a larger producer for fig fruit \nespecially in this Southeast Asia region. A fig fruit-planting project is \njointly developed between the state government and a private company \nbased in Penang Island. Through this, Perlis will become such Egypt, which \nis among the countries with largest producer of fig fruit in particular for \nthe worldwide market. As shown in Table 2 is total import and export figs \ndried Malaysia. \n\n\n\nTable 2: Total Import and Export Figs Dried. \n\n\n\nImport and Export (Thousand tons) 2010 2011 2012 2013 Total \nImport 180 209 222 252 863 \nExport 18 19 21 35 93 \n\n\n\nSource: FAO (2017). \n\n\n\nBased on a study, figs have been used for human consumption for \ncenturies, and recently their nutritive and pharmacological values have \nbeen investigated [25,26]. Fig is economically important because of its \nhigh nutrition and medicinal properties (anticancer, anti- diabetic, anti-\ninflammatory etc) [27]. Figs are predominantly rich in amino acids, \nvitamins, carotenoids, minerals, antioxidant polyphenols, sugars, and \norganic acids, which serve as a nourishing food and are used in industrial \nproducts [28,29]. However, fig fruits are considered to be free from \nsodium, fat, and cholesterol [30]. It is suggested that this species plays an \nimportant role in human health. It is possibly due to their phytochemical \ncomposition preventing serious health disorders including obesity, \ndiabetes, cardiovascular diseases, neurodegenerative disorders and even \ncertain types of cancer [31,32]. Figs are rich in phenolic compounds and \ncontain antioxidants. They play key roles in the prevention of pathogenic \nprocesses associated with cancer, cardiovascular disease, diabetes and can \nenhance immune function [33]. In a study conducted on fig fruits \nantioxidant activity contributes to the concentration of polyphenols in fig \nfruits. Since secondary metabolites exist commonly in figs, several studies \nhave been conducted on their health-promoting potential [34]. Fig fruit \nhave been studied in recent years being part of the healthy diet. In Islam, \nfig fruit was mentioned several times due to its benefit to human. Fig fruit \nwas mentioned in the Quran because of its nutritious. \u201cBy the fig and the \nolive\u201d \n(Quran: Surah At-Tin: 95:1) \n\n\n\nIbn al-Qayyim also said that figs are more nourishing than all other fruit, \nthat they should be ripe and peeled before eating, and that the best type of \nfig is the white variety. He said that eating fresh figs can prevent the \ndevelopment of urotoxicity (toxic quality of urine) and can cleanse the \nkidney and bladder. Fresh figs are healthier and more wholesome than \ndried figs and can benefit the throat, chest and trachea. He mentioned that \ndried figs are beneficial for the nerves and that eating dried figs combined \nwith almonds and walnuts on an empty stomach in the morning can be \nexceedingly beneficial in opening up the alimentary canal (a tube that runs \nthrough the body, from the mouth to the end of the large intestine). In \naddition, Prophet Muhammad (pbuh) was mentioned about figs as below: \n\u201cIf I say, indeed the fruit descends from heaven then I say this is the fruit \n(figs), the fruit of heaven is no doubt.\u201d \n(Hadith Riwaat Abu Darba; Suyuti). \n\n\n\nHowever, figs planting remains underutilized in Malaysia because \n\n\n\ninadequate information about its feasibility. In addition, traditional fig \nplantations have low productivity and are often no longer profitable. Thus, \nplanting figs in mass productivity have a potential to grow into one of the \nsources of economic growth since the fruit cost RM120 for a kilogram. \nCurrently, there is no technical study on commercial figs plant in Malaysia. \nFigs tree potentially to be grown in Malaysia with further study by the \nresearcher in this country. \n\n\n\n4.3 Pumpkin \n\n\n\nThe pumpkin (Cucurbita) is a cultivar of a squash plant that is round, with \nsmooth, slightly ribbed skin, and deep yellow to orange coloration. The \nthick shell contains the seeds and pulp. Pumpkins, like other squash, are \nthought to have originated in North America. The oldest evidence, \npumpkin-related seeds dating between 7000 and 5500 BC, was found in \nMexico. Then, pumpkin is cultivated to Argentina and Chile and has spread \nto Europe, Asia and Western America. Pumpkin is an annual vine or \ntrailing plant and can be cultivated from sea level to high altitudes [35]. \n\n\n\nPhysically, pumpkins in Malaysia are from the species of Cucurbita \nmoschata and Cucurbita moschata Duchesne [36]. Locally, they are known \nas labu manis and labu loceng among the community. Labu manis is \nplanted almost in every state in Malaysia. Meanwhile, labu loceng majorly \ncame from Kedah [37]. They are varied in size and colour; the young fruit \nis green while the older is pale yellow. The flesh thickness is around three \ncentimetres and they have sweetish taste with a very good market \ncompared to other species due to its size with an average of 1.4 kg per \npiece. These physical features allow farmers and wholesalers to plan the \nproduction and marketing of the crops [38]. \n\n\n\nMalaysia produces pumpkin of its own, with considerably large areas of \nproduction comparable to its high global demand. In Malaysia, there are \n138 hectares of pumpkin plants with production of 6,240 metric tons in \n2014 worth RM 7.3 million [39]. The major pumpkin producing states in \nMalaysia are Johor, Terengganu, Kelantan and Kedah. According to a \nscholar, the area for pumpkin plantations in Malaysia was around 138 \nhectares, where pumpkin is abundantly planted in Kelantan (79.6 \nhectares), Terengganu (59.6 hectares) and Johor (93.5 hectares). A shown \nin Table 3 below is the total production of pumpkin in Malaysia for the \nyear 2011 until 2015. Despite of producing, Malaysia also imported and \nexported pumpkin. \n\n\n\nTable 3: Total Production (Tonnes) of Pumpkin in Malaysia for Year 2011 - 2015. \n\n\n\nItem/Year 2011 2012 2013 2014 2015 Total \nPumpkin (Tonnes) 21,534.40 17,382.50 111,144.30 44,525.70 25,651.70 22,023,860.00 \n\n\n\nSource: DOA (2016). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 13-19 \n\n\n\nCite The Article: Aimi Fadzirul Kamarubahrin, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul Aini Muhamed \nAnd Abu Hassan Makmun Abdul Qadir (2019). An Overview Malaysia As A Hub Of Planting Prophetic Fruits .  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 3(1) : 13-19. \n\n\n\nLocal production of pumpkin is for trade in international and domestic \nmarket purposes. Thus, as shown in Table 4 below is the total import and \nexport for Malaysia pumpkin for the year 2013. Total exported is more \n\n\n\ncompare than imported, this due to surplus on local production of \npumpkin. Besides that, pumpkin is believed to have health benefits and \nnutritious content\n\n\n\nTable 4: Total Import and Export Pumpkin. \n\n\n\nPumpkin (Thousand tons) 2013 (Year) \nImport 2502 \nExport 4411 \n\n\n\nSource: FAO (2017). \n\n\n\nPumpkin has been considered as beneficial to health because it contains \nvarious biologically active components such as polysaccharides, para-\namino benzoic acid, fixed oils, sterols, proteins and peptides. The fruits are \na good source of carotenoids and gamma-aminobutyric acid. Pumpkin \nseeds are valued for their high protein content and useful amounts of the \nessential fatty acid, linoleic acid. Besides that, pumpkin also provide health \nbenefits such as sharp eyesight, aid weight loss, reducing cholesterol, \nreducing cancer risk, protect the skin and boost immune system [40]. \nBased on a study, pumpkin is rich in carotene, vitamins, minerals, dietary \nfibre, pectin and several compounds beneficial for human health [41]. \n\n\n\nAccording to research, pumpkins are annual or perennial climbing or \ntrailing herbs, comprising about 25 species, some of which are \neconomically important, such as Cucurbita maxima, Cucurbita moschata \nand Cucurbita pepo [42]. Pumpkins are good sources of many important \nnutrients, including potassium, vitamin C, folate, fibre, and numerous \nphytochemicals as shown in Table 2 [43]. Furthermore, pumpkins contain \npolysaccharides, proteins and peptides, para-amino benzoic acid, phenolic \ncompounds, terpenoids and sterols [44]. Pumpkin is medically proven as \na source of lowering the risk of prostate cancer, protecting against \nswelling of joints, lessening of wrinkles on face, stimulating the \nfunctioning of kidneys and others. Moreover, pumpkin is also listed as \nsunnah food, it was also the top most desired food consume regularly by \nProphet Muhammad (pbuh). \nAs one of prophetic fruits, pumpkin is stated in the following Quranic verse \nand a Hadith stated below which are: \n\u201cAnd We caused a plant of yaqteen (pumpkin) to grow over him.\u201d \n(Quran: Surah As-Saffat 37:146) \n\"I accompanied Allah's Apostle to that meal. He served the Prophet with \nbread and soup made with pumpkin and dried meat. I saw the Prophet \ntaking the pieces of pumpkin from the dish.\" Anas added, \"Since that day I \nhave continued to like pumpkin.\" \n(Hadith No.305, Vol.3, Book 34, Sahih Bukhari) \n\n\n\nThus, the availability of pumpkin is a good potential to boost Malaysian \neconomy, especially to the farmers in divers their source of income. This \nin line with the government's policy to promote commodities which have \nhigh added values and good export potential. In addition, with government \nagencies support towards planting pumpkin will help in increase total \nexport of agriculture sectors of Malaysia in the future. \n\n\n\n4.4 Watermelon \n\n\n\nWatermelon (Citrullus lanatus (Thunb.) Matsum. et Nakai) widely planted \nand consumed around the world is a popular and important fruit [45]. \nWatermelon are major crops of the gourd family Cucurbitaceae, which do \nnot interbreed and draw from distinct botanical origins [46,47]. \nWatermelon has a narrower genetic base than melon, and it is native to \nthe drier areas of south-central Africa, near the Kalahari Desert (Namibia \nand Botswana), where bitter and sweet forms were found in the wild and \n\n\n\nconsumed by humans and animals [48-51]. Watermelon has been \ncultivated in Africa for over 4000 years. Seeds and plants parts found in \nEgyptian tombs indicate that watermelons were widely cultivated in the \nNile valley before 2000 BCE. From Africa, they were introduced to India at \nabout 800 CE and China at 900 CE, and then extended to Southeast Asia, \nJapan, Europe and the Americas in the 1500\u2019s [52]. Watermelon, is an \nannual plant with long angular trailing vines bearing lobed leaves, \nbranched tendrils and separate solitary male and female flowers. The \nplant is typically monoecious with alternating staminate (male) flowers \nappearing first and pistillate (female) flowers later with ratios in favour of \nmaleness (e.g. 7 staminate: 1 pistillate). Watermelon fruits are round, oval \nor elongated with a size typically ranging from 1.5 to 15 kg. The rind is \nlight to dark green with stripes of various patterns. The flesh may range \nfrom white, green, yellow, orange to red, though consumers associate the \ninternal quality with deep red, pink or intense yellow, in addition to \nsweetness and texture. Watermelon (Citrullus lanatus) is a popular fruit \namong Malaysians locally known as Tembikai. Red-fleshed seeded and \nseedless, and yellow-fleshed watermelons are mostly selected as a dessert \nand available throughout the year in local markets. \n\n\n\nBased on a study, watermelon in scientific name known as Citrullus lanatus \nare plant in Malaysia with total areas is 13,814 hectares [53]. To meet the \ngrowing consumption demand worldwide, monocultures become the \nmajor cropping system for watermelon production recently. According to \na scholar, the period from fruit setting to optimal harvest maturity varies \nwith cultivar earliness, generally ranging from 30 to 45 days, and \nconstitutes a reliable but cultivar specific harvest maturity index [54]. \nWatermelon yield losses are highest during rainy and humid seasons. In \nMalaysia, about 11,270 hectares was grown in 2009 producing 228,880 \nmillion tons of watermelon. Most area planted with watermelon was \nRompin, Pahang (2,543 ha) followed by Kluang, Johor (1,119 ha) and \nMersing, Johor (828 ha). Other states growing watermelon was Kelantan \n(1,006 ha), Pahang (1,777 ha) and Terengganu (1,128 ha) respectively \n(FAMA, 2014). For domestic market, watermelon demand throughout the \nyear but increase in hot season and fasting month. Most watermelon from \nMalaysia exported to Singapore, Hong Kong and Brunei [55]. Production \nof watermelon is for domestic and export market as shown in Table 5 \nbelow. \n\n\n\nMoreover, based interview conduct with farmer: \n\u201cCost of production for watermelon is about RM 10,431.00/hectare. Farm \ngross production about RM 12,500/ha based on 25,000 kg fruit/ha at RM \n0.50/kg giving net farm income about RM 2,067.00/ha for 2.5 months. \nReturn for RM1.00 about RM1.20 and cost of per kilogram calculated at \nRM0.42. This crop is high risk commodity to establish. Most farmers jointly \ncapitalised between input suppliers, marketers and grower in their activity. \nThus, this commodity needs lots of experience farmers to venture as there \nwas technology available in the country.\u201d \n(Watermelon Farmer: Mr. Mohd Anim Hosnan) \n\n\n\nTable 5: Total Import and Export Watermelons. \n\n\n\nImport and Export (Thousand tons) 2010 2011 2012 2013 Total \nImport 1347 558 781 553 3239 \nExport 54977 50643 49591 50688 205,899 \n\n\n\nSource: FAO (2017). \n\n\n\nDespite of large export value to neighbour countries, watermelon has \nemerged to the forefront in research advances due to its attractive high \nnutrient value. Study showed quality attribute of watermelon is a critical \naspect of postharvest storage, consumer preference, and commercial \nacceptability of the fruit [56]. Watermelon mostly contain water (93.2%) \n\n\n\nand other nutrient such as sugar many vitamins (Thiamine, Riboflavin and \nNiacin). It\u2019s an herb type crop and creeps with oval leaf shape. The flower \nis monoicous and yellow colour. Watermelon fruit was roundish or oval or \nlong oval shape with dark green colour and weighed between 7 - 15 kg \ndepending on variety. According to a scholar, different varieties of \nwatermelon had different nutritional contents and physico-chemical \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 13-19 \n\n\n\nCite The Article: Aimi Fadzirul Kamarubahrin, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul Aini Muhamed \nAnd Abu Hassan Makmun Abdul Qadir (2019). An Overview Malaysia As A Hub Of Planting Prophetic Fruits .  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 3(1) : 13-19. \n\n\n\ncharacteristics [57]. The watermelon is really benefitting to health. The nutritional contained as shown below in Table 6: \n\n\n\nTable 6: Nutritional Content of Watermelon. \n\n\n\nNutritional Per 100 grams \nProtein  0.61 \nAsh 0.25 \nWater  91.45  \nCarbohydrates  7.55 \nFibre  0.4 \nFructose  3.36 \nCalcium  7 mg \n\n\n\nSource: Malaysian Fruit (2010). \n\n\n\nIn addition, there are many benefits of watermelon to the human body. \nWatermelon fruit consist of potassium and magnesium is essential to bring \ndown the blood pressure. It is decreasing the risk of getting heart disease \nand stroke. Among others, potassium in watermelon is helping cleaning \ntoxic material in kidney stone and this will maintain a good function of \nkidney. Watermelon juicy sweet fresh water can effectively rehydrate \nbody heat [58]. In Malaysia, the fruit is selling at fruit hawker, market. \nMoreover, watermelon has fibre, zero fats and low in calories. It is a good \nfruit to lose weight [59]. Various mineral and vitamin in watermelon \nmaintain the good level of insulin in body which can help in control blood \nsugar. Even though diabetes patient can eat the watermelon. For example, \nVitamin B in the fruit produce energy to body. It is good to eat the fruit \ninstead of drinking chemical drink. Meanwhile, Vitamin C protect eye of \ndrying up and protect from illness of glaucoma and optical nerves [60]. \nThe antioxidant profile of watermelon. It is helping to kill the cancer \ncausing free radical especially asthma, colon, prostate and hearth cancers. \nThe red component Carotenoid in watermelon can prevent getting risk of \ncancer. Phytonutrients is effective in protect and maintain good health and \nproper functioning of eye, organ, constipation and secretion system. \nWatermelon is full of vitamins and minerals. Watermelon is the perfect \nsunnah accompaniment to dates when breaking fast. \n\n\n\nAccording Ibn Qayim Al Jawziyyah in Medicine of the Prophet, Prophet \nMuhammd (pbuh) used to eat watermelon with dates to balance out the \neffects of these foods, date is hot and moist while watermelon is cool and \nwet. Meanwhile, Aishah reports that, \u201cProphet Muhammad (pbuh) ate \nwatermelon with fresh dates.\u201d. In Tirmidhi and other narrations, in \nexplaining this, Prophet Muhammad (pbuh) also said, \u201cThe cold effect of \none removes the heat of the other, and the heat of one removes the cold effect \nof the other.\u201d (Hadith Tirmidhi: 189, 2). \n\n\n\nWatermelon [Citrullus lanatus (Thunb.) Matsum. et Nakai] are popular \nannual fruit crops of the gourd family Cucurbitaceae, drawing from \ndiscrete botanical backgrounds. The current review is instrumental in the \nefforts for improving quality and expanding market share for watermelon. \nThus, there is potential for watermelon to plant in mass scale productivity \nin Malaysia due to its demand, nutritious and impact to the economy since \nit been exported to several countries in this region. \n\n\n\n5. CONCLUSIONS \n\n\n\nAs recommendation, several strategies have been outlined to increase the \nproduction of prophetic fruits. Traditional farms will be converted into \nmodern farms using latest technologies and knowledge-based production \nsystems. Similarly, idle lands will also be consolidated and rehabilitated \nwith improved infrastructures, drainage and irrigation facilities.  \nPermanent food production parks will be set up to ensure sustainability of \nfood production in the country. For priority prophetic fruits, the Malaysian \ngovernment will set up contract farms with guaranteed markets, \nminimum prices and easier access to markets. The government will also \nensure adequate funds are available for investment and the farmers can \nget easier access to credits. The small and medium enterprises are also \nencouraged to process prophetic fruits into ready to eat minimally \nprocessed or fully processed products. There is a need to develop specific \ntargeted programmes to increase the production of prophetic fruits in \nMalaysia, especially among the farmers. In conclusion, the prophetic fruit \nindustries in Malaysia have the potential to further grow and contribute to \nthe expansion of the agricultural sector. The supportive government \npolicies had given the fruit industry the necessary impetus for accelerated \ndevelopment and had encouraged the commercial production of prophetic \nfruits for local consumption, export and processing. The growth of the fruit \nindustry further gathered momentum with the active participation of the \n\n\n\ngovernment and private sectors. The Economics Transformation \nProgramme (ETP) under the Entry Point Project (EPP) number seven has \nstressed that fruit productions be upgraded to meet the growing domestic \nand export markets. Production is expected to increase through expansion \nin cultivation area as well as improvement in productivity per unit area of \ncultivation. At the moment, Malaysia does not have a specific campaign to \npromote planting prophetic fruits due to its availability to be plant and \nharvest. Concerted efforts in promoting planting of domestic prophetic \nfruits must be properly planned and implemented for benefit of the \ncountry. The review has shown that potential to made Malaysia as a hub \nof planting prophetic fruits among others is dates palm, figs, pumpkin and \nwatermelon which know consume regularly by Prophet Muhammad \n(pbuh) in daily life. Thus, planting and cultivate local prophetic fruit is not \nonly for domestic consumption, also benefits to the Malaysia economics in \nterm of exporting fresh fruit and secondary products made from these \nprophetic fruits. This study contributes to the literature of planting several \nprophetic fruits which is available to cultivate, plant and harvest in \nMalaysia. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThis research was funded by the Ministry of Higher Education (MOHE) \nunder the Niche Research Grant Scheme (NRGS) \nUSIM/NRGS_P6/FEM/8406/52113. Faculty of Economics and Muamalat. \nUniversiti Sains Islam Malaysia. \n\n\n\nREFERENCES \n\n\n\n[1] Salleh Mohamed Mohd, Hussein Yunus, Normah Osman 2007. Status \nand Perspectives on Good Agricultural Practices in Malaysia. Fruits and \nVegetables for Health Workshop. \n\n\n\n[2] DOS. 2017. Department of Statistics of Malaysia. Total GDP and \nPopulation of Malaysia.  \n\n\n\n[3] Norsida Man, Nolila Mohd. Nawi, Mohd.Mansor Ismail. 2009. An \nOverview of The Supply Chain Management of Malaysian Vegetable and \nFruit Industries Focussing on The Channel of Distribution. Journal of \nAgribusiness Marketing, 2. \n\n\n\n[4] Mohamad Bin Osman. 1994. Tropical Fruit Crops of Malaysia. \nProceedings: 4th Annual Hawaii Tropical Fruit Growers Conference. Kona \nSurf Resort Keauhou, Hawaii. \n\n\n\n[5] ETP. 2010. Economic Transformation Programme. Entry Points \nProject: Agriculture: EPP 7: Premium Fruit and Vegetables. \n\n\n\n[6] Tengku Ahmad, T.M.A., Suntharalingam, C. 2009. Transformation and \neconomic growth of the Malaysian agricultural sector. Management \nReview, 4, 1\u201310. \n\n\n\n[7] DOS. 2014. Department of Statistics of Malaysia. Statistik Utama \nPemasaran FAMA 2014. Lembaga Pemasaran Pertanian Persekutuan \n(FAMA). Kementerian Pertanian dan Industri Asas Tani. \n\n\n\n[8] Masi L., De, F.M., Vella, B., Laratta, M.G., Volpe, M., Tiseo, La Cara, F. \n2017. Biochemical and genetic characterization of a red fig cultivar (Ficus \ncarica) from Southern Italy. Acta Horticulturae. 1173. \n\n\n\n[9] Asmaddy Haris, Zurina Kefeli @ Zulkefli, Nursilah Ahmad, Siti \nNurazira Mohd Daud, Nurul Aini Muhamed, Syadiyah Abdul Shukor and \n\n\n\n\nhttp://www.malaysiafruit.com/\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 13-19 \n\n\n\nCite The Article: Aimi Fadzirul Kamarubahrin, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul Aini Muhamed \nAnd Abu Hassan Makmun Abdul Qadir (2019). An Overview Malaysia As A Hub Of Planting Prophetic Fruits .  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 3(1) : 13-19. \n\n\n\nAimi Fadzirul Kamarubahrin. 2017. Consumers\u2019 Intention to Purchase \nDates: Application of Theory of Reasoned Action (TRA). Malaysian Journal \nof Consumer Family Economics, 20, 1-15. \n\n\n\n[10] Aimi Fadzirul Kamarubahrin, Muhammad Iqmal Hisham \nKamaruddin, Asmaddy Haris, Siti Nurazira Mohd Daud, Zurina Kefeli @ \n\n\n\nZulkefli, Nursilah Ahmad, Syadiyah Abdul Shukor, Nurul Aini Muhamed. \n2018. Dates Palm Farming in Malaysia: Current Challenges and Future \nBaraqah. International Seminar on Islam & Science 2018. Contemporary \nIssues: Islam & Science, 25. \n\n\n\n[11] Mohd Anim Hosnan. 2011. Kurma. \n\n\n\n[12] Mohd Hazrol Zainal. 2016. Ladang Kurma di Pontian. 6 September, \nUtusan Online.  \n\n\n\n[13] Abul-Soad, A.A. 2011. Date Palm in Pakistan, Current Status and \nProspective. USAID Firms Project, 1-86. \n\n\n\n[14] Syful Tamar. 2018. Pokok Kurma \u201cMade in Malaysia\u201d.  \n\n\n\n[15] FAMA. 2014. Statistik Utama Pemasaran FAMA 2014 (Edaran \nUmum). Lembaga Pemasaran Pertanian Persekutuan (FAMA).  \n\n\n\n[16] DOS. 2014. Total Import and Export Dates Fruit Malaysia. Putrajaya: \nDepartment of Statistics Malaysia. \n\n\n\n[17] FAO. 2015. Food Agriculture Organization. World Food and \nAgriculture. Rome: Food Agriculture Organization (FAO). \n\n\n\n[18] Ismail, B., Haffar, I., Baalbaki, R., Mechref, Y., Henry, J. 2006. Physico-\nChemical Characteristics and Total Quality of Five Date Varieties Grown in \nUnited Arab Emirates. International Food Science and Technology, 41(8), \n919-926. \n\n\n\n[19] Sedaghat Sahar, Rahemi Majid. 2018. Effects of Physio-Chemical \nChanges During Fruit Development on Nutritional Quality of Fig (Ficus \nCarica L. Var. \u2018Sabz\u2019) Under Rain-Fed Condition. Scientia Horticulturae, \n237, 44\u201350.  \n\n\n\n[20] Duenas, M., Perez-Alonso, J.J., Santos-Buelga, C., Escribano-Bailon, T. \n2008. Anthocyanin composition in fig (Ficus carica L.). Journal of Food \nComposition and Analysis, 21, 107\u2013115.  \n\n\n\n[21] Sadder, M.T., Ateyyeh, A.F. 2006. Molecular assessment of \npolymorphism among local Jordanian genotypes of the common fig (Ficus \ncarica L.), Scientia Horticulturae, 107, 347\u2013351. \n\n\n\n[22] Khoshbakht, K., Hammer, K. 2006. Savadkouh: An evolutionary \ncentre for fruit trees and shrubs. Genetic Resources, Crop Evolution, 53, \n641\u2013651. \n\n\n\n[23] Stover, E., Aradhya, M., Ferguson, L., Crisosto, C.H. 2007. The fig: \noverview of an ancient fruit. HortScience 42, 1083\u20131087. \n\n\n\n[24] Barolo, M.I., Ruiz Mostacero, N., L\u00f3pez, S.N. 2014. Ficus carica L. \n(Moraceae): An ancient source of food and health. Food Chemistry, 164, \n119\u2013127. \n\n\n\n[25] Yang, X.M., Yu, W., Ou, Z.P., Ma, H.L., Liu, W.M., Ji, X.L. 2009. \nAntioxidant and immunity activity of water extract and crude \npolysaccharide from Ficus carica L. fruit. Plant Foods for Human Nutrition, \n64, 167\u2013173. \n\n\n\n[26] Lazreg, A.H., Gaaliche, B., Fekih, A., Mars, M., Aouni, M., Pierre, C.J., \nSaid, K. 2011.   In vitro cytotoxic and antiviral activities of Ficus carica latex \nextracts. Natural Product Research, 25, 310\u2013319. \n\n\n\n[27] Moniruzzaman. M.Z., Yaakob, Taha, R.A. 2017. In vitro production of \nfig (Ficus carica L.) plantlets. Acta Horticulturae. 1173.  \n\n\n\n[28] Lianju, W., Weibin, J., Kai, M., Zhifeng, L., Yelin, W. 2003. The \nProduction and Research of Fig (Ficus Carica L.) In China. Acta \nHorticulturae, 605, 191\u2013196. \n\n\n\n[29] Vinson, J.A., Zubik, L., Bose, P., Samman, N., Proch, J. 2005. Fruits: In \nVivo Excellent In Vitro and In Vivo Antioxidants. Nutrition, 24, 44\u201350. \n\n\n\n[30] Solomon, A., Golubowicz, S., Yablowicz, Z., Grossman, S., Bergman, \nM., Gottlieb, H.E., Altman, A., Kerem, Z., Flaishman, M.A. 2006. Antioxidant \nActivities and Anthocyanin Content of Fresh Fruits of Common Fig (Ficus \nCarica L.). Journal of Agricultural and Food Chemistry, 54, 7717\u20137723.  \n\n\n\n[31] Mawa, S., Husain, K., Jantan, I. 2013. Ficus Carica L. (Moraceae): \nPhytochemistry, Traditional Uses and Biological Activities. Evident Based \nComplement. Alternative Medicine.  \n\n\n\n[32] Wojdy\u0142o, A., Nowicka, P., Carbonell-Barrachina, \u00c1.A., Hern\u00e1ndez, F. \n2016. Phenolic compounds, antioxidant and antidiabetic activity of \ndifferent cultivars of Ficus carica L. fruits. Journal of Functional Foods, 25, \n421\u2013432.  \n\n\n\n[33] Cai, Y., Luo, Q., Sun, M., Corke, H. 2004. Antioxidant activity and \nphenolic compounds of 112 traditional Chinese medicinal plants \nassociated with anticancer. Life Science, 74, 2157\u20132184.  \n\n\n\n[34] Vallejo, F., Mar\u00edn, J.G., Tom\u00e1s-Barber\u00e1n, F.A. 2012. Phenolic \ncompound content of fresh and dried figs (Ficus carica L.). Food Chemistry. \n130, 485\u2013492.  \n\n\n\n[35] Yadav, M., Jain, S., Tomar, R., Prasad, G.B.K.S., Yadav, H. 2010. \nMedicinal and Biological Potential of Pumpkin: An Updated Review. \nNutrition Research Reviews, 23(2), 184\u2013190. \n\n\n\n[36] Margaret Chan Kit Yoka, Seraphina Anak Dominic Gisong, Beatrice \nAnak Modon, Rogaya Rusim. 2016. Creating New Market in Integrated \nAgriculture Development Area in Samarahan, Sarawak, Malaysia \u2013 Case \nStudy in the Supply Chain of Cucurbita sp. (Pumpkin). Procedia - Social and \nBehavioral Sciences 224 (2016) 516 \u2013 522.  \n\n\n\n[37] Norshazila, S., Irwandi, J., Othman, R., Yumi Zuhanis, H.H. 2014. \nCarotenoid content in different locality of pumpkin (Cucurbita moschata) \nin Malaysia. International Journal of Pharmacy and Pharmaceutical \nSciences, 6. \n\n\n\n[38] Zaharah, A., Wan Azman, W.I., Albahari, S. 2006. Planting of Pumpkin \non Bris Soil. Buletin Teknologi Tanaman, 3, 35\u201342. \n[39] DOA. 2016. Department of Agriculture Malaysia. Perangkaan \nAgromakanan 2015 (Buah-Buahan). Putrajaya: Department of \nAgriculture.  \n\n\n\n[40] Klein, S. 2014. 8 Impressive Health Benefits of Pumpkin.  \n\n\n\n[41] Djutin, K.E. 1991. Pumpkin: Nutritional Properties. Potatoes and \nVegetables, 3, 25-26. \n\n\n\n[42] Mayor, L., Moreira, R., Chenlo, F., Sereno, A.M. 2007. Osmotic \nDehydration Kinetics of Pumpkin Fruits using Ternary Solutions of \nSodium Chloride and Sucrose. Drying Technology, 25(10), 1749\u20131758. \n\n\n\n[43] CDC. 2011. Strategies to Prevent Obesity and Other Chronic \nDiseases: The CDC Guide to Strategies to Increase the Consumption of \nFruits and Vegetables. Atlanta, U.S: Centers for Disease Control and \nPrevention (CDC). Department of Health and Human Services. \n\n\n\n[44] Kuhlmann, H.K., Koetter, U., Theurer, C. 1999. Sterol Contents in \nMedicinal Pumpkin (Cucurbita Pepo Convar. Citrullinina Var. Styriaca). In \nInternational Symposium of Cucurbits, 492, 175\u2013178. \n\n\n\n[45] Liu, L., Shuhua Chen, Jun Zhao, Xing Zhou, Baoying Wang, Yunlong \nLi, Guoqiang Zheng, Jinbo Zhang, Zucong Cai, Xinqi Huang. 2018. \nWatermelon Planting is Capable to Restructure the Soil Microbiome That \nRegulated by Reductive Soil Disinfestation. Applied Soil Ecology. \n\n\n\n[46] Paris, H.S., Amar, Z., Lev, E. 2012. Medieval emergence of sweet \nmelons, Cucumis melo (Cucurbitaceae). Annals of Botany110, 23\u201333. \n\n\n\n[47] Paris, H.S. 2015. Origin and emergence of the sweet dessert \nwatermelon, Citrullus lanatus. Annals of Botany, 116, 133\u2013148. \n\n\n\n[48] Nimmakayala, P., Levi, A., Abburi, L., Abburi, V.L., Tomason, Y.R., \nSaminathan, T., Vajja, V.G., Malkaram, S., Reddy, R., Wehner, T.C., Mitchell, \nS.E. 2014. Single nucleotide polymorphisms generated by genotyping by \nsequencing to characterize genome-wide diversity, linkage \ndisequilibrium, and selective sweeps in cultivated watermelon. BMC \nGenomics, 15, 767. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 13-19 \n\n\n\nCite The Article: Aimi Fadzirul Kamarubahrin, Asmaddy Haris, Syadiyah Abdul Shukor, Siti Nurazira Mohd Daud, Nursilah Ahmad, Zurina Kefli @ Zulkefli, Nurul Aini Muhamed \nAnd Abu Hassan Makmun Abdul Qadir (2019). An Overview Malaysia As A Hub Of Planting Prophetic Fruits .  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 3(1) : 13-19. \n\n\n\n[49] Sebastian, P., Schaefer, H., Telford, I.R.H., Renner, S.S. 2010. \nCucumber (Cucumis sativus) and melon (C. melo) have numerous wild \nrelatives in Asia and Australia, and the sister species of melon is from \nAustralia. Proceedings of the National Academy of Sciences of the United \nStates of America, 107, 14269\u201314273. \n\n\n\n[50] Rubatzky, V.E. 2001. Origin, distribution, and uses. In: Maynard, D.N.  \n\n\n\n[51] (Ed.), Watermelons: Characteristics, Production and Marketing. \nASHS Press, Alexandria, 21-26. \n\n\n\n[52] Wehner, T., Shetty, N.V., Elmstrom, G.W. 2001. Breeding and seed \nproduction. In: Maynard, D.N. (Ed.), Watermelons: Characteristics, \nProduction and Marketing. ASHS Press, Alexandria, pp. 27\u201376. \n\n\n\n[53] Kyriacou, M.C, Leskovar, D.I., Colla, G., Rouphael, Y. 2018. \nWatermelon and Melon Fruit Quality: The Genotypic and Agro-\nEnvironmental Factors Implicated. Scientia Horticulturae. \n\n\n\n[54] Rasmuna Mazwan Muhammad and Nik Rozana Nik Mohd Masdek. \n2016. Overview of Melon Industry in Malaysia. Malaysian Agricultural \nResearch and Development Institute, MARDI. \n\n\n\n[55] Soteriou, G.A., Kyriacou, M.C., Siomos, A.S., Gerasopoulos, D. 2014. \nEvolution of watermelon fruit physicochemical and phytochemical \ncomposition during ripening as affected by grafting. Food Chemistry, 165, \n282\u2013289. \n\n\n\n[56] Mohd Anim Hosnan. 2010. Watermelon. \n\n\n\n[57] Maimunah Mohd Ali, Norhashila Hashima, Siti Khairunniza Bejo, \nRosnah Shamsudin. 2017. Rapid and nondestructive techniques for \ninternal and external quality evaluation of watermelons: A review. Scientia \nHorticulturae 225, 689\u2013699. \n\n\n\n[58] Sabeetha, S., Amin, I., Nisak, M.Y.B. 2017. Physico-Chemical \nCharacteristics of Watermelon in Malaysia. Journal of Tropical Agriculture \nand Food Science, 45(2) 209-223. \n\n\n\n[59] FAO. 2017. Food Agriculture Organization. 2017. Crops and \nlivestock products: Pumpkin; Figs; Watermelons.  \n\n\n\n[60] Malaysian Fruit. 2010. Watermelon Fruit.  \n\n\n\n[61] Zain. 2017. Owner of Dates Palm Farm in Kelantan. (Personal \nInterview). 25 April 2017. \n\n\n\n\n\n\n\n\n\n"
"\n\n Malaysian Journal of Sustainable Agriculture (MJSA)1(2) (2017) 09-11\n\n\n\nRAINWATER HARVESTING SYSTEM: LOW AWARENESS LEVEL AMONG \nUNIVERSITY STUDENTS IN A HIGH RAINFALL TROPICAL COUNTRY\nPey Fang Tan1, Marlia M. Hanafiah1, *, Mazlin B. Mokhtar2, Siti Norliyana Harun1\n\n\n\n1School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti \nKebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.\n2LESTARI, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.\n*Corresponding author E-mail address: mhmarlia@ukm.edu.my\n\n\n\nARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 September 2017 \nAccepted 19 October 2017 \nAvailable online 30 October 2017 \n\n\n\nKeywords: \n\n\n\nRainwater harvesting system; \nawareness level; water quality; \nsustainable water resource; Malaysia\n\n\n\nABSTRACT\n\n\n\nWater scarcity has emerged as a global issue and the situation is getting worse. In accordance with the urgency, this \nstudy aimed to assess the suitability of a rainwater harvesting system (RWHS) to supply water for domestic uses in \nresidential colleges in the Bangi campus of Universiti Kebangsaan Malaysia (UKM). The study also analysed the level \nof awareness on the importance of rainwater and RWHS among the students residing in the 10 residential colleges. \nThe study used Likert scale 1 -5 questionnaire survey method and the sample involved 1,075 respondents randomly \nselected from 10 residential colleges in UKM. The findings showed that the suitability of rainwater collected by the \nRWHS for domestic uses had a mean of 3.45, while the mean value of awareness level towards the importance of \nrainwater and RWHS was 3.75. The questions group with the lowest mean score was \u201cknowledge regarding the \nobjectives of collection and reuse of rainwater\u201d, with a mean of 3.28. A case study of RWHS was carried out by \ninstalling a RWHS in one of the residential college, namely Ungku Omar College. Residents at Ungku Omar College got \nthe highest total percentage for \"agree\" and \"strongly agree\" scores when being asked if rainwater collected by RWHS \nis suitable for domestic uses, as compared to the scores obtained by other residential colleges. Among the important \naspects of RWHS include safety of water collected, sustainability of the system and quantity of water collected with \nmean of 4.39, 4.19 and 4.07, respectively. In conclusion, RWHS is a method that can be widely accepted among college \nstudents at UKM, mainly for domestic uses. However, further efforts should be incorporated to increase the awareness \nlevel and knowledge on the importance of conserving water resource.\n\n\n\nCite this article as: Pey Fang Tan, Marlia M. Hanafiah,  Mazlin B. Mokhtar, Siti Norliyana Harun  (2017).  Rainwater Harvesting System: Low \nAwareness Level Among University Students In a High Rainfall Tropical Country.  Malaysian Journal of Sustainable Agriculture,  1(2):09-11.\n\n\n\n1. INTRODUCTION\n\n\n\nMalaysia is a tropical country that received an average amount of rainwater \ndistribution of 1151 - 5687 mm per year. However, if it is not fully utilized, \nit is feared Malaysia will face an acute water crisis due to rapid population \nincrease, water pollution and water resources depletion during dry season. \nThe water crisis is predicted to be more acute in near decades if the \npopulation growth is keep rising [1]. The volume of rainwater which is high \nin Malaysia allows the exploration of alternative sources of water supply \nusing a rainwater harvesting system (RWHS). Rainwater harvesting for \ndomestic uses have been identified in terms of its importance as an \nalternative water source, especially during water shortages. Various aspects \ninvolved including weather, characteristics of the building, effectiveness of \nthe tank size, economy and ecology are crucial to ensure the success of \nRWHS. Rainwater harvesting for domestic uses have been carried out in \nTaiwan, where the problem of water supply is one of the major issues. \nAlthough Taiwan received a total rainfall of 2457 mm per year, which is 2.6 \ntimes the average for that obtained by other countries of the world, but only \n4074 m3 per capita per year of rainwater can be used. Construction of dam \ncan be done but high erosion causing sedimentation contributes to siltation \nof dams. Groundwater extraction cannot be done excessively because of the \nfear of underground movement contributes to the collapse of land mainly in \nthe coastal region [2]. Therefore, green building policy has been introduced \nby suggesting that all buildings with a floor area of more than 10,000 m2 are \nrequired to install RWHS [3].\n\n\n\nThis research was conducted to assess the level of awareness among the \nstudents on the importance of water resources and RWHS in 10 residential \ncolleges in the Bangi campus of UKM. This paper also reviewed the \nsuitability of RWHS to supply water for domestic uses in residential colleges \nof the Bangi campus of UKM. The results obtained could be used to \ndetermine the effectiveness of the system and this can be used to forecast its \napplication in other buildings.\n\n\n\n2. METHODOLOGY\n\n\n\nThis research was based on a literature review and distribution of \nquestionnaire survey to 10 residential colleges of the Bangi campus of \nUniversiti Kebangsaan Malaysia (UKM), located in Selangor. Data obtained \nfrom questionnaires was developed using Likert scale of the interval and \nordinal types. For questions with Likert scale interval type, the appropriate \ntype of analysis is the mean, median, mode and standard deviation. The \nlevel of awareness towards the issue of water supply are categorized \naccording to the composite score of such questions. Respondents who \n\n\n\nContents List available at RAZI Publishing \nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nJournal Homepage:  : http://www.razipublishing.com/journals/malaysian- journal- \nofsustainable-agriculture-mjsa/\n\n\n\nISSN: 2521-2931 (Print)\nISSN: 2521-294X (Online)\n\n\n\nanswered a score of 1, 2, 3, 4 and 5 have a low, moderate, neutral, good and \nhigh level of awareness, respectively. Likert scale questionnaire was chosen \nbecause it suitable for surveying the level of awareness regarding \nenvironmental issues [4]. Measurement in Likert scale was used for each \nstatement of water supply crisis and each item was given a scale of 1 to 5, \nnamely:\n\n\n\n1 = Strongly disagree\n2 = Disagree\n3 = Unsure / neutral \n4 = Agree\n5 = Strongly agree\n\n\n\nThe data was inserted into the computer software namely Statistical Package \nfor the Social Sciences (SPSS) version 22, which was developed by IBM. \nMean for each group of questions was investigated to determine whether the \nlevel of awareness among students is high or low. Analysis was done to \ndetermine the demographic factors. One of the 10 residential colleges, \nnamely Kolej Ungku Omar (KUO) was selected as the case study area. Since \nFebruary 2015, KUO has been facing very serious water supply problem. \nAccording to the UKM Infrastructure Unit, KUO was in a zone where water \nsupply comes from Semenyih Water Treatment Plant which caters to many \ndensely populated areas. Some part of the UKM Bangi campus facing less \nwater supply problems because the source of water was from the Langat \nWater Treatment Plant.\n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nIn this study, the sample size was 1,075 respondents, following the sample \nsize proposed by a researcher. For students in residential colleges, the \nsuitability of collecting rainwater with the RWHS method for supplying \nwater for domestic uses has a mean of 3.45, with a standard deviation of \n0.801.\n\n\n\nAs shown in Table 1, among the 10 groups of questions asked, the group \nquestion that has the lowest score is the mean for the group S8 (Knowledge \nrelated to objectives of the collection and reuse of rainwater) which was \nonly 3.28 (standard deviation of 0.775). These statistics show that the \nknowledge of students in the residential colleges related to the objectives of \ncollecting and re-use of rainwater is very low compared with the other \nquestions developed in the questionnaire survey.\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.09.11\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.09.11\n\n\n\n\n\n\n10  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 09-11  \n\n\n\nTable 1: Mean for 10 main groups of questions Figure 1: Comparison between the percentage of respondents in each \nresidential college for question S8 (Rainwater was collected for domestic \nuses)  \n\n\n\nRWHS may also contribute to saving money or profit generation. Sub-\nSaharan in Africa is facing the effects of climate change, increasing \nfarmers' income and secure food supplies were done by collecting \nrainwater in-situ [7]. RWHS is not only for supplying water to the area \nprone to water shortage and lack of rain, but the use of RWHS can \ndelaying storm water runoff into water bodies.  \n\n\n\nTable 2: Statistics for questions group S10 (Practices related to the \napplication of RWHS for water supply in residential colleges) \n\n\n\nQuestions group S2 (Steps of resources conservation / reduction of \nenvironmental pollution) was a collection of question that has the highest \nmean score of 4.30 (standard deviation of 0.503). Knowledge associated \nwith measures of reduction of environmental pollution has long existed \namong the population of Malaysia. However, in this context the \nimplementation of the measures is much more crucial.  \n\n\n\nCompared to other groups of questions, the lowest mean score was found \nfor the question related to collection and re-use of rainwater. According to a \nresearch, most of the rainwater harvested with RWHS can only be used for \nthe supply of daily domestic activities such as gardening, washing vehicles \nand cleaning [5]. Perceptions of the respondents regarding whether the \nrainwater collected by RWHS is fit for drinking is minimal (min 2.59, \nstandard deviation 1.183, median 3.00). For the bathroom uses, the \nrespondents' confidence rose slightly to 2.94 in mean (standard deviation of \n1.162, median 3.00). For the use of washing clothes, the mean was 3:59 min \n(0.959 standard deviation, median 4.00). In various uses of the rainwater \ncollected as proposed in the questionnaire, the highest mean is for washing \nthe toilets, with a mean of 3.84 (standard deviation of 0.920, median 4.00). \n\n\n\nHowever, the mean for all questions related to the use of rainwater \ncollected with RWHS were found to be less than 4.  \n\n\n\nSome of research paper stated that in contrast to the believe that water \ncollected from the roof is safe, the data showed chemical-physical aspects of \npollution and microbiology, leaching and erosion of roof materials, storage \nfacilities or channelling, and faecal contamination could affect the quality of \nthe rainwater harvested [6]. Epidemiological studies linking the use of \nrainwater with the public health risk is limited, especially in developing \ncountries. The findings also showed that the perception of college students \nin KUO towards the suitability of using RWHS for domestic purposes was \nhigher as compared to all 10 residential colleges in the campus. The total \npercentages of \"agree\" and \"strongly agree\" among residents were 46.5% \nand 41.4%, respectively as shown in Figure 1.  \n\n\n\nTable 2 above shows the mean, median and standard deviation of the S10 \ngroup of questions concerning priorities for the use of RWHS in residential \ncolleges. The most preferred aspect of concern is the safety aspects of \nwater (S10_46 I give priority to the safety of water for water supply) with \na mean of 4.39 (median of 5.00 and standard deviation of 0.726). A \nresearch reported a total of 3840 RWHS was assessed at daily intervals \nand various aspects including cost for installation are important to be \nconsidered. The main cost of RWHS is related to the installation of the \nRWHS itself. Regarding the cost of installation, (S10_48 \" I give priority to \nthe installation costs of RWHS.), the government will introduce the \nconcept of rainwater harvesting so that the cost of water treatment can be \nreduced [5].  \n\n\n\n4. CONCLUSIONS\n\n\n\nThis study has been successfully reviewing the opinion related to RWHS \namong the students who occupy the residential colleges in the Bangi \ncampus of UKM, one of the leading research universities in Malaysia. The \nviews and the level of awareness of the importance of water resources and \nRWHS among students that occupied residential colleges in the campus \nwere analysed. In accordance with the urgency of the water crisis in recent \nyears, the installation of RWHS as alternative for water supply for \ndomestic uses in residential colleges of UKM needs to be encouraged.   \n\n\n\nThe findings show that the level of agreement among students in the \nresidential college regarding the importance of water resources and the \nsuitability of water collected by RWHS for domestic uses has a mean of \n3.45 (standard deviation 0.801) and awareness of the importance of rain \nas water resources and RWHS for supplying water for domestic uses were \nat the mean of 3.75 (with a standard deviation of 0.677). Among the 10 \ngroups of questions asked in the questionnaire, the questions with lowest \nmean score was the group that refers to knowledge of objective of \ncollection and reuse of rainwater, which has a mean of 3.28 (standard \ndeviation of 0.775). In conclusion, rainwater harvesting is an initiative that \ncan be accepted among students in the residential college mainly for \ndomestic uses only. However, there is still room to improve knowledge or \nexposure at this early stage. It is recommended that the study on the safety \nof the water collected by RWHS in terms of microbiology should be \ncontinued. Knowledge on RWHS still need to be improved among students \nof UKM so that they are more confident in using the water collected from \nthe rainfall in the future. Hopefully, RWHS will be practiced in order to \nachieve sustainable development of the country. \n\n\n\nACKNOWLEDGEMENT\n\n\n\nMarlia Mohd Hanafiah was financed by research grants: FRGS/2/2013/\nSTWN01/UKM/03/1 and TD-2014-012.\n\n\n\nREFERENCES\n\n\n\n[1] Falkenmark, M. 1987. Water-related constraints to African \ndevelopment in the next few decades. Water for the Future: Hydrology in \n\n\n\n0.00 % 20.00 % 40.00 % 60.00 % 80.00 % 100.00 %\nKUO\n\n\n\nKDO\n\n\n\nKIY\n\n\n\nKKM\n\n\n\nKPZ\n\n\n\nKIZ\n\n\n\nKTHO\n\n\n\nKBH\n\n\n\nKAB\n\n\n\nKRK\n\n\n\nStrongly Agree and Agree\n\n\n\nPercentage (%) \n\n\n\nUnsure / Neutral Disagree and Strongly Disagree\n\n\n\nCite this article as: Pey Fang Tan, Marlia M. Hanafiah,  Mazlin B. Mokhtar, Siti Norliyana Harun  (2017).  Rainwater Harvesting System: Low \nAwareness Level Among University Students In a High Rainfall Tropical Country.  Malaysian Journal of Sustainable Agriculture,  1(2):09-11.\n\n\n\n\n\n\n\n\n11  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 09-11  \n\n\n\nPerspective (Proceedings of the RomeSymposium, April 1987) IAHS Publ, \n164, 439.\n\n\n\n[2] Falkenmark, M. 1989. The Massive Water Scarcity Now Threatening \nAfrica \u2013 WhyIsn\u2019t It Being Addressed? Royal Swedish Academy of Sciences, \n18 (2), 112.\n\n\n\n[3] Liaw, C.H., and Chiang, Y.C. 2014. Framework for Assessing the \nRainwaterHarvesting Potential of Residential Buildings at a National Level \nas an Alternative WaterResource for Domestic Water Supply in Taiwan. \nWater, 6 (10), 3224-3246.\n\n\n\n[4] Asmawi, A. P. M. Z., and Ibrahim, A. N. 2011. The Level of Awareness \nTowardsEnvironmental Issues and Concern among Students in Tertiary \nLevel. 11th InternationalCongress of Asian Planning Schools Association \n(APSA 2011),\n\n\n\n19-21 September 2011.Tokyo: University of Tokyo Hongo Campus.\n\n\n\n[5] Shamsuddin, M., Hashim, N.M., Ahmad, A.H., Khin, M.T., and Sidek, N.S. \n2014.Kebolehupayaan sistem penuaian hujan sebagai bekalan air alternatif \ndi Malaysia: Suatupenelitian awal. Geografia: Malaysian Journal of Society \nand Space, 10 (6), 97-104.\n\n\n\n[6] Gwenzi, W., Dunjana, N., Pisa, C., Tauro, T., and Nyamadzawo, G. 2015. \nWaterQuality and Public Health Risks Associated with Roof Rainwater \nHarvesting Systemsfor Potable Supply: Review and Perspectives. \nSustainability of Water Quality andEcology, 6, 107\u2013118.\n\n\n\n[7] Vohland, K., and Barry, B. 2009. A review of in situ rainwater harvesting \n(RWH)practices modifying landscape functions in African drylands. \nAgriculture, Ecosystems &Environment, 131 (3-4), 119\u2013127.\n\n\n\nCite this article as: Pey Fang Tan, Marlia M. Hanafiah,  Mazlin B. Mokhtar, Siti Norliyana Harun  (2017).  Rainwater Harvesting System: Low \nAwareness Level Among University Students In a High Rainfall Tropical Country.  Malaysian Journal of Sustainable Agriculture,  1(2):09-11.\n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 52-57 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2023.52.57 \n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Autoregressive Distributed Lag Modeling of Impact of Climatic and Non-Climatic  \n\n\n\nFactors Influencing Sorghum Production in Ethiopia. Journal of Sustainable Agricultures, 7(1): 52-57. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.52.57 \n\n\n\n\n\n\n\nAUTOREGRESSIVE DISTRIBUTED LAG MODELING OF IMPACT OF CLIMATIC AND \nNON-CLIMATIC FACTORS INFLUENCING SORGHUM PRODUCTION IN ETHIOPIA \n\n\n\nAbera Gayesa Tirfi \n\n\n\nAddis Ababa, ETHIOPIA \n*Corresponding Author Email: aberagayesa@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 25 September 2022 \nRevised 28 October 2022 \nAccepted 07 November 2022 \nAvailable online 28 March 2023 \n\n\n\n This study examined factors influencing sorghum output in Ethiopia using ARDL model over the period 1981 \nto 2020. The elasticity coefficient of crop growing period mean temperature showed negatively significant \nimpact on sorghum production in the long-run, aligning with theory. Conversely, main-season rainfall had \npositively significant impact on sorghum output, contrasting with the theory. Among non-climatic variables, \nsorghum price and area under sorghum had affirmatively considerable contribution to sorghum production \nas expected in theory. In the short-run, mean temperature revealed negatively significant impact on sorghum \nproduction, supporting the theory. Conversely, the main season rainfall and area under sorghum production \ndemonstrated positively significant impact on sorghum production. Furthermore, sorghum output is \npositively responsive to own price during the second lag differences, implying that any price incentive strategy \nshould be released before the last year. Equally, sorghum output is positively responsive to fertilizers applied \nin the first lag, which implies that fertilizers applied on sorghum cultivation during first lag difference have \npositive contribution to sorghum output supply. In view of the results of the current study, it is strongly \nrecommended that the government should come up with strategies and policies that help sorghum farmers to \nmitigate and adapt to climate change. \n\n\n\nKEYWORDS \n\n\n\nRainfall, Temperature, Elasticities, Tropical Cereal Crop  \n\n\n\n \n1.   INTRODUCTION \n\n\n\nGlobally, shreds of evidence show that sorghum (Sorghum bicolor (L.) \nMoench) is the fourth most important tropical cereal crop next to wheat, \nrice and maize (Alemu and Haji, 2016). Globally, sorghum production \nsupply is approximately 70 million tons of grains from 50 million hectares \nof land (Mojapelo, 2019). Sorghum is the major staple food for about 500 \nmillion family members who live in the hot semi-arid tropics of the \ncontinents of Africa and Asia, which covers nearly 80% of the world\u2019s land \narea. Surprisingly, over 100 million people use sorghum as the main and \nstaple food (Alemu and Haji, 2016). Practically, sorghum was produced \nand used by resource-poor small-scale producers, who predominantly \ngrow the crop under conditions of low-rainfall and arid to semiarid \nenvironments. \n\n\n\nIn Ethiopia, sorghum is primarily grown and used as major food crop and \nranks third in terms of land area it covers next to teff and maize crops. It \nis grown on 1.828 million hectares with a total production of 5.265 million \ntons comprising about 15.7% of the total production next to maize, teff and \nwheat (CSA, 2020). The grain of sorghum is consumed as human food, \nwhereas the residue part is utilized as livestock feed. In terms of altitude, \nthe crop is extensively cultivated in the tropics amid elevation of 1400 to \n2100 meters above sea level (m.a.s.l). Sorghum crop is considered to have \nhigh adaptive capacity to adverse environmental conditions which made \nit a popular crop worldwide. \n\n\n\nHowever, sorghum cultivation in Ethiopia is adversely affected by climate \n\n\n\nand non-climate variables. These days, climate change is becoming a global \nand regional concern that is seriously affecting developing and least \ndeveloped countries, which predominantly depend on rain-fed \nagricultural production (FAO, 2015). The adverse impacts of variability as \nwell as change climate factors, in developing and least developed countries \nlike Ethiopia, are recently growing over time and exerts pressure on crop \nproduction systems which changes the balance among key determinants \nof sorghum crop output and yield enhancement efforts. Shreds of evidence \nshow that agriculture in Ethiopia is highly vulnerable to climatic extremes \nand variability, primarily caused by its high dependence on rain-fed \nsystems (MOA, 2011). According to Aragie, many researchers found that \nclimate change caused disparities have contributed to occurrence of \nfrequent droughts, flooding as well as mounting mean surface \ntemperatures, which in return critically affected production of crops over \nhuge areas of Ethiopia (Aragie, 2013).  \n\n\n\nWith an increasing inconsistency and extremes of climate variables in \nEthiopia, it becomes very important to investigate potential impacts of the \nlikely changes taking place in climatic factors (extreme, sub-seasonal \nrainfall deficit, and continuous warming of temperature) on sorghum crop \nproduction. The main aim of this study was, therefore, to investigate the \npotential impacts of factors influencing the supply of sorghum crop in \nEthiopia. Such a study is important to enhance the knowledge and \nunderstanding of readers, policy makers, and scholars on the \nconsequences of climate change and global warming and helps them to \ndesign strategies that mitigate and adapt to the likely impacts of climate \nchange. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 52-57 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Autoregressive Distributed Lag Modeling of Impact of Climatic and Non-Climatic  \n\n\n\nFactors Influencing Sorghum Production in Ethiopia. Journal of Sustainable Agricultures, 7(1): 52-57. \n\n\n\n\n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1   Description of Study Area \n\n\n\n\n\n\n\nFigure 1: Major Sorghum Growing Belts of Ethiopia \n\n\n\n2.2   Data Type and Source \n\n\n\nIn this study, secondary time series data were used for all the variables \ncovering the period from 1981 to 2020.  The study used one independent \nvariable, viz. sorghum output expressed in a million tons; and explanatory \nvariables, viz. crop growing period rainfall expressed in millimeters (mm), \ncrop growing period mean temperature expressed in (\u00baC), price of \nsorghum output expressed in ETB, land area cultivated under sorghum \nexpressed in a million hectares, and fertilizer quantity used on sorghum \ncultivation). Data on sorghum output as well as area allocated for sorghum \ncultivation have been taken from the Ethiopia Agricultural Sample Survey \nReports of CSA, which covered the period from 1981 to 2020. Secondary \ndata on weather variables (minimum and maximum temperatures and \ncrop growing season rainfalls, i.e. short-season/belg and long-\nseason/meher rainfalls) were obtained and compiled from the National \nMeteorological Agency (NMA) of Ethiopia. Representative weather \nstations from sorghum crop growing belts were selected (12 stations) and \ncrop growing period precipitation and atmospheric temperature data \nwere taken as recorded in NMA database. Then, nationally aggregated \naverage data of crop growing period climate data were pooled by taking \naverage of the weather stations selected for the study. Historical producer \nprices of sorghum crop over the observation period of 1981 to 2020 were \nalso compiled from FAOSTAT database, CSA, and EGTE.   \n\n\n\n2.3   Empirical Model Specification \n\n\n\nThe investigator adopted an autoregressive distributed lag (ARDL) model \ndeveloped by some researchers to establish the relationship existing \namong the variables selected for the study (Pesaran et al., 1996; Pesaran, \n1997; Pesaran et al., 2001). An ARDL is a least squares regression \ncontaining lags of the dependent and explanatory variables. According to \nDuasa, the model is appropriate for estimating short- and long-run \nelasticity coefficients of small sample size using ordinary least square \n(OLS) for cointegration between variables incorporated in the study \n(Duasa, 2007). The model allows using appropriate and optimal lags, \nwhich otherwise is not possible with the use of standard cointegration \ntest. Above all, ARDL can be used in cases the sample data or observations \nare small (30 \u2013 80 observations) and where the set of critical values were \noriginally developed by Narayan using GAUSS technique (Narayan, 2005). \nARDL approach gives flexibility in order of variables under consideration \nare co-integrating and is appropriate for a mixture of variables of order \nI(0), I(1), or mutually cointegrated variables (Frimpong and Oteng, 2006). \nHowever, the model fails in case any of the variables co-integrated with \norder I (2). \n\n\n\nThe common form of ARDL model that establishes the association \n\n\n\nbetween dependent and explanatory variables can be expressed as:  \n\n\n\nSProt = \u03b10 + \u03b21SPrit + \u03b22SArt + \u03b23SFertt + \u03b24LSRFt + \u03b25MeanTempt + \n\u03b5t \n\n\n\n(1) \n\n\n\nWhere: SPro represents sorghum production, SPri represents price of \nsorghum output, SAr represents land area cultivated under sorghum crop, \nSFert indicates fertilizer input used in sorghum cultivation, LSRF \nrepresents long/ main season rainfall, and MeanTemp shows crop \ngrowing period mean temperature. Furthermore, \u03b10 represents the \nconstant, \u03b21, \u03b22, \u03b23, \u03b24, \u03b25 are coefficients to be estimated and \u03b5t represents \nthe error term.   \n\n\n\nFurther, equation (1) should be transformed into logarithm form to \nachieve a suitably proficient estimated parameter from the crop supply \nresponse model, which gives Equation 2 below:  \n\n\n\nlnSProt = \u03b10 + \u03b21lnSPrit + \u03b22lnSArt + \u03b23lnSFertt + \u03b24lnLSRFt + \n\u03b25lnMeanTempt + \u03b5t \n\n\n\n(2) \n\n\n\nAn ARDL approach is the best tool to test the association existing among \nthe variables incorporated in the study in the long run. The conditional \nARDL model in equation (2) should then be expressed in ARDL model form \nas follows: \n\n\n\n\u2206lnSProt = \u03b10 + \u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 1\u2206lnSProt-k + \u2211 \ud835\udefd\ud835\udc5b\n\n\n\n\ud835\udc58=1 2\u2206lnSPrit-k + \n\u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 3\u2206lnSArt-k + \u2211 \ud835\udefd\ud835\udc5b\n\n\n\n\ud835\udc58=1 4\u2206lnSFertt-k + \u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 5\u2206lnLSRFt-k + \n\n\n\n\u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 6\u2206lnMeanTempt-k + \u03bb1lnSPRot-1 + \u03bb2lnSPRit-1 + \u03bb3lnSArt-1 + \n\n\n\n\u03bb4lnSFertt-1 + \u03bb5lnLSRFt-1 + \u03bb6lnMeanTempt-1 + \u03b5t   \n\n\n\n(3) \n\n\n\nWhere: \u03b10 represents a drift, \u0394 shows first order difference, \u03b5t shows the \nerror term. To choose an optimum lag length, the study used the Akaike \ninformation criterion (AIC). Subsequent to the establishment of existing \nlong-run relationship among the variables, specification of the error \ncorrection models (ECM) has been carried out for the short-run dynamics \nof the variables incorporated in the study. The general form of the ECM \nfrom Equation (3) can be specified in Equation (4) as: \n\n\n\n\u2206lnSProt = \u03b10 + \u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 1\u2206lnSProt-k + \u2211 \ud835\udefd\ud835\udc5b\n\n\n\n\ud835\udc58=1 2\u2206lnSPrit-k + \n\u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 3\u2206lnSArt-k + \u2211 \ud835\udefd\ud835\udc5b\n\n\n\n\ud835\udc58=1 4\u2206lnSFertt-k + \u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 5\u2206lnLSRFt-k + \n\n\n\n\u2211 \ud835\udefd\ud835\udc5b\n\ud835\udc58=1 6\u2206lnMeanTempt-k + \u2205ECMt-1 + \u03b5t \n\n\n\n(4) \n\n\n\nWhere: \u0394 indicates first order difference operator, ECMt-1 is the error \ncorrection model while \u2205 reflects the speed at which deviations from long-\nrun equilibrium are corrected for the short-run.  \n\n\n\nAfter estimation of the ARDL model, various tests have been carried out to \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 52-57 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Autoregressive Distributed Lag Modeling of Impact of Climatic and Non-Climatic  \n\n\n\nFactors Influencing Sorghum Production in Ethiopia. Journal of Sustainable Agricultures, 7(1): 52-57. \n\n\n\n\n\n\n\ndetermine the trend of causality among the independent and explanatory \nvariables incorporated in the model. Towards this end, Wald test was \nconducted to verify the long-run association existing among the variables. \nFurthermore, normality test (Jaque-Bera test); Heteroscedasticity test \n(Breusch and Godfray LM test); multicollinearity Test and Serial \ncorrelation test (Brush & Godfray LM test), Functional form test (Ramseys \nRESET); and Unit Root test have been carried out.  \n\n\n\n3. RESULTS  \n\n\n\nThis section presents the results of the various diagnostic tests and the \nsupply response of sorghum crop models used in the current study.  \n\n\n\n3.1   Results of the Unit Root Tests \n\n\n\nThe investigator has conducted unit root tests of all the time series and \nmulticollinearity tests between the variables included in the model. \nAugmented Dickey Fuller (ADF) and Phillips Perron (PP) tests were used \nto test the presence of unit root in the data series.  In the ADF and PP tests \nfor stationarity of the time series, the null hypothesis for presence of unit \nroot is rejected when the test statistic is greater than the critical value at \ndesired significance level, otherwise the null hypothesis is not rejected \n(Dickey and Fuller, 1979).  Table 1 presents the results of the unit root \ntests. The estimated product of both tests reflected that all the study \nvariables (lnSPro, lnSPri, lnSAr, lnSFert, lnLSRF, and lnMeanTemp) are \nstatistically significant and are stationary at levels or order I (0). The \n\n\n\nestimated results suggest that an ARDL model could be employed for \nexamining both the long- and short-run interrelationships existing among \nthe variables selected for the study. \n\n\n\n3.2   Diagnostic, Robustness and Stability Tests \n\n\n\nIn this study, an ARDL bound cointegration test technique was employed \nto detect the existence of long-run cointegration between the variables \nincluded in the model. Table 2 presents the outcomes of the bound\u2019s co-\nintegration tests. As can be seen from the table, there exists long-run \ncointegration among the dependent and explanatory variables \nincorporated in the model, since the F-Statistics 3.6985 exceeds the critical \nupper bound 3.534 at 10% level of significance. This indicates the \nexistence of long-run relationships among the dependent and explanatory \nvariables incorporated in the estimated model. \n\n\n\nThe error term from the sorghum output response model was also \nsubjected to certain residual tests to detect non-normality, serial \ncorrelation, and heteroscedasticity. As can be seen from Table 3 below, the \ndistribution follows normal distribution on the basis of statistical \ninsignificance of the Jarque\u2013Bera statistic. Therefore, t and F tests can be \ncorrectly used for hypothesis testing in respect of the series. In addition, \nthe results show nonexistence of autocorrelation as revealed by Breush\u2013\nGodfrey Lagrange Multiplier (LM) test statistics. Nevertheless, there is \npresence of heteroscedasticity as shown by Lagrange Multiplier (LM) test \nfor no autoregressive conditional heteroscedasticity (ARCH). \n\n\n\nTable 1: Results of the Unit Root Test \n\n\n\nVariable \nADF PP \n\n\n\nResults \nLevel First Difference Level First Difference \n\n\n\nLNSPRO -3.6895** -7.25513 -3.31685*** -9.21889 I(0) \n\n\n\nLNSPRI -2.3912*** -5.56108 -1.7495*** -8.78981 I(0) \n\n\n\nlNSAR -3.1324*** -6.28477 -2.77889*** -6.37645 I(0) \n\n\n\nLNSFERT -3.9043*** -6.70024 -4.01282*** -8.96121 I(0) \n\n\n\nLNLSRF -3.2833*** -15.64517 -6.98374 -43.5617 I(0) \n\n\n\nLNMeanTemp -3.9043*** -7.15842 -3.91892*** -7.29074 I(0) \n\n\n\n*, ** and *** denotes significance level at 10%, 5% & 1%, respectively. \n\n\n\n\n\n\n\nTable 2: Estimation of Cointegration Equations \n\n\n\nDependent variable Type of test Test statistics Critical values Conclusion \n\n\n\nSorghum output supply response Wald test 3.6985* 3.534 Long-run cointegration exists \n\n\n\nNote: * indicates significance level at 10%. \n\n\n\n\n\n\n\nTable 3: Residual Properties of Barley Output Response Equation \n\n\n\nType of test Test statistic Test statistic value Probability \n\n\n\nNormality test - Histogram Jarque-Bera 3.5362 0.17066 \n\n\n\nLM  Obs*R-squared 2.2644 0.3750 \n\n\n\nHeteroscedasticity (ARCH)  Obs*R-squared 16.93030 0.0498 \n\n\n\n\n\n\n\nTable 4: Results of the Ramsey RESET Test \n\n\n\nDependent Variable F \u2013 statistic Probability Conclusion \n\n\n\nLog sorghum output 9.763851 0.0053 No indication of misspecification \n \n\n\n\n\n\n\n\nFigure 2: Plot of CUSUM of Squares of recursive Residuals \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 52-57 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Autoregressive Distributed Lag Modeling of Impact of Climatic and Non-Climatic  \n\n\n\nFactors Influencing Sorghum Production in Ethiopia. Journal of Sustainable Agricultures, 7(1): 52-57. \n\n\n\n\n\n\n\nFurthermore, a test for Ramsey RESET has been carried out to detect the \npresence of any misspecification. Table 4 presents the results of the \nRamsey RESET tests. It can be seen from the table that the model does not \nsuffer from any form of misspecification. Equally, the robustness of the \nestimated parameters has been evaluated from the response equation \nemploying the cumulative sum (CUSUM) and cumulative sum of squares \n(CUSUMSQ) techniques of recursive residuals test. The results, as can be \nseen from Figure 2, reveal non-significant divergence of the plots from the \nzero line, which suggests the stability of parameters incorporated in the \nestimated equation.  \n\n\n\n3.3   Modeling Factors Influencing the Supply Response of Sorghum \nOutput \n\n\n\nThe study examined how sorghum production/ output supply responds to \nclimatic and non-climatic variables. To achieve the intended objective, the \ncrop output supply model was estimated with both climatic (rainfall and \nmean temperature) and non-climatic (lagged sorghum output, producer \nprice of sorghum, land area cultivated under sorghum crop, chemical \nfertilizers used under sorghum cultivation) variables. Irrigated area under \nsorghum crop and improved sorghum seed were initially included into the \nmodel but were dropped since the test results exhibited presence of serial \ncorrelation and multicollinearity with other variables. \n\n\n\nThe adjusted R2 and F-statistic estimated for the ARDL regression model \nexhibited good fitness of the model for the sorghum output supply data \nseries, with a value of adjusted R2 (0.6905). The adjusted R2 value of \n0.6905 in sorghum output model implies that 69% of the disparity in \nsorghum output production has been explained by the explanatory \nvariables included in the model (see Table 6). The F-statistics (9.2555) \nshow that the model is well fitted to the data series. The Durban-Watson \ntest further showed no existence of serial autocorrelation. The model \nbecomes viable and fit at lag length 1 and first-order difference only; lag \n\n\n\nlength 2 and second-order difference were tried but revealed high serial \nautocorrelation. \n\n\n\nSince the previous test for cointegration revealed the presence of long-run \ncointegration, long-run elasticity coefficients have been estimated for the \nsorghum output supply model. Based on F-statistics, adjusted-R2, and the \nAIC, an ARDL of (1, 0, 1, 0, 1, 0, 0) was selected as the best model. Table 5 \npresents the long-run elasticity coefficients estimated for sorghum output \nsupply associated to climate and non-climate variables. After dropping \nserially correlated variables of irrigated area and improved seeds used, the \nclimatic and non-climatic variables selected for the analysis were: log \nmean temperature recorded during crop-growing period, log long/main-\nseason rainfall over crop growing period, log sorghum producer price, log \narea cultivated under sorghum crop, and log chemical fertilizers used on \nsorghum production.  \n\n\n\nThe estimated elasticity coefficients show that mean temperature over \ncrop growing period had negative and significant (10% level) relationship \nwith sorghum output supply in the long run. This indicates that a 1% \nincrease or alter in crop growing period mean temperature would \ndecrease sorghum output by 5.5%. Conversely, the estimated elasticity \ncoefficient of main/long-season rainfall showed affirmative and \nsignificant (10% level) impact on the supply of sorghum crop output. The \nresult indicates that a 1% increase/change in main-season rainfall would \nincrease sorghum output by 0.66%. Conversely, all the non-climatic \nexplanatory variables incorporated in sorghum output model showed \npositive impact on sorghum output supply in the long run. However, only \nthe coefficients estimated for log sorghum price and log area cultivated \nunder sorghum are statistically significant. The results designate that a 1% \nboost in log price of sorghum and log area under sorghum cultivation over \nthe long run would increase sorghum output supply by 0.11% and 1.51% \nrespectively. \n\n\n\nTable 5: Long-Run Elasticity Estimates of Variables Considered in Sorghum Output Model \n\n\n\nVariable Coefficient Std. Error t-Statistics Prob. \n\n\n\nCons 11.18472 7.676728 1.456964 0.1549 \n\n\n\nLNSPri 0.114021** 0.055378 2.058974 0.0477 \n\n\n\nLNSAr 1.5132*** 0.215898 7.008846 0.0000 \n\n\n\nLNFert 0.019875 0.066032 0.300995 0.7654 \n\n\n\nLNLSRF 0.664308* 0.394939 1.682053 0.1023 \n\n\n\nLNMeanTemp -5.481787* 2.931363 -1.870047 0.0706 \n\n\n\n*, ** and *** indicate statistical significance level at 10%, 5% and 1%, respectively \n \n \n\n\n\nTable 6: Short-Run Coefficient Estimaes of Variables Included in Sorghum Output Model \n\n\n\nDependent Variable: D(LNSPRO) \n\n\n\nMaximum dependent lags: 2 (Automatic selection) \n\n\n\nModel selection method: Akaike info criterion (AIC) \n\n\n\nDynamic regressors (1 lag, automatic): ECT (-1) D(LNSPRI) D(LNSAR) D(LNSFERT) D(LNLSRF) D(LNMEANTEMP) \n\n\n\nFixed regressors: C @TREND \n\n\n\nSelected Model: ARDL (1, 0, 1, 0, 1, 0, 0) \n\n\n\nVariable Coefficient Std. Error t-Statistic Prob. \n\n\n\nC 3.22144** 1.24111 2.59560 0.0151 \n\n\n\nD (LNSPRO (-1)) -0.10398 0.110095 -0.94450 0.3533 \n\n\n\nECT (-1) -0.29913** 0.114885 -2.603699 0.0148 \n\n\n\nD(LNSPRI) -0.18932* 0.096796 -1.95585 0.0609 \n\n\n\nD (LNSPRI (-1)) 0.10222 0.09743 1.04923 0.3034 \n\n\n\nD(LNSAR) 1.0184*** 0.15604 6.52649 0.0000 \n\n\n\nD(LNSFERT) 0.04896 0.03621 1.35222 0.1875 \n\n\n\nD (LNSFERT (-1)) -0.04884 0.03805 -1.28365 0.2102 \n\n\n\nD(LNLSRF) 0.017363 0.22675 0.07657 0.9395 \n\n\n\nD(LNMEANTEMP) -1. 98619* 1.11224 -1.78576 0.2993 \n\n\n\n@TREND 0.00109 0.00226 0.482599 0.6333 \n\n\n\nR-squared 0.774162 Mean dependent var 0.038775 \n\n\n\nAdjusted R-squared 0.690519 S.D. dependent var 0.259452 \n\n\n\nS.E. of regression 0.144336 Akaike info criterion -0.796149 \n\n\n\nSum squared resid 0.562487 Schwarz criterion -0.322111 \n\n\n\nLog likelihood 26.12683 Hannan-Quinn criter. -0.627490 \n\n\n\nF-statistic 9.255496 Durbin-Watson stat 2.159750 \n\n\n\nProb(F-statistic) 0.000002    \n\n\n\n*, ** and *** indicate significance level at 10%, 5% and 1%, respectively \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 52-57 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Autoregressive Distributed Lag Modeling of Impact of Climatic and Non-Climatic  \n\n\n\nFactors Influencing Sorghum Production in Ethiopia. Journal of Sustainable Agricultures, 7(1): 52-57. \n\n\n\n\n\n\n\nTo depict the dynamic adjustment of all the variables considered in the \nmodel, short-run coefficients were estimated with an ECM following the \nARDL bounds test approach. Table 6 shows the short-run elasticity \ncoefficients of the variables estimated using the selected ARDL model (1, \n0, 1, 0, 1, 0, 0) with optimum lag length.  The selection of the model with \noptimum lag length was done by employing AIC procedures. It can be seen \nfrom the table that the estimated lagged ECM (-1), is negative (-0.29913) \nas expected and highly significant (at 5% level), with probability value less \nthan 5% (0.0148). These results support the short-run relationship or co-\nintegration between the regressors represented by equation (1). The pace \nof adjustment (-0.29913) suggests that approximately 41.93% of the \nshort-run disequilibrium due to the previous year\u2019s shocks experienced in \nequation (4) can be appropriately corrected in the long-run. \n\n\n\nIn the short run, the coefficient estimates for crop growing period mean \ntemperature had negative and significant (10% level) effect on sorghum \ncrop output supply, which aligns with the theory. The result indicates that \na 1% mount in crop growing period mean temperature would decrease \nsorghum output by 1.99%. Conversely, the main-season rainfall exerts \naffirmative effect on sorghum output supply in the short-run, although \nstatistically insignificant. Equally, elasticity coefficient for the non-climatic \nfactors, which include own price of sorghum, land area cultivated under \nsorghum, chemical fertilizers applied on sorghum production have been \nestimated for the short run. Among these variables, areas cultivated under \nsorghum crop showed affirmative and significant (1% level) impact on \nsorghum output supply over the short-run.  \n\n\n\nThe result indicates that an expansion of land area allocated under \nsorghum cultivation by 1% would lead to an enlargement of sorghum crop \noutput by 1.02%. Conversely, sorghum output supply is depressingly and \nconsiderably responsive to own price in the first lag difference and \npositively responsive to own price in second lag difference. This implies \nthat a 1% increase/change in sorghum crop own price will diminish \nsorghum crop output by 0.19% in first lag difference (last year) and \nboosting sorghum crop output by 0.102% during second lag difference \n(before last year). Furthermore, sorghum output is positively responsive \nto chemical fertilizers applied during the first lag difference (last year) and \nnegatively responsive to chemical fertilizers applied on sorghum \nproduction before last year (2nd lag difference), although the outcomes are \nstatistically insignificant. \n\n\n\n4. DISCUSSION \n\n\n\nThe elasticity coefficients estimated for temperature showed that mean \ntemperature over crop growing period had negative and significant \nrelationship with sorghum output supply in the long run. The result was \nas expected and aligns with the theory proposition. Conversely, the \nestimated elasticity coefficient for long-season rainfall showed a positive \nand significant impact on the supply of sorghum crop output, which \ncontrasts with the theory. This may be because, in Ethiopia, agriculture is \nprimarily rain-fed-based with insufficient irrigation works. Furthermore, \nas temperature at earth\u2019s surface rises, more evaporation occurs, which, \nin turn, increases overall precipitation. Therefore, a warming climate is \nexpected to increase precipitation in many areas, which would have a \npositive impact on sorghum production. This phenomenon has been \nconfirmed by the IPCC\u2019s Fifth Assessment Report (IPCC, 2013).  \n\n\n\nThis finding is consistent with empirical research findings of Ali, who in \ntheir modeling of the influence of climatic and non-climatic factors on \ncereal crop production in India reported that average temperature had \nnegative and significant impact on cereal production (Ali et al., 2021). The \noutcome implied that a 1\u00baC increase in average temperature will decrease \ncereal production by 2.31%. Further, they reported that the estimated \nelasticity coefficient of average rainfall in the long-run showed positive \neffects on cereal production. A similar study by Asfew and Bedemo [16] \nalso supports the result of this study. In their study of the impact of climate \nchange on cereal crops production in Ethiopia, they found that rainfall has \na positive and significant effect on cereal crops production both in the \nlong- and short-runs, while temperature change has a significant negative \neffect (Asfew and Bedemo, 2022). The study on the impact of precipitation \non bean farming in China also supports the current study, which exhibited \na positive and significant result (Li et al., 2021). \n\n\n\nAmong the non-climatic explanatory variables, the coefficients estimated \nfor log sorghum price and log area cultivated under sorghum crop are \nfound to have positive and significant relationship impact on sorghum \ncrop output in the long run. The results are as expected in theory; i.e. \neconomic theory states that a positive association exists among own price \nof a commodity and the output in question. The finding in land area under \nsorghum farming implies that sorghum crop output is highly responsive \nto changes in the area allocated under sorghum cultivation, which is also \n\n\n\nin line with the theory. The outcomes of this study are similar to the \nfindings of (Alemu et al., 2003; Muchapondwa, 2009).   \n\n\n\nA group researcher who examined supply response grain output in \nEthiopia, reported that own price of sorghum depicted affirmative and \nmomentous impact on sorghum output supply over the long-run (Alemu \net al., 2003). The result indicates that a 1% raise in sorghum own price \nwould lead to a boost of sorghum output supply by 0.43%. Equally, \nMuchapondwa studied the supply response of Zimbabwean agriculture \nand reported that land area under aggregate agricultural supply and \nfertilizer quantity used on aggregate agricultural production have positive \nimpact on supply of agricultural output over the long run (Muchapondwa, \n2009). In the short run, the coefficient estimates for crop growing period \nmean temperature had negative and significant effect on sorghum crop \noutput supply, which aligns with the theory.  \n\n\n\nConversely, the main-season rainfall exerts affirmative effect on sorghum \noutput supply in the short-run, although statistically insignificant. The \nfindings of the study are analogous with the study findings of who modeled \nclimatic and non-climatic factors influencing agricultural crop production \nin India and reported that coefficient estimates of average temperature \nduring short run had depressing and momentous (1% level) impact on \ncereal output while the coefficient estimates of average rainfall had \naffirmative effect on cereal crops output (Chandio, 2021). The study \noutcomes depict that a 1% mount in mean temperature would lead to \ndecrease of cereal crops output by 2.25% while 1% enlargement in \naverage rainfall increases cereal crop output by 0.05%. Conversely, the \noutcomes of the current investigation contrasts with the findings of who \nstudied factors (climate and non-climate) impacting rice crop cultivation \nin South Korea (Nasrullah, 2021).  \n\n\n\nThey reported that coefficient estimates of mean temperature had positive \nand momentous influence on the output of rice crop during the short run \nwhile estimates for mean rainfall had positive and momentous shock on \nthe production of rice crop. Among the non-climatic variables, area \ncultivated under sorghum crop showed positive and significant (1% level) \nimpact on sorghum output supply over the short run. Conversely, sorghum \noutput supply is depressingly and considerably responsive to own price in \nfirst lag difference and positively responsive to own price in second lag \ndifference. The results can be justified with the expression that sorghum \noutput is negatively responsive to any own price incentive put in place \nduring first lag difference (last year) and positively and moderately \nresponsive to own price incentive strategies released before last year.  \n\n\n\nIn other words, any own price of sorghum incentive policy and strategy \nshould be released during the second lag difference (before last year) to \nyield positive increment of sorghum crop output supply. Furthermore, \nsorghum output is positively responsive to chemical fertilizers applied \nduring the first lag difference (last year) and negatively responsive to \nchemical fertilizers applied on sorghum production before last year (2nd \nlag difference). These study outcomes resonate to the study outcomes \n(Chandio, 2021; Nasrullah, 2021; Munchapondwa, 2009). Chandio in their \nmodeling of the influence of climatic and non-climatic factors on cereal \nproduction in India reported that cereal cropped area negatively and \ndrastically influenced agricultural value-added during second lag \ndifference and positively affected agricultural value added during the first \nlag difference (last year) (Chandio, 2021).  \n\n\n\nThe outcomes point out that any increase/ change in area allocated under \nproduction of cereal crops during second lag difference (before last year) \nby 1% has decreased agriculture output supply by 0.59% and a 1% \nincrease/change in area allocated under production of cereal crop during \nfirst lag difference increases agriculture output supply by 0.33%. \nNasrullah, in their study of variables influencing the supply of rice crop \noutput in South Korea reported that area cultivated under rice during first \nlag difference (last year) showed positive and considerable impact on \nsupply of rice output whereas chemical fertilizer applied on rice crop \nfarming during the first lag difference (short-run) showed harmful shock \non supply of rice crop output (Nasrullah, 2021). The results implied that \nany enlargement in area cultivated under rice crop during the first lag \ndifference (last year) by 1% would increase rice output by 0.69%; \nsimilarly, any increase in quantity of chemical fertilizer application by 1% \nwould lead to a decrease of rice output supply by 0.08%.  \n\n\n\nFurthermore, Muchapondwa in his study on supply response of \nagriculture in Zimbabwe reported that the short-run elasticity with \nrespect to the lagged price variable is affirmative and momentous at 5% \nlevel (Muchapondwa, 2009). Conversely, the elasticity estimates of both \nthe short- and long-run with respect to the current prices are depressing \nand considerably influenced aggregate agricultural output supply. He \nfurther reported that chemical fertilizer application and land area \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 52-57 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Autoregressive Distributed Lag Modeling of Impact of Climatic and Non-Climatic  \n\n\n\nFactors Influencing Sorghum Production in Ethiopia. Journal of Sustainable Agricultures, 7(1): 52-57. \n\n\n\n\n\n\n\nallocated under cereal crops cultivation had affirmative effect on supply of \naggregate agricultural output in the short run (current year), although not \nsignificant. The result exhibits that any boost in chemical fertilizer \napplication and area cultivated under cereal crops by 1% would increase \naggregate agricultural production by 0.39% and 0.388% respectively.  \n\n\n\n5. CONCLUSION \n\n\n\nThe ultimate purpose of this study was to examine the factors influencing \nthe supply of sorghum crop output in the country. An ARDL model \noriginally developed have been employed to set up the relationship \nprevailing among the variables incorporated in the crop output supply \nmodel. The study used time series secondary data of the selected variables \ncovering the period from 1981 to 2020. The estimated elasticity \ncoefficients for crop growing period average surface temperature had \nnegative and considerable (10% level) impact on sorghum crop output \nsupply during both the long- and short-run. Conversely, the estimated \nelasticity coefficient for main/long-season rainfall showed positive and \nsignificant (10% level) effect on sorghum crop output supply in both the \nlong- and short-run.  \n\n\n\nThe regression coefficient estimates for crop growing period average \natmospheric temperature is found to be consistent with the theory \nproposition whereas that of main season rainfall contrasts with the theory. \nAmong the non-climatic variables, all the regressors incorporated in the \ncrop output supply model showed positive effect on the supply of sorghum \ncrop output in the long-run, as expected in theory proposition. However, \nonly the elasticity estimates of log sorghum own price and log area \ncultivated under sorghum are statistically significant. The finding may \nproof the claim towards increase in supply of sorghum crop output in the \ncountry was partly due to expansion of area cultivated under sorghum \nproduction rather than other inputs.  \n\n\n\nIn the short-run, land area cultivated under sorghum crop showed \naffirmative and momentous (1% level) impact on the production of \nsorghum crop. Conversely, sorghum output supply is depressingly and \nconsiderably responsive to its own price during the last year (1st lag \ndifference) and positively responsive to own price during the second lag \ndifference (year before last year). The study outcomes imply that sorghum \noutput is negatively responsive to any sorghum price incentive put in place \nlast year (1st lag difference) and positively and moderately responsive to \nown price incentive strategies released during the second lag differences \n(before last year).  \n\n\n\nIn other words, any own price of sorghum incentive policy and strategy \nshould be released during the second lag difference (before last year) to \nbring any positive increment in sorghum output supply. Furthermore, \nsorghum output supply is positively responsive to fertilizer applied in the \nfirst lag difference (last year) and negatively responsive to fertilizer used \nbefore last year (during 2nd lag difference), although the outcomes are \nstatistically insignificant. This result implies that application of chemical \nfertilizer on sorghum crop output supply during first lag difference (last \nyear) will have positive input towards the expansion in volume of sorghum \noutput.  \n\n\n\nThe study indicated that climate variables, particularly temperature had \nnegative impact on sorghum crop production. It is also expected that \nfuture production of sorghum crop would be adversely affected by the \nconsequences of climate change if technical and tactical measures are not \ntaken. Therefore, it is strongly recommended that the government should \ncome up with strategies and policies that help sorghum farmers to adapt \nto the impacts exerted by climate change. Some of the strategies may \ninclude changes in planting date, practicing irrigation work, use of short \nduration crop variety, and fertilizer application. It is also recommended \nthat further investigation should be undertaken to clearly assess the \nnegative impacts of climate change on future food production and explore \nalternative measures that enhance and sustain sorghum crop production. \nThe output of the study is important to enhance the knowledge and \nunderstanding of readers, policy makers, and scholars on the \nconsequences of climate change and global warming and helps them to \ndesign strategies that mitigate and adapt to the likely impacts of climate \nchange.   \n\n\n\nREFERENCES \n\n\n\nAlemu, G., and Haji, J., 2016. Economic Efficiency of Sorghum Production \nfor Smallholder Farmers in Eastern Ethiopia: The Case of Habro \n\n\n\nDistrict. Journal of Economics and Sustainable Development, 7 (15), \nPp. 44 \u2013 51. \n\n\n\nAlemu, Z.G., Oosterhuizen, K., and van Schalkwyk, H., 2003. Grain-Supply \nResponse in Ethiopia: An Error -Correction Approach, Agrekon, 42, \nPp. 389\u2013404. \n\n\n\nAli, C.A., Jiang, Y., Amin, A., Akram, W., Ozturk, I., Sinha, A., 2021. Modeling \nthe impact of climatic and non-climatic factors on cereal production: \nevidence from Indian agricultural sector, MPRA Paper No. 110065, \nposted 08 Oct 2021, 18:29 UTC. \n\n\n\nAragie, A.E., 2013. Climate change, growth and poverty in Ethiopia. \nVolume-3. \n\n\n\nAsfew, M., and Bedemo, A., 2022. Impact of Climate Change on Cereal Crops \nProduction in Ethiopia; Advances in Agriculture, Pp. 1 \u2013 8. \nhttps://doi.org/10.1155/2022/2208694.  \n\n\n\nChandio, A.A., 2021. Modeling the impact of climatic and non-climatic \nfactors on cereal production: evidence from Indian agricultural \nsector, MPRA Paper No. 110065, https://mpra.ub.uni-\nmuenchen.de/110065/.  \n\n\n\nCSA, 2020. Report on area and production of major crops, Agricultural \nSample Survey 2019-20, Addis Ababa, Ethiopia. \n\n\n\nDickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for \nautoregressive time series with a unit root. Journal of the American \nStatistical Association, 74 (366a), Pp. 427-431. \n\n\n\nDuasa, J., 2007. Determinants of Malaysian trade balance: an ARDL bound \ntesting approach. Global Economic Review, 36 (1), Pp. 89\u2013102. \n\n\n\nFAO (Food and Agriculture Organization), 2015. Adaptation to climate risk \nand food security: Evidence from smallholder farmers in Ethiopia. \nFood and Agriculture Organization, Rome, Italy. \n\n\n\nFrimpong, M.J., and Oteng, E.F., 2006. Bound Testing Approach: An \nExamination of Foreign Direct Investment, Trade and Growth \nRelationships. MPRA Paper No. 352, Pp. 1\u201319. \n\n\n\nIPCC, 2013. Climate change 2013: The physical science basis, Working \nGroup I contribution to the IPCC Fifth Assessment Report, \nCambridge, UK. Cambridge University Press.  \n\n\n\nLi, S., You, S., Song, Z., Zhang, L., and Liu, Y., 2021. Impacts of climate and \nenvironmental change on bean cultivation in China. Atmosphere, 12, \nPp. 1591. \n\n\n\nMoA (Ministry of Agriculture), 2011. Agriculture Sector Programme of \nPlan on Adaptation to Climate Change. Ministry of Agriculture, Addis \nAbaba, Ethiopia. \n\n\n\nMojapelo, M.C., 2019. Estimation of sorghum supply elasticity in South \nAfrica. Journal of Agribusiness and Rural Development, 2 (52), Pp. \n131 \u2013 138. \n\n\n\nMuchapondwa, E., 2009. Supply response of Zimbabwean agriculture: \n1970\u20131999. Afjare, 3 (1), Pp. 28 \u2013 42. \n\n\n\nNarayan, P.K., 2005. The Saving and Investment Nexus for China: Evidence \nfrom Cointegration Tests. Applied Economics, 37, Pp. 1979\u20131990. \n\n\n\nNasrullah, M., 2021. Autoregressive distributed lag (ARDL) approach to \nstudy the impact of climate change and other factors on rice \nproduction in South Korea. Journal of Water and Climate Change, 12 \n(6), Pp. 2256 \u2013 2270. \n\n\n\nPesaran, H.M., 1997. The role of economic theory in modelling the long run. \nEconomic Journal, 107 (440), Pp. 178 - 191. \n\n\n\nPesaran, H.M., Shin, Y., and Smith, R.J., 1996. Testing the Existence of a \nLong-run Relationship, DAE Working Paper Series No. 9622, \nCambridge, Department of Applied Economics, University of \nCambridge. \n\n\n\nPesaran, H.M., Shin, Y., and Smith, R.J., 2001. Bounds testing approaches \nthe analysis of level relationships. Journal of Applied Econometrics, \n16, Pp. 289 \u2013 326.\n\n\n\n \n\n\n\n\nhttps://doi.org/10.1155/2022/2208694\n\n\nhttps://mpra.ub.uni-muenchen.de/110065/\n\n\nhttps://mpra.ub.uni-muenchen.de/110065/\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2022.57.64 \n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growing of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2022.57.64 \n\n\n\n\n\n\n\nSOIL TEMPERATURE CONTROL FOR GROWING OF HIGH-VALUE TEMPERATE \nCROPS ON TROPICAL LOWLAND \n \nRasaq Adekunle Olabomia*, Bakar Jaafarb, Md Nor Musab, Shamsul Saripb \n \naNational Institute for Policy and Strategic Studies, Jos, Plateau State Nigeria \nbUniversiti Teknologi Malaysia \n*Corresponding Author E-Mail: rasaqolabomi@yahoo.com \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 08 November 2021 \nAccepted 10 December 2021 \nAvailable online 21 December 2021 \n\n\n\n\n\n\n\nLow soil temperature (14\u2103\u201320\u2103) favours growing of high-value temperate crops that are known to have \nhigher return per hectare of land than other widely cultivated crops, thereby presenting increased income to \nfarmer. However, due to high soil cooling load, growing these crops on tropical lowland area is a challenge \nexcept through greenhouse farming or on few cool higher altitudes with resemblance of temperate climate. \nGreenhouse farming involves cooling the entire volume of planting zone and is energy intensive, while few \ncool highlands are not sufficient to achieve food security in this direction. This study aims at application of \nchilled water for direct cooling of soil, to create favorable soil conditions for optimal performance of planted \ntemperate crops.  However, soil cooling using vapour compression refrigeration system may not be \neconomically viable. Solar thermal chilled water production system is presented in this study to supply the \ncooling.  The system consists of absorption refrigeration system and dimensioned size of soil bed with chilled \nwater pipe network.  The study includes modeling of soil cooling load to determine the refrigeration power \nrequired to overcome such load.  The modeled system matched well with the experiment; having standard \ndeviation of 1.75 and percentage error of 12.24%. Parametric analysis of the soil cooling showed that \ntemperatures of cooled soil were significantly affected by chilled water flow rates. The regression equation \ndeveloped from the Analysis of Variance (ANOVA) is suitable for predicting cooled soil temperature.  The \ncooling process is technically feasible, with potential for greenhouse gas emission reduction.  \n\n\n\nKEYWORDS \n\n\n\nSoil Temperature, Greenhouse, Farming, Temperate Climate, Vapour Compression Refrigeration System, \nSolar Thermal \n\n\n\n1. INTRODUCTION \n\n\n\nMost of the physiological processes of planted crops are controlled by soil \ntemperature which if higher than optimal, will alter the root growth and \nfunctionalities of the crops; a phenomenon that is very common with the \nhot tropical climate soil (Kim and Joo, 2020; Mongkon et al., 2014).  \nHowever, Temperature crops, mostly as high-value, crops such as cabbage, \nlettuce, broccoli and carrots are generally referred to as cold season crops \nwith their adaptability to low temperature; usually between 18\u2103 and 20\u2103 \n(Sabri et al., 2018).  Hence difficulty in their cultivation on hot tropical \nlowland areas except on some few highland such as Jos Plateau in Nigeria \nand Cameroon highland in Malaysia.  The effects of climate change have \nequally led to decline in temperate crops farming in these areas.  \nAlternatively, greenhouse farming system is commonly used in the tropics \nto cool down the air volume of planting zones of some high-value crops \nagainst excessive heat (Campiotti et al., 2016). However, soil temperature \nhas been found to have more effects on the development of planted crops \nthan air temperature (Labeke et al., 1993; Ogbodo et al., 2010).  \nFurthermore, radiant floor cooling has been reported in the literature as a \nmore energy efficient system than air-cooling system due to the better \nthermal capacity and less pumping energy requirement of water than air \n(Seo et al., 2014). Like the greenhouse cooling, soil temperature control \nrequires energy expenditure which if alternatively provided, will not only \n\n\n\nmake the system economically viable but also environmentally benign \n(Wongkee et al., 2014; Zarella et al., 2014; Zhou et al., 2014; Zhou et al., \n2019).  A prominent alternative cooling system is absorption chillers that \nutilize low-grade energy such as solar for chilled water production.  Chilled \nwater production through application of solar thermal technology is one \nof the interesting research areas in the tropics where there is high solar \npotential that is available in phase with the cooling load. \n\n\n\nPresented in this paper is a system comprising of a solar thermal chilled \nwater via absorption refrigeration to offset the cooling load of a \ndimensioned soil bed. Absorption cooling system is a typical thermally \nactivated technology that is found suitable for utilization of low-grade \nthermal energy such as solar energy via solar collector. The soil cooling \nprocess involves channeling chilled water through chilled water pipe laid \nunder the surface of the soil for heat removal.   The objectives of the paper \ninclude; to develop mathematical model for soil load and equivalent \nrefrigeration plant, to develop model equation for chilled water flow rates \nsuitable for a range of soil temperatures and to validate the model with \nexperimental results, to perform sensitivity analysis of the soil cooling to \nchilled water flow rates and temperature, and ambient air temperature. \nThe significance of the study can be found in the areas of better utilization \nof free energy source (solar energy), not only for agricultural soil cooling \napplications but also for comfort cooling in building. Successful \n\n\n\n\nmailto:rasaqolabomi@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growi ng of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n\n\n\n\napplication of this study equally aims to aid domestication of high-value \ntemperate crops in the tropical regions which could help to contribute to \nnational economy via reduction in importation of the temperate crops.   \n\n\n\n2. MATHEMATICAL MODELS DEVELOPMENT \n\n\n\nSoil temperature is direct consequence of the solar radiation heat on the \n\n\n\nearth surface. mathematical models are developed to estimate the amount \n\n\n\nof the heat gained by the affected area of soil (Niu et al., 2015; Wu et al., \n\n\n\n2015). This helps to determine the equivalence amount of the cooling \n\n\n\ncapacity required to offset the heat to appropriately control the soil \n\n\n\ntemperature. The heat removal is achieved through the heat exchange \n\n\n\nbetween the surrounding soil and the buried chilled water pipe. \n\n\n\n2.1 Soil \u2013 chilled water pipe heat transfer model \n\n\n\nAs presented in Fig. 1, conductive heat transfer from the top soil to the \n\n\n\nchilled water is due to the temperature difference between the two \n\n\n\nsections.  However, in the absence of a heating source, an homogenous \n\n\n\ntemperature is possible between the two sections; when the temperature \n\n\n\ndifference decay brings the conductive heat transfer close to zero (Feng et \n\n\n\nal., 2016). The summary of the soil and chilled water pipe heat transfer \n\n\n\nmodel is as itemized below; \n\n\n\n\u2022 Heat transfer resulting from the temperature difference between the \n\n\n\nsoil surface and the ambient environment under the influence of solar \n\n\n\nradiation (\u01ec\ud835\udc60) \n\n\n\n\u2022 Vertical heat conduction from the soil surface down to buried chilled \n\n\n\nwater pipe (\ud835\udc44) \n\n\n\n\u2022 Heat gained by the chilled water through the pipe via convection and \n\n\n\nconduction (\ud835\udc48\ud835\udc61). \n\n\n\n \nFigure 1: Heat transfer model \n\n\n\n \nThe net heat energy balance between the soil surface and the ambient \n\n\n\natmosphere is expressed as; \n\n\n\n\n\n\n\n\u01ec\ud835\udc60 = \ud835\udc45\ud835\udc5b \u2212 (\u01ec\u210e +\u01ec\ud835\udc3f) (Ricardo et al., 2008).  \n\n\n\n\n\n\n\nWith respect to Stefan-Boltzmann law, the net radiation absorbed by soil \n\n\n\n(\ud835\udc45\ud835\udc5b) is expressed as; \n\n\n\n\n\n\n\n\ud835\udc45\ud835\udc5b = (1\u2212\u221d)\ud835\udc44\ud835\udc56\ud835\udc5b\ud835\udc50 + \ud835\udf0e\u2107\ud835\udc60(\u2107\ud835\udc4e\ud835\udc47\ud835\udc4e\n4 \u2212 \ud835\udc47\ud835\udc60\n\n\n\n4) (Tsoutsos et al., 2009; Novak, 2010) \n\n\n\n\n\n\n\nThe heat transfer between the chilled water and the surrounding soil is \n\n\n\ndetermined using the \u2018total heat transfer coefficient\u2019 (\ud835\udc48\ud835\udc34) across the wall \n\n\n\nof the chilled water pipe from the surrounding soil (Fig. 2). \n\n\n\n\n\n\n\n\ud835\udc44 = \ud835\udc48\ud835\udc34(\ud835\udc47\ud835\udc5d \u2212 \ud835\udc47\ud835\udc64) \n\n\n\n\n\n\n\n \nFigure 2: (a \u2013 c) Chilled water tube cross section and flow of heat across \n\n\n\nthe sections \n \nThe overall heat transfer coefficient (UA) is expressed in terms of the \n\n\n\nresistance to heat transfer across the chilled water pipe (Lee and Strand, \n\n\n\n2008).  The resistance against the flow of heat across the chilled water \n\n\n\npipe, from the surrounding soil is represented in Fig. 3.  Perfect contact is \n\n\n\nassumed between the soil and the chilled water pipe, thus temperature of \n\n\n\nthe soil in contact with the pipe and that of the pipe are equal (Niu et al., \n\n\n\n2015). \n\n\n\n\n\n\n\n \nFigure 3: Heat transfer resistance across chilled water pipe \n\n\n\nResistance to convective heat transfer between the inner surface of the \npipe and the chilled water (\ud835\udc451) is expressed as; \n\n\n\n\n\n\n\n\ud835\udc451 =\n1\n\n\n\n\u210e\ud835\udc34\n=\n\n\n\n1\n\n\n\n2\ud835\udf0b\ud835\udc5f\ud835\udc61\ud835\udc59\u210e\ud835\udc50\n \n\n\n\n\n\n\n\nWhere; \u210e\ud835\udc50 =\n\ud835\udc41\ud835\udc62\ud835\udc58\ud835\udc64\n\n\n\n2\ud835\udc5f\ud835\udc61\n  (Subramaniam, 2008)  \n\n\n\nResistance to conduction heat transfer from the outer surface of the pipe \nto its inner surface (R2) is given as; \n \n\n\n\n\ud835\udc452 =\n1\n\n\n\n2\ud835\udf0b\ud835\udc59\ud835\udc58\ud835\udc5d\nln\u2061(\n\n\n\n\ud835\udc5f\ud835\udc61 + \ud835\udc61\ud835\udc5d\n\ud835\udc5f\ud835\udc61\n\n\n\n) \n\n\n\n \nResistance to conduction heat transfer between the surrounding soil and \nthe external wall of pipe (\ud835\udc453) is expressed as; \n \n\n\n\n\ud835\udc453 =\n1\n\n\n\n2\ud835\udf0b\ud835\udc59\ud835\udc58\ud835\udc60\nln\u2061(\n\n\n\n\ud835\udc5f\ud835\udc61 + \ud835\udc61\ud835\udc5d + \ud835\udc51\ud835\udc60\n\ud835\udc5f\ud835\udc61 + \ud835\udc61\ud835\udc5d\n\n\n\n) \n\n\n\n \nTotal resistance; \ud835\udc45\ud835\udc61 = \ud835\udc451 + \ud835\udc452 + \ud835\udc453 \nTotal heat transfer coefficient; \n \n\n\n\n\ud835\udc48\ud835\udc61 = \ud835\udc48\ud835\udc34 =\n1\n\n\n\n\ud835\udc45\ud835\udc61\n \n\n\n\n \nTotal heat transfer is expressed as; \n \n\n\n\n\ud835\udc48\ud835\udc61[\ud835\udc47\ud835\udc50\ud835\udc64_\ud835\udc5c\ud835\udc62\ud835\udc61 \u2212 \ud835\udc47\ud835\udc50\ud835\udc64_\ud835\udc56\ud835\udc5b] \n\n\n\n \nThus, the heat transfer occurring between the surrounding soil and the \nchilled water in the pipe is equivalent to the amount of heat gained by the \nchilled water as it flows through the earth pipe.  The chilled water outlet \ntemperature (\ud835\udc47\ud835\udc50\ud835\udc64_\ud835\udc5c\ud835\udc62\ud835\udc61) is majorly influenced by the amount of heat \ntransferred from the soil (cooling load), through the pipe to the chilled \nwater. \n\n\n\n2.2 Soil cooling load \n\n\n\nCooling load calculation plays an important role in the estimation of heat \n\n\n\nfrom heating, ventilation and air-conditioning (HVAC) facility that needs \n\n\n\nto be overcome by HVAC equipment (Huang et al., 2015; Yue et al., 2016).  \n\n\n\nMeanwhile, it is a common practice in load design to always consider \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growi ng of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n\n\n\n\nuncertainties that may arise from service conditions due to load variations \n\n\n\n(Moser and Folkman, 2008; Liu et al., 2014). This is usually taken care of \n\n\n\nby \u2018factor of safety\u2019, that is used either to allow future expansion in loads \n\n\n\nso that the equipment can operate within the safe range or to reduce the \n\n\n\npossibility of oversizing or under-sizing of HVAC equipment (Ahmedullah, \n\n\n\n2006; Gang et al., 2015; Gang et al., 2016; Parameshwaran et al., 2012). \n\n\n\nThe cooling load in this study is calculated as the product of the net heat \n\n\n\nflux on the soil and a safety factor, given as; \n\n\n\n\u01ec\ud835\udc59\ud835\udc5c\ud835\udc4e\ud835\udc51 = \u01ec\ud835\udc60 \u00d7 \ud835\udefe \n\n\n\nWhere  \ud835\udefe is the factor of safety and \u01ec\ud835\udc60 is the net heat flux on the soil. \n\n\n\nConduction heat transfer within the soil is considered as the net exchange \n\n\n\nof kinetic energy by the soil molecules.  This usually occurs from a higher \n\n\n\ntemperature region to the lower temperature region (Muerth and Mauser, \n\n\n\n2012).  However, researches have shown that at any given depth (z) below \n\n\n\nthe surface, the undisturbed ground temperature (\ud835\udc47\ud835\udc60\n\ud835\udc62) follows a \n\n\n\nperiodical/harmonic variation with time as (Novak, 1981; Krarti and \n\n\n\nKreider, 1996).  \n\n\n\n\ud835\udc47\ud835\udc60\n\ud835\udc62(\ud835\udc67, \ud835\udc61) = \ud835\udc47\ud835\udc5a + \ud835\udc47\ud835\udc4e\ud835\udc5a\ud835\udc45\ud835\udc52(\ud835\udc52\n\n\n\n\ud835\udc56\ud835\udc64\ud835\udc61) \n\n\n\nThis has been demonstrated in an experiment conducted by a researcher \nas shown in Fig. 4 (Qin et al., 2002). Thus; \n\n\n\n\u01ec\ud835\udc50\ud835\udc5c\ud835\udc5b\ud835\udc51(\ud835\udc56,\ud835\udc56+1) = \u2212\ud835\udc58\ud835\udc60(\ud835\udc56)\n\u2206\ud835\udc47\ud835\udc56(\ud835\udc61)\n\n\n\n\u2206\ud835\udc4d\ud835\udc56\n\ud835\udc34\ud835\udc60 \n\n\n\nWhere \ud835\udc58\ud835\udc60\u2061is the soil thermal conductivity, \ud835\udc34\ud835\udc60 is the surface area of the soil \n\n\n\nbed and \u2206\ud835\udc4d is the vertical depth between the top soil and the chilled water \n\n\n\npipe, \ud835\udc47\ud835\udc60\n\ud835\udc62is the undisturbed soil temperature,\u2061\ud835\udc47\ud835\udc5a\u2061&\u2061\ud835\udc47\ud835\udc4e\ud835\udc5a are mean and \n\n\n\namplitude of the ground surface temperature variations, \ud835\udc64 is the angular \n\n\n\nfrequency of the periodic variation (\ud835\udc64 = 2\ud835\udf0b/\ud835\udc51\ud835\udc4e\ud835\udc66) \n\n\n\n \nFigure 4: Daily soil and soil temperature profile [31] \n\n\n\n2.3 Cooling plant model \n\n\n\nAn ammonia based vapour absorption refrigeration (VAR) system is the \n\n\n\ncooling plant considered in this study for chilled water production.  The \n\n\n\nsystem is designed to operate on low-grade heat such as solar energy using \n\n\n\nevacuated tube solar collector.  Considering solar energy as the heat \n\n\n\nsource for the absorption cooling plant, the effective solar collector size \n\n\n\nrequired for absorption refrigeration system has been determined using \n\n\n\nthe following relation (Yeh et al., 2002; Bajpai, 2012; Mumtaz et al., 2016; \n\n\n\nSingh and Mishra, 2018): \n\n\n\n\ud835\udc34\ud835\udc50 =\n\ud835\udc44\ud835\udc62\n\ud835\udc44\ud835\udc56\ud835\udc5b\ud835\udc50\n\n\n\n\n\n\n\n\ud835\udc44\ud835\udc62 =\n\ud835\udc44\ud835\udc4f\n\n\n\n\ud835\udc58\ud835\udc50\n \n\n\n\nPerformance of HVAC system is usually measured in terms of the \n\n\n\nCoefficient of Performance (COP), which relates the cooling output to the \n\n\n\nenergy input for driving the system. In terms of the energy required to \n\n\n\nvaporize the working fluid, the COP is expressed as; \n\n\n\n\ud835\udc36\ud835\udc42\ud835\udc43 =\n\u01ec\ud835\udc86\ud835\udc97\ud835\udc91\n\n\n\n\u01ec\ud835\udc83\ud835\udc90\ud835\udc8a\ud835\udc8d\ud835\udc86\ud835\udc93\n\n\n\n\n\n\n\nHowever, in terms of the overall solar energy available for the system \nactivation, the COP is expressed as;  \n\n\n\n \ud835\udc36\ud835\udc42\ud835\udc43\ud835\udc61\u210e\ud835\udc52\ud835\udc5f\ud835\udc5a\ud835\udc4e\ud835\udc59 =\n\u01ec\ud835\udc52\ud835\udc63\ud835\udc4e\ud835\udc5d\n\n\n\n\u01ec\ud835\udc62\n   \n\n\n\nWhere; \u01ec\ud835\udc62, \u01ec\ud835\udc4f, \u01ec\ud835\udc52\ud835\udc63\ud835\udc4e\ud835\udc5d,\u2061\ud835\udc4e\ud835\udc5b\ud835\udc51 \ud835\udc34\ud835\udc50 , \ud835\udc58\ud835\udc50are the energy received by collector, heat to \n\n\n\nvaporize working fluid, cooling effect, collector effective area, collector \nthermal efficiency, and pump efficiency respectively. \n\n\n\n2.4 Chilled water flow rate model \n\n\n\nThe study considers a dimensioned size of soil bed in which chilled water \n\n\n\npipe is evenly networked.  The passage of chilled water through the \n\n\n\nnetwork of pipe results in heat transfer due to temperature difference.  \n\n\n\nThe temperature difference between the undisturbed soil surrounding the \n\n\n\npipe and soil surface causes difference in heat content between soil load \n\n\n\n(\ud835\udc44\ud835\udc59\ud835\udc5c\ud835\udc4e\ud835\udc51\u2061) and chilled water cooling capacity (\ud835\udc44\ud835\udc60\ud835\udc64). This is otherwise \n\n\n\nreferred to as conduction heat (\ud835\udc44\ud835\udc50\ud835\udc5c\ud835\udc5b\ud835\udc51), mathematically expressed as; \n\n\n\n\ud835\udc44\ud835\udc50\ud835\udc5c\ud835\udc5b\ud835\udc51(\ud835\udc61) = \ud835\udc44\ud835\udc59\ud835\udc5c\ud835\udc4e\ud835\udc51(\ud835\udc61) \u2212 \ud835\udc44\ud835\udc60\ud835\udc64 \n\n\n\nTherefore, soil temperature corresponding to the optimized chilled water \n\n\n\nflow rate is expressed in the following equation as; \n\n\n\n\ud835\udc7b\ud835\udc94\n\ud835\udc95 =\n\n\n\n\ud835\udc85\ud835\udc9b\n\n\n\n\ud835\udc8c\ud835\udc94\ud835\udc68\ud835\udc94\n\n\n\n\ud835\udf49{\ud835\udc78\ud835\udc8d\ud835\udc90\ud835\udc82\ud835\udc85(\ud835\udc95) \u2212 \u1e41\ud835\udc98\ud835\udc6a\ud835\udc91\ud835\udc98\u2206\ud835\udc7b} + \ud835\udc7b\ud835\udc91 \n\n\n\nRelating the chilled water cooling capacity (with respect to soil bed area) \n\n\n\nwith the cooling plant capacity gives the approximate length of the chilled \n\n\n\nwater pipe.  With all specifications appropriately selected, the calculated \n\n\n\nlength of the pipe is evenly networked through the soil bed for even \n\n\n\ntemperature distribution within the soil bed. Table 1 shows the soil bed \n\n\n\nspecifications as used for the experimental soil cooling in this study. \n\n\n\n3. EXPERIMENTAL SET-UP \n\n\n\nThe test rig developed in this study consists of an absorption chiller and \n\n\n\nthe soil bed conducted at the back of Ocean Thermal Energy Centre, \n\n\n\nUniversiti Teknologi Malaysia.  Data collected from the set-up include \n\n\n\nchilled water flow rates and temperatures from the chiller, the soil bed and \n\n\n\nthe chilled water pipes, using T-type thermocouples.  The temperatures \n\n\n\nrecorded during the experimental study were logged to the computer hard \n\n\n\ndisk through the National instrument data logger and compared with \n\n\n\nthose obtained from the mathematical models. \n\n\n\n3.1 Soil cooling \n\n\n\nThe soil cooling system consists of two soil beds; cooled soil bed and \n\n\n\ncontrol soil bed.  Each of the soil beds was filled with loamy soil obtained \n\n\n\nat the experimental site.  The chilled water pipe was buried at 0.15 m \n\n\n\ndepth below the soil bed surface (Fig. 5).  The properties of the soil and \n\n\n\nthat of the soil bed, and chilled water pipe are shown in Table 1.  Type-T \n\n\n\nthermocouples were used to measure temperatures at 8 points on the set-\n\n\n\nup (Fig. 7); four on the cooled soil bed, one on the control soil bed, one each \n\n\n\non chilled water inlet and return, and one to measure the ambient air \n\n\n\ntemperature. \n\n\n\nTable 1: Soil bed and chilled water pipe specifications \n\n\n\nSoil bed container Soil Chilled water pipe \n\n\n\nMaterial Surface area Type Thermal cond. pH Material Length OD Thickness Thermal cond. \n\n\n\nPolystyrene box 0.25 m2 Loamy 1.5 W/m-K 4.85-5.0 HDPE 1.0 m 0.02m 0.0025m 0.42W/m-K \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growi ng of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n\n\n\n\nHigh-density polyethylene pipe (HDPE) was evenly networked through \n\n\n\nthe soil bed (Fig. 5) and chilled water was pumped through the buried pipe \n\n\n\nat controlled rate to cool the soil. \n\n\n\n \nFigure 5: Schematics of the experimental soil bed and piping \n\n\n\nconfiguration \n\n\n\n3.2 Chilled water production \n\n\n\nExperimental investigation of the chilled water production system had \n\n\n\nbeen done via a lab-size absorption cooling unit (Fig. 6), taking its \n\n\n\noperational parameters at steady state to determine its performance \n\n\n\ncharacteristics.   \n\n\n\nPrior to the experimental study, sections of the evaporator, solution heat \n\n\n\nexchanger, and boiler pipes were insulated against the ambient \n\n\n\nenvironment to reduce interference and losses to negligible minimum.  \n\n\n\nCondenser and absorber are air-cooled and were allowed to be fully \n\n\n\nexposed to the ambient air; the air-cooling of the components was aided \n\n\n\nby a mini fan with 2.5 W capacity.  Chiller\u2019s required energy was regulated \n\n\n\nthrough its thermostat. \n\n\n\n \nFigure 6: Experimental absorption refrigeration system \n\n\n\n3.3 Data collection \n\n\n\nThe overall set up contained 14 numbers of data collection points; five \n\n\n\npoint on the chiller, eight on the soil beds and one for the ambient air \n\n\n\ntemperature.  Chiller\u2019s operating temperatures at the boiler, condenser, \n\n\n\nevaporator, absorber and solution heat exchanger were measured via T-\n\n\n\ntype thermocouples as shown in Fig 7A. The soil bed contained nine data \n\n\n\npoint; one flow sensor for measuring the chilled water flow rate to the soil \n\n\n\nbed, and eight number of thermocouples for temperature measurement \n\n\n\n(Fig. 7B).  The experimental data were taken for a period of two weeks.  \n\n\n\nMalaysia has a relatively uniform climatic condition over the year hence \n\n\n\nthe period of the experiment is a representative of the annual climatic \n\n\n\nsituation. \n\n\n\n3.4  Soil cooling parametric analyses \n\n\n\nAssessment of the system performance and its optimization has always \n\n\n\nbeen based on investigation of the influence of each of the key parameters \n\n\n\n(Chen et al., 2017). The effects of air flow rates, and pipe dimension \n\n\n\n(length, diameter, and thickness) on heat transfer between ground buried \n\n\n\npipes were investigated through parametric analysis (Ahmed et al., 2016).  \n\n\n\nIn this study, parametric analysis is conducted on the soil cooling process \n\n\n\nusing the response surface methodology (RSM) of the Design Expert\u00ae \n\n\n\nsoftware.  Response surface methodology (RSM) comprises of \n\n\n\nmathematical and statistical analysis tools suitable for defining effects of \n\n\n\nindependent parameters on the output (Hariharan et al., 2013; Ciarrocchi \n\n\n\net al., 2017). With RSM, performance evaluation can be made at \n\n\n\nintermediate levels that might not have been experimentally studied \n\n\n\n(Hariharan et al., 2013; Rout et al., 2014; Suliman et al., 2017). Central \n\n\n\ncomposite design (Custom) of the RSM was used in this study with a 20 \n\n\n\nnumber of totally randomized runs to optimize the parameters for the soil \n\n\n\ncooling model. Apart from the chilled water flow rates, the effects of other \n\n\n\ninfluencing factors such as chilled water temperature and ambient air \n\n\n\ntemperatures, on the cooled soil temperature were analysed to gain \n\n\n\ninsight to the extents of their effects on the soil temperature \n\n\n\n \nFigure 7: Schematic diagram of experimental set-up with temperature \n\n\n\nand flowrate measurement \n\n\n\n4. RESULTS AND DISCUSIONS \n\n\n\n4.1 Soil cooling performance \n\n\n\nThe peak load during the daytime has significant effects on the \n\n\n\nperformance of the modelled cooling system (Fig. 8). It is also considered \n\n\n\nthat in the absence of external heat (solar radiation heat) on the soil, its \n\n\n\ntemperature gradient decays over time, leading to a thermal equilibrium \n\n\n\nwithin the soil bed (\u2206\ud835\udc47 = 0, thus; \ud835\udc44\ud835\udc50\ud835\udc5c\ud835\udc5b\ud835\udc51 \u2248 0); and bringing the conductive \n\n\n\nheat flux close to zero.  Considering the climatic condition of the \n\n\n\nexperimental site, modelled soil temperature was studied between \n\n\n\n7:00am and 7:00pm during which soil cooling load is at peak due to solar \n\n\n\nradiation (Fig. 4). Chilled water flow rates between 0.06 kg/min to 0.6 \n\n\n\nkg/min were selected for the cooling process and their effects were \n\n\n\nobserved for optimal performance in offsetting the peak load. The cooled \n\n\n\nsoil temperature profile (Fig. 8) shows the effects of the midday peak load \n\n\n\non the cooling performance. \n\n\n\n4.2 Chilled water production \n\n\n\nExperimental tests were carried out on lab size absorption chiller, by \ntaking its temperature profiles at different stages to determine its steady \nstate performance characteristics. Chilled water production was achieved \nfrom the chiller\u2019s evaporator (Fig. 6) whose cooling rates were used to \ndetermine the chilled water production rates (Fig. 9). With sets of inlet and \noutlet chilled water temperature to the evaporator, the mass flow rates of \nthe chilled water were determined with an average flow rate of 0.3kg/hr; \nkeeping the chilled water temperature at the exit and inlet of the chiller \nare at an average of 5 \u2103 and 10 \u2103, respectively. \n\n\n\n \nFigure 8: Modelled soil cooling profile during daytime \n\n\n\nChilled \n\n\n\nwater tank\n\n\n\nChilled \nwater pump\n\n\n\nChilled water pipeT_inlet\n\n\n\nT_s1F_c.w\n\n\n\nT_s4\n\n\n\nT_s1\nT_s2\n\n\n\nT_outlet\n\n\n\nAir cooled \ncondenser \n\n\n\nNH3 liq\n\n\n\nEvaporator \n\n\n\nAbsorber \n\n\n\nRectifier \n\n\n\nChilled water \n\n\n\ntank\n\n\n\nNH3/H2O \n\n\n\nmix\n\n\n\nNH3 vap\n\n\n\nH2O liq\n\n\n\nvalve\n\n\n\nChilled water \n\n\n\npumpPump\n\n\n\nHeat input\n\n\n\nHeat \n\n\n\nexchanger\n\n\n\nBoiler\n(NH3/H2O mixture)\n\n\n\nThx\n\n\n\nTab\n\n\n\nH2O traces\n\n\n\nChilled water\n\n\n\nA\n\n\n\nB\n\n\n\n-50\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n7\n:0\n\n\n\n0\n\n\n\n8\n:0\n\n\n\n0\n\n\n\n9\n:0\n\n\n\n0\n\n\n\n1\n0\n:0\n\n\n\n0\n\n\n\n1\n1\n:0\n\n\n\n0\n\n\n\n1\n2\n:0\n\n\n\n0\n\n\n\n1\n3\n:0\n\n\n\n0\n\n\n\n1\n4\n:0\n\n\n\n0\n\n\n\n1\n5\n:0\n\n\n\n0\n\n\n\n1\n6\n:0\n\n\n\n0\n\n\n\n1\n7\n:0\n\n\n\n0\n\n\n\n1\n8\n:0\n\n\n\n0\n\n\n\n1\n9\n:0\n\n\n\n0\n\n\n\nS\no\nil\n\n\n\n L\no\na\nd\n\n\n\n (\nW\n\n\n\n)\n\n\n\nT\nem\n\n\n\np\ner\n\n\n\na\ntu\n\n\n\nre\n (\n\u2103\n\n\n\n)\n\n\n\nDaytime (hr)\n\n\n\nAmb Air Temp Ts @ m = 0.24 kg/min\n\n\n\nTs @ m = 0.6 kg/min Ts @ m = 0.06 kg/min\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growi ng of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n\n\n\n\n \nFigure 9: Experimental chilled water flow rate at 5 \u2103 \n\n\n\n4.3 Soil temperature profile \n\n\n\nChilled water temperature to the soil bed was maintained at maximum of \n\n\n\n10 \u2103, while the flow rates were ranged between 0.24 kg/min and 0.6 \n\n\n\nkg/min for the experimental soil cooling. The effects of chilled water flow \n\n\n\nrates and ambient air temperature were observed on the cooled soil. Fig. \n\n\n\n10 (A-D) show that the selected chilled water flow rates are normally \n\n\n\nsuitable to offset the soil load considering the soil bed size. However, \n\n\n\nbesides the chilled water flow rates and temperatures, the effects of \n\n\n\ndaytime weather condition were observed on the soil temperature profile \n\n\n\nduring the mid-day, when the air temperature and solar radiation are at \n\n\n\nthe peak. \n\n\n\n\n\n\n\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: (A-D): Experimental soil temperature profiles \n\n\n\nChilled water application was observed to have significant effects on soil \n\n\n\ncooling as indicated by wide difference between the cooled soil and \n\n\n\ncontrol soil temperatures (T_cooled and T_contr, respectively) as shown \n\n\n\nin Fig 10.  It was also observed that all the T_s are at lower range of \n\n\n\ntemperatures starting from 18:00hr, hence chilled water pumping may not \n\n\n\nbe required at night-time to save pumping energy. \n\n\n\n4.4 Model comparison and validation \n\n\n\nTo check the model reliability, variations between the analytical results \n\n\n\nand measured values from the experiment have been quantified using the \n\n\n\npercentage errors and standard deviation under the same conditions. \n\n\n\nWith the chilled water inlet temperatures and flow rates, as shown in Fig. \n\n\n\n11 (A \u2013 D), both of the experimental and modelled soil temperatures are \n\n\n\nobserved to respond to chilled water flow rates and temperatures, and the \n\n\n\nambient conditions of each of the scenarios. It is observed that modelled \n\n\n\nsoil temperatures Fig. 11A are significantly lower than the experimental \n\n\n\nvalues. This is observably due to some simplification assumptions in the \n\n\n\nmodel and the use of historical climatic data for the peak load analysis. \n\n\n\nHowever, results of the model equations and experimental test are \n\n\n\nreasonably close with average values of the standard deviation and \n\n\n\npercentage error of 1.75 and 14.24 %, respectively (Table 2) \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\n4\n.7\n\n\n\n-2\n.4\n\n\n\n-7\n.5\n\n\n\n-1\n1\n\n\n\n.0\n\n\n\n-1\n3\n\n\n\n.4\n\n\n\n-1\n5\n\n\n\n.2\n\n\n\n-1\n6\n\n\n\n.5\n\n\n\n-1\n7\n\n\n\n.5\n\n\n\n-1\n8\n\n\n\n.2\n\n\n\n-1\n8\n\n\n\n.5\n\n\n\n-1\n8\n\n\n\n.7\n\n\n\n-1\n9\n\n\n\n.0\n\n\n\n-1\n9\n\n\n\n.4\n\n\n\n-1\n9\n\n\n\n.7\n\n\n\n-1\n9\n\n\n\n.8\n\n\n\n-1\n9\n\n\n\n.7\n\n\n\n-1\n9\n\n\n\n.6\n\n\n\nC\nh\n\n\n\nil\nle\n\n\n\nd\n W\n\n\n\na\nte\n\n\n\nr\n F\n\n\n\nlo\nw\n\n\n\n R\na\nte\n\n\n\n (\nk\n\n\n\ng\n/h\n\n\n\nr\n)\n\n\n\nEvaporator Temperature (\u2070C)\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\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_C.W inlet T_Amb air\n\n\n\nT_Contr soil T_Cooled 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\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_C.W inlet T_Amb air\n\n\n\nT_Contr soil T_Cooled 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\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_CW inlet T_Amb air\n\n\n\nT_Contr soil T_Cooled 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\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_C.W Inlet T_Amb air\n\n\n\nT_Contr soil T_Cooled soil\n\n\n\nA: (0.24 kg/min) \n\n\n\n\n\n\n\nB: (0.36 kg/min) \n\n\n\n\n\n\n\nD: (0.6 kg/min) \n\n\n\nC: (0.48 kg/min) \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growi ng of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n\n\n\n\n\n\n\n\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: (A-D): Experimental and modelled soil temperature profiles \n \n\n\n\n4.5 Parametric analysis of soil cooling system \n\n\n\nObservations of the soil temperatures against the chilled water flow rates \n\n\n\nshown in Fig. 10 and Fig. 11 indicated that soil temperature is not only \n\n\n\neffected by the variations in chilled water flow rates but also by the \n\n\n\nvariations in chilled water temperatures and ambient environment. \n\n\n\nFurther analysis of the results was carried out with RSM of Design Expert\u00ae \n\n\n\nto assess the extent of the effects of variations of all the three parameters \n\n\n\non the cooling performance \n\n\n\nThe analysis of variance (ANOVA) performed on the interactions of the \n\n\n\nfactors and responses shows that the model is significant, with F and p \n\n\n\nvalues of 7.09 and 0.0030 respectively, and \u201cLack of Fit\u201d with p and F \n\n\n\nvalues of 2.14 and 0.299.  The regression equation developed from the \n\n\n\nANOVA for the cooled soil temperature (T_s) is given as; \n\n\n\n\ud835\udc47\ud835\udc60 = 17.777 + 0.073\u2061(\ud835\udc34) + 0.318(\ud835\udc35) \u2212 466.693\u2061\u2061\u2061\u2061\u2061\u2061\u2061\u2061\u2061\u2061(\ud835\udc36) \n\n\n\nWhere; A, B, and C are the values of ambient air temperature, chilled water \n\n\n\ntemperature, and chilled water flow rates, respectively \n\n\n\nThe equation above can be used to predict the soil temperature (T-s) at any \n\n\n\ngiven condition of ambient air, and chilled water temperature and flow \n\n\n\nrate. \n\n\n\nModel diagnostic plot (Fig. 12) shows the \u201cLambda value\u201d of 1.61, \n\n\n\nregarded as the best, with respect to Lambda\u2019s low and high values.  \n\n\n\nHowever, with respect to the size of the experimental soil bed, 0.42 kg/min \n\n\n\nflow rate is sufficient for the cooled soil temperature can be kept below \n\n\n\n19.5 \u2103, even when the ambient and chilled water temperatures are \n\n\n\nobservably high. Indicating that flow rate beyond 0.42 kg/min may not be \n\n\n\nrequired at any time of the cooling process in this particular case (Fig. 13). \n\n\n\nThis indicates that considering the size of the soil bed, 0.42kg/min of the \n\n\n\nchilled water (5\u2103 \u2013 10\u2103) will keep the soil temperature at 18 \u2103 \u00b12 for \n\n\n\noptimal performance of the temperate crops. \n\n\n\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n45\n\n\n\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_C.W inlet T_Amb air Ts_Model Ts_Exp\n\n\n\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n45\n\n\n\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_C.W inlet T_Amb air\nTs_Model Ts_Exp\n\n\n\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n45\n\n\n\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_C.W inlet Amb Air Ts_Model Ts_Exp\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\nT\nem\n\n\n\np\ner\n\n\n\nat\nu\n\n\n\nre\n (\n\n\n\n\u00baC\n)\n\n\n\nDaytime (hr)\n\n\n\nT_C.W Inlet T_Amb air Ts_(model) Ts_(exp)\n\n\n\nTable 2: Experimental and model soil temperature comparison \n\n\n\nTime \n\n\n\n(hr) \n\n\n\nC.W Inlet Temp (\u00baC) Soil Temp (\u00baC) \n% Error STDev \n\n\n\nExp. Model \n\n\n\n7:00 4.74 17.31 13.61 21.39 2.62 \n\n\n\n8:00 2.13 16.50 12.32 17.53 2.96 \n\n\n\n9:00 2.28 17.05 13.24 27.76 2.69 \n\n\n\n10:00 3.00 17.88 16.06 25.97 1.29 \n\n\n\n11:00 4.29 18.65 18.18 13.89 0.33 \n\n\n\n12:00 3.92 19.55 19.15 6.98 0.28 \n\n\n\n13:00 5.27 19.98 19.47 4.18 0.36 \n\n\n\n14:00 4.12 19.92 18.76 2.27 0.82 \n\n\n\n15:00 3.74 19.28 17.14 2.69 1.51 \n\n\n\n16:00 6.01 18.47 15.58 7.17 2.04 \n\n\n\n17:00 6.06 17.72 13.49 12.10 2.30 \n\n\n\n18:00 5.12 16.55 12.26 18.53 3.04 \n\n\n\n19:00 5.28 16.26 13.66 24.62 1.84 \n\n\n\nAve. 4.30 18.09 15.61 14.24 1.75 \n\n\n\nD: (0.6 kg/min) \n\n\n\nA: (0.24 kg/min) \n\n\n\nC: (0.48 kg/min) \n\n\n\nB: (0.36 kg/min) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growi ng of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n\n\n\n\n \nFigure 12: Model diagnostics plots for soil cooling analysis \n\n\n\n \nFigure 13: 3-D plots of chilled water and ambient air effects on cooled \n\n\n\nsoil \n \n\n\n\n5. CONCLUSION \n\n\n\nApart from high altitude farming, temperate crops are mainly cultivated in \nthe tropics via the use of cool greenhouses.  A more energy efficient soil \ncooling could be a better alternative and could aid small size home \ngardening of the high-value crops with appropriate chilled water \ntemperature and flow rates. This process requires right sizing of HVAC \nequipment for load estimation to be optimally applied. The use of \nabsorption powered by solar energy for this application makes it more \neconomically reasonable. Using various earth-to-air modelling \napproaches, soil cooling load and equivalent absorption chiller\u2019s capacity \nhad been analytically determined with experimental setup conducted to \nverify the model. The analytical models results have been validated with \nthe results obtained from the experimental set-up. Good agreements were \nfound between the analyses of the results obtained from both the \nexperimental and analytical models. The models agreed well, with \nstandard deviation and percentage error of 1.75 and 14.24%, respectively \nfor the soil cooling. Hence, it could be concluded that the application of this \nphysical model could be extended beyond the experimental size of soil bed \nin this study with acceptable range of variations. From the parametric \nanalyses, regression equation developed from the ANOVA of the RSM for \nthe cooled soil temperature is suitable for predicting the soil temperature \n(T_s), given ambient air temperature, and chilled water flow rate and \ntemperature. The RSM showed that cooled soil temperature (T_s) is more \nsignificantly affected by the chilled water flow rate than ambient air and \nchilled water temperatures. This shows the feasibility of controlling the \nsoil temperature through the chilled water flow rate. More so, while there \nis ease of controlling chilled water flow rate and temperature, the ambient \nair temperature is only controlled by the nature. It has also been shown \nthat maximum flow rate of 0.42kg/min would be sufficient, even during \nthe hottest days to keep the soil temperature at 19.5\u2103 or below which is \nconsidered to be   the soil temperature range suitable for the temperate \ncrops. This system of application of renewable energy shows the potential \nfor upgrade and substantial contribution to Nigeria national economy. \n\n\n\nNomenclature \n\n\n\n\ud835\udc45\ud835\udc5b Net solar radiation \ud835\udc52\ud835\udc4e(\ud835\udc61) Water vapor pressure at \nreference height \n\n\n\n\ud835\udc44\ud835\udc56\ud835\udc5b\ud835\udc50 Incident solar \nradiation on the soil \n\n\n\nR Ideal gas constant \n\n\n\n\u221d Soil surface albedo \ud835\udc40\ud835\udc64 Molar mass of water \n\n\n\n\ud835\udf0e Boltzmann constant \ud835\udc34\ud835\udc60 Surface area of the soil bed \n\n\n\n\u2107\ud835\udc60&\u2107\ud835\udc4e soil and air emissivity \u210e\ud835\udc50 Convective heat transfer \ncoefficient \n\n\n\n\ud835\udc47\ud835\udc60\u2061&\u2061\ud835\udc47\ud835\udc4e Temperatures at soil \nsurface and air \n\n\n\nl Length of chilled water tube \n\n\n\n\u01ec\u210e Sensible heat flux \ud835\udc5f\ud835\udc61  Inner radius of the chilled water \ntube \n\n\n\n\u01ec\ud835\udc3f Latent heat due to \nvaporization \n\n\n\n\ud835\udc61\ud835\udc5d Tube thickness, \n\n\n\n \ud835\udf0c\ud835\udc4e Density of air above \nthe soil \n\n\n\n\ud835\udc58\ud835\udc64 Thermal conductivity of water \n\n\n\n\ud835\udc36\ud835\udc5d\ud835\udc4e Specific heat of air \ud835\udc41\ud835\udc62 Nusselt number. \n\n\n\n \ud835\udc5f\ud835\udc4e Boundary layer \nresistance \n\n\n\n\ud835\udc48 Component heat transfer \ncoefficient \n\n\n\n\ud835\udc4d\ud835\udc5f Reference height \u2206\ud835\udc47\ud835\udc59\ud835\udc5a Log temperature difference \nbetween inlet and outlet fluid  \n\n\n\n\ud835\udc48\ud835\udc5f Wind speed   \n\n\n\n\ud835\udc3e Van Karman \nconstant. \n\n\n\n\ud835\udc44\ud835\udc59\ud835\udc5c\ud835\udc4e\ud835\udc51(\ud835\udc61) Cooling load imposed on the soil \nover the measurement period \n\n\n\n\ud835\udf0c\ud835\udc630 Soil surface vapor \ndensity \n\n\n\n\ud835\udc7b\ud835\udc91 Undisturbed soil temperature \nalong the chilled water tube \n\n\n\n\ud835\udf0c\ud835\udc63\ud835\udc4e Water vapor density \nat reference height \n\n\n\n\ud835\udc7b\ud835\udc94\n\ud835\udc95  Soil surface temperature at time \n\n\n\nt \n\n\n\nREFERENCES \n\n\n\nAhmed, S. F., M. T. O. Amanullah, M. M. K. Khan, M. G. Rasul, N. M. S. Hassan. \n2016. Parametric study on thermal performance of horizontal earth \npipe cooling system in summer, Energy Convers. Manag., 114, pp. 324\u2013\n327. \n\n\n\nAhmedullah, S. S. 2006. Integrated Solar Energy and Absorption Cooling \nModel for HVAC (Heating, Ventilation and Air Conditioning) \nApplication in Building, Michigan Technological University. \n\n\n\nBajpai, V. K. 2012. Design of Solar Powered Vapour Absorption System, in \nProceedings of the World Congress on Engineering, 3, pp. 2\u20136. \n\n\n\nCampiotti, C. A., G. Morosinotto, G. Puglisi, E. Schettini, G. Vox. 2016. \nPerformance Evaluation of a Solar Cooling Plant Applied for \nGreenhouse Thermal Control, Agric. Agric. Sci. Procedia, 8, pp. 664\u2013\n669. \n\n\n\nChen, Y., H. Yang, Y. Luo. 2017. Parameter sensitivity analysis and \nconfiguration optimization of indirect evaporative cooler (IEC) \nconsidering condensation, Appl. Energy, 194, pp. 440\u2013453. \n\n\n\nCiarrocchi, E., A. Arteconi, X. Zheng, F. Polonara. 2017. Assessment of the \nEnergy Performance of an Air Source Heat Pump by Response Surface \nMethodology, pp. 1\u20136. \n\n\n\nFeng, J. D., S. Schiavon, F. Bauman. 2016. New method for the design of \nradiant floor cooling systems with solar radiation 1 Introduction, 125, \npp. 9\u201318. \n\n\n\nGang, W., G. Augenbroe, S. Wang, C. Fan, F. Xiao. 2016. An uncertainty-\nbased design optimization method for district cooling systems, \nEnergy, 102, pp. 516\u2013527. \n\n\n\nGang, W., S. Wang, K. Shan, D. Gao. 2015. Impacts of cooling load calculation \nuncertainties on the design optimization of building cooling systems, \nEnergy Build., 94, pp. 1\u20139. \n\n\n\nHariharan, N. M., P. Sivashanmugam, S. Kasthurirengan. 2013. \nOptimization of thermoacoustic refrigerator using response surface \nmethodology, J. Hydrodyn., 25(1), pp. 72\u201382. \n\n\n\nHuang, C., T. Bai, J. Cai, L. Lv, J. Chen, L. Li. 2015. Experimental Study on the \nRadiant Cooling Load of Floor based on the Radiant Time Series \nMethod, Procedia Eng., 121, pp. 45\u201351. \n\n\n\nKim, G. S., S. J. Joo. 2020. Seasonal Variation of Soil Respiration in the \nMongolian Oak (Quercus mongolica Fisch. Ex Ledeb.) Forests at the \nCool Temperate Zone in Korea, Forest, 11(984), pp. 1\u201322. \n\n\n\nDesign-Expert\u00ae Software\n\n\n\nT_s\n\n\n\nLambda\n\n\n\nCurrent = 1\n\n\n\nBest = 1.61\n\n\n\nLow C.I. = -3.95\n\n\n\nHigh C.I. = 7.16\n\n\n\nRecommend transform:\n\n\n\nNone\n\n\n\n (Lambda = 1)\n\n\n\nLambda\n\n\n\nL\nn\n\n\n\n(R\ne\n\n\n\nsi\nd\n\n\n\nu\na\n\n\n\nlS\nS\n\n\n\n)\n\n\n\nBox-Cox Plot for Power Transforms\n\n\n\n2.7\n\n\n\n2.75\n\n\n\n2.8\n\n\n\n2.85\n\n\n\n2.9\n\n\n\n2.95\n\n\n\n3\n\n\n\n-3 -2 -1 0 1 2 3\n\n\n\n2.95754\n\n\n\nDesign-Expert\u00ae Software\n\n\n\nT_s\n\n\n\nLambda\n\n\n\nCurrent = 1\n\n\n\nBest = 1.61\n\n\n\nLow C.I. = -3.95\n\n\n\nHigh C.I. = 7.16\n\n\n\nRecommend transform:\n\n\n\nNone\n\n\n\n (Lambda = 1)\n\n\n\nLambda\n\n\n\nL\nn\n\n\n\n(R\ne\n\n\n\nsi\nd\n\n\n\nu\na\n\n\n\nlS\nS\n\n\n\n)\n\n\n\nBox-Cox Plot for Power Transforms\n\n\n\n2.7\n\n\n\n2.75\n\n\n\n2.8\n\n\n\n2.85\n\n\n\n2.9\n\n\n\n2.95\n\n\n\n3\n\n\n\n-3 -2 -1 0 1 2 3\n\n\n\n2.95754\n\n\n\nDesign-Expert\u00ae Software\n\n\n\nFactor Coding: Actual\n\n\n\nT_s (C)\n\n\n\n20.996\n\n\n\n15.864\n\n\n\nX1 = A: Amb Air Temp\n\n\n\nX2 = B: Chilled water Temp\n\n\n\nActual Factor\n\n\n\nC: Flow Rates = 0.007\n\n\n\n1.5  \n2.6  \n\n\n\n3.7  \n4.8  \n\n\n\n5.9  \n7  \n\n\n\n  25\n  27\n\n\n\n  29\n  31\n\n\n\n  33\n  35\n\n\n\n15  \n\n\n\n16  \n\n\n\n17  \n\n\n\n18  \n\n\n\n19  \n\n\n\n20  \n\n\n\n21  \n\n\n\nT\n_\ns\n (\n\n\n\nC\n)\n\n\n\nA: Amb Air Temp (C)B: Chilled water Temp (C)\n\n\n\nDesign-Expert\u00ae Software\n\n\n\nFactor Coding: Actual\n\n\n\nT_s (C)\n\n\n\n20.996\n\n\n\n15.864\n\n\n\nX1 = A: Amb Air Temp\n\n\n\nX2 = B: Chilled water Temp\n\n\n\nActual Factor\n\n\n\nC: Flow Rates = 0.007\n\n\n\n1.5  \n2.6  \n\n\n\n3.7  \n4.8  \n\n\n\n5.9  \n7  \n\n\n\n  25\n  27\n\n\n\n  29\n  31\n\n\n\n  33\n  35\n\n\n\n15  \n\n\n\n16  \n\n\n\n17  \n\n\n\n18  \n\n\n\n19  \n\n\n\n20  \n\n\n\n21  \n\n\n\nT\n_\ns\n (\n\n\n\nC\n)\n\n\n\nA: Amb Air Temp (C)B: Chilled water Temp (C)\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 57-64 \n\n\n\n\n\n\n\n \nCite The Article: Rasaq Adekunle Olabomi, Bakar Jaafar, Md Nor Musa, Shamsul Sarip (2022). Soil Temperature Control For Growi ng of High-Value Temperate Crops on \n\n\n\nTropical Lowland. Malaysian Journal of Sustainable Agricultures, 6(1): 57-64. \n\n\n\n\n\n\n\nKrarti M., J. F. Kreider. 1996. Analytical model for heat transfer in an \nunderground air tunnel, Energy Convers. Manag., 37(10), pp. 1561\u2013\n1574. \n\n\n\nLee, K. H., R. K. Strand. 2008. The cooling and heating potential of an earth \ntube system in buildings, Energy Build., 40(4), pp. 486\u2013494. \n\n\n\nLiu, M., W. Saman, F. Bruno. 2014. Computer simulation with TRNSYS for \na mobile refrigeration system incorporating a phase change thermal \nstorage unit, Appl. Energy, 132, pp. 226\u2013235. \n\n\n\nMongkon, S., S. Thepa, P. Namprakai, N. Pratinthong. 2014. Cooling \nperformance assessment of horizontal earth tube system and effect on \nplanting in tropical greenhouse, Energy Convers. Manag., 78, pp. 225\u2013\n236. \n\n\n\nMoser, A., S. Folkman. 2008. Buried Pipe Design 3/E. \n\n\n\nMuerth M., W. Mauser. 2012. Environmental Modelling & Software \nRigorous evaluation of a soil heat transfer model for mesoscale \nclimate change impact studies, Environ. Model. Softw., 35, pp. 149\u2013\n162. \n\n\n\nMumtaz, M., A. Khan, N. I. Ibrahim, R. Saidur, I. M. Mahbubul, F. A. Al-\nsulaiman. 2016. Performance assessment of a solar powered ammonia \n\u2013 water absorption refrigeration system with storage units, Energy \nConvers. Manag., 126, pp. 316\u2013328. \n\n\n\nNiu, F., Y. Yu, D. Yu, H. Li. 2015. Heat and mass transfer performance \nanalysis and cooling capacity prediction of earth to air heat exchanger, \nAppl. Energy, 137, pp. 211\u2013221. \n\n\n\nNiu, F., Y. Yu, D. Yu, H. Li. 2015. Heat and mass transfer performance \nanalysis and cooling capacity prediction of earth to air heat exchanger, \nAppl. Energy, 137, pp. 211\u2013221. \n\n\n\nNovak, M. D. 1981. The Moisture and Thermal Regimes of a Bare Soil in the \nLower Fraser Valley During Spring, University of British Columbia. \n\n\n\nNovak, M. D. 2010. Agricultural and Forest Meteorology Dynamics of the \nnear-surface evaporation zone and corresponding effects on the \nsurface energy balance of a drying bare soil, Agric. For. Meteorol., \n150(10), pp. 1358\u20131365. \n\n\n\nOgbodo, E. N., P. O. Okorie, E. B. Utobo. 2010. Growth and Yield of Lettuce \n(Lactuca sativa L.) At Abakaliki Agro-Ecological Zone of Southeastern \nNigeria, World J. Agric. Sci., 6(2), pp. 141\u2013148. \n\n\n\nParameshwaran, R., S. Kalaiselvam, S. Harikrishnan, A. Elayaperumal. \n2012. Sustainable thermal energy storage technologies for buildings: \nA review, Renew. Sustain. Energy Rev., 16(5), pp. 2394\u20132433. \n\n\n\nQin, Z., P. Berliner, A. Karnieli. 2002. Numerical solution of a complete \nsurface energy balance model for simulation of heat fluxes and surface \ntemperature under bare soil environment, Appl. Math. Comput., \n130(1), pp. 171\u2013200. \n\n\n\nRicardo, C., R. Gil, A. Cooman. 2008. Original papers Use of geostatistical \nand crop growth modelling to assess the variability of greenhouse \ntomato yield caused by spatial temperature variations, 5, pp. 219\u2013227. \n\n\n\nRout, S. K., B. K. Choudhury, R. K. Sahoo, S. K. Sarangi. 2014. Multi-objective \nparametric optimization of Inertance type pulse tube refrigerator \nusing response surface methodology and non-dominated sorting \ngenetic algorithm, Cryogenics (Guildf)., 62, pp. 71\u201383. \n\n\n\nSabri, N. S. A., Z. Zakaria, A. B. Jaafar, S. E. Mohamad. 2018. The use of soil \ncooling for growing temperate crops under tropical climate, Int. J. \nEnviron. Sci. Technol., 0123456789. \n\n\n\nSeo, J. M., D. Song, K. H. Lee. 2014. Possibility of coupling outdoor air \ncooling and radiant floor cooling under hot and humid climate \nconditions, Energy Build., 81, pp. 219\u2013226. \n\n\n\nSingh, H., R. S. Mishra. 2018. Detailed parametric analysis of solar driven \nsupercritical CO 2 based combined cycle for power generation, cooling \nand heating effect by vapor absorption refrigeration as a bottoming \ncycle, Therm. Sci. Eng. Prog. \n\n\n\nSubramanian, R. S. 2008. Heat transfer in Flow Through Conduits. \n\n\n\nSuliman, R., A. F. Mitul, L. Mohammad, G. Djira, Y. Pan, Q. Qiao. 2017. \nModeling of organic solar cell using response surface methodology, \nResults Phys., 7, pp. 2232\u20132241. \n\n\n\nTsoutsos, T., M. Karagiorgas, G. Zidianakis. 2009. Development of the \nApplications of Solar, 18(7), pp. 1\u201315. \n\n\n\nVan Labeke, M., P. Dambre, M. C. van Labeke, P. Dambre. 1993. Response \nof five Alstroemeria cultivars to soil cooling and supplementary \nlighting, Sci. Hortic. (Amsterdam)., 56(2), pp. 135\u2013145. \n\n\n\nWongkee, S., S. Chirarattananon, P. Chaiwiwatworakul. 2014. A Field Study \nof Experimental of Radiant Cooling for Residential Building in a \nTropical Climate, J. Autom. Control Eng., 2(1), pp. 67\u201370. \n\n\n\nWu, X., J. Zhao, B. W. Olesen, L. Fang, F. Wang. 2015. A new simplified model \nto calculate surface temperature and heat transfer of radiant floor \nheating and cooling systems, pp. 285\u2013293. \n\n\n\nYeh, H.M., C.D. Ho, J.Z. Hou. 2002. Collector efficiency of double-flow solar \nair heaters with fins attached, Energy, 27(8), pp. 715\u2013727. \n\n\n\nYue, C., D. Han, W. Pu, W. He. 2016. Parametric analysis of a vehicle power \nand cooling/heating cogeneration system, Energy, 115, pp. 800\u2013810. \n\n\n\nZarrella, A., M. De Carli, C. Peretti. 2014. Radiant floor cooling coupled with \ndehumidification systems in residential buildings: A simulation-based \nanalysis, Energy Convers. Manag., 85, pp. 254\u2013263. \n\n\n\nZhou, X., Y. Liu, M. Luo, L. Zhang, Q. Zhang, X. Zhang. 2019. Energy & \nBuildings Thermal comfort under radiant asymmetries of floor \ncooling system in 2 h and 8 h exposure durations, Energy Build., 188\u2013\n189, pp. 98\u2013110.  \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 79-80 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.79.80 \n\n\n\nCite The Article: L. Chervinsky, M. Tregub, S. Makoda (2022). Pre-Sowing Stimulation of Wheat Seed Growth By  \nInfrared Radiation. Journal of Sustainable Agricultures, 6(2): 79-80 \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.79.80\n\n\n\nPRE-SOWING STIMULATION OF WHEAT SEED GROWTH BY INFRARED \nRADIATION \n\n\n\nL. Chervinskya*, M. Treguba, S. Makodab \n\n\n\na Department of Electrical Power Engineering, Electrical Engineering and Electromechanics, BilaTserkva National Agrarian University.  \nb National University of Life and Environmental Sciences of Ukraine. UKRAINE \n*Corresponding Author E-mail: lchervinsky@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 28 November 2021 \nAccepted 31 December 2021 \nAvailable online 11 January 2022\n\n\n\nWheat production is the most common edible crop in the world, accounting for one third of the world's diet. \nTherefore, the main thesis of the development of modern crop production in Ukraine is to reduce costs and \nintroduce innovative technologies for the production of quality wheat. The quality of grain and seed depends \non many factors, namely: agro-climatic conditions, sowing condition of the seed material, quality \ncharacteristics of the soil, yielding properties of seeds, pre-sowing seed treatment. etc. For this purpose, the \nphotosynthesis and intensity of photosynthesis need to be limited to the width of the leaf and the height of \nthe leaves by a smaller cut of the stem. It is extremely important to ensure that the head and side pagons of \nwheat are in good condition. All parameters are often secured by the technology of grain preparation before \ndelivery. Prior to this technology, it is possible to introduce processing of the material in the form for the \ndevelopment of the material. This article presents the effectiveness of the use of infrared irradiation for the \npre-sowing treatment of winter wheat seeds in Sekobra Research, Germany.  \n\n\n\nKEYWORDS \n\n\n\nInfrared Irradiation, pre-sowing stimulation, Growth Parameters, quality wheat, Thermostat. \n\n\n\n1. INTRODUCTION\n\n\n\nIncreasing crop yields is a major issue in agricultural development in \n\n\n\nUkraine. To improve the sowing qualities of seeds and increase the \n\n\n\ngermination energy, various methods of removing their biological system \n\n\n\nfrom rest, including optical methods are used (Cherenkov and Kosulin, \n\n\n\n2005). In particular, stimulation of seeds with laser irradiation allows to \n\n\n\nincrease germination and growth energy within 20% and, as a \n\n\n\nconsequence, to obtain a yield increase of 11\u201312% at low energy costs \n\n\n\n(Velsky, 1996). Infrared irradiation can be attributed to both photovoltaic \n\n\n\nand thermal methods because the rays of this range have a high \n\n\n\npermeability and cause the seeds to heat up. The positive effect of this \n\n\n\ntreatment is to increase the germination and growth energy at the initial \n\n\n\nstages of plant development within 11% (Altukhov and Fedotov, 2011). \n\n\n\nUltraviolet irradiation of seeds and plants has also become widespread, \n\n\n\nespecially in closed soil conditions. The method is used for \n\n\n\ndecontamination of seed material, air, soil, control of plant diseases, \n\n\n\ncontinuation of daylight (Chervinsky and Pashkovska, 2018). \n\n\n\n2. METHODS \n\n\n\nEffects of pre-sowing treatment of winter wheat seed by infrared \n\n\n\nirradiation on its germination and germination energy were studied in \n\n\n\nbiosensor laboratories and electrical laboratories of illumination and \n\n\n\nirradiation of NUBAN (\u201cNUBIP\u201d) of Ukraine. Seeds of winter wheat \n\n\n\nproduced by Sekobra Research were treated with infrared irradiation by \n\n\n\nIKZK -250 lamp at a distance of 2 cm from the lamp for a time from 30 sec \n\n\n\nto 2 min. Then the energy of germination, seed germination and growth \n\n\n\nparameters of seedlings were studied. To determine the similarity of \n\n\n\nwheat seeds the existing method according to DSTU (GOST) 4138 \u2013 2002 \n\n\n\nwas used. \n\n\n\n3. RESULTS\n\n\n\nThe seed germination energy is an important indicator of the quality of the \nseed. It characterizes the degree of viability of the seeds, the ability to give \nquick and large-scale harvest, which is of great importance for obtaining a \nhigh yield. Laboratory seed germination is a quantitative indicator of its \nquality, which is a measure of viability. Seeds with low germination \nsharply deteriorate the yielding properties and quite often, even with \nincreasing seeding rates, it is impossible to reach a high yield. To \ndetermine the germination and germination energy from a batch of seeds, \nsamples were taken in four replicates of 100 seeds each. Each seed sample \nwas spread on moistened filter paper placed on the bottom of the grow \nbox. The grow box was covered with a glass plate, signed and placed in a \nthermostat for seed germination. The thermostat maintained a constant \nhumidity of the filter paper and a temperature of about 20 \u00b0 C. Seed \ngermination of different cultures will be determined after a certain period \nof stay in the thermostat, wheat seeds are determined after 7 days.  \n\n\n\nGerminated is considered to be a seed in which the seedlings and roots are \nnormally developed, and the main root is not shorter than the length of the \nseed. The sprouted seeds have underdeveloped roots or they do not have \nor they have decayed, and the seedlings have the form of a single stalk or \nit\u2019s absent at all. The number of sprouted seeds in a 100-seed sample \ndetermines the germination of seeds in percentage. Out of the four \nreplicates, the average percentage is obtained, which will characterize a \n\n\n\n\nmailto:lchervinsky@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 79-80 \n\n\n\nCite The Article: L. Chervinsky, M. Tregub, S. Makoda (2022). Pre-Sowing Stimulation of Wheat Seed Growth By  \nInfrared Radiation. Journal of Sustainable Agricultures, 6(2): 79-80. \n\n\n\nparticular variant (batch) of seeds (Kindruk et al., 2003). Simultaneously \nwith the laboratory similarity, the energy of seed germination - its ability \nof quick and large-scale germination (DSTU (GOST) 2949-94, p.15) is also \nunder determination. Naturally, the seeds that germinate first have a \n\n\n\nhigher vitality and form a more productive offspring than those that \nsprout later, precisely by measuring the top and bottom of the seedlings \nwith a millimeter ruler (Gritsaenko et al., 2003). The results of the study \nare summarized in Table 1. \n\n\n\nTable 1: Effect of pre-sowing infrared irradiation of seeds on sowing qualities of winter wheat \n\n\n\nOptions Germination energy, % Seed germination, % \n\n\n\nSeeds chemically treated (standard) 90,0   96,7 \n\n\n\nRaw seeds 88,0   96,0 \n\n\n\nThe seeds were treated with infrared    radiation \n\n\n\n30 sec 92,0   94,7 \n\n\n\n45 sec 97,3 100,0 \n\n\n\n60 sec 94,7   99,3 \n\n\n\n2  minutes 93,3   97,3 \n\n\n\nAccording to Table 1 it is visible that pre-sowing treatment of winter \nwheat seeds by infrared irradiation improves sowing quality of seeds. In \nparticular, it was noted that the energy index of germination of wheat \nseeds treated with infrared radiation for 30, 45, 60 seconds exceeded the \nseeds treated with chemicals (standard) by 2, 7.3 and 4.7%, respectively. \nBy increasing the processing time to two minutes, the indicator decreased, \nbut remained higher than the standard by 3.3%. The germination of wheat \nseeds treated with chemical agents was 96.7%, while that of untreated \n96%. When exposed to infrared radiation for 45 and 60 seconds, this \nindicator increased to 100 and 99.3%, respectively, which confirms the \nhigh stimulating effect of pre-sowing in this period of time. Pre-treatment  \n\n\n\nwith infrared irradiation for 45 and 60 sec results the best stimulation of \nthe germination energy and germination of wheat seeds. The effect of \ninfrared radiation on the biometric parameters of winter wheat seedlings \nwas studied for 7 days. From the Table  2 and Figure 1 shows that the \ntreatment of seeds with infrared radiation for 30 to 60 seconds stimulated \ngrowth processes in plants. Thus, according to Table 2 we can conclude \nthat in the laboratory the highest stimulating effect was observed during \nseed treatment with infrared radiation for 45 and 60 sec. The lengths of \nthe underground parts of the seedlings increased by 6.5 and 6.6 cm, \nrespectively, compared to the standard. \n\n\n\nTable 2: Effect of pre-sowing infrared irradiation on winter wheat growth rates \n\n\n\nOption Length of the underground part of the seedling, cm Length of the aboveground part of the seedling, cm \n\n\n\nThe seeds are chemical processing \n(standard) \n\n\n\n7,2 7,5 \n\n\n\nRaw seeds 4,1 4,3 \nThe seeds were treated with \n\n\n\ninfrared radiation \n30 sec 7,4 8,2 \n45 sec 13,7 10,1 \n60 sec 13,8 10,3 \n\n\n\n2 minutes 7,1 6,1 \n\n\n\nFigure 1: Undergroundparts of seedlings Stand.  Raw seeds  30 sec  45 \nsec  60 sec  120 sec \n\n\n\n4. CONCLUSION\n\n\n\nThus, according to the experimental studies, it can be concluded that pre-\nsowing treatment of winter wheat seeds with infrared radiation lamp \nIKZK-250 improves sowing qualities and stimulates the growth of \nseedlings. The highest rates of germination energy and germination of \nseeds were observed by treating the seeds with IR radiation for 45 sec. \nThese figures were 97.3 and 100% respectively. The highest growth \nparameters were observed in the case of 60 seconds treatment, the \naboveground part of the seedling increased by 37.3% compared to the \nstandard, and the underground part - 91.7%. The results obtained indicate \nthe prospect of pre-sowing winter wheat seed infrared radiation. \n\n\n\nREFERENCES \n\n\n\nKindruk, M., Slyusarenko, O., Getch, V., 2003. Seeds  of agricultural crops. \nMethods  fordetermining quality. Kyiv,   Ukraine:  State Consumption \nStandard of Ukraine. \n\n\n\nGritsaenko, Z.M., Gritsaenko, A.O., Karpenko, V.P., 2003. Methods of \nbiological and agrochemical studies  of plants and soils    Kyiv, Ukraine: \nJSC \u201cNICHLAVA\u201d. \n\n\n\nCherenkov, A.D., Kosulin, N.G., 2005. Application of information \nelectromagnetic fields in technological processes of   agriculture. Journal \nof Light engineering and electricity, 5, Pp. 77-80. \n\n\n\nVelsky, A.I., 1996. Application of laser radiation in crop production. Journal \nof  Sumy State Agrarian University, 1, Pp. 67\u201368. \n\n\n\nAltukhov, I.V.,  Fedotov, V.A., 2011. Influence of infrared radiation of \ndifferent wavelengths on wheat seeds. Journal of Polzunovsky Herald, 2 \n(1), Pp. 156\u2013159. \n\n\n\nChervinsky, L.S., Pashkovska, N.I., 2018. Investigation of Infrared \nRadiation Influence on Sowing Quality and Growth       Indices of Winter \nWheat. Journal of Visnyk KhNTUSG, 195, Pp. 119-112. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.43.50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2021.43.50 \n\n\n\nNUTRIENT COMBINATION WITH BIOCHAR: IMPROVING YIELD AND QUALITY OF \nJUTE SEED \n\n\n\nSyed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana and Md. Abu Sadat* \n\n\n\nBangladesh Jute Research Institute, Manik Mia Avenue, Dhaka-1207, Bangladesh. \n*Corresponding Author Email: sadat@snu.ac.kr\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 26 October 2020 \nAccepted 27 November 2020 \nAvailable Online 24 December 2020\n\n\n\nPlants are naturally growing on the soil without hampering the eco-friendly environment. Intensive \ncultivation of crops with high yielding verities (HYV) hampering the soil health resulting unfertile soil. In \naddition, frequent uses of chemicals as well as overdose of synthetic fertilizer creating hazardous \nenvironment for the living things.  However, to meet up the demand of foods as well as other basic \nrequirements of increasing population of the world crop cultivation techniques need to be modernized. To \novercome this situation, application of organic fertilizer need to apply regularly. Biochar can be a good source \nof organic fertilizer and it is currently using to improve the soil health globally. To examine the effect of \nbiochar on jute growth and yield an experiment was set up in two different locations (Jute Agriculture \nExperimental Station (JAES), Manikganj, and Jute Research Sub Station (JRSS), Jashore of Bangladesh) during \nthe period from August to December, 2019. Results revealed that location (JAES) had significant and positive \neffect on jute physiology and seed yield and as well as seed quality. Among fourteen treatments, treatment T2 \n(Recommended dose of fertilizer (RDF)) showed the highest plant height (163.5 cm), base diameter (7.44 \nmm) and pod per plant (16.5) compare to the rest treatments. Treatment T6 (RDF 75% + 25%\nbiochar@3.0tonha-1) showed the best seed weight (2.13 g) and seed yield/ plant (3.98 g) among all\ntreatments. Interaction among treatments and locations, T2 x L2 affected seed germination (99%) and field\nemergence (92.33%) positively. From this research it was cleared that biochar alone may not enough but\ncombination is required for improving jute seed yield and quality. \n\n\n\nKEYWORDS \n\n\n\nBiochar, location, jute growth, seed yield, seed quality and seed germination. \n\n\n\n1. INTRODUCTION\n\n\n\nJute (Corchorus spp.) belonging to the family Malvaceae, is one of the \n\n\n\nimportant fibre and cash crops of Bangladesh (Islam and Ali, 2017). In \n\n\n\nagricultural sector of Bangladesh, jute is called golden fibre because of \n\n\n\nearning foreign currency to strong the national GDP (Anonymous, 2013). \n\n\n\nJute covers around 80% of bast fibre production world-wide (FAO, 2014). \n\n\n\nJute fiber is well known for its versability, durability and fitness and fiber \n\n\n\nis used for making different type of products including carpet, mat and \n\n\n\ncloths (Zhang et al., 2013). In addition, young leaves of jute are popular as \n\n\n\nvegetables because of containing several proteins, vitamins, minerals and \n\n\n\nantioxidants (Islam, 2007; Mollah et al., 2020; Tareq et al., 2019). Study \n\n\n\nalso showed that jute plant has significant importance for pharmacological \n\n\n\nresearch as it contained several secondary metabolites important for \n\n\n\nhuman health issues (Al-Snafi, 2016).  \n\n\n\nJute had a significant contribution to the national economy of Bangladesh. \n\n\n\nHowever, total area of land under jute cultivation is decreasing \n\n\n\ncontinuously due to the more production of food crops and moreover jute \n\n\n\nprice is not attractive compare to other crops in Bangladesh (Hossain et \n\n\n\nal., 2017). In addition, availability of quality seed and total yield also \n\n\n\naffected jute production in Bangladesh (Sikder et al., 2008). Recently, \n\n\n\nBangladesh government has employed policy to promote investment and \n\n\n\nexport of jute products to regain the lost position of jute in the world \n\n\n\nmarket (Islam and Ali, 2017). Recently increasing trend of jute cultivation \n\n\n\nhas been observed in last few years because of gaining profit in jute \n\n\n\ncultivation (Hossain et al., 2017). Moreover, Bangladesh Jute Research \n\n\n\nInstitute released new variety BJRI tossa pat-8 is playing an important role \n\n\n\nin solving the both problems. \n\n\n\nJute cultivation is highly favored by the environment of Bangladesh. \n\n\n\nHowever, jute production is also dependent on the area or location of jute \n\n\n\ncultivation (Rashid et al., 2007). Jute requires total 1500 mm or more \n\n\n\nrainfall/ water and minimum 250 mm water of each month during jute \n\n\n\ngrowing season in summer \n\n\n\n(http://en.banglapedia.org/index.php?title=Jute).  Jute can be grown in \n\n\n\ndifferent types of soil ranging from clay to sandy loam. Bangladesh Jute \n\n\n\nResearch Institute (BJRI) applied different dose of fertilizer based on the \n\n\n\nvarietal recommendation (Islam and Rahman, 2008). Jute plant need \n\n\n\nadequate amount of essential nutrients for optimum plant growth and \n\n\n\ndevelopment leading to yield however, continuous application of N, P and \n\n\n\nK can create imbalance effect of nutrients leading to decrease fertilizer \n\n\n\nefficiency (Lin et al., 2019; Ali et al., 2019). To overcome this problem \n\n\n\napplication of biochar might be one of the proper solutions to solve this \n\n\n\nproblem.  \n\n\n\n\nmailto:biochar@3.0tonha-1\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\nBiochar is the solid product of incomplete combustion of in an oxygen \n\n\n\nlimited environment, which is widely using in agricultural sector (Hussain \n\n\n\net al., 2017; Zhu et al., 2014). Application of biochar to the soils can \n\n\n\npotentially help to reduce climatic change and concurrently improve soil \n\n\n\nfunction leading to high crop production (Nair et al., 2017; Verheijen et al., \n\n\n\n2009). In addition, biochar application increase soil water-holding \n\n\n\ncapacity, enhanced soil water permeability, increase saturated hydraulic \n\n\n\nconductivity (SHC), reduced soil strength as well as modify soil bulk \n\n\n\ndensity (Asai et al., 2009; Chan et al., 2007; Laird et al., 2010). Biochar can \n\n\n\nalso increase nutrient availability of plants by changing the physical and \n\n\n\nchemical properties of soils which help to root colonization by mycorrhizal \n\n\n\nfungi (Yamato et al., 2006). In addition, biochar may also reduce emissions \n\n\n\nof greenhouse gases from soil such as N2O and CH4 leading to eco-friendly \n\n\n\nenvironment (Lehmann and Rondon, 2006). \n\n\n\nImprovement of crop yield in agricultural sector can be achieved by soil \n\n\n\nimprovement through using biochar (Srinivasarao et al., 2013). Biochar \n\n\n\nincrease N availability in soil, reduce leaching loss of N by retain water \n\n\n\nhowever, N can be improved by application of biochar produced from slow \n\n\n\npyrolysis rather than fast pyrolysis (Bruun et al., 2012). It was reported \n\n\n\nthat biochar increases the crop yield without hampering the existing \n\n\n\nnatural environment (Rawat et al., 2019). Report showed that application \n\n\n\nof biochar increases the yield of tomato significantly compare to the \n\n\n\ncontrol treatment (Yilangai et al., 2014). In addition, vegetable yield was \n\n\n\nincreased by 5%-25% with the using of biochar than the traditional \n\n\n\nagricultural practices (Vinh et al., 2014). Recent research on mung bean \n\n\n\nwith biochar revealed the positive effect of biochar on mung bean physical \n\n\n\ngrowth and yield (Toma et al., 2017). However, effect of biochar on the jute \n\n\n\nproduction and seed quality is still unknown. So, this experiment was \n\n\n\nconducted to see the effect of biochar and geographical location on jute \n\n\n\nvariety BJRI tossa-8 seed yield and quality.  \n\n\n\n2. MATERIALS AND METHODS\n\n\n\nThe experiment was conducted at the Jute Agriculture Experimental \n\n\n\nStation, Manikganj, and Jute Research Sub Station, Jashore of Bangladesh \n\n\n\nJute Research Institute (BJRI), Bangladesh during the period from August \n\n\n\nto December, 2019. BJRI tossa pat-8 was used for this research and seeds \n\n\n\nwere collected from Bangladesh Jute Research Institute (BJRI), Manik Mia \n\n\n\nAvenue, Dhaka. The research was laid out in randomized complete block \n\n\n\ndesign (RCBD) with three replications. A total 14 treatment combinations \n\n\n\nwere applied for this experiment (Table 1). The experimental plot was 3.1 \n\n\n\nm \u00d7 3.1 m in size. At the beginning of the experiment, the soil was ploughed \n\n\n\nin two directions and smoothed. Seeds were sown on 10th August, 2019 \n\n\n\nafter seed treating with Vitavax 200\u00ae (0.04%). All cultural operations \n\n\n\nwere according to Chowdhury and Hassan (2013). Jute was harvested on \n\n\n\n30th December, 2019. Data were subjected to ANOVA using MSTAT-C \n\n\n\n(Gomez and Gomez, 1984). Treatment means were separated with LSD \n\n\n\n(Least significant difference). Correlation studies were carried out using \n\n\n\nSTAR software (ver. 2.0.1, IRRI, Los Banos, Laguna, Philippines).  \n\n\n\nTable 1: Treatments combinations used in this experiment. \n\n\n\nT1=Control (No fertilizer),  \n\n\n\nT2=RDF (Recommended dose of fertilizer), \n\n\n\nT3=Biochar@3.0tonha-1 \n\n\n\nT4=Biochar@5.0tonha-1 \n\n\n\nT5=Biochar@8.0tonha-1 \n\n\n\nT6=RDF 75% + 25% Biochar@3.0tonha-1 \n\n\n\nT7= RDF 75% + 25% Biochar@5.0tonha-1 \n\n\n\nT8= RDF 75% + 25% Biochar@8.0tonha-1 \n\n\n\nT9= RDF 50% + 50% Biochar@3.0tonha-1 \n\n\n\nT10= RDF 50% + 50% Biochar@5.0tonha-1 \n\n\n\nT11= RDF 50% + 50% Biochar@8.0tonha-1 \n\n\n\nT12= RDF 25% + 75% Biochar@3.0tonha-1 \n\n\n\nT13= RDF 25% + 75% Biochar@5.0tonha-1 \n\n\n\nT14= RDF 25% + 75% Biochar@8.0tonha-1  \n\n\n\n3. RESULTS\n\n\n\n3.1 Effect of location on vegetative growth, seed yield and seed \n\n\n\nquality of jute \n\n\n\nLocations showed significant effect on plant height (Table 2).  The highest \n\n\n\nplant height (274.74 cm) and base diameter (9.08) was recorded in Jute \n\n\n\nAgriculture Experimental Station (JAES), Manikganj whereas the lowest \n\n\n\nwas recorded in Jute Research Sub-Station (JRSS), Jashore. In addition, \n\n\n\nbranches per plant, pod per plant was higher in JAES, Manikganj. However, \n\n\n\nplant population per plot was not affected by the locations. Interestingly, \n\n\n\npod length was higher in JRSS, Jashore (6.64 cm). Number of seed in each \n\n\n\npod was similar but seed yield per plant was higher in JAES, Manikganj \n\n\n\n(3.5 g). Seed size normally determine the seed weight. Weight of \n\n\n\nthousands of seed was higher in JRSS (2.20 g), Jashore compare to the \n\n\n\nseeds of JAES, Manikganj (1.88 g) (Table 3). However, percent of \n\n\n\ngermination (90%) and field emergence percent (83%) of seeds were \n\n\n\nhigher in JAES, Manikganj (Table 3). These results clearly suggesting that \n\n\n\nlocation had significant effect on vegetative growth of jute, seed yield as \n\n\n\nwell as seed quality\n\n\n\nTable 2: Effect of location on yield and yield contributing characters of jute seed. \n\n\n\nLocation \nName \n\n\n\nPlant \nheight (cm) \n\n\n\nBase \nDiameter \n(mm) \n\n\n\nPlant \npopulation / \nplot \n\n\n\nBranches / \nplant \n\n\n\nPod / \nplant \n\n\n\nPod \nlength \n(cm) \n\n\n\nPod \ndiameter \n(mm) \n\n\n\nNo. of seed \nper pod \n\n\n\nSeed \nyield \nper \nplant \n(g) \n\n\n\nJRSS 76.12b* 5.03b 167.57a 2.65b 9.66b 6.64a 4.91b 172.81a 3.05b \n\n\n\nJAES 234.74a 9.08a 168.55a 3.43a 21.44a 6.270b 5.09a 176.86a 3.50a \n%CV 9.68 11.90 11.97 14.79 19.23 6.21 4.68 9.87 19.75 \nLSD 6.59 0.37 12.18 0.20 1.31 0.18 0.10 7.56 0.28 \n\n\n\nNote: JRSS: Jute Research Sub-Station, Monirumpur, Jashore; JAES: Jute Agriculture Experimental Station, Manikganj.  \n\n\n\n* values in columns followed by the same letter are not significantly different, P\u2264 0.05, LSD\n\n\n\nTable 3: Effect of location on quality of jute seed \nLocation \nName \n\n\n\n1000-seed \nweight (g) \n\n\n\nGermination \npercent \n\n\n\nField Emergence \npercent \n\n\n\nJRSS 2.20a* 84.79b 79.62b \nJAES 1.88b 89.83a 83.14a \n%CV 5.45 8.63 8.73 \nLSD 0.12 3.30 3.11 \n\n\n\nNote: JRSS: Jute Research Sub-Station, Jashore; JAES: Jute Agriculture \n\n\n\nExperimental Station, Manikganj. \n\n\n\n* values in columns followed by the same letter are not significantly\n\n\n\ndifferent, P\u2264 0.05, LSD.  \n\n\n\n3.2 Effect of biochar and fertilizer on jute growth and yield \n\n\n\ncontributing characters \n\n\n\nRecommended dose of fertilizer (RDF), biochar and combination of RDF \n\n\n\nand biochar had significant effect on jute vegetative growth and yield \n\n\n\ncontributing characters (Table 4). Results showed that treatment T2  \n\n\n\n(RDF) and T7 (RDF 75% + 25% Biochar@5.0tonha-1) increased plant \n\n\n\nheight significantly whereas treatment T3 (T3=Biochar@3.0tonha-1) and \n\n\n\nT14 (RDF 25% + 75% Biochar@8.0tonha-1) showed reduction in plant \n\n\n\nheight among all treatments including control (T1) (Table 4). In case of \n\n\n\nplant base diameter, expanded plant diameter was observed in three \n\n\n\ntreatments (T2, T11 and T12) however, lower plant base diameter was \n\n\n\nobserved in two treatments T3, T14. Branches per plant were increased \n\n\n\nafter application of RDF 25% + 75% Biochar@3.0tonha-1 (T12) but most \n\n\n\nof the treatments negatively affected in increasing branches of jute.  \n\n\n\nApplication of treatments showed negative effect on number of pods per \n\n\n\nplant and similarly pod length was not increased after application of either \n\n\n\nsole RDF and biochar or combinations of RDF with biochar (Table 4).  \n\n\n\nHowever, pod diameter was increased (5.2 mm) by the treatment T7 (RDF \n\n\n\n75% + 25% Biochar@5.0tonha-1) compare to rest of the treatments. Seed \n\n\n\nyield was highest in T6 and lowest in T14 compare to other treatments \n\n\n\nalong with control (T1). Number of seed per pod was negatively affected \n\n\n\nby the treatments. It was quite interesting that plant population per plot \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\nhad no effect by the treatments (Table 4). Form the above results it can be \n\n\n\nsaid that jute vegetative growth affected by different treatments were not \n\n\n\nthe related to the jute seed yield. From these results it can be said that \n\n\n\nsingle treatment is not enough to contribute the vegetative growth of jute \n\n\n\nand seed production. \n\n\n\nTable 4: Effect of biochar treatments on yield and yield contributing characters of jute seed \n\n\n\nTreatments \n\n\n\nPlant \nheight \n(cm) \n\n\n\nBase \nDiameter \n(mm) \n\n\n\nPlant \npopulation / \nplot \n\n\n\nBranches \n/ plant \n\n\n\nPod / \nplant \n\n\n\nPod length \n(cm) \n\n\n\nPod Diameter \n(mm) \n\n\n\nNo. of seed / \npod \n\n\n\nSeed yield / \nplant (g) \n\n\n\n1 145.52bc* 7.18ab 159.3a 3.07ab 15.78a 6.93 a 4.82c 191.90a 2.91cd \n\n\n\n2 163.50a 7.44a 161.67a 3.23ab 16.52a 6.28b 4.98abc 171.03b 3.45abc \n\n\n\n3 143.22c 6.42b 187.17a 2.83 b 14.35ab 6.35b 5.09abc 179.07ab 2.92cd \n\n\n\n4 149.95abc 6.99ab 168.50a 2.75b 14.53ab 6.66ab 4.98abc 179.53ab 3.10bcd \n\n\n\n5 157.90abc 6.90ab 185.67a 3.08ab 15.97a 6.29b 4.88bc 179.05ab 2.89cd \n\n\n\n6 157.25abc 7.04ab 166.50a 2.93 b 15.65ab 6.67ab 4.98abc 170.27b 3.98a \n\n\n\n7 163.83a 7.35ab 170.00 a 2.98ab 15.82 a 6.41b 5.20a 184.75ab 3.53abc \n8 152.75abc 6.95ab 158.17a 3.25ab 17.13a 6.32b 5.04abc 165.93b 3.80ab \n\n\n\n9 158.35abc 6.90ab 158.67a 3.03ab 15.50ab 6.48ab 4.99abc 179.68ab 3.26abcd \n10 160.18abc 7.00ab 162.00a 3.05ab 16.45a 6.33 b 5.14ab 169.25b 3.27abcd \n\n\n\n11 159.60abc 7.49a 167.33a 2.98ab 15.05ab 6.51ab 5.06abc 170.58b 3.25abcd \n\n\n\n12 161.92ab 7.47a 171.50a 3.48 a 16.05a 6.42b 4.97abc 170.05b 3.54abc \n\n\n\n13 157.97abc 7.25ab 167.67a 3.07ab 16.62a 6.39b 4.91bc 165.02b 3.43abc \n\n\n\n14 144.07c 6.38b 168.67a 2.77b 12.28 b 6.36b 4.98abc 171.53b 2.61d \n\n\n\n%CV 9.69 11.90 11.97 14.79 19.24 6.21 4.68 9.87 19.75 \n\n\n\nLSD 17.44 0.97 32.27 0.52 3.47 0.46 0.27 19.99 0.75 \nvalues in columns followed by the same letter are not significantly different, P\u2264 0.05, LSD. \n\n\n\n3.3 Effect of biochar and fertilizer on quality of jute seed \n\n\n\nSeed quality of jute was affected by the application of fertilizer and biochar \n\n\n\n(Table 5). Thousand seeds weight was positively affected by three \n\n\n\ntreatments (T6, T7 and T10). However, T5 treatment reduced seed weight \n\n\n\ncompare to the rest of the treatments and even less to the control \n\n\n\ntreatment (T1). Results showed that application of RDF and biochar has \n\n\n\nno effect on seed germination. It was also found that treatments T4, T5, T7 \n\n\n\nand T9 affected negatively in field emergence percent and these was \n\n\n\nsimilar with the control treatment (T1). Treatment T14 (RDF 25% + 75% \n\n\n\nBiochar@8.0tonha-1) alone showed the positive effect of field emergence \n\n\n\nand the rest treatments showed the intermediate effect. These results \n\n\n\nindicate that seed weight and seed quality of jute are not reliant on the \n\n\n\nmanure or fertilizer status of the growing field.  \n\n\n\nTable 5: Effect of biochar treatments on quality of jute seed \nTreatments \n\n\n\n1000 seed \nweight (g) \n\n\n\nGermination \npercent \n\n\n\nField Emergence \npercent \n\n\n\n1 1.98bcd* 85.33a 78.00b \n\n\n\n2 2.09ab 91.83a 85.17ab \n\n\n\n3 2.01abcd 88.83a 83.33ab \n\n\n\n4 1.96cd 84.17a 78.17b \n\n\n\n5 1.94d 85.33a 78.33b \n\n\n\n6 2.13a 86.33a 81.00ab \n\n\n\n7 2.12a 84.17a 78.17b \n\n\n\n8 2.08abc 86.00a 79.67ab \n\n\n\n9 1.98bcd 85.17a 79.00b \n\n\n\n10 2.12a 86.00a 85.17ab \n\n\n\n11 2.00abcd 90.50a 80.17ab \n\n\n\n12 2.09ab 88.50a 84.67ab \n\n\n\n13 2.05abcd 92.17a 82.83ab \n\n\n\n14 1.98bcd 88.00a 87.83a \n\n\n\n%CV 5.46 8.63 8.72 \n\n\n\nLSD 0.13 8.73 8.23 \n\n\n\n* values in columns followed by the same letter are not significantly\n\n\n\ndifferent, P\u2264 0.05, LSD. \n\n\n\n3.4 Interaction effect of location and treatments on jute growth and \n\n\n\nyield contributing characters \n\n\n\nThe highest plant height 250.7 cm and 268.7 cm was found from the \n\n\n\ninteraction L2 \u00d7 T12 and L2 \u00d7 T7, respectively; all treatments in location 1 \n\n\n\n(L1) showed the lowest plant height (Table 6). In case of plant base \n\n\n\ndiameter, highest result (10.2 mm) was found in L2 \u00d7 T12 and the lowest \n\n\n\n(4.4 mm) was in L1 \u00d7 T14. Plant population per plot was not affected by \n\n\n\nthe interaction of locations and treatments (Table 6). Branches per plant \n\n\n\nwere higher in L2 \u00d7 T12 and lower was in L1 \u00d7 T4. Remaining interactions \n\n\n\nshowed the intermediate result for branches per plant. Jute plant contain \n\n\n\nmore pods in location 2 (L2) compare to the location 1 (L1) with \n\n\n\ninteraction of all treatments. It was quite interesting that pod length was \n\n\n\npositively affected by location 1 (L1) compare to location 2 (L2). The \n\n\n\nhighest pod length was found in L1 \u00d7 T1 and L1 \u00d7 T6 whereas lowest length \n\n\n\nwas observed in L2 \u00d7 T3.  \n\n\n\nPod diameter was significantly, positively increased in all interaction \n\n\n\nexcept L1 \u00d7 T1 and the highest diameter was found in L2 \u00d7 T7. Number of \n\n\n\nseed per pod was higher (200) in treatment combination L2 \u00d7 T1 and \n\n\n\nlower in two treatments L1 \u00d7 T8 and L2 \u00d7 T13. Higher seed weight per \n\n\n\nplant was higher in location 1 (L1) with T6 treatment and lower in location \n\n\n\n1 (L1) with control (T1). Remaining interaction showed the intermediate \n\n\n\nresult. It was also observed that treatment combination L2 \u00d7 T12 showed \n\n\n\nthe positive effect on plant vegetative growth compare to the other \n\n\n\ntreatment combination. From these results it could be concluded that \n\n\n\nlocation affect significantly for growth and seed production of jute plant. \n\n\n\n3.5 Interaction effect of location and treatments on quality of jute \nseed \n\n\n\nCombination of interaction showed significant effect on thousand seed \nweight (Table 7). Highest seed weight was observed in the treatment \ncombination L1 \u00d7 T10 and lower weight was observed in location 2 (L2) with \nthe most of the treatments. Higher seed germination percent (99.0) was \nfound in two treatments L2 \u00d7 T2 and L2 \u00d7 T13, respectively. Treatment \ncombination L1 \u00d7 T6, L1 \u00d7 T10, L2 \u00d7 T4 and L2 \u00d7 T7 showed the lower seed \ngermination percentage (Table 7). Most of the treatment combination \nshowed the negative effect on field emergence percentage; only the \ncombination L2 \u00d7 T2 shoed the positive effect. In addition, L2 \u00d7 T2 showed \npositive effect on both seed germination and field percentage (Table 7). It \nwas also observed that L1 \u00d7 T10 had positive effect on seed weight however, \nseed germination and field emergence percentage was negative. These \nresults pointed that location along with different treatment had no \ninteraction effect on seed quality. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\nTable 6: Location \u00d7 treatment interaction effect on yield and yield contributing characters of jute seed \nInteraction Plant height \n\n\n\n(cm) \nBase \nDiameter \n(mm) \n\n\n\nPlant \npopulation / \nplot \n\n\n\nBranches / \nplant \n\n\n\nPod / \nplant \n\n\n\nPod length \n(cm) \n\n\n\nPod \nDiameter \n(mm) \n\n\n\nNo. of seed / \npod \n\n\n\nSeed yield / \nplant (g) \n\n\n\nL1 \u00d7 T1 72.70 e* 5.16e 144.00a 2.73efgh 10.47 e 7.18 a 4.75e 183.80abcd 1.93h \nL1 \u00d7 T2 84.67e 5.20ef 164.33a 2.83defgh 10.67e 6.60abcde 4.88bcde 177.73abcd 3.65abcde \n\n\n\nL1 \u00d7 T3 74.10e 4.75ef 189.00a 2.43gh 8.37e 6.77 ab 4.97 bcde 174.47abcd 2.60efgh \nL1 \u00d7 T4 72.23e 4.63ef 181.67a 2.27 h 8.53e 6.65abcd 4.92bcde 180.73abcd 2.98cdefg \nL1 \u00d7 T5 73.8e 4.52ef 183.67a 2.43gh 8.53e 6.55 abcdef 4.90 bcde 169.43bcd 2.30gh \nL1 \u00d7 T6 82.83e 5.83e 168.0 a 2.93cdefgh 12.33de 7.17a 4.90bcde 179.20abcd 4.56a \n\n\n\nL1 \u00d7 T7 79.00e 5.40ef 177.00a 2.57fgh 9.43e 6.78ab 4.90bcde 180.83abcd 3.62abcde \nL1 \u00d7 T8 76.50e 5.43ef 157.67a 3.27bcdef 11.83de 6.33bcdef 5.00bcde 159.53d 3.71abcd \nL1 \u00d7 T9 78.37e 5.08ef 160.33a 2.80defgh 9.633e 6.53abcdef 5.18 abc 167.36bcd 3.30bcdefg \nL1 \u00d7 T10 75.37e 5.12 ef 156.67a 2.47 gh 9.60e 6.42bcdef 5.02 bcde 168.17bcd 3.65abcde \nL1 \u00d7 T11 81.87e 5.27ef 159.67a 2.67efgh 9.63e 6.60abcde 4.783de 175.17abcd 2.84defgh \n\n\n\nL1 \u00d7 T12 73.17e 4.72ef 172.67 a 2.63 fgh 8.07e 6.20 bcdef 4.88bcde 163.43cd 2.98cdefgh \nL1 \u00d7 T13 74.93e 4.95ef 171.33a 2.60fgh 10.00e 6.50bcdef 4.87bcde 170.70bcd 2.96cdefgh \n\n\n\nL1 \u00d7 T14 66.13e 4.40f 160.00a 2.40gh 8.13e 6.68abc 5.18 abc 168.73bcd 3.08bcdefg \n\n\n\nL2 \u00d7 T1 218.33cd 9.19abcd 174.67a 3.40bcde 21.10abc 6.67 abcd 4.90bcde 200.00a 2.34fgh \n\n\n\nL2 \u00d7 T2 242.33abc 9.68ab 159.00a 3.63abc 22.37ab 5.95ef 5.07bcde 164.33bcd 3.91abc \n\n\n\nL2 \u00d7 T3 212.33d 8.08d 185.33a 3.23 bcdef 20.33abc 5.93f 5.21 ab 183.67abcd 3.26bcdefg \n\n\n\nL2 \u00d7 T4 227.67abcd 9.35abcd 155.33a 3.23 bcdef 20.53abc 6.67abcd 5.04bcde 178.33abcd 3.25bcdefg \n\n\n\nL2 \u00d7 T5 242.00abc 9.28abcd 187.67 a 3.73 ab 23.40ab 6.03def 4.85bcde 188.67abc 3.22bcdefg \n\n\n\nL2 \u00d7 T6 231.67abcd 8.25cd 165.00a 2.93cdefgh 18.97bc 6.18bcdef 5.05bcde 161.33cd 3.49bcde \n\n\n\nL2 \u00d7 T7 248.67a 9.31abcd 163.00a 3.40bcde 22.20ab 6.03def 5.49a 188.67abc 3.39bcdef \n\n\n\nL2 \u00d7 T8 229.00abcd 8.47bcd 158.67a 3.23bcdef 22.43ab 6.31bcdef 5.07bcde 172.33abcd 3.44bcde \n\n\n\nL2 \u00d7 T9 238.33abc 8.71bcd 157.00a 3.27bcdef 21.37ab 6.43bcdef 5.18 abc 192.00ab 3.88abcd \n\n\n\nL2 \u00d7 T10 245.00ab 8.89abcd 167.33a 3.63abc 23.30 ab 6.23bcdef 5.09bcde 170.33bcd 3.69abcd \n\n\n\nL2 \u00d7 T11 237.33abc 9.72ab 175.00a 3.30bcdef 20.47abc 6.42 bcdef 5.08bcde 166.00bcd 3.53abcde \n\n\n\nL2 \u00d7 T12 250.67a 10.22a 170.33 a 4.33a 24.03a 6.63abcd 5.15 abcd 176.67abcd 4.12ab \n\n\n\nL2 \u00d7 T13 241.00abc 9.55abc 164.00a 3.53bcd 23.23ab 6.29bcdef 4.93bcde 159.33d 3.78abcd \n\n\n\nL2 \u00d7 T14 222.00bcd 8.37bcd 177.33 a 3.13bcdefg 16.43cd 6.03 cdef 5.09bcde 174.33abcd 2.88cdefgh \n\n\n\n%CV 9.68 11.92 11.98 14.79 19.23 6.21 4.68 9.87 19.75 \n\n\n\nLSD 24.66 1.38 45.63 0.74 4.90 0.66 0.38 28.27 1.06 \n\n\n\nL1 = Jute Research Sub-Station, Monirumpur, Jashore; L2 = Jute Agriculture Experimental Station, Manikganj. \n\n\n\n* values in columns followed by the same letter are not significantly different, P\u2264 0.05, LSD.\n\n\n\nTable 7: Location \u00d7 treatment interaction effect on quality of jute seed \n\n\n\nLocation \u00d7 treatment Interaction 1000-Seed weight Germination percent Field Emergence percent \nL1 \u00d7 T1 2.20bcd* 84.33bc 77.00d \nL1 \u00d7 T2 2.33abc 84.67bc 78.00d \n\n\n\nL1 \u00d7 T3 2.12de 84.67bc 79.67cd \nL1 \u00d7 T4 2.11de 86.00bc 80.00cd \nL1 \u00d7 T5 2.10de 86.00bc 78.33d \nL1 \u00d7 T6 2.35ab 82.00c 78.00d \nL1 \u00d7 T7 2.19bcd 86.00bc 80.67bcd \nL1 \u00d7 T8 2.21abcd 84.33bc 79.33cd \n\n\n\nL1 \u00d7 T9 2.16cd 84.67bc 79.00cd \nL1 \u00d7 T10 2.39a 81.33c 76.67d \nL1 \u00d7 T11 2.19bcd 84.67bc 79.33cd \nL1 \u00d7 T12 2.32abc 86.00bc 81.67abcd \nL1 \u00d7 T13 2.08def 85.33bc 83.33abcd \nL1 \u00d7 T14 2.11de 87.00abc 76.67d \n\n\n\nL2 \u00d7 T1 1.78h 86.33bc 79.00cd \nL2 \u00d7 T2 1.86gh 99.00a 92.33a \nL2 \u00d7 T3 1.90fgh 93.00abc 87.00abcd \nL2 \u00d7 T4 1.81h 82.33c 76.33d \nL2 \u00d7 T5 1.78h 84.67bc 78.33d \nL2 \u00d7 T6 1.91fgh 90.67abc 75.67d \nL2 \u00d7 T7 2.05def 82.33c 80.00cd \n\n\n\nL2 \u00d7 T8 1.96efgh 87.67abc 79.00cd \nL2 \u00d7 T9 1.81h 85.67bc 76.33d \nL2 \u00d7 T10 1.86gh 90.67abc 83.67abcd \nL2 \u00d7 T11 1.82h 96.33ab 90.00abc \nL2 \u00d7 T12 1.86gh 91.00abc 84.00abcd \nL2 \u00d7 T13 2.03defg 99.00a 92.00ab \n\n\n\nL2 \u00d7 T14 1.84h 90.67abc 82.67abcd \n%CV 5.45 8.63 8.73 \nLSD 0.18 12.34 11.64 \n\n\n\nL1 = Jute Research Sub-Station, Monirumpur, Jashore; L2 = Jute Agriculture Experimental Station, Manikganj. \n\n\n\n* values in columns followed by the same letter are not significantly different, P\u2264 0.05, LSD.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\n3.6 Correlation coefficient analysis of vegetative growth and quality \n\n\n\nof seed of jute \n\n\n\nStatistical analyses were carried out to understand the associations among \n\n\n\nvegetative growth and seed quality of jute. Analysis revealed that \n\n\n\nsignificant, and positive correlation was found for plant height (PH) with \n\n\n\nbase diameter (BD), branches per plant (BP), plant population per plot \n\n\n\n(PPP), pod diameter (PD) and percentage of seed germination (PGR). \n\n\n\nHowever, pod length (PL), thousand seed weight (TSW) and seed weight \n\n\n\nper plant (SWP) was significantly, and negatively correlated with plant \n\n\n\nheight (Table 8). Plant base diameter (BD) had strong positive relation \n\n\n\nwith branches per plant (BP), plant population per plot (PPP) and \n\n\n\nsignificant negative correlation with thousand seed weight (TSW) and \n\n\n\nseed weight per plant (SWP) (Table 8). It was also observed that \n\n\n\nvegetative growth had negative correlation with jute seed yield and seed \n\n\n\nquality. In addition, seed weight showed negative relation with seed \n\n\n\ngermination (PGR) and field emergence (FE).  \n\n\n\n4. DISCUSSION \n\n\n\nIn agricultural sector, deterioration and impotence of soil are common \n\n\n\nobstacle for crop production (Chan and Xu, 2009). Increasing number of \n\n\n\nstudies focused on the application of biochar for the improvement of soil \n\n\n\nproperties for proper production of crop. Biochar improve soil structure \n\n\n\nto increase water holding capacity and nitrogen availability for the crop \n\n\n\n(Bruun et al., 2012; Laird et al., 2010). Crop production is highly depended \n\n\n\non soil type, temperature, annual rainfall etc. (Mall et al., 2017; Masikati et \n\n\n\nal., 2019). Bangladesh has been divided in thirty agro-ecological zones \n\n\n\n(AEZ) based on the physiography, soils, land levels in relation to flooding \n\n\n\nand agro-climatology. It has been reported that crop productivity varies in \n\n\n\ndifferent AEZs due to several phenomena (Quddus, 2009). In our research \n\n\n\nwe also observed variation in vegetative growth and seed production of \n\n\n\njute (Table 2 and Table 3).  \n\n\n\nJute Agriculture Experimental Station, Manikganj (JAES) is situated in \n\n\n\nAEZ-8 and Jute Research Sub-Station, Monirumpur, Jashore (JRSS) is \n\n\n\nunder the AEZ-11 \n\n\n\n(http://en.banglapedia.org/index.php?title=Agroecological_Zone). The \n\n\n\nsilt-loam soil of JAES might play significant role not only to improve \n\n\n\ngrowth and yield but also quality of seed of jute variety BJRI tossa-8. \n\n\n\nSimilar report was found in the cultivation of edible Amaranthus tricolor \n\n\n\nlines in Japan where production of Amaranthus tricolor was higher in grey \n\n\n\nsoil (Oshiro et al., 2016).  \n\n\n\nPlant requires various types of soil nutrients like N, P and K for their \n\n\n\nproper growth but those nutrients decrease over time in the soil (Rawat \n\n\n\net al., 2019). To fulfill the required N, P and K in the soil, huge amount of \n\n\n\nchemical fertilizer is used however, plant can uptake a small amount from \n\n\n\nthe soil and rest are converted into insoluble forms leading to reducing soil \n\n\n\nfertility, soil degradation, soil compaction and loss of soil carbon \n\n\n\n(Hariparasad and Dayananda, 2013; Lin et al., 2019). Biochar is a soil \n\n\n\namendment agent which has unique physical and chemical properties that \n\n\n\ncan promote crop growth and development as well as crop yield (Gwenzi \n\n\n\net al., 2016; Hammer et al., 2014; Olmo et al., 2016). In this research it was \n\n\n\nfound that most of the plant growth parameters were negatively affected \n\n\n\nby biochar application (Table 4).  \n\n\n\nHowever, pod diameter and seed production of each plant was somehow \n\n\n\npositively affected. In addition, plant population and pods per plant was \n\n\n\nsame in all treatments including control (Table 4). These results indicated \n\n\n\nthat application of biochar alone or combination with fertilizer may not \n\n\n\nprovide sufficient nutrients for proper plant growth. Crops respond to \n\n\n\nbiochar in various ways and sometimes may not give the expected \n\n\n\npotential yield directly but improve the soil health directly (Brandstaka et \n\n\n\nal., 2010; Chan et al., 2008; Deenik et al., 2010; Jien and Wang, 2013). Plant \n\n\n\ngrowth is highly depended on the soil type, cultivated crops, biochar \n\n\n\nsource and cultivation techniques (Jeffery et al., 2015; Oshiro et al., 2016). \n\n\n\nIn addition, crop yield is highly related to the plant population and growth \n\n\n\nstatus of specific crop (Haarhoff and Swanepoel, 2018; Jolliffe et al., 1990).  \n\n\n\nCrop production and seed quality are the fundamental parameters \n\n\n\nreflecting the efficacy of plant production (Salentnik et al., 2019). Seed \n\n\n\nweight, germination percentage and field emergence of seeds provide the \n\n\n\nseed quality (Bekele et al., 2019). Reports showed that application of \n\n\n\nbiochar increase seed germination capacity of potato and wheat (Bamberg \n\n\n\net al., 1986; Van Zwieten et al., 2010). However, maize seed germination \n\n\n\nand was not significantly affected by the application of biochar (Free et al., \n\n\n\n2010). It has been reported that biochar contains unwanted compounds \n\n\n\nas well as small to high concentration of nutrients that may inhibit seed \n\n\n\ngermination (Garnett et al., 2004; Gaskin et al., 2008; Hille and den Quden, \n\n\n\n2005). Seed weight of jute in our study positively affected by the biochar \n\n\n\napplication with inorganic fertilizer indicating the proper development of \n\n\n\nseed through soil treated with biochar (Table 5). \n\n\n\nPercentage of seed germination in laboratory condition was not affect by \n\n\n\nbiochar application but field emergence percentage was highly affected by \n\n\n\nthe seeds grown with biochar. Biochar might alter the organic \n\n\n\nmineralization which may be linked to the nitrogen mineralization and \n\n\n\nchange in nutrient status which may affect both seed germination in field. \n\n\n\nSimilar mechanism was reported by the experiments (Murphy et al., 2003; \n\n\n\nSteiner et al., 2008). Plant growth often affected by the temperature, soil \n\n\n\ntype, annual rainfall of a particular area and understanding how plant \n\n\n\ninteract with environmental factors is extremely important (Fourcaud et \n\n\n\nal., 2008; Mall et al., 2017). Jute can be grown in various soil but soil pH \n\n\n\nranging between 5.0-8.6 is optimum for best production.  \n\n\n\nJAES is under AEZ-8 having good soil organic matter and JRSS in under \n\n\n\nAEZ-11 having low soil fertility condition. Interaction between location \n\n\n\nand different treatments found the lower vegetative growth in location \n\n\n\n1(L1) compare to the location 2 (L2). However, plant population of plant \n\n\n\nwas not affected by the different interactions (Table 6). These results \n\n\n\nsuggesting the importance of location for jute cultivation. Our prediction \n\n\n\nwas supported by the study where location was reported to be influenced \n\n\n\nby the environmental factors (Baker and Capel, 2011). Field emergence \n\n\n\npercentage also supporting our observation of soil and environmental \n\n\n\ncondition leading to jute growth and germination in the field level (Table \n\n\n\n7). \n\n\n\nTable 8: Correlation coefficients of vegetative growth and seed quality. \nPH BD PPP BP PPP PL PD NSP TSW SWP PGR FE \n\n\n\nPH 1.00 \nBD 0.94** 1.00 \nPPP 0.03 -0.06** 1.00 \n\n\n\nBP 0.67** 0.79** -0.14 1.00 \nPPP 0.94** 0.91** -0.02 0.74** 1.00 \nPL -0.37** -0.23** -0.06 -0.13 -0.27* 1.00 \nPD 0.37** 0.32** 0.07 0.23** 0.30 -0.15 1.00 \nNSP 0.11 0.17** 0.08 0.14 0.15 0.37** 0.07 1.00 \nTSW -0.74** -0.67** -0.15 -0.43** -0.62** 0.31** -0.15** -0.10 1.00 \n\n\n\nSWP -0.90** -0.82** 0.03 -0.53** -0.80** 0.42** -0.33** -0.08** 0.74 1.00 \nPGR 0.29** 0.30** 0.15 0.17 0.19 -0.18 -0.07** -0.11** -0.34 -0.30 1.00 \n\n\n\nFE 0.21 0.21 0.17 0.10 0.12 -0.20 -0.10 -0.13** -0.28 -0.23 0.97 1.00 \nPH- Plant height; BD- Base diameter; PPP- Plant population/ plot; BP- Branches/ plant; PP- Pod/ plant; PL- Pod length; PD- Pod diameter; NSP- No. of seed/ \n\n\n\npod; TSW- 1000 seed weight; SWP- Seed weight/ plant; PGR- % germination rate; FE- Field emergence.    \n\n\n\n**. Correlation is significant at the 0.01 level  \n\n\n\n*. Correlation is significant at the 0.05 level  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\nCorrelation coefficient indicates the strong relationship between to \n\n\n\nvariables (Ratner, 2009). For optimum production of a plant, leaf area, \n\n\n\nplant height and total plant biomass are important because these involve \n\n\n\nin photosynthesis leading to yields (Arif et al., 2017). In this study plant \n\n\n\ngrowth parameters were significantly, and negatively correlated with \n\n\n\nyield contributing characters and finally yield (Table 8). However, plant \n\n\n\nheight was positively correlated with base diameter, branches per plant \n\n\n\nand population per plot. These results clearly indicated that changes of \n\n\n\nplant growth parameter may change the yield and yield contributing \n\n\n\nparameter.   \n\n\n\n5.CONCLUSION\n\n\n\nJute plant need numbers of nutrients for its growth and proper \n\n\n\ndevelopment. Although applying of inorganic fertilizers increase crop \n\n\n\nyield but deteriorate soil health as well resulting decrease of crop yield. \n\n\n\nBiochar is a charcoal which is used to improve soil health and creates a \n\n\n\nscope to release nutrients slowly. In this present study the combination of \n\n\n\ninorganic fertilizer and biochar was applied in various combination. \n\n\n\nApplication of biochar alone had not significant effect on vegetative \n\n\n\ngrowth of jute however, some combination with inorganic fertilizer \n\n\n\nshowed the positive effect. Treatment T2 increased plant height, base \n\n\n\ndiameter and pod/plant. In addition, treatment T6 gave the highest seed \n\n\n\nyield per plant. Seed quality was significantly affected by the interaction of \n\n\n\ntreatment T2 with location L2.  \n\n\n\nACKNOWLEDGEMENT\n\n\n\nThis research did not receive any significant grant from any funding \n\n\n\nagencies in the public, commercial, or not for profit sectors.  \n\n\n\nCONFLICT OF INTEREST\n\n\n\nThe authors declare no conflict of interest. \n\n\n\nAUTHOR CONTRIBUTION\n\n\n\nSNI, MLR, MZT and MAS designed the study. SNI, MLR, BM, MMK, AS and \n\n\n\nMZT conducted field works and collected data. SNI analyzed the data. SNI, \n\n\n\nMLR, MZT and MAS worked on the manuscript.  \n\n\n\nREFERENCES\n\n\n\nAli, M.S., Hoque, M.M., Alim, M.A., Islam, M.M., 2019. A nutrient \n\n\n\ncombination that can affect yield of Olitorius jute. Plant, 7 (3), Pp. 42-\n\n\n\n46. \n\n\n\nAl-Snafi, A.E., 2016. The contents and pharmacological importance of \n\n\n\nCorchorus capsularis- a review. IOSR Journal of Pharmacy, 6 (6), Pp. \n\n\n\n58-63. \n\n\n\nAnonymous., 2013. Monthly Economic Trends, October 2013. Statistics \n\n\n\nDepartment, Bangladesh Bank, Dhaka, Pp. 27. \n\n\n\nArif, M., Ilyas, M., Riaz, M., Ali, K., Shah, K., Haq, I.U., Fahad, S., 2017. Biochar \n\n\n\nimproves phosphorus use efficacy of organic-inorganic fertilizer, \n\n\n\nmaize-wheat productivity and soil quality in low fertility alkaline soil. \n\n\n\nField Crops Research, 214, Pp. 25-37. \n\n\n\nAsai, H., Samson, B.K., Stephan, H.M., Songyikhangsuthor, K., Homma, K., \n\n\n\nKiyono, Y., Inoue, Y., Shiraiwa, T., Horie, T., 2009. Biochar amendment \n\n\n\ntechniques for upland rice production in Northern Laos 1. Soil \n\n\n\nphysical properties, leaf SPAD and grain yield. Field Crop Research, \n\n\n\n111 (1-2), Pp. 81-84. \n\n\n\nBamberg, J.B., Hanneman Jr, R.E., Towill, L.E., 1986. Use of activated \n\n\n\ncharcoal to enhance the germination of botanical seeds of potato. \n\n\n\nAmerican Potato Journal, 63, Pp. 181-189. \n\n\n\nBaker, N.T., Capel, P.D., 2011. Environmental factors that influence the \n\n\n\nlocation of crop agriculture in the conterminous United States: U.S. \n\n\n\nGeological Survey Scientific Investigations Report, Pp.72.  \n\n\n\nBekele, N., Tesso, B., Fikre, A., 2019. Assesment of seed quality parameters \n\n\n\nin different seed sources of chickpea (Cicer arietinum (L.). African \n\n\n\nJournal of Agricultural Research, 14 (33), Pp. 1649-1658. \n\n\n\nBrandstaka, T., Helenius, J., Hovi, J., Kivela, J., Koppelmaki, K., Simojoki, A., \n\n\n\nSoinne, H., Tammeorg, P., 2010. Biochar filter: use of biochar in \n\n\n\nagriculture as soil conditioner. University of Helsinki, Helsinki, \n\n\n\nFinland. \n\n\n\nBruun, E.W., Ambus, P., Egsgaard, H., Hauggaard-Nielsen, H., 2012. Effects \n\n\n\nof slow and fast pyrolysis biochar on soil C and N turnover dynamics. \n\n\n\nSoil Biology and Biochemistry, 46, Pp. 73-9. \n\n\n\nChan, K.Y., Zwieten, L.V., Meszaros, I., Downie, A., Joseph, S., 2007. \n\n\n\nAgronomic values of green waste biochar as a soil amendment. \n\n\n\nAustralian Journal of Soil Research, 45 (8), Pp. 629-634. \n\n\n\nChan, K.Y., Xu, Z., 2009. Biochar: Nutrient properties and their \n\n\n\nenhancement. In: Lehmann, J. Joseph, S. (Eds.), Biochar for \n\n\n\nEnvironmental Management: Science and Technology. Earthscan, \n\n\n\nLondon, Pp. 67-84. \n\n\n\nChan, K., Van Zwieten, L., Meszaros, I., Downie, A., Joseph, S., 2008. \n\n\n\nAgronomic values of green waste biochar as a soil amendment. Soil \n\n\n\nResearch, 45, Pp. 629-34. \n\n\n\nChowdhury, M.A.H., Hassan, M.S., 2013. Hand book of agricultural \n\n\n\ntechnology. Bangladesh Agricultural Research Council, Dhaka, \n\n\n\nBangladesh. \n\n\n\nDeenik, J.L., McClellan, T., Uehara, G., Antal, M.J., Campbell, S., 2010. \n\n\n\nCharcoal volatile matter content influences plant growth and soil \n\n\n\nnitrogen transformations. Soil Science Society of America Journal, 74 \n\n\n\n(4), Pp. 1259-1270. \n\n\n\nFAO (Food and Agriculture Organization of the United Nations)., 2014. \n\n\n\nStatistical bulletin-2014. \n\n\n\nFourcaud, T., Zhang, X., Stokes, A., Lambers, H., Korner, C., 2008. Plant \n\n\n\ngrowth modelling and applications: the increasing importance of plant \n\n\n\narchitecture in growth models. Annals of Botany, 101 (8), Pp. 1053-\n\n\n\n1063. \n\n\n\nFree, H.F., McGill, C.R., Rowarth, J.S., Hedley, M.J., 2010. The effect of \n\n\n\nbiochars on maize (Zea mays) germination. New Zealand Journal of \n\n\n\nAgronomy Research, 53 (1), Pp. 1-4. \n\n\n\nGarnett, E.L., Jonsson, M., Dighton, J., Murnen, K., 2004. Control of pitch \n\n\n\npine seed germination and initial growth exerted by leaf litters and \n\n\n\npolyphenolic compounds. Biology and Fertility of Soils, 40, Pp. 421-\n\n\n\n426. \n\n\n\nGaskin, J.W., Steiner, C., Harris, K., Das, K.C., Bibens, B., 2008. Effect of low-\n\n\n\ntemperature pyrolysis conditions on biochar for agricultural use. \n\n\n\nTransaction of ASABE, 51 (6), Pp. 2061-2069. \n\n\n\nGomez, K.A., Gomez, A.A., 1984. Statistical procedures for agricultural \n\n\n\nresearch. 2nd ed. John Willy Sons, New York. \n\n\n\nGwenzi, W., Muzava, M., Mapanda, F. Tauro, T.P., 2016. Comparative short-\n\n\n\nterm effects of sewage sludge and its biochar on soil properties, maize \n\n\n\ngrowth and uptake of nutrients on a tropical clay soil in Zimbabwe. \n\n\n\nJournal of Integrative Agriculture, 15 (6), Pp. 1395-1406. \n\n\n\nHammer, E.C., Balogh-Brunstad, Z., Jakobsen, I., Olsson, P.A., Stipp, S.L.S., \n\n\n\nRillig, M.C., 2013. A mycorrhizal fungus grows on biochar and captures \n\n\n\nphosphorus from its surface. Soil Biology & Biochemistry, 77, Pp. 252-\n\n\n\n260. \n\n\n\nHariparasad, N.Y., Dayananda, H.S., 2013. Environmental impact due to \n\n\n\nagricultural runoff containing metals- a review. International Journal \n\n\n\nof Scientific and Research Publication, 3 (5), Pp. 1-6. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\nHaarhoff, S.J., Swanepoel, P.A., 2018. Plant population and maize grain \n\n\n\nyield: a global systematic review of rainfed trials. Crop Science, 58, Pp. \n\n\n\n1819-1829.  \n\n\n\nHille, M., den Quden, J., 2005. Charcoal and activated carbon as adsorbate \n\n\n\nto phytotoxic compounds-a comparative study. Oikos, 108 (1), Pp. \n\n\n\n202-207. \n\n\n\nHossain, M.B., Uddin, M.N., Alam, M.J., Ali, M.Y., Rahman, M.R., 2017. \n\n\n\nEconomic performance of jute cultivation at farm level in some \n\n\n\nselected areas of Bangladesh. Journal of Experimental Bioscience, 5 \n\n\n\n(1), Pp. 97-104. \n\n\n\nHussain, M., Farooq, M., Nawaz, A., Al-Sadi, A.M., Solaiman, Z.M., Alghamdi, \n\n\n\nS.S., Ammara, U., Ok, Y.S. Siddique, K.H.M., 2017. Biochar for crop \n\n\n\nproduction: potential benefits and risks.Journals of Soils and \n\n\n\nSediments, 17, Pp. 685-716.  \n\n\n\nIslam, M.M., Ali, M.S., 2017. Economic importance of jute in Bangladesh: \n\n\n\nproduction, research achievements and diversification. International \n\n\n\nJournal of Economic Theory and Application, 4 (6), Pp. 45-57. \n\n\n\nIslam, M.M., Rahman, M., 2008. In: Hand book on agricultural technologies \n\n\n\nof jute, kenaf and mesta crops. Bangladesh Jute Research Institute, \n\n\n\nManik Mia Avenue, Dhaka-1207, Bangladesh.   \n\n\n\nIslam, M.S., 2007. Management of some jute diseases. MS Thesis. \n\n\n\nBangladesh Agricultural University, Mymensingh, Bangladesh. \n\n\n\nJeffery, S., Abalos, D., Spokas, K., Verheijen, F.G.A., 2015. Biochar effect on \n\n\n\ncrop yield. Biochar for environmental management, Science, \n\n\n\nTechnology and Implementation. 2nd edition, Routledge. \n\n\n\nJien, S.H., Wang, C.S., 2013. Effects of biochar on soil properties and erosion \n\n\n\npotential in a highly weathered soil. CATENA, 110, Pp. 225-233. \n\n\n\nJolliffe, P.A., Tarimo, A.J.P., Eaton, G.W., 1990. Plant growth analysis: \n\n\n\ngrowth and yield component responses to population density in \n\n\n\nforage maize. Annals of Botany, 6 (2), Pp. 139-147.  \n\n\n\nLaird, D.A., Fleming, P.D., Davis, D.D., Horton, R., Wang, B., Karlen, D.L., \n\n\n\n2010. Impact of biochar amendments on the quality of a typical \n\n\n\nMidwestern agricultural soil. Geoderma in press: Geoderma. doi: \n\n\n\n10.1016/j.geoderma.2010.1005.1013. \n\n\n\nLehmann, J., Rondon, M., 2006. Bio-char soil management on highly \n\n\n\nweathered soils in the humid tropics. In Uphoff, N. (ed.) Biological \n\n\n\nApproaches to Sustainable Soil Systems, \n\n\n\nCRC Press, Boca Raton, FL, USA, Pp. 517-530. \n\n\n\nLin, W., Lin, M., Zhou, H., Wu, H., Li, Z., Lin, W., 2019. The effects of chemical \n\n\n\nand organic fertilizer usage on rhizosphere soil in the tea orchards. \n\n\n\nPLoS ONE, 14 (5), Pp. e0217018.  \n\n\n\nMall, R.K., Gupta, A., Sonkar, G., 2017. Effect of climate change on \n\n\n\nagricultural crops. In Current developments in biotechnology and \n\n\n\nbioengineering, crop modification, nutrition, and food production. Pp. \n\n\n\n23-46. \n\n\n\nMasikati, P., Descheemaeker, K., Crespo, O., 2019. Understanding the role \n\n\n\nof soils and management on crops in the face of climate uncertainty in \n\n\n\nZimbabwe: a sensivity analysis. In: Rosenstock T., Nowak A., Girvetz E. \n\n\n\n(Eds), The Climate-Smart Agriculture Papers. Springer, Cham, Pp.49-\n\n\n\n64.   \n\n\n\nMollah, M.A.F., Tareq, M.Z., Bashar, K.K., Hoque, A.B.M.Z., Karim, M.M., \n\n\n\nRafiq, M.Z.A., 2020. Antioxidant properties of BJRI vegetable mesta-1 \n\n\n\n(Hibiscus sabdariffa L.). Plant Science Today, 7 (2), Pp. 154-156. \n\n\n\nMurphy, D.V., Recous, S., A.Stockdale, E., Fillery, I.R.P., Jensen, L.S., Hatch, \n\n\n\nD.J., Goulding, K.W.T., 2003. Gross nitrogen fluxes in soil: theory, \n\n\n\nmeasurement and application of 15N pool dilution techniques. \n\n\n\nAdvance in Agronomy, 79, Pp. 69-119. \n\n\n\nNair, V.D., Nair, P.K.R., Dari, B., Freitas, A. M., Chatterjee, N., Pinheiro, F, M., \n\n\n\n2017. Biochar in the agroecosystem-climate-change-sustainability \n\n\n\nnexus. Frontiers in Plant Science, 8, Pp. 2501.    \n\n\n\nOlmo, M., Villar, R., Salazar, P., Alburquerque, J.A., 2016. Changes in soil \n\n\n\nnutrient availability explain biochar\u2019s impact on wheat root \n\n\n\ndevelopment. Plant & Soil, 399 (1), Pp. 1-11. \n\n\n\nOshiro, M., Hossain, M.A., Nakamura, I., Akamine, H., Tamaki, M., Bhowmik, \n\n\n\nP.C., Nose, A., 2016. Effect of soil types and fertilizer on growth, yield \n\n\n\nand quality of edible Amaranthus tricolor lines in Okinawa, Japan, \n\n\n\nPlant Production Science, 19 (1), Pp. 61-72. \n\n\n\nQuddus, M.A., 2009. Crop production growth in different agro-ecological \n\n\n\nzones of Bangladesh. Journal of Bangladesh Agricultural University, 7 \n\n\n\n(2), Pp. 351-360. \n\n\n\nRashid, M.M., Khan, M.M.R., Hossain, M.A., Hossain, M.M., 2007. \n\n\n\nManagement of seed borne fungi of jute in Mymensingh region. \n\n\n\nBangladesh Journal of Crop Science, 18, Pp. 209-214. \n\n\n\nRatner, B., 2009. The correlation coefficient; its value range between +1/-\n\n\n\n1, or do they? Journal of Targeting, Measurement and Analysis for \n\n\n\nMarketing, 17, Pp. 139-142. \n\n\n\nRawat, J., Saxena, J., Sanwal, P., 2019. Biochar: a sustainable approach for \n\n\n\nimproving plant growth and soil properties. An Imperative \n\n\n\namendment for soil and the environment. \n\n\n\nSalentnik, B., Bajcar, M., Zagula, G., Salentnik, A., Tarapatskyy, M., \n\n\n\nPuchalski, C., 2019. Biochar as a stimulator for germination capacity in \n\n\n\nseeds of Virginia mallow (Sida hermaphordita (L.) Rsuby). Applied \n\n\n\nSciences, 9 (16), Pp. 3213. \n\n\n\nSikder, F.S., Saha, C.K., Rahman, M., Alam, A.K.N.M., Haque, S., 2008. Jute \n\n\n\nproduction in Bangladesh- an overview. Abstracts of papers, \n\n\n\nInternational symposium on jute and allied fibres production, \n\n\n\nutilization and marketing. National library, Kolkata, India, Pp. 9-12. \n\n\n\nSrinivasarao, Y., Gopinath, K.A., Venkatesh, G., Dubey, A.K., Wakudar, H., \n\n\n\nPurakayastha, T.J., Pathak, H., Jha, P., Lakaria, B.L., Rajkhowa, D.J., \n\n\n\nMandal, S., Jeyaraman, S., Venkateswarlu, B., Sikka, A.K., 2013. Use of \n\n\n\nbiochar for soil health enhancement and greenhouse gas mitigation in \n\n\n\nIndia: potential and constraints. Central Research Institute for Dryland \n\n\n\nAgriculture, Hyderabad, Andhra Pradesh, Pp. 51. \n\n\n\nSteiner, C.K., Das, C., Garcia, M., Forster, B., Zech, W., 2008. Charcoal and \n\n\n\nsmoke extract stimulate the soil microbial community in a highly \n\n\n\nweathered xanthic Ferralsol. Pedobiologia, 51 (5-6), Pp. 359-366. \n\n\n\nTareq, M.Z., Bashar, K.K., Amin, M.R., Sarker, M.D.H., Moniruzzaman, M., \n\n\n\nSarker, M.S.A., Islam, M.S., 2019. Nutritional composition of some jute \n\n\n\ngenotypes as vegetables. International Journal of Vegetable Science, \n\n\n\nDOI 10.1080/19315260.2019.1658686. \n\n\n\nToma, K., 2017. Effect of biochar on growth and yield of mung bean \n\n\n\n(BARIMung-6). MS thesis. Department of Soil Science, Sher-e-Bangla \n\n\n\nAgricultural University, Dhaka, Bangladesh. \n\n\n\nVan Zwieten L., Kimber, S., Morris, S. Chan, K.Y., Downie, A., Rust, J., Joseph, \n\n\n\nS., Cowie, A., 2010. Effects of biochar from slow pyrolysis of paper mill \n\n\n\nwaste on agronomic performance and soil fertility. Plant Soil, 27, Pp. \n\n\n\n235-246. \n\n\n\nVerheijen, F.G.A., Jeffery, S., Bastos, A.C., Velde, V.D., Diafas, M.I., 2009. \n\n\n\nBiochar application to soils: A critical scientific review of effects on soil \n\n\n\nproperties, processes and functions. Luxembourg: Office for Official \n\n\n\nPublications of the European Communities. EUR 24099 EN. 61. \n\n\n\nVinh, N., Hien, N., Anh, M., Lehmann, J., Joseph, S., 2014. Biochar treatment \n\n\n\nand its effects on rice and vegetable yields in mountainous areas of \n\n\n\nnorthern \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)5(1) (2021) 43-50 \n\n\n\nCite The Article: Syed Nazrul Islam, Md. Lutfar Rahman, Md. Zablul Tareq, Bornali Mostofa, Md. Meftahul Karim, Abida Sultana And Md. Abu Sadat(2021). Nutrient \nCombination With Biochar: Improving Yield And Quality Of Jute Seed. Malaysian Journal Of Sustainable Agriculture, 5(1): 43-50 .\n\n\n\nVietnam. International Journal of Agricultural and Soil Science, 2 (1), Pp. \n\n\n\n5-13. \n\n\n\nYamato, M., Okimori, Y., Wibowo, I.F., Anshori, S., Ogawa, M., 2006. Effects \n\n\n\nof the application of charred bark of Acacia mangium on the yield of \n\n\n\nmaize, cowpea and peanut, and soil chemical properties in South \n\n\n\nSumatra, Indonesia. Soil Science and Plant Nutrition, 52 (4), Pp. 489-\n\n\n\n495. \n\n\n\nYilangai, R.M., Manu, A., Pineau, W., Mailumo, S., Okeke-Agulu, K., 2014. The \n\n\n\neffect of biochar and crop veil on growth and yield of Tomato \n\n\n\n(Lycopersicum esculentus Mill) in Jos, North central Nigeria. Current \n\n\n\nAgriculture Research Journal, 2 (1), Pp. 37-42. \n\n\n\nZhu, Q.H., Peng, X.H., Huang, T.Q., Xie, Z.B., Holden, N.M., 2014. Effect of \n\n\n\nbiochar addition on maize growth and nitrogen use efficiency in acidic \n\n\n\nred soils. Pedosphere, 29 (2), Pp. 699-708.  \n\n\n\nZhang, G., Qi, J., Xu, J., Niu, X., Zhang, Y., Tao, A., Zhang, L., Fang, P., Lin, L., \n2013. Overexpression of UDP-glucose pyrophosphorylase gene could \nincrease cellulose content in jute (Corchorus capsularis L.). \nBiochemical and Biophysical Research Communications, 442 (3-4), Pp. \n153-158. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 54-58 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.54.58 \n\n\n\nCite the Article: Santosh Bharati, Binod Joshi, Roshan Dhakal, Sushma Paneru, Shiva Chandra Dhakal, Khem Raj Joshi (2020). Effect Of Different Mulching On Yield And Yield \nAttributes Of Potato In Dadeldhura District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2):  54-58. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.54.58 \n\n\n\nEFFECT OF DIFFERENT MULCHING ON YIELD AND YIELD ATTRIBUTES OF \nPOTATO IN DADELDHURA DISTRICT, NEPAL \n\n\n\nSantosh Bharatia*, Binod Joshia, Roshan Dhakala, Sushma Panerua, Shiva Chandra Dhakalb, Khem Raj Joshic  \n\n\n\na Faculty of Agriculture, Agriculture and Forestry University, Rampur, Chitwan, Nepal \nb Department of Economy, Agriculture and Forestry University, Chitwan, Nepal \nc Senior Agriculture officer, PMAMP, Super-Zone,  \n*Corresponding author e-mail: santoshbharati896@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 26 February 2020\n\n\n\nA study on \"Effect of different mulching materials on yield and yield attributes of potato in Dadeldhura, \nDistrict\" was conducted from March to June, 2019 in Tadibata, the commanding area of potato superzone, \nDadeldhura to detect the effective mulching materials for potato. Lack of irrigation and labor shortage along \nwith high weed infestation were the problem found in the potato production in farmers level .To find out the \nefficiency of different mulching materials, a field experiment was conducted in RCBD design with five \ntreatments: T1: Control, T2: Saw dust, T3: Rice straw, T4: Black plastic and T5: Rice husk and were replicated \nfour times to find the best mulching materials that can help the farmers to improve their production practice. \nThe data on plant height, aerial stem number, canopy and number of leaves were taken at 45, 60 and 75 days \nafter planting (DAP) and the data on grading, diameter, dry weight were taken after harvesting of potato. \nAfter the data collection, data entry was done is MS- Excel and analysis was done by using R-studio software. \nFrom the experiment it was found that the highest tuber yield was obtained in black plastic (3.33 kg/m2) \nwhich was followed by rice straw (2.74 kg/m2) , saw dust (2.63 kg/m2), rice husk (2.55kg/m2) and lowest \ntuber yield was obtained in control condition (2.39 kg/m2). Similarly, the soil temperature was influenced \nby the use of mulching material as compared to the bare soil with highest soil temperature being recorded in \nblack plastic and lowest recorded in control condition. In case of economics, the highest B: C ratio was found \nin black plastic (2.01) and minimum found in rice husk (1.64). Thus, black plastic is the most effective \nmulching material for the high production of potato in Dadeldhura.  \n\n\n\nKEYWORDS \n\n\n\nPotato, Temperature, Mulch, Tuber, Yield.\n\n\n\n1. INTRODUCTION \n\n\n\nPotato (Solanum tuberosum L.) is the first non-cereal food crops and \n\n\n\nfourth most important crops in the world after wheat, rice and maize and \n\n\n\nis considered as the major crops in the hilly region of Nepal. It is also \n\n\n\nconsidered as the major cash crops that are grown to satisfy the food \n\n\n\ndemand and to improve the living standard of the farmers (Shijie, 2011). \n\n\n\nIt consists of high starch (16.1/100 g), vitamin C (17.1 mg/100 g), protein \n\n\n\n(2.1/100 g), potassium (443 mg/100 g) and essential amino acids and it is \n\n\n\nconsidered as the nutrient rich food. Potato is a major food- security crop \n\n\n\nthat can substitute for cereal crop considering its high yield and great \n\n\n\nnutritive value (Zhang, 2017). Potato is native to Peru and Chile in the \n\n\n\nAndes Mountains of South America as well as the alpine zone with an \n\n\n\nelevation of 3000-4000 m in Mexico (Ahmed, 2017). \n\n\n\nMulching also has some disadvantages by the continuous use of plastic \n\n\n\nmulch over an entire growing season it may reduce crop yield due to \n\n\n\nprolonged higher temperature (Zhou, 2009; Dong, 2014). It is the more \n\n\n\ntime consuming and labor intensive for the installation of mulching in the \n\n\n\nmain field (Dong, 2014). The ability to suppress weeds by various plant \n\n\n\nspecies sown as living mulches is presented in the literature and ranges \n\n\n\nwidely from 34 to 96%. Mulching can be done by organic materials like \n\n\n\nstraw, husk, cover crop etc. or by inorganic black or silver plastic that \n\n\n\nprovide one or more ecological services such as enriching or protecting \n\n\n\nthe soil or preventing pest attack or for enhancement of the production of \n\n\n\ncrop. Potato is harvested twice a year in this area of the country. Certain \n\n\n\ncultural practices can be imposed or modified to increase the yields such \n\n\n\nas the use of improved hybrid varieties, use of modern techniques like \n\n\n\nirrigation, and higher plant populations which are tedious along with \n\n\n\nbeing expensive.  \n\n\n\nIn Nepal potato is cultivated in the 18,587-hectare area producing \n\n\n\n2,591,686 tons and yield of 13,943 kg per hectare (MOAD2016/17). \n\n\n\nVarious factors affect the productivity of potato in Nepal. In such situations \n\n\n\nuse of mulching can be cheap way to increase potential yield. Farmers are \n\n\n\nnot aware about the potential benefits from use of mulching on plants. \n\n\n\nAlthough the initial cost is high but it gives service for longer period of \n\n\n\ntime. Diseases, insects, weeds and others pests causes the substantial loss \n\n\n\nin the yield and quality of the crop yearly which can be controlled by the \n\n\n\nuse of proper mulching method and proper management of the field. As, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Santosh Bharati, Binod Joshi, Roshan Dhakal, Sushma Paneru, Shiva Chandra Dhakal, Khem Raj Joshi (2020). Effect Of Different Mulching On Yield And Yield \nAttributes Of Potato In Dadeldhura District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 54-58. \n\n\n\nthe geographical situation of this area is not plain the irrigation becomes \n\n\n\na major factor for decrease in the yield of the potato. In such scenario \n\n\n\nmulching of potato can be highly significant for conserving the moisture. \n\n\n\nAn important step in substituting the problems of irrigation and weed \n\n\n\ninfestation in potato cultivation is through the use of proper mulching \n\n\n\ntechniques. Mulch plays an important role in the high production of the \n\n\n\npotato. Soil water does not escape from under plastic mulch. Plant growth \n\n\n\non mulch is often higher as compared on bare soil. Farmers are not aware \n\n\n\nabout these benefits of the mulches. Mulching techniques is widely \n\n\n\npracticed on the wide range of the vegetables while its application has \n\n\n\nbeen limited to the potato production. As, the potato is the one of the major \n\n\n\nstaple crops of this region its demand is always high. So, to meet its \n\n\n\nincreasing demand from potato-based industries it is necessary to use \n\n\n\nvarious techniques like mulching to reduce the import of potato from the \n\n\n\nother country. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Experimental site \n\n\n\nThe research experiment entitled \u201cEffect of different planting depth and \n\n\n\nmulching on yield and yield attributes of potato in Dadeldhura district, \n\n\n\nNepal \u201cwas carried out at Dadeldhura district Nepal during 2018-\n\n\n\n2019.The study site is located at 28\u00ba59\u201dN to 29\u00ba26\u201dN latitude and 80\u00ba12\u201d \n\n\n\nto 80\u00ba47\u201d longitude in the humid sub-tropical zone with elevation of 17, \n\n\n\n45masl. \n\n\n\n2.1.1 Climatic condition at experimental site \n\n\n\nThe data regarding the maximum and minimum temperature, rainfall, \n\n\n\nrelative humidity and bright sunshine during the experiment period of 3.5 \n\n\n\nmonth from March to June 2019 was recorded from the Department of \n\n\n\nHydrology and Meteorology, Dadeldhura. The average relative humidity \n\n\n\nwas found 54.22% during the experiment. Similarly, the average \n\n\n\nmaximum temperature was 23.53\u00bac which ranged from 13.70-33.10\u00bac \n\n\n\nwhereas the average minimum temperature was 12.09\u00bac which ranged \n\n\n\nfrom 1.30-17.70\u00bac. The average precipitation of 1.09mm with100.6 mm \n\n\n\nrainfall was observed in the experimental field. The average bright \n\n\n\nsunshine of 8.07 hundredths of an hour with total 742.0 hundredths of an \n\n\n\nhour was observed in the experimental area. \n\n\n\nFigure 1: Meteorological parameters (Relative humidity, Maximum \n\n\n\nTemperature, Minimum Temperature, Rainfall, Bright Sunshine) during \n\n\n\nthe crop duration at experimental site \n\n\n\n2.2 Experimental design, layout and methodology \n\n\n\nThe field experiment was carried out on RCBD with four replication and \n\n\n\nfive treatment in total land area of 131.2m2(16.4m*8m) at Tadibata \n\n\n\n(Bhatkada ) farm of Dadeldhura, district. Each replication consists of five \n\n\n\ntreatment plot each of area 3.6m*1m that were placed through \n\n\n\nrandomization. The Desiree cultivar of potato was taken for the \n\n\n\nexperimental study of our research project. The Desiree variety of potato \n\n\n\nwas sown on March 1, 2019. Crop geometry was maintained of 60*20 cm. \n\n\n\nPlot size was 3.6*1 m2 where 6 rows with 5 plants/ row was planted. \n\n\n\nThe soil was made harrowed until completely free of weed roots. About \n\n\n\nthree ploughing, along with the harrowing, was done before the soil would \n\n\n\nreach a suitable condition: soft, well-drained and well-aerated. Ridge and \n\n\n\nfurrow will be made on the field and black-plastic mulch (silver color on \n\n\n\nunderside) will be used to cover the ridges. For Desiree variety of potato \n\n\n\nthe recommended dose of fertilizers is used i.e, FYM: 1500kg/ropani, \n\n\n\nUrea: 7kg/ropani,DAP: 11 kg/ropani and MOP: 5 kg/ropani. All the \n\n\n\nfertilizers according to above doses were incorporated into the field \n\n\n\nbefore sowing. Due to mulches on the field, all the doses of nitrogen were \n\n\n\ngiven as the basal dose at the time of sowing. \n\n\n\nTable 1: Different treatment and their abbreviation used in the \n\n\n\nexperiment \n\n\n\nS.N. Treatment Abbreviation \n\n\n\n1. Control T1 \n2. Saw dust T2 \n3. Rice straw T3 \n4. Black polythene T4 \n\n\n\n5. Rice husk T5 \n\n\n\n2.3 Data collection and analysis \n\n\n\nThe five plants were tagged randomly from each plot for the collection of \n\n\n\ndata. The data regarding to plant height, canopy, leaves number and aerial \n\n\n\nstem per plant were taken at 45, 60 and 75 days after planting as well as \n\n\n\nother data of tuber number. Yield, marketable tuber and unmarketable \n\n\n\ntuber were also taken. The data collected were entered in MS-Excel and \n\n\n\nthen analyzed by using R studio software. The means were compared by \n\n\n\nusing Duncan\u2019s Multiple Range test (DMRT) at 5% level of significance. \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Effects of different types of mulching on 90% germination \n\n\n\nTable 2: Effect of different treatments on 90 percentage germination \n\n\n\nafter sowing of potato \n\n\n\nTreatments Days to 90 % germination \n\n\n\nRice straw 37.25b \n\n\n\nSaw dust 36.50b \nRice husk 35.75b \n\n\n\nBlack plastic 30.75a \n\n\n\nControl 40.00c \nGrand mean 36 \n\n\n\nCV 4.85 \n\n\n\nLSD 2.69*** \n\n\n\nSEM(\u00b1) 0.87 \n\n\n\nA perusal of the data revealed that the days to germination was \n\n\n\nsignificantly influenced by mulching. In case of black plastic quicker \n\n\n\ngermination was observed as compared to control condition and other \n\n\n\nmulching materials. In case of black plastic to get 90 percent of \n\n\n\ngermination it took (30.75 days). The days to germination for rice husk \n\n\n\nwas found at (37.25 days) which was significantly at par with saw dust \n\n\n\n(36.50 days) and rice straw (37.25 days).The highest days for 90 percent \n\n\n\ngermination was observed in control condition (40 days). The quicker \n\n\n\ngermination in mulch condition was due to increase in soil temperature by \n\n\n\nthe application of mulching material as compared to control condition.  \n\n\n\n3.2 Effect of different types of mulching on plant height and canopy \n\n\n\nlength \n\n\n\nTable 3: Effect of different treatment on plant height and canopy length \n\n\n\nat different days after planting \n\n\n\nTreatments Plant height(cm) Canopy(cm) \n\n\n\n45 DAP 60DAP 75DAP 45 DAP 60DAP 75DAP \n\n\n\nRice straw 22.31b 32.85b 36.52b 36.800ab 46.550ab 53.375 \n\n\n\nSaw dust 20.65b 33.05b 36.27b 35.775ab 44.900bc 51.550 \n\n\n\nRice husk 22.55b 33.52b 36.25b 35.425b 43.725bc 52.325 \n\n\n\nBlack plastic 28.15a 38.15a 38.20a 38.850a 50.475a 55.325 \n\n\n\nControl 15.79c 27.93c 36.20b 31.025c 40.925c 49.300 \n\n\n\nGrand mean 21.9 33.1 36.7 35.6 45.3 52.4 \n\n\n\nCV 11.9 7.64 1.32 5.74 7.55 6.33 \n\n\n\nLSD 4.02*** 3.9** 0.744*** 3.15** 5.27* 5.11NS \n\n\n\nSEM(\u00b1) 1.30 1.26 0.24 1.02 1.70 1.65 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Santosh Bharati, Binod Joshi, Roshan Dhakal, Sushma Paneru, Shiva Chandra Dhakal, Khem Raj Joshi (2020). Effect Of Different Mulching On Yield And Yield \nAttributes Of Potato In Dadeldhura District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 54-58. \n\n\n\n(Means with the same letter do not differ significantly at p=0.05 by DMRT. \n\n\n\nCV =Coefficient of variation. LSD=least significant difference, \n\n\n\nSEM=Standard error of mean. DAP= Days after planting). Plant height is \n\n\n\none of yield attributing parameter. Plant height was found significantly \n\n\n\nhigher in black plastic (28.15, 38.15 and 38.20) which was found \n\n\n\nsignificantly at par with the rice straw (22.31, 32.85, 36.52) and at 45, 60 \n\n\n\nand 75 DAP respectively. The plant height of rice straw (22.31, 32.85, \n\n\n\n36.52) was found significantly at par with saw dust (20.65, 33.05, 36.27) \n\n\n\nand rice husk (22.55, 33.52, 36.25) at 45, 60 and 75 DAP respectively. The \n\n\n\nincreased plant height in mulched plants was possibly due to better \n\n\n\navailability of soil moisture and optimum soil temperature provided by the \n\n\n\nmulches. Changes in the plant height of potato have been observed by \n\n\n\nusing different mulches and plastic mulch increased the plant height than \n\n\n\nother mulches. Similar result with application of black plastic in plant \n\n\n\nheight was reported by the research carried (Ahmed, 2017). \n\n\n\nCANOPY: \n\n\n\nCanopy length is another important parameter, in which tuber yield \n\n\n\ndepends to certain extent. At 45 DAP the canopy was found significantly \n\n\n\nhigh in black plastic (38.85) which was found significantly at par with the \n\n\n\nrice straw (36.80) and saw dust (35.75) respectively at 45 DAP. The lowest \n\n\n\ncanopy was found in control condition (31.02) at 45 DAP. At 60 DAP the \n\n\n\ncanopy was found significantly higher in black plastic (50.47) which was \n\n\n\nfound statistically significant at par with the rice straw (46.55) while the \n\n\n\nsmallest canopy was found in control condition (40.92). At 75 DAP no \n\n\n\nsignificant result was observed in all treatment while the highest canopy \n\n\n\nwas observed in black plastic (55.32) and lowest canopy was found in \n\n\n\ncontrol condition (49.30). \n\n\n\nMulch materials created favorable condition for the growth of plant. Such \n\n\n\nresponse was mainly due to the physiochemical and biological \n\n\n\nimprovement occurred in the soil including favorable temperature and \n\n\n\nmoisture regimes, nutrient availability and microbial activity in mulch \n\n\n\ncondition. Among the different mulch materials black polythene was more \n\n\n\neffective, which ensured maximum vegetative growth with maximum \n\n\n\nfoliage coverage. \n\n\n\nFigure 2: Relationship between Canopy coverage at 60 DAP and yield \n\n\n\nper plant at Tadibata, Dadeldhura \n\n\n\n3.3 Effect of different mulching on the aerial stem per plant \n\n\n\nTable 4: Effect of different treatment on aerial stem per plant \n\n\n\nTreatments Aerial stem number \n\n\n\nRice straw 5.70ab \n\n\n\nSaw dust 5.50abc \n\n\n\nRice husk 5.40bc \n\n\n\nBlack plastic 6.17a \n\n\n\nControl 4.87c \n\n\n\nGrand mean 5.53 \n\n\n\nCV 9.09 \n\n\n\nLSD 0.774* \n\n\n\nSEM(\u00b1) 0.25 \n\n\n\nAerial stem number per plant is one of the major contributing on the yield \n\n\n\nattributes of the potato. The numbers of aerial stems per plants were found \n\n\n\nhighest in black plastic as compared to the other mulching materials and \n\n\n\ncontrol conditions. In case of black plastic, the number of aerial stems per \n\n\n\nplant found was 6.17 which was statistically at par with rice straw (5.70) \n\n\n\nand saw dust (5.50). The lowest number of aerial stems was found in \n\n\n\ncontrol condition (4.87). The mulching condition produced significantly \n\n\n\nhigher number of aerial stems per plant as compared to the control \n\n\n\ncondition (no use of any mulch) and in case of mulching material highest \n\n\n\nnumber of aerial stems per plant was found in black plastic mulch. Mulch \n\n\n\nmaterials created favorable condition for the growth of plant which leads \n\n\n\nto production of maximum number of main stems per hill. The mulching \n\n\n\ntreatment increased tuber yields of potato, with significantly higher tuber \n\n\n\nyields for full mulching than no mulching at Munsigonj. These could be \n\n\n\nattributed to the higher temperature and humidity under mulched during \n\n\n\nthe early development. As a result, mulching led to the higher emergence \n\n\n\nrate and strong seedling, accordingly increased the stems and branches per \n\n\n\nplant, leading to a greater number of tubers in tuber initiation (Ahmed, \n\n\n\n2017). \n\n\n\nFigure 3: Relationship between Aerial stem numbers at 60 DAP and \n\n\n\nyield per plant at Tadibata, Dadeldhura \n\n\n\n3.4 Effect of different types of mulching on leaves number \n\n\n\nTable 5: Effect of different treatment on leaf number \n\n\n\nTreatments No of leaves \n\n\n\n45 DAP 60DAP 75DAP \n\n\n\nRice straw 35.15ab 67.400ab 60.500 \n\n\n\nSaw dust 33.60b 64.100abc 55.250 \n\n\n\nRice husk 32.17bc 58.38bc 53.450 \n\n\n\nBlack plastic 38.52a 71.600a 61.750 \n\n\n\nControl 28.95c 53.85c 53.425 \n\n\n\nGrand mean 33.7 63.07 56.9 \n\n\n\nCV 8.18 11.8 13.9 \n\n\n\nLSD 4.24** 11.4* 12.1NS \n\n\n\nSE(\u00b1) 1.34 3.70 3.94 \n\n\n\nNumber of leaves is one of the major yields attributing parameter. Number \n\n\n\nof leaves per plant was found significantly higher in mulch condition as \n\n\n\ncompared to control condition. At 45 DAP the highest number of leaves \n\n\n\nwas found higher in black plastic (38.52) which was statistically at par with \n\n\n\nrice straw (35.15) and lowest number of leaves was found in control \n\n\n\ncondition (28.95). AT 60 DAP the number of leaves was found higher in \n\n\n\nblack plastic (71.60) which was statistically at par with rice straw (67.40) \n\n\n\nand saw dust (64.10) while the lowest  number of leaves per plant was \n\n\n\nfound in control condition (71.60). At 75 DAP no significant difference was \n\n\n\nfound in number of leaves in mulch and control condition. In case of \n\n\n\nmulching, it helps to reduce the evaporation and maintain proper moisture \n\n\n\ncontent  due to which maximum number of leaves is found at mulching as \n\n\n\ncompared to un-mulching. Similar result with maximum number of leaves \n\n\n\nin mulching as compared to un-mulching was reported (Dong, 2014). \n\n\n\n0.45 \n\n\n\n0.4 \n\n\n\n0.35 \n\n\n\n0.3 \n0.25 \n\n\n\nyield per plan t \n0.2 \n\n\n\n0.15 \n\n\n\n0.1 \n\n\n\n0.05 \n\n\n\n0 \n\n\n\ny = 0.0085x + 0.0468 \nR\u00b2 = 0.533 \n\n\n\nYPP \n\n\n\nLinear (YPP) \n\n\n\n0 20 40 60 \n\n\n\nCanopy \n\n\n\nYield per plant \n\n\n\n0.45 \n\n\n\n0.35 \n\n\n\n0.3 \n\n\n\n0.25 \n\n\n\n0.2 \n\n\n\n0.15 \n\n\n\n0.1 \n\n\n\n0.05 \n\n\n\ny = 0.0085x + 0.0468 \n\n\n\nR\u00b2 = 0.533 \n\n\n\nYPP \n\n\n\nLinear (YPP) \n\n\n\n0    20   40 60 \n\n\n\nAerial stem number \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Santosh Bharati, Binod Joshi, Roshan Dhakal, Sushma Paneru, Shiva Chandra Dhakal, Khem Raj Joshi (2020). Effect Of Different Mulching On Yield And Yield \nAttributes Of Potato In Dadeldhura District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 54-58. \n\n\n\nTable 6: Effect of different treatment on diameter of tuber \n\n\n\nTREATMENT UMS(<1cm) MRS \n\n\n\n(1-2.5cm) \n\n\n\nMRST \n\n\n\n(2.6-3.5cm) \n\n\n\nMT(>3.5cm) \n\n\n\nRice straw 1.35bc 7.75ab 3.80ab 2.025b \n\n\n\nSaw dust 1.70b 7.00bc 3.15bc 1.850b \n\n\n\nRice husk 1.87ab 6.65bc 2.75bc 1.750b \n\n\n\nBlack plastic 0.75c 8.80a 4.80a 3.075a \n\n\n\nControl 2.35a 5.75c 2.25c 1.575b \n\n\n\nGrand mean 1.6 7.19 3.35 2.06 \n\n\n\nCV 24.5 15.7 21.3 15.2 \n\n\n\nLSD 0.605** 1.74* 1.1** 0.482*** \n\n\n\nSE(\u00b1) 0.19 0.56 0.35 0.15 \n\n\n\nMeans with the same letter do not differ significantly at p=0.05 by DMRT. \n\n\n\nCV =Coefficient of variation. LSD=least significant difference, \n\n\n\nSEM=Standard error of mean. DAP= Days after planting (UMS indicates \n\n\n\n\u201cUnmarketable seed\u201d, MRS indicates \u201cMarketable seed\u201d used for tuber, \n\n\n\nMRST indicate \u201cMarketable seed for tuber as well as for seed purpose\u201d and \n\n\n\nMT indicate \u201cMarketable tuber\u201d used for consumption only). The \n\n\n\nUnmarketable seeds per plant were found significantly different in mulch \n\n\n\nand control condition. The lowest numbers of unmarketable seed were \n\n\n\nfound in black plastic (0.75) which was statistically at par with rice straw \n\n\n\n(1.35) while the highest numbers of unmarketable seed were found in \n\n\n\ncontrol condition (2.35).In case of Marketable seed used for tuber the \n\n\n\nhighest number of tubers per plant the highest number of tubers per plant \n\n\n\nwas found in was found in black plastic (8.80) which was significantly at \n\n\n\npar with rice straw (7.75) and lowest number  were found in control \n\n\n\ncondition(5.75). \n\n\n\nThe number of marketable tubers and seeds per plant were found \n\n\n\nsignificantly higher in black plastic (4.80) which was statistically at par \n\n\n\nwith rice straw (3.80) and lowest number were found in control condition \n\n\n\n(2.25). In case of marketable tuber significant difference was found \n\n\n\nbetween mulched and control condition. In case of black plastic, the \n\n\n\nnumbers of marketable tuber per pant were found (3.07) as compared to \n\n\n\nthe control condition (1.57). Similarly, the marketable tubers in rice straw \n\n\n\nwas found (2.02) which was statistically at par with saw dust(1.85) and \n\n\n\nrice husk (1.75). Mulch materials created favorable condition for the \n\n\n\ngrowth of plant. Such response was mainly due to the physiochemical and \n\n\n\nbiological improvement occurred in the soil including favorable \n\n\n\ntemperature and moisture regimes, nutrient availability and microbial \n\n\n\nactivity in mulch condition. Among the different mulch materials black \n\n\n\npolythene was more effective, which ensured maximum vegetative growth. \n\n\n\nThe above results are in accordance with the findings of (Ahmed, 2017). \n\n\n\n3.5 Effect of different types of mulching on yield of tube \n\n\n\nTable 7: Effect of different treatment on yield of tuber \n\n\n\nTreatments TUBER YIELD (kg/m2) \n\n\n\nRice straw 2.74b \n\n\n\nSaw dust 2.63b \n\n\n\nRice husk 2.55b \n\n\n\nBlack plastic 3.33a \n\n\n\nControl 2.39b \n\n\n\nGrand mean 2.73 \n\n\n\nCV 8.77 \n\n\n\nLSD 0.36** \n\n\n\nSEM(\u00b1) 0.11 \n\n\n\nMulch materials showed significant difference on yield per meter square \n\n\n\nof potato. The maximum (3.33 kg) yield per meter square was recorded \n\n\n\nfrom black plastic while the lowest yield was recorded in control condition \n\n\n\n(2.39) which was statistically at par with rice straw (2.74), saw dust (2.63) \n\n\n\nand rice husk (2.55). Mulch materials created favorable condition for the \n\n\n\ngrowth of plant which leads to the production of maximum yield per \n\n\n\nhectare. Mulch application resulted in a significant decrease  in soil \n\n\n\ntemperature in the root zone and the conservation of soil moisture. The \n\n\n\nnumber and weight of tubers and tuber yield in the mulch treatment were \n\n\n\nsignificantly greater than on plots without mulching. Similar result with \n\n\n\napplication of black plastic was reported (Farrag, 2016). \n\n\n\n3.6 Effect of different types of mulching on number of tubers \n\n\n\nTable 8: Effect of different treatments on number of tubers \n\n\n\nTreatments Tuber No (number/m2) \n\n\n\nRice straw 101.62b \n\n\n\nSaw dust 99.96b \n\n\n\nRice husk 102.87b \n\n\n\nBlack plastic 125.15a \n\n\n\nControl 93.29b \n\n\n\nGrand mean 105 \n\n\n\nCV 11.7 \n\n\n\nLSD 18.9* \n\n\n\nSEM(\u00b1) 6.14 \n\n\n\nStatistically significant difference was recorded due to different mulch \n\n\n\nmaterials on number of tubers per meter square of potato. The maximum \n\n\n\n(125.15) number of tubers per plant was found in black plastic. The \n\n\n\nminimum number (93.29) was found in control condition which was \n\n\n\nstatistically at par with rice straw (101.62), rice husk (102.87) and saw \n\n\n\ndust (99.96) Mulch materials created favorable condition for the growth of \n\n\n\nplant which leads to the production of maximum vegetative growth with \n\n\n\nmaximum number of tubers per hill. Plots covered with black polyethylene \n\n\n\nmulch recorded significantly higher average number of tubers per square \n\n\n\nmeter and statistically superior to no mulch and other mulching. \n\n\n\n3.7 Effect of different types of mulching on grading of tubers \n\n\n\nTable 9: Effect of different treatment on grading of tuber (large, medium \n\n\n\nand small) \n\n\n\nTreatments LST \n\n\n\n(>50gm)/plant \n\n\n\nMST(25-\n\n\n\n50gm)/plant \n\n\n\nSST \n\n\n\n(<25gm)/plant \n\n\n\nRice straw 4.25b 4.30b 4.05bc \n\n\n\nSaw dust 3.80bc 4.22b 4.90ab \n\n\n\nRice husk 3.46c 4.00bc 5.15ab \n\n\n\nBlack plastic 5.50a 4.65a 3.25c \n\n\n\nControl 3.22c 3.88c 5.85a \n\n\n\nGrand mean 4.05 4.21 4.64 \n\n\n\nCV 10 4.67 21.5 \n\n\n\nLSD 0.624*** 0.303** 1.54* \n\n\n\nSEM(\u00b1) 0.2 0.09 0.49 \n\n\n\nMulching produces a significant difference in tuber weight per plant as \n\n\n\ncompared to the control condition. In case of large of large size \n\n\n\ntubers(>50gm) the maximum number of large tubers were found in black \n\n\n\nplastic (5.50) while the minimum number were found in control condition \n\n\n\n(3.22) which was statistically at par with rice straw (4.25) and saw dust \n\n\n\n(3.80). Medium sized tuber (25 -50 gm) were found significantly higher in \n\n\n\nmulch condition as compared to the control condition. In black plastic the \n\n\n\nmaximum numbers of medium sized tubers per plant were found \n\n\n\n(4.65).The minimum number of medium sized tubers were found in \n\n\n\ncontrol condition was found in control condition (3.88) which was \n\n\n\nstatistically at par with rice husk (4.00).  \n\n\n\nThe number of medium sized tubers in rice straw was found (4.30) which \n\n\n\nwas significantly at par with saw dust (4.22) and rice husk (4.00). Small \n\n\n\nsized tubers (<25 gm) numbers per plant were found significantly higher \n\n\n\nin control condition as compared to the mulch condition. The highest \n\n\n\nnumbers of small sized tubers were found in control condition (5.85) \n\n\n\nwhich was statistically at par with rice husk (5.15) and saw dust (4.90). \n\n\n\nThe minimum numbers of small sized tubers were found in black plastic \n\n\n\n(3.25). The higher yield of large sized tubers and medium sized tubers with \n\n\n\nmulch was due to the less resistance by soil and more up take of water and \n\n\n\nnutrients which might have led to better development and growth of \n\n\n\nindividual tuber and hence large sized potato (Zhao, 2012). The results \n\n\n\nwere more pronounced in case of black polyethylene mulch compared to \n\n\n\nother mulches and control condition because of more soil moisture and \n\n\n\nnutrient retention due to lesser weed competition. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Santosh Bharati, Binod Joshi, Roshan Dhakal, Sushma Paneru, Shiva Chandra Dhakal, Khem Raj Joshi (2020). Effect Of Different Mulching On Yield And Yield \nAttributes Of Potato In Dadeldhura District, Nepal. Malaysian Journal of Sustainable Agriculture, 4(2): 54-58. \n\n\n\n3.8 Effect of different mulching materials on soil temperature \n\n\n\nFigure 4: Effect of different mulching material on soil temperature \n\n\n\nAverage soil temperature at 15 cm depth during the experimental period \n\n\n\n(Figure 6) was affected by the mulching type and materials applied. Results \n\n\n\nindicated that the type of mulch improved the soil temperature following \n\n\n\nthe order of Black plastic > Saw dust > Rice husk > Rice straw > Control. \n\n\n\nThe soil temperature under the different mulches is affected by the type of \n\n\n\nmaterial employed and the temperatures registered in bare soil are always \n\n\n\nlower than under mulch treatments. Applying the black plastic mulch \n\n\n\nincreased soil temperature by 1.5 -2.47 C\u00b0 as compared to other mulching \n\n\n\nmaterials and bare soils. These findings are in agreement with many other \n\n\n\nfield studies (Singh, 2012; Moursy, 2015; Li, 2018; Kumari, 2012; Simsek, \n\n\n\n2017; Xing, 2012; Yaghi, 2013). The warmer soil temperatures can quicken \n\n\n\nseedling emergence and growth to achieve the desired population \n\n\n\nstructure at an earlier growth stage which maximize the absorption of \n\n\n\nsolar radiation and enhance the yield (Zhou, 2009; Li, 2018). Furthermore, \n\n\n\nelevated soil temperature can be lethal for nematode and soil borne \n\n\n\npathogens as well as many weed seeds before its germination through \n\n\n\nsolarization (Singh, 2012). \n\n\n\n4. CONCLUSION \n\n\n\n\u27a2 Black polyethylene mulch was found to be more suitable mulching \n\n\n\nmaterial compared to saw dust, rice straw, rice husk, mulch for potato \n\n\n\ncrop. \n\n\n\n\u27a2 The maximum yield was obtained from black plastic which was followed \n\n\n\nby rice straw, saw dust, rice husk and control condition. \n\n\n\n\u27a2 The soil temperature was found maximum in black plastic followed by \n\n\n\nsaw dust, rice husk, rice straw and control condition. \n\n\n\n\u27a2 The production of potato with the use of black plastic was found \n\n\n\neconomical. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThis research was supported by the Agriculture and Forestry University, \n\n\n\nRampur, Nepal through providing financial aid for the research. Authors \n\n\n\nare totally obliged to Mr. Prakash Ayer helping us to complete research. \n\n\n\nREFERENCES \n\n\n\nAhmed, N.U., 2017. Performance of mulching on the yield and quality of \n\n\n\npotato. International Journal of Natural and Social Sciences, 4(2), Pp. \n\n\n\n07-13. \n\n\n\nDong, B.L., 2014. Growth, grain yield and water use efficiency of rain-fed \n\n\n\nspring hybrid millet (Setaria italica) in plastic mulched and \n\n\n\nunmmulched fields. Agricultural water management, 143, 93-101. \n\n\n\nFarrag, K.A., 2016. Growth and productivity of potato under different \n\n\n\nirrigation levels and mulch types in the noerth west of the Nile delta, \n\n\n\nEgypt. Middle East. Journal of Applied Sciences, 6, 774-786. \n\n\n\nKumari, S., 2012. Influence of drip irrigation and mulch on leaf area \n\n\n\nmaximization, water use efficiency and yield of potato (Solanum \n\n\n\ntuberosum L.). Journal of Agricultural Science, 4(1), Pp. 79-86. \n\n\n\nLi, Q.L., 2018. Mulching improves yield and water -use efficiency of potato \n\n\n\ncropping in China: A meta-analysis. Field rops research, 221, 50-660. \n\n\n\nMoursy, S.A., 2015. Polyethylene nd Rice Straw as Soil Mulching: Reflection \n\n\n\nof Soil Mulch Type on Soil Temperature, Soil Borne Diseases, Plant \n\n\n\nGrowth and Yield of Tomato. Global Journal of Advance Research, 2(10), \n\n\n\nPp. 1497-1519. \n\n\n\nShiJie, F.W.D., 2011. Effects of different cultivation techniques on soil \n\n\n\ntemperature, moisture and potato yield. Transactions of the Chinese \n\n\n\nsociety of Agricultural Engineering, 27(11), 216-221. \n\n\n\nSimsek, U.E., 2017. Effect of mulching on soil moisture and some soil \n\n\n\ncharacteristics. Feb-Fresenius Environmental Bulletin, 7437. \n\n\n\nSingh, A.K., 2012. Effect of black plastic mulch on soil temperatuure and \n\n\n\ntomato yield in mid hills of Garhwal Himalayas\u201d, Journal of Horticulture \n\n\n\nand Forestry, 4(4), 77-79. \n\n\n\nXing, Z.T., 2012. Effects of Hay Mulch on Soik Properties and POtato Tuber \n\n\n\nYield under Irrigation and Non-irrigation in New Brunswick,Canada. \n\n\n\nJournal of Irrigation and Drainage Engineerring, 138(8), 703-714. \n\n\n\nYaghi, T.A., 2013. Cucumber (Cucumis sativus, L.) water use efficiency \n\n\n\n(WUE) under plastic mulch and drip irrigation. Agricultural water \n\n\n\nmanagement, 128, Pp. 149-157. \n\n\n\nZhang, H.F., 2017. Progress of potato staple food reserach and industry \n\n\n\ndevelopment in China. Journal of integrayive agriculture, 16(12), Pp. \n\n\n\n2924-2932. \n\n\n\nZhao, H.X., 2012. Plastic filmm mulch for half growing - season maximized \n\n\n\nWUE and yield of potato via moisture-temperature improvement in a \n\n\n\nsemi-arid agroecosystem. Agricultural Water Mangement, 104, Pp. 68-\n\n\n\n78. \n\n\n\nZhou, L.F., 2009. How Two Ridges and the Furrow Mulched with Plastic \n\n\n\nFilm Affect Soil water, Soil Temperature and Yield of Maize on the \n\n\n\nSemiarid Loess Plateau of China. Field crops Reserach, 113.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 99-103 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.99.103 \n\n\n\nCite the Article: Dhruba Baral, Anup Paudel, Himal Acharya, Madhav Prasad Neupane (2021). Evaluation of Soil Nutrient Status in Apple Orchards Located in Different \nAltitudes in Kalikot District, Nepal.  Malaysian Journal of Sustainable Agriculture, 5(2): 99-103. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2021.99.103 \n\n\n\nEVALUATION OF SOIL NUTRIENT STATUS IN APPLE ORCHARDS LOCATED IN \nDIFFERENT ALTITUDES IN KALIKOT DISTRICT, NEPAL \n\n\n\nDhruba Baral *, Anup Paudel, Himal Acharya, Madhav Prasad Neupane\n\n\n\nDepartment of Agronomy, Agriculture and Forestry University, Rampur, Chitwan, Nepal \n\n\n\n*Corresponding Author Email: baralafu2015@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 19 November 2020 \nAccepted 23 December 2020 \nAvailable online 25 March 2021\n\n\n\nThis study was conducted to assess the fertility status of different altitude of apple orchard and their effect \nupon soil nutrients and to study the relationship between different altitude and their availability. Seven \ndifferent orchards located in 2800, 2700 2600, 2500, 2400, 2300 and 2200 at Apple Zone, Raskot, Kalikot \nwere selected as treatments. They were replicated three times in Randomized Complete Block Design. \nComposite soil samples were collected in each study site from 0-3 ft soil depth in \u2018W\u2019 pattern from each plot. \nAnalyses of soil samples were done in regional soil testing laboratory, Surkhet for chemical properties. There \nwas a significant effect (p<0.05) of altitude on soil macronutrients except available potassium level. Maximum \namounts of soil organic matter, acidic and soil rich in nitrogen and phosphorus were found in 2800 masl \nwhereas more basic soil , poor soil organic matter and soil poor in nitrogen and phosphorus were found in \n2200 masl. Result showed that in altitude of 2200 masl has poor soil nutrients compared to apple orchards \nin higher altitude. Kalikot is the top producer of apple in Nepal. This assessment will helps apple growers for \nadopting better nutrient management plan in their orchards according to the altitude in the district. Further, \nit is recommended to conduct soil nutrient assessments for all other apple growing regions in the country.  \n\n\n\nKEYWORDS \n\n\n\nAssess, fertility, macronutrients, Malus, pumila.\n\n\n\n1. INTRODUCTION \n\n\n\nAgriculture is the main economic activity of Nepal, employing about 65% \nof the population and providing 27.10% of GDP with annual growth rate \nof 2.72% (AITC, 2019). Fruits are consumed for rituals and cultural \npurposes since ancient time in Nepal (Karki et al., 2017). There is a need \nto explore the potentiality of fruit cultivation in Nepal for export \npromotion as well as import substitution (MoAD, 2017). Apple contributes \nabout 4.2 percent of the total fruit production and occupies 5.08 percent \nof the total fruit area in Nepal (MoALD, 2017). Apple (Malus pumila) is an \nimportant fruit crop of world and it is also called as the king of temperate \nfruits (USAID, 2008). Apple accounts for more than 50% of the deciduous \ntree fruit production in the world. Nepal\u2019s total and productive area under \napple was 12,025 ha and 3,707 ha respectively with 19,850 mt and 5.36 \nmt ha-1 production and productivity respectively (MoALD, 2017). The \naverage production, area and productivity of the apple in Kalikot district \nwas 836 mt, 805 ha and 8.09 mt ha-1 in the fiscal year. \n\n\n\nTopographically, Kalikot district has 53.01% forest land, 2.31% bushes \nland, 29.35% bare area, 5.83% snow cover area and only 9.46% \nagriculture land (GoN, 2011). The district has 136,948 of total population \ninvolving 94.82% in the agriculture (GoN, 2011). Healthy soils which help \nto increase production and address food security of nation. Soil health can \nbe evaluated by soil characteristics like pH, soil organic matter (SOM), \ntotal nitrogen (N), available phosphorus (P) and available Potassium (K). \nPeople plant the apple in unfertile and ignore land than fertile. No any  \nexcess of transportation facilities in the upper part and farmers have \nless knowledge about the soil nutrients. According to local farmer, \n\n\n\nsoil test was done only through kit-box which was not accurate and \nauthentic. The national recommended dose for apple is 300 kg ha-1 of \nFarm Yard Manure, 8.7 kg ha -1 of diammonium phosphate, 14 kg ha -1 \n\n\n\nof urea, 2.7 kg  ha-1 of muriate of potash respectively in a year (AITC, \n2019). Arguably, Kalikot district is emerging as the apple grower \nfrom last decade.  \n\n\n\nThis is one of the major temperate fruit. It has been cultivated in the \nsandy loam soil having pH range 6.5 to 8 and ignored lands in its few \nfirst of cultivation (DoSM, 2015). This study focusses on the \ncomparison of the different altitude soil nutrient. This will give an \nidea about the soil nutrients and health status of soil under different \naltitude apple cultivation. This information will help the farmers and \nthe policy makers to make plans according to the profitability.  This \nwill make the block to think about how to accommodate the other \ngrowers by raising the awareness about these effects of cultivation \non soil. This will make the adoption of the new cultivation technology \nmore effectively. The objective is to study about different altitude soil \nchemical properties on apple orchard. \n\n\n\n2. MATERIALS AND METHODOLOGY\n\n\n\n2.1 Treatment Details \n\n\n\nThe sampling activities were conducted in different altitude of apple \norchards in Apple Zone, Raskot, Kalikot. The statistical method adopted in \nthis study was Randomized Complete Block Design (RCBD) by selecting \napple orchards from different altitudes (i.e. 2200, 2300, 2400, 2500, 2600, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 99-103 \n\n\n\nCite the Article: Dhruba Baral, Anup Paudel, Himal Acharya, Madhav Prasad Neupane (2021). Evaluation of Soil Nutrient Status in Apple Orchards Located in Different \nAltitudes in Kalikot District, Nepal.  Malaysian Journal of Sustainable Agriculture, 5(2): 99-103. \n\n\n\n2700 and 2800 masl) as seven treatments area of each treatment was 500 \nm2 and contain 20 apple trees. Each altitude was replicated three times \ncomprising a total of twenty-one soil sampling plots. To maintain \nhomogeneity within a replication, the samples from each block were \ncollected within the same vicinity. Each block was located in south facing \nslope. The density of apple trees was almost similar in all the treatments.  \n\n\n\nTable 1: Basic information of the study \nDesign : RCBD \nTreatment combinations : 7  \nReplications  : 3 \nTotal number of plots : 7 \u00d7 3 = 21 \nCrop : Apple \nSeason : Spring \n\n\n\nLocation  \n: Raskot (Septi and Bisali), \n\n\n\nKalikot \nSampling time : Last week of March, 2019  \nClimatic region : Temperate region \nSlope of Area : South facing slope \n\n\n\nEvaluations of soil nutrients (N, P, K, pH & Organic matter) according to \n\n\n\naltitude with 7 treatment and 3 replications of each. \n\n\n\nT1= 2800masl (soil nutrient test; N, P, K, pH & OM) \nT2=2700masl (soil nutrient test; N, P, K, pH & OM) \nT3= 2600masl (soil nutrient test; N, P, K, pH & OM) \nT4= 2500masl (soil nutrient test; N, P, K, pH & OM) \nT5= 2400masl (soil nutrient test; N, P, K, pH & OM) \nT6=2300masl (soil nutrient test; N, P, K, pH & OM) \nT7=2200masl (soil nutrient test; N, P, K, pH & OM) \n\n\n\n2.2 Soil sample collection and preparation \n\n\n\nSoil samples were collected from each treatment. Three sub samples were \ncollected randomly from each plot within a replication (i.e., 1ft, 2ft, and \n3ft). These subsamples were then collected and standard procedure was \nfollowed for obtaining 0.5 kg of composite sample. A total of twenty-one \ncomposite soil samples were collected. The collected soil samples were \nlabeled, brought to Directorate of Soil testing laboratory, Birendranagar, \nSurkhet, Karnali Province. These samples were air dried and then ground \nand sieved through 2mm sieve for chemical analysis and through 0.2mm \nsieve for SOM analysis. \n\n\n\n2.3 Laboratory analysis \n\n\n\nSoil samples collected from each location and orchards were analyzed for \nsoil pH, soil organic matter, total nitrogen, available phosphorus and \npotassium content of the soil. Laboratory methods used for analysis of \ndifferent soil fertility parameters are depicted in Table 02. \n\n\n\nTable 2: Laboratory analytical techniques for different soil physical \nand chemical properties \n\n\n\nParameters Study method \nSoil texture Mechanical analysis method (Day,1965) \nOrganic \nmatter \n\n\n\nWalkley \u2013 Black method (Houba et al., 1989) \n\n\n\nSoil pH \nGlass-calomel electrode pH meter using 1:2 soil \nwater ratio (Cottenie et al., 1982) \n\n\n\nTotal nitrogen Kjeldhal distillation unit (Bremner and Hauck, 1982) \nAvailable \nphosphorus \n\n\n\nModified Olsen bicarbonate method  \n\n\n\nAvailable \npotassium \n\n\n\nAmmonium acetate extraction method  \n\n\n\n2.4 Statistical analysis and data presentation \n\n\n\nData pertaining to soil organic matter and nitrogen were rated according \nto standard rating of Soil Science Division, Khumaltar, Lalitpur and data \nrelated to phosphorus and  potassium were recorded based on Ward lab \nlaboratories rating Table 03. The pH obtained from laboratory analysis \nwere rated according to Khatri- Chhetri, 1991 Table 04 and analyzed using \nGen STAT software 15th Edition and Microsoft Excel. The data were \nsubjected to analysis of variance (ANOVA) appropriate to randomized \ncomplete block design technique. When significant difference existed \nbetween treatment means, comparison of the means was done using \nDuncan\u2019s Multiple Range Test (DMRT) at 5% probability levels. \nCorrelation analysis was done between organic matter and nitrogen level \nto know the effect of organic matter on nitrogen content of soils. SPSS \nsoftware was used for Correlations analysis. \n\n\n\nTable 3: Rating chart for classification of fertility status according to \nSoil Science Division, Khumaltar, Lalitpur (2002) and Ward lab \n\n\n\nlaboratories \nNutrient \n\n\n\nstatus \nSOM \n(%) \n\n\n\nTotal N \n(%) \n\n\n\nAvailable \nP (mg/kg) \n\n\n\nAvailable \nK (mg/kg) \n\n\n\nVery low <1 <0.05 0-3 0-40 \nLow 1-2.5 0.05-0.1 4-9 41-80 \n\n\n\nMedium 2.5-5 0.1-0.2 10-16 81-120 \nHigh 5-10 0.2-0.4 17-30 121-200 \n\n\n\nVery high >10 >0.4 >30 >200 \n\n\n\nTable 4: Rating chart for soil reaction according to Khatri-Chhetri \nSoil pH value Soil reaction rating \n\n\n\n<6 Acidic \n6.0-7.5 Neutral \n\n\n\n>7.5 Alkaline \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Soil pH \n\n\n\nThe pH level in different altitude of apple orchard is shown in Table 05. \nThe result of the study indicated that the effect of altitude on pH was highly \nsignificant (P<0.001). \n\n\n\nTable 5: pH variation in different altitude apple orchards in Apple \nZone, Kalikot \n\n\n\nAltitude of apple orchard (masl) pH \n2800 5.50c \n2700 5.88bc \n2600 6.31b \n2500 6.28b \n2400 6.31b \n2300 6.38b \n2200 7.19a \n\n\n\nSEm(\u00b1) 0.177 \nLSD (0.05) 0.54*** \n\n\n\nCV,% 4.9 \nGrand mean 6.264 \n\n\n\nMeans followed by same letter (s) in a column are not significantly \ndifferent at 5% level of significance in DMRT test. NS: Non-significant SEm: \nStandard Error of Mean,Coefficient of Variance (CV), *** is significant at \nP<0.001  \n\n\n\nThe altitude had significant impact upon the pH level (P<0.001). The \nmaximum pH was observed in 2200m (7.1) and lowest pH was observed \nin 2800m (5.5) which is acidic in nature. The pH of 2300m (6.38) to 2600m \n(6.31) were not significantly different and remained closer neutral range.  \nAccording to the rating chart adopted from (Khatri-Chhetri,1991). Soil \nreaction was acidic in the elevations of 2700 (5.88) and 2800m (5.50). The \nlower soil pH in higher height might be due to its higher slope, and less \nevaporation (Yeshaneh, 2015). Yeshaneh reported lower soil pH in the \nsoils at higher slopes as soils of sloppy areas with good drainage are often \nacidic in nature due to loss of soluble basic cations (Pal, 2016). \nDecomposition of leaf litter from trees releases organic acids lowering the \nsoil pH (Gustafson, 1937). The results indicated that the pH is increasing \nwith decreasing height. These findings are similar with the findings of in \nLadakh India (Charan et al., 2013). \n\n\n\n3.2 Soil organic matter (SOM) \n\n\n\nThe SOM level in different altitude is shown in Table 6. Soil organic matter \ncontent was more in higher altitude compared to lower altitude. \n\n\n\nTable 6: Soil organic matter variation in different altitude apple \norchards in Apple Zone, Kalikot \n\n\n\nAltitude of apple orchard (masl) SOM (%) \n2800 2.15a \n2700 1.94a \n2600 0.86b \n2500 0.68b \n2400 0.60b \n2300 0.43b \n2200 0.41b \n\n\n\nSEm(\u00b1) 0.183 \nLSD (0.05) 0.56*** \n\n\n\nCV, % 31.3 \nGrand mean 1.01 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 99-103 \n\n\n\nCite the Article: Dhruba Baral, Anup Paudel, Himal Acharya, Madhav Prasad Neupane (2021). Evaluation of Soil Nutrient Status in Apple Orchards Located in Different \nAltitudes in Kalikot District, Nepal.  Malaysian Journal of Sustainable Agriculture, 5(2): 99-103. \n\n\n\nMeans followed by same letter (s) in a column are not significantly \n\n\n\ndifferent at 5% level of significance in DMRT test. NS: Non-significant SEm: \n\n\n\nStandard Error of Mean, Coefficient of Variance (CV), *** is significant at \n\n\n\nP<0.001  \n\n\n\nThe results of the study indicated that the effect of altitude on SOM was \nhighly significant (P<0.001). The highest amount (2.15%) of SOM was \nfound in 2800m whereas the lowest (0.41%) found in 2200m. This result \nwas consistent with the research outcomes of Charan, et al.(2013), they \nfound that SOM was increases with increase in altitude from 10000ft to \n>12000ft. The SOM % of other altitude are similar because of similar \npractices of inter cropping pattern and supply of OM (Bot and Benites, \n2005). The SOM level is high in higher altitude because of not leaching of \norganic matter in uncultivable land where soil wasn\u2019t disturbed (Brady \nand Weil, 2005). In inter cropping system due to the more tillage practices \nsoil erosion and leaching occurs which wash out the surface organic \nmatter (Funderburg, 2016). \n\n\n\n3.3 Soil nitrogen \n\n\n\nThe total nitrogen was lowest (0.02 %) in 2200m altitude level where as \nthe highest N percentage (0.15%) was reported in the orchard located in \n2800 m. Nitrogen percentages in 2600m (0.070) and 2500m (0.066) were \nsimilar whereas no significant difference was reported in 2400m (0.033), \n2300m (0.021) and 2200m (0.021) altitude levels (Table 7).  \n\n\n\nTable 7: Nitrogen variation in different altitude apple orchards in \nApple Zone, Kalikot \n\n\n\nAltitude of apple orchard (masl) Nitrogen \n2800 0.157a \n2700 0.136a \n2600 0.070b \n2500 0.066b \n2400 0.033c \n2300 0.021c \n2200 0.021c \n\n\n\nSEm(\u00b1) 0.00892 \nLSD (0.05) 0.02749*** \n\n\n\nCV,% 21.5 \nGrand mean 1.01 \n\n\n\nMeans followed by same letter (s) in a column are not significantly \n\n\n\ndifferent at 5% level of significance in DMRT test. NS: Non-significant SEm: \n\n\n\nStandard Error of Mean, Coefficient of Variance (CV), *** is significant at \n\n\n\nP<0.001  \n\n\n\nThere was a positive correlation (r=.917**) between SOM and N level \nindicating that the highest nitrogen level was in 2800 m. Similar findings \nwere put forward by they found that amount of nitrogen increases with \nincreases in SOM because soil required nitrogen to decompose the \norganisms into organic matter (Charan et al., 2013). The availability of \nnitrogen to plants is substantially affected by quantity and type of soil \n(Chen and Avnimelech, 1986). The total nitrogen was found more in high \naltitude which are uncultivated land and had no leaching and soil problem \nthen lower altitude which were intercropping land having leaching and \nerosion problem. In high hilly area there is problem of washing up surface \nnutrient due to sloppy land (Brady and Weil, 2005). The nitrogen level in \nRaskot area is low below the 2600masl so recommendation of Organic \nmatter and cultivation of legumes crops can be given. \n\n\n\n3.4 Available phosphorus \n\n\n\nComparison of available phosphorus was done on the basis of Ward lab \nratings, the P in soils of 2800m found to be high following 2500m (Table \n8). The lowest available phosphorus was reported in 2200m, 2600m and \n2400m and no significant difference was observed. \n\n\n\nTable 8: Phosphorus variation in different altitude apple orchards in Apple \nZone, Kalikot \n\n\n\nAltitude of apple orchard (masl) Phosphorus (mg/kg) \n\n\n\n2800 23.50a \n2700 19.09ab \n\n\n\n2600 17.21bc \n2500 17.76b \n\n\n\n2400 16.47bc \n\n\n\n2300 12.85cd \n2200 11.21d \n\n\n\nSEm(\u00b1) 2.058 \nLSD (0.05) 4.484** \n\n\n\nCV,% 14.9 \nGrand mean 16.87 \n\n\n\nMeans followed by same letter (s) in a column are not significantly \n\n\n\ndifferent at 5% level of significance in DMRT test. NS: Non-significant SEm: \n\n\n\nStandard Error of Mean, Coefficient of Variance (CV), *** is significant at \n\n\n\nP=0.001  \n\n\n\nAs shown in Table 08 the altitude had a significant (P<0.001) effect on \navailable phosphorus. Maximum P was observed in 2800m (23.50) and \n2500m (19.09). It indicated that the soil of Raskot area contains medium \nlevel of phosphorus. On the basis of Ward lab ratings available P ranges \nfrom 10 mg kg-1 to 16 mg kg-1 is medium range. A positive correlation was \nobserved (r =.629**) between SOM and available phosphorus. Sharma \nreported 21.99% less annual phosphorus input via tree litter fall than \nother macronutrients (Sharma, 2004). This might be the possible cause of \nlower available phosphorus in this study. The acidic condition of soils \nmight have caused transition of phosphate into less soluble compounds \nwith the reaction of Fe and Al. \n\n\n\n3.5 Available potassium \n\n\n\nThe effect of stand age on available potassium levels is given in. There was \nno significant effect of stand age on the soil potassium level shown in Table \n9 \n\n\n\nTable 9: Potassium variation in different altitude apple orchards in \nApple Zone, Kalikot \n\n\n\nAltitude of apple orchard (masl) Potassium \n2800 70.53a \n\n\n\n2700 72.69a \n\n\n\n2600 77.90a \n\n\n\n2500 71.62a \n\n\n\n2400 76.44a \n\n\n\n2300 78.33a \n\n\n\n2200 67.11a \n\n\n\nSEm(\u00b1) 4.59 \nLSD (0.05) NS \n\n\n\nCV,% 7.70% \nGrand mean 73.5 \n\n\n\nMeans followed by same letter (s) in a column are not significantly \n\n\n\ndifferent at 5% level of significance in DMRT test. NS: Non-significant SEm: \n\n\n\nStandard Error of Mean, Coefficient of Variance (CV) \n\n\n\nThe highest available Potassium (mg/kg) was reported as 2300m (78.33) \nfollowed by 2600m (77.90), 2400m (76.44), 2700m (72.69), 2500m \n(71.62). The lowest available potassium was found in 2200m (67.11) but \nall of these results were statistically non-significant. The soil potassium \nwas high in all soil samples according to the Ward lab chart. Carson also \nreported that Nepalese soils are rich in potassium (Carson, 1992). The soil \npotassium is affected by the parental material of the soil in bed rock. \nPotassium from deep subsoil horizons is taken up by deep rooted \nperennials and recycled to soil surface through translocation into leaves \nand following leaf fall and decomposition (Lehmann and Schroth, 2002). \nThe low level of available soil K in 2200masl might be due to higher \nleaching loss and more K harvest from the soil. Under irrigated condition, \navailable K is subjected to considerable leaching loss (Brady and Weil, \n2005). \n\n\n\n3.6 Simple correlation coefficient (r) among different soil nutrient \n\n\n\nparameters \n\n\n\nThe correlation analysis was done in soil pH with soil nitrogen content, \nphosphorus availability and potassium availability. The correlation of SOM \nwith soil pH, soil nitrogen content, plant available phosphorus and \navailable potassium are shown in Table 10. \n\n\n\nTable 10: Soil pH and SOM with different soil nutrient parameters in \ndifferent altitude of apple orchards \n\n\n\nParameters pH \nSOM \n(%) \n\n\n\nPhosphorus \n(ppm) \n\n\n\nPotassium \n(ppm) \n\n\n\nNitrogen \n(%) \n\n\n\n-.707** .917** .778** -.133 \n\n\n\npH -.716** -.796** -.096 \nSOM (%) .693** -.186 \n\n\n\n**Correlation is significant at the 0.01 level (2-tailed). \n*Correlation is significant at the 0.05 level (2-tailed). \n\n\n\n3.6.1 Relationship of soil pH with different soil nutrient parameters \n\n\n\nRelationship between soil pH and Soil organic matter - The correlation \nbetween soil pH and SOM was significant (r= -0.716**) negatively. The \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 99-103 \n\n\n\nCite the Article: Dhruba Baral, Anup Paudel, Himal Acharya, Madhav Prasad Neupane (2021). Evaluation of Soil Nutrient Status in Apple Orchards Located in Different \nAltitudes in Kalikot District, Nepal.  Malaysian Journal of Sustainable Agriculture, 5(2): 99-103. \n\n\n\ncoefficient of determination (R2) value was 0.512 that the SOM contributes \n51.2% to the change in soil pH level while the rest effects was due to other \nfactors which is shown in Figure 1.SOM content had significant effect on \nchange in soil pH. \n\n\n\nFigure 1: The relationship between SOM content and the soil pH \n\n\n\nRelationship between soil pH and soil nitrogen content: - The correlation \nbetween soil pH and N was significant (r =-0.707**) but negatively. The \ncoefficient of determination (R2) value was 0.500 that the in-soil pH level \n50% to the change in soil nitrogen level while the rest effects was due to \nother factors as shown in Figure 2. Soil pH had significant effect on change \nin soil nitrogen level. \n\n\n\nFigure 2: The relationship between nitrogen content and the soil pH \n\n\n\nRelationship between soil pH and available phosphorus: - The soil pH and \nplant available phosphorus content had negative correlation (r= -0.796**) \nwith each other. The coefficient of determination (R2=0.633) indicated \nthat the contribution of SOM content to the available phosphorus was \n63.3% and rest of the effects was due to other factors which is shown \nFigure 3. Soil organic matter had significant effect on phosphorus \navailability. \n\n\n\nFigure 3: The relationship between available phosphorus and the soil pH \n\n\n\n3.6.2 Relationship of Soil organic matter with different soil nutrient \n\n\n\nparameters \n\n\n\nRelationship between soil organic matter content and soil nitrogen: - The \nsoil organic matter and soil nitrogen content had a highly significant \npositive correlation (r= 0.917**) with each other. The coefficient of \ndetermination (R2=0.841) indicated that the contribution of SOM content \nto the soil nitrogen content was 84.1% and rest of the effects was due to \nother factors as shown in Figure 4. Soil organic matter had highly \nsignificant effect on soil nitrogen level changes. \n\n\n\nFigure 4: The relationship between SOM content and nitrogen content \n\n\n\nRelationship between soil organic matter content and available \nphosphorus: - The correlation between SOM content and available \nphosphorus was highly significant (r = 0.693**) positively. The coefficient \nof determination (R2) value was 0.479 that the contribution of the SOM \ncontent to the amount of available phosphorus content was 47.9% and \nrest of the effect was due to other factors as shown in Figure 5. Soil organic \nmatter had significant effect on plant phosphorus availability. \n\n\n\nFigure 5: The relationship between SOM content and available \n\n\n\nphosphorus \n\n\n\n4. CONCLUSION \n\n\n\nThe result of present findings concluded that the altitudinal variation of \napple orchard had a considerable effect on soil nutrient pool except for \npotassium availability. Soil nutrient contents increased significantly from \nthe lower altitude to higher altitude. The highest amount of SOM was \nfound in 2800 masl which was significantly higher with 2200 masl. The \nSOM level is high in higher altitude because of not leaching of organic \nmatter in uncultivable land where soil wasn\u2019t disturbing and also due to \ntemperature effects. The soil of Raskot, Kalikot consists of low SOM and \nnitrogen whereas medium to high in the case of phosphorus and \npotassium. Soil pH was acidic in higher altitude and neutral in lower \naltitude. The pH is inversely related with the SOM, nitrogen and \nphosphorus. Highest soil chemical nutrients (SOM, N, P) was found in 2800 \nmasl and lowest was found in 2200 masl but pH was maximum in 2200 \nmasl and minimum in 2800 masl. SOM, N and P showed decreasing trend \nwith decreasing altitude and pH was increasing with altitude. This \nresearch was conducted in south facing slope and further investigations \nare needed to cover all directions to come up with more precise findings.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe research was conducted as a part of Learning for Entrepreneurial \nexperience supported by Prime Minister Agriculture Modernization \nProject. The author is thankful to all supervising committee, Project \nImplementation Unit (Apple Zone), Kalikot and Agriculture and Forestry \nUniversity, Rampur, Chitwan. \n\n\n\nREFERENCES \n\n\n\nAITC, A.I., 2019. Krishi Diary. Hariharbhawan, Lalitpur: Agriculture \n\n\n\nInformation and Training Centre, Ministry of Agriculture Development. \n\n\n\nBot, A., Benites, J., 2005. The importance of soil organic matter: Key to \n\n\n\ndrought resistant soil and sustained food production. Rome, Italy: Food \n\n\n\nand Agriculture Organization of the United Nations. \n\n\n\nBrady, N.C., Weil, R.R., 2005. The Nature and Properties of the Soil. New \n\n\n\nJersey: Prentice-hall, India, Pp. 643-697. \n\n\n\nBremner, J.M., Hauck, R.D., 1982. Advances in methodology for research on \n\n\n\nnitrogen transformations in soils. Nitrogen in agricultural soils, 22, Pp. \n\n\n\n467-502. \n\n\n\nCarson, B., 1992. The land, the farmer and the future: A soil fertility \n\n\n\nmanagement strategy for Nepal. Kathmandu, Nepal: ICIMOD. \n\n\n\nCharan, G., Bharti, V., Jadhav, S., Kumar, S., Kumar, P., Gongoi, D., Srivastava, \n\n\n\nR., 2013. Altitudinal variations in soil physico-chemical properties at \n\n\n\ncold desert high altitude. Journal of Soil Science and Plant Nutrition, Pp. \n\n\n\n267-277. DOI: http://dx.doi.org/10.4067/S0718-95162013005000023 \n\n\n\nChen, Y., Avnimelech, Y., 1986. Effects of Organic Matter on Nitrogen and \n\n\n\nPhosphorus supply to plants. In Y. Chen, & Y. Avnimelech, The role of \n\n\n\norganic matter in modern agriculture Pp. 13-27. Dordrecht: Martinus \n\n\n\nNijhoff Publishers. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 99-103 \n\n\n\nCite the Article: Dhruba Baral, Anup Paudel, Himal Acharya, Madhav Prasad Neupane (2021). Evaluation of Soil Nutrient Status in Apple Orchards Located in Different \nAltitudes in Kalikot District, Nepal.  Malaysian Journal of Sustainable Agriculture, 5(2): 99-103. \n\n\n\nCottenie, A., Verloo, M., Kiekens, L., Velghe, G., Camerlynck, R., 1982. \n\n\n\nChemical analysis of plants and soils. Lab. Agroch. State Univ. Gent, \n\n\n\nBelgium. \n\n\n\nDado, D.A., 2013. Annual Agriculture Development Programe and \n\n\n\nStatistics Book. Kalikot, Nepal: District Agriculture Development Office. \n\n\n\nDay, P.R., 1965. Particle fractionation and particle\u2010size analysis. Methods \n\n\n\nof Soil Analysis: Part 1 Physical and Mineralogical Properties. Including \n\n\n\nStatistics of Measurement and Sampling, 9, Pp. 545-567. \n\n\n\nDoSM, D.O., 2015. Fertility mapping of Kalikot District. Hariharbhawan, \n\n\n\nLalitpur: Directorate of Soil Management, Ministery of Agriculture \n\n\n\nDevelopment. \n\n\n\nFunderburg, E., 2016. Organic matter serves important role in soil health. \n\n\n\nArdmore, Oklahoma: Noble Research Institute, Pp. 30-70. \n\n\n\nGoN, G.O., 2011. National Census. Thapathali, Kathmandu: Central Bureau \n\n\n\nof Statistics. \n\n\n\nGustafson, A.F., 1937. Conservation of the soil. New York: The maple press \n\n\n\ncompany. \n\n\n\n Houba, V.J.G., Van der Lee, J.J., Novozamsky, I., Walinga, I., 1989. Soil and \n\n\n\nplant analysis. Part, 5, Pp. 4-10. \n\n\n\nKarki, S., Rizal, G., Manandhar, R., Atreya, P.N., Gotame, T.P., 2017. Minor \n\n\n\nFruits in Nepal: Utilization and Conservation Efforts. National Workshop \n\n\n\n22-23 May 2017, Dhulikhel, Pp. 143-155. Kathmandu,Nepal: NAGRC, \n\n\n\nFDD, DoA and MoAD. \n\n\n\nLehmann, J., Schroth, G., 2002. Nutrient Leaching. In F. L. Sinclair, Trees, \n\n\n\ncrops and soil fertility: concepts and research, Pp. 151-166. Blanus: Cabi \n\n\n\npub. \n\n\n\nMoAD, M.O., 2017. Costs of Production and Benefit Cost Analysis. Journal \n\n\n\nof Chemical Information and Modeling, 53, Pp. 1689-1699. \n\n\n\nMoALD, M.O., 2017. Statistical Information on Nepalese Agriculture \n\n\n\n2016/2017, Kathmandu, Nepal: Agri-Business Promotion and Statistics \n\n\n\nDivision, Ministry of Agriculture and Livestock Development. \n\n\n\nPal, S.K., 2016. Textbook of Soil Science. New Delhi: Oxford and IBH \n\n\n\nPublishing Co. Pvt. Ltd. \n\n\n\nSharma, P., 2004. Effects of land-use change on soil microbial C, N, Pi n a \n\n\n\nHimalayan watershed. Pedobiologia, 48, Pp. 83-92. \n\n\n\nUSAID. 2008. Fruit Orchards. Perennial Crop Support Series, Jalalabad, \n\n\n\nAfghanistan. Publication No. 2008-005-AFG. Retrieved from \n\n\n\nhttp://www.rootsofpeace.org/documents/Fruit_Orchard_Manual-\n\n\n\nNov%202008.pdf \n\n\n\nYeshaneh, G.T., 2015. Effect of slope position on Soil Physico- Chemical \nproperties with different management practices in Small Holder \nCultivated Farms of Abuhoy Gara Catchment, Gidan District, North \nWollo. American Journal of Environmental Protection, Pp. 174-179.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 20-26 \n\n\n\nCite The Article: Manoj Kandel, Narayan Bhadhur Dhami, Jiban Shrestha (2019). Phenotypic Diversity Of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes. \nMalaysian Journal of Sustainable Agriculture, 3(2): 20-26. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 1 January 2019 \nAccepted 19 February 2019 \nAvailable online 22 February 2019 \n\n\n\nABSTRACT\n\n\n\nAn experiment was conducted at Hill Crops Research Program (HCRP), Kabre, Dolakha under Nepal Agricultural \nResearch Council, Nepal (NARC), during rainy season of 2018 with objective to identify the level of genetic \ndifference present in the finger millet genotypes being cultivated for selecting genotypes in different agro-climatic \nregion in Nepal using descriptive statistics, correlation analysis, cluster analysis and principle component \nanalysis. P value of REML procedure revealed that significant variation was observed in 16 finger millet genotypes \nfor baring head, days to 50% flowering, days to 50 % heading, days to 75 % maturity, finger length, flag leaf length, \nplant stand, plant height, number of finger, peduncle length, no of productive tiller, thousand grain weight, grain \nyield and straw yield showed selection and development of suitable varieties for different agro-climatic region of \nNepal. Traits baring head, finger length, number of fingers, flag leaf length, peduncle length, productive tiller, \nthousand kernel weight, plant stand, straw yield were positively correlated with grain yield revealed that \nselection within this is importance for improvement grain yield. Cluster I consists up six early mature genotypes \nnamed as KLE-178,GE-0383, ACC#6022,GE-0382,KLE-0150,ACC#0124  can be used to development of early \nmature genotypes  for mountain regions where chilling stress occurs at maturity period whereas similarly cluster \nII, III and IV consisted up 10 late mature genotypes named as ACC#2843, ACC#2860, ACC#8827-1,Sailung-Kodo-\n1,NE-1703-34,KLE-236,ACC#2311,GE-0356, farmer\u2019s variety, GE-0480 can be used to develop high yielding late \nmature varieties for mid hill and terai regions these genotypes may be of interest to researcher for selection of \nmaterials for breeding program in different agro-climatic region of Nepal. \n\n\n\nKEYWORDS \n\n\n\nGenetic variation, Traits, Multivariable analysis, Genotypes, Finger millet \n\n\n\n1. INTRODUCTION \n\n\n\nFinger millet (Eleusine coracana L. Gaertn) (2n=4x=36) is most important \nsmall minor grain cereals grain crop which is suitable for traditional low \ninput-based cereals farming system [1]. In world it is ranks 4th among the \nmost grown cereals after pearmillet, sorghum and prossomilet and foxtail \nmillet and in Nepalese context it is also ranked 4th position in the case of \nproduction (2,66,799 hectare), total production (3,02,397M tons) and \nproductivity after paddy, maize and wheat [2]. It is grown in marginal land \nof tropical to subtropical areas of world due to its hardy nature and in our \ncontext, it grown up to 3150 m due to rich in finger millet genotypes \ndiversity [3]. It has been found large diversity in various region of Nepal \nabout 790 accessions including two wild species\u2014E. indicaand E. \naegyptica [4]. Its variety improvement is most possible area in our context \ndue to rich at both varietal and population levels [5]. In this scenario \nchanging climatic scenario, finger millet taking into consideration scope \nfor increasing the areas due to adaptability to is suited for low and \nmarginal lands and also for harsh weather conditions. Thus, phenotypic \nvariability evaluation is one of importance steps for drawing meaningful \nconclusions for its crop improvement [6,7]. Nutritional values of finger \nmillet contain, moisture 13.24%, protein 7.6%, carbohydrate 74.36%, \nfiber 1.52%, minerals 2.35%, fat 1.35%, energy 341.6 cal/100g [8]. It is \nrich in micronutrient such calcium and iron in other cereals crops so it \nhelps to alleviation of malnutrition and anemia in countries where it is  \n\n\n\nwidely consumed as a staple food [9]. Due to its nutritional awareness to \npublic and more focuses research in major cereals it gets less importance \nin breeding program [10]. \n\n\n\nBut now days because of its nutritional and health benefits awareness, \nPlant breeders give more attention for its research. The status of finger \nmillet is now changing from neglected and underutilized crop to future \nsmart crops for health food and functional food product with high value. \nFor breeding programs of crop improvement information on genetic \nvariation in the landraces, accession and genotypes is a must essential \n[11,12]. Grain yield is dependent traits and selection based on yield \nattributing traits help plant breeder indirectly improve yield [13]. Thus, \nrich at both varietal and population levels this crop indicates there is much \nscope for crop improvement [14].  \n\n\n\nThe aim of this study was to conduct to know the availability of genetic \ndiversity in the available accessions or genotypes. The objective of this \nstudy was to determine genetic variations in 16 finger millet genotypes for \nquantitative traits, which may contribute to formulation of suitable \nselection indices for crop improvement as well as will identify the level of \ngenetic difference present in the finger millet genotypes being cultivated \nfor selecting genotypes in different agro-climatic region in Nepal. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.02.2019.20.26 \n\n\n\n   RESEARCH ARTICLE \n\n\n\nPHENOTYPIC DIVERSITY OF FINGER MILLET (Eleusine coracana (L.) Gaertn.) \nGENOTYPES \n\n\n\nManoj Kandel1*, Narayan Bhadhur Dhami1, Jiban Shrestha2 \n\n\n\n1Nepal Agricultural Research Council, Hill Crops Research Program (HCRP), Baiteshwor-4, Dolakha, Nepal \n2 Nepal Agricultural Research Council, Agriculture Botany Division (ABD), Khumaltar, Lalitpur, Kathmandu, Nepal \n*Corresponding author email: manojkandel24@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:manojkandel24@gmail.com\n\n\nmailto:manojkandel24@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)20-26 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Manoj Kandel, Narayan Bhadhur Dhami, Jiban Shrestha (2019). Phenotypic Diversity Of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes. \nMalaysian Journal of Sustainable Agriculture, 3(2): 20-26. \n\n\n\nTable 1: Comparison of nutritional values of finger millet and other cereals. \n \n\n\n\nCrop Product    Amount of Nutrients per 100 gram raw grain \n\n\n\nCarbohydrate(g) Protein (g)  Fiber (g) Iron (mg) Calcium (mg) \n\n\n\nFinger millet  72 7.3 3.6 3.9 344 \n\n\n\nBuckwheat 72 13.3 1.7 2.3 18 \n\n\n\nBarley 74 13.6 2.4 1.7 26 \n\n\n\nAmaranthus 68 9.4 2.2 5.2 37 \n\n\n\nFoxtailmillet 66 12.3 8 2.8 31 \n\n\n\nProsomillet 73 12.5 2.2 3 14 \n\n\n\nSorghum 72 8.5 1.6 11.2 22 \n\n\n\nPotato 23 1.6 0.6 0.5 10 \n\n\n\nRice 78 6.8 0.2 0.7 10 \n\n\n\nMaize   72 9.2 - 1.2 \n\n\n\nWheat 71 11.8 1.2 5.3 41 \n\n\n\nSource: Nutritive value of Nepali Foods: National Nuritional Program 2061, Nutritive Value of Indian Foods, NIN, 1993. Millets: Future Food and Farming, \nIndia \n  \n2.  MATERIALS AND METHODS \n\n\n\n \nThe field experiment was conducted on finger millet research field at Hill \nCrop Research Program (HCRP), Kabre, Dolakha under Nepal Agricultural \nResearch Council, Nepal (NARC), during rainy season of 2018. Dry nursery \nbeds were established of each genotype on July 2018. Each nursery row \nwas 1 m in length and applied with equal amount of farmyard manure. No \nchemical fertilizers were applied on nursery beds. The seed rate was 10 kg \nha-1. The age of seedlings was 27 days during transplanting. The field \nexperiment was conducted in Random complete block design (RCBD) with \nthree replications. Each replication comprised of sixteen blocks/plots. \nRandomization of experimental materials was done with the software \nCROPSTAT. The plot size was 6 m2. Seeding was done continuously by \nmanually and seed rate was 10 kg/ha Fertilizers were applied at the rate \nof 30:30:0kg/ha N: P2O5: K2O respectively and in addition to this 10-ton \nfarmyard manure per hectare as applied before one month of sowing [16]. \n \nHalf dose of N and full doses of P and K were applied basal dose and \nremaining half of N was applied as side dressing at tillering growth stage. \nTransplanting of 2-3 seedlings per hill was done on 13th July. with a \nspacing of 10cm between rows and 10 cm between hills. There was a gap \nof 0.5m between plots and 2 m between replications. Bunds were \nconstructed between the plots and replications. Weeds are the major \nproblem in finger millet, especially during 2-3 weeks after sowing. The \nplots were kept free of weeds manually. Data 14 quantitative (days to \n\n\n\nheading, days to flowering, days to maturity, flag leaf length, finger length, \nPlant stand per square meter, peduncle length, bearing head per square \nmeter, productive tillers number per plant, plant height, finger number per \near, thousand kernel weight, straw yield per plot, grain yield per plot ) \ntraits were recorded following finger millet descriptors [17]. Grain yield \nwas recorded for net harvested area.  \n \nEach plot was harvested excluding border rows and grain moisture \ncontent for each plot was recorded and grain yield was adjusted to 12 % \nmoisture basis. The grain yield per plot was converted into ton/ha by \nusing formula as given below [17]. The data recorded on different \nparameters from field were first tabulated and processing in Microsoft \nexcel (MS- Excel, 2010), then subjected GenStat to obtain ANOVA and all \nvalues were expressed as mean values. Correlation coefficients of different \ntraits using SPSS program were carried out using the formula [18]. \nHierarchical clustering using Ward\u2019s minimum-variance method with \nsquared Euclidean distance and principal component analysis using \ncorrelation matrix for 14 quantitative characters was performed using \nMINITAB 14 software [19]. P values less than 0.05 and 0.01 were \nconsidered statistically significant and statistically highly significant, \nrespectively. \n \n\n\n\nGrain yield(\nt\n\n\n\nha\n) =  \n\n\n\nYield of plot(kg) \u00d7 10 \u00d7 (100 \u2212 Grain moisture)\n\n\n\nNet harvested area(m2) \u00d7 (100 \u2212 12)\n \n\n\n\n \nTable 2: Names genotypes of finger millets used for research at HCRP (2018). \n\n\n\n \nS. N Name of genotypes  S.N. Name of genotypes  S.N. Name of genotypes  \n\n\n\n1 KLE-178 7 GE-0356 13 ACC#8827-1 \n\n\n\n2 ACC#2843 8 KLE-0150 14 ACC#2860 \n\n\n\n3 GE-0383 9 KLE-236 15 ACC#2311 \n\n\n\n4 ACC#6022 10 GE-0480 16 Farmer\u2019s variety \n\n\n\n5 GE-0382 11 ACC#0124 \n  \n\n\n\n6 Sailung-Kodo-1 12 NE-1703-34 \n  \n\n\n\n3.  RESULTS \n \n\n\n\n3.1 Analysis of Variance \n \n\n\n\nThe result of ANOVA of fourteen yield related traits for sixteen genotypes \nis presented in Table 2. The ANOVA showed highly significant difference \n(p<0.01) among the tested genotypes for all characters except for days to \nflowering and plant height showed significant difference (p<0.05) \nindicating the presence of variability which can be exploited through \nselection. Thus, there is ample scope for selection for finger millet \nimprovement. Mean, F-test value, least significant value (LSD 0.05) and \ncoefficient of variation (CV %) for fourteen characters were presented in \n\n\n\nTable 3. Variation in grain yield was high, genotypes GE-0356 had \nproduces maximum grain yield (0.8533 t/ha) fallowed by Sailing kodo-1 \n(0.78 t/ha) and GE-0480(0.76 t/ha). Days to flowering and days to \nmaturity were ranges of KLE-178(79) - ACC# 0124 (90) days and Local \n(130) - KLE-0150 (143) days, respectively. These traits showed high \nvariation and selection of maturity classes that are suitable for cropping \nsystem is possible. \n \nGrowth characters showed high variation ranging from ranging from KLE-\n236 (7.67)\u2013Sailung kodo-1 (11.67) cm in flag leaf length, KLE-236(70.90)- \nACC#2311 (137.1) cm in plant height, KLE-178 (3)\u2013 GE-0382(6) in \nproductive tiller number and ACC#6022(59.33) \u2013ACC#2311(71) in plant \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)20-26 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Manoj Kandel, Narayan Bhadhur Dhami, Jiban Shrestha (2019). Phenotypic Diversity Of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes. \nMalaysian Journal of Sustainable Agriculture, 3(2): 20-26. \n\n\n\nstand per meter square. Yield attributing traits showed high variation \nranging from GE-0383(69)- Sailung Kodo-1 (114) in bearing head per \nsquare meter, KLE-178 (5.0) \u2013ACC#2311 (9.3) in number of finger per \nhead,NE-1703-34 (2.6) \u2013 GE-0480(3.4) gram in thousand grain weight and  \nFarmer\u2019s (local) variety (7.94)\u2013GE-0480 (10.78) ton per hectare in straw \nyield. Studying the variation among agronomic and yield attributing traits \nof finger millet genotypes is very important for every breeding program as \nthe most of them are highly correlated and have direct effect on yield, they \ncan either affects yield positively or negatively depending upon their \nvariation. High variability existing in these genotypes brings forward the \nmuch-needed information for genetic improvement program of finger \nmillet. Thus, measurement and evaluation of variability are essential steps \nin drawing meaningful conclusions from a given set of phenotypic \nobservations [6,7]. A researcher also observed substantial variation \namong finger millet entries has also been reported in previous studies \n\n\n\n[10]. Other researchers also reported that significant difference in the \nresults would be due to the inclusion of diverse accessions in Asia in this \nstudy especially those from India [20]. Thus, basic objective of study the \nanalysis of variance in any breeding program is the improvement of crop \nyield and quality for increases productivity of that crop association of \ncharacter. \n \nEstimates of phenotypic correlation coefficient for most of characters are \npresented in Table 4. Grain yield per plot had positive and significant \nphenotypic association with baring head, finger length, number of fingers \nper head, flag leaf length, peduncle length, productive tiller, thousand \nkernel weight, plant stand, straw yield. Similarly, the positive and \nsignificant association of straw yield with plant height, number of fingers \nper head, days to 50 % flowering, days to maturity. \n  \n\n\n\n \nTable 3: Mean, F-test, coefficient of variation and least significant difference for yield and yield attributing traits among 16 genotypes of Finger millet \n(Eleusine coracana L. Gaertn) \n \n\n\n\nEntries BH DAF DOH DTM FL FLL PS PH  NF/H PL PT TGW     GY        SY  \n\n\n\nKLE-178 77 85 79 133 7.00 8.67 62.33 114.90 5.10 17.33 3.0 3.00 0.3767 8.33 \n\n\n\nACC#2843 97 81 74 139 7.67 8.67 67.33 110.10 5.17 18.67 4.3 2.90 0.6411 9.22 \n\n\n\nGE-0383 69 89 82 137 8.33 10.00 61.00 126.00 6.40 21.33 4.3 3.27 0.6811 8.89 \n\n\n\nACC#6022 82 89 82 136 7.00 8.00 59.33 109.80 6.77 16.00 3.3 3.07 0.4517 10.33 \n\n\n\nGE-0382 102 86 80 136 9.67 11.33 69.00 117.30 7.27 21.67 6.0 3.17 0.8533 9.11 \n\n\n\nSailung-K.1 114 88 82 137 10.00 11.67 69.67 127.30 7.40 22.33 5.0 3.40 0.7811 9.50 \n\n\n\nGE-0356 77 79 72 137 8.33 10.00 64.67 113.20 7.43 18.33 3.7 2.63 0.5567 8.56 \n\n\n\nKLE-0150 111 87 80 143 9.67 10.33 68.00 107.40 7.70 20.33 4.0 3.10 0.7000 6.94 \n\n\n\nKLE-236 91 88 81 140 6.67 7.67 68.33 70.90 8.17 21.67 4.0 3.10 0.6256 8.44 \n\n\n\nGE-0480 87 88 82 139 9.33 10.67 62.00 112.40 8.40 18.33 4.3 3.40 0.7683 10.78 \n\n\n\nACC#0124 98 90 84 137 6.33 8.00 63.33 109.40 8.43 18.33 3.3 3.30 0.5389 8.00 \n\n\n\nNE-1703-34 95 80 73 132 7.00 8.33 61.67 123.30 8.47 16.67 3.3 2.60 0.4528 8.67 \n\n\n\nACC#8827-1 95 82 75 139 7.00 8.67 62.00 115.00 8.83 17.33 4.0 3.20 0.5289 9.44 \n\n\n\nACC#2860 112 83 75 139 9.33 10.33 69.00 105.00 9.03 19.00 4.0 3.00 0.7100 8.61 \n\n\n\nACC#2311 109 83 76 134 8.00 8.67 71.00 137.10 9.30 18.00 4.7 3.10 0.6917 8.56 \n\n\n\nLocal variety 82 81 75 130 7.33 9.00 69.33 123.20 6.40 18.00 3.7 3.20 0.5289 7.94 \n\n\n\nG.Mean 94 85 78 137 8.04 9.38 65.50 113.90 7.51 18.96 4.1 3.09 0.6180 8.83 \n\n\n\nF-test <.001 0.003 <.001 <.001 <.001 <.001 <.001 0.028 <.001 <.001 <.001 <.001 <.001 <.001 \n\n\n\nCV(%) 3.1 4.2 3.8 2.71 10.2 8.6 3.1 14.5 0.69 7 14.3 4.8 6.4 5.6 \n\n\n\nLSD(0.05) 4.84 5.8 5.02 1.2 1.3 1.33 3.4 27.5 5.5 2.2 0.96 0.24 0.066 0.831 \n\n\n\nBH=Baring Head per square meter, DAF= Days to 50% flowering, \nDOH=Days to 50 % Heading, DTM= Days to 75 % maturity,FL=finger \nlength(cm),FLL=flag leaf length(cm), PS=plant stand per square \nmeter,PH=Plant height(cm), NF/H=Number of finger per \n\n\n\nhead,PL=Peduncle length(cm),PT=No of productive tiller,TGW=Thousand \ngrain weight (gram),GY=Grain yield (t/ha), SY= Straw yield \n(t/ha),CV=coefficient of variation (%), LSD=least significant difference at \n0.05.<.005 significant at 0.05% level,<.001 significant at 0.01% level. \n\n\n\n \nTable 4: Pearson\u2019s Correlation coefficient among thirteen traits of fifty sixteen genotypes of finger millet landraces, at HCRP, Dolakha (2018) \n\n\n\n\n\n\n\n \n          BH      DAF       DOH     DTM      FL      FLL      GY       NF/H      PL       PT     TGW      PS      PH \n\n\n\nDAF       0.005    1 \n \nDOH      -0.023    0.989**  1 \n \nDTM       0.336    0.351   0.273   1 \n \nFL        0.454    0.111   0.106   0.364   1 \n \nFLL       0.304    0.104   0.127   0.237   0.951**  1 \n \nGY        0.512*   0.268   0.258   0.460   0.820**  0.756*  1 \n \nNF/H      0.517*   0.008  -0.041   0.325*  0.105    0.022   0.367*   1  \n \nPL        0.280    0.410   0.405   0.412   0.566*0.579*0.747*  -0.023   1 \n \nPT        0.437    0.117   0.133   0.208   0.685*0.671*0.884**  0.129   0.683**1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)20-26 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Manoj Kandel, Narayan Bhadhur Dhami, Jiban Shrestha (2019). Phenotypic Diversity Of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes. \nMalaysian Journal of Sustainable Agriculture, 3(2): 20-26. \n\n\n\n \nTGW       0.140    0.715*0.748** 0.204   0.256*   0.306   0.463*   0.083   0.414   0.375   1 \n \nPS        0.650*  -0.195  -0.187   0.061   0.429    0.324   0.565*   0.158   0.513*0.562*  0.104   1 \n \nPH        0.035   -0.243  -0.192  -0.539*0.343*   0.316   0.054   -0.047  -0.196   0.214   0.066  -0.002  1 \n \nSY       -0.179    0.217*  0.182   0.222*0.307*   0.148   0.253*0.210* -0.160   0.218   0.243  -0.403  0.197* \n\n\n\nBH=Baring Head per square meter,DAF= days to 50% \nflowering,DOH=days to 50 % Heading,DTM= days to 75 % \nmaturity,FL=finger length,FLL=flag leaf length,GY=Grain \nyield,NF/H=Number of finger per head,PL=Peduncle length,PT=No of \nproductive tiller,TGW=Thousand grain weight, PS=plant stand per square \nmeter,PH=Plant height, SY=Straw yield.** significant at 0.05% level,** \nsignificant at 0.01% level. \n \n3.2 Cluster analysis \n\n\n\n \nAll the entries were clustered using baring head, days to 50 % flowering, \ndays to 50 % heading, days to 75% maturity, finger length, flag leaf length, \nnumber of fingers per head, peduncle length, productive tiller, thousand \nkernel weight, plant stand per square meter, plant height, grain yield and \nstraw yield as variable. The dendrogram reveled four clusters with \nminimum 39.13% similarity level in UPGMA clustering. The distance \nbetween the cluster centroid was found highest between cluster 3 and 4. \nand lowest between cluster 1and 2. The cluster was divided in Group A \nand Group B.Th. Group A consisted of two cluster named as Cluster I and \ncluster IV whereas Group B consisted of two cluster named as Cluster II \nand III. \n \nCluster I consisted of 6 genotypes named as KLE-178, GE-0383ACC#6022, \nGE-0382, KLE-0150, ACC#0124 which represent 37.5% of total genotypes. \nThe genotypes of in this cluster had high straw yield and lower value for \nnumber of fingers per head, peduncle length, productive tiller, plant stand \nper square meter. This cluster had lower grain yield as compared to other \ncluster under study condition. \n \nCluster II consisted of 7 genotypes named as ACC#2843, ACC#2860, \n\n\n\nACC#8827-1, Sailung-K.1, NE-1703-34, KLE-236, ACC#2311which \nrepresented 43.75 % of total genotypes. The genotypes grouped in this \ncluster had shorter days to 50% flowering and other intermediate traits \nvalues. These genotypes due to shorter days to 50 % flowering and \nmaturity classes that is suitable for different cropping system is possible. \n \nCluster III consisted of 2 genotypes named as GE-0356, Local variety which \nrepresented 12.5 % of total genotypes. This cluster III genotypes had high \nvalue for bearing head, finger length, flag leaf length, number of finger per \nhead, productive tiller, thousand kernel weight, plant stand, plant height \nand grain yield. Since this cluster of lines had superior traits value for \nstudy condition, these lines may be of interest to researchers. \n \nCluster IV consisted up 1 genotype named as GE-0480 which represented \n6.25% of total genotypes had high value for days to 50 % flowering, Days \nto 50 % heading, days to 75 % maturity and peduncle length whereas \nlower value for finger length, flag leaf length, plant height and straw yield. \nGrouping accessions with related morphological traits is very critical in \nevery breeding program so as to understand and to have basic information \non which and how many accessions possess traits of importance. Group \ninformation on which a superior accession with economic traits belong \nwill in future help to check more accessions from the same group with \nsimilar or closely related economic traits and further be used in finger \nmillet breeding program [21].  \n \nEvaluation of genetic variation based on morphological characters has \nproved to be very informative enough and can also be manipulated into \neither selecting superior accessions or to be utilized to select parents for a \nbreeding program [22,23]. \n \n\n\n\n \nTable 5: Distance among the different cluster centroid of 16 genotypes of Finger millet (Eleusine coracana L. Gaertn). \n\n\n\n\n\n\n\nCluster I Cluster II Cluster III Cluster IV \n\n\n\nCluster I 0 23.4758 36.9069 48.0874 \n\n\n\nCluster II  0 22.7882 43.5687 \n\n\n\nCluster III   0 65.0195 \n\n\n\nCluster IV \n   0 \n\n\n\n \nTable 6:  Cluster mean of yield and yield attributing traits among 16 genotypes of Finger millet (Eleusine coracana L. Gaertn). \n\n\n\n\n\n\n\nVariable Cluster I Cluster II ClusterIII Cluster IV Centroid \n\n\n\nNo of genotypes 6 7 2 1 20 \n\n\n\nBearing head 79 101.7 111.5 91 93.7 \n\n\n\nDays to flowering 85.1 84.1 85.5 88 84.9 \n\n\n\nDays to heading 78.6 77.2 79 81 78.2 \n\n\n\nDays to maturity 135.3 137.8 135.5 140 136.7 \n\n\n\nFinger length 7.88 8.09 9 6.67 8.04 \n\n\n\nFlag leaf length 9.39 9.38 10.17 7.67 9.37 \n\n\n\nGrain yield 0.56 0.63 0.73 0.63 0.61 \n\n\n\nNumber of finger per head 6.75 7.843 8.35 8.17 7.517 \n\n\n\nPeduncle length 18.22 18.85 20.16 21.67 18.95 \n\n\n\nProductive tiller 3.7 4.1 4.8 4 4.0 \n\n\n\nThousand grain weight 3.09 3.03 3.25 3.1 3.09 \n\n\n\nPlant stand 63.11 65.76 70.33 68.33 65.49 \n\n\n\nPlant height 116.5 112.5 132.2 70.9 113.8 \n\n\n\nStraw yield 9.13 8.57 9.03 8.44 8.83 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 20-26 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Manoj Kandel, Narayan Bhadhur Dhami, Jiban Shrestha (2019). Phenotypic Diversity Of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes. \nMalaysian Journal of Sustainable Agriculture, 3(2): 20-26. \n\n\n\n\n\n\n\nFigure 1: Cluster analysis of yield and yield attributing traits among 16 genotypes of Finger millet (Eleusine coracana L. Gaertn). \n \n \nThe PCA showed close resemblance with clustering and partition of total \nvariation into 4 PCs having eigen value > 1 explaining about first four \nprinciple components and first two principle components revealed that 79 \n% and 57% of variability among 16 finger millet accessions respectively \n(Figure 1 and Table 5). However, the remaining component contributed \nonly 21 % towards total diversity for this finger millet accession. Most of \nvariation was contributed from phonological character plant height, \nproductive tiller, and days to flowering, days to heading, flag leaf length, \npeduncle length, plant stand, bearing head, number of fingers per head, \nstraw yield, finger length and grain yield. This yield attributing traits of \nfinger millet was correlated and can be used in selection for breeding \nprograms. The first principle components were positively contributed by \ngrain yield (0.418), productive tiller (0.368), peduncle length (0.366) and \nflag leaf length (0.341).Similarly traits Day to flowering (0.517) and days \nto heading (0.515) were positive contributed to second principle \n\n\n\ncomponent. The third principle component were negative contributed by \nplant height (-0.554) and straw yield (-0.398) and positive contributed by \ndays to maturity (0.389).Similarly four principle component were positive \ncontributed by traits peduncle length(0.381) and plant stand (0.226) and \nnegative contributed by number of finger per head(-0.677) and straw yield \n(-0.467)  and bearing head(-0.292).Thus positive relation with grain yield \nwith Productive tiller, Peduncle length, flag leaf length, finger length, \nnumber of finger per head, thousand kernel weight, plant stand, straw \nyield lead to first principle component had variability and selection within \nthis is importance for improvement grain yield under study condition as \ncompared to 4 principle component. The present research revealed that \ngenotypes formed in cluster one in study condition were found most \nsuperior than cluster 4 genotypes. This finding PCA supported the result \nobtained by cluster analysis. \n \n\n\n\n \nTable 7: The first four principal components of traits used for cluster analysis and PCA and the eigen analysis of the correlation matrix at HCRP, Dolakha \n(2018). \n \n\n\n\nVariable \n\n\n\n Principle component \n\n\n\n          PC1              PC2              PC3             PC4 \n\n\n\nEigenvalue \n 5.215 2.7677 1.8258 1.2529 \n\n\n\nProportion \n 0.372 0.198 0.13 0.089 \n\n\n\nCumulative (%) \n 0.372 0.57 0.701 0.79 \n\n\n\nBearing head \n 0.25 -0.232 0.292 -0.292 \n\n\n\nDays to flowering  0.183 0.517 0.041 0.016 \n\n\n\nDays to heading  0.181 0.515 -0.011 0.052 \n\n\n\nDays to maturity  0.216 0.152 0.389 -0.133 \n\n\n\nFinger length  0.366 -0.158 -0.173 0.01 \n\n\n\nFlag leaf length  0.345 -0.128 -0.285 0.06 \n\n\n\nGrain yield  0.418 -0.069 -0.027 -0.05 \n\n\n\nNumber of finger per head  0.102 -0.097 0.281 -0.677 \n\n\n\nPeduncle length  0.355 0.053 0.113 0.381 \n\n\n\nProductive tiller  0.368 -0.135 -0.16 0.01 \n\n\n\nThousand kernel weight  0.253 0.341 -0.124 -0.059 \n\n\n\nPlant stand  0.244 -0.335 0.226 0.226 \n\n\n\nPlant height  0.014 -0.22 -0.554 -0.104 \n\n\n\nStraw yield   0.04 0.193 -0.398 -0.467 \n\n\n\n\n\n\n\nObservations\n\n\n\nS\nim\n\n\n\nil\na\nri\n\n\n\nty\n\n\n\n16715912613142101153481\n\n\n\n-39.13\n\n\n\n7.25\n\n\n\n53.62\n\n\n\n100.00\n\n\n\nDendrogram with Ward Linkage and Euclidean Distance\n\n\n\nI\n\n\n\nII\n\n\n\nIII\n\n\n\nIV\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 20-26 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Manoj Kandel, Narayan Bhadhur Dhami, Jiban Shrestha (2019). Phenotypic Diversity Of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes. \nMalaysian Journal of Sustainable Agriculture, 3(2): 20-26. \n\n\n\n\n\n\n\nFigure 2: The score plot of first two components among 16 genotypes of Finger millet (Eleusine coracana L. Gaertn). \n \n4.  DISUSSION \n\n\n\n \nGenotypes under study showed significant difference at P= 0.05 level of \nsignificant which indicates there is ample of variability which indicates \npresence of substantial variation which can be exploited through selection \n(Table 3) and Similar finding was also reported in previous studies [8,24]. \nThe study showed that grain yield per plot had positive and significant \nphenotypic association with baring head, finger length, number of fingers \nper head, flag leaf length, peduncle length, productive tiller, thousand \nkernel weights, plant stand, straw yield. This indicates that increases in \nthese characters may result in increases in grain yield. Found that grain \nyield correlated positively with straw yield [25]. Similar finding similar \nresult between grain yield and no of finger and flag leaf length [26]. A \ngroup researcher found similar association with productive tiller number \nand 1000 kernel weight on finger millet [27]. The positive and significant \nassociation of straw yield with plant height, number of fingers per head, \ndays to 50 % flowering, days to maturity indicates that these traits can be \nimproved simultaneously through selection. Straw yield showed positive \nand significant phonotypic correlation with number of fingers per head \nand grain yield, which indicate impressing straw yield increases grain \nyield too. Days to flowering had high negative genotypic and phenotypic \ncorrelations with the key yield related traits of finger width, peduncle \nlength, panicle exertion grains per spikelet, and threshing percent, \ncorroborating results reported [28]. The positive association between \nplant height and finger length and number of fingers per plant in this study \nwas also reported [29]. Among the four cluster, cluster I consists up early \nmatured type genotypes with low productive tiller and grain yield. The \ngenotypes of this cluster can be used to development of early mature line \nfor mountain regions where chilling stress occurs at maturity period [30]. \nSimilarly, cluster II, III and IV can be used to develop high yielding late \nmature lines for mid hill and terai regions. The clustering of genotypes \nbased on traits value was confirmed by the principal component analysis \n[31]. Thus, this study of principal component uses to reduces of original \nvariables into four principal component and information about each \nvariable which support cluster analysis result. \n \n5.  CONCLUSION \n\n\n\n \nThus, natural variation present within genotypes is important for \nselection and development of suitable varieties for different agro-climatic \nregion of Nepal. Thus, significant variation was observed in 16 finger \nmillet genotypes for baring head, days to 50% flowering, days to 50 % \nheading, days to 75 % maturity, finger length, flag leaf length, plant stand, \nplant height, number of finger, peduncle length, no of productive tiller, \nthousand grain weight, grain yield and straw yield. Grain yield had positive \nand significant phenotypic association with baring head, finger length, \nnumber of fingers, flag leaf length, peduncle length, productive tiller, \nthousand kernel weight, plant stand, straw yield and selection within this \nis traits importance for grain yield improvement. Cluster I consist up early \n\n\n\nmatured low grain yield type genotypes and these genotypes can be used \nto development of early mature varieties for mountain regions where \nchilling stress occurs at maturity period. Similarly, cluster II, III and IV can \nbe used to develop high yielding late mature varieties for mid hill and terai \nregions these genotypes may be of interest to researcher for selection of \nmaterials for breeding program in different agro-climatic region of Nepal. \n \nREFERENCES \n \n[1] Wolie, A., Dessalegn, T. 2011. Correlation and path coefficient analysis \nof some yield related traits in finger millet (Eleusine coracana L. Gaertn) \ngermplasms in Northwest Ethiopia. African Journal of Agricultural \nResearch, 6, 5099-5105. \n \n[2] MoAD. 2016/17. Statistical Information on Nepalese Agriculture. Agri \nbusiness promotion and statistical division, Agristatistic section, \nSinghdurbar, Kathmandu. \n \n[3] Upreti, R.P. 1999. Status of millet genetic resources in Nepal: Wild \nrelatives of cultivated plants in Nepal. Pp. 78-82 in Wild relatives of \ncultivated plants in Nepal (R. Shrestha and B. Shrestha, eds.). Proceedings \nof National conference on wild relatives of cultivated plants in Nepal, 2\u20134 \nJune 1999, Kathmandu. Green Energy Mission (GEM), Kathmandu, Nepal. \n \n[4] Upadhyay, M.P., Joshi, B.K. 2003. Plant genetic resources in SAARC \ncountries: Their conservation and management. pp. 297-422, Nepal \nchapter. SAARC Agriculture Information Center. \n \n[5] Baniya, B.K., Riley, K.W., Dongol, D.M.S., Sherchand, K.K. 1992. \nCharacterization of Nepalese hill crops landraces (Barley, Buckwheat, \nFinger millet, Grain Amaranth, Foxtail, Proso and Barnyard millets). NARC-\nIBPGR, Kabre, Dolakha, Nepal. \n \n[6] Joshi, A.K., Kumari, M., Singh, V.P., Reddy, C.M., Kumar, S., Rane, J., \nChand, R. 2007. Stay Green Trait: Variation, inheritance and its association \nwith spot blotch resistance in spring wheat (TriticumaestivumL.). \nEuphytica, 153, 59-71. \n \n[7] Reddy, C.V.C.M., Reddy, Y.R. 2011. Genetic parameters for yield and \nquality traits in desi cotton (GossypiumarboreumL). Journal of Cotton \nResearch and Development, 25, 168-170. \n \n[8] Chetan, S., Malleshi, N.G. 2007. Finger mille polyphenols: \nCharacterization and their nutraceuticalpotential. American Journal of \nFood Technology, 2, 582\u2013592. \n \n[9] Babu, B.K., Senthil, N., Gomez, S.M., Biji, K.R., Rajendraprasad, N.S., \nKumar, S.S., Babu, R.C. 2006. Assessment of genetic diversity among finger \nmillet (Eleusine coracana L. Gaertn) accession using molecular markers. \n\n\n\nFirst Component\n\n\n\nS\ne\nc\no\nn\n\n\n\nd\n C\n\n\n\no\nm\n\n\n\np\no\nn\n\n\n\ne\nn\n\n\n\nt\n\n\n\n543210-1-2-3-4\n\n\n\n3\n\n\n\n2\n\n\n\n1\n\n\n\n0\n\n\n\n-1\n\n\n\n-2\n\n\n\nCluster\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\nLocal variety\n\n\n\nACC#2311\n\n\n\nACC#2860\n\n\n\nAcc#8827-1\n\n\n\nNE-1703-34\n\n\n\nACC#0124\n\n\n\nGE-0480\nKLE-236\n\n\n\nKLE-0150\n\n\n\nGE-0356\n\n\n\nSailung-K.1\n\n\n\nGE-0382\n\n\n\nACC#6022\n\n\n\nGE-0383\n\n\n\nACC#2843\n\n\n\nKLE-178\n\n\n\nPrinciple component analysis using correlation matrix\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)20-26 \n \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Manoj Kandel, Narayan Bhadhur Dhami, Jiban Shrestha (2019). Phenotypic Diversity Of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes. \nMalaysian Journal of Sustainable Agriculture, 3(2): 20-26. \n\n\n\nGenetic Research and Crop Evolution, 54, 339-404. \n \n[10] Daba, C. 2000. Variability and Association among Yield and Related \nTraits in Finger Millet [Eleusine coracana (L) Gaertn]. M.Sc. thesis, Alemaya \nUniversity. \n \n[11] Adeniji, O.T., Peter, J.M., Bake, I. 2008. Variation and interrelationship \nfor pod and seed yield characters in Bambara groundnut \n(Vignasubterrenea) in Adamawa state, Nigeria. Africaan Journal of \nAgricultural Research, 3, 617-621.  \n \n[12] Basafa, M., Taherian, M. 2009. A study of agronomic and \nmorphological variations in certain Alfalfa (Medicago sativa L.) Ecotypes \nof the cold region of Iran. Asian Journal of Plant Science, 8, 293-300. \n \n[13] Wilson, J.P., Sanogo, M.D., Nutsugah, S.K., Angarawai, I., Fofana, A., \nTraore, H., Ahmadou, I., Muuka, F.P. 2008. Evaluation of pearl millet for \nyield and downy mildew resistance across seven countries in sub \u2013\nSaharan Africa. African Journal of Agricultural Research, 3, 371-378. \n \n[14] Poehlman, J.M. 1987. Breeding Field Crops. 3rd ed. AVI Publishing \nCompany, Inc. West Port, CT. Pp. 187-213 \n \n[15] Singh, K.B., Bejiga, G., Malhotra, R.S. 1990.  Association of some \ncharacters with seed yield in chickpea collections.  Euphytica, 49, 83-88. \n \n[16] MoAC. 2011. Agriculture Diary. 2010-2011.Agriculture information \nand communication centre. Harihar Vawan, Nepal. \n \n[17] IBPGR. 1985. Descriptors for finger millet (Elusine coracana (L.) \nGaertn). Rome, Italy:  International Board for Plant Genetic Resources. Pp \n20. [Online] available: \nhttp://www2.bioversityinternational.org/publications/Web_version/41\n7/(Retrieved in 20 Feb, 2018). \n \n[18] Steel, R.G.D., Torrie, J.H. 1980. Principles and procedures of statistics, \na biochemical approach. McGraw Hill, Inc. New York. \n \n[19] MINITAB. 2004. Minitab user guide, Releases 14.3 Minitab Inc., UK. \n \n[20] Upadhyaya, H.D., Gowda, C.L.L., Reddy, V.G. 2007. Morphological \ndiversity in finger millet germplasms introduced from Southern and \nEastern Africa. Journal of SAT Agricultural Research, 3, 1-3. \n \n\n\n\n[21] Upadhyaya, H.D., Gowda, C.L.L., Pundir, R.P.S., Reddy,V.G., Singh, S. \n2004. Development of core subset of finger millet germplasm using \ngeographical origin and data on 14 quantitative traits. Genetic Resource \nand Crop Evolution, 53, 679-685. \n \n[22] Khan, S., Latif, A., Ahmad, Q., Ahmad, F., Fida, M. 2011. Genetic \nvariability analysis in some advanced lines of soybean (Glycine max L.). \nAsian Journal of Agricultural Science, 3, 138-141. \n \n[23] Lang, M.T., Tu, P.T.B., Thanh, N.C., Buu, B.C., Ismail, A. 2009. Genetic \ndiversity of salt tolerance rice landraces in Vietnamese. Journal of Plant \nBreed and Crop Science, 1, 230-243. \n \n[24] Naik, B.J., ShankareGowda, B.T., Seetharam, A. 1994. Pattern of \nvariability in relation to domestication of finger millet in Africa and India, \npp. In [K.E. Riley, S.C. Gupta, A. Seetharam and J.N. Mushonga (eds.)]. \nAdvances in Small Millets. International Science, New York, 347-363. \n \n[25] Tazeen, M., Nadia, K., Farzana, N.N. 2009. Heritability, phenotypic \ncorrelation and path coefficient studies for some agronomic characters in \nsynthetic elite lines of wheat. Journal of Food, Agriculture and \nEnvironment, 7 (3&4), 278-282. \n \n[26] Sharathbabu, K.S., Shantakumar, G., Salimath, P.M. 2008. Genetic \nvariability and character association in white ragi (Eleusine coracana \nGaertn). Karnataka Journal of Agricultural Sciences, 21 (4), 572-575 \n \n[27] Nandini, B., Ravishankar, C.R., Mahesha, B., Shailaja, H., Kalyana, \nM.K.N.  2010. Study of correlation and path analysis in F2 population of \nfinger millet. International Journal of Plant Sciences, 5 (2), 602-605. \n \n[28] Bezawelataw, K., Sripichitt, P., Wongyaiw, Hongtrakul, V. 2006. \nGenetic variation,heritability and path-analysis in Ethiopian finger millet \n(Eleusine coracana (L.) Gaertn) landraces. KasetsartJournal (National \nScience), 40, 322-334. \n \n[29] Suyambulingam, C., Jebarani, W. 1977. Genetic divergence in short \nduration ragi (Eleusine coracana Gaertn). Madras Agricultural Journal, 64, \n816-818. \n \n[30] Dida, M.M., Wanyera, N., Ramakrishnan, S., Harrison-Dunn, M.L., \nBennetzen, J.L., Devos, K.M. 2008. Population structure and diversity in \nfinger millet (Eleusine coracana) germplasm. Tropical Plant Biology, 1, \n131-141.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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 Sustainable Agriculture (MJSA) 2(1) (2018) 05-06 \n\n\n\nCite the article: Galal Ahmed El Toum Mohammed (2018). Productivity Of Pure Stands And Intercropped Forage Sorghum And Hyacin th \nBean .  Malaysian Journal of Sustainable Agriculture, 2(1) : 05-06. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ    \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nEnhanced biodiversity in intercropping systems can increase productivity, stability, resilience and resource-use \n\n\n\nefficiency of the intercropped species compared with sole-cropping. A randomized complete block design with four \n\n\n\nreplications was used to compare the productivity of pure stand of fodder sorghum \" Abu sabein\" and hyacinth bean \n\n\n\n\" Lubia afin\" with the mixture of the two fodders. The analysis of variance showed significant differences in fresh and \n\n\n\ndry weight at plant age 30, 40, 50 and 60 days and leaves to stem ratio at 30 days. This study revealed that the \n\n\n\ncontribution of green and dry weight of fodder sorghum was greater than that of hyacinth bean and leaf to stem ratio \n\n\n\nfor both fodders was declined with plant age.  \n\n\n\nKEYWORDS \n\n\n\nPure stand, Mixture, fodder sorghum, hyacinth bean.\n\n\n\n1. INTRODUCTION \n\n\n\nFodder sorghum (Sorghum bicolor L. (Moench) cv. \"Abu sabein\" is one of \nthe most important native cereal crop in Africa and the fifth area-wise of \nthe world cereals. The crop is grown in the tropical and sub-tropical \nregions of the world [1]. The crop is grown for green fodder and for grains. \nHyacinth bean (Lablab purpureus L.) cv. \"Lubia afin\" is known in different \nparts of the world by different names, in the Sudan it is called \"Lubia afin \n\". The most common commercial cultivars are Rongi and High worth [2]. \nIntercropping is the cultivation of two or more crops in the same area and \nat the same time. Accordingly intercropping promotes the interaction \nbetween the different plants [3]. Intercropping is becoming more \nimportant to increase crop productivity to satisfy food demands of \nincreasing population, especially in developing countries [4]. \nIntercropping has long been practiced in the Sudan as a traditional system \n[5]. The productivity of forage crops in the Sudan is limited by the lack of \nhybrids with high yield and good quality. A researcher stated that very few \nefforts have been exerted to develop improved forage cultivars from the \nlocal stocks [1]. A scientist reported that shortage of forage seeds of \nsorghum, poor methods of production and preservation techniques such \nas hay and silage making are limiting the productivity of fodders in the \nSudan [6]. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nA pot experiment was carried out at Demonstration Farm of the Faculty of \nAgriculture, University of Khartoum (Latitude 15\u02da \u2013 40\u02dd N and Longitude \n32\u02da 32 \u02ddE) \u2013 Shambat-Sudan in season 2010. The climate of the locality is \nsemi-desert with low relative humidity, maximum temperature is about \n40 \u02daC in summer and 20 \u02daC in winter, but night temperatures are lower. \nThe range of temperature varies from about 20\u201326MJ/m-2 day-1 [7]. A \nrandomized complete block design(RCBD) with four replications was used \nto execute the experiment. Sowing was done in season 2010 with seed rate \n50% of each crop. Four samples were taken during the growing period. \nThe first sample was taken 30 days from sowing and the other three \nsamples were taken at intervals of 10 days. The collected data include \nfresh and dry weight and leaves to stems ratio. The data were submitted \nto standard procedure of analysis of variance and means were separated \nby using least significant difference (LSD) as described in a study [8]. \n\n\n\nLand equivalent ratio (LER) was used to compare the dry weight yield \nresulting from the same crops in mixture and as a pure stand using the \nformula:  \n\n\n\nLER=Crop A yield in mixture \u0338 crop A yield in pure stand + Crop B yield in \nmixture \u0338 crop B yield in pure stand  \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Fresh and dry weight \n\n\n\nResults in table-1 and table-2 showed progressive increase in the fresh \nand dry weights of fodder sorghum and hyacinth bean as the plants grew \nfrom age 30 to 60 days after sowing. This trend is true for both sowing the \ncrop as a pure stand and as a mixture with the other fodder. However, the \npure stand attained greater fresh and dry weight at intervals of ten days \nfor the four samples (30, 40, 50 and 60 days after sowing). The explanation \nfor this fact is possibly due to competition of the mixed crops for moisture \nand also for possible shading of hyacinth bean by the taller fodder \nsorghum. The total yield of the mixture of fodder sorghum and hyacinth \nbean was greater than the yield of the pure stand of hyacinth bean, but the \npure stand of fodder sorghum was greater than that of the mixture. The \ncontribution of hyacinth bean based on green yield and dry weight is lower \nthan fodder sorghum this is due to the lower moisture content of the stems \nof this legume, compared with the stems of the cereal fodder which is \nmade of the folding of the base of leaves. Therefore, comparisons of the \ncontribution of this legume when mixed with fodder sorghum based on \nthe dry weight gives the real value of the share of this legume in the \nmixture. Similar results found by a group of researchers who all found the \nsame result. Intercropping gave advantageous of land equivalent ratio of \n1.19 when plant age at 40 days which means that an area planted as a pure \nstand would require 19% more land to produce the same yield as the same \narea planted in mixture combination [9-12].  \n\n\n\nTable 1: Fresh weight of fodder sorghum and hyacinth bean in tons/ha \nduring the growing season. \n\n\n\nTreatment \nPlant age (days after sowing) \n\n\n\n30 40 50 60 \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.01.2018.05.06 \n\n\n\nPRODUCTIVITY OF PURE STANDS AND INTERCROPPED FORAGE SORGHUM AND \n\n\n\nHYACINTH BEAN \nGalal Ahmed EL Toum Mohammed \n\n\n\nDepartment of Agronomy, Faculty of Agric. Sciences, University of Dongola, Sudan. \n\n\n\n*Corresponding Author E-mail: galaleltoum1234@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited.\n\n\n\n\nmailto:galaleltoum1234@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 05-06 \n\n\n\nCite the article: Galal Ahmed El Toum Mohammed (2018). Productivity Of Pure Stands And Intercropped Forage Sorghum And Hyacin th \nBean .  Malaysian Journal of Sustainable Agriculture, 2(1) : 05-06. \n\n\n\nPure stand of hyacinth bean 1.16c 4.87c 14.10c 20.37c \n\n\n\nHyacinth bean in mixture 0.65c 3.10c 6.10d 9.85d \n\n\n\nPure stand of fodder sorghum 21.81a 44.36a 62.96a 75.67a \n\n\n\nFodder sorghum in mixture 8.27b 24.34b 28.94b 35.33b \n\n\n\nLSD 4.41 3.0 4.80 3.75 \n\n\n\nSE\u00b1 1.21 1.89 1.45 1.25 \n\n\n\nC.V% 11.49 24.67 14.59 10.01 \n\n\n\nMeans followed by the same letters within each column for each treatment \nare not significantly different at 5% level of probability. \n\n\n\nLSD= least significant difference. SE\u00b1= standard error   C.V.%= coefficient \nvariation \n\n\n\nTable 2: Dry weight of fodder sorghum and hyacinth bean in tons/ha \nduring the growing season \n\n\n\nTreatment \nPlant age (days after sowing) \n\n\n\n30 40 50 60 \n\n\n\nPure stand of hyacinth bean 0.37c 1.80c 6.34c 10.99c \nHyacinth bean in mixture 0.18c 0.96d 2.38d 4.83d \n\n\n\nPure stand of fodder sorghum 8.51a 19.51a 40.92a 58.27a \nFodder sorghum in mixture 3.14b 10.26b 16.21b 21.91b \n\n\n\nLSD 1.51 3.54 2.13 2.18 \nSE\u00b1 0.94 2.22 1.33 1.31 \n\n\n\nC.V% 0.41 0.87 0.39 0.32 \n\n\n\nLER 0.94 1.19 0.89 0.95 \n\n\n\nMeans followed by the same letters within each column for each treatment \nare not significantly different at 5% level of probability. \n\n\n\n3.2 Leaves to stems ratio (LSR) \n\n\n\nTable (3) shows that leaf to stem ratio for fodder sorghum and hyacinth \nbean grown as pure stand or as a mixture declined with age (from 30 to 60 \ndays). This fact can be attributed to the increase in the fibre content of the \nstem with age. The leaves, however, decreased in weight after reaching full \nsize due to senescence which results in loss of water and drying. Therefore, \nLeaf to stem ratio based on dry weight also decreased [13,14].  \n\n\n\nTable 3: Leaves to stems ratio (LSR) of fodder sorghum and hyacinth bean \nduring the growing season. \n\n\n\nTreatment \nPlant age (days after sowing) \n30 40 50 60 \n\n\n\nPure stand of hyacinth bean 1.39b 1.27a 0.89a 0.69a \nHyacinth bean in mixture 1.34b 1.23a 0.81a 0.68a \n\n\n\nPure stand of fodder sorghum 1.51a 1.32a 0.94a 0.71a \nFodder sorghum in mixture 1.39b 1.25a 0.83 0.69a \n\n\n\nLSD 0.11 0.31 0.14 0.04 \nSE\u00b1 0.05 0.19 0.09 0.02 \n\n\n\nC.V% 2.63 11.44 7.69 2.71 \n\n\n\nMeans followed by the same letters within each column for each \ntreatment are not significantly different at 5% level of probability. \n\n\n\n4. CONCLUSION \n\n\n\nThe result of this study indicated that the contribution of green and dry \nweight of fodder sorghum was greater than the contribution of hyacinth \nbean in the mixture. \n\n\n\nACKNOWLEDGEMENT  \n\n\n\nThis study was conducted at the Demonstration Farm of the Faculty of \nAgriculture, University of Khartoum-Sudan. I gratefully thank my students \nand friends for their helps on data processing and suggestions to improve \nexperimental design. \n\n\n\nREFERENCES \n\n\n\n[1] Mohammed, M. A. R. 2005. Effect of irrigation intervals and sowing \nmethods on productivity and water use efficiency fodder Maize. M.Sc.\nThesis Faculty of Agriculture, University of Khartoum, Shambat, Sudan. \n\n\n\n[2] Cook, B.G., Pengelly, B. C., Brown, S.D., Donnelly. J.L., Eagles, D.A., \nFranco, M.A., Hanson, J., Mullen, B.F., Partridge, I.J., Peter, M., Schultz\u2013 Kraft,\nR. 2005. Tropical Forages: an interactive selection tool., {CD \u2013ROM}, CSIRO,\nDPI and F(QLD), CIAT and ILRI, Brisbone, Australia. htt://www. Tropical\nforage. \n\n\n\n[3] Sullivan, P. 2003. Appropriate Technology Transfer for Rural Areas. \nIntercropping principles and production practices (http: latria. Ncat.Org/ \nattar. Pub/ PDF/ intercropping. Pdf). \n\n\n\n[4] Li, Y., Li, X.L., Zhang, F.S., Christie, P. 1999. Interspecific Complementary \nand Competitive interactions between intercropped Maize and Fababean.\nPlant and soil, 212, 105\u2013114. \n\n\n\n[5] Polo, S. B. 2004. Traditional Agriculture and Sustainable Agricultural \nDevelopment in the Southern Sudan. Workshop for Combating \nDesertification Institute of Desertification Studies of K. \n\n\n\n[6] Khair. M. A. M. 1999. Principles of Forage Crops Production (in Arabic). \n1st  ed. Agricultural Research Corporation ( ARC).Wad \u2013 madni, Sudan. \n\n\n\n[7] Adam, H. S. 2005. Agroclimatology, crop water requirement and water \nmanagement. University of Gezira, water requirement and irrigation \ninstitute\u2013Wad Medani, Sudan. \n\n\n\n[8] Gomez, K. A., Gomez, A. A. 1984. Statistical Procedures for Agricultural \nResearch. 3rd Edition. John Wiley. New York. \n\n\n\n[9] Rumission, S.U. 1978. Neighbor effects between Maize and Cowpea at \nvarious levels of N and P. Journal of Experimental Agriculture, 14, 205\u2013\n212. \n\n\n\n[10] Salih, S. S. M. 2012. Symbiotic nitrogen fixation and chicken manure \nfertilization in Soybean intercropping system. Ph.D. (Agric). Thesis, Univ. \nof. Khartoum. \n\n\n\n[11] Njunie, M. N., Wagger, M.G., Luna, P. 2004. Residue Decomposition \nand Nutrient Release Dynamics from Two Tropical Forage Legumes in \nKenyan Environment. Agronomy Journal, 96, 1073\u20131081. \n\n\n\n[12] Osman, A.K.H., Elamin, E. M. 1996. Intercropping as an Instrument \nfor Optimal Land Resources Utilization and Conservation: Case of Western \nSudan Dry land Farming. Journal of Agricultural Science, 4(2). \n\n\n\n[13] Abu Elgasim, R. M. 2006. The response of three indigenous pulse \ncrops to different moisture conditions. M.Sc. Thesis Faculty of Agriculture, \nUniversity of Khartoum, Shambat, Sudan. \n\n\n\n[14] Suleiman, N. N. 2007. Comparison between the productivity of pure \nstand of fodder sorghum (Sorghum bicolor L. (Moench), Clitoria (Clitoria \nternate) and different mixtures of the two. M.Sc. Thesis Faculty of \nAgriculture, University of Khartoum, Shambat, Sudan.\n\n\n\n6\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.34.42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2021.34.42 \n\n\n\nINTEGRATED LAND COVER AND TERRAIN ANALYSIS FOR SUSTAINABLE LAND \nUSE PLANNING AT WATERSHED SCALE: A CASE STUDY OF BAN DAN NA KHAM \nWATERSHED OF NORTHERN THAILAND \n\n\n\nChike Onyeke Maduekea,d*, Dhruba Pikha Shresthab, Panagiotis Nyktasc \n\n\n\na Department of Natural Resources Management, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, \n\n\n\nHengelosestraat 99, 7514 AE, Enschede, the Netherlands.  \nb Department of Applied Earth Sciences, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, \n\n\n\nHengelosestraat 99, 7514 AE, the Netherlands.  \nc Department of Natural Resources Management, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, \n\n\n\nEnschede, Hengelosestraat 99, 7514 AE, the Netherlands.  \nd Department of Soil Science and Land Resources Management, Nnamdi Azikiwe University, P.M.B. 5025, Awka, Anambra State, Nigeria.  \n*Corresponding Author Email: co.madueke@unizik.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 September 2020 \nAccepted 28 October 2020 \nAvailable online 24 December 2020\n\n\n\nSoil is a fundamental natural resource that is vital to the sustainable development of human societies. \n\n\n\nHowever, in many developing countries, increased intensity of use and inadequate land use planning has put \n\n\n\na lot of pressure on marginal soil, leading to various forms of land degradation. The purpose of this study is \n\n\n\nto generate an integrated the land cover and terrain classification of the Ban Dan Na Kham watershed of \n\n\n\nNorthern Thailand as a tool for sustainable land use planning. The watershed boundary and slope classes \n\n\n\nwere delineated using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). The \n\n\n\nslope was subsequently classified into gentle (<8o), moderate (8-30o) and steep (>30o). The land cover map \n\n\n\nwas generated through the supervised classification of Sentinel2 satellite imagery. Both map products were \n\n\n\nthen integrated to provide the basis for land allocation and land use planning. The results show that 58 % of \n\n\n\nland currently under arable farming is either marginally suitable or practically unsuitable for that purpose. \n\n\n\nThis ultimately leads to increased land degradation and soil loss. The land should consequently be reforested. \n\n\n\nNevertheless, up to 10 km2 of the watershed that is dedicated to other land use types \u2013 almost twice the \n\n\n\ncurrent arable land area \u2013 is suitable for arable cropping. As such, given the proposed reforestation of the \n\n\n\nmarginal and unsuitable arable lands, a large proportion of suitable land is still available to make up for the \n\n\n\ndeficit. This will ultimately lead to increased productivity and reduced land degradation.  \n\n\n\nKEYWORDS \n\n\n\nRemote Sensing, Land Cover, Terrain Analysis, Land Use Planning, Land Degradation, Watershed.\n\n\n\n1. INTRODUCTION \n\n\n\nThe land use / land cover (LULC) existing in an area plays a critical role in \n\n\n\nthe prevention and control of land degradation. Nevertheless, though land \n\n\n\nuse and land cover are sometimes used interchangeably, they are quite \n\n\n\ndistinct. Land cover refers to the surface cover on the ground, like \n\n\n\nvegetation, urban infrastructure, water and bare soil; while land use refers \n\n\n\nto the purpose the land serves, like recreation, wildlife habitat or \n\n\n\nagriculture (Singh, 2015). As such, land use is often a function of human \n\n\n\nactivities, which, when inappropriate, predisposes the land to all forms of \n\n\n\ndegradation, including erosion, pollution, salinization, desertification, etc. \n\n\n\nThe threat posed by land degradation due to inappropriate land use has \n\n\n\nbecome so glaring that various researchers reported that land degradation \n\n\n\nis now a global challenge that impacts negatively on the sustenance and \n\n\n\nsurvival of billions of people (Bajocco et al., 2012; Nkonya et al., 2016; \n\n\n\nMelaku et al., 2018). This is more so in northern Thailand where \n\n\n\npopulation pressure has forced the inhabitants to expand agricultural \n\n\n\nactivities to the steeply sloping hillsides, further aggravating the state of \n\n\n\nland degradation (Tingting et al., 2008). \n\n\n\nMoreover, the impacts of land degradation are not limited to the on-site \n\n\n\nloss of soil, organic matter, soil quality and growing plants; the off-site \n\n\n\nimpacts, resulting in the submergence of downhill farms, drainage \n\n\n\nchannels and reservoirs by eroded soils, water pollution and the \n\n\n\ndegradation aquatic habitat, may even be more critical (Madueke et al., \n\n\n\n2019). These intricate and encompassing impacts necessitate the need for \n\n\n\nsustainable and proactive land management that tends more towards \n\n\n\n\nmailto:co.madueke@unizik.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nprevention, than to control of a damage that is already in progress. \n\n\n\nHowever, effective land management requires a prior understanding of \n\n\n\nthe spatio-temporal dynamics of land degradation in a region and the \n\n\n\nfactors that predispose the land to degradation. This knowledge will \n\n\n\nenable the implementation of measures that will ensure increased \n\n\n\nproductivity, while preserving the limited natural resources for the future \n\n\n\ngeneration. Indeed, as pointed out, holistic knowledge of the land use / \n\n\n\nland cover will enable an understanding of the prevalent dynamics, while \n\n\n\nalso providing the baseline for policies and programs that will ensure \n\n\n\nsustainable development (Singh, 2015; Adhikari and Hartemink 2016). \n\n\n\nHowever, it has been pointed out that land use / land cover alone, is \n\n\n\ninsufficient for effective environmental assessment and management \n\n\n\n(Jones, 2008). These data must be combined with comprehensive in-situ \n\n\n\ndata collected from numerous research and monitoring programs (Jones, \n\n\n\n2008).  \n\n\n\nThis is more so, as effective land use planning requires the coordination of \n\n\n\nplanning and management across the many sectors that critically depend \n\n\n\non the land use / land allocation in a region. Holistic resource evaluation \n\n\n\nis therefore, a capital-intensive and time-consuming project; sometimes, \n\n\n\nresulting in increased degradation, evens as plans are made to reclaim \n\n\n\nwhat has been lost. This necessitates the need for a cheaper and faster \n\n\n\noption that can be implemented when funds and time are inadequate; or \n\n\n\nas a preliminary measure while more intensive plans are ongoing. \n\n\n\nAssessing the land cover / land use of an area, alongside terrain \n\n\n\ncharacteristics like slope steepness may provide the needed insight. This \n\n\n\nis more so, as land degradation generally increases with increasing slope \n\n\n\nsteepness (Ziadat and Taimeh, 2013). Yet, it has been widely reported that \n\n\n\nthe form and extent of degradation is a product of the type of land cover \n\n\n\nexisting on the site (Tingting et al., 2008; Bajocco et al., 2012; Labri\u00e8re et \n\n\n\nal., 2015; Dos Santos et al., 2017; Nouri et al., 2018; Chen et al., 2019). \n\n\n\nConsequently, if the existing land cover types and slopes of a region are \n\n\n\nknown, it will enable the implementation of a land use plan that pairs steep \n\n\n\nslopes with land cover types like forests, which prevent degradation, while \n\n\n\nopening up land on gentler slopes to more productive use. It will also \n\n\n\nenable the implementation of measures that will ensure that the land is \n\n\n\nnot degraded further, even as it continues to generate economic returns. \n\n\n\nNevertheless, while the integrated effect of slope and land cover on land \n\n\n\ndegradation has been widely studied, not much has been done on the \n\n\n\ndelineation of land cover types in tandem with their corresponding slope \n\n\n\ntype as a tool for sustainable land use planning (Ziadat and Taimeh, 2013; \n\n\n\nNabiollahi et al., 2018; Siswanto and Sule, 2019; Wubie and Assen, 2020). \n\n\n\nBektas and Goksel (2004) explored the potential of the assessment of \n\n\n\nslope and land cover as a tool for land use planning, but they did not \n\n\n\nimplement an integrated approach resulting in the production of a single \n\n\n\nslope/land cover map. Furthermore, they seemed to focus more on the \n\n\n\nimpact of slope on the selection of potential areas for building \n\n\n\nconstruction. Similarly, though other researchers assessed the integrated \n\n\n\nimpact of land cover and slope on land degradation, they took it further, \n\n\n\nby also assessing soil quality and soil loss (Nabiollahi et al., 2018). This is \n\n\n\nadvantageous, but may still be capital-intensive, technical and time-\n\n\n\nconsuming.  \n\n\n\nThis study seeks to lay more emphasis on slope and land cover \n\n\n\ncharacterization at the watershed scale. The watershed scale is \n\n\n\nemphasized because this topographic unit governs the drainage of \n\n\n\nprecipitation and groundwater, along with the dissolved nutrients, \n\n\n\npollutants and the suspended sediments into the surrounding streams, \n\n\n\nrivers and lakes. As such, one form of land degradation in one section of \n\n\n\nthe watershed will ultimately result in another form of degradation in the \n\n\n\nadjacent section. For instance, excessive soil loss in the upland areas may \n\n\n\nresult in the inundation of growing plants in the lowland areas, silting in \n\n\n\nof riverbeds by sediments, eutrophication, flash floods and increased \n\n\n\npreponderance of waterborne diseases. Indeed, the prevalent land use / \n\n\n\nland cover of an area determines the structure, functions and dynamics of \n\n\n\nthe landscape (Shafiq et al., 2017). Land use planning at the watershed \n\n\n\nscale is therefore ideal for the holistic assessment of the underlying \n\n\n\ndynamics of natural resources, as well as for the effective implementation \n\n\n\nof well-targeted resource management options.  \n\n\n\nTherefore, the major objective of this study is to generate an integrated \n\n\n\nclassification of the land cover and slope of the Ban Dan Na Kham \n\n\n\nwatershed for effective land use planning. The specific objectives are: \n\n\n\n1. To delineate the land use / land cover (LULC) types in the Ban Dan Na \n\n\n\nKham Watershed \n\n\n\n2. To delineate the slope units of the watershed\n\n\n\n3. To make land use recommendations based on the delineated LULC-\n\n\n\nSlope units. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Study Area  \n\n\n\nThis study was carried out at the Ban Dan Na Kham Watershed located in \n\n\n\nMueang District, Uttaradit Province, Thailand (Figure 1). It is located \n\n\n\nwithin Latitudes 17o40\u2019N \u2013 17o55\u2019N, and Longitudes 99o50\u2019E \u2013 100o20\u2019E. It \n\n\n\ncovers an area of approximately 86.91 km2, with altitude ranging from 103 \n\n\n\nto 789m above sea level. The region is located in the Northern Continental \n\n\n\nHighlands (Scholten and Siriphant, 1973). It is hilly and mountainous; \n\n\n\ncrisscrossed by a network of valleys and streams, and located within the \n\n\n\nvicinity of the Nan River. Three major groups of soils predominate, viz. \n\n\n\nsoils of hills and mountains, soils of the higher terraces and low plateaus, \n\n\n\nand soils of alluvial plains and lower terraces (Figure 2). Nevertheless, due \n\n\n\nto the very coarse scale (1: 2,500,000) of the soil map, the only soil unit \n\n\n\nthat falls within the watershed is the reddish-yellow podzolic soils on \n\n\n\nsteep lands formed from acid to intermediate rocks (Figure 2).  \n\n\n\nFigure 1: Study area \u2013 Ban Dan Na Kham watershed. \n\n\n\nThe watershed is located in the humid tropics, under the influence of the \n\n\n\nnorth-eastern and south-western Monsoons. It has three seasons: dry \n\n\n\n(winter), hot (summer, with gradually increasing rainfall and \n\n\n\nthunderstorms) and rainy seasons. Over 90 % of the annual rains fall \n\n\n\nwithin the rainy season, which lasts for about 5 months (mid-May to mid-\n\n\n\nOctober), with most of the rains coming in August and September. \n\n\n\nMonsoon rains are unpredictable, so rainfall varies considerably within \n\n\n\nand between years, but generally ranges from 1,200 to 1,600 mm per \n\n\n\nannum. Cloud cover is usually least from November to March. \n\n\n\nTemperatures generally range from 18oC in winter to 37oC in summer. The \n\n\n\nmaximum temperature is usually about 40oC. The temperature decreases \n\n\n\nat the onset of the rains (mid-May), during which, it is generally below \n\n\n\n40oC. Humidity is generally high, ranging from 63 to 81%.  \n\n\n\n(Source: General Soil Condition of Thailand by produced by Moormann \nand Rojanasoontan in 1967, stored in the EuDASM Archive (Panagos et \n\n\n\nal., 2011)  \n\n\n\nFigure 2: Soil map of the study area. \n\n\n\nGeographic Coordinate System: GCS WGS 1984 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\n2.2 Data Collection and Preparation  \n\n\n\nSome of the instrument that were used during this study are the GPS \n\n\n\nreceiver, data sheets, densiometer and measuring tape. The software that \n\n\n\nwere used in this study include ArcGIS, SNAP Desktop, Erdas Imagine, \n\n\n\nPCRaster, Google Earth, Microsoft Excel and Microsoft Word. \n\n\n\n2.2.1 Watershed Delineation \n\n\n\nIn order to delineate the watershed, flow direction was computed using \n\n\n\nthe SRTM DEM of the Ban Dan Na Kham region of Thailand. Based on the \n\n\n\nflow direction, an appropriate outlet point was defined. All the \n\n\n\nsurrounding areas on higher elevation contributing runoff to the defined \n\n\n\npoint were then delineated as part of the watershed. \n\n\n\n2.2.2 Slope Classification \n\n\n\nThe slope was generated from the SRTM DEM of the watershed. The rate \n\n\n\nof change in altitude from one cell to each of the eight neighbouring cells \n\n\n\nof the DEM was calculated. The maximum rate of change was then \n\n\n\nidentified as the steepest downhill descent from that cell. Furthermore, \n\n\n\nlower slopes depict flat terrain while higher slopes depict steeper terrain. \n\n\n\nSlope being a continuous data was reclassified to generate discrete classes \n\n\n\nthat would aid in the assessment of appropriate land use types for parcels \n\n\n\nof land. In this study, three classes were generated, viz.: less that 8o as \n\n\n\ngentle slope, 8 - 30o as moderate slope and greater than 30o as steep slope. \n\n\n\n2.2.3 Definition of Land Cover Sampling Sites \n\n\n\nSentinel 2 multispectral satellite imagery of the study area acquired on \n\n\n\nNovember 6, 2018, was downloaded and pre-processed for atmospheric, \n\n\n\naerosol, terrain and cirrus correction in SNAP using the Sen2Cor \n\n\n\nalgorithm. Using ISODATA algorithm, unsupervised classification of the \n\n\n\nSentinel 2 image of the watershed was implemented in Erdas Imagine, \n\n\n\ngrouping the pixel values into 7 classes with common characteristics. \n\n\n\nUsing stratified random sampling technique, 30 sites were defined for \n\n\n\neach delineated land cover type, amounting to a total of 210 sample sites. \n\n\n\n2.2.4 Supervised Land Use / Land Cover Classification \n\n\n\n60 % of the 210 land cover sample sites, amounting to 126 sites, were used \n\n\n\nas land use / land cover training sites. These land use / land cover training \n\n\n\nsamples were used to estimate the mean and variance of the pixel values \n\n\n\nof each land cover class, enabling the determination of the appropriate \n\n\n\nrange of pixel values that belong to each land cover class. Using the \n\n\n\nMaximum Likelihood approach, the statistical probability of each grid cell \n\n\n\nbelonging to a land cover class was computed. The grid cells were \n\n\n\nsubsequently allocated to the land cover class to which they most likely \n\n\n\nbelong. The accuracy of the classification was assessed using the \n\n\n\nremaining 84 sample sites. The land cover class type of each of the sample \n\n\n\npoints was compared with the land cover class determined for that site \n\n\n\nduring the fieldwork. This enabled the calculation of the percent accuracy \n\n\n\nof each land cover class, both individually and collectively.  \n\n\n\nLand cover related parameters that were measured in the field, like plant \n\n\n\nheight (PH), canopy cover (CC) and surface cover (SC), were built into the \n\n\n\nLand Use Table that was subsequently associated with the land cover map \n\n\n\nin PCRaster. Other secondary parameters that were also built into the \n\n\n\ntable are rainfall interception by vegetation (A = Rainy Days * Smax / \n\n\n\nAnnual Rainfall), leaf area index (LAI = ln(1 \u2013 CC) / -0.4) and maximum \n\n\n\nplant canopy storage (\u201cSmax1 = 0.935 + 0.498*LAI - 0.00575 for field \n\n\n\ncrops\u201d or \u201cSmax2 = 1.46*LAI**0.56 for trees\u201d) [where A = rainfall \n\n\n\ninterception by vegetation, LAI = leaf area index, CC = canopy cover, ln = \n\n\n\nnatural logarithm, Smax = maximum plant canopy storage]. Using the \n\n\n\nlookupscalar command in PCRaster, individual maps were generated for \n\n\n\neach parameter based on the land cover map. \n\n\n\n2.2.5 Delineation of Integrated Slope and Land Cover Map \n\n\n\nThe delineated land cover and slope classes were overlaid in ArcGIS to \n\n\n\ngenerate the LULC-Slope map. This enabled the determination of the \n\n\n\nlocation and extent of each land cover type and the corresponding slope \n\n\n\nclass of the terrain. Figure 3 depicts a long kong orchard on a sloping \n\n\n\nterrain. Figure 4 shows an image of the study area, depicting its extremely \n\n\n\nundulating terrain and the diversity of the land cover types on different \n\n\n\nslope units. \n\n\n\nFigure 3: Recording the site characteristics of an orchard on a sloping \nlandscape. \n\n\n\nFigure 4: Image depicting the slope-land cover relations in the \n\n\n\nwatershed. \n\n\n\n2.3 Flowchart  \n\n\n\nThe flowchart of methods, outlining the relationships between the \n\n\n\nadopted methods and the input data is shown in figure 5. \n\n\n\nFigure 5: Flowchart of methods. \n\n\n\n3. RESULTS \n\n\n\n3.1 Land Use / Land Cover Map \n\n\n\nThe land use / land cover (LULC) map of the watershed is shown in Figure \n\n\n\n6. The dominant land uses in the watershed are arable farming, orchards \n\n\n\n\u2013 mostly long kong orchards \u2013 teak plantations, natural forests and built-\n\n\n\nup areas. Arable lands cover an area of 6.68 km2; orchards, an area of 26.07 \n\n\n\nkm2; plantations, an area of 12.76 km2; forests, an area of 39.82 km2; and \n\n\n\nbuilt-up, an area of 1.59 km2 (Table 1). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nTable 1: Attributes generated for each land cover type \n\n\n\nLand Cover \nArea \n\n\n\n(km2) \n\n\n\nArea \n\n\n\n(% of \n\n\n\nTotal) \n\n\n\nA \n\n\n\n(%) \n\n\n\nCC \n\n\n\n(%) \n\n\n\nSC \n\n\n\n(%) \n\n\n\nPH \n\n\n\n(m) \n\n\n\nLAI \n\n\n\n(m2/m2) \n\n\n\nSmax \n\n\n\n(mm) \n\n\n\nArable Land 6.68 7.69 16 49 16 1.42 1.68 1.77 \n\n\n\nOrchard 26.07 29.99 20 67 70 10.60 2.77 2.27 \n\n\n\nForest 39.82 45.81 55 95 61 19.88 7.49 6.12 \n\n\n\nBuilt-up \n\n\n\nAreas \n1.59 1.83 - - - - - - \n\n\n\nTeak \n\n\n\nPlantation \n12.76 14.68 47 92 26 20.35 6.31 5.16 \n\n\n\nWhere A = Rainfall Interception by Vegetation (%), CC = Canopy Cover \n\n\n\n(%), SC = Surface Cover (%), PH = Plant Height (m), LAI = Leaf Area Index, \n\n\n\nSmax = Maximum Canopy Storage (mm) \n\n\n\nFigure 6: Land use / land cover map of the Ban Dan Na Kham Watershed. \n\n\n\nThe accuracy assessment report and confusion matrix for the land use \n\n\n\nclassification are shown in Table 2. The overall accuracy of the map is 68 \n\n\n\n%. Natural Forest had a low accuracy of 43 % and was mostly misclassified \n\n\n\nas Teak Plantations, and to a limited extent, as Orchards. Forests and Teak \n\n\n\nplantations generally had similar canopy cover (92 \u2013 95 %), the plant \n\n\n\nheight (19 \u2013 20 m) and leaf area index (6 \u2013 7 m2/m2). Built-up areas also \n\n\n\nrecorded relatively low producer accuracy of 53 %. Forest, arable land and \n\n\n\nteak had the lowest kappa coefficient of 0.40, 0.53 and 0.56 respectively, \n\n\n\nwhereas, Built-up recorded the highest coefficient of 1.0.  \n\n\n\nTable 2: Accuracy assessment report for land use / land cover \n\n\n\nclassification \n\n\n\nError Matrix \n\n\n\nClassified \n\n\n\nData \n\n\n\nArable \n\n\n\nLand \nOrchard Forest \n\n\n\nBuilt-\n\n\n\nup \nTeak \n\n\n\nRow \n\n\n\nTotal \n\n\n\nArable \n\n\n\nLand \n28 1 0 14 2 45 \n\n\n\nOrchard 0 23 7 0 0 30 \n\n\n\nForest 1 5 13 0 6 25 \n\n\n\nBuilt-up 0 0 0 16 0 16 \n\n\n\nTeak  1 1 10 0 22 34 \n\n\n\nColumn \n\n\n\nTotal \n30 30 30 30 30 150 \n\n\n\nAccuracy Totals \n\n\n\nClass Name Reference \n\n\n\nTotals \n\n\n\nProducers \n\n\n\nAccuracy \n\n\n\n(%) \n\n\n\nUsers \n\n\n\nAccuracy \n\n\n\n(%) \n\n\n\nOverall \n\n\n\nAccuracy \n\n\n\n(%) \n\n\n\nKappa \n\n\n\nCoefficient \n\n\n\nArable Land 30 93.33 62.22 68.00 0.5278 \n\n\n\nOrchard 30 76.67 76.67 0.7083 \n\n\n\nNatural \n\n\n\nForest \n30 43.33 52.00 0.4000 \n\n\n\nBuilt-up 30 53.33 100.00 1.0000 \n\n\n\nTeak Forest 30 73.33 64.71 0.5588 \n\n\n\nOverall 150 - - 68.00 0.6000 \n\n\n\nTo further explore the impact of the misclassification of the forests as teak \n\n\n\nplantations, both classes were merged, and the image reclassified (Figure \n\n\n\n7). This means that the teak plantations were eliminated, as the regions \n\n\n\nthat were previously classified as teak, were now classified as forests, \n\n\n\nincreasing the forested area to 52.57 km2. Table 3 shows that this \n\n\n\nincreased the overall accuracy to 79 % and the overall kappa coefficient to \n\n\n\n0.70. Finally, the canopy cover (CC), surface cover (SC), plant height (PH), \n\n\n\nleaf area index (LAI), maximum plant canopy storage (Smax) and \n\n\n\nproportion of rainfall intercepted by vegetation (A) were generated for \n\n\n\neach land cover type (Table 1). The forested land had the highest canopy \n\n\n\ncover of 96 %, which was only comparable to the 92 % recorded for the \n\n\n\nteak plantation. Similarly, the maximum canopy storage of intercepted \n\n\n\nrainfall was highest in the forests and teak plantation, with a range \n\n\n\nbetween 5 to 6.2 mm (Table 1).  \n\n\n\nFigure 7: Land use / land cover map of the watershed (without the teak \n\n\n\nplantation). \n\n\n\nTable 3: Accuracy assessment report for land use / land cover \n\n\n\nclassification (without teak plantation) \n\n\n\nError Matrix \n\n\n\nClassified \n\n\n\nData \n\n\n\nArable \n\n\n\nLand \n\n\n\nOrchar\n\n\n\nd \nForest \n\n\n\nBuilt-\n\n\n\nup \nRow Total \n\n\n\nArable Land 28 1 1 14 44 \n\n\n\nOrchard 0 22 7 0 29 \n\n\n\nForests 2 7 52 0 61 \n\n\n\nBuilt-up 0 0 0 16 16 \n\n\n\nColumn \n\n\n\nTotal \n30 30 60 30 150 \n\n\n\nAccuracy Totals \n\n\n\nClass Name Reference \n\n\n\nTotals \n\n\n\nProducers \n\n\n\nAccuracy \n\n\n\n(%) \n\n\n\nUsers  \n\n\n\nAccuracy \n\n\n\n(%) \n\n\n\nOverall \n\n\n\nAccuracy \n\n\n\n(%) \n\n\n\nKappa \n\n\n\nCoefficient \n\n\n\nArable Land 30 93.33 63.64 78.67 0.5455 \n\n\n\nOrchard 30 73.33 75.86 0.6983 \n\n\n\nForests 60 86.67 85.25 0.7541 \n\n\n\nBuilt-up 30 53.33 100.00 1.0000 \n\n\n\nOverall 150 - - 78.67 0.7032 \n\n\n\n3.2 Topography and Hydrology \n\n\n\nThe watershed is located on a predominantly undulating landscape, with \n\n\n\naltitude ranging from 103 m above sea level in the southern fringes and \n\n\n\nalong river beds to 789 m above sea level in the northeastern fringes of the \n\n\n\nwatershed (Figure 8). The slope of the watershed ranges from 0 to 60o but \n\n\n\nis dominated by slope ranges between 8 to 30o (Figure 9). The gently \n\n\n\nsloping areas (less than 8o), mostly in the low-lying areas along riverbeds \n\n\n\ncover an area of 16 km2; the moderately sloping area (8 to 30o) cover an \n\n\n\narea of 64 km2; while the steeply sloping regions (greater than 30o) cover \n\n\n\nan area of 6 km2.  \n\n\n\nFigure 8: Physiography and hydrology of Ban Dan Na Kham watershed. \n\n\n\nGeographic Coordinate System: GCS WGS 1984 \n\n\n\nGeographic Coordinate System: GCS WGS 1984 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nFigure 9: Slope classes of Ban Dan Na Kham watershed. \n\n\n\n3.3 LULC-Slope Classes \n\n\n\nThe map of the watershed depicting the different land cover types and \n\n\n\ntheir corresponding slope classes is shown in Figure 10, while Table 4 \n\n\n\nshows the corresponding area of each unit and the recommended land use \n\n\n\n/ land management option. Generally, the arable lands amounted to a total \n\n\n\nof about 5.99 km2. Out of this, 2.49 km2 is located on gentle slopes and is \n\n\n\nsuitable for arable farming; 3.16 km2 on moderate slope is marginally \n\n\n\nsuitable, while the 0.34 km2 on steep slope is not suitable for crop  \n\n\n\nproduction. 24.82 km2 of the watershed is currently dedicated to orchards. \n\n\n\nOut of these, the 3.39 km2 on gentle slopes and the 9.82 km2 on moderate \n\n\n\nslopes are suitable for the prevailing land use. Nevertheless, the 1.61 km2 \n\n\n\non steep slopes is unsuitable for orchards, and may need to be reforested\n\n\n\nTable 4: Area and land use / management recommendations for the LULC-Slope classes. \n\n\n\nLandscape Unit Area \n\n\n\n(km2) \n\n\n\nArea \n\n\n\n(%) \n\n\n\nLand Use / Land Management Recommendations \n\n\n\nAL-Gentle Slope (< \n\n\n\n8o) \n\n\n\n2.49 \n3.00 \n\n\n\nSuitable for arable farming \n\n\n\nAL-Moderate Slope \n\n\n\n(8-30o) \n\n\n\n3.16 \n3.81 \n\n\n\nMarginally suitable for arable farming; may be converted to orchards \n\n\n\nAL-Steep Slope (> \n\n\n\n30o) \n\n\n\n0.34 \n0.41 \n\n\n\nUnsuitable for arable farming; needs to be forested \n\n\n\nOR-Gentle Slope (< \n\n\n\n8o) \n\n\n\n3.39 \n4.09 \n\n\n\nSuitable for orchards; may be suitable for expansion of arable farmland \n\n\n\nOR-Moderate Slope \n\n\n\n(8-30o) \n\n\n\n19.82 \n23.90 \n\n\n\nSuitable for orchards \n\n\n\nOR-Steep Slope (> \n\n\n\n30o) \n\n\n\n1.61 \n1.94 \n\n\n\nShould be reforested \n\n\n\nFO-Gentle (< 8o) 4.97 5.99 Suitable for arable farming \n\n\n\nFO-Moderate Slope \n\n\n\n(8-30o) \n\n\n\n30.25 \n36.47 \n\n\n\nSuitable for forests; moderately suitable for orchards \n\n\n\nFO-Steep Slope (8-\n\n\n\n30o) \n\n\n\n3.10 \n3.74 \n\n\n\nShould be left forested \n\n\n\nTP-Gentle Slope (< \n\n\n\n8o) \n\n\n\n2.08 \n2.51 \n\n\n\nAppropriate; may be suitable for expansion of arable land \n\n\n\nTP-Moderate Slope \n\n\n\n(8-30o) \n\n\n\n9.22 \n11.12 \n\n\n\nNeeds to be reforested or intercropped with shade-loving plants \n\n\n\nTP-Steep Slope (> \n\n\n\n30o) \n\n\n\n0.92 \n1.11 \n\n\n\nShould be reforested \n\n\n\nBA-Gentle Slope (< \n\n\n\n8o) \n\n\n\n0.90 \n1.09 \n\n\n\nAdequate for buildings, but may be prone to flash floods \n\n\n\nBA-Moderate Slope \n\n\n\n(8-30o) \n\n\n\n0.62 \n0.75 \n\n\n\nAdequate for buildings, but with appropriate soil conservation measures \n\n\n\nBA-Steep Slope (> \n\n\n\n30o) \n\n\n\n0.07 \n0.08 \n\n\n\nNot suitable for buildings; if it is to be built up, it should be sparingly distributed, as was the case; and with \n\n\n\nappropriate soil conservation measures \n\n\n\nAL = Arable Land, OR = Orchard, FO = Forests, TP = Teak Plantations, BA = Built-up Areas \n\n\n\nFigure 10: Map of the watershed showing the different land use types \n\n\n\nand their respective slope classes. \n\n\n\nThe teak plantation covers an approximate area of 12.22 km2. Out of this, \n\n\n\nthe 2.51 km2 on gentle slopes and the 11.12 km2 on moderate slopes are \n\n\n\nsuitable for teak plantations. Land conservation measures may however \n\n\n\nbe necessary on moderate slopes. 0.92 km2 of teak plantation on steep \n\n\n\nslopes is domiciled on inadequate land. 38.32 km2 of the watershed, \n\n\n\namounting to 46.2 % of the total area, is forested. 4.97 km2 is located on \n\n\n\ngentle slopes, 30.25 km2 on moderate slopes and 3.1 km2 on steep slopes. \n\n\n\n1.59 km2 of the watershed is built up. Out of this, 0.9 km2 is located on \n\n\n\ngentle slopes, 0.62 km2 on moderate slopes and 0.07 km2 on steep slopes. \n\n\n\n4. DISCUSSION\n\n\n\n4.1 Land Use / Land Cover Map \n\n\n\nTable 2 shows the tendency to misclassify Natural Forests as Teak \n\n\n\nPlantation and vice versa. This misclassification, resulting in reduced \n\n\n\noverall accuracy (68 %) and low kappa coefficient (0.60) is in consonance \n\n\n\nwith the findings of other researchers, who reported an accuracy of 50 % \n\n\n\nfor secondary forest because it was usually confused with primary forests \n\n\n\n(Gebhardt et al., 2015).  Similarly, while trying to understand the dynamics \n\n\n\nof a fragmented forest landscape in coastal Ecuador, it was reported that \n\n\n\nthe majority of the existing primary forests were classified as secondary \n\n\n\nGeographic Coordinate System: GCS WGS 1984 \n\n\n\nGeographic Coordinate System: GCS WGS 1984 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nforests; and that plantations also tend to be mistaken for forest (Haro-\n\n\n\nCarri\u00f3n and Southworth, 2018). Another group of researchers, while \n\n\n\nworking in Costa Rica, also reported that tree plantations, secondary and \n\n\n\nmature forests were usually confused with each other (Fagan et al., 2015). \n\n\n\nThese may be attributed to the fact that the forest, the teak plantations and \n\n\n\nthe orchards are populated by trees. The similarity in canopy cover (92 \u2013 \n\n\n\n96 %), plant height (approximately 20 m), leaf area index (6.3 \u2013 7.5 m2/m2) \n\n\n\nand maximum canopy storage (5 \u2013 6 mm) of the forests and teak \n\n\n\nplantations, as evident in Table 1, translates into similar pixel values.  \n\n\n\nFigure 11: Understory in the forested landscape. \n\n\n\nIndeed, the spectral confusion between tree-based land cover types has \n\n\n\nbeen widely reported (Fagan et al., 2013, 2015; Guti\u00e9rrez-V\u00e9lez and De \n\n\n\nFries, 2013; Senf et al., 2013; Haro-Carri\u00f3n and Southworth, 2018). As \n\n\n\nsuch, when teak plantations and forests were merged, and the image \n\n\n\nreclassified, the accuracy of the classification increased from 68 to 79 %, \n\n\n\nwhile the kappa coefficient increased from 0.60 to 0.70. A kappa coefficient \n\n\n\nas high as 0.70 implies substantial agreement between the classified and \n\n\n\nthe field-based data (Mchugh, 2012). Nevertheless, even though merging \n\n\n\nthe Teak Plantation and Natural Forest increased the accuracy of the \n\n\n\nclassification, the original map with 68% accuracy was retained and used \n\n\n\nfor further computations. This was because the forest (Figure 11) and the \n\n\n\nteak plantation (Figure 12) had distinct features, with particular reference \n\n\n\nto surface cover, which ranges from 26 % in the plantations to 61 % in the \n\n\n\nforests (Table 1). This, in the light of the report that in the humid tropics, \n\n\n\nforest understory plays a crucial role in determining the rate of land \n\n\n\ndegradation within the watershed, meant that they should be treated \n\n\n\ndifferently (Labri\u00e8re et al., 2015).  \n\n\n\nFurthermore, the relatively low producer accuracy recorded for built-up \n\n\n\nareas (53%) may be attributed to the misclassification of built-up areas as \n\n\n\narable land. Ideally, it is expected that arable land and built-up areas would \n\n\n\nhave very distinct pixel values. Nevertheless, the watershed is located in a \n\n\n\nrural setting, where buildings may be surrounded by or located in close \n\n\n\nproximity to farms, orchards, trees or forests (FAO, 2018).  As such, some \n\n\n\nbuildings may be located in pixels dominated by arable land, resulting in \n\n\n\ntheir classification as arable land. This phenomenon, referred to as mixed \n\n\n\npixels, has been widely reported (Amalisana et al., 2017; Maclachlan et al., \n\n\n\n2017; Yang et al., 2017). However, with respect to the impact of the \n\n\n\nmisclassification on the assessment and management of land degradation \n\n\n\nin the watershed, most of the built-up areas are located on gentle to \n\n\n\nmoderately sloped low-lying areas that are less prone to erosion and other \n\n\n\nforms of degradation, especially when they are surrounded by trees with \n\n\n\nunderstory of farmlands. \n\n\n\nThe forested land had the highest canopy cover (96 %), as also reported \n\n\n\nby other researchers (Siswanto and Sule, 2019). This value was only \n\n\n\ncomparable to the 92 % recorded in the teak plantation. The high canopy \n\n\n\ncover engenders a greater shield for the land from the direct impact of \n\n\n\nprecipitation and sunlight (Zuazo and Pleguezuelo, 2008; Siswanto and \n\n\n\nSule, 2019). As such, the loss of forest canopy through deforestation has a \n\n\n\ndevastating effect on the environment and the ecosystem (Olagunju, 2015; \n\n\n\nChen et al., 2019). Nevertheless, while high canopy cover can reduce soil \n\n\n\ndetachment by raindrops, it has been reported that it may also result in \n\n\n\nretarded growth for shaded plants (Hou et al., 2018; Wagner et al., 2011). \n\n\n\nAs such, if crops are to be grown under the canopy of the teak plantation, \n\n\n\nshade-loving plants should be preferentially selected (Wagner et al., \n\n\n\n2011). \n\n\n\nThe high maximum canopy storage of forests and teak plantations (Table \n\n\n\n1) buttress the impact of canopy cover on the prevention and control of \n\n\n\nsoil erosion and other forms of land degradation. This is reiterated by the \n\n\n\nhigher leaf area index (7.50 m2/m2) and rainfall interception by vegetation \n\n\n\n(55%) recorded in the forests, which reportedly, reduces soil degradation \n\n\n\n(Seitz et al., 2016). Nevertheless, with a plant height of approximately 20 \n\n\n\nm, the intercepted rainfall that falls through the tree foliage will have a \n\n\n\ngreater erosive energy due to the accumulated kinetic energy of the \n\n\n\nthroughfall. Therefore, increase in tree height and the attendant erosive \n\n\n\nenergy exposes the soil to increased risk of degradation (Seitz et al., 2016). \n\n\n\nThe least erosive throughfall was recorded on the arable land due to a low \n\n\n\nplant height of 1.42m (Table 1). \n\n\n\nFortunately, irrespective of the erosive energy of the throughfall, a high \n\n\n\nsurface cover of 61 %, which is a product of the forest understory (Figure \n\n\n\n11), will reduce detachment by throughfall on forested land as has also \n\n\n\nbeen reported by other researchers (Seitz et al., 2016). This is not the case \n\n\n\nin the teak plantation (Figure 12), with a surface cover of 26 %. \n\n\n\nNevertheless, it is recommended that an understory of annual plants will \n\n\n\nrestrict soil erosion to negligible levels (Zuazo and Pleguezuelo, 2008). \n\n\n\nIncidentally, arable land also had the lowest surface cover of 16 % and is \n\n\n\nconsequently least protected from the erosive impact of rainfall, runoff \n\n\n\nand other agents of land degradation, irrespective of its low plant height. \n\n\n\nFigure 12: Lack of understory in the teak plantation. \n\n\n\n4.2   Topography and Hydrology \n\n\n\nIt has been reported that the slope of the landscape is one of the most \n\n\n\nimportant factors that must be taken into consideration in the course of \n\n\n\nland use planning (Tilahun and Teferie, 2015).  This is because the \n\n\n\nsteepness, shape (uniform, concave, convex or complex) and length of the \n\n\n\nslope has a fundamental impact on the rate and degree of runoff and soil \n\n\n\nerosion within a landscape (Sensoy and Palta, 2009; Gray, 2016; Ahmadu \n\n\n\net al., 2019). Consequently, land uses that predispose the land to \n\n\n\ndegradation should be avoided in specific sections of the landscape, \n\n\n\nparticularly on steep slopes (Tilahun and Teferie, 2015; Karamage et al., \n\n\n\n2016; Madueke et al., 2019).  \n\n\n\nIn line with this, arable cropping should be discouraged on the steep \n\n\n\nslopes, which amounts to 6.98 % of the Ban Dan Na Kham watershed. The \n\n\n\nsteep slopes are better left forested as reduced erosion is evident on \n\n\n\nforested soils (Chalise et al., 2019). This is also in line with the contention \n\n\n\nthat steeply sloping land are unsuitable for crop production and hill slopes \n\n\n\nare generally suitable for tree crops (AbdelRahman et al., 2016; Mazahreh \n\n\n\net al., 2018). \n\n\n\nOn the moderate slopes (74.42 % of the watershed), cropping has to be \n\n\n\naccompanied by extensive investment on soil conservation measures like \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nterracing, contour ploughing, alley farming, cover cropping, etc. Orchards \n\n\n\nand plantations may be established on the moderate slopes, with arable \n\n\n\ncrops grown between the rows of trees. This would increase the economic \n\n\n\nreturns of the farmers, while at the same time, preventing excessive soil \n\n\n\ndegradation (Zuazo and Pleguezuelo, 2008). Arable farming may be \n\n\n\nsustainably implemented on the gentle slopes (18.60 % of the watershed). \n\n\n\nThis agrees with the recommendation of that gentle slopes are optimal for \n\n\n\narable crop production (Li et al., 2015; Mazahreh et al., 2018).  \n\n\n\n4.3  LULC-Slope Classes \n\n\n\nDue to intense monsoon rains in Southeast Asia, the sloping lands are \n\n\n\nsusceptible to landslides, flash floods, soil erosion and other forms of land \n\n\n\ndegradation (Xu et al., 2013). This is more so for arable lands on slopes. In \n\n\n\nline with this, destructive degrees of soil degradation has been reported \n\n\n\nfor arable lands on slope classes above 7 % (Nabiollahi et al., 2018). It is \n\n\n\nnoteworthy that trees play a crucial role in the prevention and control of \n\n\n\nthese diverse forms of land degradation (Zuazo and Pleguezuelo, 2008; Xu \n\n\n\net al., 2013; Nabiollahi et al., 2018). As such, the marginal arable lands on \n\n\n\nthe moderate slopes of the Ban Dan Na Kham watershed may need to be \n\n\n\nconverted to orchards or forests to forestall further land degradation. If \n\n\n\nplantations or orchards are to be established on such land, agroforestry \n\n\n\nsystems like alley farming may be recommended. This will provide \n\n\n\nadditional returns to the farmer while maintaining the integrity of the soil. \n\n\n\nFurthermore, the arable land on the steep slopes should be reforested. \n\n\n\nThis is in line with the contention that steep slopes are unsuitable for \n\n\n\narable crop production (AbdelRahman et al., 2016; Karamage et al., 2016; \n\n\n\nMazahreh et al., 2018; Madueke et al., 2019). \n\n\n\nOut of the 24.82 km2 currently dedicated to orchards, 23.21 km2 may be \n\n\n\nsuitable for orchards, but, in line with the contention of Li et al. (2015), the \n\n\n\n3.39 km2 on gentle slopes may also be suitable for the expansion of arable \n\n\n\nland. The remaining 1.61 km2 is located on steep slopes and should be \n\n\n\nreforested. This will forestall aggravated land degradation (AbdelRahman \n\n\n\net al., 2016; Karamage et al., 2016; Mazahreh et al., 2018; Madueke et al., \n\n\n\n2019). The 38.32 km2 of forested land is not at risk of degradation, a \n\n\n\nsituation which is attributed to the increased canopy cover, soil organic \n\n\n\nmatter, structural stability, porosity and capacity to conserve water \n\n\n\nefficiently (Tingting et al., 2008; Siswanto and Sule, 2019). Nevertheless, if \n\n\n\nthere is the need for expansion of arable farmlands, the 4.97 km2 of \n\n\n\nforested land on gentle slopes may be suitable, while the 3.10 km2 on the \n\n\n\nsteep slopes should be left untouched. This is because steep slopes are \n\n\n\nneither suitable for arable farming, plantations or orchards (Madueke et \n\n\n\nal., 2019). \n\n\n\nThe 2.08 km2 of teak plantation on gentle slope may be left in its current \n\n\n\nstate. However, these plantations, along with the 9.22 km2 on moderate \n\n\n\nslopes, need to be intercropped with shade-loving plants to protect the \n\n\n\nbare soil from the direct impact of raindrops and throughfall. The crops \n\n\n\nwill provide the needed understory which, reduces soil degradation in the \n\n\n\nin the humid tropics (Zuazo and Pleguezuelo, 2008; Labri\u00e8re et al., 2015; \n\n\n\nSeitz et al., 2016). The remaining 0.92 km2 of teak plantation on steep slope \n\n\n\nmay need to be reverted to natural forests as it has been reported that \n\n\n\nplantations are relatively more prone to erosion (Tingting et al., 2008; \n\n\n\nMadueke et al., 2019). \n\n\n\nOnly 1.59 km2, amounting to 1.83 % of the watershed, is currently \n\n\n\ndedicated to built-up areas. This buttresses the rural setting of the \n\n\n\nwatershed as FAO (2018) pointed out that the inhabitants of a rural area \n\n\n\nusually live far apart from one another in relatively small, widely spaced \n\n\n\nsettlements that may be located in close proximity with farms or forests.  \n\n\n\nOut of the total built-up area, the 0.90 km2 on gentle slope is adequate, but \n\n\n\nmay be prone to flash floods. The 0.62 km2 on moderate slope is also \n\n\n\nadequate but may require the implementation of soil and water \n\n\n\nconservation measures like proper drainage / waterways and prevention \n\n\n\nof unnecessary deforestation. The 0.07 km2 on steep slope may be \n\n\n\ninadequate, but its sparing distribution, and the implementation of \n\n\n\nadequate soil conservation measures, including maintenance of adequate \n\n\n\nforest and understorey, may preclude undue land degradation. \n\n\n\n5. CONCLUSION \n\n\n\nThe integrated land cover and slope map of a watershed can be used as a \n\n\n\npreliminary tool for land use and land management planning. \n\n\n\nFurthermore, when constrained by time and finance, the integrated \n\n\n\nassessment can serve as a sustainable and proactive instrument for land \n\n\n\nallocation to ensure increased productivity with minimal land \n\n\n\ndegradation. Generally, with regards to the Ban Dan Na Kham watershed, \n\n\n\nthe current extent of the arable lands is 5.99 km2. Out of this, 0.34 km2 is \n\n\n\nunsuitable due to its steep slope, while 3.16 km2 is marginally suitable due \n\n\n\nto its moderate slope. Nevertheless, the total area on gentle slope which \n\n\n\nmay be suitable for the expansion of the arable land in the watershed \n\n\n\namounts to as much as 10.44 km2; about 12.14 % of the total extent of the \n\n\n\nwatershed, as opposed to the current total arable land proportion of 6.97 \n\n\n\n%. As such, a coordinated land use planning based on the integrated land \n\n\n\ncover and slope map will engender sustainably increased soil productivity. \n\n\n\nRECOMMENDATIONS \n\n\n\na. Up to 2.87 km2 of land occupied by agricultural fields, orchards and \n\n\n\nplantations may need to be afforested due to the steepness of the \n\n\n\nslope. \n\n\n\nb. The portion of the watershed located on moderate slopes, amounting \n\n\n\nto about 32.20 km2 may need to be reforested or subjected to soil and \n\n\n\nland management options that would ensure sustainable use. \n\n\n\nc. The teak plantation should be intercropped with shade-loving plants \n\n\n\nto protect the soil from the direct impact of precipitation, runoff and \n\n\n\nother agents of denudation. \n\n\n\nFURTHER STUDIES \n\n\n\nGiven the relatively low accuracy of the LULC classification of the rural \n\n\n\nwatershed, there may be the need to explore other classification options, \n\n\n\nlike the fuzzy classification and the random forest, both of which may give \n\n\n\nbetter accuracy. It may be necessary to assess the rate of land degradation \n\n\n\nwithin each delineated unit, to ensure a more precise land use and \n\n\n\nmanagement recommendation. Finally, determining the land capability \n\n\n\nclasses within the watershed may also be necessary as it will provide \n\n\n\ninsight on proactive and sustainable land use / land management options. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nI am profoundly grateful to the government and people of the Netherlands \n\n\n\nfor awarding me the NFP fellowship that enabled me to further my \n\n\n\neducation and conduct this and allied research. I must also express my \n\n\n\nimmense gratitude to the National Aeronautics and Space Administration \n\n\n\n(NASA), the Asian Disaster Preparedness Centre (ADPC) and the Civil \n\n\n\nEngineering Department of Naresuan University, Phitsanulok, for \n\n\n\nproviding the framework for this project and the attendant fieldwork. Also \n\n\n\nworthy of special mention are my fieldwork colleague, Kasimir Orlowski \n\n\n\nand our benevolent technologist / guide at Naresuan University, Wannica \n\n\n\nKankomnanta. \n\n\n\nFUNDING \n\n\n\nThis study was funded by the Netherlands Fellowship Programme [NFP] \n\n\n\n(2017/2019) as part of the thesis research and requirements for the \n\n\n\naward of a M.Sc. Degree in Geoinformation Science and Earth Observation \n\n\n\nfor Natural Resources Management at ITC, University of Twente, \n\n\n\nEnschede, the Netherlands.  \n\n\n\nREFERENCES \n\n\n\nAbdelRahman, M.A.E., Natarajan, A., Hegde, R., 2016. Assessment of Land \nSuitability and Capability by Integrating Remote Sensing and GIS for \nAgriculture in Chamarajanagar District, Karnataka, India The Egyptian \nJournal of Remote Sensing and Space Science, 19, Pp. 125 \u2013 141.  \n\n\n\nAdhikari, K., Hartemink, A., 2016. Linking Soils to Ecosystem Services\u2014A \nGlobal Review Geoderma, 262, Pp. 101 \u2013 111.  \n\n\n\nAhmadu, J., Habila Galadima, D., Blessing Chinedu, N., Franca Eze, O., 2019. \nEffect of Erosion on Agricultural Land in Agyana Community in Abaji, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nAbuja International Journal of Applied Agricultural Sciences, 5, Pp. 120. \nhttps://doi.org/10.11648/j.ijaas.20190505.14 \n\n\n\nAmalisana, B., Rokhmatullah, Hernina, R., 2017. Land Cover Analysis by \nUsing Pixel-Based and Object-Based Image Classification Method in \nBogor IOP Conference Series: Earth and Environmental Science, 98, Pp. \n012005 https://doi.org/10.1088/1755-1315/98/1/012005 \n\n\n\nBajocco, S., De Angelis, A., Perini, L., Ferrara, A., Salvati, L., 2012. The \nImpact of Land Use / Land Cover Changes on Land Degradation \nDynamics: A Mediterranean Case Study Environmental Management, \n49, Pp. 980 \u2013 989.  \n\n\n\nBektas, F., Goksel, C., 2004. Remote Sensing and GIS Integration for Land \nCover Analysis, a Case Study: Gokceada Island in: Proceedings of the \nInternational Society for Photogrammetry and Remote Sensing (ISPRS) \nCongress of 12 - 23 July 2004 in Istanbul Commission IV, WG IV/7, \nInternational Society for Photogrammetry and Remote Sensing (ISPRS), \nIstanbul. \n\n\n\nChalise, D., Kumar, L., Kristiansen, P., 2019. Land Degradation by Soil \nErosion in Nepal: A Review Soil Systems, 3, Pp. 1\u201318 \nhttps://doi.org/10.3390/soilsystems3010012 \n\n\n\nChen, L., Guo, Z., Yin, K., Pikha Shrestha, D., Jin, S., 2019. The influence of \nland use and land cover change on landslide susceptibility: A case study \nin Zhushan Town, Xuan\u2019en County (Hubei, China) Natural Hazards and \nEarth System Sciences, 19, Pp. 2207\u20132228 \nhttps://doi.org/10.5194/nhess-19-2207-2019 \n\n\n\nDos Santos, J.C.N., De Andrade, E.M., Henrique, P.H.A., Medeiros, A., \nGuerreiro, M.J.S., De Queiroz Pal\u00e1cio, H.A., 2017. Land Use Impact on Soil \nErosion at Different Scales in the Brazilian Semi-Arid Revista Ci\u00eancia \nAgron\u00f4mica, 48, Pp. 251 \u2013 260.  \n\n\n\nFagan, M.E., DeFries, R.S., Sesnie, S.E., Arroyo-Mora, J.P., Soto, C., Singh, A., \nTownsend, P.A., Chazdon, R.L., 2015. Mapping species composition of \nforests and tree plantations in northeastern Costa Rica with an \nintegration of hyperspectral and multitemporal landsat imagery \nRemote Sensing, 7, Pp. 5660\u20135696 \nhttps://doi.org/10.3390/rs70505660 \n\n\n\nFagan, M.E., Defries, R.S., Sesnie, S.E., Arroyo, J.P., Walker, W., Soto, C., \nChazdon, R.L., Sanchun, A., 2013. Land cover dynamics following a \ndeforestation ban in northern Costa Rica Environmental Research \nLetters, 8, Pp. 034017. https://doi.org/10.1088/1748-\n9326/8/3/034017 \n\n\n\nFAO, 2018. Guidelines on defining rural areas and compiling indicators for \ndevelopment policy, Food and Agriculture Organization of the United \nNations, Rome. \n\n\n\nGebhardt, S., Maeda, P., Espinoza, J.E., Wehrmann, T., Schmidt, M., 2015. A \nProper Land Cover and Forest Type Classification Scheme for Mexico in: \nProceedings of the 36th International Symposium on Remote Sensing \nof Environment held from 11\u201315 May 2015 in Berlin, Germany, The \nInternational Archives of the Photogrammetry, Remote Sensing and \nSpatial Information Sciences, Berlin. \n\n\n\nGray, D., 2016. Effect of Slope Shape on Soil Erosion Journal of Civil and \nEnvironmental Engineering, 6, Pp. 1000231 \nhttps://doi.org/10.4172/2165-784x.1000231 \n\n\n\nGuti\u00e9rrez-V\u00e9lez, V.H., De Fries, R., 2013. Annual multi-resolution detection \nof land cover conversion to oil palm in the Peruvian Amazon Remote \nSensing of Environment, 129, Pp. 154\u2013167 \nhttps://doi.org/doi.org/10.1016/j.rse.2012.10.033 \n\n\n\nHaro-Carri\u00f3n, X., Southworth, J., 2018. Understanding land cover change \nin a fragmented forest landscape in a biodiversity hotspot of coastal \nEcuador Remote Sensing, 10, Pp. 1\u201321 \nhttps://doi.org/10.3390/rs10121980 \n\n\n\nHou, W., Luo, Y., Wang, X., Chen, Q., Sun, B., Wang, Y., Liu, Z., Tang, H., Zhang, \nY., 2018. Effects of shading on plant growth, flower quality and \nphotosynthetic capacity of Rosa hybrida AIP Conference Proceedings, \n1956, Pp. 020005-1-020005\u20136 AIP Publishing, New \nYorkhttps://doi.org/10.1063/1.5034257 \n\n\n\nJones, K.B., 2008. Importance of Land Cover and Biophysical Data in \nLandscape-Based Environmental Assessments in: Campbell, J. C., Jones, \nK. B., Smith, J. H., and Koeppe, M. T. (Eds), North America Land Cover \n\n\n\nSummit, Association of American Geographers, Washington, D.C., Pp. \n215 \u2013 249. \n\n\n\nKaramage, F., Zhang, C., Ndayisaba, F., Shao, H., Kayiranga, A., Fang, X., \nNahayo, L., Nyesheja, E.M., Tian, G., 2016. Extent of Cropland and \nRelated Soil Erosion Risk in Rwanda Sustainability, 8, Pp. 1\u201319 \nhttps://doi.org/10.3390/su8070609 \n\n\n\nLabri\u00e8re, N., Locatelli, B., Laumonier, Y., Freycon, V., Bernoux, M., 2015. Soil \nErosion in the Humid Tropics: A Systematic Quantitative Review \nAgriculture, Ecosystems and Environment, 203, Pp. 127 \u2013 139. Elsevier \nB.V.https://doi.org/10.1016/j.agee.2015.01.027 \n\n\n\nLi, Y., Yang, X., Cai, H., Xiao, L., Xu, X., Liu, L., 2015. Topographical \nCharacteristics of Agricultural Potential Productivity during Cropland \nTransformation in China Sustainability, 7, Pp. 96\u2013110 \nhttps://doi.org/10.3390/su7010096 \n\n\n\nMaclachlan, A., Roberts, G., Biggs, E., Boruff, B., 2017. Subpixel land-cover \nclassification for improved urban area estimates using landsat \nInternational Journal of Remote Sensing, 38, Pp. 5763\u20135792 Taylor & \nFrancis. https://doi.org/10.1080/01431161.2017.1346403 \n\n\n\nMadueke, C.O., Shrestha, D.P., Nyktas, P., Orlowski, K., 2019. Spatial \nVariability of Soil Loss in the Ban Dan Na Kham Watershed of Northern \nThailand in: 43rd Annual Conference of the Soil Science Society of \nNigeria held at University of Agriculture, Makurdi, Benue State, Nigeria, \nSoil Science Society of Nigeria, Makurdi. \n\n\n\nMazahreh, S., Bsoul, M., Ziadat, F., Hamoor, D.A., 2018. Participatory Land \nSuitability Analysis to Identify the Optimum Land Use for a \nMountainous Watershed in Jordan Safa Mazahreh Journal of \nEngineering Research and Application, 8, Pp. 41\u201355.  \n\n\n\nMchugh, M.L., 2012. Interrater Reliability: The Kappa Statistic - \nImportance of Measuring Interrater Reliability Biochemia Medica, 22, \nPp. 276\u2013282.  \n\n\n\nMelaku, N.D., Renschler, C.S., Holzmann, H., Strohmeier, S., Bayu, W., Zucca, \nC., Ziadat, F., Klik, A., 2018. Prediction of Soil and Water Conservation \nStructure Impacts on Runoff and Erosion Processes Using SWAT Model \nin the Northern Ethiopian Highlands. Journal of Soils and Sediments, 18, \nPp. 1743\u20131755 https://doi.org/10.1007/s11368-017-1901-3 \n\n\n\nNabiollahi, N., Golmohamadi, F., Taghizadeh-Mehrjardi, R., Kerry, R., \nDavari, M., Nabiollahi, K., Golmohamadi, F., Taghizadeh-Mehrjardi, R., \nKerry, R., Davari, M., Nabiollahi, N., Golmohamadi, F., Taghizadeh-\nMehrjardi, R., Kerry, R., Davari, M., 2018. Assessing the Effects of Slope \nGradient and Land Use Change on Soil Quality Degradation through \nDigital Mapping of Soil Quality Indices and Soil Loss Rate Geoderma, \n318, Pp. 16 \u2013 28. https://doi.org/10.1016/j.geoderma.2017.12.024 \n\n\n\nNkonya, E., Mirzabaev, A., von Braun, J., 2016. Economics of Land \nDegradation and Improvement \u2013 An Introduction and Overview in: \nNkonya, E., Mirzabaev, A., and von Braun, J. (Eds), Economics of Land \nDegradation and Improvement \u2013 A Global Assessment for Sustainable \nDevelopment, Springer International Publishing, Cham, Pp. 1 \u2013 14. \nhttps://doi.org/10.1007/978-3-319-19168-3 \n\n\n\nNouri, A., Saffari, A., Karami, J., 2018. Assessment of Land Use and Land \nCover Changes on Soil Erosion Potential Based on RS and GIS, Case \nStudy: Gharesou, Iran. Journal of Geography and Natural Disasters, 8, \nPp. 222. https://doi.org/10.4172/2167-0587.1000222 \n\n\n\nOlagunju, T.E., 2015. Impacts of Human-induced deforestation, forest \ndegradation and fragmentation on food security New York Scince \nJournal, 8, Pp. 4\u201316.  \n\n\n\nPanagos, P., Jones, A., Bosco, C., Senthil, K.P.S., 2011. European Digital \nArchive on Soil Maps (EuDASM): Preserving Important Soil Data for \nPublic Free Access International Journal of Digital Earth, 4, Pp. 434 \u2013 \n443. \nhttps://doi.org/https://doi.org/10.1080/17538947.2011.596580 \n\n\n\nScholten, J.J., Siriphant, C., 1973. Soils and Land Forms of Thailand, Soil \nSurvey Division of the Ministry of Agriculture and Cooperatives, \nBangkok. \n\n\n\nSeitz, S., Goebes, P., Song, Z., Bruelheide, H., H\u00e4rdtle, W., K\u00fchn, P., Li, Y. and \nScholten, T., 2016. Tree species and functional traits but not species \nrichness affect interrill erosion processes in young subtropical forests \nSoil, 2, Pp. 49\u201361. https://doi.org/10.5194/soil-2-49-2016. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 34-42 \n\n\n\nCite the Article: Chike Onyeke Madueke, Dhruba Pikha Shrestha, Panagiotis Nyktas (2021). Integrated Land Cover and Terrain Analysis for Sustainable Land Use \nPlanning at Watershed Scale: A Case Study of Ban Dan Na Kham Watershed of Northern Thailand. Malaysian Journal of Sustainable Agriculture, 5(1): 34-42. \n\n\n\nSenf, C., Pflugmacher, D., van der Linden, S., Hostert, P., 2013. Mapping \nrubber plantations and natural forests in Xishuangbanna (Southwest \nChina) using multi-spectral phenological metrics from modis time \nseries Remote Sensing, 5, Pp. 2795\u20132812. \nhttps://doi.org/10.3390/rs5062795. \n\n\n\nSensoy, H., Palta, S., 2009. Effect of Slope Shape on Soil Erosion Bart\u0131n \nOrman Fak\u00fcltesi Dergisi, 11, Pp. 95\u201398.  \n\n\n\nShafiq, M., Mir, A.A., Rasool, R., Singh, H., Ahmed, P., 2017. A Geographical \nAnalysis of Land Use/Land Cover Dynamics in Lolab Watershed of \nKashmir Valley, Western Himalayas Using Remote Sensing and GIS \nJournal of Remote Sensing and GIS, 6, Pp. 189. \nhttps://doi.org/10.4172/2469-4134.1000189 \n\n\n\nSingh, Y., 2015. Significance of Land Use / Land Cover (LULC) Maps. \nAccessed: 05/04/2020 from: \nhttps://www.satpalda.com/blogs/significance-of-land-use-land-\ncover-lulc-maps.  \n\n\n\nSiswanto, S.Y., Sule, M.I.S., 2019. The Impact of slope steepness and land \nuse type on soil properties in Cirandu Sub-Sub Catchment, Citarum \nWatershed IOP Conference Series: Earth and Environmental Science, \nPp. 393. https://doi.org/10.1088/1755-1315/393/1/012059 \n\n\n\nTilahun, A., Teferie, B., 2015. A GIS based slope analysis for recommended \nsoil and water conservation techniques in Sekela District, Amhara State, \nEthiopia. International Journal of Advance Agricultural Research, 3, Pp. \n45\u201354.  \n\n\n\nTingting, L.V.V., Xiaoyu, S., Dandan, Z., Zhenshan, X., Jianming, G., 2008. \nAssessment of Soil Erosion Risk in Northern Thailand International \n\n\n\nArchives of the Photogrammetry, Remote Sensing and Spatial \nInformation Sciences, XXXVII, Pp. 703 \u2013708.  \n\n\n\nWagner, S., Fischer, H., Huth, F., 2011. Canopy effects on vegetation caused \nby harvesting and regeneration treatments European Journal of Forest \nResearch, 130, Pp. 17\u201340 https://doi.org/10.1007/s10342-010-0378-\nz \n\n\n\nWubie, M.A., Assen, M., 2020. Effects of land cover changes and slope \ngradient on soil quality in the Gumara watershed, Lake Tana basin of \nNorth\u2013West Ethiopia Modeling Earth Systems and Environment, 6, Pp. \n85\u201397. Springer International \nPublishinghttps://doi.org/10.1007/s40808-019-00660-5 \n\n\n\nXu, J., Mercado, A., He, J., Dawson, I., 2013. An Agroforestry Guide for Field \nPractitioners, World Agroforestry Centre (ICRAF), Kunming. \n\n\n\nYang, C., Wu, G., Ding, K., Shi, T., Li, Q., Wang, J., 2017. Improving land \nuse/land cover classification by integrating pixel unmixing and decision \ntree methods Remote Sensing, 9, Pp. 1222 \nhttps://doi.org/10.3390/rs9121222 \n\n\n\nZiadat, F.M., Taimeh, A.Y., 2013. Effect of Rainfall Intensity, Slope, Land Use \nand Antecedent Soil Moisture on Soil Erosion in an Arid Environment \nLand Degradation and Development, 24, Pp. 582 \u2013 590.  \n\n\n\nZuazo, V.H.D., Pleguezuelo, C.R.R., 2008. Soil-erosion and runoff \nprevention by plant covers. A review Agronomy for Sustainable \nDevelopment, 28, Pp. 65\u201386. https://doi.org/10.1051/agro:2007062.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.86.93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.86.93 \n\n\n\nBIOCHAR AS A FEED ADDITIVE FOR IMPROVING THE PERFORMANCE OF FARM \nANIMALS \n\n\n\nEmanuel Joel Laoa,b*, Ernest Rashid Mbegaa  \n\n\n\na Department of Sustainable Agriculture, Biodiversity and Ecosystem Management, The Nelson Mandela African Institution of Science and \nTechnology (NM-AIST). P. O. Box 447, Tengeru, Arusha - Tanzania. \nb Centre for Research, Agriculture Advancement, Teaching Excellence and Sustainability (CREATES), The Nelson Mandela African Institution of \nScience and Technology, Arusha, Tanzania \n* Corresponding Author\u2019s E-mail: laoemanueljoel@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 10 March 2020\n\n\n\nBiochar, also known as biomass-derived char or charcoal is a dark/black carbonaceous material generated \nfrom the pyrolysis process under temperature averagely 700 0C and low oxygen levels. Depending on the \nintended objectives and conditions of the pyrolysis, the biochar, syngas and bio-oils are the three primary \nproducts generated. The quality of biochar is a function of its primary biomass source, residence time and \ntemperature during pyrolysis which in turn results in variations of its physicochemical characteristics such \nas porosity, carbon content, elemental composition, surface area, retention capacity, and overall applications. \nThe physical and chemical activation techniques to produce the activated charcoal is often done to improve \nthe effectiveness of these carbonaceous materials. The biochar has broadly been used globally in agro-\nenvironmental management including in livestock production. Its inclusion at 1 - 3 % of DM of animal feed \nrations have been studied to improve health conditions and performance of farm animals such as weight gain, \nimmunity response, feed intake, feed conversion rates, carcass characteristics and overall quality of animal \nproducts. The mechanisms associated with the beneficial impacts rely on adsorption ability of these materials \nin detoxifying the mycotoxins in feed, regulating plant-produced toxins, having a high affinity to pollutants as \nwell as improvement of the beneficial microbial populations in animals' gastrointestinal tract. However, the \ncurrent literature indicates there is still a need for more investigation on the effectiveness of biochar in animal \nproduction due to either limited knowledge or contrasting findings reported. Also, there are imperative \nchallenges which need to be addressed such as safety standards, specificity, potential contamination, \naffordability, and level of awareness by farmers who are end-users of biochar and its products.  \n\n\n\nKEYWORDS \n\n\n\nActivated charcoal, Adsorption, Animal performance, Biochar, Detoxification, Pyrolysis.\n\n\n\n1. INTRODUCTION \n\n\n\n1.1 Background \n\n\n\nEven though research and biochar usage has gained considerable \nattention from the late 19th century, its application for different purposes \nsuch as detoxification of animal feed is acknowledged to have been \npracticed back in ancient times among different global cultures (Gerlach \nand Schmidt, 2012). The inclusion of biochar in the production of pigs has \nwidely been used from the 1880s while also near mid 20th centuries (the \n1940s), it has reportedly been applied in poultry feeding (Totusek and \nBeeson, 1953). From the current literature, the benefits that can be \nobtained by animals are quite diverse which range from detoxification of \nanimal feed, enhancing feed intake and digestion, promoting animal live \nweight gain as well as improving the quantity and quality of animal \nproducts such as milk, eggs and meat (Toth and Dou, 2016). While most of \nthe current research on biochar and activated charcoal are more focusing \non its potential in mitigating climate change, improving soil \ncharacteristics, managing the wastes and modulating environmental \npollution, relatively little attention is being paid to its role as a feed \n\n\n\nadditive to farm animals' productivity (McHenry, 2010). This article has \ntherefore reviewed the current knowledge on the production of biochar, \nits conversion to activated form and the primary factors influencing its \ncharacteristics and hence the application. Also, mechanisms of biochar as \na potential feed additive as well as the benefits related to the improvement \nof the health and performance of farm animals specifically the ruminants, \nswine and poultry being thoroughly presented. Additionally, its \nlimitations as a feed supplement and future suggestions for improvement \nare briefly highlighted.   \n\n\n\n1.2 Production of Biochar by Pyrolysis  \n\n\n\nBiochar is a highly porous, recalcitrant and non-soluble organic powder \nmaterial that is generated when biomass has undergone pyrolysis at \ntemperatures averagely 700 0C and low oxygen levels (Toth and Dou, \n2016; Tang et al., 2013). A wide range of biomass sources has been globally \nused which include animal manure, crop residues, agro-industrial by-\nproducts as well as forestry wastes (Toth and Dou, 2016; Ahmad, 2014; \nJindo et al., 2014; Olieveira et al., 2017; Weber and Quicker, 2018). The \ncommon characteristics for all the biomass sources are their cost-\neffectiveness, environmentally friendly as well as ability to enhance the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\nrecycling of organic wastes generated from agricultural, forestry and \nprocessing industries. The first step of biochar production is the \nconversion of biomass into char (Tang et al., 2013; Oliveira et al., 2017). \nThe components of biomass will be degraded by depolymerization of \nbiomass components which are primarily cellulose, hemicellulose, and \nlignin (Weber and Quicker, 2018). This process involves drying of the \nbiomass by heating where volatile organic matters are released from the \nsolid fraction which can be permanent gases (methane, carbon dioxide, \ncarbon monoxide, and hydrogen gas) or some organic compounds that can \nbe condensed such as acetic acid and methanol (Weber and Quicker, 2018; \nNovak, 2009; Cantrell and Martin, 2012). The production of three fractions \nof gas, liquid-oil or solid fractions depends highly on pyrolysis conditions \nespecially temperature and residence time of the biomass. With fast \npyrolysis, where the temperature of nearly 1000 0C is used, the main \nproduct is liquid oil since these volatile compounds are fast released from \nthe biomass (Tang et al., 2013). This kind of pyrolysis can produce a \nsignificant quantity of liquid oil which can be up to three-quarters of dry \nmatter of the biomass (Tang et al., 2013).  \n\n\n\nHowever, to generate biochar, temperatures around 700 0C is usually \napplied with longer residence time and can result in production of biochar \nof up to 95% carbon content (Tang et al., 2013; Mukome et al., 2013). In \nproducing biochar, the temperatures vary with the nature of biomass that \nis being used. For example, woody-based materials may require a higher \ntemperature of 1000 0C to obtain good biochar as compared to the \nbiomass obtained from agro-industrial wastes where temperatures \naround 300 0C are sufficient (Weber and Quicker, 2018). The activation of \nbiochar by using physical or chemical treatment techniques involves the \nconversion of biochar into activated carbon which is more porous, having \nimproved carbon content and surface area, low ash, low moisture content, \nand long life span (Borchard, 2012; Romanos, 2011). Biochar and \nactivated charcoals cover a wide range of application which varies from \nthe manufacturing of medicine, filtration of water, carbon sequestration, \nameliorate environmental pollution, water treatment, crop production, \nlivestock husbandry and improvement of soil characteristics (Weber and \nQuicker, 2018; Guo et al., 2016).  \n\n\n\n1.3 Conversion of Biochar to Activated Charcoal  \n\n\n\nThe activated carbon constitutes mainly with carbon which varies \nbetween 87 to 97 % with the rest being other elements greatly depend on \nbiomass source and employed methods of its production (Jankowska et al., \n1991). Its pore volume usually range from 0.2 - 0.6 m2 per gram of charcoal \nwhile the network micropores contain pores with diameters of less than 2 \nnm (Bansal and Goyal, 2005; Beguin and Frackowiak, 2009). To produce \nbiochar with improved surface area and considerable adsorption aptitude, \nthese materials need to undergo treatment or activation and hence the \nfinal product is referred to as the activated charcoal, or also known as \nactivated carbon (Chada et al., 2012). The surface area as high as 500 - \n3000 m2 per gram of activated carbon can be produced by either physical \nor chemical treatment (Dillon et al., 1989; Soo et al., 2013). With physical \ntreatment, the first phase involves carbonization of the biomass whereby \nthese materials undergo pyrolysis at temperatures range between 300 - \n1000 0 C in inert conditions constitutes of nitrogen gas (H2) and argon (Ar).  \n\n\n\nThe activation of the carbonized materials is done by subjecting them in \nthe oxidizing condition in the presence of either oxygen gas or steam at \ntemperatures that can be as high as 1200 0C. For the case of chemical \nactivation, the biomass to be used in pyrolysis is impregnated with \nselected chemicals, preferably strong alkali, acid or salt such as sulphuric \nacid (H2SO4), caustic potash (KOH), zinc chloride (ZnCl2), sodium \nhydroxide (NaOH) and calcium chloride (CaCl2) (Romanos,  2011). The \nbiomass then goes through the carbonization process at temperatures \nslightly lower compared to physical activation (\u2248 700 0C). The chemical \nactivation is the most preferential over physical treatment as the former \ntechnique requires relatively lower temperatures as well as short \nresidence time for activating the biomass (Romanos, 2011).  \n\n\n\nWith existing complexity in their physical structure, the activated charcoal \nclassification can be grouped due to their micropores network, \n\n\n\napplications and techniques or methods used in its production. For pore \nsize, they can be powdery (< 1 mm in size), granular with relatively larger \nparticles often used for treating polluted water or can be extruded \ncharcoals (between 1 - 130 mm diameter size). There are also \nimpregnated carbons that contain inorganic compounds (e.g. some cations \nas well as iodine and silver) which are made for special applications such \nas regulation of air pollution. Therefore, all the activated charcoals are \nbiochar in nature, and the difference which is due to \"activation\" is what \nmakes the activated charcoal having improved surface area, porosity, as \nwell as being 5 to 10 times expensive compared to biochar (Gerlach and \nSchmidt, 2012). Given that biochar can be produced from a wide range of \nbiomass resources which range from forestry/woody materials, \nagricultural and industrial processing byproducts, there is a need for more \nexploration in research and utilization to capitalize on these valuable \nresources.  \n\n\n\nFigure 1: The graphic illustration highlighting biomass sources, pyrolysis \n\n\n\nand general applications of biochar and activated charcoal \n\n\n\n1.4 Factors influence Physicochemical Characteristics of Biochar  \n\n\n\nThe temperature used during pyrolysis, residence time and biomass \nsources are the three acknowledged factors having considerable influence \non characteristics of the generated biochar (Weber and Quicker, 2018). \nThe higher temperatures during pyrolysis frequently result in biochar \nwith the larger surface areas as compared to when low temperatures are \nused. For instance in a study that analyzed the sorptive capacity of crop \nresidues observed wheat residues heated between 500 to 700 0C was \nrelatively well carbonized and having the larger surface area of nearly 300 \nm2/g compared to the 300 to 400 0C during pyrolysis where the surface \narea was less than 200 m2/g (Chun et al., 2004). Similarly, with pine needle \nas biomass source reported the pyrolysis temperature of 700 o C had a \nsignificant surface area (490.8 m2/g) and adsorption capacity as compared \nto when lower temperatures were used in pyrolysis which was 600 0C \n(206.7 m2/g), 500 0 C (236.4 m2/g), 400 0C (112.4 m2/g) and 100 0C (0.65 \nm2/g) (Chen et al., 2008).  \n\n\n\nVariations of biomass sources have been studied to affect the \ncharacteristics of the biochar produced. For instance, in a study  whereby \nthree different sources were used (soot, black carbon from coal and rice \nstraws), the authors observed that both surface area and porosity (234.9 \nm2/g and 0.4392 ml/g, respectively) were significant in rice straw \ncharcoal compared to the other two biomasses (Luo, 2011). Additionally, \nthe literature has shown variations of physicochemical characteristics of \nbiochar when the same temperature was applied using different sources \nof biomass. For example, at the pyrolysis temperature of 600 0C studies \nhave shown different values for the specific surface area which include \n179.03 m2/g for soybean stalk, 527 m2/g for poultry manure, 642 m2/g for \noak wood chips and 206.7 m2/g for pine needles (Chen et al., 2008; Kong \net al., 2011; Nguyen et al., 2010; Nguyen et al., 2009).  \n\n\n\nTable 1: The summary of variations of biochar characteristics with different biomass sources and production conditions from selected literature \n\n\n\nBiomass source Pyrolysis temperature Total Carbon (%) pH Volatile matter (%) Ash (%) References \n\n\n\nGreen waste 300\u00b0C 64 8.1 6.8 6.8 (Ronsse et al., 2013) \n\n\n\n600\u00b0C 77 11.3 8.8 13 (Ronsse et al., 2013) \n\n\n\n750\u00b0C 81 11.6 1.9 13 (Ronsse et al., 2013) \n\n\n\nSugar cane bagasse 350\u00b0C 75 5.0 39 3.6 (Spokas, 2011) \n\n\n\nRice straw 300\u00b0C 55 9.2 40 23 (Wu, 2012) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\n500\u00b0C 56 10.5 23 32 (Wu, 2012) \n\n\n\n700\u00b0C 65 10.8 14 29 (Wu, 2012) \n\n\n\nPoultry litter 350\u00b0C 46 8.7 37 36 (Novak, 2009) \n\n\n\n700\u00b0C 44 10.3 14 52 (Novak, 2009) \n\n\n\nPigs Litter 350\u00b0C 52 8.2 50 33 (Cantrell and Maretin, 2012) \n\n\n\n700\u00b0C 44 8.2 13 53 (Cantrell and Martin, 2012) \n\n\n\nSwitchgrass 250\u00b0C 55 5.4 74 2.6 (Novak, 2009) \n\n\n\n500\u00b0C 84 8.0 13 7.8 (Novak, 2009) \n\n\n\nWood 300\u00b0C 71 5.7 43 0.5 (Ronsse et al., 2013) \n\n\n\n450\u00b0C 85 6.7 17 1.2 (Ronsse et al., 2013) \n\n\n\n600\u00b0C 91 9.1 6.4 1.3 (Ronsse et al., 2013) \n\n\n\n700\u00b0C 92 10.4 2.6 1.1 (Ronsse et al., 2013) \n\n\n\n2. MECHANISMS OF ACTIVATED BIOCHAR IN RELATION TO FARM \n\n\n\nANIMALS' PRODUCTIVITY \n\n\n\n2.1   Detoxification of Mycotoxins in Feed \n\n\n\nAbout 25 % of all the cereals in the world are estimated by the United \nNation's Food and Agriculture Organization  (FAO) to be annually affected \nby mycotoxins contaminations (Mezes et al., 2010). The contamination by \nthese secondary metabolites can occur while crops are still in the field, \nduring storage and when feeding animals (Wild et al., 2015). The \nmycotoxin groups acknowledged to be of great importance to humans, \ncrop yields and animal health are aflatoxins B1 (AFB1), deoxynivalenol \n(DON), zearalenone (ZEN) and ochratoxin A (OCHRA) (Keller et al., 2012). \nWhen animals consume contaminated feed for an extended period, they \nare associated with numerous health-related complications and diseases \nnotably the teratogenic, immunosuppressive disorders, carcinogenic and \nmutagenic effects while also gastrointestinal activity impairment and \noverall reduced production (Wild et al., 2015; Anukul et al., 2013; \nMisihairabgwi et al., 2017). Human beings contact the mycotoxins through \nthe contaminated animal products including milk, eggs, meat, and \nliver (Sobrova et al., 2010).  \n\n\n\nThe use of adsorbents such as biochar, activated carbon and other non-\ncharcoal adsorbents including zeolites, bentonites, and aluminosilicates \nhave shown promising results in reducing the assimilation of these toxic \ncompounds into animal bloodstream (Dakovic et al., 2005). The \nadsorption capacity of biochar and activated charcoal as influenced by \ntotal surface area and pore size distribution is reported to bind the \nmycotoxins and hence reduce the bioavailability and meanwhile improve \nanimal productivity (Galvano, 1996; Galvano et al., 2001). An in vitro study \ndone whereby two types of adsorbents (activated charcoal and \naluminosilicates) were supplemented at the rate of 2 % of DM of dairy \ncows diet observed the significant reduction of aflatoxins B1 of up to 70% \n(Galvano, 1996). In this feeding study that was conducted for 14 days, the \nlevels aflatoxin in milk were also reduced by 45 % as compared to control \ncows which didn\u2019t receive any adsorbents.  \n\n\n\nThe underlined mechanism suggested by authors is the ability of \nadsorbents being able to convolute these toxic metabolites and hence \nreduce their intestinal adsorption which in turn lessens their levels in \nanimal products. A similar study was done using Holstein cows breed \nobserved up to 65% reduction in aflatoxin in milk with the \nsupplementation of 0.25% activated charcoal (Diaz, 2004). Comparable \nfindings on the detoxification potential of mycotoxins have been reported \nin small ruminants as well. With supplementation of activated charcoal at \n1.0 % of the feed for 2 weeks, the aflatoxins levels in goat milk have been \nable to be reduced by 76 % which was higher compared to other types of \nadsorbents used in the study, the bentonite with 65% (Rao et al., 2004). \nEven when lethal doses of aflatoxins were fed to goats together with \nactivated charcoal, observed no significant indication of internal organ \ndamage while the levels of these toxic compounds were high (Hatch et al., \n\n\n\n1982). The authors suggested the main reason behind was the inability of \naflatoxins to be absorbed by the goats' intestines.  \n\n\n\n2.2  Control of Pathogenic activity  \n\n\n\nThe two mechanisms associated with the reduced pathogenic activity are \nrelated to physicochemical characteristics of activated charcoal and \nmicrobial status in animals' gastrointestinal tract (GIT). A study reported \nthat the inclusion of 5 mg/ml of activated charcoal in feed can reduce the \nlevels of Escherichia coli and Salmonella as low as 10 mg/ml (Naka, 2001). \nThe authors suggest the importance of a combination of pore size and its \ndiameter in binding these microscopic organisms. However, a subsequent \nstudy using matured ewe observed contrasting results that the activated \ncharcoal didn\u2019t bind of either E.coli nor the Salmonella typhimurium \n(Knutson et al., 2006). Another possible mechanism is the increased \nactivity of beneficial microbes in the GIT particularly species from three \ngenera Bifidobacterium, Enterococcus and Lactobacillus. Through the \nimproved activity, these beneficial bacteria colonize the gut \nenvironmental niches and with the competitive exclusion principle they \ntend to outweigh the pathogenic population (Knutson et al., 2006; \nCallaway et al., 2012). Given that there is currently, no sufficient evidence \nto support the role of activated charcoal in controlling these hazardous \nmicrobes, there is a need for further in vivo research to be done. \n\n\n\n2.3  Regulation of Plant-derived Toxins in Feed \n\n\n\nApart from having structural features such as thorns, spines, and prickles, \nplants also produce hundreds of toxins that serve as a means of protection \nby deterring any kind of physio-biological disturbances (Wittstock and \nGershenzon, 2002). However, while these compounds are beneficial to \nplants, they have detrimental impacts when consumed by herbivores as \nthey often result in injuries, illness or even death. The high affinity of \nactivated charcoal in binding the plant-produced toxins and mycotoxins is \nassociated with a combination of its physicochemical characteristics that \ninclude pore network, surface area, and surface acidity. Studies have \nshown that activated charcoal with predominant micropores structure \n(less than 2 nm pore size) tend to have lower adsorption rate due to \nreduced diffusion of these toxic compounds while also high surface acidity \nof charcoal has repulsing effect of the positively charged of some \nmycotoxins such as aflatoxins. On the other hand, as suggested, the \nimproved adsorption efficiencies can be achieved with activated charcoal \nconstitutes mesopores network (between 2 - 50 nm pore sizes) as well as \nlow acidic surface areas which favors the binding of mycotoxins and other \ntoxic compounds produced by plants (Galvano, 1996). However, the \noverall efficiency will depend on numerous factors including \nphysicochemical characteristics of activated charcoal, amount of charcoal \nsupplemented to an animal, concentration of toxins in feed, species, and \nanimal breed used as well as feeding management (Bansal and Goyal, \n2005; Dillon et al., 1989; Hatch et al., 1982; Kim, 2006). The current \nliterature indicates the promising results of biochar and activated charcoal \nin detoxifying plant toxins especially those produced by some shrubs and \nforbs which are feed sources for livestock as shown in table 2.\n\n\n\nTable 2: The summary from selected literature indicating the effect of activated carbon in regulating plant-produced toxins \n\n\n\nAnimal \nsubjects \n\n\n\nTypes of Feed Plant-produced toxins \nComposition of \nactivated charcoal \n\n\n\nObserved effects References \n\n\n\nDairy heifers \nand steers  \n\n\n\nLantana camara \nTriterpene acids, lantadene A \nand lantadene B \n\n\n\n5.0 g/kg of live weight \n\n\n\nRecovery times from liver \ndamage for animals \nconsumed activated \ncharcoal was less than 2 \nweeks compared to 3 \nweeks for control animals \n\n\n\n(McLennen and \nAmos, 1989) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\nCattle \n\n\n\nShrubs known as \nyellow tulp (Moraea \npallida) \n\n\n\nGlycoside  2.0 g/kg of live weight \n\n\n\nAll cattle provided \nactivated charcoal shown \nrelatively faster recovery \nfrom the posterior paresis \n(clinical condition \ncharacterized with limited \nvoluntary movement) \nwithin 48 hours.  \n\n\n\n(Snyman et al., \n2009) \n\n\n\nSheep and \ngoats \n\n\n\nMediterranean \nshrubs  \n\n\n\nTerpene and tannins  \n0.7, 0.8 and 1.0 g/kg of \nlive weight \n\n\n\nA general increase in \nshrub consumption for \nboth goats and sheep \nsupplemented with \ncharcoal \n\n\n\n(Rogosic et al., \n2006) \n\n\n\nGoats \n\n\n\nJuniper (Juniperus \npinchotii Sudw) and \n(Juniperus ashei \nBuch) \n\n\n\nTerpenoids 1.0 g/kg of live weight \n\n\n\nIncreased consumption of \nJuniper during early \nexposure in a 10 days \nstudy \n\n\n\n(Bisson et al., \n2001) \n\n\n\nSheep \n\n\n\nAlfalfa, maize diet \nand Bitter \nrubberweed  \n(Hymenoxys \nodorata) \n\n\n\nSesquiterpene lactones  \n0.5,1.0 and 1.5 % of \nfeed DM  \n\n\n\nContinuous consumption \nof treated bitterweed in \nwhich suggests reduced \ntoxicity \n\n\n\n(Poage et al., \n2000) \n\n\n\n2.4  Regulating Heavy metals, Organic pollutants and residues from \n\n\n\nPesticides and Herbicides \n\n\n\nHeavy metals (e.g. lead, arsenic, chromium, mercury, cadmium,  \nchromium), organic pollutants (e.g. polycyclic aromatic hydrocarbons and \nsulfamethoxazole) and residues from pesticides and herbicides are of \ngreat health risk to animals and human being (Pandey and Madhuri, 2014; \nUchimiya et al., 2012). These pollutants that originate from both natural \nto anthropogenic activities can emanate from the feed, food, air and water \nwith their toxicity result from bioaccumulation tends to disrupt balance \nand activity of ecosystems of most living organisms (Schwarzenbach et al., \n2010). Consumption of these pollutants don\u2019t assist in any physiological \nconditions and can lead to the formation of toxic soluble compounds but \nalso being detrimental when they are in specific form in animals' bodies \n(Pandey and Madhuri, 2014).  \n\n\n\nA significant number of research studies have documented the \neffectiveness of biochar and activated charcoal in reducing the levels of \nthese pollutants in soil and water where the majority of animal feed is \nobtained. The mechanism behind the existing potential is ascribed to the \nelectrostatic interaction between biochar, heavy metals and soil \nconditions. When added to the soil, the biochar induces the cation \nexchange capacity which increases negatively charged ions and with the \nmetals possessing positively charged ions, the latter tend to be bound to \nbiochar surface areas (Peng et al., 2011). Also, as the biochar incorporation \nincreases the water retention which in turn increase the pH levels of the \nsoil and as a result, these conditions lead to decreased mobility of heavy \nmetals and so is their effects (Peng et al., 2011). There exist variations of \nbiochar effectiveness as summarized in table 3 and the main influential \nfactors include the type of biomass source, pyrolysis conditions, type and \nconcentration of pollutants and general experimental setup.\n\n\n\nTable 3: The effectiveness of different sources of biochar in modulating the pollutants \n\n\n\nPrimary Biomass \nSources\n\n\n\nPyrolysis \nTemperature \n\n\n\nGroups of \nContaminants \n\n\n\nNames of \nContaminants \n\n\n\nEffect of Biochar and Activated Charcoal References \n\n\n\nCattle manure \u2264 500 0C Heavy metals Lead and atrazine Nearly 100 % Lead deduction and about 77 % atrazine (Cao and \nHarris, \n2010) Rice straw NR Heavy metals The acid \n\n\n\nextractable \nCopper and Lead \n\n\n\nThe decrease of 20 - 100 % Copper, and 19 - 77 % Lead (Jiang et al., \n2012) \n\n\n\nGreen waste \ncompost \n\n\n\nNR Heavy metals Copper and Lead Significant reduction of Cu and Pb levels from the soil to \nthe plants (which plant) \n\n\n\n(Karami et \nal., 2011) \n\n\n\nSewage sludge 500 0C Heavy metals Copper, Cadmium, \nZinc, Lead,  and \nNickel \n\n\n\nReduced plant availability for Pb, Cd, Zn and Ni. Also the \nNi, Zn and Cu in leached decreased significantly \n\n\n\n(Mendez et \nal., 2012) \n\n\n\nBamboo, bagasse \nand hickory wood \n\n\n\n450 - 600 0C Organic \npollutants \n\n\n\nSulfamethoxazole \n(an antibiotic) \n\n\n\n\u2264 14 % of Sulfamethoxazole transported with \nincorporation of biochar compared to 60 % with no \nbiochar \n\n\n\n(Yao, 2012) \n\n\n\nSewage sludge \nand maize stover \n\n\n\n600 0C Organic \npollutants \n\n\n\nPolycyclic \naromatic \nhydrocarbons  \n(PAH) \n\n\n\nThe reduction of up to 57 % of PAH can be achieved \ndepend on the amount of biochar used while for \nactivated charcoal the PAH were reduced by 56 - 95 % \n\n\n\n(Oleszczuk \net al., 2012) \n\n\n\nSewage sludge 500 - 900 0C Organic \npollutants \n\n\n\nPolychlorinated \nbiphenyls (PCB) \n\n\n\nThe potential of 5 % of bamboo biochar to reduce \nleaching of PCB up to 65 % have been observed \n\n\n\n(Mendez et \nal., 2012) \n\n\n\nDeciduous \nhardwood \n\n\n\n600 0C Organic  \npollutants \n\n\n\nPAH Reduction of between 306 to 449 mg/kg of PAH while \nalso up to 45 % of PAH was reduced in earthworm \nspecies called Eisenia fetida (red or earthworm) \n\n\n\n(Gomez-\nEyles et al., \n2011) \n\n\n\nHardwood 600 0C Pesticides and \nInsecticides \n\n\n\nPesticides (enta-\nchlorobenzenes \nand carbofuran) \nand insecticides \n(chlorpyrifos and \nfipronil) \n\n\n\nInclusion of 0.5 to 1.0 % of biochar significantly reduce \nthe toxicity and levels of contaminants in the soil (Kookana, \n\n\n\n2010) \n\n\n\nCotton straw and \nwoodchips \n\n\n\n> 400 0C Pesticides Fipronil, \nChlorpyrifos and \nCarbofuran \n\n\n\nAbout 0.1 to 1.0 % biochar was able to reduce 32-51% \nmobilization of the pesticides from the contaminated \nsoil \n\n\n\n(Yang, \n2010) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\n3. IMPROVEMENT OF FARM ANIMALS' PERFORMANCE BY \n\n\n\nBIOCHAR AND ACTIVATED CHARCOAL \n\n\n\nBiochar and activated charcoal are important in modulating different \nfarming practices. Up to 90 % of the produced biochar in Europe is used in \nvarious farming practices including treatment of slurry, in production of \nsilage, as a vital feed additive, as a litter component, in production of \ncompost, while also in some fish farming has been included to treat water \nfrom pollutants (Gerlach and Schmidt, 2012). Regarding farm animals \nparticularly poultry, pigs and ruminants, studies have come up promising \nfindings that highlight biochar and activated charcoal being vital feed \nadditive material for enhancing their health and performance. \n\n\n\n3.1   Poultry \n\n\n\nA feeding experiment using broiler chicks that were fed biochar made \nfrom hardwood at the inclusion rate of 0, 2, 4 and 8 % of total DM were \ndone (Bakr, 2008). The study that lasted for 6 weeks observed that the 2 \n% biochar having a significantly higher return in terms of feed intake of \nchicks, body weight gain and overall feed conversion rates. Similar results \non broiler chicks were observed of when up to 1.0 % of DM of biochar \nproduced from maize cob was used (Kana et al., 2011). Also, two different \nstudies were done using hardwood biochar in a six to seven weeks study \nreported that the chicks that received diets having biochar tended to have \nimproved feed conversion rates and weight gain (Majewska and \nZaborowski, 2003; Majewska et al., 2011). The mechanisms behind these \nbenefits as postulated by these authors are due to detoxification potential \nof biochar of the feed as well as the reduced surface tension of \nthe digesta inside the animal's gastrointestinal tract. Another mechanism \nas acknowledged by other researchers includes the biochar potential to \nbind to antinutritional factors in the feed (Kutlu, 1998).  \n\n\n\nOn the other hand, the studies that involve laying hens, biochar inclusion \nhas been studied to improve the quality and quantity of eggs and egg \ncomponents. A studied that inclusion of 1 - 4% of biochar have a significant \nreduction of number cracked eggs as compared to hens received no \nbiochar (Kutlu et al., 2001). Moreover, the 1 % of biochar mix (a mixture \nof carbonaceous biochar and woody vinegar) showed an augment of \nmembrane collagen of eggs by more than 33 % (Yamauchi et al., 2010). \nIncreasing the production of eggs by nearly 5 % as well as the strength of \neggshells have been also observed (Kim, 2006; Yamauchi et al., 2013). \nAdditionally, a study involving ducks with biochar rates used at 0.1, 0.5 \nand 1.0 % of DM in a diet composed of seaweed and with the control diet \nwhich included the chlortetracycline antibiotic (C22H23ClN2O8) was done \n(Islam et al., 2014)  The results from this study shown the best outcomes \nin terms of feed intake by ducks and the feed efficiency for all the diets \ncontain biochar as compared to control, which suggests these \ncarbonaceous materials can be used as a potential alternative to \nantibiotics.  \n\n\n\n3.2   Pigs  \n\n\n\nAs discussed for poultry, biochar also provides benefits to pigs in terms of \nhealth and performance even though some studies didn\u2019t come up with \nconsistent findings. A studied feed utilization, immunity response and \ncarcass characteristics of finishing pigs with biochar supplemented at 0, \n0.3 and 0.6 % of total DM of the feed (Choi et al., 2012). The authors \nobserved the highest performance in terms of feed conversion rates, the \ncarcass characteristics, live weight gain and immune response at 0.3 % \nsupplementation as compared to all other treatments. The comparable \nfindings were also reported with the study which investigated carcass \nquality of finishing pigs and apart from the above-mentioned \nimprovements, the meat marbling, meat color traits as well as tenderness \nof the meat when cooked were significantly enhanced (Lee et al., 2011). \nMoreover, observed an increase of up to 13 % of live weight gain as well \nas about 15 % feed conversion rates when fattening pigs were \nsupplemented between 0.3 and 0.6 % of DM of diet as biochar compared \nto control with no biochar (Chu, 2013). As experimented by series of \nstudies, the pig's responses to biochar vary with the rate of biochar \nsupplement, the primary source of biochar, the length/duration of the \nfeeding experiment and specific parameter involved in the assessment \n(Chu, 2013a; Chu, 2013b; Chu et al., 2013).  \n\n\n\nNevertheless, several studies involving pigs have also reported some \ncontrasting findings. A feeding experiment involving piglets \nsupplemented with biochar that is commercially prepared constitutes of \nwoody vinegar and biochar (ratio of 4:1) at 0, 3 and 5 % of feed DM was \nconducted (Mekbungwan et al., 2004). Unlike the previous studies above \nwhich reported increased weight gain and feed utilization efficiency, this \nstudy found the two parameters were not different as when compared to \n\n\n\npiglets received no biochar. However, there was a reported improvement \nin gastrointestinal architecture for some features specifically the villi \ngrowth, especially at 1 and 3 % biochar supplementation. The results from \nthis study are in agreement with other authors in subsequent research \nstudy whereby pigs received dietary feed constitute of pigeon pea \n(Cajanus cajan) (Mekbungwan et al., 2008).  \n\n\n\n3.3   Ruminants  \n\n\n\nSimilar to poultry and pigs, studies have been done using ruminants where \nanimal performances have been evaluated. An experiment done whereby \nthe local cattle were supplemented with 1 % biochar in a diet composed \nof cassava foliage, cassava root and urea observed the increased growth of \n20 % (Leng et al., 2012).  Also, a feeding study has been done by Mui et al., \n(2006) using different levels of biochar at 0, 0.5, 1.0 and 1.5 % of DM in \ngoats rations made of concentrate and forage. The study observed \nconsiderable the highest dietary protein digestion and dry matter intake \n(p<0.05) at 0.5% compared to 0 % as well as 1.5%. The authors suggested \nthe unexpected lower DM intake and protein digestibility at 1.5% was due \nto impairment of optimum rumen activity. There are however some \ninconsistent results that showed no positive results with biochar inclusion \nsuch as the study done using goats and the other where studied \nparameters such as live weight gain and carcass quality of beef steers were \nnot different from the animal subjects which received no biochar \n(Phongpanith et al., 2013; Kim and Kim, 2005). \n\n\n\n4. CHALLENGES OF BIOCHAR UTILIZATION IN FARM ANIMALS \n\n\n\n4.1   Inadequate and Contradictory Research Findings \n\n\n\nThe limited knowledge and contrasting research findings are of great \nconcern especially when biochar is used as feed supplement to farm \nanimals. While the underlined mechanisms of these adsorbent materials \nhave mostly been done on a small scale, short duration and in vitro \nconditions, it is important to for the scientific community to investigate \nand establishing clear-cut mechanisms using in vivo studies done for an \nextended period of time. This is due to existing variations that result from \ncharacteristics of biochar itself, different responses to species and group \nof animals, climatic conditions and time factor. For instance, different \nfindings have been reported on the role of biochar in regulating pathogens \nsuch as E.coli and Salmonella by similar studies that were done (Khutson \net al., 2006; Naka, 2001). \n\n\n\n4.2   Specificity of Biochar \n\n\n\nA comparative study was done in evaluating the effectiveness of different \nadsorbents in regulating different groups of mycotoxins observed a \nspecificity effect of biochar in mollifying these toxic metabolites (Huwig et \nal., 2001). In this study that involved activated carbon and other non-\ncharcoal adsorbents such as hydrated sodium calcium aluminosilicates \n(HSCAS), bentonite, montmorillonite, sepiolite, and cholestyramines \nobserved high levels of activated carbon are not beneficial as these \nmaterials are not very specific to mycotoxins only and can bind to \nnutrients too. Even though in vitro studies have shown promising results, \nthe amount supplemented in vivo is critical to obtain desirable results. \nThere are however few exceptional findings such as the one done whereby \nhigh doses of biochar were beneficial to goats that were exposed to higher \naflatoxin poisoning (Hatch et al., 1982). It is important to understand that, \nso far, no single adsorbent is capable of alleviating all types of mycotoxins \nuniformly as from summary shown whereby even for some non-charcoal \nbinders like HSCAS which are considered very effective can bind nearly \ncompletely for all AFB1, in small extent on ZEN and  OCHRA while they are \nalmost not effective against trichothecenes (Huwig et al., 2001).    \n\n\n\n4.3   Potential Contaminations \n\n\n\nUsually, the heavy metals are not generated during pyrolysis, even though \nthey may be present in biomass sources mostly from industrial \nbyproducts as well as sewages. At the optimum pyrolysis temperature, \nthese metals are not volatile and hence remained as an ash component of \nthe biochar. However, some hazardous organic contaminants including \ndioxins and polycyclic aromatic hydrocarbons are often produced during \npyrolysis. For instance, reported the favorable conditions for the \nformation of dioxins are the presence of chlorine, the open burning of the \nbiomass materials and also temperatures around 450 - 850 0C (Shibamoto \nand Yasuhara, 2007). The impact of these compounds can range from \nbeing completely not toxic to carcinogenic and mutagenic, and often \nassociated with the factors such as shorter residence time and low \npyrolysis temperature. Therefore, it is important to carefully evaluate the \nbiomass sources and applying optimum conditions during pyrolysis to \nminimize the risks.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\n4.4   Farmers' Awareness \n\n\n\nEven though there have been promising findings on its diverse application \nglobally, there is still existing a knowledge gap of farmers who are end-\nusers of biochar. There is a contrasting practical application as well as the \nlevel of interest by farmers and other stakeholders such as environmental \nmanagers and policymakers on the global scale. While nearly 90% \nproduced biochar in regions like Europe plays an important role in agro-\nenvironmental activities including livestock husbandry, crop production \nand modulation of the environment, the case is different in most of \ndeveloping countries. The woody-based biomass is heavily utilized in \ncharcoal production as a source of fuel while the agri-industrial \nbyproducts from processing industries are less recycled to produce value-\nadded materials including biochar. There is misapprehension by farmers \nthat all biochars are of the same physicochemical characteristics while \nmeanwhile, the inquiries such appropriate rates how often biochar can be \napplied are yet to be clearly understood. This hence calls for necessary \npractical initiatives such as an establishment of best-use biochar \napplication protocols and programs in educating farmers through \nextension and livestock field officers. Additionally, the use of media, \ninitiatives on biochar projects and markets will contribute more of its \nadoption.   \n\n\n\n4.5   The Affordability \n\n\n\nMost of the currently used biochar are commercially produced and they \nare associated with higher prices and limited available markets. For \nexample, the average cost of biochar in the United States by 2014 is 2.87 \nUS dollars per kilogram (Guo et al., 2016). For resource-constrained \nfarmers, the production of low cost and desirable biochar using locally \navailable biomass resources is restricted due to higher production costs \nwhile in most countries this technology is nonexistent. Since there are \nabundant and inexpensive raw materials, governments need to attract \ninvestments in commercial biochar production by the local industries \nwhere the availability and affordability will be enhanced while meanwhile, \njob creation is enabled. Supported with research findings, economical \nanalysis, and improved awareness, it is then possible for decision-making \nbodies to be interested in supporting the biochar through regulations and \nsafety standards.  \n\n\n\n5. CONCLUSION \n\n\n\nThe current literature highlight how the biochar and activated charcoal \ncan be useful in promoting farm animals' health and performance. These \ncarbonaceous materials are potentially safe and promising feed additives \nin improving the animal performance and hence can be an alternative for \nsubstances such as antibiotics. Their role in regulating mycotoxins in feed, \nthe plant-derived toxins from the plant materials, potent pollutants such \nas heavy metals, organic pollutants and residues from pesticides and \nherbicides as well as pathogenic activities of E.coli and the Salmonella is \ncrucial for the safety of animal feed. This in return has been directly linked \nto the enhancement of health and performances indicators to farm animals \nspecifically weight gain, immunity response, feed intake, feed conversion \nrates, carcass characteristics and the overall quality of animal products. \nThere is, therefore, a need to emphasize the establishment of local-based \nprotocols and recommendations for farmers to improve the utilization of \nbiochar for different purposes such as agronomical practices, animal \nhusbandry, and environmental modulation. Furthermore, the \ncollaborations among researchers, extension workers, policymakers and \nfarmers are important in the dissemination of basic knowledge and \ninformation that aims at capitalizing the utilization of biochar. Even \nthough studies have indicated the improved animal performance with \nbiochar supplementation, very few studies have been able to vividly verify \nthe underlined mechanisms. With the current advances in technology, \nthere is, therefore, a need for more investigation to explain mechanisms as \nwell as the existing high variations on biochar and activated charcoal \neffectiveness.    \n\n\n\nCONFLICT OF INTEREST \n\n\n\nThe authors of this review article declare no conflict of interest. \n\n\n\nACKNOWLEDGMENT \n\n\n\nThe authors received no specific financial support for the reviewing, \nauthorship, and/or publication of this article. \n\n\n\nREFERENCES  \n\n\n\nAhmad, M., 2014. Biochar as a sorbent for contaminant management in \n\n\n\nsoil and water: a review. Chemosphere, 99, pp. 19\u201333. \n\n\n\nAnukul, N., Vangnai, K., Mahakarnchanakul, W., 2013. Significance of \n\n\n\nregulation limits in mycotoxin contamination in Asia and risk \n\n\n\nmanagement programs at the national level. J. Food Drug Anal., 21 (3), \n\n\n\npp. 227\u2013241. \n\n\n\nBakr, B.E.A., 2008. The effect of using citrus wood charcoal in broiler \n\n\n\nrations on the performance of broilers. An-Najah Univ. J. Res., 22, pp. \n\n\n\n17\u201324. \n\n\n\nBansal, R.C., Goyal, M., 2005. Activated carbon adsorption. CRC press. \n\n\n\nBeguin, F., Frackowiak, E., 2009. Carbons for electrochemical energy \n\n\n\nstorage and conversion systems. Crc Press. \n\n\n\nBisson, M.G., Scott, C.B., Taylor Jr, C.A., 2001. Activated charcoal and \n\n\n\nexperience affect intake of juniper by goats. J. Range Manag., pp. 274\u2013\n\n\n\n278. \n\n\n\nBorchard, N., 2012. Physical activation of biochar and its meaning for soil \n\n\n\nfertility and nutrient leaching\u2013a greenhouse experiment. Soil Use \n\n\n\nManag., 28 (2), pp. 177\u2013184. \n\n\n\nCallaway, T.R., Edrington, T.S., Harvey, R.B., Anderson, R.C., Nisbet, D.J., \n\n\n\n2012. Prebiotics in food animals, a potential to reduce foodborne \n\n\n\npathogens and disease. Rom. Biotechnol. Lett., 17 (6), p. 7809. \n\n\n\nCantrell, K.B., Martin, J.H., 2012. Stochastic state-space temperature \n\n\n\nregulation of biochar production. Part I: Theoretical development. J. Sci. \n\n\n\nFood Agric., 92 (3), pp. 481\u2013489. \n\n\n\nCantrell, K.B., Martin, J.H., 2012. Stochastic state-space temperature \n\n\n\nregulation of biochar production. Part II: Application to manure \n\n\n\nprocessing via pyrolysis. J. Sci. Food Agric., 92 (3), pp. 490\u2013495. \n\n\n\nCao, X., Harris, W., 2010. Properties of dairy-manure-derived biochar \n\n\n\npertinent to its potential use in remediation. Bioresour. Technol., 101 \n\n\n\n(14), pp. 5222\u20135228. \n\n\n\nChada, N., Romanos, J., Hilton, R., Suppes, G., Burress, J., Pfeifer, P., 2012. \n\n\n\nActivated carbon monoliths for methane storage. APS Meeting \n\n\n\nAbstracts. \n\n\n\nChen, B., Zhou, D., Zhu, L., 2008. Transitional adsorption and partition of \n\n\n\nnonpolar and polar aromatic contaminants by biochars of pine needles \n\n\n\nwith different pyrolytic temperatures. Environ. Sci. Technol., 42 (14), \n\n\n\npp. 5137\u20135143. \n\n\n\nChoi, J.S., Jung, D.S., Lee, J.H., Choi, Y.I., Lee, J.J., 2012. Growth performance, \n\n\n\nimmune response and carcass characteristics of finishing pigs by \n\n\n\nfeeding stevia and charcoal. Korean J. Food Sci. Anim. Resour., 32 (2), \n\n\n\npp. 228\u2013233. \n\n\n\nChu, G.M., 2013. Effects of bamboo charcoal and bamboo vinegar as \n\n\n\nantibiotic alternatives on growth performance, immune responses and \n\n\n\nfecal microflora population in fattening pigs. Anim. Sci. J., 84 (2), pp. \n\n\n\n113\u2013120. \n\n\n\nChu, G.M., 2013. Effects of bamboo charcoal on the growth performance, \n\n\n\nblood characteristics and noxious gas emission in fattening pigs. J. Appl. \n\n\n\nAnim. Res., 41 (1), pp. 48\u201355. \n\n\n\nChu, G.M., Kim, J.H., Kang, S.N., Song, Y.M., 2013. Effects of dietary bamboo \n\n\n\ncharcoal on the carcass characteristics and meat quality of fattening \n\n\n\npigs. Korean J. Food Sci. Anim. Resour., 33 (3), pp. 348\u2013355. \n\n\n\nChun, Y., Sheng, G., Chiou, C.T., Xing, B., 2004. Compositions and sorptive \n\n\n\nproperties of crop residue-derived chars. Environ. Sci. Technol., 38 (17), \n\n\n\npp. 4649\u20134655. \n\n\n\nDakovi\u0107, A., Toma\u0161evi\u0107-\u010canovi\u0107, M., Dondur, V., Rottinghaus, G.E., \n\n\n\nMedakovi\u0107, V., Zari\u0107, S., 2005. Adsorption of mycotoxins by \n\n\n\norganozeolites. Colloids Surfaces B biointerfaces, 46 (1), pp. 20\u201325. \n\n\n\nDiaz, D.E., 2004. Aflatoxin binders II: Reduction of aflatoxin M1 in milk by \n\n\n\nsequestering agents of cows consuming aflatoxin in feed,\u201d \n\n\n\nMycopathologia, 157 (2), pp. 233\u2013241. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\nDillon, E.C., Wilton, J.H., Barlow, J.C., Watson, W.A., 1989. Large surface area \n\n\n\nactivated charcoal and the inhibition of aspirin absorption. Ann. Emerg. \n\n\n\nMed., 18 (5), pp. 547\u2013552. \n\n\n\nGalvano, F., 1996. Activated carbons: in vitro affinity for aflatoxin B1 and \n\n\n\nrelation of adsorption ability to physicochemical parameters. J. Food \n\n\n\nProt., 59 (5), pp. 545\u2013550. \n\n\n\nGalvano, F., Piva, A., Ritieni, A., Galvano, G., 2001. Dietary strategies to \n\n\n\ncounteract the effects of mycotoxins: a review. J. Food Prot., 64 (1), pp. \n\n\n\n120\u2013131. \n\n\n\nGerlach, A., Schmidt, H.P., 2012. The use of biochar in cattle farming. Ithaka \n\n\n\nJ., pp. 281\u2013285. \n\n\n\nGomez-Eyles, J.L., Sizmur, T., Collins, C.D., Hodson, M.E., 2011. Effects of \n\n\n\nbiochar and the earthworm Eisenia fetida on the bioavailability of \n\n\n\npolycyclic aromatic hydrocarbons and potentially toxic elements. \n\n\n\nEnviron. Pollut., 159 (2), pp. 616\u2013622. \n\n\n\nGuo, M., Uchimiya, S.M., He, Z., 2016. Agricultural and environmental \n\n\n\napplications of biochar: Advances and barriers, no. sssaspecpub63. Soil \n\n\n\nScience Society of America, Inc. \n\n\n\nHatch, R.C., Clark, J.D., Jain, A.V., Weiss, R., 1982. Induced acute \n\n\n\naflatoxicosis in goats: treatment with activated charcoal or dual \n\n\n\ncombinations of oxytetracycline, stanozol, and activated charcoal \n\n\n\n[Aflatoxins, Aspergillus flavus]. Am. J. Vet. Res., \n\n\n\nHuwig, A., Freimund, S., K\u00e4ppeli, O., Dutler, H., 2001. Mycotoxin \n\n\n\ndetoxication of animal feed by different adsorbents. Toxicol. Lett., 122 \n\n\n\n(2), pp. 179\u2013188. \n\n\n\nIslam, M.M., Ahmed, S.T., Kim, Y.J., Mun, H.S., Yang, C.J., 2014. Effect of sea \n\n\n\ntangle (Laminaria japonica) and charcoal supplementation as \n\n\n\nalternatives to antibiotics on growth performance and meat quality of \n\n\n\nducks. Asian-Australasian J. Anim. Sci., 27 (2), p. 217. \n\n\n\nJankowska, H., \u015awi\u0105tkowski, A., Choma, J., 1991. Active carbon. Ellis \n\n\n\nHorwood Ltd. \n\n\n\nJiang, J., Xu, R., Jiang, T., Li, Z., 2012. Immobilization of Cu (II), Pb (II) and \n\n\n\nCd (II) by the addition of rice straw derived biochar to a simulated \n\n\n\npolluted Ultisol. J. Hazard. Mater., 229, pp. 145\u2013150. \n\n\n\nJindo, K., Mizumoto, H., Sawada, Y., Sanchez-Monedero, M.A., Sonoki, T., \n\n\n\n2014. Physical and chemical characterization of biochars derived from \n\n\n\ndifferent agricultural residues. Biogeosciences, 11 (23), pp. 6613\u20136621. \n\n\n\nKana, J.R., Teguia, A., Mungfu, B.M., Tchoumboue, J., 2011. Growth \n\n\n\nperformance and carcass characteristics of broiler chickens fed diets \n\n\n\nsupplemented with graded levels of charcoal from maize cob or seed of \n\n\n\nCanarium schweinfurthii Engl. Trop. Anim. Health Prod., 43 (1), pp. 51\u2013\n\n\n\n56. \n\n\n\nKarami, N., Clemente, R., Moreno-Jim\u00e9nez, E., Lepp, N.W., Beesley, L., 2011. \n\n\n\nEfficiency of green waste compost and biochar soil amendments for \n\n\n\nreducing lead and copper mobility and uptake to ryegrass. J. Hazard. \n\n\n\nMater., 191 (1\u20133), pp. 41\u201348. \n\n\n\nKeller, L.A.M., Pereyra, C.M., Cavaglieri, L.R., Dalcero, A.M., Rosa, C.A.R., \n\n\n\n2012. Fungi and mycotoxins from pre-and poststorage brewer\u2019s grain \n\n\n\nintended for bovine intensive rearing. ISRN Vet. Sci.,  \n\n\n\nKim, B.K., Kim, Y.J., 2005. Effects of Feeding Charcoal Powder and Vitamin \n\n\n\nA on Growth Performance, Serum Profile and Carcass Characteristics of \n\n\n\nFattening Hanwoo Steers. J. Anim. Sci. Technol., 47 (2), pp. 233\u2013242. \n\n\n\nKim, K.E., 2006. Effect of Dietary Activated Charcoal Mixed with Wood \n\n\n\nVinegar on Quality and Chemical Composition of Egg in Laying Hens. J. \n\n\n\nAnim. Sci. Technol. \n\n\n\nKnutson, H.J., Carr, M.A., Branham, L.A., Scott, C.B., Callaway, T.R., 2006. \n\n\n\nEffects of activated charcoal on binding E. coli O157: H7 and Salmonella \n\n\n\ntyphimurium in sheep. Small Rumin. Res., 65 (1\u20132), pp. 101\u2013105. \n\n\n\nKong, H., He, J., Gao, Y., Wu, H., Zhu, X., 2011. Cosorption of phenanthrene \n\n\n\nand mercury (II) from aqueous solution by soybean stalk-based \n\n\n\nbiochar. J. Agric. Food Chem., 59 (22), pp. 12116\u201312123. \n\n\n\nKookana, R.S., 2010. The role of biochar in modifying the environmental \n\n\n\nfate, bioavailability, and efficacy of pesticides in soils: a review. Soil Res., \n\n\n\n48 (7), pp. 627\u2013637. \n\n\n\nKutlu, H.R., 1998. Effects of dietary wood charcoal on performance and \n\n\n\nfatness of broiler chicks. Br. Poult. Sci., 39 (S1), pp. 31\u201332. \n\n\n\nKutlu, H.R., \u00dcnsal, I., G\u00f6rg\u00fcl\u00fc, M., 2001. Effects of providing dietary wood \n\n\n\n(oak) charcoal to broiler chicks and laying hens. Anim. Feed Sci. \n\n\n\nTechnol., 90 (3\u20134), pp. 213\u2013226. \n\n\n\nLee, J.J., Park, S.H., Jung, D.S., Choi, Y.I., Choi, J.S., 2011. Meat quality and \n\n\n\nstorage characteristics of finishing pigs by feeding stevia and charcoal. \n\n\n\nKorean J. Food Sci. Anim. Resour., 31 (2), pp. 296\u2013303. \n\n\n\nLeng, R.A., Preston, T.R., Inthapanya, S., 2012. Biochar reduces enteric \n\n\n\nmethane and improves growth and feed conversion in local \u2018Yellow\u2019 \n\n\n\ncattle fed cassava root chips and fresh cassava foliage. Livest. Res. Rural \n\n\n\nDev., 24 (11). \n\n\n\nLuo, L., 2011. Sorption and desorption of pentachlorophenol to black \n\n\n\ncarbon of three different origins. J. Hazard. Mater., 185 (2\u20133), pp. 639\u2013\n\n\n\n646. \n\n\n\nMajewska, T., Pudyszak, K., Koz\u0142owski, K., 2011. The effect of charcoal \n\n\n\naddition to diets for broilers on performance and carcass parameters. \n\n\n\nProduktyvumui ir skerdenos Rodikl., 55, pp. 10\u201312. \n\n\n\nMajewska, T., Zaborowski, M., 2003. Charcoal in the nutrition of broiler \n\n\n\nchickens. Med. Weter., 59 (1), pp. 81\u201383. \n\n\n\nMcHenry, M.P., 2010. Carbon-based stock feed additives: a research \n\n\n\nmethodology that explores ecologically delivered C biosequestration, \n\n\n\nalongside live weights, feed use efficiency, soil nutrient retention, and \n\n\n\nperennial fodder plantations. J. Sci. Food Agric., 90 (2), pp. 183\u2013187. \n\n\n\nMcLennan, M.W., Amos, M.L., 1989. Treatment of lantana poisoning in \n\n\n\ncattle [Lantana camara; activated charcoal]. Aust. Vet. J. \n\n\n\nMekbungwan, A., Yamauchi, K., Sakaida, T., 2004. Intestinal villus \n\n\n\nhistological alterations in piglets fed dietary charcoal powder including \n\n\n\nwood vinegar compound liquid. Anat. Histol. Embryol., 33 (1), pp. 11\u2013\n\n\n\n16. \n\n\n\nMekbungwan, A., Yamauchi, K., Sakaida, T., Buwjoom, T., 2008. Effects of a \n\n\n\ncharcoal powder\u2013wood vinegar compound solution in piglets for raw \n\n\n\npigeon pea seed meal. Animal, 2 (3), pp. 366\u2013374. \n\n\n\nM\u00e9ndez, A., G\u00f3mez, A., Paz-Ferreiro, J., Gasc\u00f3, G., 2012. Effects of sewage \n\n\n\nsludge biochar on plant metal availability after application to a \n\n\n\nMediterranean soil. Chemosphere, 89 (11), pp. 1354\u20131359. \n\n\n\nM\u00e9zes, M., Balogh, K., T\u00f3th, K., 2010. Preventive and therapeutic methods \n\n\n\nagainst the toxic effects of mycotoxins\u2014A review. Acta Vet. Hung., 58 \n\n\n\n(1), pp. 1\u201317. \n\n\n\nMisihairabgwi, J.M., Ezekiel, C.N., Sulyok, M., Shephard, G.S., Krska, R., \n\n\n\n2017. Mycotoxin contamination of foods in Southern Africa: A 10-year \n\n\n\nreview (2007\u20132016). Crit. Rev. Food Sci. Nutr., pp. 1\u201316. \n\n\n\nMukome, F.N.D., Zhang, X., Silva, L.C.R., Six, J., Parikh, S.J., 2013. Use of \n\n\n\nchemical and physical characteristics to investigate trends in biochar \n\n\n\nfeedstocks. J. Agric. Food Chem., 61 (9), pp. 2196\u20132204. \n\n\n\nNaka, K., 2001. Adsorption effect of activated charcoal on \n\n\n\nenterohemorrhagic Escherichia coli. J. Vet. Med. Sci., 63 (3), pp. 281\u2013\n\n\n\n285. \n\n\n\nNguyen, B.T., Lehmann, J., Hockaday, W.C., Joseph, S., Masiello, C.A., 2010. \n\n\n\nTemperature sensitivity of black carbon decomposition and oxidation. \n\n\n\nEnviron. Sci. Technol., 44 (9), pp. 3324\u20133331. \n\n\n\nNguyen, B.T., Lehmann, J., Kinyangi, J., Smernik, R., Riha, S.J., Engelhard, \n\n\n\nM.H., 2009. Long-term black carbon dynamics in cultivated soil. \n\n\n\nBiogeochemistry, 92 (1\u20132), pp. 163\u2013176. \n\n\n\nNovak, J.M., 2009. Characterization of designer biochar produced at \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 86-93 \n\n\n\nCite the Article: Emanuel Joel Lao, Ernest Rashid Mbega (2020). Biochar As A Feed Additive For Improving The Performance Of Farm Animals.  \nMalaysian Journal of Sustainable Agriculture, 4(2): 86-93. \n\n\n\ndifferent temperatures and their effects on a loamy sand. Ann. Environ. \n\n\n\nSci. \n\n\n\nOleszczuk, P., Hale, S.E., Lehmann, J., Cornelissen, G., 2012. Activated \n\n\n\ncarbon and biochar amendments decrease pore-water concentrations \n\n\n\nof polycyclic aromatic hydrocarbons (PAHs) in sewage sludge. \n\n\n\nBioresour. Technol., 111, pp. 84\u201391. \n\n\n\nOliveira, F.R., Patel, A.K., Jaisi, D.P., Adhikari, S., Lu, H., Khanal, S.K., 2017. \n\n\n\nEnvironmental application of biochar: Current status and perspectives. \n\n\n\nBioresour. Technol., 246, pp. 110\u2013122. \n\n\n\nPandey, G., Madhuri, S., 2014. Heavy metals causing toxicity in animals and \n\n\n\nfishes. Res. J. Anim. Vet. Fish. Sci., 2 (2), pp. 17\u201323. \n\n\n\nPeng, X., Ye, L.L., Wang, C.H., Zhou, H., Sun, B., 2011. Temperature-and \n\n\n\nduration-dependent rice straw-derived biochar: Characteristics and its \n\n\n\neffects on soil properties of an Ultisol in southern China. Soil Tillage \n\n\n\nRes., 112 (2), pp. 159\u2013166. \n\n\n\nPhongpanith, S., Inthapanya, S., Preston, T.R., 2013. Effect on feed intake, \n\n\n\ndigestibility and N balance in goats of supplementing a basal diet of \n\n\n\nMuntingia foliage with biochar and water spinach (Ipomoea aquatica). \n\n\n\nLivest. Res. Rural Dev. 25 (35). \n\n\n\nPoage, G.W., Scott, C.B., Bisson, M.G., Hartmann, F.S., 2000. Activated \n\n\n\ncharcoal attenuates bitterweed toxicosis in sheep. J. Range Manag., pp. \n\n\n\n73\u201378. \n\n\n\nRao, S.B.N., Chopra, R.C., Radhika, V., 2004. Sodium bentonite or activated \n\n\n\ncharcoal supplementation on dry matter intake and growth rate of \n\n\n\nyoung goats fed diets with aflatoxin B1. Indian J. Anim. Sci,. \n\n\n\nRogosic, J., Pfister, J.A., Provenza, F.D., Grbesa, D., 2006. The effect of \n\n\n\nactivated charcoal and number of species offered on intake of \n\n\n\nMediterranean shrubs by sheep and goats. Appl. Anim. Behav. Sci., 101 \n\n\n\n(3\u20134), pp. 305\u2013317. \n\n\n\nRomanos, J., 2011. Nanospace engineering of KOH activated carbon. \n\n\n\nNanotechnology, 23 (1), p. 15401. \n\n\n\nRonsse, F., Van Hecke, S., Dickinson, D., Prins, W., 2013. Production and \n\n\n\ncharacterization of slow pyrolysis biochar: influence of feedstock type \n\n\n\nand pyrolysis conditions. Gcb Bioenergy, 5 (2), pp. 104\u2013115. \n\n\n\nSchwarzenbach, R.P., Egli, T., Hofstetter, T.B., Von Gunten, U., Wehrli, B., \n\n\n\n2010. Global water pollution and human health. Annu. Rev. Environ. \n\n\n\nResour., 35, pp. 109\u2013136. \n\n\n\nShibamoto, T.K., Yasuhara, A., 2007. Dioxin Formation from Waste \n\n\n\nIncineration. Rev Env. Contam Toxicol, 190, pp. 1\u201341. \n\n\n\nSnyman, L.D., Schultz, R.A., Botha, C.J., Labuschagne, L., Joubert, J.P.J., 2009. \n\n\n\nEvaluation of activated charcoal as treatment for Yellow tulp (Moraea \n\n\n\npallida) poisoning in cattle. J. S. Afr. Vet. Assoc., 80 (4), pp. 274\u2013275. \n\n\n\nSobrova, P., Adam, V., Vasatkova, A., Beklova, M., Zeman, L., Kizek, R., 2010. \n\n\n\nDeoxynivalenol and its toxicity. Interdiscip. Toxicol., 3 (3), pp. 94\u201399. \n\n\n\nSoo, Y., Chada, N., Beckner, M., Romanos, J., Burress, J., Pfeifer, P., 2013. \n\n\n\nAdsorbed Methane Film Properties in Nanoporous Carbon Monoliths. \n\n\n\nAPS Meeting Abstracts. \n\n\n\nSpokas, K.A., 2011. Qualitative analysis of volatile organic compounds on \n\n\n\nbiochar. Chemosphere, 85 (5), pp. 869\u2013882. \n\n\n\nTang, J., Zhu, W., Kookana, R., Katayama, A., 2013. Characteristics of \n\n\n\nbiochar and its application in remediation of contaminated soil. J. Biosci. \n\n\n\nBioeng., 116 (6), pp. 653\u2013659. \n\n\n\nToth, J.D., Dou, Z., 2016. Use and impact of biochar and charcoal in animal \n\n\n\nproduction systems. Agric. Environ. Appl. biochar Adv. barriers, no. \n\n\n\nsssaspecpub63, pp. 199\u2013224. \n\n\n\nTotusek, R., Beeson, W.M., 1953. The nutritive value of wood charcoal for \n\n\n\npigs. J. Anim. Sci., 12 (2), pp. 271\u2013281. \n\n\n\nUchimiya, M., Bannon, D.I., Wartelle, L.H., 2012. Retention of heavy metals \n\n\n\nby carboxyl functional groups of biochars in small arms range soil. J. \n\n\n\nAgric. Food Chem., 60 (7), pp. 1798\u20131809. \n\n\n\nWeber, K., Quicker, P., 2018. Properties of biochar. Fuel, 217, pp. 240\u2013261. \n\n\n\nWild, C.P., Miller, J.D., Groopman, J.D., 2015. Mycotoxin control in low-and \n\n\n\nmiddle-income countries. International Agency for Research on Cancer \n\n\n\nLyon, France. \n\n\n\nWittstock, U., Gershenzon, J., 2002. Constitutive plant toxins and their role \n\n\n\nin defense against herbivores and pathogens. Curr. Opin. Plant Biol., 5 \n\n\n\n(4), pp. 300\u2013307. \n\n\n\nWu, W., 2012. Chemical characterization of rice straw-derived biochar for \n\n\n\nsoil amendment. Biomass and bioenergy, 47, pp. 268\u2013276. \n\n\n\nYamauchi, K., Manabe, N., Matsumoto, Y., Yamauchi, K., 2013. Increased \n\n\n\ncollagen accumulation in eggshell membrane after feeding with dietary \n\n\n\nwood charcoal powder and vinegar. Connect. Tissue Res., 54 (6), pp. \n\n\n\n416\u2013425. \n\n\n\nYamauchi, K., Ruttanavut, J., Takenoyama, S., 2010. Effects of dietary \n\n\n\nbamboo charcoal powder including vinegar liquid on chicken \n\n\n\nperformance and histological alterations of intestine. J. Anim. Feed Sci., \n\n\n\n19 (2), pp. 257\u2013268. \n\n\n\nYang, X.B., 2010. Influence of biochars on plant uptake and dissipation of \n\n\n\ntwo pesticides in an agricultural soil. J. Agric. Food Chem., 58 (13), pp. \n\n\n\n7915\u20137921. \n\n\n\nYao, Y., 2012. Adsorption of sulfamethoxazole on biochar and its impact on \nreclaimed water irrigation. J. Hazard. Mater., 209, pp. 408\u2013413.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 1(1) (2017) 08-10 \n\n\n\n\n\n\n\nCite this article  Song-Quan Ong1,5*, Bui-Bui Lee2, Geok-Pin Tan3 and Saravanan A/L Maniam 4  Capacity of black soldier fly and house fly larvae in treating the wasted \nrice in Malaysia . of Sustainable Agriculture (MJSA) 1(1) (2017) 08-10 \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 August 2016 \n\n\n\nAccepted 12 December 2016 \n\n\n\nAvailable online 20 January 2017 \n\n\n\nKeywords: \n\n\n\nrice waste management, black \nsolider fly, house fly, survival \nrate  \n\n\n\nABSTRACT\n\n\n\nRice is the major source of carbohydrate in the world and also the common composition in avoidable food waste. Due \n\n\n\nto the rich food culture in Malaysia, different pretreated rice waste is generated and the pretreat-component may affect \n\n\n\nthe outcome in managing the rice waste using fly larvae. In this study, black soldier fly larvae (BSFL; Hermetia illucens) \n\n\n\nand house fly larvae (HFL; Musca domestica) are introduced to four types of rice waste: 1) steamed white rice (WR), \n\n\n\n2) rice with curry (CR), 3) rice with coconut milk (CCR), and 4) fried rice (FR). The reduction rate of rice waste and \n\n\n\nlarval survival rate, and nutrient analysis were measured by prepupal stages of both fly larvae. BSFL showed no \n\n\n\nsignificant difference in the reduction for four types of rice wastes (p= 0.28) and significantly higher survival rate than \n\n\n\nHFL for the CR and CCR wastes; indicating better tolerant to the feeding substrate. Although BSFL has significantly \n\n\n\ngreater reduction rate (3.03 \u2013 3.26 g /10 larvae/ day) than HFL, but in a fixed timeframe (20-25 days) four batches of \n\n\n\nHFL were generated and therefore having significantly more mass production than BSFL (500g of substrates \n\n\n\ngenerating 11.96g of BSFL but 22.62g of HFL). Rice waste management using fly larvae is effective subjected to the \n\n\n\nneeds and purpose; BSFL is more adaptive to different types of rice waste and high in fat content, whereas HFL is \n\n\n\nsensitive to the waste but high in protein content. \n\n\n\n1. INTRODUCTION \n\n\n\nThe amount of food waste in Malaysia is estimated to increase to more \nthan 6 million ton per day by 2020 [1]. Food waste in Malaysia was \nmanaged as the municipal solid waste (MSW) and avoidable trashed \nfood was the dominant composition that has occupied almost 60% of \nthe MSW [1]. Moreover, an internal study for food waste was \nconducted by School of Biological Sciences, Universiti Sains Malaysia \n(USM) and KDU Penang University College, which finds rice is the \nmain disposed avoidable waste in five cafeteria of USM healthy \ncampus. These food wastes may bring significant impact to the \nenvironment as they emitt greenhouse gasses that cause climate \nchanges when decomposing at the landfill [2]. Transforming rice and \nkitchen waste into animal feed should be the most effective food \nwaste management in terms of minimizing energy and heat loss; \nhowever this practice is restricted by the FEED ACT (Act 698) (Law of \nMalaysia, 2009) [3] in Malaysia which prohibits the use of food waste \nas animal feed.  \n\n\n\nThe idea of converting food waste into fly larvae is commendable. As \ndemonstrated by numerous studies [4, 5, 6], fly larvae such as black \nsoldier fly larvae (BSFL) and house fly larvae (HFL) has significant \nrole in reducing organic waste volume and biologically convert the \nwaste into useful protein for livestock animal feed [6]. By using BSFL \nand HFL on the organic waste, it is crucial to understand the capacity \nand nutrition value of the larvae on the feeding substrate. As \ndemonstrated by Oonincx et al (2015) [7] their survival rate and \ngrowth rate were greatly affected when the BSFL grew on different \nculture environment and medium and (Cickova et al. 2012) [5] had  \n\n\n\nshown that house fly preferred on relatively moister breeding \nmedium. Most of the kitchen wastes in Malaysia were unsegregated  \nand consisted of unknown pretreated components such as curry \npaste.Therefore in order to manage rice waste using fly larvae, the \neffect of the pretreated components and the capacity of the fly larvae \nin reducing rice waste have to be investigated. \n\n\n\n2. Material & Methods \n\n\n\n2.1 Fly culture  \n\n\n\nA population of black soldier fly, Hermetia illucens L., was maintained \nunder a poultry house at Balik Pulau, Penang.  The poultry house was \nplaced on a meadow, exposed daily to direct sunlight for about 8 h. A \nblack plastic foil-covered tray (2 m) containing organic waste was \nused to attract ovipositing females and eggs laying. Larvae hatched \napproximately 3 days after oviposition and the young larvae at an age \nof 4\u20136 days were then introduced into the pretreated rice waste.   \n\n\n\nFor the house fly, Musca domestica L. the WHO/VCRU house fly strain \nwas used in the study. The population was reared at 25\u00b12\u00b0C and \n67\u00b15% relative humidity (RH) with a photoperiod of 8:16 (L:D) h in \nInsectarium II at the Vector Control Research Unit (VCRU) of \nUniversity Science Malaysia. First to second instar larvae were \nidentified by their body size (2-5mm) and introduced to the rice waste \nsubstrate.  \nBoth pre-pupae of BSFL and HFL were identified with the presence of \nlarvae in the drying agent and morphological identification: BSFL \nforming blackish exoskeleton and HFL forming reddish exoskeleton \n[8].   \n\n\n\n2.2 Rate of reduction and Survival rate  \n\n\n\nThe experimental methods used in this study are slight modified from \nNguyen et al. [6, 9]. Four types of wasted rice: 1) white rice (WR), 2) \ncurry rice (CR), 3) coconut milk rice (CMR), and 4) fried rice (FR) were \nobtained mainly from kitchen and plate waste from the cafeteria in \nUniversity Sains Malaysia. To keep the waste medium consistent, \nlarge quantity of rice waste was first grinded into a homogenous \nmixture, packaged and frozen for use throughout the experiment [9].  \nBriefly, fifty BSFL and HFL were placed in a container cup (16 oz) with \n50g of medium and drying agent was placed externally for harvesting \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nJournal Homepage: http://www.razipublishing.com/journals/malaysian- journal-\n\n\n\nISSN: 2521-2931  (Print) \nISSN: 2521-294X  (Online) \n\n\n\nCapacity of black soldier fly and house fly larvae in treating the wasted rice in \nMalaysia. \n\n\n\nSong-Quan Ong1,5*, Bui-Bui Lee2, Geok-Pin Tan3 and Saravanan A/L Maniam 4  \n1,2,3,4KDU Penang University College. 32, Jalan Anson 10400 Penang Malaysia   \n5School of Biological Sciences, Universiti Sains Malaysia 11800 Pulau Pinang Malaysia *corresponding author Email:songguan26@gmail.com\n\n\n\nof-sustainable-agriculture-mjsa/ \n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.08.10\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.09.10\n\n\nhttps://doi.org/10.26480/mjsa.01.2017.08.10\n\n\n\n\n\n\nSong-Quan Ong1,5*, Bui-Bui Lee2, Geok-Pin Tan3 and Saravanan A/L Maniam 4  Capacity of black soldier fly and house fly larvae in treating the wasted rice in \nMalaysia . of Sustainable Agriculture (MJSA) 1(1) (2017) 08-10 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite this article  Song-Quan Ong1,5*, Bui-Bui Lee2, Geok-Pin Tan3 and Saravanan A/L Maniam 4  Capacity of black soldier fly and house fly larvae in treating the wasted \nrice in Malaysia . of Sustainable Agriculture (MJSA) 1(1) (2017) 08-10 \n\n\n\n\n\n\n\n9 \n\n\n\nthe pre-pupae. The containers were monitored daily and the presence \nof larvae on the drying agent indicating the prepupae formation of the \nlarvae [9], and the remaining waste medium was weighed and the \ntime in days were recorded. Each waste medium repeated for 5 times \nand the control replicates were set up at the same time as described \nabove but contained no larvae. This control was used to counter for \nany reduction in waste weight due to water evaporation and microbes \ndecomposition, and the daily waste reduction from the larvae was \ncorrected by this loss. As the pupa were collected, they were \nproceeded to the nutritional study.  \nDue to different development time for BSHL and HFL to pre-pupae \nstage (in this study BSHL takes 19.2\u00b13.6 days; HFL takes 4.5\u00b11.9 days) \nand therefore the reduction waste was measured as the mass reduced \nper larvae per days [9] and overall deduction percentage from initial \n500g of rice waste. The capacity of BSFL and HFL in reducing four \ntypes of rice waste was compared using one-way analysis of variance \n(ANOVA) with post-hoc least significant difference (LSD) at level of \np<0.05 in SPSS 17.0.  \n \n2.3 Nutrition analysis- proximate analysis The nutrition of pre-pupae \nof BSFL and HFL were determined according to the Association of \nOfficial Analytical Chemists International (2002) [10]. The moisture \nof the pre-pupae was conducted according to the AOAC (2002), \nOfficial Method 934.01, in which two subsamples of each sample \nweighing 2g respectively were placed in a crucible drying in an oven \nfor 24 hours at 100\u00baC. The samples were weighed in an electrical \nbalance before and after drying and moisture was indicated by the \ndifferences of mass and it was converted into percentages. As for the \nash content, dry ashing method was used to determine the content. \nThe samples were put in a preweighed ceramic cup and incinerated \nin a furnace. The crude protein content of the pre-pupae meal \nsubsamples were determined by measuring the total nitrogen (N) \ncontent according to the method described by Association of Official \nAnalytical Chemists International (2002), Official Method 4.2.07, \nKjeldahl method. Approximately 0.10 g dried sample digested with \nconcentrated sulfuric acid and catalyst in a Kjeldahl flask. The \nproducts were later cooled down at room temperature and sodium \nhydroxide was added into the flask. The flask was subjected to the \ndistillation connection unit and the distillate was mixed with boric \nacid and few drops of methyl red. The distillate mixture was titrated \nwith 0.40% hydrochloric acid and calculated the protein in \npercentage. The Crude Fat or Ether Extract (EE) content was \ndetermined by making use of the diethyl ether reagent method using \nthe Tecator Soxtec System HT 1043 Extraction Unit according to \nAssociation of Official Analytical Chemists International (2002), \nOfficial Method 920.39. Two subsamples of each sample weighing 2g \nrespectively were placed in a soxhlet fat beaker. Thereafter 50ml of \ndiethyl ether was added to the subsample and placed into the Tecator \nSoxtec  \n\n\n\nSystem HT 1043 Extraction Unit. The subsamples were placed in a \ndrying oven for 2 hours at 100\u00baC. The residue weight was the lipid \nand was expressed in the percentage. The total carbohydrate content \nin the samples was calculated by difference method. The food's \nconstituents (protein, fat, water, ash) were determined individually, \nsummed and subtracted from the total weight of the food. This is \nreferred as the total carbohydrate by difference and it should be clear \nthat the carbohydrate estimated in this method included fibers [17]. \n  \nThe nutritional contents were expressed in percentage and the value \nwill be prior transformed by arcsine transformation and compared t-\ntest in SPSS 17.0 at p< 0.05.  \n\n\n\n3. Results & Discussion  \n \nFor the BSFL, no significant difference in reducing four types of waste, \nin which BSFL can reduced 3.03 \u2013 3.48 g/ 10 larvae / day an overall \n71.24 to 73.16% of rice waste. The BSFL reduction rate on rice waste \nwas parallel to the studies of Diener et al. (2011) [4] and Oonincx et \nal. (2015) [7] that having the reduction rate ranged from 60 to 80% \non the organic waste. The capability of BSFL in bioconversion of rice \nmaterial had also been reviewed by Zheng et al. (2012) [11] in which \napplying BSFL and microbes Rid-X converting rice straw into larval \ngrease that able to use as biodiesel. Comparing mass of the rice waste \nreduction rate per larvae between BSFL and HFL for all rice waste, \nBSFL showed significantly greater reduction rate than HFL (P<0.05, \nTable 1). This greater reduction rate could be explained by the nature \nof consumption of BSFL that having larger body size compared with \nHFL and required more calorie consumption [12] although digestive \nphysiology and metabolism should also play a role in animal food \nconsumption. In addition, BSFL demonstrated no significant \ndifference on the survival rate for four rice waste; indicating growth \n\n\n\nof BSFL not influenced by the pretreated components of rice waste.  \nAlthough BSFL showed promising reduction properties on four rice \nwaste, but when comparing the mass conversion from rice waste to \nlarvae mass, HFL generated significantly higher mass (22.62 \u00b1 0.84g) \ncompared to BSFL (11.96 \u00b1 0.81g) in the experiment time frame (20-\n25 days). This is mainly attributable to the shorter development time \ntaken by HFL in which it takes 4.5 days to become prepupae \ncompared with BSFL which takes 19.2 days and in this study an \naverage of 4 batches of house fly prepupae can be produced relative \nto one batch of BSFL, and therefore the mass of HFL is greater than \nBSFL.  \n \nNevertheless, due to the concern of pest status, house fly seldom used \nas the food waste management agent and most of the HFL organic \nwaste studies were focused on the animal manure [5, 13] to the best \nof our knowledge, this study is the first one reporting the use of rice \nwaste as the growth substrate for HFL. The reduction and survival \nrate of HFL are showed in Table 1 in which HFL demonstrate \nrelatively inconsistent reduction as the reduction rate for curry and \ncoconut milk rice waste are significantly lower than the WR and FR \n(p<0.05, Table 1). This may be due to the presence of curry having \ncertain amount of spices such as citronella that poses as a repellent of \nthe insect [14]. The inconsistency in the survival rate of HFL also \nsuggests the house fly growth is sensitive to the environment and \npose challenges when applying HFL in the larvae management on the \nfood waste with different pretreated components.   \n \nAs for the nutritional analysis, prepupal of house fly significantly \ncomposed of high crude protein whereas prepupal of BSF significantly \ncomposed of high crude fat (p<0.05, Table 2). This study, HFL that \nfeeds on the rice waste generated crude protein and fat percentage in \nthe range as demonstrated by Pretorius (2011) [15] that using \nstandard larvae meal as the substrate. BSFL consist of high level of \ncrude fat but low protein content as showed in many studies [4, 16] \nand this was suggested as the adult black soldier fly do not eat and \ntherefore, they usually need to build up a fat body that  is necessary to \ncomplete development and survive as adults long enough to mate and \nlay eggs.  \n \n4. Conclusion   \nRice waste management using fly larvae is effective subjected to the \nneeds and purpose; BSFL is more adaptive to different types of rice \nwaste and high in fat content, whereas HFL is sensitive to the waste \nbut high in protein content.  \n  \nAcknowledgements  \n\n\n\nWe thank the Vector Control Research Unit for providing the fly larvae \nand the first author was financially supported by MyBrain15 program \nunder the Department of Education, Malaysia. This study is partly \nsupported by KDU Penang University College Internal Research Grant \nScheme. \n\n\n\n \nReferences \n \n[1] A.A. Hamid, Ahmad, A., Ibrahim, M.H., Rahman, N.N.N.A. \nFood Waste Management in Malaysia- Current situation and future \nmanagement options. Journal of Industrial Research & Technology \n(2015), 2(1): 36-39 \n \n\n\n\n[2] N.B.D. Thi, Kumar, G. and Lin, C.Y. 2015. An overview of \nfood waste management in developing countries: current status and \n\n\n\n\n\n\n\n\nSong-Quan Ong1,5*, Bui-Bui Lee2, Geok-Pin Tan3 and Saravanan A/L Maniam 4  Capacity of black soldier fly and house fly larvae in treating the wasted rice in \nMalaysia . of Sustainable Agriculture (MJSA) 1(1) (2017) 08-10 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite this article  Song-Quan Ong1,5*, Bui-Bui Lee2, Geok-Pin Tan3 and Saravanan A/L Maniam 4  Capacity of black soldier fly and house fly larvae in treating the wasted \nrice in Malaysia . of Sustainable Agriculture (MJSA) 1(1) (2017) 08-10 \n\n\n\n\n\n\n\n10 \n\n\n\nfuture perspective. Journal of Environmental Management 157: 220-\n229 \n \n\n\n\n[3] Law of Malaysia. 2009. FEED ACT (Act 698). Retrieved on \nMay 3, 2015 from http://www.agc.gov.my/agcportal/uploads/files/ \nPublications/LOM/EN/Act%20698%20-%20 \nFeed%20Act%202009.pdf \n \n\n\n\n[4] S. Diener, M. Nandayure, Studt Solano, Floria Roa \nGutie\u00b4rrez, Christian Zurbru\u00a8gg, Klement Tockner Biological \nTreatment of Municipal Organic Waste using Black Soldier Fly Larvae. \nWaste Biomass Valor (2011) 2:357\u2013363 DOI 10.1007/s12649-011-\n9079-1 \n \n\n\n\n[5] H. Cickova, H. Pastor, M. Kozanek, Martnez-Sanchez, A., \nRojo, S. and Takac, P. Biodegradation of pig manure by the housefly, \nMusca domestica: A viable ecological strategy for pig manure \nmanagement. PLoS ONE, (2012), 7(3): e32798. \n \n\n\n\n[6] T.T.X. Nguyen, K. Jeffery, Tomberlin and S. Vanlaerhoven. \nAbility of Black Soldier Fly (Diptera: Stratiomyidae) Larvae to Recycle \nFood Waste. Environ. Entomol. (2015), 44(2): 406\u2013 410; DOI: \n10.1093/ee/nvv002 \n \n\n\n\n[7] D.G.A.B. Oonincx, van Broekhoven S, van Huis A, van Loon \nJJA. Feed Conversion, Survival and Development, and Composition of \nFour Insect Species on Diets Composed of Food By- Products. PLoS \nONE (2015), 10(12): e0144601. doi:10.1371/ journal.pone.0144601 \n \n\n\n\n[8] P. Haasbroek. The use of Hermetia illucens and Chrysomya \nchloropyga larvae and pre-pupae meal in ruminant nutrition (2016) \nMaster of Science in Agriculture (Animal Sciences) at Stellenbosch \nUniversity \n \n\n\n\n[9] T.T.X. Nguyen, J. K. Tomberlin, and S. Vanlaerhoven. \nInfluence of resources on Hermetia illucens (Diptera: Stratiomyidae) \nlarval development. J. Med. Entomol. (2013) 50: 898\u2013906. \n \n\n\n\n[10] Association of Official Analytical Chemists (AOAC) \nInternational. 2002. Official methods of analysis of AOAC \ninternational. 17th ed. Arlington, Virginia, USA. \n \n\n\n\n[11] L. Zheng, Y. Hou, W. Li, S. Yang, Q. Li, Z. Yu. Biodiesel \nproduction from rice straw and restaurant waste employing black \nsoldier fly assisted by microbes Energy 47 (2012) 225e229 \n \n[12] M. Clauss, P. Steuer, D. M\u00fcller, W. H., Codron, D., & Hummel, \nJ. Herbivory and Body Size: Allometries of Diet Quality and \nGastrointestinal Physiology, and Implications for Herbivore Ecology \nand Dinosaur Gigantism. PLoS ONE (2013),8(10)e68714. \nhttp://doi.org/10.1371/journal.pone.0068714 \n \n\n\n\n[13] M. Hussein, V.V. Pillai, J.M. Goddard, H.G. Park, K.S. \nKothapalli, D.A. Ross. Sustainable production of housefly (Musca \ndomestica) larvae as a protein-rich feed ingredient by utilizing cattle \nmanure. PLoS ONE (2017), 12(2): e0171708. \ndoi:10.1371/journal.pone.0171708 \n \n\n\n\n[14] M. F. Maia, S. J Moore. Plant-based insect repellents: a \nreview of their efficacy, development and testing Malaria Journal \n(2011), 10:S11 http://www.malariajournal.com/content/10/S1/S11 \n \n\n\n\n[15] Q. Pretorius. The Evaluation of Larvae of Musca Domestica \n(Common House Fly) As Protein Source for Broiler Production (2011) \nMaster of Science in Agriculture (Animal Sciences) at Stellenbosch \nUniversity \n \n\n\n\n[16] S. Thomas, M. Eeckhout, P. D. Clercq, S. De Smet. Nutritional \ncomposition of black soldier fly (Hermetia illucens) prepupae reared \non different organic waste substrates. Journal of the Science of Food \nand Agriculture (2016). \n \n\n\n\n[17] G. Nantel, 1999. Carbohydrates in human nutrition. Food \nand Nutrition Division, FAO FNA/ANA 24. \n \n\n\n\n\nhttp://www.agc.gov.my/agcportal/uploads/files/\n\n\nhttp://www.agc.gov.my/agcportal/uploads/files/\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 20-24 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2023.20.24 \n\n\n\n \nCite The Article: Benazir Iqbal, Iffat Jahan Nur, Md Shariful Islam, Bishwajit Kundu, Dr. Nazmun Naher, Dr. Md. Forhad Hossain (2023).  \n\n\n\nTree Species Diversity and Carbon Stock in Charland Homegardens of Bangladesh. Journal of Sustainable Agricultures, 7(1): 20-24. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.20.24 \n\n\n\n\n\n\n\nTREE SPECIES DIVERSITY AND CARBON STOCK IN CHARLAND HOMEGARDENS OF \nBANGLADESH \nBenazir Iqbal1*, Iffat Jahan Nur2, Md Shariful Islam3, Bishwajit Kundu4, Dr. Nazmun Naher5, Dr. Md. Forhad Hossain6 \n\n\n\n1M.S., Department of Agroforstry and environmental science, Sher-r-bangla Agricultural University, sher-e-bangla Nagar, Dhaka-1207, \nBangladesh \n2Scientific Officer, Bangladesh Jute Research Institute, Dhaka-1207, Bangladesh \n3Assistant professor, Sher-r-bangla Agricultural University \n4Scientific Officer, Bangladesh Jute Research Institute, Dhaka-1207, Banglades \n5Professor, Sher-r-bangla Agricultural University, sher-e-bangla Nagar, Dhaka-1207, Bangladesh \n6Professor, Sher-r-bangla Agricultural University, sher-e-bangla Nagar, Dhaka-1207, Bangladesh \n*Corresponding Author E-mail: benaziriqbal301@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 28 October 2022 \nRevised 12 November 2022 \nAccepted 20 December 2022 \nAvailable online 30 December 2022 \n\n\n\n Four Bangladeshi villages were chosen at random to represent a total of 64 home gardens.The study's \n\n\n\nobjective was to evaluate the variety of tree species and the carbon store in the tree biomass on Char Island, \n\n\n\nboth above and below groun. The Shannon Wiener index was used to evaluate the variety of tree species, and \nallometric equations were used to estimate the carbon stock, with the assumption that the stock represented \n\n\n\n50% of the carbon in the tree biomass. The findings indicated that home garden ecosystems could store an \n\n\n\naverage of 18.00 Mg of carbon per hectare, ranging from 1.66 Mg of carbon per hectare to 58.93 Mg per \nhectare and tree species diversity ranged from 0 to 1.84 with a mean value of 1.05 where most abundant was \n\n\n\nEucalyptus camaldulensis (44.21%) which stored the most C (33.62Mg ha-1) followed by Moringa oleifera \n\n\n\n(29.37 Mg ha-1) in tree biomass. The study provides a evidence of tree diversity and carbon storage in char \nisland. \n\n\n\nKEYWORDS \n\n\n\nCarbon stock, Tree diversity, homegarden \n\n\n\n1.   INTRODUCTION \n\n\n\nA home garden, according to a study, is a sophisticated and sustainable \nland use system that integrates a range of agricultural products, meets \nhousehold requirements, creates employment opportunities, and \ngenerates money (Weerahewa et al., 2012).  It can also be a source of \ncarbon sequestration, a strategy for preventing climate change. The \nadditional carbon that can be stored in the environment of trees is known \nas C sequestration (Bernouxet al., 2006). According to a study, tropical \nagro-forestry systems have the ability to sequester 95 t CO2 ha-1, ranging \nfrom 12 to 228 tCO2 ha-1 (Albrecht and Serigne., 2003). Depending on its \ncharacteristics, management strategies, land uses, and geography, \ncomplex agro-ecosystems can exhibit significant variation in C \nsequestration and biodiversity (Montagnini and Nair, 2004). While a home \ngarden, as a scope of agro-ecosystems with a great range of plant species, \nliving forms, and production activities, may offer higher levels of \nproductivity and more stable C stocks, carbon sequestration is also a \nstrategy for mitigating climate change (Houghton et al., 1993; Yachi and \nLoreau, 1999).  \n\n\n\nHowever, in Bangladesh the situation on Char Island is very different. The \nterm \"char island\" refers to any accumulation in a river course or estuary \nthat is surrounded by the waters of an ocean, sea, lake, or stream \n(Chowdhury, 1988). At the current study, biodiversity and carbon stock \nwere measured in a homestead on Char Island in North-Western \nBangladesh, which  \n\n\n\nmay be important for carbon stock. These Char islands' residents rely on \n\n\n\nagriculture and their backyard gardens for a living. Additionally, by storing \nCO2 through a variety of multilayer tree species, these home gardens give \nthem a steady environment. According to research by Roshetko and \nPurnomosidhi, the average above-ground carbon stocks in Lumpung home \ngardens in Indonesia were assessed to be 56.5 Mg C/ha after taking into \naccount species, classes, rotation durations, and time (Roshetko and \nPurnomosidhi, 1998). A well-established home garden can also provide \n29% lumber, 32.26% fuel, 38.71% medicinal plants, and 45% fruit and \nfood (Roy et al., 2013).  \n\n\n\nSimilar to this, Bangladesh's char island home gardens can be used as a \nsource of biodiversity preservation and carbon storage to lessen climate \nchange. More consideration must be given to adaptation strategies that \nlocal land users might adopt with the effective cooperation of stakeholders \nand policymakers in order to address future issues of biodiversity \nconservation and the negative consequences of climate change (Murthy et \nal., 2013). In light of this, research into the home gardens on Char Island \nwas important to educate the public about the value of a well-established \nhome garden in enhancing plant diversity and creating a more favorable \nclimate. As a result, the study's attention was drawn to tree diversification \nand storage carbon.  \n\n\n\n2.   METHOD AND MATERIALS  \n\n\n\n2.1   Location and Study Area  \n\n\n\nIn Kurigram district, four villages within two upazillas (administrative \nunits) participated in the study. The Kurigram district is situated in \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 20-24 \n\n\n\n\n\n\n\n \nCite The Article: Benazir Iqbal, Iffat Jahan Nur, Md Shariful Islam, Bishwajit Kundu, Dr. Nazmun Naher, Dr. Md. Forhad Hossain (2023).  \n\n\n\nTree Species Diversity and Carbon Stock in Charland Homegardens of Bangladesh. Journal of Sustainable Agricultures, 7(1): 20-24. \n\n\n\n\n\n\n\nBangladesh's northern region. The district has a total size of 2255.29 sq \nkm and is situated between latitudes 20\u00b003' and 26\u00b003' N and longitudes \n89\u00b027' and 89\u00b032' E. The four villages that were under study are Dagarkuti, \nKolakata, Borovita, and Charuapara. These include the communities of \nBorovita and Charuapara in Chilmary upazilla, and Dagarkuti and Kolakata \nin Ulipur upazilla.The area of Ulipur Upazilla is 504.19 square kilometers, \nand it is situated between latitudes 25\u00b033' and 25\u00b049' north and 89\u00b029' \nand 89\u00b051' east. Chilmary Upazilla is situated at 25.5667\u00b0N and 89.6917\u00b0E.  \n\n\n\n\n\n\n\nPlate 1: Kurigram district \n\n\n\nKurigram experiences tropical dry and wet weather. In general, the \nclimate is characterized by monsoons, high temperatures, high levels of \nhumidity, and significant rainfall. Early in April, the hot season starts, and \nit lasts through July. The lowest and maximum mean temperatures for the \nmonths of January, February, March, and April were between 7 and 16 \u00b0C \nand 32 and 36 \u00b0C, respectively (BBS, 2012). The district's average annual \nrainfall is approximately 1587 mm, with the greatest rainfall recorded \nduring the monsoon months being 1378.6 mm (BMD, 2014). Alluvial soil \nmakes up 80% of the soil in the study area, with bare soil accounting for \nthe remaining 20% (SRDI., 2014).  \n\n\n\n\n\n\n\nPlate 2: Ulipurupazillaplate \n\n\n\n\n\n\n\nPlate 3: Chilmariupazilla \n\n\n\n2.2   Sampling Technique  \n\n\n\nUlipur and Chilmari were two of the nine upazillas (administrative units) \nof Kurigram that were randomly chosen. There are 14 unions \n(administrative unit) in Ulipur and 6 in Chilmari Upazilla, respectively. \nHatia and Buraburi were randomly chosen from among the 14 unions in \nUlipur, and Ranigang and Nayarhat were chosen from among the 6 unions \nin Chilmari Upazilla. One village named Dagarkuti and one village named \nKolakata from the Htia and Buraburi union were arbitrarily chosen to be \nin Ulipur upazilla.Two villages, Borovita in the Ranigang union and \nCharuapara in the Nayarhat union, were arbitrarily chosen from the \nChilmari upazilla. A sample of 15% out of 712 farm families, was taken \n(Jaman et al., 2016). By using the stratified random sample method, 178 \nhouseholds were chosen (table 1) Finally, 64 representative farm families \nwere chosen from homegardens rich in various species to participate in \nquestionnaire surveys, carbon stock measurements, and tree diversity \nassessments. The formula of garden Yamane was employed to determine \nthe ultimate home (Yamane, 1967). \n\n\n\nTable 1: Distribution of Population and Sample Size in Four Selected Village \n\n\n\nUpazilla Union Village No. of total households \nNo. of households primary \n\n\n\nselected selected (N) \nultimate number of households \n\n\n\nchosen for data gathering (n) \n\n\n\nUlipur \nHatia Dagarkuti 247 62 22 \n\n\n\nBuraburi Kolakata 135 60 12 \n\n\n\nChilmary \nRanigang Borovita 260 65 24 \n\n\n\nNayarhat Churoapara 70 17 6 \n\n\n\nTotal 712 178 64 \n\n\n\n2.3   Survey of Backyard Plots  \n\n\n\nFirst, the home gardens were separated into three categories for \ncomparison purposes: tiny (0.01-0.03 ha), medium (0.03-0.05 ha), and \nlarge (> 0.05 ha). There were 23, 17, and 24 home gardens in each of the \n\n\n\nthree categories (Jaman et al., 2016). The breast height (1.37 m) of each \nperennial tree was taken into consideration when selecting it, and each \ntree's local name and botanical name were documented down to the \nspecies level. Using a measuring tape, the DBH of each chosen species was \ncalculated. The biomass of the trees was determined for each kind of tree \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 20-24 \n\n\n\n\n\n\n\n \nCite The Article: Benazir Iqbal, Iffat Jahan Nur, Md Shariful Islam, Bishwajit Kundu, Dr. Nazmun Naher, Dr. Md. Forhad Hossain (2023).  \n\n\n\nTree Species Diversity and Carbon Stock in Charland Homegardens of Bangladesh. Journal of Sustainable Agricultures, 7(1): 20-24. \n\n\n\n\n\n\n\nusing an allometric equation developed (Chave et al., 2005). Using the \nglobal wood density database and the FAO list of wood densities for tree \nspecies from Tropical Asia, the wood density for the species under study \nwas determined (Zanne et al., 2009).  Climbers were not chosen for this \nstudy because the study plots lacked palm trees and because it was \ndifficult to tell stems apart.  \n\n\n\n2.4   Estimation of Biodiversity \n\n\n\nUsing the Shannon Wiener Diversity Index, biodiversity with an emphasis \non tree diversity was estimated (SWI). Each of the homesteads served as a \nsample plot, and the variety of the tree species was quantified therein by \ncreating an index based on their frequency and number. The Shannon-\nWiener diversity index (SWI), which is effective for evaluating the \ndiversity of tree species, was employed in this study. The Shannon-Wiener \ndiversity index exhibits the maximum diversity when all species are \nequally plentiful relative to the fraction of species abundance in the \npopulation; when the sample only contains one species, it displays the \nleast diversity, or 0 diversity. The species (i) to species total ratio (Pi) was \ncalculated, and the same ratio's natural logarithm was multiplied (Ln Pi). \nThe total across all species is multiplied by -1 to arrive at the final value \n(Shannon et al., 1963) \n\n\n\nH =\u2211 P\ud835\udc56 Ln P\ud835\udc56\ud835\udc5b\n\ud835\udc56=1   \n\n\n\nWhere, \u03a3= Summation.  \n\n\n\npi = The percentage of the entire sample of that species. Total number of \ndistinct species i , divided by total no. of plant species discovered in a \nsample community.      \n\n\n\n H = Shannon index  \n\n\n\n n = No. of species \n\n\n\n2.5   Allometric Equation for Above and Below Ground Biomass:  \n\n\n\n2.5.1   Tree Biomass  \n\n\n\nTree biomass equations relate to diameter at breast height (dbh) and \ndiffer depending on the species. This is due to the fact that trees in \ncomparable functional groups can have significantly diverse growth forms \nin various geographic locations (Pearson et al., 2007). Allometric \nequations for tropical trees were developed (Chave et al., 2005). These \nequations can be used for a wide range of morphological and diameter \ncircumstances.  \n\n\n\n2.5.2   Above-Ground Biomass \n\n\n\nThe following equation has been used to calculate above-ground biomass:  \n\n\n\nAGB = \u03c1\u00d7 exp (-1.499+2.148\u00d7ln (DBH) + 0.207\u00d7 (ln (DBH))2 - 0.028 (ln \n(DBH))3) (Chave et al., 2005)  \n\n\n\n\u03c1 = Wood density (g cm-3): - 1.499, 2.148\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u20260.207 and \n0.0281= Constant.  \n\n\n\n2.5.3   Below Ground Biomass \n\n\n\nThe model equation created by Cairns et al., 1997, which is based on \ninformation of above ground biomass, was used to determine the below \nground biomass and carbon. It is the most practical and economical way \nto calculate root biomass.  \n\n\n\nBGB = exp (-1.0587 + 0.8836 x ln AGB)  \n\n\n\nWhere; BGB = Below ground biomass, ln = Natural logarithm, AGB = Above \nground biomass, -1.0587 and 0.8836 are constant. \n\n\n\n2.5.4   Conversion of Biomass to Carbon  \n\n\n\nUsing an allometric relationship to estimate biomass, it was then \nmultiplied by the wood carbon content's (50%) value. Nearly all tropical \nforest carbon measuring experiments make the assumption that all \ntissues, including wood, leaves, and roots, contain 50% carbon on a dry \nmass basis.  \n\n\n\nCarbon (Mg) = Biomass estimated by allometric equation \u00d7 Wood carbon \ncontent % = Biomass estimated by allometric equation \u00d7 0.5. (Chave et al., \n2005).  \n\n\n\n2.6   Analysis of Data \n\n\n\nUsing MS Excel 2007 and SPSS-23, field data that were gathered through \nquestionnaire surveys were processed and analyzed. Using international \nstandard common tree allometries and regional tables of wood density by \ntree species, above-ground biomass carbon was calculated. Regression \nanalyses were applied in order to examine the association between \nvarious variables.  \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1   Tree Diversity at Various Homegardens in Kurigram District \n\n\n\nA substantial difference was discovered across 64 home gardens in the \nresearch area when tree diversity in various home gardens was assessed \nusing the Shannon-Winner diversity index. Table 2 displayed the diversity \nof trees, and the Shannon-Winner diversity index indicated that the home \ngardens' diversity value ranged from 0 to 1.84. This diversity index \nrevealed that large homegarden (n = 23) had the highest mean value of \n1.17 \u00b1 0.1 and small homegarden (n = 24) had lowest mean value of 0.86 \n\u00b1 0.09 where medium homegarden (n = 17) had moderate mean value of \ntree diversity (1.12 \u00b1 0.09). Large > medium > small is the order of the \nresults. The average number of tree species per hectare in large home \ngardens was 13 with 17 different types, in medium home gardens it was \n24 with 15 different types, in small home gardens it was 33 with 14 \ndifferent types (table 2). According to the study, differences in species \ncomposition and richness, soil properties, climate, topography, and garden \nsize account for the variation.  \n\n\n\nTable 2: Tree Diversity at Various Homegardens in Kurigrm District \n\n\n\nHomegarden \nsize \n\n\n\nMean number of tree \nspecies per hectare \n\n\n\nSpecies recorded in homegardens Shannon-Winner Index (SWI) \n\n\n\nTotal Mean Mean \u00b1 SE Range \n\n\n\nSmall (24) 33 14 16.21 0.86  \u00b1 0.09 0-1.66 \n\n\n\nMedium (17) 24 15 14.93 1.12  \u00b1 0.09 0-1.54 \n\n\n\nLarge (23) 13 17 21.76 1.17  \u00b1 0.1 0-1.84 \n\n\n\nDrescher and Karyono carried out similar research and discovered that \nShannon-Wiener diversity indices in tropical home gardens varied widely, \nranging from 0.93 in rural Zambia, which was lower than the study's mean \nvalue, to almost 3.0 in West Java, Indonesia, which was higher than the \npresent result (Drescher, 1998; Karyono, 1990). A group researchers \nconducted a study that was comparable to the current study but had the \nopposite outcome (Jaman et al., 2016). The small size home garden had the \nhighest mean tree diversity (1.66 \u00b10.05), followed by medium (1.65 \u00b10.05) \nand large (1.6 \u00b1 0.06) home gardens, with a range of 1 to 2.2 and a mean \nvalue of 1.64 0.03.  \n\n\n\n3.2   Tree Species and Their Presence in Various Backyard Gardens \n\n\n\nThere were five major species found in the homegardens namely, \nEucalyptus which is 44.21 % of total number of species followed by neem \n(9.5%), shojna (8.40%), payara (7.06%) and ipil-ipil (5.72%) (Table 3). \nAccording to a survey conducted in Bangladesh's hoar homestead, the \npercentage of fruit species included coconut (80.67%), mango (79.33%), \nguava (63.67%), and papaya (51.67%) (Mannan et al., 2013).  \n\n\n\n3.3   Tree Carbon Stock at Various Home Gardens in Kurigram District  \n\n\n\nSignificant differences were discovered when the carbon stock at different \nhomes was measured. The average tree carbon stock (both above and \nbelow ground) was determined to be 18.00 Mg ha-1 among 64 home \ngardens, with values ranging from 1.66 Mg C ha-1 to 58.93 Mg ha-1. Among \nthe home gardens large home gardens (> 0.05 ha) had the highest carbon \nstock (20.55 \u00b1 2.66 Mg ha-1) with a number of 23 and lowest carbon stock \n\n\n\nTable 3: Presence of Five Major Species Present in Study Areas \n\n\n\nSl. \nNo. \n\n\n\nSpecies \nname \n\n\n\nScientific name \n% of \n\n\n\noccurrence \n\n\n\n1. Eucalyptus Eucalyptus camaldulensis 44.21 \n\n\n\n2. Neem Meliaazedarach 9.25 \n\n\n\n3. Shojna Moringaoleifera 8.40 \n\n\n\n4. Peyara Psidiumguajava 7.06 \n\n\n\n5. Ipil-ipil Leucaenaleucocephala 5.72 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 20-24 \n\n\n\n\n\n\n\n \nCite The Article: Benazir Iqbal, Iffat Jahan Nur, Md Shariful Islam, Bishwajit Kundu, Dr. Nazmun Naher, Dr. Md. Forhad Hossain (2023).  \n\n\n\nTree Species Diversity and Carbon Stock in Charland Homegardens of Bangladesh. Journal of Sustainable Agricultures, 7(1): 20-24. \n\n\n\n\n\n\n\n(15.68 \u00b1 3.00 Mg ha-1) was found in small home gardens (0.02 > 0.03 ha) \nwith a number of 24 while medium carbon stock (17.82 \u00b1 4.20 Mg ha-1) \nwas found in medium home gardens with a number of 17 (table 4). As the \nlarge home garden had the highest tree ha-1 carbon content was higher in \nlarge home garden. \n\n\n\nSimilar research was conducted in central Kerala, India, by Kumar, where \naverage standing carbon stocks of home gardens ranged from 16 to 36 Mg \nha-1per unit area, with small home gardens having higher standing carbon \n\n\n\nstocks than large and medium home gardens due to species richness and \ntree density (Kumar, 2011). The average carbon stock (AGB C stock + BGB \nC stock) was 53.53 Mg ha-1; n=64; ranging from 6.25 to 193.83 Mg ha-1; \nand tiny home gardens had a higher amount of carbon (69.15 Mg ha-1) than \nmedium-sized (47.96 Mg ha-1) and large-sized (39.93 Mg ha-1) home \ngardens. The size of the home gardens, species mix, soil qualities, \nmanagement practices, and financial circumstances of the homestead \nowner all affect the carbon stock inside the home gardens in the Kurigram \ndistrict.  \n\n\n\nTable 4: Tree Carbon Stocks at Various Home Gardens in Kurigram District \n\n\n\nCategory of Home \nGarden \n\n\n\nNumber of home \ngarden \n\n\n\nCarbon stock range Mg/Ha \nMean \u00b1 SE \n\n\n\nHighest Lowest \n\n\n\nSmall 24 45.52 1.66 15.68 \u00b1 3.00 \n\n\n\nMedium 17 58.93 3.38 17.82 \u00b1 4.20 \n\n\n\nLarge 23 48.56 6.52 20.55 \u00b1 2.66 \n\n\n\n3.3.1   Above and Below Ground Carbon (AGC and BGC) Stock in \nDifferent Home Gardens \n\n\n\nAccording to measurements of above- and below-ground carbon stocks \nlarge home gardens had highest quantity of above ground (17.28 Mg ha-1) \nand below ground (3.27 Mg ha-1) carbon and small home gardens had the \nlowest amount of above ground C (12.66 Mg ha-1) but medium amount \nbelow ground C (3.02 Mg ha-1) where medium home gardens had a \nmoderate amount of above ground C (14.19 Mg ha-1) but the lowest \namount of  below ground C ( 2.9 Mg ha-1) (Figure 1). Smaller homesites \n(<0.4 ha) had more soil carbon per unit area (119.3 Mg ha-1) than bigger \nones [0.4 ha] with C stock of 108.2 (Mg ha-1) because they had more trees \nand plant types (Subhrajit et al., 2009). \n\n\n\n\n\n\n\nFigure 1: Above and below ground carbon stocks (Mg ha-1) at \nvarioushomegarden in Kurigram district \n\n\n\n3.3.2  Major Tree Species and Their Carbon Content at Various \nHomegardens \n\n\n\n \nFigure 2: Five major tree species and their C content (Mg) \n\n\n\nAccording to the study, Eucalyptus camaldulensis stored the most carbon \n(33.62Mg ha-1), followed by Moringao leifera (29.37Mg Mg ha-1), \nLeucaena leucocephala (4.89 Mg ha-1), Melia azedarach (3.48 Mg ha-1), \nand Psidium guajava (1.18 Mg ha-1) (Figure: 2). According to the results \nof the current study, Eucalyptus trees were the most prevalent (44.21% \noccurance; Table 4), and as a result, they contain the most carbon. \nAccording to a similar study conducted betel nuts were the most prevalent \n\n\n\nspecies (453 no.), with 15.59 Mg of carbon, followed by mango (362 no., \n26.7 Mg), jackfruit (178 no., 29.71 Mg), Mahagani (146 nos., 17.24 Mg), \nGora neem (128 nos., 5.65 Mg) and Eucalyptus (98 nos., 6.4 Mg) at various \nhomegarden (Jaman et al., 2016). \n\n\n\n3.4   The Relationship Between Tree Diversity and Tree Carbon (Mg \nHa- 1).  \n\n\n\nA linear association between tree diversity and biomass carbon (Mg ha-1) \nwas investigated using the equation y = 12.22x + 5.132 (R2 = 0.154), which \nis depicted in Figure 3 and has a positive R2 value, r=0.041, and a \nsignificance level of p < 0.05 (figure 3). It showed that there was a very \nslight but statistically significant association between tree diversity and \ntree carbon (5% threshold of significance). According to the calculation, \nthe amount of carbon in the atmosphere grew at a rate of 12.22 Mg ha-1 \nfor every unit change in tree diversity. A group researchers conducted a \nsimilar investigation, and, in his research, he discovered a favorable \nrelationship between tree diversity and tree carbon supply (Jaman et al., \n2016). Another study by indicates that increased structural diversity \nraises aboveground carbon in Canadian forests (Weifeng et al., 2011). \n\n\n\n\n\n\n\nFigure 3: The relationship between tree diversity and tree carbon (Mg \nha- 1) at various homegardens in Kurigram \n\n\n\n4.   CONCLUSION \n\n\n\nDue to carbon dioxide emissions, deforestation, and over-exploitation by \nhumans, two pressing challenges in today's globe are climate change and \nbiodiversity destruction. However, the current study demonstrated that \nbecause of their multifaceted functions, home gardens had a potential role \nin mitigation and adaptation to climate change. The study displayed an \noverall picture of the homesteads on Bangladesh's North-Western Char \nIsland in terms of plant diversity and carbon stock, showing how these \nvariables differed depending on the size of the home gardens and their \nvegetational traits. According to the study, household gardens in Charlatan \nhave the potential to play a part in biodiversity preservation as well as \nclimate change adaptation and mitigation. Due to the unfavorable climatic \nconditions, the residents of Char Island are unable to improve their home \ngardens, which forces them to relocate. However, people can make \nimprovements to their home gardens, which are a significant source of \nplant diversity and a carbon store.  \n\n\n\nAUTHOR\u2019S CONTRIBUTION \n\n\n\nBI planed, conduct experiments, gathered and analysed data.  IJN, BK , MSI \nand DNN helped in writing, editing and revising the manuscript. DMFH \nhelped in supervising the experiment. All the authors read and approve \nthe final manuscript. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 20-24 \n\n\n\n\n\n\n\n \nCite The Article: Benazir Iqbal, Iffat Jahan Nur, Md Shariful Islam, Bishwajit Kundu, Dr. Nazmun Naher, Dr. Md. Forhad Hossain (2023).  \n\n\n\nTree Species Diversity and Carbon Stock in Charland Homegardens of Bangladesh. Journal of Sustainable Agricultures, 7(1): 20-24. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\nWe thank the People's Republic of Bangladesh's Ministry of Science and \nTechnology for funding the investigation. We also acknowledge Sher-e-\nBangle Agricultural University for providing support for this investigation.  \n\n\n\nCOMPETING INTEREST \n\n\n\nAll authors accept and declare that there is no conflict of interest either \nfinancially or otherwise. \n\n\n\nREFERENCES \n\n\n\nAhmed, M.F.U., and Rahman, S.M.L., 2004. Profile and use of multi-species \ntree crops in the homesteads of Gazipur district, central Bangladesh. \nJournal of Sustainable Agriculture, 24 (1), Pp. 81\u2013 93. \n\n\n\nAhmed, R.U., 2001. Impact of bank erosion of the Jamuna River on selected \ntowns in the Northern region of Bangladesh. Ph.D. dissertation, \nDepartment of Geography and Environment, Jahangirnagar \nUniversity, Savar, Dhaka. \n\n\n\nAlbrecht, A., Serigne, T.K., 2003. Carbon sequestration in tropical \nagroforestry systems. Agriculture, Ecosystems and Environment, 99, \nPp. 15\u201327. \n\n\n\nBBS, 2011. Statistical Yearbook of Bangladesh. Bangladesh Bureau of \nStatistics. Ministry of Planning, Government of the People of \nBangladesh, Dhaka, Bangladesh. \n\n\n\nBBS., 2012. Statistical Yearbook of Bangladesh. Bangladesh Bureau of \nStatistics.Ministry of Planning, Government of the People of \nBangladesh, Dhaka, Bangladesh. \n\n\n\nBernoux, M., Feller, C., Cerri, C.C., Eschenbrenner, V., Cerri, C.E.P., 2006. Soil \ncarbon sequestration. In: Roose, E., Lal, R., Feller, C., Barthes, B., \nStewart, R. (Eds.), Soil erosion and carbon dynamics. CRC Press, Boca \nRaton, Pp. 13\u201322. \n\n\n\nBMD. 2014. Annual Rainfall data, Bangladesh Meteorological Department, \nDhaka, Bangladesh. \n\n\n\nChave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J.Q., Eamus, D., \nFolster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.P., Nelson., \nB.W., Ogawa, H., Puig, H., Riera, B., and Yamakura, T., 2005. Tree \nallometry and improved estimation of carbon stocks and balance in \ntropical forests. Oecologia, 145, Pp. 87\u201399. \n\n\n\nChowdhury, E.H., 1988. Human adjustment to riverbank erosion hazard in \nthe Jamuna Flood plain, Bangladesh. Human ecol., 16 (40), Pp. 421-\n437. \n\n\n\nHoughton, J., Unruh, J.D., Lefebvre, P., 1993. Current land use in the tropics \nand its potential for sequestering carbon. Global Biogeochimical \nCycles, 7, Pp. 305\u2013320. \n\n\n\nKaryono, 1990. Home Gardens in Java. Their Structure and Function. In: \nLandauer K. and Brazil M. (eds). Tropical Home Gardens. The United \nNations University, Tokyo, Japan, Pp. 138. \n\n\n\nKumar, B.K., 2011. Species Richness and Aboveground Carbon stocks in \nthe Home gardens of Central Kelara, India. J. Agric. Ecos. Env., DOI: \n10.1016/j.agee.  \n\n\n\nKumar, B.M., and Nair, P.K.R., 2004. The Enigma of Tropical Home Gardens. \nAgro-forestry Systems, 61, Pp. 135- 142. \n\n\n\nKumar, B.M., 2011. Species richness and aboveground carbon stocks in the \nhomegardens of central Kerala, India. Agr. Ecosyst. Environ., 140, Pp. \n430\u2013440. \n\n\n\nMannan, M.A., Haque, M.M., and Islam, M.S., 2013. Plant Biodiversity in the \nHoar Homesteads of Bangladesh. IRJALS, 2 (5) Article No. 02 \n\n\n\nJaman, M.S., Islam, M.S., Jamil, M., and Hossain, M.F., 2016. Soil organic \ncarbon and tree diversity in home gardens of Rangpur district, \nBangladesh. International journal of plant and soil science, 13 (4), \nXX-XX, article no IJPSS.29617 \n\n\n\nMontagnini, F., Nair, P.K.R., 2004. Carbon sequestration: underexploited \nenvironmental benefit of agroforestry systems. Agroforestry \nSystems, 61, Pp. 281\u2013295. \n\n\n\nMurthy, I.K., Gupta, M., Tomar, S., Munsi, M., Tiwari, R., 2013. Carbon \nSequestration Potential of Agroforestry Systems in India. J. Earth Sci. \nClimate Change. 4 (1), Pp. 1-7. doi:10.4172/2157-7617.1000131  \n\n\n\nOkigbo, B., 1990. Home Gardens in Tropical Africa. Tropical Home \nGardens. Edited by: Landauer K, Brazil M., Tokyo, Japan: United \nNations University Presson-farm management of plant genetic \nresources and to improve the livelihoods of Nepalese farmers: \nFindings of Baseline Surveys of Four Project Sites (Jhapa, Ilam, \nRupandehi and Gulmi). Working Paper, Local Initiatives for \nBiodiversity, Research and Development, Pokhara, Nepal. \n\n\n\nPearson, T.R.H., Brown, S.L., Birdsey, R.A., 2007. Measurement guidelines \nfor the sequestration of forest carbon. General Technical Report- \nNRS-18, USDA Forest Service, Northern Research Station Newtown \nSquare, USA. \n\n\n\nRoshetko, J.M., and Purnomosiddhi, P., 1998. Second Field Report-\nExpanding Option for Smallholder Tree Production in North \nLumpung.21-24 October. Internationa Center For Research in \nAgroforestry-Winrock International, Bogor, Indonasia. \n\n\n\nRoy, B., Rahman, M.H., and Fardusi, M.J., 2013. Status, Diversity, and \nTraditional Uses of Homestead Gardens in Northern Bangladesh: A \nMeans of Sustainable Biodiversity Conservation. ISRN \nBiodiversity.2013(Article ID 124103):11. \n\n\n\nSaikia, P., Choudhury, B.I., and Khan, M.L., 2012. Floristic composition and \nplant utilization pattern in homegardens of Upper Assam, India. \nTropical Ecology, 53 (1), Pp. 105-118. \n\n\n\nSaikia, P., Choudhury, B.I., Khan, M.L., 2012. Floristic composition and \nplant utilization pattern in homegardens of Upper Assam, India. \nTropical Ecology, 53 (1), Pp. 105-118. International Society for \nTropical Ecology \n\n\n\nShannon, C.E., Weaver, W., 1963. The Mathematical Theory of \nCommunication. University of Illinios Press, Urbana, USA, Pp. 117. \n\n\n\nSRDI. 2014. Soil Survey Report, Soil Resource Development Institute, \nDhaka, Bangladesh. \n\n\n\nSubhrajit, K., Saha, P.K., Ramachandran, N., Vimala, D., Nair, B., Kumar, M., \n2009. Soil carbon stock in relation to plant diversity of home gardens \nin Kerala, India. Agroforest Syst., 76, Pp. 53\u201365 DOI 10.1007/s10457-\n009-9228-8. \n\n\n\nWeerahewa, J., Pushpakumara, G., Silva, P., Daulagala, C., Punyawardena, \nR., Premalal, S., Miah, G., Roy, J., 2012. Are home garden ecosystems \nresilient to climate change? An analysis of the adaptation strategies \nof home gardeners in Sri Lanka. APN Science Bulletin, 2, Pp. 22\u201327. \n\n\n\nWeifeng, W., Xiangdong, L., Zhihai, M., Daniel, D.K., and Changhui, P., 2011. \nPositive Relationship between Aboveground Carbon Stocks and \nStructural Diversity in Spruce-Dominated Forest Stands in New \nBrunswick, Canada. Forest Science, 57 (6), Pp. 506-515. \n\n\n\nYachi, S., Loreau, M., 1999. Biodiversity and ecosystem productivity in a \nfluctuating environment: the insurance hypothesis. Ecology, 96, Pp. \n1463\u20131468. \n\n\n\nYamane, T., 1967. Statistics, An Introductory Analysis, 2nd Ed., New York: \nHarper and Row. \n\n\n\nZaman, S., Siddique, S.U., and Kotoh, M., 2010. Structure and Diversity of \nHomegarden Agroforestry in Thakurgaon District, Bangladesh. The \nopen forest scince journal, 3, Pp. 38-44. \n\n\n\nZanne, A.E., Lopez-Gonzalez, G., Coomes, D.A., Ilic, J., Jansen, S., Lewis, S.L., \nMiller, R.B., Swenson, N.G., Wiemann, M.C., Chave, J., 2009, Data from: \ntowards a worldwide wood economics spectrum, Dryad Digital \nRepository, Global Wood Density Database, Retrieved from: \nhttp://dx.doi.org/10.5061/dryad.234 (accessed on December 26, \n2014). \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 39-43 \n\n\n\nCite The Article: Jha Ritesh Kumar, Bhattarai Natasha, KC Suraj, Shrestha Arjun Kumar, Kadariya Manahar (2019). Rooftop Farming: An Alternative To Conventional \nFarming For Urban Sustainability. Malaysian Journal of Sustainable Agriculture, 3(1): 39-43. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 April 2019 \nAccepted 27 May 2019 \nAvailable online 29 May 2019 \n\n\n\nABSTRACT\n\n\n\nIn ecological terms, modern cities consume 75% of world resources with 2% of global land area and have become a parasite \n\n\n\nand a resource sink. Unmanaged planning and rapid development often result in the destruction of natural resources and \n\n\n\nloss of greenery. Pokhara is rapidly urbanizing into a megacity in Nepal and climate change caused by global warming is a \n\n\n\ngreat menace here. To support the rising requirement of quality food for the skyrocketing population, the main consumption \n\n\n\ncentre should be mobilized for food production.  Rooftop gardens are gaining relevance as they have the potential to meet \n\n\n\nthe growing demand for food in cities and enhance the ecosystem along with the conservation of biodiversity. Thus, the \n\n\n\naddition of greenery element such as a green roof is becoming a trend to solve this problem in Pokhara. Establishment of \n\n\n\ngreen roofs in Pokhara city is arousing the interest of the government and public due to their demonstrated environmental \n\n\n\nbenefits. The objective of this research is to inspect the existing practice and obstacles in rooftop farming that is faced by \n\n\n\npractitioners. Nagdhungha and Birauta are the areas of research here. Two practitioners are interviewed and sixty \n\n\n\nnonpractitioners are surveyed. The result shows that rooftop farming can benefit the environment by greatly reducing \n\n\n\ncarbon in the atmosphere and can assist urban areas by reducing stormwater management cost. Furthermore, the paper \n\n\n\ndemonstrates that the willingness to practice rooftop farming is high among urban dwellers and for future scope, some \n\n\n\nrecommendations are provided in this research. \n\n\n\nKEYWORDS \n\n\n\nBenefit, Rooftop, Urban \n\n\n\n1. INTRODUCTION \n\n\n\nIn the Meantime, Urbanization and Human Activities induced climate \n\n\n\nchange impacts are two distinct hot topics that are worth \n\n\n\ndiscussion.  Urbanization brings various challenges like greater ambient \n\n\n\nnoises, increased environmental stressors and massive demand for food. \n\n\n\n54% of the total world population is urbanized, the share is expected to \n\n\n\nreach up to 66% in less developed regions and 86% in most developed \n\n\n\nregions by 2050 [1]. Moreover, many urban residents are facing problems \n\n\n\ndue to lack of space for vegetation. The pensive problem of urbanization \n\n\n\nand destruction of fertile soils cordially invites the solution of rooftop \n\n\n\ngardening. Where the lives of people are obstructed and there is a scarcity \n\n\n\nof soil and land to cultivate plants, Rooftop gardening is itself a prodigious \n\n\n\nidea for pitching a road towards sustainability. Some cities are trying to \n\n\n\nboost sustainability through urban farming as a possible remedy to these \n\n\n\nproblems [2]. Though it may look a stressful and energy draining job to get \n\n\n\nengaged in gardening or either to fill our own plates of vegetables from \n\n\n\nour own free spaces of the roof, it doesn\u2019t take much time. Side by side \n\n\n\nrooftop gardening is itself a better way of utilizing free family time. There \n\n\n\nis a Chinese Proverb \u2018\u2019We all are Farmers by birth\u2019\u2019 The time we spend in \n\n\n\ngrowing vegetables and plants for ourselves we mend our own souls. \n\n\n\nIn the modern times of massive misuse of pesticide and degrading soil \n\n\n\nfertility, the fright of health hazards while consuming the market  \n\n\n\nvegetables are inside the minds of people somewhere.  Roof Top \n\n\n\nGardening may seem a small step but it is a leap ahead for sustainability \n\n\n\nand combating the havoc of climate change hazards. Microclimate can be \n\n\n\nmodified by rooftop farming because of its contribution to mitigate the \n\n\n\necological problems and promotion of metropolitan food system. Rooftop \n\n\n\ngarden regulates the temperature on the roof as well as the room below \n\n\n\nthe roof garden [3]. 60% of heat gain can be prevented from the vegetation \n\n\n\nof the green roof system. This result to decrease in temperature as \n\n\n\ncompared to other buildings which lack the rooftop garden [4]. Moreover, \n\n\n\ngreen roofs provide insulation by decreasing transport heat which leads \n\n\n\nto a reduction in electricity as well as natural gas consumption. Green roof \n\n\n\ncut off 30% of all CO2 emissions for heating or cooling the building in many \n\n\n\ndeveloped nations. \n\n\n\nVarious environmental issues like massive pollution, waste generation, \n\n\n\nthe rapid growth of population, high consumption and unsustainable use \n\n\n\nof natural resources challenge the urban centre in developing countries. \n\n\n\nThe rooftop garden captures rainwater through absorption by the \n\n\n\nvegetation and minimizes overflowing impact on roads [5]. Carbon \n\n\n\nemitted at the local level and various infrastructures are absorbed by \n\n\n\nplants in the rooftop garden and used as a source for photosynthesis. \n\n\n\nGreen roof on top of the building reduces air pollution by removing \n\n\n\nparticulate matter and pollutant gases like nitrous oxide, sulfur dioxide \n\n\n\nand carbon monoxide, in turn, cut off greenhouse gas emissions [6,7]. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.01.2019.39.43 \n\n\n\n  RESEARCH ARTICLE \n\n\n\nROOFTOP FARMING: AN ALTERNATIVE TO CONVENTIONAL FARMING FOR \nURBAN SUSTAINABILITY \n\n\n\nJha Ritesh Kumar1*, Bhattarai Natasha1, KC Suraj1, Shrestha Arjun Kumar2 and Kadariya Manahar3 \n\n\n\n1Faculty of Agriculture, Agriculture and Forestry University, Rampur, Chitwan, Nepal \n2Department of Horticulture, Agriculture and Forestry University, Rampur, Chitwan, Nepal \n3Senior Horticulture Development Officer, Government of Nepal \n*Corresponding Author Email: ritesh.lord.of.truth@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:ritesh.lord.of.truth@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 39-43 \n\n\n\nCite The Article: Jha Ritesh Kumar, Bhattarai Natasha, KC Suraj, Shrestha Arjun Kumar, Kadariya Manahar (2019). Rooftop Farming: An Alternative To Conventional \nFarming For Urban Sustainability. Malaysian Journal of Sustainable Agriculture, 3(1): 39-43. \n\n\n\nBesides, the benefit of recreation, fresh air and good ambience is an added \n\n\n\nbonus. By 2050, the food production will drop by more than 50% whereas, \n\n\n\nthe population is estimated to reach 9 billion [8]. If we take some hours off \n\n\n\nfrom our robotic lives in our roofs, we will be able to discover the panacea \n\n\n\nfor many big problems like food insecurity and hunger. The vegetables \n\n\n\nwhich are grown in roofs are far better than those in fields as we can easily \n\n\n\nmodify the microclimate of kitchen gardens according to our will and we \n\n\n\ncan deny using harmful pesticides. We can use locally available resources \n\n\n\nincluding local varieties of vegetables which are prolific but less in use, we \n\n\n\ncan use abandoned bins, drums, plastic bags as pots for the vegetables, \n\n\n\nlocal ropes as staking, rainwater as irrigation [9]. Roof Top gardening \n\n\n\ncertainly considers some important points to note. We must be well aware \n\n\n\nof the load-bearing ability of the roofs; we can in the other hand modify \n\n\n\nthe engineering of the roof for cultivation taking help of engineers and \n\n\n\ntechnicians too.  The problem of wind and direct rainfall can be an issue \n\n\n\nbut on the other hand, we can develop sustainable infrastructure which \n\n\n\nwill be suitable for carrying out this eminently beneficial process in our \n\n\n\nroofs. For controlling pests and insects we can also use the concepts of \n\n\n\nIntegrated Pest Management. \n\n\n\nOn the basis of the above consideration, a need to study rooftop gardening \n\n\n\nin Pokhara is realized. This study was conducted for the evaluation of \n\n\n\nquantifying soft benefits of the rooftop garden in Pokhara. Besides this, the \n\n\n\nstudy discusses future prospects and the potential benefits of a rooftop \n\n\n\ngarden. The important things are objectives, value and sustainability. If \n\n\n\nrooftop gardening comes with a plethora of advantages like sustainable \n\n\n\nproduction, decreasing family monthly costs, improving the quality of air \n\n\n\nin roofs and providing healthy nutritious vegetables straight from roofs to \n\n\n\nplates, it certainly deserves some efforts. We can't deny rooftop gardening \n\n\n\nis the bright solution for dozens of urban problems. \n\n\n\n2. STUDY METHODS \n\n\n\n2.1 Study vicinity \n\n\n\nThis research was conducted in an urban corridor of Pokhara city. In 2018, \n\n\n\nPokhara metropolitan city started promoting rooftop farming by \n\n\n\nproviding training and subsidies on Rooftop farming. For the purpose of \n\n\n\nthis study, Nagdhungha and Birauta areas have been chosen as a study site \n\n\n\nas there was the highest number of enlisted practitioners according to the \n\n\n\ndatabase of Pokhara metropolitan city. Most of the buildings where the \n\n\n\npractitioners did urban farming in the selected areas were two to three \n\n\n\nstorey high. \n\n\n\n2.2 Data collection \n\n\n\nTwo practitioners were selected from each of the selected sites and were \n\n\n\ninterviewed to discover the existing situation as well as the opportunities \n\n\n\nand obstacles of rooftop farming. Again 30 practitioners from each site \n\n\n\nwere surveyed with a semi-structured questionnaire to perceive their \n\n\n\npoint of view regarding rooftop gardening. \n\n\n\nTable 1: Basic Description of two practitioners' of Birauta and Nagdhunga \n\n\n\nAreas \n\n\n\nPractitioner Gayatri Aryal Damar Subedi \nAddress Birauta Naghdhungha \nType of Building Private Private \nStorey number in Building 2 and half 3 and half \nTenure Type Building Owner Building Owner \nRoof Area of Building 4 Aana 5 Aana \nGardening Area 1.9336 Aana 2.56 Aana \nStarting Time of Garden 2068 BS 2074 BS \nSatisfaction status High High \n\n\n\n3. DETAIL ABOUT TWO ROOF GARDEN OWNERS\n\n\n\n3.1 Basic information on practitioners \n\n\n\nMiss Gayatri Aryal is a Housewife. She lives in Birauta Pokhara, Kaski.  \n\n\n\nInspired from Rooftop gardening she started growing vegetables on her \n\n\n\nroof from 2068 BS. She began gardening with the basic knowledge she had \n\n\n\nwithin herself with Rs. 2000 (18$ Approx.) At first, she started the roof \n\n\n\ngardening with basic plants like onions, garlic, coriander.  Two years later \n\n\n\nafter getting support from District Agriculture Office, she got knowledge \n\n\n\nabout cultivation practice of other vegetables like brinjals, tomato, chillies \n\n\n\netc. She has now become a role model in her community as a rooftop \n\n\n\ngardener and her roof is nowadays crowded with the local enthusiasts \n\n\n\nwho are inclined in rooftop gardening. \n\n\n\nIn the other hand, Damar Subedi from Naghdhungha Pokhara is the \n\n\n\npresident of a community cooperative. He started the practice from 2074 \n\n\n\nBS. His initial cost of the Rooftop gardening was Rs1500 (13$ Approx.) \n\n\n\nAlthough he started as a capricious practitioner, the level of satisfaction is \n\n\n\nhigh in himself now. He started with the early growing species and flowers \n\n\n\nlike petunias and marigold. Later he started to thrive on the rooftop \n\n\n\ngarden for the consumption of vegetables. As a cooperative leader, his \n\n\n\nwork has impressed the society and many are following him now. \n\n\n\n3.2 Cultivation Methodology \n\n\n\nThe cultivation practices in both cases of practices are more influenced by \n\n\n\nconventional technology. The use of soil in abandoned pots, tubes, utensils \n\n\n\nhas been practised. Side by side use of modern clay pots and plastic bags \n\n\n\nare also been used. The leafy vegetables can be seen in both pots and beds \n\n\n\ntoo. \n\n\n\n3.3 Plants and Production at the existing stage \n\n\n\nAt the present moment tomatoes, coriander, onions, garlic, eggplant, peas, \n\n\n\nleafy vegetables, cabbages, cauliflower, lady\u2019s finger and gourds are the \n\n\n\ndominant vegetables covering the gardens. Some plants of citrus, lime, \n\n\n\nmint and beans are also seen. The rotation of vegetables is practised in the \n\n\n\ngardens according to seasons.  Offseason practise is not being adopted \n\n\n\nthough. \n\n\n\nThe winter vegetables like brinjal, cabbage, cauliflower, tomatoes are seen \n\n\n\nmore in the garden in the cold season and the summer crops include \n\n\n\ngourds. Lime and citrus are also grown in dominant form. The production \n\n\n\nof tomatoes, brinjal, cabbage and many other vegetables are satisfactory \n\n\n\nand adequate enough to fill the plates and stomach of Aryal and Subedi \n\n\n\nfamilies. \n\n\n\nIn Recent years, Dambar Subedi has been assisted from various agriculture \n\n\n\ncooperatives and has been enthusiastically growing fruit trees like guava \n\n\n\nand pomegranate. On the other hand in the garden of Gayatri Aryal leafy \n\n\n\nvegetables are the most grown plants which have been keeping the roof \n\n\n\nbusy and green. \n\n\n\n3.4 Maintenance and Reclamation \n\n\n\nIn both cases, the use of organic fertilizers and organic way of pests control \n\n\n\nare being adopted. The use of the chemical is very low. An additional \n\n\n\ngardener is not hired. The use of compost manure and eco prangarik mal \n\n\n\nhas been practised. Use of drums, bricks, pots have been done. In the case \n\n\n\nof soil loss and leaching, new nutrition mixed soil is refilled and a proper \n\n\n\ndrainage system is maintained to avoid damping and water stagnation in \n\n\n\nthe roof. \n\n\n\n4. ESTIMATION OF SOFT BENEFITS OF THE ROOFTOP GARDEN\n\n\n\nNo, any work has been carried out yet in Nepal for the quantifying roof \n\n\n\ngarden benefits, the methodologies and standard values for quantifying \n\n\n\nthese benefits are in accordance with [10]. Five perspectives (i.e. property \n\n\n\nvalue amplification, food production, stormwater retention, air quality \n\n\n\nenhancement, carbon sequestration) are used to calculate the pecuniary \n\n\n\nworth of the soft benefit of rooftop farming. \n\n\n\n4.1 Social benefits \n\n\n\nThe natural character of the green roof provides solace from concrete \n\n\n\nconstruction in busy and noisy urban areas. Presence of greenery in roof \n\n\n\ngenerate the feeling of safety, calm psychological effect, helps to reduce \n\n\n\nblood pressure and maintain heartbeat. The green ambience and lively \n\n\n\nenvironment created by the plants also counts in social benefit. \n\n\n\nAdditionally, it also adds aesthetic value and can be a perfect place for \n\n\n\ngatherings and informal meetings and foster the social relationship. \n\n\n\nWe asked two practitioners from Nagdhunga and Birauta of Pokhara. Mrs \n\n\n\nGayatri Aryal of Birauta stated that the opinions and perspective of her \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 39-43 \n\n\n\nCite The Article: Jha Ritesh Kumar, Bhattarai Natasha, KC Suraj, Shrestha Arjun Kumar, Kadariya Manahar (2019). Rooftop Farming: An Alternative To Conventional \nFarming For Urban Sustainability. Malaysian Journal of Sustainable Agriculture, 3(1): 39-43. \n\n\n\nneighbours have been changed since she started to adopt rooftop gardens. \n\n\n\nThey ask her about the techniques and quite often they also visit her roof. \n\n\n\nShe herself admits that she has been in limelight and a focal person in her \n\n\n\ncommunity regarding gardening and rooftop practice. \n\n\n\nMr Dambar raj subedi of Nagdhungha have also resembling experiences. \n\n\n\nIn the scarce time of vegetables in the market and the time when there is \n\n\n\nhavoc of chemically sprayed vegetables in the market, the rooftop garden \n\n\n\ncomes to great advantage. His family of 4 members are sustainably \n\n\n\nconsuming vegetables from their roof and they spend their family time \n\n\n\ntogether in the roof. People also come to his house and ask about the way \n\n\n\nto start rooftop gardening and they are in other words lured by his \n\n\n\npractice. \n\n\n\n4.2 Financial and Ecological benefits \u2013 the pecuniary value of soft \n\n\n\nbenefits \n\n\n\n4.2.1 Property value \n\n\n\nGreen infrastructure increases the property value as well as increases the \n\n\n\nmarketability of nearby real estate. Thus, the rooftop garden favours both \n\n\n\nto owner and surrounding. The green roof on the building increases the \n\n\n\nproperty value by 7% if it is productive and by 11% if it is recreational. \n\n\n\nThe formula widely used to assess the property value is, \n\n\n\nb = 0.07 * v  \n\n\n\nIn the above-mentioned formula, \"b\" means the benefit value and \"v\" \n\n\n\nrepresents the \\ roof garden value of owning property. Both prices of land \n\n\n\nand building price are important to calculate the property value. On an \n\n\n\naverage range, the present price of land per ropani in Birauta area is \n\n\n\napproximately found to be Nrs.  2,50,00,000. The building price per square \n\n\n\nfeet is Nrs. 1600. The area in which the building is constructed is \n\n\n\napproximately found to be 4.5 anna, the area of the building was recorded \n\n\n\nas 127.185 square meters and the building is 2 and a half storeyed, so the \n\n\n\nfinal price of the land comes to be Nrs. 70,03,125 and the building is Nrs. \n\n\n\n54,76,000. Thus, the value of the owner\u2019s roof garden property is Nrs. \n\n\n\n1,24,79,725. \n\n\n\nTherefore, value of benefit, b = 0.07* 1,24,79,725 =NRs. 8,73,580.75 \n\n\n\ni.e.US$7769.30 \n\n\n\nOn the other hand, the average price of land per ropani in Nagdhungha \n\n\n\narea is Nrs. 3,20,00,000.  The price of the building per square feet is Nrs. \n\n\n\n2200. The plot of the building is over 7 annas, the building area is \n\n\n\napproximately 5 annas and the structure is 3 and a half storeyed, So the \n\n\n\nprice of land in all total is Nrs. 1,40,00,000.00   and that of the building is \n\n\n\nNrs. 1,31,76,625. Thus, the value of the roof garden of the owner is Nrs. \n\n\n\n2,71,76,625. \n\n\n\nHence, value of benefit, b = 0.07*2,71,76,625 = NRs.19,02,363.75 = \n\n\n\nUS$16918.92 \n\n\n\n4.2.2 Stormwater retention \n\n\n\nGreen roofs retain stormwater which mainly is dependent primly over the \n\n\n\ncomponents like which plants are used, what is the depth of the planting \n\n\n\nmedia used and its formation and the environmental effects can be caused \n\n\n\nto lessen the effects of impervious runoff. However, when the media used \n\n\n\nin green roof become saturated, the runoff will occur but it takes some \n\n\n\ntime.  This delay in the runoff can control the overflow of stormwater This \n\n\n\ndelay can prevent overflowing of stormwater in the system and the \n\n\n\nintensity of rainwater leaving the roof is pretty slower than in case of \n\n\n\nconcrete floor which reduces the erosive power of runoff. The rooftop \n\n\n\ngarden also reduces diurnal fluctuations of temperature at membranes of \n\n\n\nthe roof that prevents the pressure of per day expanding and contractions \n\n\n\nand can increase the life of membrane life by three or two times. In \n\n\n\nWashington DC, if all roofs in the city are replaced with the green roof, the \n\n\n\nair pollutants that can be removed would be accountable with the figure \n\n\n\nof 58 metric tons. If we use a combination of 80% extensive and 20% \n\n\n\nintensive ratio in the green roof, it is possible to reduce runoff volume by \n\n\n\n69% as compared to conventional roofs [11]. \n\n\n\nThe formula widely used to calculate stormwater retention benefit is, \n\n\n\nb = (R+E)*C*d  \n\n\n\nIn this formula, the value \u2018b' denotes the yearly value of benefit and \u2018a' \n\n\n\ndenotes the garden area of the roof in square meters. Value of erosion \n\n\n\nmitigation, E is worth of $13.66/m3 i.e. Nrs. 1535.93 /m3 (Tomalty & \n\n\n\nKomorowski, 2010). The lowest value for retention of stormwater services \n\n\n\nis $20.13/m3 (R) signifies retention pond and the highest value found to \n\n\n\nbe $1059.44/m3 (R) denotes retention basin. The capacity of retention was \n\n\n\n42.7 L/m2roof (C) as used by (Carter & Keeler, 2008) has been adopted for \n\n\n\nthe calculation purpose. \n\n\n\nIn this research study, the stormwater management benefit gained from \n\n\n\nrooftop farming of the Birauta practitioner is estimated to be in the range \n\n\n\nof Nrs. 9972.30 and Nrs. 3,16,703 i.e. US$ 88.69 and US$ 2816.64 and in \n\n\n\ncase of the practitioner from Nagdhunga area is estimably found to be in \n\n\n\nthe range of Nrs.13201.58 and Nrs.419282 i.e. US$ 117.41 and \n\n\n\nUS$3728.94. As these two buildings reside in a city environment where \n\n\n\nthere is significantly high urbanization with very high values of land, low-\n\n\n\ncost solutions for managing stormwater cannot be an option. For all of \n\n\n\nthese reasons, the values of the benefits are likely to be high (around \n\n\n\nNrs.3,16,703   i.e. US$ 2816.64 and around Nrs. 4,19,282 i.e. US$3728.94). \n\n\n\n4.2.3 Air quality \n\n\n\nGreen roof on top of the building reduces air pollution by removing \n\n\n\nparticulate matter and pollutant gases like nitrous oxide, sulfur dioxide \n\n\n\nand carbon monoxide as well as reduces energy demand for regulating \n\n\n\nhousing temperature which in turn cut off greenhouse gas emissions. \n\n\n\nFurthermore, green roof increases air quality through carbon \n\n\n\nsequestration which mainly depends upon the size of the plant and \n\n\n\nthickness of substrate used in green roofs [12]. In Singapore, Sulphur \n\n\n\ndioxide and particles level after installation of the green roof on a 4000 \n\n\n\nsquare metre roof was reduced by 6% and 37% respectively above the \n\n\n\ngreen roof in the air [13]. In Bologna, Italy, Rooftop top garden could \n\n\n\ncapture an estimated 624 tons of CO2 every year and meet 77% of \n\n\n\nresidents need for vegetables in the city if all suitable flat roof space can \n\n\n\nbe used for urban agriculture. \n\n\n\nThe formula widely used to calculate air quality benefit is, \n\n\n\nb =  (g/12months) * [Hsg * asg + Htg * atg  + Hd* ad]  \n\n\n\nIn this formula, b denotes the yearly value of benefit whereas g means the \n\n\n\ngrowing period in months.; Hsg, Htg and Hd simultaneously denotes the \n\n\n\nhealth benefit for short grass pollution absorption, for tall herbaceous \n\n\n\nplant pollution absorption and  deciduous plant pollution absorption in \n\n\n\n$/m2*year respectively and asg, atg and ad turn by turn denotes the area \n\n\n\ncovered with short grass, with tall herbaceous plant and with deciduous \n\n\n\nplants in m2 respectively. Duration of the growing period i.e. in months (g) \n\n\n\nof fruits, vegetables and other plants is 12 months because in Nepal crops \n\n\n\nare grown all year round. The annual pollutant removal health benefit \n\n\n\nvalue for different types of vegetation has been used as 0.0521 US$/m2 for \n\n\n\nshort grass, 0.0673 US$/m2 for tall herbaceous plants and 0.0839 US$/m2 \n\n\n\nfor deciduous trees. Therefore, along with this study, the benefit in air \n\n\n\nquality from rooftop farming for the practitioner of Birauta area is found \n\n\n\nto be of worth Nrs. 469.45 i.e. US$ 4.175 and for the case of the practitioner \n\n\n\nof Nagdhungha area is found to be of worth Nrs. 761.15 i.e. US$ 6.77. \n\n\n\n4.2.4 Food value \n\n\n\nPokhara is a Metropolitan City. It is the second most urbanized city in \n\n\n\nNepal. The prime occupation of people here in Pokhara is business \n\n\n\nincluding hotel, restaurants, tourism industry and others but not \n\n\n\nagriculture. The shifting occupation of people from agriculture to non-\n\n\n\nagriculture is significantly prevalent here. People generally buy vegetables \n\n\n\nfrom the market. Here the trend of supermarkets and shopping complexes \n\n\n\nis massive now. For increasing the post-harvest life and shelf life of \n\n\n\nvegetables, the use of chemicals is not that uncommon. Rooftop \n\n\n\ngardeners/practitioners are completely away from these problems. They \n\n\n\nare also non-agriculture occupants but they spend their fixed time of day \n\n\n\nhours in the rooftop for growing vegetables and plants and this culture \n\n\n\nseems to be impactful here. The spaces of house which are generally \n\n\n\nabandoned in other cases are utilized well in Rooftop gardening for food \n\n\n\nproduction. Singapore imports a large number of vegetables to meet \n\n\n\npresent-day needs and after implementation of rooftop farming across \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 39-43 \n\n\n\nCite The Article: Jha Ritesh Kumar, Bhattarai Natasha, KC Suraj, Shrestha Arjun Kumar, Kadariya Manahar (2019). Rooftop Farming: An Alternative To Conventional \nFarming For Urban Sustainability. Malaysian Journal of Sustainable Agriculture, 3(1): 39-43. \n\n\n\npublic housing estates, the production can meet 35.5% of food demand \n\n\n\n[14-15]. \n\n\n\nThe formula widely used to calculate food production benefit is, \n\n\n\nb = P * g * a  \n\n\n\nIn the above formula, \"b'' denotes the yearly value of the benefit and \"a\" \n\n\n\ndenotes the roof garden area in meter square. The period of duration(g) of \n\n\n\nfruits, vegetable and any other plants is 12 months in Nepal because the \n\n\n\ncrop is grown year around in the country. In the case of mixed fruit and \n\n\n\nvegetables (low case scenario), productivity (P) is found to be Nrs. 224.88 \n\n\n\nor $2 per square meter per month and for lettuces, herbs and flowers (high \n\n\n\ncase scenario) like plants, productivity (P) is found as Nrs. 2248.8 or $20 \n\n\n\nper square meter per month. Therefore, for this study, the food production \n\n\n\nvalue from rooftop farming of the practitioner of Birauta area is estimated \n\n\n\nto be between the range of Nrs.1,65,880.48 and Nrs. 16,58,804.83 i.e. US$ \n\n\n\n1475.28 and US$ 14752.8. We found that the garden generally produces \n\n\n\nmixed fruits and vegetables, so the total value of production is likely to be \n\n\n\nat the lower end of this range (around Nrs. 1,65,880.48 i.e. US$ 1475.28). \n\n\n\nHere again, the food production value from rooftop farming of the \n\n\n\npractitioner of Nagdhungha area is estimated to be in between the range \n\n\n\nof Nrs.2,19,608.81 and Nrs.21,96,088 i.e. US$ 1953.12 and US$ 19531.2. \n\n\n\nThis garden also produces mixed fruits and vegetables, so the total value \n\n\n\nof production is likely to be at the lower end of this range (around \n\n\n\nNrs.2,19,608.81 i.e. US$1953.12 ). \n\n\n\n4.2.5 Carbon sequestration \n\n\n\nRapidly developing cities like Pokhara are dealing with population growth, \n\n\n\npoor resource endowments, pollution and environmental degradation. \n\n\n\nGlobally warming climate could have detrimental effects on the \n\n\n\nenvironment. Employing vegetation in highly populated areas as a carbon \n\n\n\ncapture and storage system encase an engineering strategy that can be \n\n\n\neasily installed by the urbanites. Rooftop farming on an urban fringe could \n\n\n\npotentially reduce greenhouse gas emissions and is one of the potential \n\n\n\nways of mitigating climate change. \n\n\n\nThe formula widely used to calculate carbon sequestration  benefit is, \n\n\n\nb = Sd * ad + Sg * ag + Sf * af  \n\n\n\nIn this formula \u201cb\u201d  means the value of benefit in $/year; Sd, Sg and Sf \n\n\n\nrepresents  the value of carbon sequestration by deciduous plants, by \n\n\n\ngrasses and by productive agriculture in $/ha*year respectively and ad, ag \n\n\n\nand af denotes the area of roof garden covered by deciduous plants (ha), \n\n\n\ncovered by grasses and covered by productive crops in hectare \n\n\n\nrespectively. The total value of carbon sequestration by deciduous plants, \n\n\n\nby grasses and by productive agriculture has been estimated to be worth \n\n\n\nof $ 39.11/ha, $ 28.46/ha and $28.59 /ha respectively. For this study, the \n\n\n\ncarbon sequestration benefit from rooftop farming of the practitioner of \n\n\n\nBirauta area is found as the worth of Nrs. 20.70   i.e. US$ 0.184 and the \n\n\n\npractitioner of Nagdhungha area is found to be worth of Nrs. 27.84 i.e. US$ \n\n\n\n0.248. \n\n\n\n5. OBSTACLES ON ROOFTOP GARDEN\n\n\n\n5.1 For Practitioners \n\n\n\nThe availability of true to type plant varieties is one of the major problems \n\n\n\nfaced as there is no quality assurance by any of the nursery providing the \n\n\n\nseedlings. Generally, People cultivate locally available and inferior plant \n\n\n\nvarieties instead of improved varieties. Another big problem is the lack of \n\n\n\nhelp from the government sector. If little more assisted, the productivity \n\n\n\ncan get raised. The shadow of other buildings and structures is also the \n\n\n\nproblem. \n\n\n\n5.2 For Non-Practitioners \n\n\n\nOne of the problems for non-practitioners for not establishing Roof-Top \n\n\n\nGardening has been observed that a Roof is a vital place for recreation of \n\n\n\nchildren and families. If Roof Top gardening is practiced the Children \n\n\n\nbecome dependent on technology; it is the fear of most of the householders \n\n\n\nIn Pokhara most of them are tenants and it is very difficult to convince \n\n\n\nlandlords to allow them to Practice Roof Top Gardens because of several \n\n\n\nfears high load and damping. \n\n\n\n6. OPPORTUNITIES FOR ROOFTOP GARDENING IN POKHARA KASKI\n\n\n\n6.1 Household demand thrives and supply \n\n\n\nThe production of different vegetables according to seasons and \n\n\n\nappropriate conditions are of great benefit for supplying the family \n\n\n\ndemand for vegetables. They are being able to cut off the cost of vegetables \n\n\n\nwhich was prevalent before adopting rooftop gardening. When the \n\n\n\nscarcity of vegetables and the supply chain of vegetables get distorted in \n\n\n\nthe market, householders are okay to stay calm because the rooftop \n\n\n\ngarden is their back support. It has also been found the householders who \n\n\n\nwant to consume more vegetables are involved in managing their rooftop \n\n\n\ngarden in a more efficient way. \n\n\n\nThe rooftop gardening is certainly serving the demand of households and \n\n\n\nsaving a major portion of household expense very decently. \n\n\n\n6.2 Scope and Support of Roof Top Gardening \n\n\n\nThere are certain conditions and requirements for operating a rooftop \n\n\n\ngardening. Every person cannot adopt rooftop gardening because there \n\n\n\nare limitations too. A portion of space is very preliminary for Rooftop \n\n\n\ngardening. The house owners with small houses cannot adopt rooftop \n\n\n\ngardens. The difficulty in management occurs if the roof is too small. If the \n\n\n\nroof is too large, the management cost of rooftop gardening can be \n\n\n\novertiring. If rooftop gardening has to be adopted the previously used \n\n\n\nspace for sports, recreation, family time and cloth drying must be \n\n\n\nforgotten. Rooftop gardening involves extra costs, doesn't matter it might \n\n\n\nbe of a small amount. The owner must spend extra and the only rooftop \n\n\n\ngarden is possible to say the truth. \n\n\n\n7. THREATS TO ROOFTOP FARMING IN POKHARA \n\n\n\nThis study figures out why non-practitioners are yet not involved in \n\n\n\nrooftop farming. Majority of them answered that they did not have \n\n\n\ntechnical knowledge about farming on the roof. 31.66% of people stated \n\n\n\nthat they are afraid of using soil as the media on their roof because it leads \n\n\n\nto heavy load which may create the problem of seepages in the roof. Lack \n\n\n\nof leisure time is also a constraint for not practicing.  \n\n\n\nFigure 1: Reasons behind not willing to practice Rooftop Farming \n\n\n\n8. RECOMMENDATIONS \n\n\n\n\u2022 Most of the urban pedestrians are aware and well conscious about \n\n\n\nRoof Top Gardening, at least they have heard about it once but the fear \n\n\n\nof extra load to be thrived due to the soil and the hefty containers \n\n\n\nremain a major problem. However, If the design of the building and \n\n\n\nroof are modified in such a way that it can hold and thrive certain load \n\n\n\nof vegetations, growing media and containers, these problems can be \n\n\n\nsolved. Use of light rooting media other than soil like coco peat, clay \n\n\n\nballs, can also be practised. Durable grow bags can be used instead of \n\n\n\nheavy clay pots. \n\n\n\n\u2022 Ceiling dampness/Roof dampness is also a great fear and a threat to \n\n\n\nthe householders. Placing the containers, drums, pots above brick or \n\n\n\nany firm substance can help to avoid this problem. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 39-43 \n\n\n\nCite The Article: Jha Ritesh Kumar, Bhattarai Natasha, KC Suraj, Shrestha Arjun Kumar, Kadariya Manahar (2019). Rooftop Farming: An Alternative To Conventional \nFarming For Urban Sustainability. Malaysian Journal of Sustainable Agriculture, 3(1): 39-43. \n\n\n\n\u2022 Although being a Metropolitan City, the extension of the education of \n\n\n\nrooftop farming and the proper way to carry it out still remain in the \n\n\n\ndark corners. The government sectors should make people aware of \n\n\n\nthe probable good impact of training and seminars about rooftop \n\n\n\ngardening in growers/residents through training. \n\n\n\n\u2022 Choice of crops is also another important point to consider. Instead of \n\n\n\nharvesting in bulk and once, multiple picking can be of great \n\n\n\nadvantage. For this advantage, the crop which can grow moderately \n\n\n\nfor a longer period with multiple picking can be selected. On an \n\n\n\nimportant note, they can be of great advantage for evergreen \n\n\n\nfreshness and ambience in the roof if done so. \n\n\n\n\u2022 The problem of the scorching sun is prevalent on the roof which \n\n\n\nreduces the moisture level in the growing substrate and leads to \n\n\n\nvolatilization of nutrients. For avoiding this problem, continuous \n\n\n\nmoisture should be maintained by installing a drip irrigation system. \n\n\n\n\u2022 Hailstones and strong wind prevalence in monsoon are the problems \n\n\n\nof concern in Pokhara. For avoiding all these we can use roof boundary \n\n\n\nwall, hail nets to avoid hail problem and good staking must be \n\n\n\nmaintained to make plants more stable. \n\n\n\n9. CONCLUSION\n\n\n\nThus, this study concludes that green roofs can be a potential way to meet \n\n\n\nthe growing demand of fresh food and also make a major improvement in \n\n\n\nthe quality life of urban people by contributing to the various \n\n\n\nenvironmental benefits such as purifying the air by acting as a reservoir of \n\n\n\ncarbon dioxide and place for stormwater management. It also has benefits \n\n\n\nat the societal level as it can be a perfect place for gatherings and relaxing \n\n\n\nand also helps to increase the property value of the rooftop garden host \n\n\n\nproperty as well as the surrounding. Being the rapidly developing city the \n\n\n\npopulation of Pokhara is skyrocketing day by day resulting in the higher \n\n\n\ndemand for quality food and fresh air. If the government, as well as the \n\n\n\nother responsible organizations, step forward to foster rooftop gardening, \n\n\n\na sustainable and beautiful city with plenty of quality and fresh food can \n\n\n\nbe maintained. It is hoped that this study will be beneficial to the ongoing \n\n\n\nresearch on environmental, economic and social benefits of green roof as \n\n\n\nwell as support the Pokhara Metropolitan city to develop the policy for \n\n\n\npromotion of rooftop farming in the beautiful city of lakes. \n\n\n\nREFERENCES \n\n\n\n[1] Nations, U. 2014. World Urbanization Prospects: The 2014 Revision.\n\n\n\n[2] Smit, J., Nasr, J., Ratta, A. 2001. Urban Agriculture Yesterday and \n\n\n\nToday. In J. Smit, J. Nair, & A. Ratta, Urban Agriculture (2001 ed.). The \n\n\n\nUrban Agriculture Network, Inc. \n\n\n\n[3] Gupta, G., & Mehta, P. (n.d.). Roof Top Farming a Solution to Food \n\n\n\nSecurity and Climate Change Adaptation for Cities. Springer International \n\n\n\nPublishing AG 2017. \n\n\n\n[4] Williams, N. S., Rayner, J. P., Raynor, K. J. 2010. Green roofs for a wide \n\n\n\nbrown land: Opportunities and barriers for rooftop greening in Australia. \n\n\n\nUrban Forestry and Urban Greening, 9 (3), 169-272. \n\n\n\n[5] Ries, A. 2014, April 23. Green Roofs- Drawbacks and Benefits.\n\n\n\n[6] Currie, B. A., Bass, B. 2008. Estimates of air pollution, mitigation with \n\n\n\ngreen plants and green roofs using the UFORE model. Urban Ecosystem, \n\n\n\n409-422. \n\n\n\n[7] Heisler, G. M. 1985. Effects of Individual Trees on the Solar Radiation \n\n\n\nClimate of Small Buildings. Urban Ecology. \n\n\n\n[8] 2017. World population projected to reach 9.8 billion in 2050, and \n\n\n\n11.2 billion in 2100. United Nations Department of Economic and Social \n\n\n\nAffairs. \n\n\n\n[9] Rain Gardens. (n.d.). Stormwater Pub. \n\n\n\n[10] Tomalty, R., Komorowski, B. 2010. The Monetary Value of the Soft \n\n\n\nBenefits of Green Roofs. Montreal: Canada Mortgage and Housing \n\n\n\nCorporation (CMHC). \n\n\n\n[11] Deutsch, B., Whitlow, H., Sullivan, M., Savineau, A. 2005. A Green \n\n\n\nRoof Vision Based on Quantifying Stormwater and Air Quality Benefits. \n\n\n\n[12] Getter, K. L., Rowe, B. D., Robertson, P. G., Cregg, B. M., Anderesen, J. \n\n\n\nA. 2009. Carbon Sequestration Potential of Extensive green roofs.\n\n\n\nEnvironment Science and Technology, 43(19), 7564-7570. \n\n\n\n[13] Tan, P. Y., Sia, A. (n.d.). A Pilot Green roof Research Projects In \n\n\n\nSingapore. \n\n\n\n[14] Astee, L., Kishnani, D. (n.d.). Building integrated agriculture utilising \n\n\n\nrooftops for sustainable food crop cultivation in Singapore. Journal of \n\n\n\nGreen Building. \n\n\n\n[15] Getter, K. L., Rowe, B. D. 2006. The Role of Extensive Green Roofs in \n\n\n\nSustainable Development. Hort.Science, 41(5), 1276-1285. \n\n\n\n[16] World, N. T. (n.d.). Solutions For Rooftop Gardening Challenges.\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nThe purpose of this study is to evaluate the performance and change in the technical as well as technological \nefficiency in the total factor productivity of the 34 food processing industries in Malaysia, and to investigate the \nchanges in their efficiency from 2009 to 2010 by applying two recent methods of data analysis, namely order-m and \nMalmquist productivity index. The results show that almost all industries have experienced an efficient \ntechnological contribution in their respective production functions, but there are wide dissimilarities in the \ntechnical efficiency of the organic composition of each industry. Also, there are variations in the change in efficiency \nscores from 2009 to 2010.  \n\n\n\n KEYWORDS \n\n\n\nMalmquist Productivity Index, Order-m, Technical Efficiency, Technological Efficiency, Total Factor Productivity, \nOrganic composition.\n\n\n\n1. INTRODUCTION \n\n\n\nMalaysia is a highly open, upper-middle income economy. The food \nprocessing industry, along with other industries, plays a vital role in the \neconomy of Malaysia by creating employment, market outlets and adding \nvalue to primary agricultural products [1]. Without the proper processing \nof food, higher productivity of both the industry and the economy in \ngeneral is, perhaps, unachievable. Moreover, if a country cannot stock its \nproduced food for a long time, the possibilities for exporting are limited to \nfresh food with the associated higher costs. The more a country is able to \nefficiently and productively produce a good, the more likely the country \nwill have an absolute and a comparative advantage in the international \nmarket. And a country needs comparative advantage to acquire higher \ngains from trade [2]. This study will focus on efficiency and productivity of \nthe Malaysian food processing industry by applying recent non-\nparametric approaches to data interpretation. \n\n\n\nThough Malaysia has been exporting processed food since 1964, it \ntypically runs a trade deficit in food, although this has declined recently \n[3]. In 2012, Malaysia exported more than RM 11 billion of food to 200 \ncountries with imports of processed food valued at more than RM 30 \nbillion [4]. In 2015, Malaysia achieved a trade surplus in processed food \nwith exports of approximately RM 18.02 billion, and imports of RM 17.8 \nbillion [5]. Gains from trade have increased from the export of edible \nproducts and preparations, cocoa and cocoa preparations, cereals and \nflour. Its major export destinations were Singapore, Indonesia, USA, \nThailand, and Republic of China [4, 5].  To promote growth, the Malaysian \nGovernment has launched the National Agricultural Policy (NAP), the \nBalance of Trade (BOT) Policy, the Industrial Master Plan (for 1986-1995, \n1996-2005 and 2006-2020) and the National Agro-Food Policy (2011-\n2020) [6].  \n\n\n\nIn order to understand and sustain the efficiency and productivity of \nMalaysian food processing industries for future gains of trade, there is a \nneed for in-depth analysis with sectoral data using recently developed \nstatistical methods. This study is an attempt to illustrate the ranking, \nefficiency, total factor productivity and overall competitiveness of \nMalaysian food processing industries. The data is collected by the survey  \n\n\n\nconducted in the study year as the part of post-doctoral study of the \ncorresponding author. Since it is difficult to collect several years\u2019 data, the \nkey focus of this study is not only to analyze the data but also demonstrate \nhow recent statistical methods can be used for this type of analysis. The \noutcome of this research can be applicable to other industries especially \nat the sectoral level.  The article is organized as follows: Section 1 contains \nthe introduction, literature review, research gap or problem and objective \nof the study; Section 2 discusses the methodology; Section 3 illustrates the \ndata and variables; Section 4 presents results and interpretation of those \nresults and finally Section 5 discuss the conclusion and policy implications \nof this research. \n\n\n\n2. LITERATURE REVIEW \n\n\n\nThere have been a number of studies on the same or similar topics using \nconventional approaches to data interpretation. For example, a group \nscientist investigated the competitiveness and comparative advantage of \nthe Malaysian food processing industry by introducing net social profit \n(NSP), domestic resource cost (DRC) and the social cost-benefit (SCB) ratio \nat the production level and Porter\u2019s diamond approach at the firm level \n[7]. They proposed that the industry has comparative advantages at \ndifferent magnitudes. They found the NSP indices to be quite wide and \nsuggested that there is a need to improve the resource allocation from low \nto high comparative advantage sectors. Their result also suggested that the \nfood processing industry in Malaysia was gaining competitiveness. \n\n\n\nA group researcher has investigated a very similar topic but applied \nslightly different methods. The main objective of their study was to \ninvestigate and measure competitiveness among various producers of \nfood products in Malaysia [2]. Their study involved analysis of quantitative \ndata of 20 food processing industries in Malaysia from the year 2000 to \n2008 by implementing financial analysis using net present value (NPV), \ninternal rate of return (IRR), profitability index (PI) and pay-back period \n(PP), as well as the Policy Analysis Matrix (PAM) model. They found that \nMalaysia enjoys an above average level of comparative advantage in the \nproduction of twenty food products, especially in fish and palm oil, the \nlatter of which has greater comparative advantage than other food \nproduction processes because it had the lowest DRC among all products.  \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.01.2018.19.28 \n\n\n\nA COMPARATIVE ANALYSIS OF THE EFFICIENCY AND PRODUCTIVITY OF SELECTED FOOD \n\n\n\nPROCESSING INDUSTRIES IN MALAYSIA \n\n\n\nMunshi Naser Ibne Afzal1*, Roger Lawrey2, Mir Shatil Anaholy3, Jhalak Gope4 \n\n\n\n1 Faculty of Business, Economics and Accountancy, University Malaysia Sabah, Kota KInabalu, Sabah, Malaysia. \n2 School of Commerce, University of Southern Queensland (USQ), Toowoomba, Australia. \n3 Research Assistant, Shahjalal University of Science and Technology, Sylhet. \n4 Shahjalal University of Science & Technology (SUST), Bangladesh.  \n\n\n\n*Corresponding Author email: munshi.naser@gmail.com; munshi.naser@ums.edu.my\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ \n \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\nThe main objective of this study is to comprehend the competitiveness and \nefficiency of the food processing industry during the initial period of the \nintroduction of various policies which were intended to promote growth \nof this sector. The key difference between the previous studies and this \nstudy is that this study analyses data for the years 2009 and 2010 by \napplying two contemporary non-parametric methods of analysis (see Note \n2 in Appendix), namely the Malmquist productivity index (MPI) and order-\nm partial frontier analysis. It is believed that the application of these two \nmodern approaches to data interpretation will enable future analysts of \nsimilar topics to compare results of the most recent years with those of the \ninitial period more comprehensively, broadly and systematically. \n\n\n\nMany previous studies have used MPI analysis and order-m analysis. Some \nresearcher performed an empirical investigation into the regional \ninnovation systems (RIS) which studied the influence of interrelationships \namong education, knowledge transfer, linkage and communications, \nregulatory quality, cost of doing business, trade openness, R&D \nexpenditure and high-tech exports in overall economic growth [8]. This \npaper applied the non-parametric robust partial frontier order-m \napproach in cross-section data analysis. This enabled the study of \nbehaviors of individual sectors in the course of the overall performance of \nthe economy. \n\n\n\nIn other hand, study of productivity analysis of ASEAN economies in the \ntransition towards a knowledge-based economy, applied the Malmquist \nProductivity Index (MPI) analysis [9]. The main purpose of this study was \nto analyze the nature and extent of productivity changes in Cobb-Douglas \nproduction function components and the growth of the knowledge \neconomy of selected ASEAN countries, namely Malaysia, Indonesia, \nPhilippines, Thailand, Singapore plus South Korea. This study used data \nenvelopment analysis (DEA) and MPI analysis to estimate the individual \ncountry\u2019s efficiency and productivity changes. This aided the analysis of \nthe contribution of technological as well as technical efficiency in the \nefficiency change of total factor productivity, which is similar to the \napproach of this study.  \n\n\n\nA researcher also has use the random coefficient frontier production \nfunction to show that input growth is a key factor contributing to output \ngrowth in Bangladesh for the period 1981 to 1991 [10]. This empirical \nstudy showed that low capital realization lowered the performance of the \noverall food processing sector despite economic reforms.  \n\n\n\nThe first empirical work separating technical efficiency from technological \nprogress as contributors to total factor productivity was introduced by a \ngroup researcher [11]. Technical efficiency is the extent to which firms are \nable to produce on the \u201cbest-practice\u201d production function that specifies \nthe frontier of outputs for all possible input-output combinations. This \ntechnical efficiency may be the result of things such as learning by doing, \ndiffusion of new technological knowledge, improved managerial practice, \nshort run adjustment to external shocks and changes in the organic \ncomposition of the firm (see Note 1 in Appendix). The extent to which \nfirms are unable to produce on this frontier is referred to as technical \ninefficiency. On the other hand, technological progress (change, efficiency) \nis defined as a rise in the best-practice production frontier.  \n\n\n\n2.1 Research Gaps \n\n\n\nPrevious studies that have measured productivity and efficiency in the \ncontext of the Malaysian food processing industry appear to have left \nsignificant research gaps as follows: \n\n\n\n1. Very few empirical works have measured total factor productivity \nand technological change efficiency of the Malaysian food \nprocessing industry. Separate investigations of the performance of \ntechnological and technical efficiency have also not been found. In \naddition, a comprehensive analysis of the sectoral contributions of \norganic composition and technology to total factor productivity is \ninadequate. These gaps support the application of a precise \ncomparative analysis method for measuring the competitiveness of \nthe industry. \n\n\n\n2. The clarification of technical and technological intensiveness for \ndetermining the comparative advantage among the industries is \nalso absent in the prior studies. \n\n\n\n3. The methodology used in this study can be extended to other \nindustries while measuring total factor productivity, \ncompetitiveness and efficiency using up to date methodologies such \nas MPI and Order m. \n\n\n\n2.2 Research Objective \n\n\n\nThe main purpose of this study is to investigate, in depth, the \ncompetitiveness and the performance of the food processing industries in \nMalaysia by using two new non-parametric methods, namely Malmquist \nProductivity Index and Order-m analysis. The main reason behind \napplying these methods is to observe not only the efficiency scores but also \nthe sensitivity of the organic composition and production technology \nwhich play significant roles in increasing the total factor productivity of \nthe industries. \n\n\n\n3. METHODOLOGY \n\n\n\nAs stated before, according to the purpose of the study, this study will \ninvolve two methods of data analysis for the decision making, namely \nMalmquist Productivity Index (MPI) analysis and Order-m Partial Frontier \napproach. \n\n\n\n3.1 Malmquist Productivity Index (MPI) \n\n\n\nThe Malmquist Productivity Index is a bilateral index that can be used to \ncompare the production technology of two or more economies or sectors. \nIt was developed by Sten Malmquist. This method will be used because it \nhas a number of desirable features suitable for this study. First, Malmquist \nindexes are unit independent and they do not require input or output \nprices in their construction. Second, the computation is relatively \nstraightforward, as demonstrated by some researcher [12]. Third, the MPI \ncan accommodate multiple inputs and outputs without having to \naggregate them. Fourth, MPI has two components \u2013 technical efficiency \nchange and technological change [12]. Technical efficiency refers to the \nability to use a minimal amount of input to produce a given level of output. \nOn the other hand, technological efficiency means the ability to combine \nthe inputs most efficiently in order to produce the maximum level of \noutput. Over time, the level of the output of an industry will increase due \nto technological changes that affect the ability to optimally combine inputs \nand outputs. Thus, for any organization in an industry, productivity \nimprovements over time may be either technical efficiency improvements \n(catching up with their own frontier) or technological improvements \n(because the frontier is shifting up over time), or both [13].  \n\n\n\nA study has shown that productivity and efficiency are the indexes of \ncompetitiveness [14]. Another studies also stated that productivity and \nefficiency are the most reliable measurest of competitiveness [15]. The \nimportance of analyzing these two components is that it provides insight \ninto the sources of change in total factor productivity. The fifth desirable \nfeature is that the original MPI assumes constant returns to scale for the \nproduction process. As a result, if the production process displays \ndecreasing returns to scale the original MPI typically overestimates \nproductivity change or underestimates it for increasing returns to scale. A \ngroup researcher recommended the use of a generalized MPI, to cope with \nthe issue of variable returns to scale, that includes an additional \ncomponent, called scale index, to represent the effect of economies of scale \non productivity [12]. Scale efficiency refers to the extent an organization \ncan take advantage of returns to scale by altering its size towards optimal \nscale. A researcher also echoed that MPI does have the accuracy in \nmeasuring the productivity change under an appropriate characterization \nof the technology [16]. Sixth, the Malmquist DEA approach measures \nefficiency for one year, relative to the prior year, while allowing the \nefficiency frontier to shift. So positive total factor productivity growth is \nindicated by a value greater than unity, whereas a value less than unity \nindicates productivity decline. \n\n\n\nThere are two approaches to measuring productivity by using the \nMalmquist productivity index. One is the output-oriented Malmquist \nproductivity index which is the way to measure a change in productivity \nto see how much more output has been produced, using a given input level \nand the present state of technology, relative to what could be produced \nunder a given reference technology using the same input level. Another is \ninput-oriented Malmquist productivity index which is the way to measure \nthe change in productivity by examining the reduction in input use that is \nfeasible given the need to produce a given level of output under a reference \ntechnology [17]. This study concentrates on the output-oriented \nMalmquist productivity index for analysis. \n\n\n\nThe functional definition of DEA MPI is as follows: \n\n\n\n\ud835\udc40\ud835\udc61+1(\ud835\udc4b\ud835\udc61+1, \ud835\udc66\ud835\udc61+1, \ud835\udc4b\ud835\udc61, \ud835\udc66\ud835\udc61) = [\n\ud835\udc37\ud835\udc61(\ud835\udc65\ud835\udc61+1,\ud835\udc66\ud835\udc61+1)\n\n\n\n\ud835\udc37\ud835\udc61(\ud835\udc65\ud835\udc61,\ud835\udc66\ud835\udc61)\n\n\n\n\ud835\udc37\ud835\udc61+1(\ud835\udc65\ud835\udc61+1,\ud835\udc66\ud835\udc61+1)\n\n\n\n\ud835\udc37\ud835\udc61+1(\ud835\udc65\ud835\udc61,\ud835\udc66\ud835\udc61)\n]\n\n\n\n1\n\n\n\n2 (1) \n\n\n\nWhere \ud835\udc37\ud835\udc61 is a distance function measuring the efficiency of conversion of \ninputs \ud835\udc65\ud835\udc61 to outputs \ud835\udc66\ud835\udc61  in the period t. DEA efficiency is considered a \n\n\n\n20\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\ndistance measure in the literature as it reflects the efficiency of \ninput/output conversion of DMUs. In fact, if there is a change in technology \nthe following year which is (t+1), then \ud835\udc37\ud835\udc61+1(\ud835\udc65\ud835\udc61, \ud835\udc66\ud835\udc61) will be the efficiency of \naltering input in period t to output in period t \u2260 \ud835\udc37\ud835\udc61(\ud835\udc65\ud835\udc61, \ud835\udc66\ud835\udc61). Hence, it can \nbe said that technically Malmquist Productivity Index (MPI) is a geometric \naverage of the efficiency and technological changes in the two referenced \nperiods and it is thus can be written as: \n\n\n\n\ud835\udc40\ud835\udc61+1(\ud835\udc4b\ud835\udc61+1, \ud835\udc66\ud835\udc61+1, \ud835\udc4b\ud835\udc61, \ud835\udc66\ud835\udc61) = [\n\ud835\udc37\ud835\udc61+1(\ud835\udc65\ud835\udc61+1,\ud835\udc66\ud835\udc61+1)\n\n\n\n\ud835\udc37\ud835\udc61(\ud835\udc65\ud835\udc61,\ud835\udc66\ud835\udc61)\n][\n\n\n\n\ud835\udc37\ud835\udc61(\ud835\udc65\ud835\udc61+1,\ud835\udc66\ud835\udc61+1)\n\n\n\n\ud835\udc37\ud835\udc61+1(\ud835\udc65\ud835\udc61+1,\ud835\udc66\ud835\udc61+1)\n\n\n\n\ud835\udc37\ud835\udc61(\ud835\udc65\ud835\udc61,\ud835\udc66\ud835\udc61)\n\n\n\n\ud835\udc37\ud835\udc61+1(\ud835\udc65\ud835\udc61,\ud835\udc66\ud835\udc61)\n]\n\n\n\n1\n\n\n\n2     (2) \nor, M = ET \n\n\n\nwhere M stands for productivity index, E is the technical efficiency change \nand T is the technology change. E measures the change in the CRS technical \nefficiency of period t+1 over that in period t. If E is greater than 1, It is \nassumed that there is an increase in the technical efficiency. However, T \nrepresents the average technological change over the two referred \nperiods. \n\n\n\n3.2 Order-m Frontier Approach \n\n\n\nThe study discusses order-m frontier in a non-technical way for easier \naccess to a broader audience. In contrast to the FDH or DEA approach, the \nidea behind the order-m approach is to compare one sector\u2019s performance \nwith a randomly drawn sub-sample of sectors\u2019 performance instead of \nevaluating one sector with respect to the performance of all other sectors \n[18]. The researcher has to specify the sub-sample size, which is denoted \nas m, giving the name to the procedure. For instance, this study worked \nover 34 observations; therefore, the m can be 5, 10, 15, 20, 25, 30 etc. For \nsimplicity, the study took m = 20 and m = 25 for partial frontier and m = \n34 for full frontier analysis. Afzal applied the nonparametric robust partial \nfrontier order-m approach to determine the frontier region in his study \n[8]. The evaluation of sectors\u2019 individual performances is done in an \nidentical style to that of the DEA or FDH approach based on partial \nfrontiers. The order-m performance measure contains most of the \ncharacteristics of the FDH or DEA model. In addition, it is less sensitive to \n\n\n\noutliers and noise in the data as the partial frontier is not enveloping all \nobservations [18]. \n\n\n\nThe primary idea of the unconditional order-m is straightforward. For \ninstance, in a multivariate case, consider (\ud835\udc650, \ud835\udc660) as the inputs and outputs \nof the unit of interest. (\ud835\udc4b1, \ud835\udc4c1), ......., (\ud835\udc4b\ud835\udc5a, \ud835\udc4c\ud835\udc5a) are the inputs and outputs of \nm randomly drawn units that satisfy \ud835\udc4b\ud835\udc56 \u2264 \ud835\udc650.  \ud835\udf06\ud835\udc5a(\ud835\udc650, \ud835\udc66\ud835\udc5c) measures the \ndifference between point \ud835\udc660 and the order-m frontier of \ud835\udc4c1,......, \ud835\udc4c\ud835\udc5a. This can \nbe written as: \n\n\n\n\ud835\udf06\ud835\udc5a(\ud835\udc4b0, \ud835\udc660) = \ud835\udc5a\ud835\udc4e\ud835\udc65(\ud835\udc56=1\u2026\ud835\udc5a){\ud835\udc5a\ud835\udc56\ud835\udc5b\ud835\udc57=1\u2026\ud835\udc5e(\n\ud835\udc4c\ud835\udc56\n\n\n\n\ud835\udc57\n\n\n\n\ud835\udc66\ud835\udc57\n)}  (3) \n\n\n\nWith \ud835\udc4c\ud835\udc56\n\ud835\udc57\n(\ud835\udc66\ud835\udc57) with the \ud835\udc57\ud835\udc61\u210e component of \ud835\udc4c\ud835\udc56(of \ud835\udc660 respectively) the order-m \n\n\n\nefficiency measure of unit (\ud835\udc650, \ud835\udc660) is defined as: \n\n\n\n\ud835\udf06\ud835\udc5a(\ud835\udc4b0, \ud835\udc660) = \ud835\udc38[\ud835\udf06\ud835\udc5a(\ud835\udc4b0, \ud835\udc660) \u2195 \ud835\udc4b \u2264 \ud835\udc650] (4) \n\n\n\nThe obtained performance measures the radial distance of the unit to the \norder-m frontier. Note that in any case, a unit is at least compared to itself \nwhich results in a performance score of one. For an extensive treatment of \nthe conditional and unconditional order-m approach see [19, 20]. \n\n\n\n4. DATA AND VARIABLES \n\n\n\nTable 1 shows the summary of different numerical indicators used as input \nand output variables. This study used Cost of Input, Total Employment, \nSalaries & Wages Paid, Value of Assets Owned, and Number of \nEstablishments as input variables. This study also applied Value of Gross \nOutput and Value-Added as the output variables. For all the variables, the \n2009 and 2010 data has been collected and analyzed for 34 food \nprocessing industries operating in Malaysia. Due to a lack of panel data for \nrecent years, this study has used this data set to investigate the initial stage \nof policies implemented by the Malaysian government during 2009-2010. \nThe data were collected from the Department of Statistics, Malaysia and \nannual report of food industry in Malaysia.\n\n\n\nTable 1: Summary of the indicators used as variables: \n\n\n\nType Indicator Unit \n\n\n\nOutput Variables \nValue of gross output RM'000 \n\n\n\nValue added RM'000 \n\n\n\nInput Variables \n\n\n\nCost of input RM'000 \n\n\n\nTotal employments No.s \n\n\n\nSalaries & wages paid RM'000 \n\n\n\nValue of assets owned RM'000 \n\n\n\nNo. of establishments No.s \n\n\n\n5. RESULTS DISCUSSION\n\n\n\n5.1 Malmquist Summary Index Analysis \n\n\n\nTable 2 shows the descriptive summary of the results obtained from the \nMPI index analysis. In the MPI analysis, any efficiency scores greater than \nunity mean an increase in efficiency, and any efficiency scores less than \nunity means declines in efficiency. The result shows some significant \noutcomes which need to be evaluated. Almost all the industries show a \nnegative change in the technical efficiency except for Manufacturer of \nPalm Kernel Oil and Manufacturer of Glucose & Glucose Syrup, Maltose. On \nthe other hand, all the industries show a positive change in the \ntechnological efficiency which outweighs the changes in technical \nefficiency. As a result, almost all the industries show positive changes in \nthe efficiency of total factor productivity. Five industries show a negative \nchange in the efficiency of total factor productivity, namely Manufacturer \nof Coconut Oil, Manufacturer of Flour Milling, Manufacturer of Tea, \nManufacturer of Sauces including Flavoring Extracts such as Monosodium \nGlutamate, Manufacturer of Other Food Products. The cause of this decline \nis a negative change in the technical efficiency of these industries that \noutweighs the positive change in technological efficiency. The data show \nthat from 2009 to 2010 the value-added by these industries has increased, \nbut employment and average salary have also increased. Hence, there is a \ndecline in efficiency. \n\n\n\nIt is evident from the results obtained that the highest performing industry \nis Manufacturer of Kernel Palm Oil with a score of 4.147 in the efficiency \nchange of TFP, which indicates an approximate 314.7% increase in the \n\n\n\nindustry\u2019s overall efficiency, indicating increasing returns-to-scale in the \nindustry\u2019s production function. About 1.4% of this change is due to growth \nin technical efficiency and 309% of this change is due to growth in \ntechnological efficiency. Hence, the result suggests that although the  \n\n\n\ncontribution of capital and labor to production has increased somewhat; \non the other hand, the contribution of technology has increased \nsignificantly over the last few years. The second highest performing \nindustry is Manufacturer of Glucose & Glucose Syrup, Maltose. This \nindustry shows no change in the technical efficiency but a score of 3.939 \nin the change in the technological efficiency, which means about 293.9% \nincrease in the efficiency of total factor productivity caused solely by the \nincrease in technological efficiency. Since technological efficiency \nincreases are likely the result of developments external to the industry \nitself, this suggests that there is still scope for improvement in the organic \ncomposition of both these high performing industries.  \n\n\n\nThe industry with the highest decline in the change of total factor \nproductivity is Manufacturer of other Food Products, with an approximate \n45.7% decrease and a score of 0.162 in the change of technical efficiency, \nwhich means about an 83.8% decline in the technical efficiency. The \ntechnological efficiency has increased by about 234.7%, therefore the \ndecline can be attributed to the negative change in the technical efficiency. \nThe second highest decline in the change in the efficiency of total factor \nproductivity is attained by Flour Milling industry, which has about a 39.5% \ndecrease in the efficiency of total factor productivity. The approximate \n271.7% increase in technological efficiency could not compensate for the \n83.7% decrease in technical efficiency, hence the decline. In fact, this is \nevident in almost all industries where there is a decline in the change of \n\n\n\n21\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\nefficiency of total factor productivity: the increase in the technological \nefficiency could not offset the decrease in technical efficiency. Therefore, \nit might be said that these industries are more sensitive to their organic \ncomposition rather than to their production technology. This suggests that \nthere is still much scope to further investigate the industry-wide \nsensitiveness to the organic composition as well as technology using more \n\n\n\nrigorous data, which will help policy makers to take appropriate and \nnecessary steps to boost the food processing industries in Malaysia. Figure \n1 shows a comparative visualization of all the factors\u2019 efficiency changes \nby indicating industries in the horizontal axis and change in efficiencies of \ntechnical, technological and TFP in vertical axis. \n\n\n\nTable 2: Summary of the results obtained from the MPI Analysis \n\n\n\nDMU Industry Name \nChange in \nTechnical \nEfficiency \n\n\n\n% Change in \nTechnical \nEfficiency \n\n\n\nChange in \nTechnological \nEfficiency \n\n\n\n% Change in \nTechnological \nEfficiency \n\n\n\nChange in TFP \nEfficiency \n\n\n\n% Change in \nTFP Efficiency \n\n\n\n1 \nManufacturer of meat & meat \nproducts \n\n\n\n0.218 -78.2 6.461 546.1 1.41 41 \n\n\n\n2 \nManufacturer of poultry & \npoultry products \n\n\n\n0.511 -48.9 4.444 344.4 2.269 126.9 \n\n\n\n3 \nManufacturer of fish & fish \nproducts \n\n\n\n0.266 -73.4 6.956 595.6 1.849 84.9 \n\n\n\n4 \nCanning & preservation of \nother fruits & vegetables \n\n\n\n0.236 -76.4 6.392 539.2 1.509 50.9 \n\n\n\n5 Pineapple canning 0.331 -66.9 7.352 635.2 2.432 143.2 \n\n\n\n6 \nManufacturer of nut & nut \nproducts \n\n\n\n0.258 -74.2 6.319 531.9 1.632 63.2 \n\n\n\n7 Manufacturer of crude palm oil 0.358 -64.2 5.457 445.7 1.951 95.1 \n\n\n\n8 \nManufacturer of refined palm \noil 0.387 -61.3 4.196 319.6 1.625 62.5 \n\n\n\n9 \nManufacturer of palm kernel \noil 1.014 1.4 4.09 309 4.147 314.7 \n\n\n\n10 \nManufacturer of other \nvegetable and animal oils & fats \n\n\n\n0.554 -44.6 3.9 290 2.16 116 \n\n\n\n11 Manufacturer of coconut oil 0.198 -80.2 4.677 367.7 0.925 -7.5\n12 Manufacturer of ice cream 0.171 -82.9 6.056 505.6 1.034 3.4 \n\n\n\n13 \nManufacturer of condensed, \npowdered and evaporated milk \n\n\n\n0.404 -59.6 3.339 233.9 1.349 34.9 \n\n\n\n14 Rice milling 0.397 -60.3 3.716 271.6 1.474 47.4 \n\n\n\n15 Flour milling 0.163 -83.7 3.717 271.7 0.605 -39.5\n\n\n\n16 \nManufacturer of other \nflour/grain mill products \n\n\n\n0.466 -53.4 4.534 353.4 2.111 111.1 \n\n\n\n17 \nManufacturer of glucose, \nglucose syrup & maltose \n\n\n\n1 0 3.939 293.9 3.939 293.9 \n\n\n\n18 \nManufacturer of sago & tapioca \nflour products \n\n\n\n0.483 -51.7 5.067 406.7 2.448 144.8 \n\n\n\n19 \nManufacturer of biscuits and \ncookies \n\n\n\n0.258 -74.2 4.831 383.1 1.244 24.4 \n\n\n\n20 \nManufacturer of bread, cake \nand other bakery products \n\n\n\n0.225 -77.5 4.725 372.5 1.064 6.4 \n\n\n\n21 \n\n\n\nManufacturer of snacks, \ncrackers & chips (e.g. \nprawn/fish crackers, \npotato/banana/tapioca chips) \n\n\n\n0.284 -71.6 4.625 362.5 1.314 31.4 \n\n\n\n22 Manufacturer of sugar 0.449 -55.1 2.587 158.7 1.162 16.2 \n\n\n\n23 \nManufacturer of cocoa \nproducts \n\n\n\n0.59 -41 2.536 153.6 1.496 49.6 \n\n\n\n24 \nManufacturer of chocolate \nproducts & sugar confectionery \n\n\n\n0.233 -76.7 3.892 289.2 0.907 -9.3\n\n\n\n25 \nManufacturer of macaroni, \nnoodles & similar products \n\n\n\n0.232 -76.8 4.208 320.8 0.978 -2.2\n\n\n\n26 Manufacturer of coffee 0.214 -78.6 4.075 307.5 0.874 -12.6\n27 Manufacturer of tea 0.233 -76.7 3.36 236 0.783 -21.7\n\n\n\n28 \nManufacturer of sauces \nincluding flavoring extracts \nsuch as monosodium glutamate \n\n\n\n0.191 -80.9 4.006 300.6 0.764 -23.6\n\n\n\n29 \nManufacturer of spices & curry \npowder \n\n\n\n0.226 -77.4 3.764 276.4 0.849 -15.1\n\n\n\n30 \nManufacturer of other food \nproducts \n\n\n\n0.162 -83.8 3.347 234.7 0.543 -45.7\n\n\n\n31 \n\n\n\nDistilling, rectifying and \nblending of spirits; ethyl \nalcohol production from \nfermented materials \n\n\n\n0.503 -49.7 2.297 129.7 1.156 15.6 \n\n\n\n32 \nManufacturer of wines, malt \nliquors & malt 0.596 -40.4 2.156 115.6 1.285 28.5 \n\n\n\n33 Manufacturer of soft drinks 0.323 -67.7 2.126 112.6 0.688 -31.2\n34 Production of mineral water 0.316 -68.4 3.658 265.8 1.155 15.5 \n\n\n\n22\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\nFigure 1: Comparative visualization of the MPI analysis (Source: Author calculation) \n\n\n\n5.2 Order-m Analysis \n\n\n\nTables 3 & 4 report the results obtained from the order-m analysis for the \nyears 2009 and 2010 respectively. As stated before, the study has taken m \n= 20 and m = 25 for partial frontier and m = 34 for full frontier analysis. \nThe comparative efficiency performance of each industry in the years \n2009 and 2010 are shown in Figures 2 and 3 respectively. Figure 4 shows \nthe percentage increase or decrease in the efficiency score for the partial \nfrontiers and the full frontier separately for each industry. By taking a \ncloser look at those figures, it can be seen that for both the years the partial \nfrontier analysis for m = 20 and m = 25 show almost similar results, but \nthey vary to a significant extent with that of full frontier results. In the \npartial frontier analysis, the best performing industry is the Manufacturer \nof Crude Palm Oil for both m = 20 and m = 25 in both the years. The \nManufacturer of Palm Kernel Oil shows a significant rise in the efficiency \nscore over the one-year period, ranking 4th in 2010 from 10th in 2009, with \nan almost 29% increase in the efficiency score. The poorest performing \nindustry is the Manufacturer of Wine Liquor and Malt for both partial \nfrontiers in both years, also having the highest decline in the efficiency \nscore from 2009 to 2010. \n\n\n\nThe full frontier order-m analysis for the years 2009 and 2010 shows that \nthe best performing industries for both the years are the Manufacturer of \nCrude Palm Oil, Manufacturer of Refined Palm Oil, Manufacturer of Palm \nKernel Oil, Manufacturer of Other Vegetables and Animal Oils and Fats, \nManufacturer of Condensed Powdered and Evaporated Milk, \nManufacturer of Bread Cake and Other Bakery Products, Manufacturer of \nSugar, Manufacturer of Cocoa Products, and Manufacturer of Wines Malt \nLiquors and Malt, all having an efficiency score of 1 in both the years. The \nhighest scorer in the order-m analysis is Manufacturer of Coconut Oil, with \nan efficiency score of 2.953242 in the year 2009 and 2.829483 in the year \n2010, which is an approximate 4.19% decrease over the one-year period. \n\n\n\nThe outcome indicates that this industry is performing well and the \nmechanism of its production process is working efficiently, although it is \nexhibiting significantly high diminishing returns-to-scale in its production. \n\n\n\nThe lowest performing industry is the Manufacturer of Biscuits and \nCookies with an efficiency score of 0.7177833 in the year 2009 and \n0.716358 in the year 2010, which is about a 0.20% decrease over the one-\nyear period. The MPI analysis also shows that this industry has a decline \nof 74.2% in its technical efficiency. This means there is scope to improve \nits production process and its organic composition, and since it exhibits \nincreasing returns-to-scale in its production process, the improvement \ncan further secure its future sustainability. The analysis suggests that this \nindustry should follow the mechanism of Manufacturer of Wines Malt \nLiquors and Malt as pseudo reference (see Note 3 in Appendix); in an \nattempt to eventually reach an efficient point of production. \n\n\n\nBy analyzing these results, it can be seen that the highest increase in the \nefficiency score from 2009 to 2010 is held by Pineapple Canning industry. \nIt shows about a 4.78% increase in its efficiency score. This is because \nalthough the amount of value-added by the industry fell, so did the amount \nof input cost including salaries and wages paid to employees. As a result, \noverall efficiency has increased. On the other hand, the highest decline in \nthe efficiency score is exhibited by the Manufacturer of Tea which is \napproximately a 14.67% decrease. Data suggests that this is because the \nincrease in the industry\u2019s value-added could not offset the increase in the \ncost of salaries and wages paid to the employees. Twelve industries among \nthe 38-show constant efficiency score over the one-year period. Figures 2 \nand 3 show the relative performance of all the industries for the years \n2009 and 2010 respectively. Figure 4 shows the comparative percentage \nincrease or decrease in the efficiency score from 2009 to 2010 for each \nindustry \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\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nm\nea\n\n\n\nt \n&\n\n\n\n m\ne\n\n\n\nat\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\np\no\n\n\n\nu\nlt\n\n\n\nry\n &\n\n\n\n p\no\n\n\n\nu\nlt\n\n\n\nry\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nfi\nsh\n\n\n\n &\n f\n\n\n\nis\nh\n\n\n\n p\nro\n\n\n\nd\nu\n\n\n\nct\ns\n\n\n\nC\nan\n\n\n\nn\nin\n\n\n\ng \n&\n\n\n\n p\nre\n\n\n\nse\nrv\n\n\n\nat\nio\n\n\n\nn\n o\n\n\n\nf \no\n\n\n\nth\ner\n\n\n\n f\nru\n\n\n\nit\ns \n\n\n\n&\n\u2026\n\n\n\nP\nin\n\n\n\ne\nap\n\n\n\np\nle\n\n\n\n c\nan\n\n\n\nn\nin\n\n\n\ng\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nn\nu\n\n\n\nt \n&\n\n\n\n n\nu\n\n\n\nt \np\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\ncr\nu\n\n\n\nd\ne \n\n\n\np\nal\n\n\n\nm\n o\n\n\n\nil\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nre\nfi\n\n\n\nn\ned\n\n\n\n p\nal\n\n\n\nm\n o\n\n\n\nil\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\n p\nal\n\n\n\nm\n k\n\n\n\ne\nrn\n\n\n\ne\nl o\n\n\n\nil\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\no\nth\n\n\n\ner\n v\n\n\n\ne\nge\n\n\n\nta\nb\n\n\n\nle\n a\n\n\n\nn\nd\n\n\n\n a\nn\n\n\n\nim\nal\n\n\n\n o\nils\n\n\n\n\u2026\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nco\nco\n\n\n\nn\nu\n\n\n\nt \no\n\n\n\nil\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nic\ne \n\n\n\ncr\nea\n\n\n\nm\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nco\nn\n\n\n\nd\nen\n\n\n\nse\nd\n\n\n\n, p\no\n\n\n\nw\nd\n\n\n\ner\ned\n\n\n\n a\nn\n\n\n\nd\n\u2026\n\n\n\nR\nic\n\n\n\ne \nm\n\n\n\nill\nin\n\n\n\ng\n\n\n\nFl\no\n\n\n\nu\nr \n\n\n\nm\nill\n\n\n\nin\ng\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\no\nth\n\n\n\ner\n f\n\n\n\nlo\nu\n\n\n\nr/\ngr\n\n\n\nai\nn\n\n\n\n m\nill\n\n\n\n p\nro\n\n\n\nd\nu\n\n\n\nct\ns\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\ngl\nu\n\n\n\nco\nse\n\n\n\n, g\nlu\n\n\n\nco\nse\n\n\n\n s\nyr\n\n\n\nu\np\n\n\n\n &\n m\n\n\n\nal\nto\n\n\n\nse\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nsa\ngo\n\n\n\n &\n t\n\n\n\nap\nio\n\n\n\nca\n f\n\n\n\nlo\nu\n\n\n\nr \np\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nb\nis\n\n\n\ncu\nit\n\n\n\ns \nan\n\n\n\nd\n c\n\n\n\no\no\n\n\n\nki\ne\n\n\n\ns\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nb\nre\n\n\n\nad\n, c\n\n\n\nak\ne \n\n\n\nan\nd\n\n\n\n o\nth\n\n\n\ner\n b\n\n\n\nak\ner\n\n\n\ny\u2026\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nsn\nac\n\n\n\nks\n, c\n\n\n\nra\nck\n\n\n\ner\ns \n\n\n\n&\n c\n\n\n\nh\nip\n\n\n\ns \n(e\n\n\n\n.g\n.\u2026\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nsu\nga\n\n\n\nr\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nco\nco\n\n\n\na \np\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nch\no\n\n\n\nco\nla\n\n\n\nte\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns \n&\n\n\n\n s\nu\n\n\n\nga\nr\u2026\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nm\nac\n\n\n\nar\no\n\n\n\nn\ni, \n\n\n\nn\no\n\n\n\no\nd\n\n\n\nle\ns \n\n\n\n&\n s\n\n\n\nim\nila\n\n\n\nr\u2026\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nco\nff\n\n\n\nee\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nte\na\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nsa\nu\n\n\n\nce\ns \n\n\n\nin\ncl\n\n\n\nu\nd\n\n\n\nin\ng \n\n\n\nfl\nav\n\n\n\no\nri\n\n\n\nn\ng\u2026\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nsp\nic\n\n\n\nes\n &\n\n\n\n c\nu\n\n\n\nrr\ny \n\n\n\np\no\n\n\n\nw\nd\n\n\n\ner\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\no\nth\n\n\n\ner\n f\n\n\n\no\no\n\n\n\nd\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nD\nis\n\n\n\nti\nlli\n\n\n\nn\ng,\n\n\n\n r\ne\n\n\n\nct\nif\n\n\n\nyi\nn\n\n\n\ng \nan\n\n\n\nd\n b\n\n\n\nle\nn\n\n\n\nd\nin\n\n\n\ng \no\n\n\n\nf \nsp\n\n\n\nir\nit\n\n\n\ns;\n e\n\n\n\nth\nyl\n\n\n\n\u2026\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nw\nin\n\n\n\ne\ns,\n\n\n\n m\nal\n\n\n\nt \nliq\n\n\n\nu\no\n\n\n\nrs\n &\n\n\n\n m\nal\n\n\n\nt\n\n\n\nM\nan\n\n\n\nu\nfa\n\n\n\nct\nu\n\n\n\nre\nr \n\n\n\no\nf \n\n\n\nso\nft\n\n\n\n d\nri\n\n\n\nn\nks\n\n\n\nP\nro\n\n\n\nd\nu\n\n\n\nct\nio\n\n\n\nn\n o\n\n\n\nf \nm\n\n\n\nin\ne\n\n\n\nra\nl w\n\n\n\nat\ne\n\n\n\nr\n\n\n\nChange in Technical Efficiency Change in Technological Efficiency Change in TFP Efficiency\n\n\n\n23\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\nTable 3: Summary of the results from Order-m Analysis for 2009 \n\n\n\nDMU Industry name \nEfficiency Score Efficiency Rank Pseudo Reference \n\n\n\nm=34 m=20 m=25 m=34 m=20 m=25 m=34 m=20 m=25 \n\n\n\n1 meat & meat products 1.18675 0.98629 0.99314 10 27 26 35 1 1 \n\n\n\n2 poultry & poultry products 0.99875 0.78942 0.81300 30 10 8 25 2 2 \n\n\n\n3 fish & fish products 1.16684 0.93207 0.96290 12 21 21 3 3 3 \n\n\n\n5 \nCanning & preservation of \nother fruits & vegetables 1.42727 0.95240 0.97532 8 22 22 5 5 5 \n\n\n\n6 Pineapple canning 1.87641 1.00000 1.00000 2 31 31 6 6 6 \n\n\n\n7 nut & nut products 1.16768 0.98710 0.98710 11 28 24 35 7 7 \n\n\n\n8 crude palm oil 1.00000 0.47320 0.54399 21 1 1 8 9 9 \n\n\n\n9 refined palm oil 1.00000 0.54529 0.62989 22 2 2 9 10 10 \n\n\n\n10 palm kernel oil 1.00000 0.61865 0.65793 23 4 5 10 21 21 \n\n\n\n11 \nother vegetable and animal \noils & fats 1.00000 0.74370 0.74119 24 6 7 11 11 11 \n\n\n\n13 coconut oil 2.95324 1.00000 1.00000 1 30 30 13 13 13 \n\n\n\n14 ice cream 1.19627 0.95875 1.00000 9 23 28 14 14 14 \n\n\n\n15 \ncondensed, powdered and \nevaporated milk  \n\n\n\n1.00000 0.57664 0.64032 25 3 3 15 23 23 \n\n\n\n16 Rice milling 1.00032 0.77132 0.84016 20 9 12 16 4 16 \n\n\n\n17 Flour milling 1.01140 0.74909 0.81308 19 7 9 17 17 17 \n\n\n\n18 \nother flour/grain mill \nproducts 1.54955 0.99816 1.00000 6 29 29 18 18 18 \n\n\n\n19 \nglucose, glucose syrup & \nmaltose \n\n\n\n1.85644 1.00000 1.00000 3 32 32 19 19 19 \n\n\n\n20 \nsago & tapioca flour \nproducts 1.46275 1.00000 1.00000 7 33 33 18 20 20 \n\n\n\n22 biscuits and cookies 0.71778 0.87095 0.92173 34 16 17 36 24 22 \n\n\n\n23 \nbread, cake and other \nbakery products 1.00000 0.64508 0.71412 26 5 6 23 23 23 \n\n\n\n24 \n\n\n\nsnacks, crackers & chips \n(e.g. prawn/fish crackers, \npotato/banana/tapioca \nchips) \n\n\n\n0.78006 0.84085 0.90803 33 12 16 36 24 24 \n\n\n\n25 sugar 1.00000 0.97580 0.99189 27 25 25 25 25 25 \n\n\n\n26 cocoa products 1.00000 0.85718 0.88342 28 15 13 26 26 26 \n\n\n\n27 \nchocolate products & sugar \nconfectionery \n\n\n\n1.08364 0.92272 0.94954 15 20 20 36 29 27 \n\n\n\n29 \nmacaroni, noodles & \nsimilar products 1.01783 0.80774 0.82757 18 11 10 29 29 29 \n\n\n\n30 coffee 1.02361 0.85281 0.89763 16 14 14 30 30 30 \n\n\n\n31 tea 1.66428 0.97441 0.99574 5 24 27 31 31 31 \n\n\n\n32 \nsauces including flavoring \nextracts 1.02265 0.89106 0.93381 17 18 18 32 29 29 \n\n\n\n33 spices & curry powder 1.15241 0.87930 0.89926 13 17 15 33 33 33 \n\n\n\n34 other food products 0.93949 0.90388 0.93614 32 19 19 36 30 34 \n\n\n\n35 \nspirits; ethyl alcohol \nproduction from fermented \nmaterials \n\n\n\n1.78884 0.98166 0.98229 4 26 23 35 35 35 \n\n\n\n36 wines, malt liquors & malt 1.00000 1.30997 1.34112 29 34 34 36 24 24 \n\n\n\n37 soft drinks 0.99524 0.75664 0.83640 31 8 11 25 16 37 \n\n\n\n38 mineral water 1.09309 0.84824 0.87893 14 13 4 38 38 38 \n\n\n\nTable 4: Summary of the results from Order-m Analysis for 2010 \n\n\n\nDMU Industry name \nEfficiency Score Efficiency Rank Pseudo Reference \n\n\n\nm=34 m=20 m=25 m=34 m=20 m=25 m=34 m=20 m=25 \n\n\n\n1 meat & meat products 1.12451 0.97153 0.98686 15 26 27 31 1 1 \n\n\n\n2 \npoultry & poultry \nproducts 0.99873 0.79522 0.83284 34 9 8 25 2 2 \n\n\n\n3 fish & fish products 1.20719 0.90412 0.92492 10 19 18 3 3 3 \n\n\n\n4 \nCanning & preservation \nof other fruits & \nvegetables \n\n\n\n1.47776 0.95020 0.98343 5 24 25 5 5 5 \n\n\n\n5 Pineapple canning 1.96614 1.00000 1.00000 2 30 30 6 6 6 \n\n\n\n6 nut & nut products 1.13087 0.97149 0.98931 13 25 28 31 7 7 \n\n\n\n7 crude palm oil 1.00000 0.46353 0.49417 24 1 1 8 33 33 \n\n\n\n8 refined palm oil 1.00000 0.48387 0.52704 24 2 2 9 10 10 \n\n\n\n9 palm kernel oil 1.00000 0.79991 0.84694 24 10 10 10 15 15 \n\n\n\n10 \nother vegetable and \nanimal oils & fats 1.00000 0.77956 0.88556 24 7 14 11 11 11 \n\n\n\n11 coconut oil 2.82948 0.98814 1.00000 1 29 29 13 13 13 \n\n\n\n24\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\n12 ice cream 1.20044 0.93696 0.97652 11 22 23 14 14 14 \n\n\n\n13 \ncondensed, powdered \nand evaporated milk  \n\n\n\n1.00000 0.69041 0.78375 24 4 4 15 33 33 \n\n\n\n14 Rice milling 1.00221 0.80411 0.80235 23 11 6 16 16 16 \n\n\n\n15 Flour milling 1.00884 0.82144 0.85638 22 13 12 17 22 17 \n\n\n\n16 \nother flour/grain mill \nproducts 1.38898 1.00000 1.00000 7 32 32 7 18 18 \n\n\n\n17 \nglucose, glucose syrup \n& maltose \n\n\n\n1.86053 1.00000 1.00000 3 31 31 13 19 19 \n\n\n\n18 \nsago & tapioca flour \nproducts 1.30239 1.00000 1.00000 9 33 33 18 20 20 \n\n\n\n19 biscuits and cookies 0.71636 0.84478 0.89773 38 14 16 32 22 22 \n\n\n\n20 \nbread, cake and other \nbakery products 1.00000 0.62448 0.71619 24 3 3 23 30 23 \n\n\n\n21 \nsnacks, crackers & \nchips 0.77893 0.91071 0.92814 37 20 19 32 24 24 \n\n\n\n22 sugar 1.00000 0.84604 0.88347 24 15 13 25 2 25 \n\n\n\n23 cocoa products 1.00000 0.75076 0.81002 24 6 7 26 2 26 \n\n\n\n24 \nchocolate products & \nsugar confectionery \n\n\n\n1.01759 0.93995 0.95312 20 23 21 27 30 27 \n\n\n\n25 \nmacaroni, noodles & \nsimilar products 1.01852 0.77976 0.84856 19 8 11 29 29 29 \n\n\n\n26 coffee 1.01325 0.91454 0.96570 21 21 22 30 29 30 \n\n\n\n27 tea 1.42011 0.97683 0.98610 6 27 26 31 31 31 \n\n\n\n28 \nsauces including \nflavoring extracts 1.01977 0.87086 0.89246 18 17 15 32 32 32 \n\n\n\n29 spices & curry powder 1.12834 0.85475 0.90256 14 16 17 33 33 33 \n\n\n\n30 other food products 0.93949 0.81265 0.84606 36 12 9 32 30 30 \n\n\n\n31 \nspirits; ethyl alcohol \nproduction from \nfermented materials \n\n\n\n1.65179 0.97793 0.97793 4 28 24 31 31 31 \n\n\n\n32 \nwines, malt liquors & \nmalt 1.00000 1.04728 1.07311 24 34 34 32 32 30 \n\n\n\n33 soft drinks 0.99524 0.71428 0.79593 35 5 5 25 4 37 \n\n\n\n34 mineral water 1.06119 0.89615 0.93962 16 18 20 34 34 34 \n\n\n\nFigure 2: Order-m efficiency score in 2009 (Source: Author calculation) \n\n\n\n0.00000\n\n\n\n0.50000\n\n\n\n1.00000\n\n\n\n1.50000\n\n\n\n2.00000\n\n\n\n2.50000\n\n\n\n3.00000\n\n\n\nmeat & meat products\npoultry & poultry products\n\n\n\nfish & fish products\nCanning & preservation of other fruits &\n\n\n\nvegetables\n\n\n\nPineapple canning\n\n\n\nnut & nut products\n\n\n\ncrude palm oil\n\n\n\nrefined palm oil\n\n\n\npalm kernel oil\n\n\n\nother vegetable and animal oils & fats\n\n\n\ncoconut oil\n\n\n\nice cream\n\n\n\ncondensed, powdered and evaporated milk\n\n\n\nRice milling\n\n\n\nFlour milling\n\n\n\nother flour/grain mill products\nglucose, glucose syrup & maltose\n\n\n\nsago & tapioca flour products\nbiscuits and cookies\n\n\n\nbread, cake and other bakery products\n\n\n\nsnacks, crackers & chips (e.g. prawn/fish\ncrackers, potato/banana/tapioca chips)\n\n\n\nsugar\n\n\n\ncocoa products\n\n\n\nchocolate products & sugar confectionery\n\n\n\nmacaroni, noodles & similar products\n\n\n\ncoffee\n\n\n\ntea\n\n\n\nsauces including flavoring extracts\n\n\n\nspices & curry powder\n\n\n\nother food products\n\n\n\nspirits; ethyl alcohol production from\nfermented materials\n\n\n\nwines, malt liquors & malt\n\n\n\nsoft drinks\nmineral water\n\n\n\nm=34 m=20 m=25\n\n\n\n25\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\nFigure 3: Order-m efficiency score in 2010 (Source: Author calculation) \n\n\n\nFigure 4: Percentage increase or decrease in efficiency score from 2009 to 2010 (Source: Author calculation) \n\n\n\n0.00000\n\n\n\n0.50000\n\n\n\n1.00000\n\n\n\n1.50000\n\n\n\n2.00000\n\n\n\n2.50000\n\n\n\n3.00000\nmeat & meat products\n\n\n\npoultry & poultry products\nfish & fish products\n\n\n\nCanning & preservation of other fruits &\nvegetables\n\n\n\nPineapple canning\n\n\n\nnut & nut products\n\n\n\ncrude palm oil\n\n\n\nrefined palm oil\n\n\n\npalm kernel oil\n\n\n\nother vegetable and animal oils & fats\n\n\n\ncoconut oil\n\n\n\nice cream\n\n\n\ncondensed, powdered and evaporated milk\n\n\n\nRice milling\n\n\n\nFlour milling\nother flour/grain mill products\n\n\n\nglucose, glucose syrup & maltose\nsago & tapioca flour products\n\n\n\nbiscuits and cookies\nbread, cake and other bakery products\n\n\n\nsnacks, crackers & chips\n\n\n\nsugar\n\n\n\ncocoa products\n\n\n\nchocolate products & sugar confectionery\n\n\n\nmacaroni, noodles & similar products\n\n\n\ncoffee\n\n\n\ntea\n\n\n\nsauces including flavoring extracts\n\n\n\nspices & curry powder\n\n\n\nother food products\n\n\n\nspirits; ethyl alcohol production from\nfermented materials\n\n\n\nwines, malt liquors & malt\nsoft drinks\n\n\n\nmineral water\n\n\n\nm=34 m=20 m=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\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\nm\ne\n\n\n\na\nt \n\n\n\n&\n m\n\n\n\ne\na\n\n\n\nt \np\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\np\no\n\n\n\nu\nlt\n\n\n\nry\n &\n\n\n\n p\no\n\n\n\nu\nlt\n\n\n\nry\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nfi\nsh\n\n\n\n &\n f\n\n\n\nis\nh\n\n\n\n p\nro\n\n\n\nd\nu\n\n\n\nct\ns\n\n\n\nC\na\n\n\n\nn\nn\n\n\n\nin\ng \n\n\n\n&\n p\n\n\n\nre\nse\n\n\n\nrv\na\n\n\n\nti\no\n\n\n\nn\n o\n\n\n\nf \no\n\n\n\nth\ne\n\n\n\nr \nfr\n\n\n\nu\nit\n\n\n\ns \n&\n\n\n\n v\ne\n\n\n\nge\nta\n\n\n\nb\nle\n\n\n\ns\n\n\n\nP\nin\n\n\n\ne\na\n\n\n\np\np\n\n\n\nle\n c\n\n\n\na\nn\n\n\n\nn\nin\n\n\n\ng\n\n\n\nn\nu\n\n\n\nt \n&\n\n\n\n n\nu\n\n\n\nt \np\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\ncr\nu\n\n\n\nd\ne\n\n\n\n p\na\n\n\n\nlm\n o\n\n\n\nil\n\n\n\nre\nfi\n\n\n\nn\ne\n\n\n\nd\n p\n\n\n\na\nlm\n\n\n\n o\nil\n\n\n\np\na\n\n\n\nlm\n k\n\n\n\ne\nrn\n\n\n\ne\nl o\n\n\n\nil\n\n\n\no\nth\n\n\n\ne\nr \n\n\n\nve\nge\n\n\n\nta\nb\n\n\n\nle\n a\n\n\n\nn\nd\n\n\n\n a\nn\n\n\n\nim\nal\n\n\n\n o\nil\n\n\n\ns \n&\n\n\n\n f\na\n\n\n\nts\n\n\n\nco\nco\n\n\n\nn\nu\n\n\n\nt \no\n\n\n\nil\n\n\n\nic\ne\n\n\n\n c\nre\n\n\n\na\nm\n\n\n\nco\nn\n\n\n\nd\ne\n\n\n\nn\nse\n\n\n\nd\n, p\n\n\n\no\nw\n\n\n\nd\ne\n\n\n\nre\nd\n\n\n\n a\nn\n\n\n\nd\n e\n\n\n\nva\np\n\n\n\no\nra\n\n\n\nte\nd\n\n\n\n m\nil\n\n\n\nk \n\n\n\nR\nic\n\n\n\ne\n m\n\n\n\nil\nli\n\n\n\nn\ng\n\n\n\nFl\no\n\n\n\nu\nr \n\n\n\nm\nil\n\n\n\nli\nn\n\n\n\ng\n\n\n\no\nth\n\n\n\ne\nr \n\n\n\nfl\no\n\n\n\nu\nr/\n\n\n\ngr\nai\n\n\n\nn\n m\n\n\n\nil\nl p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\ngl\nu\n\n\n\nco\nse\n\n\n\n, g\nlu\n\n\n\nco\nse\n\n\n\n s\nyr\n\n\n\nu\np\n\n\n\n &\n m\n\n\n\na\nlt\n\n\n\no\nse\n\n\n\nsa\ngo\n\n\n\n &\n t\n\n\n\na\np\n\n\n\nio\nca\n\n\n\n f\nlo\n\n\n\nu\nr \n\n\n\np\nro\n\n\n\nd\nu\n\n\n\nct\ns\n\n\n\nb\nis\n\n\n\ncu\nit\n\n\n\ns \na\n\n\n\nn\nd\n\n\n\n c\no\n\n\n\no\nki\n\n\n\ne\ns\n\n\n\nb\nre\n\n\n\na\nd\n\n\n\n, c\na\n\n\n\nke\n a\n\n\n\nn\nd\n\n\n\n o\nth\n\n\n\ne\nr \n\n\n\nb\na\n\n\n\nke\nry\n\n\n\n p\nro\n\n\n\nd\nu\n\n\n\nct\ns\n\n\n\nsn\na\n\n\n\nck\ns,\n\n\n\n c\nra\n\n\n\nck\ne\n\n\n\nrs\n &\n\n\n\n c\nh\n\n\n\nip\ns\n\n\n\nsu\nga\n\n\n\nr\n\n\n\nco\nco\n\n\n\na \np\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nch\no\n\n\n\nco\nla\n\n\n\nte\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns \n&\n\n\n\n s\nu\n\n\n\nga\nr \n\n\n\nco\nn\n\n\n\nfe\nct\n\n\n\nio\nn\n\n\n\ne\nry\n\n\n\nm\na\n\n\n\nca\nro\n\n\n\nn\ni,\n\n\n\n n\no\n\n\n\no\nd\n\n\n\nle\ns \n\n\n\n&\n s\n\n\n\nim\nil\n\n\n\nar\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nco\nff\n\n\n\ne\ne\n\n\n\nte\na\n\n\n\nsa\nu\n\n\n\nce\ns \n\n\n\nin\ncl\n\n\n\nu\nd\n\n\n\nin\ng \n\n\n\nfl\na\n\n\n\nvo\nri\n\n\n\nn\ng \n\n\n\ne\nxt\n\n\n\nra\nct\n\n\n\ns\n\n\n\nsp\nic\n\n\n\ne\ns \n\n\n\n&\n c\n\n\n\nu\nrr\n\n\n\ny \np\n\n\n\no\nw\n\n\n\nd\ne\n\n\n\nr\n\n\n\no\nth\n\n\n\ne\nr \n\n\n\nfo\no\n\n\n\nd\n p\n\n\n\nro\nd\n\n\n\nu\nct\n\n\n\ns\n\n\n\nsp\nir\n\n\n\nit\ns;\n\n\n\n e\nth\n\n\n\nyl\n a\n\n\n\nlc\no\n\n\n\nh\no\n\n\n\nl p\nro\n\n\n\nd\nu\n\n\n\nct\nio\n\n\n\nn\n f\n\n\n\nro\nm\n\n\n\n f\ne\n\n\n\nrm\ne\n\n\n\nn\nte\n\n\n\nd\n \u2026\n\n\n\nw\nin\n\n\n\ne\ns,\n\n\n\n m\na\n\n\n\nlt\n li\n\n\n\nq\nu\n\n\n\no\nrs\n\n\n\n &\n m\n\n\n\na\nlt\n\n\n\nso\nft\n\n\n\n d\nri\n\n\n\nn\nks\n\n\n\nm\nin\n\n\n\ne\nra\n\n\n\nl w\na\n\n\n\nte\nr\n\n\n\nm=34 m=20 m=25\n\n\n\n26\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\n6. CONCLUSION AND POLICY SUGGESTIONS \n\n\n\nThe key contributions of this study to the existing literature are as follows: \n\n\n\n1. There is an introduction to MPI analysis to the existing \nliterature for analyzing total factor productivity and \ntechnological change efficiency of the food processing industry.\n\n\n\nFindings: MPI analysis enabled us to examine the performance \nof technological and technical efficiency separately and the \ncontribution of organic composition as well as the contribution \nof technology to the total factor productivity of the sectors more \ncomprehensively; \n\n\n\n2. A search of the literature indicates this is the first time in the \nanalysis of the food processing industry that a study has used \nboth partial frontier (m = 20 and m = 25) as well as full frontier \n(m = 34) order-m analysis. The m stands for number of \nindustries in this research. \n\n\n\nFindings: This has enabled us to examine which food processing \nindustry has performed most efficiently, providing insights for \nother food processors. This approach has also helped us to \nreview the variation in the efficiency score of the industries \nover the one-year period under consideration. Moreover, the \nstudy also found the order m method to be efficient and \nconsistence unlike other non-parametric methods such as DEA, \nFDH etc. \n\n\n\n3. The study also presented technical intensiveness and \ntechnological intensiveness as factors determining comparative \nand absolute advantage for each industry in addition to the \nfactors, such as lower cost of inputs and higher value of output, \nwhich were used in previous studies. \n\n\n\nFindings: The results strongly indicate that for the majority of \nindustries, better performance is the result of an improvement \nin technological efficiency rather than organic composition. \nAlmost all industries show negativity in the improvement of \norganic composition in their production processes, with the \nexception of Manufacturer of Palm Kernel Oil for which organic \ncomposition was a significant contributor in the overall \nincrease in total factor productivity efficiency.  \n\n\n\nThis is an important finding, because since technological efficiency \nincreases are likely the result of developments external to the industry and \nthe firms that comprise it, it suggests that there is still scope for \nimprovement in the internal management practices and resource \nallocation decision of these high performing industries. In other words, the \nbest-practice production frontier is moving upward thereby increasing \ntotal factor productivity, but the extent to which firms are moving towards \nthe frontier by improving internal practices is less positive. Indeed, it \ncould be speculated that the trend of continually increasing technological \nefficiency in the food processing industry reduces the incentives for firms \nto strive for technical efficiency within their organizations. \n\n\n\nWhile this is largely a matter for the firms themselves, it has policy \nimplications simply in the recognition that firms are not moving towards \ntheir production frontiers. More work would need to be done to ascertain \nwhy but if, for example, this is because of barriers to the improvement in \norganic composition, policies could be considered to address this. This \nmethodology would be equally valuable in other industries, given the \navailability of appropriate data.The main limitation of this study is the lack \nof availability of the data. The data used here is only for two years, 2009 \nand 2010, therefore the results show some inconsistency in the outcome. \nThe dataset used in this study has been collected in association with \nUniversity Malaysia Sabah (UMS), University Putra Malaysia (UPM) and \nMalaysian government. Data for the year 2012 was available, but unusable \nfor this study because it lacked the balanced panel feature for analysis. \nAlso, a deeper understanding of how intensive an industry is to organic \ncomposition or technology wasn\u2019t possible due to lack of appropriate data. \nAdditionally, this study has used only a non-parametric approach to the \nanalysis. Hence, the suggestion to future researchers would be to use more \nyears of data in their analysis, as well as to use additional data for \nanalyzing the intensiveness to organic composition or technology of each \nindustry. It would also be possible to use parametric approaches such as \nsimple OLS or GLS regression analysis besides the non-parametric \napproaches used in this study. \n\n\n\nAPPENDIX \n\n\n\nNote 1: Organic Composition \n\n\n\nThe organic composition of a firm or industry is the ratio of constant \ncapital to variable capital which is required to produce one unit of output \nof that firm or industry. This can be referred to as an indicator of technical \nefficiency of a firm explaining how the factors of production work together \nto produce a desired amount of output. A higher value of organic \ncomposition means that the production process is capital-intensive, and \nthe lower value means that the production process is labor-intensive. Any \nincrease in organic composition will indicate improvement in the technical \nefficiency of the firm/industry. \n\n\n\nNote 2: Non-Parametric Statistics \n\n\n\nNonparametric statistics refer to a statistical method used to analyze \nordinal or nominal data with small sample sizes, wherein the data does not \nrequire any assumptions regarding the distribution of the population. \nNon-parametric methods are also referred as distribution free method. \nThe contemporary nonparametric methods are data envelopment analysis \n(DEA), free disposal hull (FDH), order-\u03b1 and order-m frontier analysis, etc.  \n\n\n\nNote 3: Pseudo Reference \n\n\n\nThe order-m analysis suggests some reference DMUs which should be \nfollowed by the respondent DMU in order to achieve higher degree of \nefficiency. This reference DMU is called pseudo reference. \n\n\n\nNote 4: Malmquist Productivity Index (MPI) \n\n\n\nMPI measures the change in efficiency of a DMU between different time \nperiods. The Malmquist productivity index does not satisfy the transitivity \nproperty and also does not adequately account for scale change. The input \nand output oriented indices coincide if the technology exhibits constant \nreturn to scale. MPI can be decomposed into efficiency change and \ntechnical change.  \n\n\n\nNote 5: Data Envelopment Analysis (DEA) \n\n\n\nIt is a nonparametric mathematical programming approach to frontier \nestimation. DEA method is popularly used to calculate MPI of TFP change.  \n\n\n\nREFERENCES \n\n\n\n[1] Alam, S., Jani, M., Senik, Z., Domil, A. 2011. Assessing barriers of growth \nof food processing SMIs in Malaysia: A factor analysis. International \nBusiness Research, 4 (1), 251-259. \n\n\n\n[2] Ramzani, S.R., Ismail, M.M., Abdurofi, I. 2015. Measuring \nCompetitiveness of Food Processing Industry in Malaysia. Australian \nJournal of Business and Economic Studies. \n\n\n\n[3] Yodfiatfinda, Mad, N., Zainalabidin, M., Md Ariff, H., Zulkornain, Y., \nAlias, R. 2012. The empirical evaluation of productivity growth and \nefficiency of LSEs in the Malaysian food processing industry. International \nFood Research Journal, 19, 287-295. \n\n\n\n[4] Malaysia\u2019s Food Industry. 2011. Malaysian Industrial Development \nAuthority (MIDA), 2012 Ministry of Agriculture and Agro-based Industry. \nNational Agro-food Policy (2011-2020). Ministry of Agriculture and Agro-\nfood Industry. Kuala Lumpur. \n\n\n\n[5] Rahman, A. 2016. Exports of processed food to grow further in 2016: \nMatrade. Ministry of International Trade and Industry (MITI), Kuala \nLampur, Malaysia. Retrieved from\nhttp://www.miti.gov.my/index.php/pages/view/3122 \n\n\n\n27\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(1) (2018) 19-28 \n\n\n\nCite the article: Munshi Naser, Roger Lawrey (2018). A Comparative Analysis Of The Efficiency And Productivity Of Selected Fo od Processing Industries In Malaysia . \nMalaysian Journal of Sustainable Agriculture, 2(1) : 19-28. \n\n\n\n[6] Fatimah, M., Ahmad, S., Alias, R., Amin, M., Azali, M. 2008. Assessment \non action plan of food sector trade balance. Ministry of Agriculture and \nAgro-based Industry, Malaysia. \n\n\n\n[7] Ismail, M.M., Yusop, Z. 2014. Competitiveness of the Malaysian Food \nProcessing Industry. Journal of Food Products Marketing, 20 (1), 164-178.\n\n\n\n[8] Afzal, M.N.I. 2013. Are Science Valleys and Clusters Panacea for a \nKnowledge Economy? An Investigation on Regional Innovation System \n(RIS) - Concepts, Theory and Empirical analysis. Asian Research Policy 4, \n114-125. \n\n\n\n[9] Afzal, M.N., Manni, U.H. 2013. An Empirical Productivity Analysis of \nASEAN Economies in Transition Towards Knowledge-Based Economy. \nAsian Research Policy, 4 (1), 1-9. \n\n\n\n[10] Salim, R. A., Kalirajan, K. 1996. Measures of capacity realization and \nproductivity growth for Bangladesh food processing industries. Australian \nNational University. \n\n\n\n[11] Nishimizu, M., Page, J.M. Jr. 1982. Total Factor Productivity Growth, \nTechnological Progress and Technical Efficiency Change: Dimensions of \nProductivity Change in Yugoslavia. The Economic Journal 92 (368), 920-\n936. \n\n\n\n[12] Fare, R., Grosskopf, S., Lovell, C. 1994. Production Frontier. \nCambridge: Cambridge University Press. \n\n\n\n[13] Afzal, M.N.I., Lawrey, R. 2012. Knowledge-Based Economy (KBE) \nFrameworks and Empirical Investigation of KBE Input-Output Indicators \nfor ASEAN. International Journal of Economics and Finance, 4 (9), 13-22. \n\n\n\n[14] Latruffe, L. 2010. Competitiveness, Productivity and Efficiency in \nthe Agricultural and Agri-Food Sectors. OECD Food, Agriculture and \nFisheries Papers, 30. doi: http://dx.doi.org/10.1787/5km91nkdt6d6-en \n\n\n\n[15] European Commission. 2009. European Competitiveness Report \n2008. Brussels: European Commission. \n\n\n\n[16] Lambert, D.K. 1999. Scale and the Malmquist productivity index. \nApplied Economics Letters, 6 (9), 593-596. \n\n\n\n[17] Coelli, T. 1996. A Guide to DEAP 2.1: A data envelopment. Armidale. \nUniversity of New England. \n\n\n\n[18] Cazals, C., Florens, J., Simar, L. 2002. Nonparametric Frontier \nEstimation: A Robust Approach. Journal of Econometrics, 106 (1), 1-25. \n\n\n\n[19] Simar, L., Wilson, P. 2006. Statistical inference in nonparametric \nfrontier models: Recent developments and perspectives. In C. L. H. Fried, \nThe Measurement of Productive Efficiency, Chapter 4. Oxford University \nPress. \n\n\n\n[20] \u010cechura, L., Hockmann, H., Kroupov\u00e1, Z. 2014. Productivity and \nEfficiency of European Food Processing Industry. International \ncomparison of product supply chains in the agri-food sector: determinants \nof their competitiveness and performance on EU and international \nmarkets, 7, 1-9. \n\n\n\n28\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 33-38 \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 April 2019 \nAccepted 10 May 2019 \nAvailable online 14 May 2019 \n\n\n\nABSTRACT\n\n\n\nA sixty days laboratory incubation study was conducted to investigate the effect of heavy metals on soil carbon, \nnitrogen and iron mineralization under aerobic condition. Sulphate salts of cadmium, zinc and copper were added \n\n\n\nindividually and in combinations to soil samples and incubated in different plastic pots. Soil organic carbon did not \n\n\n\nchange significantly throughout the incubation period. Soil microbial biomass carbon declined from 0.38mgkg-1to \n\n\n\n0.19 mgkg-1 in Cd treated soil and 0.39 mgkg-1 to 0.28 mgkg-1 in Cu treated soil which account for about 50% and \n\n\n\n28% reduction (p \u22640.05) in biomass carbon respectively. Cd:Zn and Cu:Cd treated soil had reduced 36.84% while \n\n\n\nZn:Cu had 42.11% reduction in biomass carbon.CO2-C effluxes peaked by day 15 for all the single metal amended \n\n\n\nsoil indicating that priming effects might have occurred. But in combination, metal showed some interaction for \nwhat the respiration rates were declined for the first 15 days. Rapid ammonification with presumed immobilization \n\n\n\ntook place up to day 30. The result indicated a significant (p \u22640.05) net mineralization of nitrogen for Cd:Zn \n\n\n\n(63.72%) and Cu:Cd (66.66%) treatments at the end of the experiment. Available iron content showed significant \nchanges in combined metal treatment than a single metal. \n\n\n\nKEYWORDS \n\n\n\nHeavy metals, microbial biomass carbon, respiration, nitrogen, iron, mineralization. \n\n\n\n1. INTRODUCTION \n\n\n\nHeavy metals are continuously accumulated in soil due to land disposal of \nmunicipal and industrial wastes, emissions of automobile, use of chemical \n\n\n\nfertilizers, pesticides and other toxic elements for agricultural purposes \n[1-4]. It is clear that heavy metals commenced with compost or sewage \n\n\n\nsludge, caused soil organic matter accumulation and decreased the \n\n\n\nturnover rate of organic matter [5-8]. Fertilizer applications possibly \n\n\n\ncapable of influence Cd in soil which affects the movement of Cd to plant \nroots in addition to Cd uptake [9]. Copper is mainly toxic to roots. High \n\n\n\ncopper interaction with iron metabolism is the causes of chlorosis as the \n\n\n\nsymptom of copper toxicity [10]. Along with this, higher concentrations of \ncopper can reduce the availability of zinc absorption to plant as these \n\n\n\nmicronutrients compete for the same sites of plant root [11]. Enormously \n\n\n\nslight concentrations of some metals like copper, zinc, nickel, cadmium is \nvital for the components of enzymes, pigments, structural proteins, and in \n\n\n\nmaintaining the ionic balance of cells [12]. Researchers all over the world \n\n\n\nare concerned about heavy metals mainly due to their harmful effects on \n\n\n\nplants, especially those on vegetative and generative parts of the plants. \nThey also endanger human health when the metals migrate through the \n\n\n\nfood chain [13]. Besides, heavy metals showed toxic effects on \n\n\n\nmicroorganisms in various ways such as reduced litter decomposition and \n\n\n\nnitrogen fixation, less efficient nutrient cycling [14]. There is now \n\n\n\nconsiderable amount of evidence of documenting decrease in the soil \nmicrobial biomass as a result of long-term exposure to heavy metal \n\n\n\ncontamination from past applications of sewage sludge as reviewed by \n\n\n\n[15]. The soil organic carbon content had noteworthy positive correlations \n\n\n\nwith the dehydrogenase activity, basal soil respiration, catalase activity, \nand microbial biomass-C at P<0.01 [16].  A previous researcher observed  \n\n\n\nnegative relationship of basal soil respiration with heavy metal contents \n[17]. Being a microbial dependent process, all those factors, which affect \nmicrobial activities, indirectly affect N mineralization. Several studies \n\n\n\nhave shown that metals affect N mineralization, however, results are \n\n\n\ncontradictory. A researcher reported that mineralization of soil organic N, \nas with microbial respiration, is unaffected at soil metal concentration at \naround current EU limits [18]. This is supported by a previous researcher \nwho reported that inhibition of both N mineralization and nitrification are \n\n\n\ninhibited at around 1000 mgkg-1 Zn, Cu and Ni, around 100-500 mgkg-1 Pb \n\n\n\nand Cr and around 10-100 mgkg-1 Cd. Iron deficiency is extremely rare in \n\n\n\nthe field crops [19]. As a result, very few works deal with the iron as a \n\n\n\nmajor nutrient that affects plant growth. Therefore, the present study has \nbeen conducted to determine the effects of heavy metals (Cd, Zn and Cu) \non carbon, nitrogen and iron mineralization of an agricultural soil \nartificially polluted with heavy metals. For this purpose, the rates of soil \norganic carbon, nitrogen and iron mineralization, soil respiration and \n\n\n\nmicrobial biomass carbon were monitored. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nAgricultural clayey loamy soil samples were collected from a field plot at \nNoapara in Jashore district. The surface soil samples were collected from \n\n\n\nspecific locations (N 23\u00ba 11.841\u1ffdE: 89\u00ba 14.660\u1ffd) at a depth of 0-15 cm and \n\n\n\nwere processed for the subsequent experiment and analysis. The soils \n\n\n\nwere air dried and ground to pass through a 2 mm sieve, sorted to remove \n\n\n\nstones, plant debris and any visible soil fauna and then were mixed \n\n\n\nthoroughly with hand trowel. The general description of the soil used for \nthe experiment is shown in table 1. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.01.2019.33.38\n\n\n\n  RESEARCH ARTICLE \n\n\n\nEFFECTS OF HEAVY METALS (Cd, Zn and Cu) ON CARBON, NITROGEN AND IRON \nMINERALIZATION IN SOIL \n\n\n\nFalguni Akter1*, Humaira Hasan Tinni2, Parmita Banarjee2, Mohammad Zaber Hossain1 \n\n\n\n1Soil, Water and Environment Discipline, Khulna University, Khulna, Bangladesh \n2Research fellow, Soil, Water and Environment Discipline, Khulna University, Khulna, Bangladesh \n\n\n\n*Corresponding Author Email: falgunissku@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited\n\n\n\nCite The Article: Falguni Akter, Humaira Hasan Tinni, Parmita Banarjee, Mohammad Zaber Hossain (2019). Effects Of Heavy Metals (Cd, Zn And Cu) On Carbon, \nNitrogen And Iron Mineralization In Soil. Malaysian Journal of Sustainable Agriculture, 3(1): 33-38.\n\n\n\n\nmailto:falgunissku@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 33-38 \n\n\n\nTable 1: Characteristics of the soil under study \n\n\n\nProperties Analysis \n\n\n\npH 7.30 \n\n\n\nCarbon (%) 1.56 \n\n\n\nTotal nitrogen (%) 0.11 \n\n\n\nAvailable nitrogen (mgkg-1) 25 \n\n\n\nBiomass carbon(mgkg-1) 0.38 \n\n\n\nSoil respiration(mg of Cg-1soil day-1) 5.64 \n\n\n\nField capacity (%) 60.62 \n\n\n\nTexture Clay loam \n\n\n\nCEC (cmolckg-1) 37.60 \n\n\n\nTotal Fe (%) 3.75 \n\n\n\nAvailable Fe (mgkg-1) 1.91 \n\n\n\nApproximately 0.25 kg of sieved soil samples were distributed into plastic \n\n\n\npots. Solutions of analytical grade sulphate (SO4) salt each of cadmium, \n\n\n\nzinc and copper was applied to the respective pots singly and in \n\n\n\ncombinations. Soil sample which receive no metal amendment was served \n\n\n\nas control. All the treatments (A-G) were replicated three times. Number \nof treatments and their combinations were as follows:\n\n\n\nTable 2: Pattern of metal amendment in pre-incubated soil pots \n\n\n\nTreatment Notation Metal Concentration (mgkg-1) \n\n\n\n0 Control -- \n\n\n\nA Cu 3000 \n\n\n\nB Cd 3000 \n\n\n\nC Zn 3000 \n\n\n\nD Cu:Cd 1500:1500 \n\n\n\nE Cd:Zn 1500:1500 \n\n\n\nF Zn:Cu 1500:1500 \n\n\n\nG Cu:Cd:Zn 1000:1000:1000 \n\n\n\nTotal length of the incubation period was 60 days and the time interval for \ncollection of incubated soil sample was 0, 15, 30, 45 and 60 days. The \n\n\n\nexperiment was conducted aerobically. Soil pH was determined \n\n\n\nelectrochemically at soil: water ratio of 1:2.5 by using Griffin (Model 40) \nglass electrode pH meter as described by [20]. Organic carbon of the soil \nsample was determined by Walkly and Black wet oxidation method as \n\n\n\nsuggested by [20]. Available nitrogen and total nitrogen of the soil both \n\n\n\nwere determined by colorimetric method as suggested by [21]. Soil \nmicrobial biomass carbon was determined by fumigation extraction \n\n\n\nmethod as outlined by [22]. Total iron was determined by \n\n\n\nspectrophotometer after digestion with HNO3: HClO4 (2:1) mixture. \n\n\n\nAvailable iron was determined by 1N NH4OAc (pH 7.0) as described by \n\n\n\n[23]. Total metal content of soil before incubation was determined after \nacid digestion (HNO3:HClO4; 2:1) by atomic absorption \n\n\n\nspectrophotometer. Soil respiration was done on 20g portion of soil \nincubated in a desiccator for 24 hours along with 5ml 0.1M NaOH followed \n\n\n\nby titration with 0.1M HCl as described in [24]. \n\n\n\nThe results were analyzed by two-way analysis of variance (ANOVA) at \n95% confidence interval. Differences between the control and other \ntreatments were assessed using least significant difference (LSD) [25]. \n\n\n\nTable 3: Metal of soil detected prior to amendment as determined by atomic absorption spectrophotometer \n\n\n\nMetal Concentration (mgkg-1 soil) \n\n\n\nCu 0.38 \n\n\n\nCd 0.03 \n\n\n\nZn 3.76 \n\n\n\nPb 0.33 \n\n\n\nCite The Article: Falguni Akter, Humaira Hasan Tinni, Parmita Banarjee, Mohammad Zaber Hossain (2019). Effects Of Heavy Metals (Cd, Zn And Cu) On Carbon, \nNitrogen And Iron Mineralization In Soil. Malaysian Journal of Sustainable Agriculture, 3(1): 33-38.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 33-38 \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\nTable 4: Effects of heavy metals on changes of organic carbon in soil (%) \n\n\n\nTreatment \nDays \n\n\n\nControl Cd Zn Cu Cd:Zn Zn:Cu Cu:Cd Cd:Zn:Cu \n\n\n\n0 0.89 0.90 0.89 0.89 0.90 0.89 0.90 0.89 \n\n\n\n15 0.89 0.90 0.89 0.88 0.90 0.88 0.88 0.89 \n\n\n\n30 0.88 0.88 0.89 0.88 0.90 0.87 0.88 0.89 \n\n\n\n45 0.88 0.87 0.90 0.86 0.89 0.87 0.86 0.87 \n\n\n\n60 0.87 0.85 0.90 0.85 0.89 0.85 0.84 0.87 \n\n\n\nOrganic carbon did not change significantly due to the application of heavy \n\n\n\nmetals during the entire incubation period (Table 4). Among the \n\n\n\ntreatments, Zn accumulated a little amount of carbon until 45 days while \n\n\n\nCu: Cd decreased more carbon than any other treatments. From the study, \nit was observed that soil organic carbon was unaffected due to heavy \n\n\n\nmetals application. Less incubation time may be the reason for not getting \n\n\n\nsignificant changes in the soil organic carbon content. A group of \nresearchers has worked on effects of heavy metals in acid and calcareous \nsoils for mineralization of organic carbon in incubation and found low \n\n\n\nmineralization rate of organic-C after 28 weeks of incubation [26]. \n\n\n\nTable 5: Effects of heavy metals on changes of microbial biomass carbon in soil (mg kg-1) \n\n\n\nTreatment \nDays \n\n\n\nControl Cd Zn Cu Cd:Zn Zn:Cu Cu:Cd Cd:Zn:Cu \n\n\n\n0 0.38 0.38 0.39 0.39 0.38 0.38 0.38 0.38 \n\n\n\n15 0.38 0.35 0.39 0.35 0.36 0.36 0.36 0.38 \n\n\n\n30 0.38 0.30 0.37 0.33 0.31 0.34 0.31 0.38 \n\n\n\n45 0.38 0.24* 0.37 0.31 0.30 0.30 0.32 0.38 \n\n\n\n60 0.37 0.19* 0.37 0.28* 0.24* 0.22* 0.24* 0.37 \n\n\n\n*Asterisks indicate significant difference from the control treatment\n(N=8, P\u22640.05). \n\n\n\nThe table 5 showed that microbial biomass carbon (MBC) declined due to \n\n\n\nthe application of heavy metals with incubation time. Cd has decreased \n\n\n\nmore than any other treatments. The toxic effects of metals imposed on \n\n\n\nsoil in the following order: Cd>Cu>Zn. This order probably explains the \n\n\n\ntoxicity experienced by the soil microbes by the data on soil microbial \n\n\n\nbiomass carbon. With respect to the MBC the result indicates that due to \n\n\n\napplication of different metals and their combinations, the MBC didn\u2019t \nshow any significant responses until day 45 except Cd. Cd, Cu itself and \n\n\n\ntheir combinations along with Zn showed significant (p\u22640.05) decreases \nin soil MBC at day 60 in spite of the unusually very high levels of other \nmetals [27]. The availability and toxicity of a given metal ion is always \nmuch lower in soil than in water solution as a result of adsorption of the \n\n\n\nmetal ions by soil organic and inorganic colloids and microorganisms can \n\n\n\novercome from their adverse effects. Interactions of metals used in \n\n\n\ncombination may be the result of simple competition, antagonism, \nadditive action or synergism [28, 29]. The finding is in agreement with a \n\n\n\nscholar who observed diminished microbial biomass C in cadmium-\ncontaminated sewage sludge compost to the soil [30]. \n\n\n\nTable 6: Effects of heavy metals on changes of respiration in soil (mg C g-1 soil day-1) \n\n\n\nTreatment \nDays \n\n\n\nControl Cd Zn Cu Cd:Zn Zn:Cu Cu:Cd Cd:Zn:Cu \n\n\n\n0 5.64 5.40 6.82 4.70 6.30 5.20 6.20 5.90 \n\n\n\n15 9.20  8.52* 7.98  8.85* 6.40  5.50*  5.29* 5.72 \n\n\n\n30 9.15 7.90 7.39 8.24 6.01 5.51 5.18 5.73 \n\n\n\n45 8.40 7.80 7.38 7.87  5.88* 5.30 5.91  5.55* \n60 7.26 7.50 7.38 7.44 5.88 5.40 4.68 4.02 \n\n\n\n*Asterisks indicate significant difference from the control treatment\n(N=8, P\u22640.05). \n\n\n\nInitially respiration was increased and then became slowing due to \n\n\n\napplication of heavy metals during the entire incubation period (Table 6). \nThe maximum values for respiration were found from day 0 to day 15. The \n\n\n\nresult indicated that organic carbon release occurred at this time, showing \n\n\n\nthe priming effect of the added organic materials. This finding is an \n\n\n\nagreement with the study of [31]. It was also found that soil respiration \n\n\n\nand especially the respiration per unit biomass (qCO2) increased with \n\n\n\nincreasing amounts of heavy metals due to the contribution of fungi to soil \nrespiration increased [32]. \n\n\n\nTable 7: Effects of heavy metals on changes of available N in soil(mg kg-1) \n\n\n\nSample A B C D E F G H \n\n\n\nTreatment days Control Cd Zn Cu Cd:Zn Zn:Cu Cu:Cd Cd:Zn:Cu \n\n\n\n0 82.02 86.12 95.14 61.52 83.66 77.92 66.44 83.66 \n\n\n\n15 93.16 71.36* 82.84 49.21* 58.23* 65.62* 54.92* 63.98* \n30 118.11 127.13 125.49 117.29 113.19 107.45 108.27 101.71* \n45 91.04 93.50 100.07 104.17 87.76 63.98* 104.17 77.10 \n\n\n\n60 46.75 46.75 40.19 41.83 30.35* 43.47 22.15* 44.29 \n\n\n\nCite The Article: Falguni Akter, Humaira Hasan Tinni, Parmita Banarjee, Mohammad Zaber Hossain (2019). Effects Of Heavy Metals (Cd, Zn And Cu) On Carbon, \nNitrogen And Iron Mineralization In Soil. Malaysian Journal of Sustainable Agriculture, 3(1): 33-38.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 33-38 \n\n\n\n*Asterisks indicate significant difference from the control treatment (N=8,\nP\u22640.05). \n\n\n\nNet N mineralization over the 60-day incubation period differed \n\n\n\nsignificantly (p\u22640.05) among the treatments (Table 7). Minearalization \n\n\n\nand immobilization of N were closely associated in all treatments and were \n\n\n\npredominant until day 15 of the incubation period. This finding is in \n\n\n\nagreement with [33]. The highest rate for Cd (127.13mg kg-1) displayed a \n\n\n\nhigh degree of ammonification and also for Zn (125.49 mg kg-1) and Cu \n\n\n\n(117.29 mg kg-1). The probable reason for the decrement of available N in \n\n\n\nsoil after 30 days of incubation was due to ammonia volatilization and/or \nimmobilization during large heterotrophic microbial activities which \n\n\n\nresult in similar or lesser N to be mineralized in other treatments. Besides \nthis, the recalcitrant organic fractions might slow down N mineralization \n\n\n\nover time. A scholar worked on nitrous oxide production from an ultisol \ntreated with different nitrogen sources and moisture regimes and found \n\n\n\nsimilar results [34]. \n\n\n\nTable 8: Effects of heavy metals on changes of total N in soil (%) \n\n\n\nSample A B C D E F G H \n\n\n\nTreatment days Control Cd Zn Cu Cd:Zn Zn:Cu Cu:Cd Cd:Zn:Cu \n\n\n\n0 0.46 0.53 0.45 0.45 0.49 0.40 0.34 0.41 \n\n\n\n15 0.46 0.40 0.39 0.40 0.40 0.26* 0.29* 0.33* \n30 0.46 0.44 0.38 0.44 0.52 0.46 0.50 0.43 \n\n\n\n45 0.38 0.20* 0.36 0.37 0.48 0.32 0.46 0.39 \n\n\n\n60 0.42 0.19 0.21* 0.22* 0.25* 0.21* 0.22* 0.21* \n\n\n\n*Asterisks indicate significant difference from the control treatment (N=8,\nP\u22640.05).\n\n\n\nResults indicated that net N mineralization was significantly (P\u22640.05)\ndifferent among the treatments over the 60-day incubation period (Table \n\n\n\n8). The total nitrogen content decreased throughout the incubation period. \nIt indicates mineralization and immobilization of N were closely \n\n\n\nassociated in all soils and were predominant until day 15 of the incubation \n\n\n\nperiod. These findings are in agreement with [34]. The highest value for \nCd:Zn (0.52%) at day 30 displayed a high degree of ammonification \n\n\n\nfollowed by Cu: Cd which was 0.50%and the lowest value for Cd (0.19%) \nwas found at day 60. Probably this is a result of small mineralization or \nrapid remineralization of nitrogen over a period of time. Ammonia \n\n\n\nvolatilization and/or immobilization by the heterotrophic \n\n\n\nmicroorganisms could result in similar or lesser N to be mineralized in \n\n\n\nother treatments. The recalcitrant organic fractions might slow down N \n\n\n\nmineralization over time. Cd and Zn interaction may change the pH of soil \n\n\n\n[35]. Cd toxicity increased which affect N mineralization in soil [36]. \nWorked on cadmium and zinc uptake in wheat and found similar results. \nThe existence of antagonistic relationship between Cd and Zn prevails \n\n\n\nthroughout the incubation time. This phenomenon enhances the \n\n\n\nnitrification by which nitrogen may be lost from the system. As a result, \nvalues of total nitrogen obtained were minimized. Another researcher \nfound an antagonistic relationship between zinc and cadmium [36]. On the \n\n\n\ncontrary, it is believed that an additive action or synergistic effect between \n\n\n\nCu and Zn could have been responsible for the enhanced toxicity of Cu:Zn \n\n\n\nin the soil sample and was reported by [37]. The combination of Cd:Zn:Cu \n\n\n\nwas less effective on the total nitrogen change. Based on a study, the \n\n\n\naddition of heavy metals reduced N mineralization in soil [38]. This \nphenomenon results from a decrease in nitrifying bacteria activity. \nHowever, metal variables revealed positive interactions with parameters \nand N-related indices such as urease, N-acteyl-glucosaminidase, total N \n\n\n\nmineralization, ammonification and metabolic quotient [39]. \n\n\n\nTable 9: Effects of heavy metals on changes of available iron in soil (mg kg 1) \n\n\n\nSample O A B C D E F G \n\n\n\nTreatment days Control Cd Zn Cu Cd:Zn Zn:Cu Cu:Cd Cd:Zn:Cu \n\n\n\n0 2.08 0.37 0.36 2.59 2.44 2.50 2.56 2.46 \n\n\n\n15 2.02 0.30 0.18 2.02 0.95 1.33 6.37** 7.32** \n30 1.10 1.49 1.77 1.06 1.03 0.96 5.46** 1.21 \n\n\n\n45 0.96 0.49 0.89 1.17 1.31 0.89 0.78 0.85 \n\n\n\n60 1.14 0.60 0.53 1.17 0.92 0.85 0.50 0.57 \n\n\n\n**Asterisks indicate significant difference from the control treatment \n(N=8, P\u22640.01) \n\n\n\nFrom the Table 9, we found that as compared with control treatment there \n\n\n\nwas no significant relationship among the heavy metal treatments when \n\n\n\nthey are applied singly, but in combination a significant (p\u22640.01) \nrelationship exist in Cu:Cd and Cd:Zn:Cu treatments. In case of Cu:Cd, \n\n\n\nmaximum mineralization (6.37 mgkg-1) was found at day 15 and day 30 \n\n\n\nthe result was 5.46 mgkg-1 due to the synergistic interaction between them \n\n\n\nbut after that the value decreased drastically due to antagonistic \n\n\n\ninteraction between them. Kabata-Pendias [36] found both antagonistic \n\n\n\nand synergistic relation between Cu and Cd. \n\n\n\nTable 10: Effects of heavy metals on changes of total iron in soil (%) \n\n\n\nSample O A B C D E F G \n\n\n\nTreatment days Control Cd Zn Cu Cd:Zn Zn:Cu Cu:Cd Cd:Zn:Cu \n\n\n\n0 3.63 4.11 2.14 1.85 2.14 1.96 1.85 3.63 \n\n\n\n15 3.24 3.60 2.98 3.40 3.45 3.02 3.32 3.22 \n\n\n\n30 4.68 1.42** 1.17** 0.14** 1.95* 5.74 1.38** 1.60** \n45 3.02 0.88* 0.79* 0.30 0.36** 0.36** 0.70* 1.61 \n\n\n\n60 2.36 0.53 0.84 0.15 0.19 0.16 0.32 1.95 \n\n\n\n*Asterisks indicate significant difference from the control treatment (N=8,\nP\u22640.05); **Asterisks indicate significant difference from the control \ntreatment (N=8, P\u22640.01)\n\n\n\nInitial mineralization took place in all the treatments until 15 days of the \n\n\n\nincubation together with the probability of Fe immobilization (Table 10). \nAs compared to control, at day 30 except Zn:Cu, all treatments varied \n\n\n\nsignificantly (p\u22640.05) over the incubation period. At day 45, all treatments \n\n\n\nCite The Article: Falguni Akter, Humaira Hasan Tinni, Parmita Banarjee, Mohammad Zaber Hossain (2019). Effects Of Heavy Metals (Cd, Zn And Cu) On Carbon, \nNitrogen And Iron Mineralization In Soil. Malaysian Journal of Sustainable Agriculture, 3(1): 33-38.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 33-38 \n\n\n\nshowed the decreasing pattern and varied significantly except Cu and \n\n\n\nCd:Zn:Cu. This may due to higher concentration of single metal along with \n\n\n\nthe antagonistic interaction among the metals used helps the release of \niron from exchange position. A scholar observed an antagonistic \n\n\n\ninteraction between Cu and Zn. A scholar found an antagonism relation \n\n\n\nbetween Cd and Zn.Cd:Zn:Cu showed increment in total Fe at day 45 along \n\n\n\nwith at the end of the incubation period [40]. This may due to the organism \n\n\n\ncell lyses. However, at the end of the incubation period no significant \nchange in total Fe observed. \n\n\n\n4. CONCLUSIONS \n\n\n\nThe effect of heavy metals applied in soil in single and in combination on \n\n\n\nthe changes in carbon, nitrogen and iron mineralization revealed that the \n\n\n\nsingle metal treatment showed more effect on microbial activities than in \n\n\n\ncombination. The combined effect showed interactions among the metals \nwhich was antagonistic in nature. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors thank to all staff of Soil, Water and Environment discipline, \nKhulna University, Khulna, Bangladesh. \n\n\n\nREFERENCES \n\n\n\n[1] Nouri, J., Mahvi, A.H., Jahed, G.R., Babaei, A. 2008. A regional \n\n\n\ndistribution pattern of groundwater heavy metals resulting from \n\n\n\nagricultural activities, Environmental Geology, 55:1337\u20131343\nDOI:10.1007/s00254-007-1081-3 \n\n\n\n[2] Tu, C., Zheng, C.R., Chen, H.M. 2000. Effect of applying chemical \n\n\n\nfertilizers on forms of lead and cadmium in red soil, Chemosphere.\n41:133\u2013138 DOI:10.1016/S0045-6535(99)00400-2 \n\n\n\n[3] Selene, C.H., Chou, J., De Rosa, C.T. 2003. Case studies\u2014Arsenic, \nInternational Journal of Hygiene and Environmental Health. 206:381\u2013386 \n\n\n\nDOI:10.1078/1438-4639-00234 \n\n\n\n[4] Huang, S.W., Jin, J.Y. 2008. Status of heavy metals in agricultural soils \n\n\n\nas affected by different patterns of land use, Environmental Monitoring\nand Assessment. 139(1\u20133):317\u2013327 DOI:10.1007/s10661-007-9838-4 \n\n\n\n[5] Leita, L., Nobili, M.D., Muhlbachova, G., Mondini, C., Marchiol, L., Zerbi, \nG. 1995. Bioavailability and effects of heavy metals on soil microbial \nbiomass survival during laboratory incubation, Biol Fertil Soils. 19:103\u2013\n108 \n\n\n\n[6] Leita, L., Nobili, M.D., Mondini, C. 1999. Influence of inorganic and \n\n\n\norganic fertilization on soil microbial biomass, metabolic quotient and\nheavy metal bioavailability, Biol Fertil Soils. 28:371\u2013376 \n\n\n\n[7] Chander, K., Brookes, P.C. 1991. Microbial biomass dynamics during \n\n\n\nthe decomposition of glucose and maize in metal-contaminated and non-\ncontaminated soils, Soil Biol Biochem. 23:917\u2013925 DOI:10.1016/0038-\n0717(91)90171-F \n\n\n\n[8] Chander, K., Brookes, P.C., Harding, S.A. 1995. Microbial biomass\ndynamic following addition of metal-enriched sewage sludge to a sandy \n\n\n\nloam, Soil Biol. Biochem. 27:1409\u20131421 DOI:10.1016/0038-\n0717(95)00074-O \n\n\n\n[9] Wangstrand, H., Eriksson, J., Oborn, J. 2007. Cadmium concentration in \n\n\n\nwinter wheat as affectedby nitrogen fertilization, European Journal of\nAgronomy. 26:209\u2013214 DOI:10.1016/j.eja.2006.09.010 \n\n\n\n[10] Jr. Jones, J.B., Pais, I. 1997. The Handbook of Trace Elements, CRC \n\n\n\nPress, Boka Raton, FL, USA. \n\n\n\n[11] Alloway, B.J. 2008. Zinc in Soils and Crop Nutrition, 2nd Edition, IZA \n\n\n\nand IFA, Brussels, Belgium. \n\n\n\n[12] Kosolapov, D.B., Kuschk, P., Vanishtein, M.B., Vatsourina, A.V., \nWiessner, A., K\u00e4stner, M., M\u00fcller, R.A. 2004. Microbial processes of heavy \n\n\n\nmetal removal from carbon deficient effluients inconstructed wetlands,\nEngineering Life Science. (5):403-411 DOI:10.1002/elsc.200420048 \n\n\n\n[13] Taylor, M.D., Percival, H.J. 2001. Cadmium in soil solutions from a \n\n\n\ntransect of soils away from a fertilizer bin, Environmental Pollution.\n113(1):35\u201340 DOI:10.1016/S0269-7491(00)00170-6 \n\n\n\n[14] B\u00e5\u00e5th, E. 1989. Effects of heavy metals in soil on microbial processes \nand populations, Water, Air and Soil pollution. 47(3-4):335-379 \n\n\n\n[15] McGrath, S.P., Chaudri, A.M., Giller, K.E. 1995. Long-term effects of \nmetals in sewage sludge on soils, microorganisms and plants, Journal of \nIndustrial Microbiology. 14(2):94-104 \n\n\n\n[16] K\u0131z\u0131lkaya, R., A\u015fk\u0131n, T., Bayrakl\u0131, B., Sa\u011flam, M. 2004. Microbiological \ncharacteristics of soils contaminated with heavy metals, European Journal \n\n\n\nof Soil Biology. 40(2):95-102. DOI: 10.1016/j.ejsobi.2004.10.002 \n\n\n\n[17] G\u00fclser, F., Erdo\u011fan, E. 2008. The effects of heavy metal pollution on \n\n\n\nenzyme activities and basal soil respiration of roadside \n\n\n\nsoils, Environmental monitoring and assessment. 145(1-3):127-133 \n\n\n\nDOI:10.1007/s10661-007-0022-7 \n\n\n\n[18] Brookes, P.C., McGrath, S.P. 1984. Effect of metal toxicity on the size \n\n\n\nof microbial biomass, Journal of Soil Science. 35:341-346 \n\n\n\nDOI:10.1111/j.1365-2389. 1984.tb00288.x \n\n\n\n[19] Doleman, P., Jansen, E., Michels, M., Van Til, M. 1994. Effects of heavy \n\n\n\nmetals in soil on microbial diversity and activity as shown by the\nsensitivity-resistance index, an ecologically relevant parameter, Biology \n\n\n\nand Fertility of Soils. 17:177-184 \n\n\n\n[20] Jackson, M.L. 1973. Soil Chemical Analysis, Prentice Hall of India Pvt. \nLtd., New Delhi. \n\n\n\n[21] Baethgen, W.E., Alley, M.M. 1989. A manual colorimetric procedure \n\n\n\nfor measuring ammoniumnitrogen in soil and plant Kjeldahl digests, \nCommunications in Soil Science and Plant Analysis. 20(9-10):961-969 \n\n\n\nDOI:10.1080/00103628909368129 \n\n\n\n[22] Vance, E.D., Brooks, P.C., Jenkinson, D.S. 1987. An extraction method \n\n\n\nfor measuring soil microbial C, Soil Biology and Biochemistry. 19:703-707 \n\n\n\nDOI:10.1016/00380717(87)90052-6 \n\n\n\n[23] Page, A.L., Miller, R.H., Keeney, D.R.  1982. Methods of soil analysis \n(part 2), Chemical and microbiological properties, 2nd Edition, Madison,\nWinsconsin, USA, 1982 \n\n\n\n[24] Anderson, J.M., Ingram, J.S.I. 1996. Tropical Soil Biology and \n\n\n\nFertility: A Handbook of Methods, 2nd Edition, Information press, Eynsham, \nUK.\n\n\n\n[25] Zaman, S.M.H., Rahim, K., Howlader, M. 1982. Simple Lessons from \n\n\n\nBiometry, 1stEditionBangladesh Rice Research Institute (BRRI), \nJoydevpur, Dhaka, Bangladesh. \n\n\n\n[26] Clemente, R., Bernal, M.P. 2006. Fractionation of heavy metals and \n\n\n\ndistribution of organic carbon in two contaminated soils amended with \n\n\n\nhumic acids, Chemosphere. 64(8):1264-1273 \n\n\n\nDOI:10.1016/j.chemosphere.2005.12.058 \n\n\n\n[27] Nwuche, C.O., Ugoji, E.O. 2008. Effects of heavy metal pollution on \n\n\n\nthe soil microbial activity, International Journal of Environmental Science \n\n\n\nand Technology. 5(3):409-414 \n\n\n\n[28] Diaz-Ravina, M., Baath, E. 1996. Development of metal tolerance in \n\n\n\nsoil bacterial communitiesexposed to experimentally increased metal \nlevels, Applied and Environmental Microbiology. 62(8):2970-2977 \n\n\n\nCite The Article: Falguni Akter, Humaira Hasan Tinni, Parmita Banarjee, Mohammad Zaber Hossain (2019). Effects Of Heavy Metals (Cd, Zn And Cu) On Carbon, \nNitrogen And Iron Mineralization In Soil. Malaysian Journal of Sustainable Agriculture, 3(1): 33-38.\n\n\n\n\nhttps://doi.org/10.1016/0038-0717(91)90171-F\n\n\nhttps://doi.org/10.1016/0038-0717(91)90171-F\n\n\nhttps://doi.org/10.1016/0038-0717(95)00074-O\n\n\nhttps://doi.org/10.1016/0038-0717(95)00074-O\n\n\nhttps://doi.org/10.1016/j.eja.2006.09.010\n\n\nhttps://doi.org/10.1016/S0269-7491(00)00170-6\n\n\nhttps://doi.org/10.1016/j.ejsobi.2004.10.002\n\n\nhttps://www.sciencedirect.com/science/journal/00456535\n\n\nhttps://www.sciencedirect.com/science/journal/00456535/64/8\n\n\nhttps://doi.org/10.1016/j.chemosphere.2005.12.058\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 33-38 \n\n\n\nCite The Article: Falguni Akter, Humaira Hasan Tinni, Parmita Banarjee, Mohammad Zaber Hossain (2019). Effects Of Heavy Metals (Cd, Zn And Cu) On Carbon, \nNitrogen And Iron Mineralization In Soil. Malaysian Journal of Sustainable Agriculture, 3(1): 33-38.\n\n\n\n[29] Kosolapov, D.B., Kuschk, P., Vanishtein, M.B., Vatsourina, A.V., \nWiessner, A., K\u00e4stner, M. M\u00fcller, R.A. 2004. Microbial processes of heavy \n\n\n\nmetal removal from carbon deficient effluients inconstructed wetlands,\nEngineering Life Science. 4(5):403-411 DOI:10.1002/elsc.200420048 \n\n\n\n[30] Moreno, J.L., Hernandez, T. Garcia, C. 1999. Effects of a cadmium \n\n\n\ncontaminated sewage sludge compost on dynamics of organic matter and\nmicrobial activity in an arid soil, Biol Fertil. Soils. 28:230\u2013237 \n\n\n\n[31] Khalil, M.I., Hossain, M.B., Schmidhalter, U. 2005. Carbon and \n\n\n\nnitrogen mineralization in different upland soils of the subtropics treated \n\n\n\nwith organic materials, Soil Biology and Biochemistry. 37:1507- 1518 DOI: \n10.1016/j.soilbio.2005.01.014 \n\n\n\n[32] Fliessbach, V., Martens, R. Reber, H.H. 1994. Soil microbial biomass \nand microbial activity in soils treated with heavy metal contaminated \n\n\n\nsewage sludge, Soil Biology and Biochemistry. 26(9):1201-1205 \n\n\n\nDOI:10.1016/0038-0717(94)90144-9 \n\n\n\n[33] Fu, M.H., Xu, X.C., Tabatabai, M.A. 1987. Effect of pH on nitrogen \n\n\n\nmineralization in crop residue treated soils, Biology and Fertility of Soils.\n5:115-119 \n\n\n\n[34] Khalil, M., Rosenani, A., Van Cleemput, O., Boeckx, P., Shamshuddin, \nJ., Fauziah, C. 2002. Nitrous oxide production from an ultisol of the humid \n\n\n\ntropics treated with different nitrogen sources and moisture regimes, \nBiology and Fertility of Soils. 36(1):59-65 \n\n\n\n[35] Portmann, D. 2012. Cadmium and zinc uptake in wheat as affected \n\n\n\nby nitrogen fertilization andagricultural management, M.Sc. Thesis,\nDepartment of Environmental Systems Science, Swiss Federal Institute of \nTechnology, Zurich. 66-73. \n\n\n\n[36] Kabata-Pendias, A. 2010. Trace elements in soils and plants, 4th \nEdition, CRC press, Boca Raton, FL. \n\n\n\n[37] Lighthart, B., Bond, H. 1976. Design and preliminary results from \n\n\n\nsoil/litter microcosms, International Journal of Environmental Studies. 10\n(1):51-58 DOI:10.1080/00207237608737292 \n\n\n\n[38] Dar, G.H. 1997. Impact of lead and biosolids on soil microbial \nbiomass and carbon and nitrogen mineralization, Bulletin Environmental \n\n\n\nContamination and Toxicology. 58:234-240 \n\n\n\n[39] Li, Y.T., Rouland, C., Benedetti, M., Li, F.B., Pando, A., Lavelle, P., Dai, \nJ. 2009. Microbial biomass, enzyme and mineralization activity in relation \n\n\n\nto soil organic C, N and P turnover influenced by acid metal stress, Soil \nBiology and Biochemistry. 41(5):969-977 \n\n\n\nDOI:10.1016/j.soilbio.2009.01.021 \n\n\n\n[40] Smiled, K.W., van Luit, B., Van Driel, W. 1992. The extraction by soil \nand absorption by plants of applied zinc and cadmium, Plant and Soil. \n143:233-238. \n\n\n\n\nhttps://doi.org/10.1016/0038-0717(94)90144-9\n\n\nhttps://www.sciencedirect.com/science/journal/00380717\n\n\nhttps://www.sciencedirect.com/science/journal/00380717\n\n\nhttps://doi.org/10.1016/j.soilbio.2009.01.021\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 07-11 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2022.07.11 \n\n\n\n \nCite The Article: IkramBensouf, NaceurMhamdi, Hatem OuledAhmed,FatenLasfar, Belgacem Ben Aoun, AbdessalemTrimeche  (2022). Determination of Race \n\n\n\nPerformance\u2019s Factors of Arabian Thoroughbred Tunisian Horses and The Impact of Introduction of Occidental Thoroughbred Horses Intunisia. Malaysian Journal of \nSustainable Agricultures, 6(1): 07-11. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2022.07.11 \n\n\n\n\n\n\n\nDETERMINATION OF RACE PERFORMANCE\u2019S FACTORS OF ARABIAN \nTHOROUGHBRED TUNISIAN HORSES AND THE IMPACT OF INTRODUCTION OF \nOCCIDENTAL THOROUGHBRED HORSES INTUNISIA \n \nIkramBensoufa,b*,NaceurMhamdib, Hatem OuledAhmedc ,FatenLasfard, Belgacem Ben Aound, AbdessalemTrimechea \n\n\n\n \naNational School of Veterinary Medicine,SidiThabet 2020 Ariana, University of Manouba, Tunisia \nbResearch Laboratory of Ecosystems & Aquatic Resources, National Agronomic Institute of Tunisia,Carthage University, 43 Avenue Charles \nNicolle, Tunis 1082, \nCGenetics Laboratory, Institute of Veterinary Research of Tunis, 20 Rue DjebelLakhdhar - La Rabta 1006 Tunis, Tunisia \ndNational Foundation of Amelioration of the Horse Race in Tunisia,  2020 SidiThabet \n*Corresponding author: IkramBensouf; E-mail: bensoufikram2@gmail.com \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 14 July 2021 \nAccepted 16 August 2021 \nAvailable online 23 August 2021 \n\n\n\n\n\n\n\nThe aim of the study is to investigate the effects of age, sex, running distance and origin of horse on racing \nspeed for Thoroughbred Arabian horse in Tunisia. Although the occidental type is known to be more \nsuccessful in racing than the Tunisian type, we undertook this study to try to confirm or deny this supremacy \nfor a sample of racehorses born in Tunisia from occidental father. A total of 333 racing records were \nconsidered for race performance. The effects of environmental factors on (sex, age, father's origin, race \ndistance, number of race seasons) race performance were analyzed using the least-squares method(LSM).The \nracehorses studied were all Arabian Thoroughbred horses in operation at the racecourse of Ksar Said from \n2010 to 2020. They are 180 horses, 90 horses born of a Tunisian father, and 90 horses born in Tunisia ofthe \noccidentalfather. These horses are the best and most successful in their category. The study revealed that the \ngender and age effectswere statistically insignificant onracingperformance. Race performance was \nsignificantly influenced by the distance and the origin of the father which affirms the improving role of the \noccidentalhorse in the Tunisian population. \n\n\n\nKEYWORDS \n\n\n\nArabian thoroughbred, racing performance, speed, variation\u2019s parameters, horse\u2019s origin \n\n\n\n1. INTRODUCTION \n\n\n\nThe Arabian horse is considered an international transboundary horse \ndue to its blood accessibility in all continents through various horse breeds \n(Khadka, 2015). Horse racing serves as a performance test to evaluate the \nspeed and endurance of horses. Globally, the breed most widely used for \nracing is the Thoroughbred (Klecel et al., 2021). Arabian horses are \ngenerally considered to be one of the oldest and most influential horse \nbreeds in the world (Ropka-Molik et al., 2019; Glazewska, 2010). The \nThoroughbred Arabian horse is the ultimate racehorse, especially in flat \nracing. Its strength, intelligence, elegance, and robustness make this \nanimal a cultural jewel and give it great economic and cultural importance \nthroughout the world. Nowadays, horse breeding is mostly undertaken for \nsport and recreation purposes (Taylor and Field, 2014). The performance \nof the horse in racing is measured in various forms. In flat racing, to assess \nracing performance, the most widely used measure is speed (m/sec) \n(Paksoy and \u00dcnal, 2019). In horses, racing performance is a quantitative \ntrait. Therefore, genetic and environmental factors control this trait \n(Takahashi, 2015; Paksoy and \u00dcnal, 2010). Many factors influencing the \nracing performance of Thoroughbred Horses (e.g. Breeding, nutrition, \nexercise, race distance, race track, sex, weight, age, age of dam, and \nracetrack) have been discussed in some studies (Ekiz and Kocak, 2007; \nBuxadera and Mota, 2008; Bakhtiari and Heshmat, 2009; Paksoy and \u00dcnal, \n\n\n\n2010). In Tunisia, the Arabian horse is a priceless asset that is an integral \npart of our heritage and its breeding represents an important component \nin agricultural production and the national economy and promotes \nalternative tourism.However, it is important to note that despite the \nincentives taken in favor of this sector studies have highlighted a certain \nregression of the performance of the horses, especially in comparison with \nlevels recorded in occidental countries, this finding has prompted \nprofessionals to propose the use of infusion of occidentalblood in the \nTunisian population. Stallions and frozen semens have been imported. \nThis opening should allow us to join the pack of countries that have built a \nreputation in Arabian thoroughbred racing. However, the impact of the \nintroduction of foreign blood in the Tunisian population of Arabian horses \nhas not been studied. Through this study, we will investigate some factors \nthat are supposed to influence the performance of Arabian thoroughbred \nhorses in racing and the impact of the infusion of occidentalblood in \nTunisia \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Data and animals \n\n\n\nData used in the present study was provided by the technical service of the \nHorse Racing Society in Ksar Said, Tunisia.The racehorses studied were all \n\n\n\n\nmailto:bensoufikram2@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 07-11 \n\n\n\n\n\n\n\n \nCite The Article: IkramBensouf, NaceurMhamdi, Hatem OuledAhmed,FatenLasfar, Belgacem Ben Aoun, AbdessalemTrimeche  (2022). Determination of Race \n\n\n\nPerformance\u2019s Factors of Arabian Thoroughbred Tunisian Horses and The Impact of Introduction of Occidental Thoroughbred Horses Intunisia. Malaysian Journal of \nSustainable Agricultures, 6(1): 07-11. \n\n\n\n\n\n\n\nArabian, thoroughbreds activeat the racetrack of Ksar Said from 2010 until \n2020. The study was conducted on 90 clinically healthy Tunisian Arabian \nhorses (Tunisian type) and 90 clinically healthy occidental fathers born in \nTunisia (Occidental type). The animals have an age varying from 2 to 10 \nyears old. A total of 333 race records have been considered for \nperformance in a race. These horses are all winners of group races and \nthey are the best and most successful in their category. The performance \nparameters are recorded on Historical Cards of the horses The historical \nsheets are individual documents for each horse that has run at the \nracecourses of the Horse Racing Society. This document is used to register \nthe name of the horse, sex, date of birth,and originsmainly the name of its \nmother and father. It is also used to capture the racing career data of the \nhorse, including the names of the races run, their category, date, distance, \nand times achieved as well as the ranking and earnings per race. However, \nthe time achieved during the race is recorded only for the first horse. \n\n\n\n2.2  The Parameters studied and statistical analysis \n\n\n\nThe parameters used to assess the performance of the horses were the \nspeed over the different distances covered and the factors influencing the \nperformance in races studied were the sex, age, the origin of the father, \ndistances covered, and the number of racing seasons The characteristics \nof the data used in the analyses are presented in Table 1 (Buxadera and \nMota, 2008). \n\n\n\nTable 1: Descriptive statistics for the horses used in the study \n\n\n\nInformation on class levels \n\n\n\nClass Levels Values \n\n\n\nOrigin 2 occidental/ Tunisian \n\n\n\nSex 2 Male/ female \n\n\n\nAge 9 2 3 4 5 6 7 8 9 10 \n\n\n\nSeason 9 1 2 3 4 5 6 7 8 9 \n\n\n\nDistance 6 1400,1600,1700,2000,2100,2600 \n\n\n\nThe race speed of the horses was determined according to the distance in \neach run was calculated individually. Data analysis was performed using \nthe SAS statistical package (SAS Institute Inc. 2012).The distributions of \nthe evaluated traits were used to assess according to theKolmogorov\u2013\nSmirnov test. The mean (M) value and standard error of the mean (SE) \nwerecalculated.Two sets of analyses were carried out. A \ndescriptiveexploratory analysis was carried out to summarize the main \ncharacteristics of the horses. An analysis of variance was carried out to \nidentify factors influencing racing performance using the GLM procedure \nof SAS.  \n\n\n\nThe following general linear model analyzed the effects of studied factors \non racing performance: \n\n\n\nYijklmn = \u00b5+Si+Dj+Ok+ Al+ Rm+eijklmn \n\n\n\nWhere:  \n\n\n\nYijklmn =dependent variable; \u03bc = overall mean;Si = effect of the horse\u2019s sex \n(male, female); Dj = effect of the race distance in meters (1400, 1600, 1700, \n2000, 2100, 2600); Ok = effect of the father\u2019s origin (Tunisian, occidental); \nAl = effect of the horse\u2019s age at the time of the race (2,3, 4, 5, 6, 7, 8, 9,10); \nRm = effect of number of racing seasons (1,2,3,4,5,6,7,8,9,10) and eijklmn = \nrandom error effect. \n\n\n\nResults were expressed as means\u00b1SD. Results with an associated \nprobability lessthan or equal to 0.05 were considered significant. \n\n\n\n3. RESULTS AND DISCUSSION  \n\n\n\n3.1 Characteristics of the horses \n\n\n\nData characteristics (mean, standard deviation, minimum, and \nmaximum)were shown in Table 2.The average age of the horses was 4.31 \n\u00b1 1.46 and varies from 2 to 10 years. The Tunisian horses studied had an \noverall speed of 13.58 \u00b1 0.67m/s over an average distance of 1694.29 \u00b1 \n231.28m with an average number of a racing season of 4.5 years and an \naverage number of races run of 32.01 \u00b1 16.68. \n\n\n\nThe average speed of the Tunisian Thoroughbred horses was 13.58\u00b10.67 \nm/s over an average distance of 1694 m, it is close to the value found in \nTurkey by Yildirim (13.77\u00b10.01 m/sec) and lower than that found by \nSobczy\u0144ska when the average speed of the Polish Thoroughbreds and the \naverage distance of the races was 15.57 m/s and 1509.1 m, respectively \n\n\n\n(Yildirim, 2018; Sobczy\u0144ska, 2011). The active Tunisian horses at the ksar \nSaid racetrack are aged between 2 and 10 years, this shows that \nthoroughbred horses generally complete their growth and development at \nthe age of two years, in line with the statement that thoroughbred horses \nmature early (YavuzkanPaksoy and Necmettin \u00dcnal, 2019). Thoroughbred \nhorses start their racing career one year earlier (two years) than other \nbreeds used in flat racing in many countries (YavuzkanPaksoy and \nNecmettin \u00dcnal, 2019).   \n\n\n\nTable 2: Characteristics of Tunisian thoroughbred horses \n\n\n\nParameters Mean Min Max SD \n\n\n\nAge (years) 4.31 2 10 1.46 \n\n\n\nTime (s) 125.25 79.60 248.20 20.15 \n\n\n\nDistance (m) 1694.29 1400 2600 231.28 \n\n\n\nSpeed (m/s) 13.58 8.05 20.10 0.67 \n\n\n\nNumber of race seasons 4.5 1 9 1.84 \n\n\n\nNumber of racing 32.01 1 100 16.68 \n\n\n\nThis is confirmed and explained by who revealed that the different body \nmeasurements of thoroughbred horses were similar at two and three \nyears of age, despite a significant increase from one to two years of age \n(Anderson and McIlwraith, 2004). Kocher and Staniar also estimated that \ntwo-year-old horses should reach 95% of the adult value in terms of height \nat withers (Kocher and Staniar, 2013). Based on statistical analyses, the \nvariation in velocity with age was not significant which is consistent with \nthe results found (Sobczynska, 2003; Oliveira, 1989; Mota et al., 1998). \nThis could be partly because in the mentioned study there was only the \nrace time of the winners and not all the participants in the race or also the \nretirement of less performing horses at an early age. However, even \nthough the age effect on speed in the present study was not statistically \nsignificant, the differences between the mean speeds as a function of age \nare existing (Figure 1). Indeed, the average speed was 13.50 m/s at the age \nof 2 years and increases progressively until it reaches its peak at the age \nof 5 years (13.71 m/s) then it decreases again to 13.25 m/s at the age of \n10 years. \n\n\n\nFigure 2: Age effect on horse speed \n\n\n\nThese results are similar to the results of several surveys also observed \nthat the maximum age was 4.45 years and the lowest age was two years \nfor Thoroughbred racing performance in the U.S.A (Ekiz et al., 2005; \nBuxadera and Mota, 2008; Park et al., 2011; Gramm and Marksteiner, \n2010). The fastest average speed in the Thoroughbred in Japan was at 5 \nyears of age (Oki et al., 1994). The increase in racing performance with age \nmay be related to an adaptation of horses to racing conditions and \ntraining. Young horses may exhibit behaviors that can impair racing \nperformance before and during the race (Mota, 2008). \n\n\n\n3.2 Environmental factors affecting race performance \n\n\n\nThe Least Squares Means (LSM) and standard errors of the studied effects \nfor racing performance were presented in Tables 3. In the evaluation of \nthe entire data set, statistically significant differences were found in all of \nthe fixed effects (P< 0.001).In the study, significant (P<0.0001) differences \nbetween the occidental and Tunisian blood horses were found. Tunisian \nblood horses showed lower speed (13.06m/s); however, they still fell \nwithin the reference range.These findings were described inprevious \nstudies (Paksoy and \u00dcnal 2010; Paksoy et al., 2018). Such results indicate \nsignificant differences in effort and various racing predispositions \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 07-11 \n\n\n\n\n\n\n\n \nCite The Article: IkramBensouf, NaceurMhamdi, Hatem OuledAhmed,FatenLasfar, Belgacem Ben Aoun, AbdessalemTrimeche  (2022). Determination of Race \n\n\n\nPerformance\u2019s Factors of Arabian Thoroughbred Tunisian Horses and The Impact of Introduction of Occidental Thoroughbred Horses Intunisia. Malaysian Journal of \nSustainable Agricultures, 6(1): 07-11. \n\n\n\n\n\n\n\nobserved in racing breeds, which are most likely conditioned by different \ngenetic backgrounds (Ropka-Molik et al., 2019).   \n\n\n\nThis would confirm what is known by professionals in this sector that the \noccidental horse is more successful in races than the Tunisian and we will \nbe tempted to assert the improving role of the occidental horse.The effect \nof the sex of the horse was found to be significant on race performance or \nrace speed (P= 0.0053). We observed that the race speed of the male \nhorses was found to be higher  (13.38\u00b1 0.11m/s) than the femalehorses \n(13.21 \u00b10.08 m/s). Similar to the results of this study, some researchers \nstated that male horses were faster than females (Ekiz et al., 2005; Mota et \nal, 2005; Buxadera and Mota, 2008; Park et al., 2011).  The significant \nsuperiority of males found in the current study agrees with reports of who \nreported the significant sex on race speed (Mota et al., 2005). However, \nreported no significant effect of horse sex on racing time (Yeldimir, 2018; \nBakhtiari and Kashan, 2009). In the same context, researchers reported \nthat male horses exhibited better racing performance than female horses \nin horse races due to both morphological and physiological characteristics \n(Mota et al., 2005; \u00d6zbeyaz and Ak\u00e7ap\u0131nar, 2003). \n\n\n\nThe present study indicates that the age of the horse had mixed effects on \nrace performance, in our study we reported that higher speeds were \nrecorded for the horse from 4 to 6 years old (13.63\u00b10.14 and 13.71\u00b10.09 \nm/s). However, the lowest values were reported for a horse having more \nthan 9 years (13.27\u00b10.14 m/s). The middle values were observed for 2 and \n3 and 7 and 8, respectively (ranging from 13.43\u00b10.23 to 13.50\u00b10.11). Some \nresearchers suggested that age represents the influence of accumulated \ntraining effects and racing experience upon racing performance \n(Sobczy\u0144ska, 201; Ekiz et al., 2005; Bakhtiari and Kashan, 2009). The same \nauthors reported that the racing performance would improve with \nprogressing age. The studies on the effect of the Race distances on the \nhorses\u2019 race performance were confirmed as significant (P<.0001). \n\n\n\nThe distances covered by the horses studied are between 1400 m and \n2600 m, it is close to the range cited by who indicated that this breed was \nimproved for speed at medium distance (1400-2400 m) (Taylor and Field, \n2014). The average speed is faster in shorter distance races and slower in \nlonger distance races. Increasing race distance negatively affected race \nperformance which is consistent with the study conducted (Takahashi, \n2015; \u00d6zen and G\u00fcrcan 2016). The number of laps on the track affected \nrace performance, as races up to 1500 m may include a single turn, while \ntracks from 1600 to 2600 m include two turns.Race speed tends to \ndecrease during turns. This is because horses that have to make a greater \neffort are forced to slow down to keep up over time.This result can be \nconsidered as a result of the selection and maintenance-feeding \noperations carried out over the years. In the present study, the effect of the \nrace distance on race performance or speed was significant for each track \ntype (P < 0.001). \n\n\n\nThe results of the present study did not confirm the significance of the race \nseason therefore, was excluded from the model analysis. The effect of the \ninteraction Distance*origin of the horse showed that the Tunisian horses \nran most often short distances of 1400 m,1600 m, and 1700 m. They \nparticipate less in long races 2000 and 2100 m. On the other hand, the \nhorses born of an occidental father had participated in many races over \ndistances of 2000, 2100, and 2600. The results of the statistical analysis \nshow a significant difference between the horses born of Tunisian fathers \nand the horses born of western fathers for the overall average speed and \nthe speeds on all the distances covered except the distance 2000 m where \nthe Tunisian horses were faster than the horses born of a western father \n(figure 2).  \n\n\n\nThis could be related to the fact that Tunisian horses race less over long \ndistances during the same racing season, so they would be fresher, less \nstressed, and less exposed to fatigue. Several authors report that fatigue \ncan lead to overwork if the necessary measures are not taken, and \noverwork, which is a serious pathological condition, would be detrimental \nto sports performance in the short and medium-term (Boivin, 1989; \nDenois, 1995). On the other hand, this difference could be linked to the \nnumber of races used to calculate the speed, which is lower than the actual \nnumber of races run by the horses. We are limited to the races won by the \nhorse concerned because we do not have information on the time of the \nsewing when the horse is classified. \n\n\n\nThe sex and the number of racing seasons had no significant effect (P> \n0.05) on the speed of the horses while the effect of the origin of the father \nwas well pronounced (P <0.0001). The Tunisian horses had an overall \nspeed (all distances combined) of 13.42 \u00b1 m/s lower than that of the \nhorses born from a western father who had an overall average speed of \n13.66 \u00b1 m/s. The performance in races was significantly influenced by the \ndistance covered during the race (P<0.0001).Indeed, horses born of an \n\n\n\noccidental father are more successful than those born of Tunisian father \nover all distances except for the 2000 m distance category, the horses of \nTunisian father were faster than those of occidentalfather. \n\n\n\na,b:The differences between the means of groups carrying various letters \nin the same column are significant (P<0.05 \n\n\n\nTable 3: Effects of the studied parameters on the speed of horses \n\n\n\nParameters n LSM SE P-value \n\n\n\nOrigin  \n\n\n\n<.0001 Tunisian 111 13.06b 0.09 \n\n\n\noccidental 222 13.53a 0.11 \n\n\n\nSex  \n\n\n\n0.0053 Male 237 13.38a 0.11 \n\n\n\nFemale 96 13.21b 0.08 \n\n\n\nAge  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0.0036 \n\n\n\n2 4 13.50ab 0.11 \n\n\n\n3 108 13.52ab 0.14 \n\n\n\n4 107 13.63a 0.15 \n\n\n\n5 62 13.71a 0.16 \n\n\n\n6 5 13.62a 0.14 \n\n\n\n7 8 13.46ab 0.13 \n\n\n\n8 13 13.43ab 0.12 \n\n\n\n9 3 13.27b 0.11 \n\n\n\n10 3 13.25b 0.11 \n\n\n\nDistance  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<.0001 \n\n\n\n1400 49 13.85a 0.10 \n\n\n\n1600 154 13.50ab 0.06 \n\n\n\n1700 49 13.54ab 0.10 \n\n\n\n2000 72 13.16ab 0.09 \n\n\n\n2100 3 13.04ab 0.37 \n\n\n\n2600 6 12.68b 0.27 \n\n\n\nRace season  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0.4019 \n\n\n\n1 14 13.33a 0.19 \n\n\n\n2 25 13.46a 0.15 \n\n\n\n3 56 13.39a 0.11 \n\n\n\n4 107 13.24a 0.10 \n\n\n\n5 36 13.24a 0.12 \n\n\n\n6 32 13.13a 0.15 \n\n\n\n7 45 13.41a 0.14 \n\n\n\n8 10 13.07a 0.23 \n\n\n\n9 8 13.36a 0.23 \n\n\n\nOrigine* Distance \n\n\n\n1400 Tunisian 49 \n\n\n\n\n\n\n\n13.67a 0.144 <.0001 \n\n\n\noccidental 13.89b 0.17 \n\n\n\n1600 Tunisian 154 13.23a 0.12 <.0001 \n\n\n\noccidental 13.68b 0.10 \n\n\n\n1700 Tunisian 49 13.19a 0.18 <.0001 \n\n\n\noccidental 13.66b 0.14 \n\n\n\n2000 Tunisian 72 13.32a 0.66 <.0001 \n\n\n\noccidental 13.26b 0.11 \n\n\n\n2100 Tunisian 3 - -  \n\n\n\noccidental 13.09 0.39  \n\n\n\n2600 Tunisian 6 - -  \n\n\n\noccidental 12.79 0.27  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 07-11 \n\n\n\n\n\n\n\n \nCite The Article: IkramBensouf, NaceurMhamdi, Hatem OuledAhmed,FatenLasfar, Belgacem Ben Aoun, AbdessalemTrimeche  (2022). Determination of Race \n\n\n\nPerformance\u2019s Factors of Arabian Thoroughbred Tunisian Horses and The Impact of Introduction of Occidental Thoroughbred Horses Intunisia. Malaysian Journal of \nSustainable Agricultures, 6(1): 07-11. \n\n\n\n\n\n\n\nFigure 2: Variation of the speed according to the origin of the father and \nthe different distances covered \n\n\n\n4. CONCLUSION  \n\n\n\nThe racing performance of the Tunisian Thoroughbred horses showed \nsignificant differences over the different distances covered; the increase in \ndistance negatively affects the speed of the horses. For the origin of the \nfather, the effect on the performance was highly significant, horses born in \nTunisia of an occidental father had higher speeds than horses born of \nTunisian sire over the different distances. The introduction of the \noccidental type would then be beneficial to the performance of Tunisian \nhorses. For the effect of sex and age on the performance of horses, they are \nstatistically insignificant. \n\n\n\nAUTHORS CONTRIBUTIONS \n\n\n\nI.B and H.O :Constructing hypothesis for research; I.B , F.L and B.B : Data \ncollection; N.M and I.B: Data analysis and interpretation of the results; A.T \nand N.M : supervising the course of the article; I.B and N.M: construction \nof the whole or body of the manuscript; N.M and A.T: Reviewing the article \nbefore submission not only for spelling and grammar but also for its \nintellectual content. \n\n\n\nCONFLICTS OF INTEREST  \n\n\n\nThe authors declare that there is no conflict of interest regarding the \npublication of this paper. \n\n\n\nFUNDING SOURCES \n\n\n\nThis research did not receive any specific grant from funding agencies in \nthe public, commercial, or not-for-profit sectors.  \n\n\n\nREFERENCES \n\n\n\nAnderson, T.M., McIlwraith, C.W., 2004. Longitudinal development of \nequine conformation from weanling to age 3 years in the \nThoroughbred. Equine Vet J., 36, Pp. 563\u201370. \nhttps://doi.org/10.2746/0425164044864507. \n\n\n\nBakhtiari, J., Heshmat, G., 2009. Estimation of genetic parameters of \nconformation traits in Iranian Thoroughbred horses. Livest Sci., 123, \nPp. 116\u201320. https://doi.org/10.1016/j.livsci.2008.10.014. \n\n\n\nBakhtiari, J., Kashan, N.E.J., 2009. Estimation of genetic parameters of \nracing performance in Iranian Thoroughbred horses. Livest Sci., 120, \nPp. 151\u20137. https://doi.org/10.1016/j.livsci.2008.05.007. \n\n\n\nBoivin, R., 1989. Physiologie de l\u2019exercice musculaire chez le cheval. Revue \nMed Vet., 140, Pp. 909-920. \n\n\n\nBuxadera, A.M., da Mota, M.D.S., 2008. Variance component estimations for \nrace performance of thoroughbred horses in Brazil by random \nregression model. Livest Sci., 117, Pp. 298\u2013307. \nhttps://doi.org/10.1016/j.livsci.2007.12.027. \n\n\n\n Denoix, J.M., 1995. M\u00e9decine sportive et affections locomotrices chez le \ncheval. Point Vet. 27, (171), Pp. 35-42. \n\n\n\nDo\u011fukan; G\u00dcRCAN \u00d6.,2016. Determination of factors affecting the length \nof racing career of Arabian horses in Turkey. Ankara \u00dcniversitesi Vet \nFak\u00fcltesi Derg. 63:303\u20139. \n\n\n\nhttps://doi.org/10.1501/vetfak_0000002744. \n\n\n\nEkiz, B., Kocak, \u00d6., Yilmaz, A., 2005. Phenotypic and genetic parameter \nestimates for racing traits of Thoroughbred horses in Turkey. Arch \nAnim Breed, 48, Pp. 121\u20139. https://doi.org/10.5194/aab-48-121-\n2005. \n\n\n\nEkiz, B., Ko\u00e7ak, \u00d6., 2007. Estimates of genetic parameters for racing times \nof thoroughbred horses. Turkish J Vet Anim Sci., 31, Pp. 1\u20135. \n\n\n\nG\u0142azewska, I., 2010. Speculations on the origin of the Arabian horse breed. \nLivest Sci.129, Pp. 49\u201355. \nhttps://doi.org/10.1016/j.livsci.2009.12.009. \n\n\n\nGramm, M., Marksteiner, R., 2010. The effect of age on thoroughbred racing \nperformance. J Equine Sci., 21, Pp. 73\u20138. \nhttps://doi.org/10.1294/jes.21.73. \n\n\n\nKhadka, R., 2010. Global Horse Population with respect to Breeds and Risk \nStatus. Swedish Univ Agric Sci, 81. \n\n\n\nKlecel, W., Drobik-Czwarno, W., Martyniuk, E., 2021. 36\u202f\u202fFactors \ninfluencing racing performance in Polish Thoroughbreds and \nPurebred Arabian horses. J. Equine Vet. Sci., 100, Pp. 103499. \nhttps://doi.org/10.1016/j.jevs.2021.103499. \n\n\n\nKocher, A., Burton, S.W., 2013. The pattern of thoroughbred growth is \naffected by a foal\u2019s birthdate. Livest Sci., 154, Pp. 204\u201314. \nhttps://doi.org/10.1016/j.livsci.2013.03.008. \n\n\n\nMota, M.D.S., Abrah\u00e3o, A.R., Oliveira, H.N., 2005. Genetic and \nenvironmental parameters for racing time at different distances in \nBrazilian Thoroughbreds. J. Anim. Breed Genet., 122, Pp. 393\u20139. \nhttps://doi.org/10.1111/j.1439-0388.2005.00551.x. \n\n\n\nMota, M.D.S., Oliveira, H.N., Silva, R.G., 1998. Genetic and environmental \nfactors that affect the best time of Thoroughbred horses in Brazil. J \nAnim Breed Genet., 115, Pp. 123\u20139. https://doi.org/10.1111/j.1439-\n0388.1998.tb00335.x. \n\n\n\nOki, H., Sasaki, Y., Willham, R.L., 1994. Genetics of racing performance in \nthe Japanese Thoroughbred horse: II. Environmental variation of \nracing time on turf and dirt tracks and the influence of sex, age, and \nweight carried on racing time. J. Anim. Breed. Genet., 111, Pp. 128\u201337. \nhttps://doi.org/10.1111/j.1439-0388.1994.tb00446.x. \n\n\n\n Oliveira, L.F.S., 1989. Fatores que influem no desempenho e selec\u00b8a\u02dco de \ncavalos de corrida da rac\u00b8a P.S.I. Master\u2019s thesis in Agronomy, Escola \nSuperior de Agricultura \u2018\u2018Luiz de Queiroz\u2019\u2019. Universidade de Sa\u02dco \nPaulo, Sa\u02dco Paulo, Pp. 181. \n\n\n\n\u00d6zbeyaz, C., Ak\u00e7ap\u0131nar, H., 2003. At yeti\u015ftiricili\u011fi ders notlar\u0131. Ankara \n\u00dcniversitesi Veteriner Fak\u00fcltesi, Ankara. \n\n\n\n  Paksoy, Y., \u00dcnal, N., 2010. The factors affecting horse  racing performance. \nJournal of lalahan livestock reseach institute, 50, Pp. 91-101. \n\n\n\nPaksoy, Y., \u00dcnal, N., 2019. Multivariate analysis of morphometry effect on \nrace performance in Thoroughbred horses. Rev Bras Zootec., 48, Pp. \n1\u201311. https://doi.org/10.1590/RBZ4820180030. \n\n\n\nPark, J.E., Lee, J.R., Oh, S., Lee, J.W., Oh, H.S., Kim, H., 2011. Principal \ncomponents analysis applied to genetic evaluation of racing \nperformance of Thoroughbred race horses in Korea. Livest Sci., 135, \nPp. 293\u20139. https://doi.org/10.1016/j.livsci.2010.07.014. \n\n\n\nRopka-Molik, K., Stefaniuk-Szmukier, M., Musia\u0142, A.D., Velie, B.D., 2019. The \nGenetics of Racing Performance in Arabian Horses. Int. J. Genomics. \nhttps://doi.org/10.1155/2019/9013239. \n\n\n\nSobczynska, M., 2003. Selected environmental factors influencing racing \ntime, rank at the finish, and earnings by Thoroughbred in Poland. \nPraceiMaterialyZootechniczne, 61, Pp. 49\u201359. \n\n\n\nSobczy\u0144ska, M., 2011. Environmental factors affecting the speed of \nThoroughbred horses competing in Poland. Anim Sci Pap Reports, 29, \nPp. 303\u201312. \n\n\n\nTakahashi, T., 2015. The effect of age on the racing speed of Thoroughbred \nracehorses. J Equine Sci., 26, Pp. 43\u20138. \nhttps://doi.org/10.1294/jes.26.43. \n\n\n\n Taylor, R.E., Field, T.G., 2014. Scientific animal production.10th ed. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(1) (2022) 07-11 \n\n\n\n\n\n\n\n \nCite The Article: IkramBensouf, NaceurMhamdi, Hatem OuledAhmed,FatenLasfar, Belgacem Ben Aoun, AbdessalemTrimeche  (2022). Determination of Race \n\n\n\nPerformance\u2019s Factors of Arabian Thoroughbred Tunisian Horses and The Impact of Introduction of Occidental Thoroughbred Horses Intunisia. Malaysian Journal of \nSustainable Agricultures, 6(1): 07-11. \n\n\n\n\n\n\n\nPearson Education Limited, England. \n\n\n\nWitteman, C., van der Hoek W., 2012. Introduction chapter. Synthese, 189, \nPp. 1\u20133. https://doi.org/10.1007/s11229-012-0184-x. \n\n\n\nYildirim, F., 2018. Safkan Arap Atlar\u0131n\u0131n Yar\u0131\u015f Performans\u0131na Baz\u0131 \u00c7evresel   \nFakt\u00f6rlerin Etkisi. Kocatepe Vet J . \nhttps://doi.org/10.30607/kvj.450350.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 06-08 \n\n\n\nCite the article: Ashik Bk, Jiban Shrestha, Roshan Subedi (2018). Grain Yield And Yield Attributing Traits Of Maize Genotypes Under \nDifferent Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(2) : 06-08. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online) \n\n\n\nCODEN : MJSAEJ     \n\n\n\n ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nWinter planting of maize in Inner Tarai region of Nepal is affected by planting dates. This experiment was \nconducted at research field of National Maize Research Program (NMRP) Rampur, Chitwan, Nepal from \nSeptember to March in 2016 to 2017 to evaluate the yield performance of maize varieties under various panting \ndates. The experiment was conducted in factorial randomized complete block design (RCBD) with three \nreplications in which eight treatments consisted of different combinations of two maize varieties (S03TEY-FM \nand RML-95/RML-96) and four planting dates (4th, 14th, and 24th September and 4th October). The results \nshowed that the effect of planting dates on grain yield was highly significant. Similarly, the effect of varieties on \ngrain yield was significant. Moreover, the interaction effect of them was significant. The earlier planting of maize \nvarieties (September 4) produced the higher grain yield than later planting (October 4). Therefore, maize \nvarieties should be planted in early September during winter season for achieving higher production.\n\n\n\n KEYWORDS \n\n\n\nPlanting dates, maize varieties, grain yield\n\n\n\n1. INTRODUCTION\n\n\n\nMaize (Zea mays L.) is one of the most important cereal crops grown \nduring the summer season in Nepal. It is the second most important \nstaple crops after rice both in terms of area and production. Its area, \nproduction, and productivity in Nepal are 882395 ha, 2145291 t, and \n2431 kg respectively [1]. Maize occupies about 40.6% area of the total \nfood crops in the hills and 26.05% in the country. It shares about 23.15% \nof total edible food production in Nepal [1]. The overall demand for \nmaize driven by increased demand for human consumption and \nlivestock feed is expected to grow by 4% to 6 % per year over the next \n20 years [2].\n\n\n\nIn recent years, the hybrid maize has been scaled up in the valleys and \nlow hills of different parts of the country. Commercialization of hybrid \nmaize production in the high input supply areas is strongly demanded \nand that would provide greater contribution to national economic \ngrowth [3]. Therefore, to cope the demand for feeds by various feed \nindustries located in tarai and inner tarai and to insure the food security \nin the hills of Nepal, increased in maize production per unit area through \nthe adoption of hybrid cultivars is only the viable option. So it is \nnecessary to access the performance of hybrid varieties in this climatic \nscenario with intervention of best planting and suitable maize variety for \nhigher yields.\n\n\n\nIn the tarai, valleys and low-lying river basin areas (both Bari and Khet \nlands), maize is grown in the winter and spring seasons with partial \nirrigation [3].  It had been reported that maize grain yield was reduced \nwhen sowing was delayed ending of October [4]. In Nepal very, little \nwork was done on the effect of sowing date and varieties on the \nperformance of maize. A researcher reported that sowing date had a \nsignificant effect on grain yield of maize and, October 1 seeding out \nyielded November 1 and December 1 seeding by 36.5 and 53.0 %, \nrespectively [5]. However, the cultivar varied significantly in their yield \npotential.\n\n\n\nTherefore, present works was carried out to study the effect of sowing \ndate and cultivar on grain yield of maize. Here two variety of maize \nnamely, one RML-95/ RML-96 (hybrid) and the other OPVs S03TEY-FM \nand four planting date with 10 days of intervals starting from September \n4, 2016 to October 4, 2016 were used for the experiment.\n\n\n\n2. MATERIALS AND METHODS\n\n\n\n2.1 Description of experimental site\n\n\n\nThe experiment was conducted at research field of National Maize \nResearch Program, Rampur, Chitwan, Nepal during winter season of the \nyear 2016. Maize was sown on sandy silt loam, strongly acidic soil (pH \n5.0), medium in total nitrogen (0.130%), high in soil available \nphosphorous (279 kg/ha), high in soil available potassium (215 kg/ha) \nand high in organic matter content (2.70%) (NMRP, 2012). The \ngeographical location of the experiment site was located at 27040\u2019N \nlatitude, 84o19\u2019 E and 228 masl and has sub tropical climate. The \nclimatic parameters taken during crop growing period was shown as \nbelow table.\n\n\n\nTable 1: Meteorological data during the crop growing period at Rampur, \nChitwan, Nepal, 2016- \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.02.2018.06.08 \n\n\n\nGRAIN YIELD AND YIELD ATTRIBUTING TRAITS OF MAIZE GENOTYPES \nUNDER DIFFERENT PLANTING DATES  \nAshik Bk1*, Jiban Shrestha2, Roshan Subedi1\n\n\n\n1Institute of Agriculture and Animal Science, Lamjung Campus, Lamjung \n2National Commercial Agriculture Research Program, Pakhribas, Dhankuta, Nepal\n*Corresponding Author Email: ashik.bishwo@gmail.com\n\n\n\nMonths \n\n\n\nMean daily temperature (0C) Total \n\n\n\nRainfall \n\n\n\n(mm) Maximum Minimum Average \n\n\n\nSeptember (2016) 32.32 24.89 28.60 631.40 \n\n\n\nOctober 31.82 21.25 26.54 42.70 \n\n\n\nNovember 28.58 13.71 21.14 0.00 \n\n\n\nDecember 23.81 10.15 16.98 0.00 \n\n\n\nJanuary (2017) 23.98 7.86 7.86 13.80 \n\n\n\nFebruary 26.88 12.47 19.67 3.40 \n\n\n\nMarch 29.49 16.64 23.06 64.50 \n\n\n\nApril 33.44 21.52 27.48 77.60 \n\n\n\n2.2 Experimental design and cultural practices\n\n\n\nTwo genotypes namely S03TEY-FM, and RML-95/RML-96 were sown in \nevery 10 days of interval from September 4 to October 4 of 2016. The \ndesign was randomized complete block design replicated three times \nwith each four sowing dates. In each planting; it was replicated 3 times at \nten days intervals. Spacing 60 cm row to row and 25 cm plant to plant \nspacing was maintained and two to three seeds are sown and after two \nweeks thinned one plants/hill. Plot size was 2 rows of 5 meter (1.2 m \u00d7 \n5.0 m) during 2016 in which, whole plot was used to assess final harvest. \nFertilizer @ FYM 10 t/ha and 120:60:40 kg NPK kg/ha was applied for \neach experiment. Half dose of nitrogen and full dose of phosphorous and \npotash was applied as basal dose at the time of final land preparation \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 06-08 \n\n\n\nand remaining half of nitrogen was divided into two; one part applied at \n20-24 and 40-45 days after sowing. Weeding and irrigation was done as \nper recommendations. The details of treatments used in the experiment \nwas shown as below table;\n\n\n\nTable 2: Treatments combination of experiment conducted at NMRP, \nRampur, Winter,2016\n\n\n\n2.3 Field measurements\n\n\n\nData of various yield attributes were recorded. Yield attributing \ncharacters such as; number of harvested ears, ear length and cob \ndiameter, number of kernels per ear, thousand grain weight (TGW) or \ntest weight, grain yield, were recorded. Grain yield (kg/ha) at 15% \nmoisture content was calculated using formula adopted by a group \nresearcher [6,7].\n\n\n\n2.4 Statistical analysis\n\n\n\nThe statistical analysis of data\u2019s were done using computer software \nMSTATC version 1.3 applying 5% level of significance.\n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nDetermination of sowing dates for maize varieties is very crucial for \nbetter crop yield. Grain yield of maize influenced by varieties and \nplanting dates. Cob diameter has highly significant and no. of grain per \ncob was found significant and other trait showed non-significant result \nfor variety. Whereas, in planting days, highly significant results were \nfound for cob length and cob diameter, and significant result were \nobserved for no of grains/per cob, no. of rows per cob, test weight but \nnon-significant for no of ears per plot. Along with varieties, winter maize \nhas higher production potential than the rainy season maize. Pests like \ninsects, diseases, weeds are not problem during winter season but \nsometimes parrot is a problem during maturity period. Crop receives \nlonger sunshine duration, higher rate of photosynthesis and assimilates \nutilization occurs during winter season. Fertilizer use efficiency is higher \nin winter season. These factors contribute higher production during \nwinter season.\n\n\n\n3.1 Yield attributing characters\n\n\n\nYield attributing characters such as; number of harvested ears, ear \nlength and cob diameter, number of kernels per ear, thousand grain \nweight (TGW) or test weight, grain moisture content (%), grain yield, \nwere recorded\n\n\n\nTable 3: Mean effects of planting date, and varieties on No. of ears per \nhectare, Cob length, Cob diameter, NO. of grain/cob, no. of rows/cob and \nTest weight at NMRP Rampur, during winter season, 2016/17\n\n\n\nears/ha.\n\n\n\nTable 4: Interaction result of Variety and Planting Date on No of ear/ha \nat NMRP, Rampur during winter season 2016\n\n\n\n7\n\n\n\n3.2 Number of harvested ears\n\n\n\nThe effect of planting date and all their interaction effects on Number of \nears / ha were found non-significant. Only the effect of genotypes was \nfound significant different on the No. of ears/ha. Higher no of ears/ha was \nfound in RML95/RML96 (65000 ears/ha) followed by S03TEY-FM 58889 \n\n\n\nTreatments Planting date and Variety \n\n\n\nT1 September-4+ RML-95/RML-96 \n\n\n\nT2 September-4+ S03TEY-FM \n\n\n\nT3 September-14  + RML-95/RML-96 \n\n\n\nT4 September-14 + S03TEY-FM \n\n\n\nT5 September-24  + RML-95/RML-96 \n\n\n\nT6 September-24  + S03TEY-FM \n\n\n\nT7 October-4+ RML-95/RML-96 \n\n\n\nT8 October-4+ S03TEY-FM \n\n\n\nTreatments No. of \n\n\n\nears/ha \n\n\n\nCob length \n\n\n\n(cm) \n\n\n\nCob \n\n\n\ndiameter \n\n\n\n(cm) \n\n\n\nNo. of \n\n\n\ngrains/cob \n\n\n\nNo. of \n\n\n\nrows/c\n\n\n\nob \n\n\n\n1000 grain \n\n\n\nweight (g) \n\n\n\nVarieties \n\n\n\nS03TEY-FM 58889 13.13 4.198 382.0 13.13 281.0 \n\n\n\nRML95/RML96 65000 13.57 4.485 423.6 13.87 335.2 \n\n\n\nF-test(<0.01) 0.035 0.243 0.001 0.030 0.054 0.001 \n\n\n\nLSD0.05 5597.9 0.766 0.1561 37.12 0.782 29.57 \n\n\n\nPlanting dates \n\n\n\nSept 4,2016 63611 14.32 4.573 428.2 13.60 330.3 \n\n\n\nSept 14,2016 65556 12.08 4.183 368.1 13.03 329.0 \n\n\n\nSept 24,2016 55278 13.70 4.463 386.0 12.87 301.7 \n\n\n\nOct 4,2016 63333 13.30 4.147 428.9 14.50 271.5 \n\n\n\nF-test (<0.01) 0.064 0.004 0.002 0.058 0.027 0.029 \n\n\n\nLSD0.05 7916.7 1.084 0.2207 52.50 1.106 41.82 \n\n\n\nInteraction (Varieties \u00d7 \n\n\n\nplanting dates) \n\n\n\nGrand Mean 61944 13.35 4.342 402.8 13.50 308.1 \n\n\n\nF-test (<0.01) 0.093 0.027 0.093 0.029 0.133 0.944 \n\n\n\nLSD0.05 11195.8 1.532 0.3121 74.24 1.564 59.14 \n\n\n\nCV% 10.3 6.6 4.2 10.6 6.7 11.0 \n\n\n\nVarieties \n\n\n\nPlanting Dates \n\n\n\nSept 4,2016 Sept 14,2016 Sept 24,2016 Oct 4,2016 \n\n\n\nS03TEY-FM 56111 60000 54444 65000 \n\n\n\nRML95/RML96 71111. 71111 56111 61667 \n\n\n\n3.3 Ear length\n\n\n\nThe effect of variety found non-significant result, but the planting date \nand their interaction were significant for ear length. The variety RML-95/\nRML-96 had longer ear length (15.25 cm) in September 4 planting \nfollowed by the variety S03TEY-FM in September planting (13.93 cm). \nThe variety S03TEY-FM has shortest cob length (11.4 cm) in September \n14 planting.\n\n\n\nTable 5: Interaction result of Variety and Planting Date on Cob length at \nNMRP, Rampur during winter season 2016.\n\n\n\nVarieties \n\n\n\nPlanting Dates \n\n\n\nSept 4,2016 Sept 14,2016 Sept 24,2016 Oct 4,2016 \n\n\n\nS03TEY-FM 13.39 11.40 13.81 13.93 \n\n\n\nRML95/RML96 15.25 12.77 13.60 12.67 \n\n\n\n3.4 Ear diameter\n\n\n\nThe effects of variety and planting date individually were found highly \nsignificant and the interactions effect was non-significant result. Variety \nRML-95/RML-96 has more diameters (4.80 cm) in September 4 planting \nfollowed by September 24 planting (4.40 cm).\n\n\n\nTable 6: Interaction result of Variety and Planting Date on Cob diameter \nat NMRP, Rampur during winter season 2016 \n\n\n\nVarieties \n\n\n\nPlanting Dates \n\n\n\nSept 4,2016 Sept 14,2016 Sept 24,2016 Oct 4,2016 \n\n\n\nS03TEY-FM 4.347 3.967 4.307 4.173 \n\n\n\nRML95/RML96 4.800 4.400 4.620 4.120 \n\n\n\n3.5 No of row/cob\n\n\n\nThe effects of variety and planting date individually were found \nsignificant. These results are in agreement with findings of who observed \nsignificant effect of sowing dates on number of rows/ ear [8]. Variety \nS03TEY-FM has more number of rows per cob (14.87) in October 4 \nplanting followed by RML-95/RML-96(14.27) in September 4 planting and \n14.13 in October 4 planting.\n\n\n\nTable 7: Interaction result of Variety and Planting Date on No of row/cob \nat NMRP, Rampur during winter season 2016\n\n\n\nVarieties \n\n\n\nPlanting Dates \n\n\n\nSept 4,2016 Sept 14,2016 Sept 24,2016 Oct 4,2016 \n\n\n\nS03TEY-FM 12.93 12.73 12.00 14.87 \n\n\n\nRML95/RML96 14.27 13.33 13.73 14.13 \n\n\n\n3.6 Number of kernels (grains) per ear\n\n\n\nThe effect of variety, planting date and their interactions on the no. of \ngrains /ear was significantly different. Here, the no. of grain per ear was \nhigher (487.5 grains) of Variety RML-95/RML-96 in September 4, 2016 \nplanting followed by the variety S03TEY-FM in October 4,2016 (453.0 \ngrains). The lowest no. of grains/cob was observed from the variety \nS03TEY-FM (349.3 grains) in September 14 planting. The effect of variety, \nplanting date and their interactions results on the no. of grains /ear was \nsignificantly different. Here, the no. of grain per ear was higher (487.5 \ngrains) of Variety RML-95/RML-96 in September 4, 2016 planting \nfollowed by the variety S03TEY-FM in October 4, 2016 (453.0 grains)\n\n\n\nTable 8: Interaction result of Variety and Planting Date on No of grain/cob \nat NMRP, Rampur during winter season 2016\n\n\n\nVarieties \n\n\n\nPlanting Dates \n\n\n\nSept 4,2016 Sept 14,2016 Sept 24,2016 Oct 4,2016 \n\n\n\nS03TEY-FM 369.0 349.3 356.5 453.0 \n\n\n\nRML95/RML96 487.5 386.8 415.4 404.7 \n\n\n\n3.7 Thousand Grains Weight (TGW) or Test weight\n\n\n\nThe effects of genotype were highly significant, planting dates were \nsignificant and their interactions on 1000 grain weight were non-\nsignificant differences. The variety RML-95/RML- 96 has the highest 1000 \ngains weight (361.3 g) in September 4 planting followed by September 14 \nplanting (351.7 g). The lowest 1000 grain weight was recorded from the \n\n\n\nCite the article: Ashik Bk, Jiban Shrestha, Roshan Subedi (2018). Grain Yield And Yield Attributing Traits Of Maize Genotypes Under \nDifferent Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(2) : 06-08. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 06-08 \n\n\n\nvariety S03TEY-FM (248.3 g) in October 4 planting.\n\n\n\nTable 9: Interaction result of Variety and Planting Date on 1000 grain \nweight in g (Testwt) at NMRP, Rampur during winter season 2016.\n\n\n\nThe effects of genotype were highly significant, planting dates were \nsignificant and their interactions on 1000 grain weight were non-\nsignificant differences. The variety RML-95/RML-96 has the highest 1000 \ngains weight (361.3 g) in September 4 planting followed by September 14 \nplanting (351.7 g). The lowest 1000 grain weight was recorded from the \nvariety S03TEY-FM (248.3 g) in October 4 planting.\n\n\n\n3.8 Grain yield\n\n\n\nGrain yield is determined by the yield attributes of crop. Grain yield is a \nfunction of various Combined result of two variety and four sowing date \nshowed that the effects of date of sowing was highly significant and \nvarieties on grain yield individually were significant and the Interaction \neffects of them were also found significant. The hybrid variety RML-95/\nRML-96 produced the highest grain yield (8433kg/ha) in September 4, \n2016 planting followed by 7486 kg/ha in September 14 and 6337 kg/ha in \nSeptember, 24 planting. The variety S03TEY-FM also produced more grain \nyield in October 4 planting (6012 kg/ha) followed by 5346 kg/ha in \nSeptember 14 planting respectively. The lowest yield was produced by the \ngenotype S03TEY-FM (4830 kg/ha) in September 24, planting.\n\n\n\nTable 10: Mean effect of planting date, and varieties on grain yield kg/ha \nat NMRP Rampur, during winter season, 2016.\n\n\n\n-96 hybrid and S03TEY-FM OPV maize varieties produced higher grain \nyield during first week of September planting in Inner Tarai region of \nNepal. Therefore, maize varieties should be planted in early September to \nachieve the higher grain yield. \n\n\n\nREFERENCES\n\n\n\n[1] MoAD. 2016. Statistical information on Nepalese agriculture, \n2015/2016 (2072/2073). Agribusiness Promotion and Statistics Division, \nSingha Durbar, Kathmandu, Nepal.\n\n\n\n[2] Paudyal, K.R., Ransom, J.K. 2001. Resource use efficiency and effective \nincentives of Nepalese maize farmers. In: N. P. Rajbhandari, J. K. Ransom, \nK. Adhhikari and A. E. F. Palmer (eds.) Sustainable Maize Production \nSystem for Nepal. Proceedings of a maize Symposium, December 3-5, \n239-245. \n\n\n\n[3] Pathik, D.S. 2002. Maize research achievements and constraints. In: \nRajbhandari, N.P., J.K. Ransom, K. Adhikari and A.F.E. Palmer (eds.) \nSustainable maize production systems for Nepal: Proceedings of a maize \nsympodium held, December 3-5, 7-12. Kathmandu, Nepal. Kathmandu: \nNARC and CIMMYT.\n\n\n\n[4] McCormick, S.J. 1971. The effect of sowing date on maize (Zea mays L.) \ndevelopment and yields of silage and grain. Proceedings of the Agronomy \nSociety of New Zealand, 1, 51-65.\n\n\n\n[5] Rahman, A.M., Magbou, E.L., Abdelatief, E.N. 2004. Effects of sowing \ndate and cultivar on the yield and yield components of maize in Northern \nSudan. Seventh Eastern and Southern Africa Regional Maize Conference, \nHudeiba Research Station\n\n\n\n[6] Carangal, V.R., Ali, S.M., Koble, A.F., Rinke, E.H., Sentz, J.C. 1971. \nComparison of S1 with testcross evaluation for recurrent selection in \nmaize. Crop Science, 11, 658-661. DOI: 10.2135/\ncropsci1971.0011183X001100050016x\n\n\n\n[7] Shrestha, J., Koirala, K., Katuwal, R., Dhami, N., Pokhrel, B., Ghimire, B., \nPrasai, H., Paudel, A., Pokhrel, K., KC, G. 2015. Performance evaluation of \nquality protein maize genotypes across various maize production agro \necologies of Nepal. Journal of Maize Research and Development, 1 (1), \n21-27. DOI: http://dx.doi.org/10.5281/zenodo.34282\n\n\n\n[8] Hassan, K.H. 1998. Response of some maize cultivars to early planting \ndates under saline condition at siwa oasis. Annals of Agricultural Sciences \nCairo, 43, 391-401.\n\n\n\n[9] Bahadur, B.K.S., Karki, T.B., Shrestha, J., Adhikari, P. 2015. Productivity \nof maize genotypes under different planting dates. Our Nature, 13 (1), \n45-49. DOI: http://dx.doi.org/10.3126/ on. v13i1.14208\n\n\n\n[10] Otegui, M.E., Nicolini, M.G., Ruiz, R.A., Dodds, P.A. 1995. Sowing date \neffects on grain yield components for different maize genotypes. \nAgronomy Journal, 87, 29-33.\n\n\n\n[11] Jaliya, M.M., Falaki, A.M., Mahmud, M., Sani, Y.A. 2008. Effect of sowing \ndate and NPK fertilizer rate on yield and yield components of quality \nprotein maize (Zea mays L.). ARPN Journal of Agricultural and Biological \nScience, 3 (2), 23-29.\n\n\n\n[12] Namakka, A., Abubakar, I.U., Sadik, A.I., Sharifai, A.I., Hassas, A.H. \n2008. Efect of sowing date and Nitrogen level on yield and yield \ncomponents of two extra early maize varieties (Zea mays L.) in Sudan \nSavanna of Nigeria. ARPN Journal of Agricultural and Biological Science, 3 \n(2), 01-05.\n\n\n\n[13] Aziz, A., Rahman, H., Khan, N. 2007. Maize cultivar response to \npopulation density and planting dates for grain and biomass yield. Sarhad \nJournal of Agriculture, 23 (1), 25-30.\n\n\n\n[14] Khan, H., Arif, M., Gul, R., Ahmad, N., Khan, I.A. 2002. Effect of sowing \ndates on maize cultivars. Sarhad Journal of Agriculture, 18 (1), 11-15.\n\n\n\n[15] Zaki, M.S., Shah, P., Hayat, S. 1994. Effect of sowing date on maize and \nnon-flooded land rice. Sarhad Journal of Agriculture, 10 (2), 191-199.\n\n\n\nThe hybrid variety RML-95/RML-96 produced the highest grain yield \n(8433kg/ha) in September 4, 2016 planting followed by 7486 kg/ha in \nSeptember 14 and 6337 kg/ha in September, 24 planting. The variety \nS03TEY-FM also produced more grain yield in October 4 planting (6012 kg/\nha) followed by 5346 kg/ha in September 14 planting respectively. The \nlowest yield was produced by the genotype S03TEY-FM (4830 kg/ha) in \nSeptember 24, planting. A researcher reported that grain yield of maize was \naffected by genotypes and date of planting at Rampur Chitwan Nepal [9]. \nLesser grain August was due to less population because a lot of plants were \nlodged due to the rainy season in month of August. Optimum sowing date \nresulted in higher grain yield than early and late planting dates [10]. The \nresult is in confirmation with a large group of researchers who reported \nthat grain yield was reduced by delay in sowing [11-15].\n\n\n\n4. CONCLUSION\n\n\n\nAmong the different planting date in winter season starting from \nSeptember 4 to October 4, 2016 with 10 days of interval, the RML-95/RML\n\n\n\n8\n\n\n\nTable 11: Interaction result of Variety and Planting Date on grain yield at \nNMRP, Rampur during winter season 2016\n\n\n\nVarieties \n\n\n\nPlanting Dates \n\n\n\nSept 4,2016 Sept 14,2016 Sept 24,2016 Oct 4,2016 \n\n\n\nS03TEY-FM 299.3 306.3 270.0 248.3 \n\n\n\nRML95/RML96 361.3 351.7 333.3 294.7 \n\n\n\nTreatments GrainYield (kg/ha) \n\n\n\nVarieties \n\n\n\nS03TEY-FM 5055 \n\n\n\nRML95/RML96 7116 \n\n\n\nMean \n\n\n\nF-test(<0.01) <.001 \n\n\n\nLSD0.05 420.8 \n\n\n\nPlanting dates \n\n\n\nSept 4,2016 6903. \n\n\n\nSept 14,2016 6416. \n\n\n\nSept 24,2016 5504. \n\n\n\nOct 4,2016 5519. \n\n\n\nMean \n\n\n\nF-test (<0.01) <.001 \n\n\n\nLSD0.05 595.1 \n\n\n\nInteraction (Varieties \u00d7 planting dates) \n\n\n\nGrand Mean 6085 \n\n\n\nF-test (<0.01) 0.043 \n\n\n\nLSD0.05 841.5 \n\n\n\nCV% 7.9 \n\n\n\nVarieties \n\n\n\nPlanting Dates \n\n\n\nSept 4,2016 Sept 14,2016 Sept 24,2016 Oct 4,2016 \n\n\n\nS03TEY-FM 5372 5346 4671 4830 \n\n\n\nRML95/RML96 8433 7486 6337 6207 \n\n\n\nCite the article: Ashik Bk, Jiban Shrestha, Roshan Subedi (2018). Grain Yield And Yield Attributing Traits Of Maize Genotypes Under \nDifferent Planting Dates . Malaysian Journal of Sustainable Agriculture, 2(2) : 06-08. \n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 29-33 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.29.33 \n\n\n\n \nCite the Article: Swodesh Rijal Yuvraj Devkota (2020). A Review On Various Management Method Of Rice Blast Disease.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 29-33. \n \n\n\n\n\n\n\n\n \nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n \nDOI: http://doi.org/10.26480/mjsa.01.2020.29.33 \n\n\n\n\n\n\n\n \nA REVIEW ON VARIOUS MANAGEMENT METHOD OF RICE BLAST DISEASE  \n \nSwodesh Rijala* Yuvraj Devkotab \n \na Agriculture and Forestry University, Rampur, Chitwan, Nepal \nb Institute of Agriculture and Animal Science, Paklihawa, Nepal  \n*Corresponding author email: swodeshrijal@gmail.com \n\n\n\n  \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 01 December 2019 \nAccepted 06 January 2020 \nAvailable online 05 February 2020 \n\n\n\n\n\n\n\nRice (Oryza sativa) is native to Asia and grown worldwide.  Rice feeds more than 50 % of the world population \n\n\n\nRice is predominant staple food for 17 countries in Asia and provides 20 % of world's dietary energy supply. \n\n\n\nSo, among cereal it considered as most significant crop. Both biotic and a-biotic factors adversely affect crop \n\n\n\nand yield. Among them, 70 to 80 % of annual rice yield is lost due to blast disease. Higher statical data of blast \n\n\n\ndisease is threat to growing population on food security. The objective of this review is to know the different \n\n\n\nmethods of controlling blast diseases. Management of blast can be done through various methods but eco-\n\n\n\nfriendly, integration of various cultural, Nutrient, chemical biological and botanical is best. Recent Research \n\n\n\nhas been made in biological, botanical, Resistance development and Nutritional management but \n\n\n\ndevelopment of variety and Biological are best option. Isoprothiolane at 1.5 ml/l and Tricyclazole 22 % + \n\n\n\nHexaconazole 3% SC (thrice from booting stage at weekly interval) are best chemical whereas Pseudomonas \n\n\n\nfluorescens strain Pf1 @ 10g/kg, SPM5C-1 and SPM5C-2 (aliphatic compounds obtained from Streptomyces \n\n\n\nsp), Bacillus tequilensis (GYLH001) and pseudomonad EA105 effectively inhibit the growth of M. oryzae. \n\n\n\nmore than 100 R gene are identified as Resistance in Blast. Gene Pyramiding and use of multilines varieties \n\n\n\nis efficient and able to overcome pesticide hazards. Neem extract 4ml/15ml, Coffee arabica@25%, Nicotiana \n\n\n\ntabacum@10% are effective but garlic extract @higher doses and neem extract @ 4ml/15 ml are best for \n\n\n\ncomplete control. 4 g Si/L in green house condition observed greatest reduction of blast incidence. Several \n\n\n\nforecasting model predicts probable disease outbreak and reduces crop losses. Similarly, burning of residues \n\n\n\nand flooding make unfavorable condition to pathogen. \n\n\n\n\n\n\n\nKEYWORDS \n\n\n\nRice Blast, Management Method. \n\n\n\n1. INTRODUCTION \n\n\n\nRice (Oryza sativa) is a cereal crop and belongs to family Graminae which \nis native to Asia. East and South Asia are the main regions for rice \nproduction in the world. China (over 210 million metric tons) is the world \nleading rice producer followed by india, Indonesia, Bangladesh, Vietnam \nand rest of the world (FAO, 2017). Rice is predominant staple food for 17 \ncountries in Asia and provides 20 % of world's dietary energy supply \nwhich is higher than wheat (19%) and maize (5%) (FAO, 2004). in coming \nyears it is expected that demand for rice is increasing sharply. The \nresearch conducted by Food and Agricultural Policy Research Institute \nshows that demand for milled rice can be expected to 496 million tons in \n2020 A.D. Moreover, Asian populations are still in remarkable poverty line \nwhere considerable unmet demand for rice. In spite of it great important \nproduction has remained low. Both abiotic and biotic factors adversely \naffect the crop and causes extensive losses to the yield. Drought, cold, \nacidity, salinity are abiotic factors while pests, weed and diseases are \nbiotic factors (Onyango, 2014). More than 70 % diseases have been caused \nby Fungi, viruses, bacteria and Nematode (Zhang et al., 2009). Among \nvarious Rice diseases blast is the most destructive disease in the world \n\n\n\n(Miah et al., 2013). Globally, it causes 70 to 80 % yield loss of Rice \n(Nasruddin and Amin, 2013). Rice blast disease is caused by fungus named \nPyricularia oryzae (Koutroubas et al., 2009). Pyricularia oryzae caused \ndamaged to leaf and panicle of rice which indicates that it causes damaged \nto both vegetative as well as reproductive stages (Seebold et al., 2004). The \nattacked Panicle results in partially filled or unfilled grains (IRRI, 2014).  \nMoreover, Blast in epidemics form results in complete loss of rice \nseedlings in bed (Chaudhary and DN, 1998). Being rice blast pathogen seed \nborne, it is difficult to manage easily (Hubert et al., 2015). Moreover, \nvirulence diversity of blast pathogen makes difficulties in breeding for \nresistance (Marangoni et al., 2013). So, we must follow the management \npractices to control it.  \n\n\n\n2. MATERIAL AND METHOD \n\n\n\nThis paper was based on review of various literature related with blast \ndiseases and its management. Collected information was arranged and \nfindings from them are summarized and presented in texts, table with \nconclusion. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 29-33 \n\n\n\n\n\n\n\n \nCite the Article: Swodesh Rijal Yuvraj Devkota (2020). A Review On Various Management Method Of Rice Blast Disease.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 29-33. \n \n\n\n\n\n\n\n\n3. MANAGEMENT  \n\n\n\n3.1 Cultural management \n\n\n\nInvolve all practices from sowing to after harvesting. Burning of residues \nprevents over wintering of pathogen but it is not able to prevent source of \ninoculums (Zeigler et al., 1994).   \n\n\n\n3.2 Nutrient management \n\n\n\nSoil with high organic matter and biological activities shows better fertility \nstatus which prevents infection (Luong et al., 2033). So that, Nutrient \nmanagement plays a significant role in Rice blast disease control. In \nNutrient management, Nitrogen (N) and silicon (Si) elements affect \ndisease incidence and development. Several studies have shown that \nheavy used of Nitrogen fertilizer increases susceptibility of rice plant to \nblast (Kingsolver et al., 1984; Kurschner et al., 1992). So, Application of \nNitrogen in Split doses reduced excessive vegetative growth during early \nseason and reduced blast severity (Templeton et al., 1970). Moreover, \nResearched on effect of Nitrogen fertilizer and it relation with blast \ndiseases also supported the split-N treatment for susceptible cultivar \n(Long et al., 2000). Mention that wetness of leaf increased neck blast \nrapidly (Sere et al., 2011). \n \nSilicon is commonly known as \"beneficial element\" for plants (Raij and \nCamargo, 1973). Research shows that Application of silicon to soil results \nin localized in leaf surface which act as a physical barrier against blast \n(Ishiguro, 2001). Accumulation of more silica on the shoots of rice results \nin less incidence of blast (Seebold et al., 2001). The experiment conducted \nin a green house, at the Universidadae Federal de Uberlandia, MG it was \nobserved that greatest reduction of blast incidence at 4 g Si/L, regardless \nof solution pH (Guilherme et al., 2008).  Moreover, Silicon application \nshows potential towards blast resistance but do not increases yield of rice \n(Sireger et al., 2016). Silicon is expensive in application against blast which \nis economically non viable. So, locally available straws of rice genotypes \nhave high silicon content which is best for farmer level application. So, \nSilicon is one of the best nutrient management for blast resistance. \nSimilarly, Flooding of rice field creates anaerobic condition which \neliminates diseases as water makes unfavorable condition to pathogen \n(Koutroubas and Ntanos, 2008). Water seedling reduced transmissions of \ndisease from seed to seedling. Among cereals Rice is most susceptible and \nit has low ability to tolerate water loss results into rice blast attacks. \n\n\n\n3.3 Chemical Management \n\n\n\nFarmers depend on chemical pesticides for management of blast. \nExperiment conducted by Jamal et. al concluded that Mancozeb is effective \nagainst blast at 1000 ppm and 10,000 ppm (Jamal-u-ddin et al., 2012). A \ngroup researchers reported that foliar spray of Isoprothiolane at 1.5 ml/l \ndecreased blast which was followed by carpropamid and carbendazim \n(Varma and Santhakumari, 2012). With the application of isoprothiolane \nboth grain and straw yield was increased as compared to other control. \nChemical management practices are neither practical nor environmentally \nfamiliar. Experiment conducted by Magar et al in Chitwan, Nepal \nconcluded that combination of Tricyclazole 22 % + Hexaconazole 3% SC in \nThrice from booting stage at weekly interval showed highest disease \ncontrol (87.03 % and 79.62 % in leaf and neck blast respectively) and \nhighest grain yield(4.23t/ha) (Magar et al., 2015). Earlier also revealed \nthat Captan and Acrobat controlled rice blast (Haq et al., 2015). \n\n\n\n\n\n\n\nFigure 1: Global market of leading Rice blast fungicides \n\n\n\nSource: Adopted from (McDougall, 2007; Skamnioti and Gurr, 2009). \n \nThe rest of fungicides are Benomyl, Carbendszim 12%+Mancozeb 63%, \nIprobenfos, Capropamid, Hexaconazole, Tebuconazole etc (Kapoor and \n\n\n\nSingh, 1982; Venkata and Muralidharan, 1983; Gohel and Chauhan, 2015; \nMotoyama et al., 1999; Prasanna et al., 2011; Ghazanfar et al., 2009). \n\n\n\n3.4 Biological Management \n\n\n\nEarlier, Pseudomonas fluorescens strains were used as control of soil borne \npathogens controlled foliar disease but powder formulation having long \nshelf life is more beneficial (Weller, 1988; Gnanamanickam and Mew, \n1992). So, Later Studies on biocontrol of rice blast showed that powder \nformulation of Pseudomonas fluorescens strain Pf1 at 10g/kg inhibit the \ngrowth of rice blast (Vidhyasekaran et al., 1997). \n \n\n\n\nTable 1: Efficacy of Pseudomonas fluorescens strains Pf1 against rice \nblast control in rice (cv IR50) under field trial condition Greenhouse \ncondition \n\n\n\n Treatment \n\n\n\nDAS P. fluorescens Control \n\n\n\n21 1.2a 8.2b \n\n\n\n45 2.6a 7.2b \n\n\n\n60 6.5a 7.8a \n\n\n\n75 7.1a 7.0a \n \n\n\n\nFigure in above shows disease intensity in grade values (0-9 Scale)a and \n\n\n\nLSD at P =0.05 for comparing means in column is 1.8. Among Fungus, \n\n\n\nGenus Streptomyces belongs to Actinomycetes alone occupied 50 % of \n\n\n\ntotal actinomycetes and it produced 75% of total bioactive molecule (Xu \n\n\n\net al., 1996; Demain, 2000). SPM5C-1 and SPM5C-2 were two aliphatic \n\n\n\ncompounds obtained from Streptomyces sp. PM5 were evaluated under \n\n\n\ninvitro and invivo conditions resulted into remarkably inhibition of \n\n\n\nmycelial growth of P. oryzae (Vaiyapuri et al., 2006). \n\n\n\n \nTable 1: Activity of compounds obtained from Streptomyces sp. PM5 \nin agar diffusion test using PDA against P. oryzae in lab \n\n\n\nZone of inhibition (cm)  \n\n\n\nSPM5C-1 SPM5C-2 Control \n\n\n\n2.6 \u00b1 0.17a 0.4 \u00b1 0.1 0 \n \nAbove inoculated with P. oryzae were incubated at 21 \u00b1 1\u00b0C for 9 days and \n28 \u00b1 2\u00b0C for 5 days and a are means of all values. Moreover, Culture filtrate \nproduct of Streptomyces sp. PM5 completely inhibited conidial \ngermination of P. oryzae (Prabavathy, 2005). Besides that, studies \nrevealed that Bacillus subtilis strain B-332, 1Re14, 1Pe2, 2R37 have \nantagonistic activity against P. oryzae (Yang et al., 2008). Similarly, \nResearch on biological control of blast by the use of Streptomyces \nsindeneusis isolate 263 in green house resulted in inhibition of P. oryzae \n(Ebrahimi Zarandi et al., 2009). Similarly, another fungus Trichoderma \nspp.inhibited mycelia growth of blast fungus (Quazzani et al., 1998). In the \nvery recent research carried out in China, it was proved that endophytic \nstrain of Bacillus tequilensis named GYLH001 isolated from Angelica \ndahurica has great potential as a biological control of rice blast (Li et al., \n2018). Angelica dahurica is extensively used traditional medicine in China \n(Hou et al., 2018). Similarly, Bacteria isolated from rice soil it was found \nthat pseudomonad EA105 most effectively inhibited growth of M. oryzae \n(Spence et al., 2014). In comparison with other practices Biological control \nhas minimal prejudicial effects on environment (Hyakumachi et al., 2014).   \n\n\n\n3.5 Forecasting \n\n\n\nForecasting system helps in prediction of probable disease outbreak or \nintensity and management of it (Agrios, 2005). A major purpose of \nforecasting system is to reduce uses of chemical practice and provide \naccurate prediction before crop losses in environment safety and time \nefficient way (Taylor et al., 2003). Generally Diverse modeling approaches \nis used for disease prediction in plants. But for better understanding \npurposes other approaches are followed in recent times. Support Vector \nModel (SVM) is tools which offered a prediction of plant disease which is \nbetter than conventional REG approaches (Kaundal et al., 2006). SVM is \nfirst web server for rice blast prediction which is great boon for plant \nscience community and farmers. \n \n \n \n\n\n\n% of  global leading rice blast  fungicide in market \n\n\n\nProbenazole\n\n\n\nTricyclazole\n\n\n\nAzoxystrobin\n\n\n\nIsoprothiolane\n\n\n\nPropiconazole\n\n\n\nRest\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 29-33 \n\n\n\nCite the Article: Swodesh Rijal Yuvraj Devkota (2020). A Review On Various Management Method Of Rice Blast Disease.  \nMalaysian Journal of Sustainable Agriculture, 4(1): 29-33. \n\n\n\nTable 2: Different forecasting model used to predict blast disease \n(Neck and Leaf) \n\n\n\nForecasting Model First developed  Reference  \n\n\n\nBLASTCAST Japan  (Ohta et al., 1982). \n\n\n\nBlLASTL Japan  (Hashimoto et al., 1982). \n\n\n\nBLASTAM Japan \n (Hayashi and Koshimizu, \n1988). \n\n\n\nForecasting model \nin Taiwan Taiwan  (Tsai and Su, 1984). \n\n\n\nPYTRICULARIA  Netherland  (Gunther, 1986). \nLeaf blast \nSimulation model  Philippines   (Torres, 1986). \n\n\n\nPYRNEW  Indonesia  (Tastra et al., 1987). \n\n\n\nLEAFBLAST Korea   (Choi et al., 1988). \n\n\n\nEPIBLA  India \n (Manibhushanrao and \nKrishnan, 1991). \n\n\n\nBLASTSIM.2 Philippines   (Calvero and Teng, 1991). \n\n\n\nEPIBLAST Korea  (Kim and Kim, 1993). \n\n\n\nBLASTSIM    -  (Calvero and Teng, 1992). \nDYMEX and \nCLIMEX Australia  (Lanoiselet et al., 2002). \n\n\n\nBLASTMUL Japan   (Ashizawa et al., 2005). \nMachine Learning \nTechnique India   (Kaundal et al., 2006). \nOnline information \nSystem  Korea   (Kang et al., 2010). \n\n\n\nEPIRICE  Korea   (Savary et al., 2012). \n\n\n\nModified EPIRICE  Korea   (Zhang, 2007). \n\n\n\n3.6 Botanical Management \n\n\n\nChemical practices are highly effective and low-cost management but has \nadverse impact on environment and it also makes rice blast more drug \nresistance (Slusarenko et al., 2008). Farmers of poor country cannot meet \nthe expense of chemical pesticides. Under Integrated management \npractices plant extracts can be used. Garlic juice reduced the blast disease \ncaused by P. oryzae in rice (Fiona et al., 2005). Allicin compound obtained \nfrom garlic successfully inhibited blast fungus (Rajappar et al., 2001). \nSimilarly, another Plant extract like Neem extract reduced growth of P. \noryzae (Hajano et al., 2012). It was found that extract of garlic, neem and \ncalatropis with three different doses were tested against blast it was found \nthat only garlic extract at higher doses and neem extract @ 4ml/15 ml \nmedium completely reduced growth of P. oryzae as compared to calatropis \nand controlled (Hajano et al., 2012). \n\n\n\nTable 3: Inhibitory effects of aqueous extracts of different plants \nagainst P. oryzae in % \n\n\n\nPlant extract Inhibitory effect (%) \n\n\n\nCoffee arabica@10% 81.12 \n\n\n\nCoffee arabica@25% 89.4 \n\n\n\nNicotiana tabacum@10% 80.35 \n\n\n\nAloe vera@25% 79.45 \n\n\n\nChrysanthemum coccineum@25% 78.83 \nSource: (Hubert et al., 2015). \n\n\n\nConfirmed that plant extracts were not phyto-toxic to rice seedlings So, it \ncan be used for rice seed treatment purpose to manage rice blast disease \n(Hubert et al., 2015).  \n\n\n\n3.7 Use of Resistant cultivar \n\n\n\nManagement of blast by using resistant cultivar is sustainable and eco-\nfriendly approach. Earlier, nearly 100 different resistance gene and > 350 \nQTLs of which 23 resistance genes have been identified, mapped and \ncloned and functionally validated (Fukuoka et al., 2014). Generally, in \ndevelopments of varieties rice breeders use vertical and horizontal \nresistance. Varieties develops from vertical resistance are controlled by \nfew major genes which is not durable but gene pyramiding of several \nvertical resistance gene confer durable blast resistance (Liu et al., 2004; \n\n\n\nHittalmani et al., 2000). Environment influences the expressions of \nvarieties develop from horizontal resistance and thus result in durable \nresistance (Suh et al., 2009). Using of different multilines varieties effects \nin blast control (Koizumi et al., 1996). Some of multilines varieties are \nNipponbare, Toyonishiki, Sasanishiki etc (Horisue et al., 1984; Nakajima, \n1994; Matsunaga, 1996). Use of resistant cultivar is efficient method and \nalso to overcome pesticide hazards. Genetic engineering also contributes \non sustainable disease resistance as well (Coca et al., 2004).  \n\n\n\n4. CONCLUSION \n\n\n\nLabor cost and time factor are major causes which makes unable to \ncomplete control of blast disease. For the productive results and increase \nin production of rice we must followed integrated management of rice \nblast. Isoprothiolane @ 1.5 ml/l and Tricyclazole 22 % + Hexaconazole 3% \nSC (thrice from booting stage at weekly interval) are best chemical, \nPseudomonas fluorescens strain Pf1 @ 10g/kg, SPM5C-1 and SPM5C-2 \n(aliphatic compounds obtained from Streptomyces sp), Bacillus \ntequilensis (GYLH001) and pseudomonad EA105 effectively inhibit the \ngrowth of M. oryzae. Gene Pyramiding and use of multilines varieties is \nefficient and able to overcome pesticide hazards. Neem extract 4ml/15ml, \nCoffee arabica@25%, Nicotiana tabacum@10% are effective, garlic extract \n@higher doses and neem extract @ 4ml/15 ml are best for complete \ncontrol. 4 g Si/L in green house condition observed greatest reduction of \nblast incidence. Among them development of variety and Biological are \nbest option. \n\n\n\nREFERENCES \n\n\n\nAgrios, G.N., 2005. Plant Pathology. Elsevier Academic Press, 952. \n\n\n\nAshizawa, T., Zenbayashi, K.S., Koizumi, S., Sasahara, M., Ohba, A., Hori, T., \n\n\n\n2005. Evaluation of a leaf blast simulation model (BLASTMUL) for rice \n\n\n\nmultiline in different locations and cultivar and effective blast control \n\n\n\nusing the model. Proceedings of the world Rice Research Conference \n\n\n\n(pp. 477-479). Tsukuba japan: Rice is life: Scientific perspectives for the \n\n\n\n21st century. \n\n\n\nCalvero, S.B., Teng, P.S., 1991a. BLASTSIM.2 a model for tropical leaf blast-\n\n\n\nrice pathosystem. Philippine Phytopathology, 27 (46). \n\n\n\nCalvero, S.B., Teng, P.S., 1992b. Validation of BLASTSIM.2 model in IRRI \n\n\n\nblast (Bl) nursery and cavinti, laguna, Philippines. International Rice \n\n\n\nResearch Newsletter, 17, 20-21. \n\n\n\nChaudhary, B., DN, S., 1998. Efficacy of Beam 75 WP in controlling leaf blast \n\n\n\ndisease at the seedling stage of rice. Nepal Agriculture Research Journal, \n\n\n\n2, 42-47.  \n\n\n\nChoi, W.J., Park, E.W., Lee, E.J., 1988. LEAF BLAST: A computer simulation \n\n\n\nmodel for leaf blast development on rice. Kor J Plant Pathol (Korea R), \n\n\n\n4, 25-32. \n\n\n\nCoca, M., Bortolotti, C., Rufat, M., Pen, G., Eritja, R., Tharreau, D., 2004. \n\n\n\nTransgenic rice plants expressing the antifungal AFP protein from \n\n\n\nAspergillus giganteus show enhanced resistance to the rice blast fungus \n\n\n\nMAgnaporthe grisea. Plant Mol Biol., 54 (2), 245-259. \n\n\n\nDemain, A.L., 2000. Small bugs, big business: the economic power of the \n\n\n\nmicrobe. Biotechnol Advance, 18, 499-514. \n\n\n\nEbrahimi Zarandi, M., Shahidi Bonjar, G.H., Padasht Dehkaei, F., 2009. used \n\n\n\ngical Control of Rice blast (Magnaporthe oryzae) by use of Streptomyces \n\n\n\nsindeneusis isolate 263 in Greenhouse. American Journal of Applied \n\n\n\nScience, 6 (1), 194-199. \n\n\n\nFAO., 2004. Rice is Life. Rome: Food and Agriculture organization. \n\n\n\nFAO., 2017. FAOSTAT Database. Rome: Food and Agricultural organization \n\n\n\n(FAO). \n\n\n\nFiona, H.F., Neal, O., Camilla, B.S., Michael, J.K., Nicholas, J.T., Claus, J., 2005. \n\n\n\nUse of substrate/ Alliinase combination to generate antifungal activities \n\n\n\nin Situ. J. Agri. Food Chem., 53 (3), 574-580. \n\n\n\nFukuoka, S., Yamamoto, S., Mizobuchi, R., Yamanouchi, U., Ono, K., \n\n\n\nKitazawa, N., 2014. Multiple functional polymorphisms in a single \n\n\n\ndisease resistance gene in rice enhance durable resistance to blast. Sci. \n\n\n\nRep., 4, 4550.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 29-33 \n\n\n\n\n\n\n\n \nCite the Article: Swodesh Rijal Yuvraj Devkota (2020). A Review On Various Management Method Of Rice Blast Disease.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 29-33. \n \n\n\n\n\n\n\n\nGhazanfar, M.U., Wakil, W., Sahi, S.T., Saleem-il-Yasin., 2009. Influence of \n\n\n\nvarious fungicides on the management of rice blast disease. Mycoptah, \n\n\n\n7 (1), 29-34. \n\n\n\nGnanamanickam, S., Mew, T., 1992. Biological control of blast disease of \n\n\n\nrice (Oryza sativa L.) with antagonistic bacteria and its mediation by a \n\n\n\nPseudomonas antibiotic. Annals of the Phytopathological Society of \n\n\n\nJapan, 58, 380-385. \n\n\n\nGohel, N.M., Chauhan, H.L., 2015. Integrated management of leaf and neck \n\n\n\nblast disease of rice caused by Pyricularia oryzae. African Journal Agric \n\n\n\nRes., 10 (19), 2038-2040. \n\n\n\nGuilherme, B.B., Gaspar, H.K., Antonio, N., Lisias, C., 2008. Potassium \n\n\n\nSilicate as Foliar spray and Rice blast Control. Journal of Plant Nutritioh, \n\n\n\n31 (2), 231-237. \n\n\n\nGunther, H., 1986. Simulation of the epidemiology of Pyricularia oryzae in \n\n\n\nrice. Netherlands: Department of Theoretical Production Ecology. \n\n\n\nHajano, J.U.D., Lodhi, M.A., Pathan, M.A., Khazada, M.A., Shah, G.S., 2012. In-\n\n\n\nvitro evaluation of fungicides, plant extracts and bio-control agents \n\n\n\nagainst rice blast pathogen Magnaporthe oryzae couch. Pakistan Journal \n\n\n\nof Botany, 44 (5), 1775-1778. \n\n\n\nHaq, I., Adnam, M.F., Jamil, F.F., Rehman, A., 2002. Screening of rice \n\n\n\ngermplasm against Pyricularia oryzae and evaluation of various \n\n\n\nfungitoxicants for control of disease. Pak. Journal of Pythopath, 14, 32-\n\n\n\n35. \n\n\n\nHashimoto, A., Matsumoto, K., Adachi, T., 1982. Some trials for forecasting \n\n\n\nrice leaf blast epidemics by application of the computer simulation. \n\n\n\nAnnual Report of the Society of Plant Protection of North Japan, 33, 12-\n\n\n\n14. \n\n\n\nHayashi, T., Koshimizu, Y., 1988. Computer program BLASTAM for \n\n\n\nforecasting occurrence of rice leaf blast (in Japanese). Bulletin of \n\n\n\nTohoku National Agricultural Experimental Station, 78, 123-138. \n\n\n\nHittalmani, S., Parco, A., Mew, T.V., Zeigler, R.S., Huang, N., 2000. Fine \n\n\n\nmapping and DNA marker assisted pyramiding of the three major genes \n\n\n\nfor blast resistance in rice. Theoretical and Applied Genetics, 100, 1121-\n\n\n\n1128. \n\n\n\nHorisue, N., Higashi, T., Sato, H., Koizumi, S., 1984. Breeding of isogenic \n\n\n\nlines for blast resistance in rice. Brred Sci., 34, 316. \n\n\n\nHou, K., Chen, J., Li, J., Shen, H., Chen, L., Wu, W., 2018. Effect of gibberellic \n\n\n\nacid and chlormequat chloride on growth, coumarin content and root \n\n\n\nyield of Angelica dahurica var. formosana. Journal of Agricultural \n\n\n\nScience & Technology, 15 (2), 1415-1423. \n\n\n\nHubert, J., Mabagala, R.B., Mamiro, D.P., 2015. Efficacy of selected plant \n\n\n\nextracts against Pyricularia grisea, causal agent of rice blast disease. \n\n\n\nAmerican journal of Plant Science, 6, 602-611. \n\n\n\nHyakumachi, M., Takahashi, H., Matsubara, Y., Someya, N., Shimizu, M., \n\n\n\nKobayshi, K., 2014. Recent studies on biological control of plant diseases \n\n\n\nin Japan. Journal of General Plant Pathology, 80 (4), 287-302. \n\n\n\nIRRI., 2014. Standard Evaluation system for Rice (SES). Los Banos, \n\n\n\nPhilippine: International Rice Research Institute. \n\n\n\nIRRI., Rice Knowledge Bank. Retrieved from \n\n\n\nhttp://www.knowledgebank.irri.org/training/fact-sheets/pest-\n\n\n\nmanagement/diseases/item/blast-leaf-collar \n\n\n\nIshiguro, K., 2001. Chapter 17 Review of research in Japan on the roles of \n\n\n\nsilicon in conferring resistance against rice blast. Studies in Plant \n\n\n\nscience, 8, 277-291. \n\n\n\nJamal-u-Ddin, H., Lodhi, A.M., Pathan, M.A., Khanzada, M.A., Shah, G.S., \n\n\n\n2012. In-vitro evaluation of fungicides, plant extracts and bio-control \n\n\n\nagents against rice blast pathogen Magnaporthe oryzae Couch. Pakistan \n\n\n\nJournal of Botany, 44 (5), 1775-1778. \n\n\n\nKang, W.S., Hong, S.S., Han, Y.K., Kim, K.R., Kim, S.G., Park, E.W., 2010. A \n\n\n\nWeb-based information system for plant disease Forecast based on \n\n\n\nweather data at High spatial Resolution. Plant Pathology Journal, 26, 37-\n\n\n\n48. \n\n\n\nKapoor, A.S., Singh, B.M., 1982. Evaluation of some fungicides for the \n\n\n\ncontrol of rice blast. Ind Phytopath, 53, 283. \n\n\n\nKaundal, R., Kapoor, A.S., Raghava, G., 2006. Machine learning techniques \n\n\n\nin disease forecasting: a case study on rice blast prediction. BMC \n\n\n\nBioinformatics, 7, 485. \n\n\n\nKim, C.K., Kim, C.H., 1993. The rice leaf blast simulation model EPIBLAST. \n\n\n\nThe Netherlands: Kluwer Academics Publishers. \n\n\n\nKingsolver, C.H., Barksdale, T.H., Marchetti, M.A., 1984. Rice blast \n\n\n\nepidemiiology. Agricultural Experiment Station Bull, 853. \n\n\n\nKoizumi, S., Tani, T., Fuji, S., 1996. Control of rice blast disease by \n\n\n\nmultilines. Journal of Agric. science, 51, 89-93. \n\n\n\nKoutroubas, K.S., Ntanos, D., Lupotto, E., 2009. Blast disease influence on \n\n\n\nagronomic and quality traits of rice varieties under Mediterranean \n\n\n\nconditions Turk. J. Agric., 33, 487-494. \n\n\n\nKoutroubas, S.D., Ntanos, D.A., 2008. Genotypic differences for grain yield \n\n\n\nand nitrogen utilization in Indica and Japonica rice under \n\n\n\nMediterranean conditions. Field Crop Res., 83, 251-260. \n\n\n\nKurschner, E., Bonman, J.M., Garrity, D.P., Tamisin, M.M., Pabale, D., \n\n\n\nEstrada, B.A., 1992. Effects of nitrogen timing and split application on \n\n\n\nblast disease in upland rice. Plant Disease, 76, 384-389. \n\n\n\nLanoiselet, E., Cother, E., Ash, G., 2002. CLIMEX and DYMEX simulations of \n\n\n\nthe potential occurrence of rice blast disease in south-eastern Australia. \n\n\n\nAustralasian Plant Pathology, 31, 1-7. \n\n\n\nLi, H., Guan, Y., Dong, Y., Zhao, L., Rong, S., Chen, W., 2018. Isolation and \n\n\n\nevaluation of endophytic Bacillus tequilensis GYLH001 with potential \n\n\n\napplication for biological control of Magnaporthe oryzae. PLoS ONE, 13 \n\n\n\n(10), 1-18. \n\n\n\nLiu, Y.F., Chen, Z.Y., Hu, M., Li, L., Liu, Y.Z., 2004. Distribution of \n\n\n\nMagnaporthe grisea population and virulence of predominant race in \n\n\n\nJiangsu Province, China. Rice Science, 11 (3), 324-330. \n\n\n\nLong, D.H., Lee, F.N., Tebeest, D.O., 2000. Effect of nitrogen fertilization on \n\n\n\ndisease progress of rice blast on susceptible and resistant cultivars. \n\n\n\nPlant Disease, 84, 403-409. \n\n\n\nLuong, M.C., Hoang, D., Phan, T., Jiaan, C., Heong, K.L., 2003. Impacts of \n\n\n\nnutrition management on insects pests and disease of Rice. Omon rice, \n\n\n\n11, 93-102. \n\n\n\nMagar, P.B., Acharya, B., Pandey, B., 2015. Use of chemicals for the \n\n\n\nmanagement of rice blast (Pyricularia grisea) disease at Jyotinagar, \n\n\n\nChitwan, Nepal. International Journal of Applied Science and \n\n\n\nBiotechnology, 3 (3), 474-478. \n\n\n\nManibhushanrao, K., Krishnan, P., 1991. Epidemiology of blast (EPIBLA): \n\n\n\nA simulation model and forecasting system for tropical rice in India. \n\n\n\nManila: IRRI. \n\n\n\nMarangoni, M.S., Nunes, M.P., Nelson, F., Mehta, Y.R., 2013. Pyricularia blast \n\n\n\non white oats- a new threat to wheat cultivation. Tropical Plant \n\n\n\nPathology, 38 (3), 198-202. \n\n\n\nMatsunaga, K., 1996. Breeding of a multiline rice cultivar \"Sasanishiki BL\" \n\n\n\nand its use for control of blast disease in Miyagi prefecture. J. Agric. Sci., \n\n\n\n51, 173-176. \n\n\n\nMcDougall, P., 2007. R and D expenditure in 2007 and expectations for \n\n\n\n2012 Final Report. A consultancy Study for Crop Life America and the \n\n\n\nEuropean Crop Protection Association. \n\n\n\nMiah, G., Rafii, M.Y., Ismail, M.R., Puteh, A.B., Rahim, H.A., Asfaliza, R., 2013. \n\n\n\nBlast resistance in rice: a review of conventional breeding to molecular \n\n\n\napproaches. Mol. Biol. Rep., 40, 2369-2388. \n\n\n\nMotoyama, T., Nakasako, M., Yamaguchi, M., Lyr, H., Russell, P.E., Dehne, \n\n\n\nH.W., 1999. Molecular action mechanism of a new melanin biosynthesis \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 29-33 \n\n\n\n\n\n\n\n \nCite the Article: Swodesh Rijal Yuvraj Devkota (2020). A Review On Various Management Method Of Rice Blast Disease.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 29-33. \n \n\n\n\n\n\n\n\ninhibitor. Modern Fungicides and Antifungal Compounds II (pp. 111-\n\n\n\n119). Friedrichroda, Thuringia, Germany: 12th International \n\n\n\nReihardsbrunn Symposium. \n\n\n\nNakajima, T., 1994. Mechanism of rice blast disease control by multilines. \n\n\n\nJ. Agric. Sci., 49, 390-395. \n\n\n\nNasruddin, A., Amin, N., 2013. Effects of cultivar, planting period and \n\n\n\nfungicide usage on rice blast infection levels and crop yield. Journal \n\n\n\nAgril. Science, 5 (1), 160-167. \n\n\n\nOhta, K., Chiba, S., Shimada, K., 1982. Simulation of rice leaf blast using \n\n\n\nBLASTCAST, a plant disease simulator. Annual Report of plant \n\n\n\nprotection, 33, 9-11. \n\n\n\nOnyango, A.O., 2014. Exploring options for improving rice production to \n\n\n\nreduce hunger and poverty in kenya. World Environment, 4 (4), 172-\n\n\n\n179. \n\n\n\nPapademetriou, M.K., Rice production in the Asia-pacific region: Issues and \n\n\n\nperspectives. Rome: FAO. \n\n\n\nPrabavathy, V.R., 2005. Isolation, purification and characterization of \n\n\n\nantimicrobial metabolities produced by streptomyces sp. and \n\n\n\nevaluation against blast and sheath blight diseases of rice. Chennai, \n\n\n\nIndia: Ph.D. Thesis, University of Madras. \n\n\n\nPrasanna, M.K., Siddegowda, D.K., Atheekur, H.M., Pandurange, G.K., \n\n\n\nSudarshan, G.K. 2011., New strobilurin group fungicide in rice disease \n\n\n\nmanagement. Pestology, 2 (1), 34-39. \n\n\n\nQuazzani, T.A., Mouria, A., Douira, A., Benkirane, R., Mlaiki, A., El-Yachioui, \n\n\n\nM., 1998. In vitro effect of pH and temperature on the ability of \n\n\n\nTrichoderma spp. to reduce the growth of Pyricularia oryzae. J. CAB. \n\n\n\nDirect, 96, 19-24. \n\n\n\nRaij, B.V., Camargo, O.A., 1973. Silica soluvel em solos (Soluble silicon in \n\n\n\nsoils). Bragantia, 32, 223-231. \n\n\n\nRajappar, K., Ushamalini, C., Narasimhan, N., Abdul, V., Kareem, A., 2001. \n\n\n\nManagement of grain discoloration of rice with solvent-free EC \n\n\n\nformuations of neem and pungam oils. Phytoparasitica, 29 (2), 171-174. \n\n\n\nSavary, S., Nelson, A., Willocquet, L., Pangga, I., Aunario, J., 2012. Mootential \n\n\n\nmodeling and mapping epidemics of rice diseases globally. Crop \n\n\n\nProtection, 34, 6-17. \n\n\n\nSeebold, D.J., Correa-Victoria, K., Snyder, G., 2004. Effects of Silicon and \n\n\n\nfungicides on the control of leaf and neck blast in upland rice. Plant Dis., \n\n\n\n88, 253-258. \n\n\n\nSeebold, K.W., Kucharek, T.A., Datnoff, L.E., Correa-Victoria, F.J., Marchetti, \n\n\n\nM.A., 2001. The influence of silicon on components of resistance to blast \n\n\n\nin susceptible, partially resistant cultivar of rice. Phytopathology, 91, \n\n\n\n63-69. \n\n\n\nSere, Y., Sy, A.A., Sie, M., Onasanya, A., Akator, S.K., Kabore, B., 2011. \n\n\n\nImportance of varietal improvement for blast disease control in Africa: \n\n\n\nJIRCAS (pp. 77-90). Tsukuba, Japan: International Research Center for \n\n\n\nAgriculture Sciences. \n\n\n\nSiregar, A.F., Sipahutar, I.A., Husnai, H., Wibowo, H., Sato, K., Wakatsuki, T., \n\n\n\n2016. Influence of water management and silica application on rice \n\n\n\ngrowth and productivity in central Java, Indonesia. J Agric Sci., 8, 86-96. \n\n\n\nSkamnioti, P., Gurr, S.J., 2009. Against the grain: safeguarding rice from rice \n\n\n\nblast disease. Trends in Biotechnology, 27 (3), 141-149. \n\n\n\nSlusarenko, A.J., Patel, A., Portz, D., 2008. Control of plant diseases by \n\n\n\nnatural products: Allicin from garlic as a case study. European journal \n\n\n\nof Plant pathology, 121, 313-322. \n\n\n\nSpence, C., Alff, E., Johnson, C., Ramos, C., Donofrio, N., Sundaresan, V., \n\n\n\n2014. Natural rice rhizospheric microbes suppress rice blast infections. \n\n\n\nBMC Plant Biology, 14 (130). \n\n\n\nSuh, J.P., Roh, J.H., Cho, Y.C., Han, S.S., Kim, Y.G., Jena, K.K., 2009. The Pi40 \n\n\n\ngene for durable resistance to rice blast and molecular analysis of Pi40 \n\n\n\nadvanced backcross breeding lines. Phytopathology, 99, 243-250. \n\n\n\nTastra, I.K., Irmansyah, R., Yunizar, Z.H., 1987. Epidemiology of leaf blast \n\n\n\n(Pyricularia oryzae) under various abiotic and biotic conditions. Manila, \n\n\n\nThe Philippines: International Rice Research Institute. \n\n\n\nTaylor, M., Hardwick, N., Bradshaw, N., Hall, A., 2003. Accuracy of infection \n\n\n\nwarnings and reduction of unnecessary, theoretical, fungicide \n\n\n\napplications. Crop Protection, 275-283. \n\n\n\nTempleton, G.E., Wells, B.R., Johnston, T.H., 1970. N-fertilizer application \n\n\n\nclosely related to blast at nodes and resultant lodging of rice. Rice \n\n\n\nJournal, 73 (1). \n\n\n\nTorres, C.Q., 1986. Effect of plant age on the expresion of resistance to \n\n\n\nPyricularia oryzae ca. In upland rice varieties. Los banos,Laguna, \n\n\n\nPhillippines: University of the Phillipines. \n\n\n\nTsai, W.H., Su, H.J., 1984. Prediction of rice blast meteorological variables \n\n\n\nand the development of the number of lesions. Plant protection Bulletin \n\n\n\n(Taiwan), 26, 171-180. \n\n\n\nVaiyapuri, R.P., Narayanasamy, M., Kandasamy, M., 2006. Control of blast \n\n\n\nand sheath blight diseases of rice using antifungal metabolites produced \n\n\n\nby Streptomyces sp. PM5. Biological Control, 39, 313-319. \n\n\n\nVarma, C., Santhakumari, P., 2012. Management of rice blast through new \n\n\n\nfungicidal formulations. Indian Phytopathol, 65 (1), 87-88. \n\n\n\nVenkata, R.G., Muralidharan, K., 1983. Fungicides and control of leaf blast \n\n\n\nin dry nursery. Ind Phytopath, 36, 355. \n\n\n\nVidhyasekaran, P., Rabindran, R., Muthamilan, M., Nayar, K., Rajappan, K., \n\n\n\nSubramanian, N., 1997. Development of a powdery formulation of \n\n\n\nPseudomonas fluorescens for control of rice blast. Plant Pathology, 46, \n\n\n\n291-297. \n\n\n\nWeller, D., 1988. Biological control of soilborne plant pathogens in the \n\n\n\nrhizosphere with bacteria. Annual Review of Phytopathology, 26, 379-\n\n\n\n407. \n\n\n\nworldatlas. (n.d.). Retrieved from \n\n\n\nhttps://www.worldatlas.com/articles/the-countries-producing-the-\n\n\n\nmost-rice-in-the-world.html \n\n\n\nXu, L.H., Li, Q.R., Jiang, C.L., 1996. Diversity of soil actinomycetes in Yunnan, \n\n\n\nChina. Application Environ. Microbiol, 62, 244-248. \n\n\n\nYang, J.H., Liu, H.X., Zhu, G.M., Pan, Y.L., Xu, L.P., Guo, J.H., 2008. Diversity \n\n\n\nanalysis of antagonists from rice associated bacteria and their \n\n\n\napplication in biocontrol of rice diseases. Journal of Application \n\n\n\nMicrobes, 104 (1), 91-104. \n\n\n\nZeigler, R.S., Leong, S.A., Teng, P.S., 1994. Rice Blast disease. CAB \n\n\n\nInternational, 626. \n\n\n\nZhang, H.J., Li, G. J., Li, W., Song, F.M., 2009. Transgenic strategies for \n\n\n\nimproving rice disease resistance. African Journal of Biotechnology, 8 \n\n\n\n(9), 1750-1757. \n\n\n\nZhang, Q., 2007. Strategies for developing Green Super Rice. Proceedings \n\n\n\nof the National Academy of Science, 16402-16409.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 04 January 2019 \nAccepted 07 February 2019 \nAvailable Online 12 February 2019 \n\n\n\nABSTRACT\n\n\n\nDurio zibethinus, more commonly known as Durian or the \u2018king of fruits\u2019 by locals is a Southeast Asian tropical fruit. \nSmell is a crucial factor in durian acceptance amongst consumers as many are unable to accept the pungent onion-\nlike odour liberated by durians. Due to the controversial aroma of durians, chemical compounds reportedly \ncontributing to the durian smell- volatile esters and sulfur compounds have been widely discussed in the literature. \nThis review article seeks to consolidate the literature which have identified volatile esters and sulfur-containing \ncompounds in durians from Malaysia, Indonesia and Thailand, and studies shedding light on how the economic \nvalue of durians can be enhanced. Literature review was conducted using databases Scopus and ScienceDirect and \na total of 18 articles were reviewed. In light of the rising demand for durians, factors, namely aroma, flavour and \ncolour, in which consumers consider in the purchase of durians are further looked at, to explore the potential of \nenhancing favourable traits of durians and increasing its economic value and sales in the global market. By knowing \nthe chemical compounds involved or influencing each factor, further studies can be conducted to explore methods \nsuch as breeding of new durian cultivars and metabolic or gene modification for phenotypic manifestation of \nfavourable traits attractive to consumers. \n\n\n\nKEYWORDS \n\n\n\nBreeding, Flavour, Odour, Aroma, Fragrance\n\n\n\n1. INTRODUCTION \n\n\n\nA flight was delayed as a heated argument ensued where passengers \ncomplained about an odour likened to rotten onions, which would make \nflying unbearable. Later a batch of fruits, arguably the world\u2019s smelliest \nfruit- Durian, was removed from the flight [1]. There are an estimated 27 \nspecies of Durio worldwide but Durio zibethinus remains the most popular. \nOriginating from the Malay Peninsula, Durian or Durio zibethinus belongs \nto the family of Bombacaceae, is well known by locals as the \u2018king of fruits\u2019. \nRecently, it was reported that chinese consumers bought up 80,000 \ndurians within 60 seconds upon its commencement of sales on Alibaba\u2019s \nTmall platform. It was also announced that a 3 billion yuan deal was closed \nbetween Alibaba and the Thai government [2]. Growth in durian sales in \nthe global market is remarkable seeing exports from major producers such \nas Indonesia, Malaysia and Thailand. Despite its controversial aroma, \ndurians have been proven a valuable commodity of trade and \ngovernments in South East Asia have been looking to expand their durian \nindustry.  \n\n\n\nThe smell of durians has two distinct tones: a fruity and onion-like odour, \ndue to the presence of volatile esters and sulphur-containing compounds \n[3]. Smell is a crucial factor in durian acceptance amongst consumers as \nmany are unable to accept the pungent onion-like odour liberated by \ndurians. Studies have been done to investigate characterisation of volatiles \nin durians, storage and retention of volatiles in durians, opportunities in \nthe durian biomass industry, and sensory properties and biochemical \nmetabolites in durians [3-17].  \n\n\n\nTo date, there are many publications discussing the identities and \ncontributory role of volatile esters and sulfur-containing compounds in \nthe aroma, liberated by the durian fruit. Studies have been conducted with \ndurian cultivars from various South-East Asian countries such as Malaysia,  \n\n\n\nIndonesia and Thailand. In order to gather and consolidate data from all \navailable studies that have discussed the chemical names of these esters \nand sulfur-containing compounds, their respective aromatic \ncontributions, the specific aroma of different durian cultivars, a literature \nreview was conducted. To consider how the economic value of durians can \nbe enhanced, studies discussing aromatic contributions of esters and \nsulfur-containing compounds in the lai cultivars (used in durian \nhybridisation), the metabolomics of compounds contributing to aroma, \ncolour and flavour, are also reviewed. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nThe literature review was conducted using two databases: Scopus and \nScienceDirect. This review focuses on the volatile esters and sulfur \ncompounds. Key terms \u201cDurian\u201d, \u201cDurio\u201d, \u201cEsters\u201d, \u201cSulfurs\u201d, \u201cVolatility\u201d, \n\u201cVolatile\u201d, \u201cFlavours\u201d, \u201cFragrance\u201d, \u201cAroma\u201d and \u201cOdour\u201d were included in \nthe search strategy. A filter was applied to search for articles with these \nkey terms appearing in the title, abstract or keywords. A total of 30 and 10 \narticles were found on Scopus and ScienceDirect respectively. Only \nEnglish articles (including both abstract and the full text) indexed in the \ntwo databases were utilised. There were no changes in the number of \narticles with the language filter applied. \n\n\n\n2.1 Inclusion Criteria \n\n\n\nArticles discussing esters and sulfur compounds in durians were \n\n\n\nshortlisted for review. Relevant articles were also selected from the \n\n\n\nbibliography of the articles for discussion. \n\n\n\n2.2 Exclusion Criteria \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.02.2019.05.15 \n\n\n\n  RESEARCH ARTICLE \n\n\n\nVOLATILE ESTERS AND SULFUR COMPOUNDS IN DURIANS & A SUGGESTED \nAPPROACH TO ENHANCING ECONOMIC VALUE OF DURIANS \n\n\n\nJoycelyn Soo Mun Peng \n\n\n\nOei Family Clinic, Elias Mall #02-316 Singapore 510625 \n*Corresponding Author Email: joycelyn.soomunpeng@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:joycelyn.soomunpeng@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\nArticles discussing fermentation, wine or alcoholic fermentation, volatile \ncompounds in other fruits, storage of durians and retention of volatiles, \ndevelopment of tools for quantitation of chemical compounds in durians, \nwere not considered for this literature review. \n\n\n\nA total of 18 articles were considered for final review [3-11, 18-26]. A \nflowchart reporting the steps taken to select articles for review, is shown \nbelow in Figure 1. \n\n\n\nFigure 1: Flowchart of included literature for review \n\n\n\n3. RESULTS \n\n\n\n3.1 Esters and sulfur compounds found in durians \n\n\n\nA total of 9 papers have been consolidated in Table. 1 summarising the \nesters and sulfur compounds identified in each study.  \n\n\n\nTable 1: Esters and Sulfur Compounds Identified in Studies \n\n\n\nAuthors, Year Volatile Compounds Found in Durians Country of Origin \n\n\n\n [3] Sulfur compounds: \n\u25cf Hydrogen sulphide\n\n\n\n\u25cf Methanethiol \n\n\n\n\u25cf Ethanethiol \n\n\n\n\u25cf Propanethiol \n\n\n\n\u25cf Dimethylthioether\n\n\n\n\u25cf Diethylthioether\n\n\n\n\u25cf Diethyldisulphide\n\n\n\nEster compounds: \n\u25cf Methyl acetate \n\n\n\n\u25cf Ethyl acetate \n\n\n\n\u25cf Methyl propionate \n\n\n\n\u25cf Ethyl propionate \n\n\n\n\u25cf n-Propyl propionate \n\n\n\n\u25cf Ethyl iso-butyrate \n\n\n\n\u25cf Ethyl butyrate \n\n\n\n\u25cf Methyl \ud835\udefc-methylbutyrate \n\n\n\n\u25cf Ethyl \ud835\udefc-methylbutyrate \n\n\n\n\u25cf n-Propyl \ud835\udefc-methylbutyrate \n\n\n\n\u25cf Ethyl iso-valerate \n\n\n\n\u25cf Ethyl methacrylate \n\n\n\n\u25cf Ethyl benzene \n\n\n\nUnknown durian cultivar from \nSingapore and Malaysia \n\n\n\n [4] Sulfur compounds: \n\u25cf Hydrogen sulfide \n\n\n\n\u25cf Diethyl disulfide \n\n\n\nUnknown \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\n\u25cf Ethyl propyl disulfide \n\n\n\n\u25cf Ethyl methyl trisulfide \n\n\n\n\u25cf Diethyl trisulfide \n\n\n\n\u25cf Ethyl propyl trisulfide \n\n\n\n\u25cf Diethyl tetrasulfide \n\n\n\nEster compounds: \n\u25cf Ethyl-2-methylbutanoate \n\n\n\n\u25cf Ethyl acetate \n\n\n\n [5] Sulfur compounds: \n\u25cf S-ethyl thioacetate \n\n\n\n\u25cf Methyl ethyl disulfide \n\n\n\n\u25cf 1-hydroxy-2-methylthioethane \n\n\n\n\u25cf Methyl 2-methylthioacetate \n\n\n\n\u25cf Dimethyl sulfone \n\n\n\n\u25cf Diethyl disulfide \n\n\n\n\u25cf S-ethyl thiobutyrate \n\n\n\n\u25cf Ethyl 2-(methylthio)acetate \n\n\n\n\u25cf 2-isopropyl-4-methylthiazole \n\n\n\n\u25cf S-isopropyl 3-(methylthio)- 2-butenoate \n\n\n\n\u25cf 3,5-dimethyl-1,2,4-trithiolane (I)\n\n\n\n\u25cf 3,5-dimethyl-1,2,4-trithiolane (II)\n\n\n\n\u25cf S-methyl thiohexanoate \n\n\n\n\u25cf 5-methyl-4-mercapto-2-hexanone \n\n\n\n\u25cf Benzothiazole \n\n\n\n\u25cf 3,4-dithia-2-ethylthiohexane \n\n\n\n\u25cf S-methyl thiooctanoate \n\n\n\n\u25cf 3,5-dimethyltetrathiane\n\n\n\nEster compounds: \n\u25cf Ethyl 2-methylbutanoate \n\n\n\n\u25cf Ethyl acetate \n\n\n\n\u25cf Ethyl hexanoate \n\n\n\n\u25cf Propyl 2-methylbutanoate \n\n\n\n\u25cf Ethyl caprylate \n\n\n\n\u25cf Ethyl hexadecanoate \n\n\n\n\u25cf Methyl propanoate \n\n\n\n\u25cf Methyl 2-methylbutanoate \n\n\n\n\u25cf Hexadecanyl propanoate \n\n\n\nUnknown durian cultivar from \nIndonesia \n\n\n\n [6] Sulfur compounds: \n\u25cf S-Ethyl thioacetate \n\n\n\n\u25cf Methyl ethyl disulphide \n\n\n\n\u25cf Ethyl vinyl disulphide \n\n\n\n\u25cf Diethyl disulphide \n\n\n\n\u25cf Methyl propyl disulphide \n\n\n\n\u25cf 1,1-Bis(methylthio)ethane \n\n\n\n\u25cf Dimethyl trisulphide \n\n\n\n\u25cf 3-(Ethylthio)butan-l-ol \n\n\n\n\u25cf Ethylpropyl disulphide \n\n\n\n\u25cf 1-(Ethylthio)-1-(methylthio)ethane \n\n\n\n\u25cf Ethyl methyl trisulphide \n\n\n\n\u25cf S-Ethyl 2-methylbutanethioate \n\n\n\n\u25cf 3-(Ethylthio)-2-methylbutan-l-ol (2 isomers) \n\n\n\n\u25cf 3-(Propylthio)butan-1-ol \n\n\n\n\u25cf Di-isopropyl disulphide \n\n\n\n\u25cf Dipropyl disulphide\n\n\n\n\u25cf Butyl ethyl disulphide \n\n\n\n\u25cf 1-(Methylthio)-1-(propylthio)ethane\n\n\n\n\u25cf 3,5-Dimethyl-1,2,4-trithiolane (2 isomers)\n\n\n\n\u25cf 3-Methyl-1,2,4-trithiane \n\n\n\n\u25cf 3-(Methyldithio)butan-1-ol \n\n\n\n\u25cf Diethyl trisulphide \n\n\n\n\u25cf 1-(Ethyldithio)-1-(methylthio)methane \n\n\n\n\u25cf 2-Ethyl-3-(ethylthio)butan-1-ol (2 isomers)\n\n\n\n\u25cf 3-Ethyl-5-methyl-l,2,4-trithiolane (2 isomers) \n\n\n\n\u25cf 3-(Ethyldithio)butan-1-ol \n\n\n\nUnknown durian cultivar from \nIndonesia \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\n\u25cf Ethyl isopropyl trisulphide\n\n\n\n\u25cf Ethyl propyl trisulphide \n\n\n\n\u25cf 1-(Ethylthio)-l-(methyldithio)ethane\n\n\n\n\u25cf 1-(Ethyldithio)-1-(methylthio)ethane\n\n\n\n\u25cf 1-(Ethyldithio)-1-(ethylthio)methane\n\n\n\n\u25cf 3-(Ethyldithio)-2-methylbutan-l-ol \n\n\n\n\u25cf 3-(Propyldithio)-butan-l-ol \n\n\n\n\u25cf Di-isopropyl trisulphide \n\n\n\n\u25cf lsopropyl propyl trisulphide\n\n\n\n\u25cf Dipropyl trisulphide \n\n\n\n\u25cf 1-(Ethyldithio)-1-(ethylthio)ethane\n\n\n\n\u25cf 1-(Methylthio)-1-(propyldithio)-ethane \n\n\n\n\u25cf 3,6-Dimethyl-1,2,4,5-tetrathiane \n\n\n\n\u25cf 1-(Ethyldithio)-1-(ethylthio)propane\n\n\n\n\u25cf 1-(Ethylthio)-1-(propyldithio)ethane\n\n\n\n\u25cf 1-(Ethyldithio)-1-(propylthio)ethane\n\n\n\n\u25cf 1-(Ethylthio)-1-(propyldithio)ethane\n\n\n\nEster compounds: \n\u25cf Ethyl (E,Z,Z)-deca-2,4,7-trienoate \n\n\n\n\u25cf Ethyl (E,E,Z)-deca-2,4,7-trienoate \n\n\n\n\u25cf Ethyl (Z,Z)-deca-3,6-dienoate \n\n\n\n\u25cf Ethyl (Z,Z)-deca-2,4-dienoate \n\n\n\n\u25cf Ethyl (E,Z)-deca-2,4-dienoate \n\n\n\n\u25cf Ethyl (E,E)-deca-2,4-dienoate \n\n\n\n [7] Sulfur compounds:  \n\u25cf Ethanethiol \n\n\n\n\u25cf Propanethiol\n\n\n\n\u25cf S-Ethyl thioacetate \n\n\n\n\u25cf Ethyl methyl disulphide \n\n\n\n\u25cf S-Propyl thioacetate \n\n\n\n\u25cf Methyl hexanoate \n\n\n\n\u25cf Diethyl disulphide \n\n\n\n\u25cf Methyl propyl disulphide \n\n\n\n\u25cf S-Propyl thiopropionoate\n\n\n\n\u25cf Ethyl propyl disulphide \n\n\n\n\u25cf 1-(Ethylthio)ethanethiol \n\n\n\n\u25cf Diethyl trisulphide \n\n\n\n\u25cf Ethyl propyl trisulphide \n\n\n\n\u25cf trans-3,5-Dimethyl-1,2,4-trithiolane\n\n\n\n\u25cf cis-3,5-Dimethyl-l,2,4-trithiolane \n\n\n\nEster compounds: \n\u25cf Ethyl acetate \n\n\n\n\u25cf Methyl propanoate \n\n\n\n\u25cf Ethyl propanoate \n\n\n\n\u25cf Ethyl 2-methylpropanoate \n\n\n\n\u25cf Propyl acetate \n\n\n\n\u25cf Methyl butanoate \n\n\n\n\u25cf Methyl 2-methylbutanoate \n\n\n\n\u25cf Propyl propanoate \n\n\n\n\u25cf Ethyl butanoate \n\n\n\n\u25cf Propyl 2-methylpropanoate \n\n\n\n\u25cf Ethyl 2-methylbutanoate \n\n\n\n\u25cf Ethyl 3-methylbutanoate \n\n\n\n\u25cf Butyl acetate \n\n\n\n\u25cf Diethyl carbonate \n\n\n\n\u25cf Propyl butanoate \n\n\n\n\u25cf Propyl 2-methylbutanoate \n\n\n\n\u25cf Ethyl pentanoate \n\n\n\n\u25cf Butyl propanoate \n\n\n\n\u25cf Ethyl (E)-but-2-enoate \n\n\n\n\u25cf Ethyl hexanoate \n\n\n\n\u25cf Ethyl(E)-2-methylbut-2-enoate \n\n\n\n\u25cf Ethyl heptanoate \n\n\n\n\u25cf Ethyl 2-hydroxypropanoate \n\n\n\n\u25cf Methyl Octanoate \n\n\n\n\u25cf Ethyl octanoate \n\n\n\nClones no. 15, 28, 74 durian cultivars \nfrom Malaysia \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\n\u25cf Ethyl (methylthio)acetate\n\n\n\n\u25cf Methyl 3-hydroxybutanoate \n\n\n\n\u25cf Ethyl 3-hydroxybutanoate \n\n\n\n\u25cf Ethyl decanoate \n\n\n\n\u25cf y-Butyrolactone \n\n\n\n\u25cf Ethyl dodecanoate \n\n\n\n [8] Sulfur compounds: \n\u25cf Hydrogen sulfide \n\n\n\n\u25cf Ethylene sulfide \n\n\n\n\u25cf Sulfur dioxide \n\n\n\n\u25cf Methanethiol \n\n\n\n\u25cf Ethanethiol \n\n\n\n\u25cf Propanethiol\n\n\n\n\u25cf Butanethiol \n\n\n\n\u25cf Ethanethioate, S-ethyl \n\n\n\n\u25cf Ethanethioate, S-(2-methylbutyl) \n\n\n\n\u25cf Carbon disulfide \n\n\n\n\u25cf Diethyl disulfide \n\n\n\n\u25cf Dipropyl disulfide \n\n\n\n\u25cf Methyl ethyl disulfide \n\n\n\n\u25cf Ethyl propyl disulfide \n\n\n\n\u25cf Butyl ethyl disulfide \n\n\n\n\u25cf Dipropyl trisulfide \n\n\n\n\u25cf Trans-3,5-dimethyl-1,2,4-trithiolane\n\n\n\n\u25cf Cis-3,5-dimethyl-1,2,4-trithiolane \n\n\n\nEster compounds: \n\u25cf Methyl acetate \n\n\n\n\u25cf Ethyl acetate \n\n\n\n\u25cf Butyl acetate \n\n\n\n\u25cf Methyl propanoate \n\n\n\n\u25cf Ethyl propanoate \n\n\n\n\u25cf Propyl propanoate \n\n\n\n\u25cf Butyl propanoate \n\n\n\n\u25cf Pentyl propanoate \n\n\n\n\u25cf Methyl-2-methylpropanoate \n\n\n\n\u25cf Ethyl-2-methylpropanoate \n\n\n\n\u25cf Propyl-2-methylpropanoate \n\n\n\n\u25cf Ethyl-(S)-2-hydroxypropanoate\n\n\n\n\u25cf Ethyl-2-methyl-2-propenoate \n\n\n\n\u25cf Methyl butanoate \n\n\n\n\u25cf Ethyl butanoate \n\n\n\n\u25cf Propyl butanoate \n\n\n\n\u25cf Ethyl-2-butanoate \n\n\n\n\u25cf Ethyl DL-3-hydroxybutanoate \n\n\n\n\u25cf Methyl-2-methylbutanoate \n\n\n\n\u25cf Ethyl-2-methylbutanoate \n\n\n\n\u25cf Ethyl-3-methylbutanoate \n\n\n\n\u25cf Ethyl (E)-2-methyl-2-butenoate \n\n\n\n\u25cf Propyl-2-methylbutanoate \n\n\n\n\u25cf Butyl-2-methylbutanoate \n\n\n\n\u25cf 2-methylpropyl-2-methylbutanoate \n\n\n\n\u25cf Methyl-2-ethyl acrylate \n\n\n\n\u25cf Propyl (E)-2-methyl-2-butenoate \n\n\n\n\u25cf Diethyl butanediaote \n\n\n\n\u25cf Ethyl pentanoate \n\n\n\n\u25cf Ethyl-2-methylpentanoate \n\n\n\n\u25cf Methyl-3-methyl-2-oxo-pentanoate \n\n\n\n\u25cf Methyl-4-2-oxo-pentanoate \n\n\n\n\u25cf Methyl hexanoate \n\n\n\n\u25cf Ethyl hexanoate \n\n\n\n\u25cf Propyl hexanoate \n\n\n\n\u25cf Ethyl-3-hydroxyhexanoate \n\n\n\n\u25cf Ethyl heptanoate \n\n\n\n\u25cf Propyl heptanoate \n\n\n\n\u25cf Methyl octanoate \n\n\n\n\u25cf Ethyl octanoate \n\n\n\nUnknown durian cultivar from \nMalaysia \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\n\u25cf Propyl octanoate \n\n\n\n\u25cf Ethyl (Z)-4-octenoate \n\n\n\n\u25cf Ethyl nonanoate \n\n\n\n\u25cf Ethyl decanoate \n\n\n\n\u25cf Ethyl dodecanoate \n\n\n\n\u25cf Methyl hexadecanoate \n\n\n\n\u25cf Ethyl hexadecanoate \n\n\n\n\u25cf Dimethyl carbonate \n\n\n\n\u25cf Diethyl carbonate \n\n\n\n [9] Sulfur compounds: \n\u25cf Ethanethiol \n\n\n\n\u25cf 1-Propanethiol\n\n\n\n\u25cf Methyl propyl sulphide \n\n\n\n\u25cf Methyl ethyl disulphide \n\n\n\n\u25cf Diethyl disulphide \n\n\n\n\u25cf Methyl propyl disulphide \n\n\n\n\u25cf Ethyl propyl disulphide \n\n\n\n\u25cf Dipropyl disulphide \n\n\n\n\u25cf 1-Methylethyl propyl disulphide\n\n\n\n\u25cf Diethyl trisulphide \n\n\n\n\u25cf 3,5-Dimethyl-1,2,4-Trithiolane (isomer 1) \n\n\n\n\u25cf 3,5-Dimethyl-1,2,4-Trithiolane (isomer 2) \n\n\n\n\u25cf Dipropyl trisulphide \n\n\n\n\u25cf 1,1-Bis(ethylthio)-ethane \n\n\n\nEster compounds: \n\u25cf Ethyl acetate \n\n\n\n\u25cf Methyl propionate \n\n\n\n\u25cf Ethyl propanoate \n\n\n\n\u25cf Ethyl 2-methylpropanoate\n\n\n\n\u25cf Methyl butanoate \n\n\n\n\u25cf Methyl 2-methylbutanoate\n\n\n\n\u25cf Ethyl butanoate \n\n\n\n\u25cf Propyl propanoate \n\n\n\n\u25cf Propyl 2-methylpropanoate \n\n\n\n\u25cf Ethyl 2-methyl butanoate\n\n\n\n\u25cf Ethyl 3-methylbutanoate\n\n\n\n\u25cf Propyl butanoate \n\n\n\n\u25cf Propyl 2-methylbutanoate\n\n\n\n\u25cf Ethyl but-2-enoate \n\n\n\n\u25cf Methyl hexanoate \n\n\n\n\u25cf Ethyl hexanoate \n\n\n\n\u25cf Propyl hexanoate \n\n\n\n\u25cf Ethyl heptanoate \n\n\n\n\u25cf Methyl octanoate \n\n\n\n\u25cf Ethyl octanoate \n\n\n\n\u25cf Ethyl 3-hydroxybutanoate\n\n\n\n\u25cf Ethyl decanoate \n\n\n\nD2, D24, D101, MDUR78 and Chuk \ndurian cultivars from Malaysia \n\n\n\n [10] Sulfur compounds: \n\u25cf Ethanethiol \n\n\n\n\u25cf Propanethiol\n\n\n\n\u25cf 1-(methylthio)-propane \n\n\n\n\u25cf S-ethyl ethanethioate \n\n\n\n\u25cf S-propyl ethanethioate \n\n\n\n\u25cf Diethyl disulfide \n\n\n\n\u25cf Methyl propyl disulfide \n\n\n\n\u25cf Ethyl propyl disulfide \n\n\n\n\u25cf 1-(ethylthio)-1-(methylthio)-ethane\n\n\n\n\u25cf Dipropyl disulfide \n\n\n\n\u25cf Diethyl trisulfide \n\n\n\n\u25cf Ethyl propyl trisulfide \n\n\n\n\u25cf 3,5-dimethyl-1,2,4-trithiolane \n\n\n\n\u25cf 3,5-dimethyl-1,2,4-trithiolane \n\n\n\n\u25cf Dipropyl trisulfide \n\n\n\n\u25cf 1,1-bis(ethylthio)-ethane \n\n\n\nEster compounds: \n\n\n\nD2, D24, D101 durian cultivars from \nMalaysia \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\n\u25cf Ethyl acetate \n\n\n\n\u25cf Methyl propanoate \n\n\n\n\u25cf Ethyl propanoate \n\n\n\n\u25cf Ethyl-2-methylpropanoate \n\n\n\n\u25cf Propyl acetate \n\n\n\n\u25cf Methyl butanoate \n\n\n\n\u25cf Methyl-2-methylbutanoate \n\n\n\n\u25cf Ethyl butanoate \n\n\n\n\u25cf Propyl propanoate \n\n\n\n\u25cf Ethyl-2-methylbutanoate \n\n\n\n\u25cf Ethyl-3-methylbutanoate \n\n\n\n\u25cf Propyl butanoate \n\n\n\n\u25cf Propyl-2-methylbutanoate\n\n\n\n\u25cf Propyl-3-methylbutanoate \n\n\n\n\u25cf Ethyl-2-butenoate \n\n\n\n\u25cf Methyl hexanoate \n\n\n\n\u25cf 3-methylbutyl propanoate \n\n\n\n\u25cf Ethyl hexanoate \n\n\n\n\u25cf Propyl hexanoate \n\n\n\n\u25cf Ethyl heptanoate \n\n\n\n\u25cf Methyl octanoate \n\n\n\n\u25cf Ethyl octanoate \n\n\n\n [11] Sulfur compounds: \n\u25cf Ethanethiol \n\n\n\n\u25cf Methanethiol \n\n\n\n\u25cf Ethyl (2S)-2-methylbutanoate \n\n\n\n\u25cf Hydrogen sulfide \n\n\n\n\u25cf Propane-1-thiol \n\n\n\n\u25cf Ethane-1,1-dithiol \n\n\n\n\u25cf Methyl (2S)-2-methylbutanoate \n\n\n\n\u25cf 1-(ethylsulfanyl)ethane-1-thiol \n\n\n\n\u25cf 2(5)-ethyl-4-hydroxy-5(2)-methylfuran-3(2H)-one \n\n\n\n\u25cf Diethyl trisulfide \n\n\n\n\u25cf 1-(methylsulfanyl)ethane-1-thiol \n\n\n\n\u25cf 1-(ethyldisulfanyl)-1-(ethylsulfanyl)ethane\n\n\n\n\u25cf 1-(ethylsulfanyl)propane-1-thiol \n\n\n\n\u25cf 3-methylbut-2-ene-1-thiol \n\n\n\nEster compounds: \n\u25cf Ethyl butanoate \n\n\n\n\u25cf Ethyl-2-methylpropanoate \n\n\n\n\u25cf Ethyl cinnamate \n\n\n\n\u2018Monthong\u2019 durian cultivar from \nThailand \n\n\n\n3.2 Chemical contribution of esters and sulfur compounds to durian \n\n\n\naroma \n\n\n\nThe strong smell of durians is favoured only by a limited group of \nconsumers and it has presented as an issue in the transportation of \ndurians when it is marketed in other parts of the world. Many are turned \naway by the onion-like odour of durians, which have been reported to be \ndue to sulfur-containing compounds present in durians [5]. Hence to \nenhance the favourability of durians, plant breeders have started to look \nat the cultivation of new breeds of the fruit. To breed new cultivars \nhowever, selection has to be based on the characteristics of fruits that \nbreeders would like to see manifesting in the new durian cultivar. One \npaper, namely the Belgis et al study sheds lights on how characterisation \nof cultivars can aid selection of candidates for future breeding of new \ndurian cultivars [18]. This will be elaborated in the subsequent \nsubsections. \n\n\n\n3.2.1 Sulfur compounds  \n\n\n\nBelgis et al has looked at the characterisation of volatiles and aroma of \n\n\n\nseveral lai (Durio kutejensis) and durian (Durio zibethinus) in Indonesia \n[18]. Both being of the Durio species, lai has a milder aroma than durians. \nA total of six lai cultivars (\u201cBatuah\u201d, \u201cMerah\u201d, \u201cMahakam\u201d, \u201cKutai\u201d, \u201cGincu\u201d \nand \u201cMas\u201d) and four durian cultivars (\u201cMahatari\u201d, \u201cSukarno\u201d, \u201cAjimah\u201d and \n\u201cHejo\u201d) were harvested and analysed. By Solid Phase Micro Extraction \n(SPME)/Gas Chromatography-Mass Spectrometry (GC-MS) analysis, a \ntotal of 8 sulfurs were identified in lai while 12 sulfurs were identified in \ndurians. Belgis et al reported that sulfur compounds causing the pungent \ndurian smell such as propanethiol, bis(ethylthio)methane, 3-mercapto-2-\nmethyl propanol, 1,1-bis(methylthio)-ethane, and 1,1-bis(ethylthio)-\nethane were not found in the lai cultivars sampled. It is reported that this \nmight be a reason why lai has a milder smell than durians. 3,5-dimethyl-\n1,2,4-trithiolane, reported to contribute to the durian stink, was found in \n\u201cMas\u201d lai, but not other lai cultivars. Belgis et al cited Burdock that some \nsulfur compounds are reported to be responsible for the sulfur stink in \ndurian aroma [19].  \n\n\n\n3 papers are consolidated in Table. 2 summarising the sulfur compounds \nfound and their contribution to aromatic nuances in durian cultivars [10, \n11, 18]. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\nTable 2: Aromatic Contribution of Sulfur Compounds and Cultivars They Are Found in [10, 11, 18] \n\n\n\nSulfur compounds Contribution to aromatic nuances  \n\n\n\n3-mercapto-2-methyl propanol Stinky, sulfury, durian-like aroma  \n\n\n\nBis(ethylthio)methane Stinky, sulfury, durian-like aroma  \n\n\n\n1,1-bis(methylthio)-ethane Stinky, sulfury, durian-like aroma  \n\n\n\nEthanethiol Rotten onion, rubber odour \n\n\n\nPropanethiol Rotten, durian/ Cabbage, sweet onion-like odour \n\n\n\nS-ethyl ethanethioate Alliaceous coffee odour \n\n\n\nDiethyl disulfide Sulfury, roasty, cabbage-like odour \n\n\n\nMethyl propyl disulfide Powerful penetrating sulfuraceous onion-like odour \n\n\n\nDipropyl disulfide Pungent sulfur-like onion and garlic odour \n\n\n\nDiethyl trisulfide Sweet and alliaceous odour/ fried shallot \n\n\n\nEthyl propyl trisulfide Alliaceous, roasted, rubbery odour \n\n\n\n3,5-dimethyl-1,2,4-trithiolane Sulfury, heavy, cocoa odour \n\n\n\n3,5-dimethyl-1,2,4-trithiolane Sulfury, onion odour \n\n\n\nDipropyl trisulfide Powerful diffusive garlic odour \n\n\n\n1,1-bis(ethylthio)-ethane Burnt, rubbery, alliaceous odour \n\n\n\n1-(ethylsulfanyl)ethane-1-thiol Roasted onion \n\n\n\nMethanethiol  Rotten cabbage  \n\n\n\nEthane-1,1-dithiol  sulfury , durian \n\n\n\nMethyl (2S)-2-methylbutanoate Fruity  \n\n\n\n1-(methylsulfanyl)ethane-1-thiol Roasted onion \n\n\n\n1-(ethylsulfanyl)propane-1-thiol Roasted onion \n\n\n\n3-methylbut-2-ene-1-thiol Skunky  \n\n\n\n1-(ethyldisulfanyl)-1-(ethylsulfanyl)ethane Sulfury, onion \n\n\n\nHydrogen sulfide Rotten egg \n\n\n\n3.2.2 Esters \n\n\n\nEsters are reported to be responsible for the fruity smell of durians [3]. \nStudies report that different esters contribute different aroma nuances. \n\n\n\nEsters and their aromatic nuances from 6 studies done are consolidated in \nTable. 3 and 4 for lai and durian cultivars respectively.  \n\n\n\nTable 3: Esters and their Aromatic Nuances in Lai Cultivars [5, 18, 19] \n\n\n\nEsters Contribution to aroma nuances \n\n\n\nPropyl-2-methyl butanoate Fruity, sweet and pineapple aroma \n\n\n\nPropyl propanoate Fruity, apple and banana-like aroma \n\n\n\nEthyl-2-butenoate Fruity, rum with caramel aroma \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\nEthyl octanoate Fruity and floral aroma \n\n\n\nEthyl-2-methyl butanoate Stinky aroma \n\n\n\nTable 4: Esters and their Aromatic Nuances in Durian Cultivars [5, 6, 10, 11, 19] \n\n\n\nEsters Contribution to aroma nuances \n\n\n\nEthyl (2S)-2-methylbutanoate Fruity \n\n\n\nEthyl-2-methylpropanoate Fruity \n\n\n\nEthyl cinnamate Honey \n\n\n\nEthyl acetate Pleasant, ethereal, fruity, brandy-like odour \n\n\n\nMethyl propanoate Fruity odour reminiscent of rum \n\n\n\nEthyl-2-methylpropanoate Fruity aromatic odour \n\n\n\nPropyl acetate Fruity, pear and raspberry-like odour \n\n\n\nMethyl butanoate Apple-like odour \n\n\n\nMethyl-2-methylbutanoate Sweet fruity, apple-like odour \n\n\n\nEthyl butanoate Fruity odour with pineapple undertone \n\n\n\nPropyl propanoate Complex fruity odour reminiscent of apple banana \n\n\n\nEthyl-2-methylbutanoate Powerful, green, fruity, apple-like odour \n\n\n\nEthyl-3-methylbutanoate Fruity odour reminiscent of apple \n\n\n\nPropyl butanoate Pineapple and apricot-like odour \n\n\n\nPropyl-3-methylbutanoate Fruity odour \n\n\n\nMethyl hexanoate Ether-like odour reminiscent of pineapple \n\n\n\n3-methylbutyl propanoate Pineapple-apricot like odour \n\n\n\nEthyl hexanoate Powerful fruity odour with pineapple-banana note \n\n\n\nPropyl hexanoate Ether-like odour reminiscent of pineapple \n\n\n\nEthyl heptanoate Fruity odour reminiscent of cognac, wine-like brandy odour \n\n\n\nMethyl octanoate Powerful winey, fruity, orange-like odour \n\n\n\nEthyl octanoate Pleasant, fruity, floral odour with wine apricot note \n\n\n\n3.2.3 Characteristic Aroma of Durian and Lai Cultivars \n\n\n\nBelgis et al reported on the characterisation of lai and durian cultivars in \nIndonesia by Quantitative Descriptive Analysis (QDA), a method used to \ncharacterise aroma through evaluation of sensory properties and \nsubsequent description of these sensory attributes by categories, \nhighlights the type of aroma of each cultivar and identifies chemical \n\n\n\ncompounds possibly influencing these aroma nuances in durian and lai \ncultivars (as summarised in Table 6 and Table 7) [18]. By identifying the \ncharacteristic aroma of the different durian and lai cultivar, it will better \ninform decisions on breeding and the potential traits that might arise in \nthe new breeds of durian fruits. \n\n\n\nTable 5: Characteristic Aroma in Durian Cultivars \n\n\n\nDurians Characteristic aroma Chemical contribution \n\n\n\nMatahari Strongest sulfury and fruity aroma amongst durians used in \nstudy \n\n\n\nContains highest amount of sulfurs including  \n\u25cf diethyl disulfide, \n\n\n\n\u25cf ethyl propyl disulfide,\n\n\n\n\u25cf 1,1-bis(methylthio)-ethane, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\n\u25cf 1,1-bis(ethylthio)-ethane, \n\n\n\n\u25cf 3-mercapto-2-methyl propanol \n\n\n\nAjimah Strongest alcohol and sweet aroma - \n\n\n\nSukarno Strongest green and sweet aroma - \n\n\n\nHejo Mildest alcohol and sulfury aroma among studied durian - \n\n\n\nTable 6: Characteristic Aroma in Lai Cultivars \n\n\n\nLai Characteristic aroma Chemical contribution \n\n\n\nMerah Strongest fruity aroma amongst lai cultivars used in study - \n\n\n\nGincu & Kutai Medium sweet and fruity aroma - \n\n\n\nMahakam Mildest fruity aroma - \n\n\n\nMas Strong beany, green, floral and nutty aroma - \n\n\n\nBatuah Mild alcohol and sulfur aroma; Characterised by pleasant \nfruity and sweet aroma which is likely favoured by \nconsumers. \n\n\n\nLikely due to the presence of highest ester ethyl-2-\nbutenoate  \n\n\n\n3.3 Enhancing Economic Value of Durians: Modifying Aroma, Color, \n\n\n\nFlavor \n\n\n\nThe sweet fruity smell of fruits is what attracts consumers but despite \nhaving a fruity aroma, the unique sulfur or onion-like odour of the durian \nfruit turns some consumers away. Smell is a critical factor in influencing \nconsumer decisions. As mentioned in the previous sections, the onion-like \nodour of durian fruits is unacceptable to many consumers in the market. \nApart from sensory attributes, the colour and appearance of durians- often \nused as a judge of aesthetics and quality- affect consumer decisions [20]. \nDurians with darker colours are usually less attractive to consumers [21]. \nTaste of the durian fruit is also an important factor of consideration. As \nsuch, these criteria in which consumers use to assess the favourability of \ndurians are important to be looked in order to be improved upon, to \nincrease the economic value of the fruit. \n\n\n\n3.3.1 Aroma \n\n\n\nMetabolomics of durians reported that amino acids leucine and cysteine \nare important precursors that contribute to the aroma of fruits, where \nleucine is responsible for volatile esters while cysteine is responsible for \nsulfur-containing aroma production [23]. Difference in amounts of the 2 \namino acids present in Chanee and Mon Thong durian cultivars reported \nin a study by Pinsorn et al could possibly account for the difference in \naroma volatiles [22]. \n\n\n\n3.3.2 Colour \n\n\n\nLai cultivars, which have a orange and red colour, are more visually \nattractive than durian cultivars, which have pale yellowish colour. \nAssociation between the yellow and orange colours of durians and lai, and \n\ud835\udefd-Carotene has been established in earlier studies [20, 24].  \n\n\n\n3.3.3 Flavour \n\n\n\n2 cultivars Chanee and Mon Thong durian cultivars from Thailand by \nPinsorn et al. The study revealed that metabolites glutamate, citrate, \ud835\udf08-\nglutamylcysteine, and glutathione, present in durian pulps from both \ncultivars are taste-related compounds. Amino acids highly abundant in \nboth cultivars, such as alanine, aspartate and glutamate are likely \nresponsible for the flavour of the durian pulps [22]. Another study found \nthat amino acids alanine, proline, phenylalanine and isoleucine are likely \nto have contributed to the bitter taste in durians sampled [20, 25]. The \nsweet taste is one of the most important traits in fruits, which is influenced \nby the sugar content (i.e. sucrose, glucose, fructose, maltose) present. By \nQDA, lai cultivars from Indonesia were found to have higher sweetness \nthan durian cultivars [20].  \n\n\n\n4. DISCUSSION\n\n\n\nTo select candidates for breeding, the analysis of the characteristics of \ndifferent cultivars could be studied and based on these characteristics\u2019 \nbreeders would like to see manifesting in new breeds of durian, they can \nselect accordingly the pair of cultivars. Belgis et al suggested that \u201cBatuah\u201d \nlai, which has a fruity and sweet aroma, and \u201cHejo\u201d durian, with the mildest \nsulfur aroma of all 4 durian cultivars, can be used to produce new cultivars, \nlikely with a milder aroma [18]. Cross breeding has been conducted in \nprevious experiments and their genetic diversity has been studied at the \nmolecular level. By using plant breeding techniques, it is believed that \ndurian cultivars of better quality can be produced [26]. Metabolomics is \nalso important in highlighting important contributors to aroma and \nflavour of durian fruits. In combination with studies such as those of Belgis \net al where they had identified volatile esters and sulfur compounds and \ntheir aromatic contribution, further studies can be conducted to \ninvestigate how certain metabolites or precursors and their eventual \nproduct formation can be manipulated to change the aromatic nuances in \ndurians [20]. Metabolites resulting in the formation of sulfur compounds \nsuch as 3,5-dimethyl 1,2,4-trithiolane, diethyl disulfide, 3-mercapto-2-\nmethyl propanol, bis(ethylthio)methane, 1,1-bis(methylthio)-ethane, and \n1,1-bis(ethylthio)-ethane, which contributes to the pungent durian odour, \nmay be further studied to look at possible inhibition of pathways leading \nto the formation of such sulfur compounds. Since research has been \nconducted on the durian genome, the identification of important \nmetabolites and precursors can potentially enable genetic modification of \nthe durian genome to enhance favourable traits in durians [23]. The genes \ncoding for metabolites and precursors can be manipulated, perhaps \nmodifying amounts of esters formed to enhance the fruity aroma, amounts \nof \ud835\udefd-Carotene produced to enhance the colour of durian cultivars, amounts \nof sugars and amino acids produced to change the flavour nuances of \ndurians, and remove unfavourable traits such as the onion-like odour \ndisliked by many consumers. Since dark colours are visually less attractive \nto consumers, molecules resulting in the dark colour of the durian pulp \nand methods to lighten the colour by modifying such molecules merits \nfurther studies. Price differentiation amongst different durian cultivars \nexist and are influenced by demand as some cultivars are preferred by \nconsumers over others. With the improvement of durian cultivars to be \nacceptable by more consumers, a general increase in demand will follow \nand as such, the economic value of the durians can be increased. \n\n\n\n5. CONCLUSION \n\n\n\nIn conclusion, considering that the aroma of durians is unbearable to \nmany, and its smell being an issue in air travel, further studies can analyses \nconsumer preferences in relation to the aromatic contributions of specific \nesters and sulfur-containing compounds in durians. This would allow \nunderstanding of the science behind the appeals or rejection of the fruit. \nFuture material science research, in relation to esters and sulfur-\ncontaining compounds identified to contribute to smell, can be conducted \nto mitigate issues regarding diffusion of the durian aroma. With this, the \nprobability of durian batches removed from air travel will be minimised, \nreducing losses and thereby also expanding the market of durians \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019)05-15 \n\n\n\nCite The Article: Joycelyn Soo Mun Peng (2019). Volatile Esters And Sulfur Compounds In Durians & A Suggested Approach To Enhancing Economic Value Of  \nDurians. Malaysian Journal of Sustainable Agriculture, 3(2): 05-15. \n\n\n\namongst individual consumers who adopt air travel transport. \nConsidering that studies report lai cultivars as good candidates for durian \nhybridisation, it highlights the possibility of other Durio cultivars as \npotential choices for durian hybrid production. Further research is \nrecommended to explore new combinations for breeding and the aromatic \nprofiles of new hybrids produced. Players in the durian industry should \nlook at enhancing factors taste, appearance and aroma according to \nconsumer preferences to leverage on the rising demands for durians. This \nis the recommended approach to increasing the economic value of durians. \n\n\n\nREFERENCES \n\n\n\n[1] Yuniar, R.W. 2018. Indonesian airline passengers\u2019 revolt over sacks of \nstinky durian. Retrieved November 07, 2018, from \nhttps://www.scmp.com/news/asia/southeast-\nasia/article/2171980/indonesian-flight-grounded-after-passengers-\nrevolt-over \n\n\n\n[2] Tao, L. 2018. Gone in 60 seconds: Chinese snap up 80,000 Thai durians \nonline. (2018, April 20). Retrieved November 07, 2018, from \nhttps://www.scmp.com/tech/china-tech/article/2142567/chinese-\nconsumers-snap-80000-durians-after-alibaba-signs-3-billion \n\n\n\n[3] Baldry, J., Dougan, J., Howard, G. 1972. Volatile flavouring constituents \nof Durian. Phytochemistry, 11 (6), 2081-2084. doi:10.1016/s0031-\n9422(00)90176-6 \n\n\n\n[4] Moser, R., D\u00fcvel, D., Greve, R. 1980. Volatile constituents and fatty acid \ncomposition of lipids in Durio zibethinus. Phytochemistry, 19 (1), 79-81. \ndoi:10.1016/0031-9422(80)85017-5 \n\n\n\n[5] Weenen, H., Koolhaas, W.E., Apriyantono, A. 1996. Sulfur-Containing \nVolatiles of Durian Fruits (Durio zibethinus Murr). Journal of Agricultural \nand Food Chemistry, 44, 3291-3293. \n\n\n\n[6] N\u00e4f, R., Velluz, A. 1996. Sulphur Compounds and some Uncommon \nEsters in Durian (Durio zibethinus Murr). Flavour and Fragrance Journal,\n11 (5), 295-303. doi:10.1002/(sici)1099-1026(199609)11:53.3.co;2-w \n\n\n\n[7] Wong, K.C., Tie, D.Y. 1995. Volatile constituents of durian (Durio \nzibethinus Murr). Flavour and Fragrance Journal, 10 (2), 79-83. \ndoi:10.1002/ffj.2730100205 \n\n\n\n[8] Jiang, J., Choo, S., Omar, N., Ahamad, N. 1998. GC-MS analysis of volatile \ncompounds in durian (Durio zibethinus Murr.). Developments in Food \nScience Food Flavors: Formation, Analysis and Packaging Influences, \nProceedings of the 9th International Flavor Conference the George \nCharalambous Memorial Symposium, 345-352. doi:10.1016/s0167-\n4501(98)80058-7 \n\n\n\n[9] Voon, Y., Hamid, N.S., Rusul, G., Osman, A., Quek, S. 2007. \nCharacterisation of Malaysian durian (Durio zibethinus Murr) cultivars: \nRelationship of physicochemical and flavour properties with sensory \nproperties. Food Chemistry, 103 (4), 1217-1227. doi: \n10.1016/j.foodchem.2006.10.038 \n\n\n\n[10] Chin, S., Nazimah, S., Quek, S., Man, Y. C., Rahman, R. A., Hashim, D.M. \n2007. Analysis of volatile compounds from Malaysian durians (Durio \nzibethinus) using headspace SPME coupled to fast GC-MS. Journal of Food \nComposition and Analysis, 20 (1), 31-44. doi: 10.1016/j.jfca.2006.04.011 \n\n\n\n[11] Li, J., Schieberle, P., Steinhaus, M. 2017. Insights into the Key \nCompounds of Durian (Durio zibethinus L. \u2018Monthong\u2019) Pulp Odor by \nOdorant Quantitation and Aroma Simulation Experiments. Journal of \nAgricultural and Food Chemistry, 65 (3), 639-647. doi: \n10.1021/acs.jafc.6b05299 \n\n\n\n[12] Voon, Y., Hamid, N.S., Rusul, G., Osman, A., Quek, S. 2007. Volatile \nflavour compounds and sensory properties of minimally processed durian \n\n\n\n(Durio zibethinus cv. D24) fruit during storage at 4\u00b0C. Postharvest Biology \nand Technology, 46 (1), 76-85. doi: 10.1016/j.postharvbio.2007.04.004 \n\n\n\n[13] Zhang, Z., Zeng, D., Li, G. 2006. The study of the aroma profile \ncharacteristics of durian pulp during storage by the combination sampling \nmethod coupled with GC\u2013MS. Flavour and Fragrance Journal, 22 (1), 71-\n77. doi:10.1002/ffj.1761 \n\n\n\n[14] Jaswir, I., Man, Y.B., Selamat, J., Ahmad, F., Sugisawa, H. 2008. \nRetention of Volatile Components of Durian Fruit Leather During \nProcessing and Storage. Journal of Food Processing and Preservation, 32 \n(5), 740-750. doi:10.1111/j.1745-4549.2008. 00211.x \n\n\n\n[15] Chin, S.T., Nazimah, S.A., Quek, S.Y., Man, Y.B., Rahman, R.A., Hashim, \nD.M. 2008. Changes of volatiles attribute in durian pulp during freeze- and \nspray-drying process. LWT - Food Science and Technology, 41 (10), 1899-\n1905. doi: 10.1016/j.lwt.2008.01.014 \n\n\n\n[16] Chin, S., Nazimah, S.A., Quek, S., Man, Y.B., Rahman, R.A., Hashim, D.M. \n2010. Effect of thermal processing and storage condition on the flavour \nstability of spray-dried durian powder. LWT - Food Science and \nTechnology, 43 (6), 856-861. doi: 10.1016/j.lwt.2010.01.001 \n\n\n\n[17] Foo, K., Hameed, B. 2011. Transformation of durian biomass into a\nhighly valuable end commodity: Trends and opportunities. Biomass and\nBioenergy, 35 (7), 2470-2478. doi: 10.1016/j.biombioe.2011.04.004 \n\n\n\n[18] Belgis, M., Wijaya, C.H., Apriyantono, A., Kusbiantoro, B., Yuliana, N.D.\n2017. Volatiles and aroma characterization of several lai (Durio \nkutejensis) and durian (Durio zibethinus) cultivars grown in Indonesia.\nScientia Horticulturae, 220, 291-298. doi: 10.1016/j.scienta.2017.03.041 \n\n\n\n[19] Burdock, G. 2004. Fenarolis Handbook of Flavor Ingredients, Fifth \nEdition. doi:10.1201/9781420037876 \n\n\n\n[20] Belgis, M., Wijaya, C.H., Apriyantono, A., Kusbiantoro, B., Yuliana, N.D. \n2016. Physicochemical differences and sensory pro ling of six lai (Durio \nkutejensis) and four durian (Durio zibethinus) cultivars indigenous \nIndonesia. International Food Research Journal, 23 (4): 1466-1473. doi: \nunavailable \n\n\n\n[21] Norjana, I., Aziah, N.A.A. 2011. Quality of durian (Durio zibethinus \nMurr) juice after pectinase enzyme treatment. International Food \nResearch Journal, 18: 1117-1122. doi: unavailable \n\n\n\n[22] Pinsorn, P., Oikawa, A., Watanabe, M., Sasaki, R., Ngamchuachit, P., \nHoefgen, R., Sirikantaramas, S. 2018. Metabolic variation in the pulps of \ntwo durian cultivars: Unravelling the metabolites that contribute to the \nflavour. Food Chemistry, 268, 118-125. doi: \n10.1016/j.foodchem.2018.06.066 \n\n\n\n[23] Teh, B.T., Lim, K., Yong, C.H., Ng, C.C., Rao, S.R., Rajasegaran, V., Tan, P. \n2017. The draft genome of tropical fruit durian (Durio zibethinus). Nature\nGenetics, 49 (11), 1633-1641. doi:10.1038/ng.3972 \n\n\n\n[24] Charoensiri, R., Kongkachuichai, R., Suknicom, S., Sungpuag, P. 2009. \nBeta-carotene, lycopene, and alpha-tocopherol contents of selected Thai \nfruits. Food Chemistry, 113 (1), 202-207. doi:\n10.1016/j.foodchem.2008.07.074 \n\n\n\n[25] Zanariah, J., Rehan, N.J. 1987. Protein and amino acid profiles of some \nMalaysian fruits. MARDI Research Bulletin Malaysia, 15: 1\u20137. doi:\nunavailable \n\n\n\n[26] Hariyati, T., Kusnadi, J., Arumingtyas, E.L. 2013. Genetic diversity of \nhybrid durian resulted from cross breeding between Durio kutejensis and \nDurio zibethinus based on random amplified polymorphic DNAs (RAPDs). \nAmerican Journal of Molecular Biology, 03 (03), 153-157.\ndoi:10.4236/ajmb.2013.33020 \n\n\n\n\nhttps://www.scmp.com/news/asia/southeast-asia/article/2171980/indonesian-flight-grounded-after-passengers-revolt-over\n\n\nhttps://www.scmp.com/news/asia/southeast-asia/article/2171980/indonesian-flight-grounded-after-passengers-revolt-over\n\n\nhttps://www.scmp.com/news/asia/southeast-asia/article/2171980/indonesian-flight-grounded-after-passengers-revolt-over\n\n\nhttps://www.scmp.com/tech/china-tech/article/2142567/chinese-consumers-snap-80000-durians-after-alibaba-signs-3-billion\n\n\nhttps://www.scmp.com/tech/china-tech/article/2142567/chinese-consumers-snap-80000-durians-after-alibaba-signs-3-billion\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 01-05 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.01.2023.01.05 \n\n\n\n \nCite The Article: Umair Ali, Muhammad Ashfaq , Abdul Rafeeh Shakeel , Asad Ali (2023). Improved Quality of Faba Beans (Vicia Faba L.) \n\n\n\n Crop with Bio-Control Against Bruchid Beetle (Bruchus Rufimanus). Journal of Sustainable Agricultures, 7(1): 01-05. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.01.05 \n\n\n\n\n\n\n\nIMPROVED QUALITY OF FABA BEANS (VICIA FABA L.) CROP WITH BIO-CONTROL \nAGAINST BRUCHID BEETLE (BRUCHUS RUFIMANUS) \n\n\n\nUmair Alia, Muhammad Ashfaqb\n, Abdul Rafeeh Shakeelc\n\n\n\n, Asad Alic \n\n\n\na Faculty of Agricultural Sciences, Department of Horticulture, University of the Punjab Lahore. \nb Faculty of Agricultural Sciences, Department of Plant Breeding and Molecular Genetics, University of the Punjab Lahore. \nc Faculty of Agricultural Sciences, Department of Entomology, University of Poonch Rawalakot. \n\n\n\n*Corresponding Author Email: umairmobashar@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 25 September 2022 \nRevised 28 October 2022 \nAccepted 14 November 2022 \nAvailable online 22 November 2022 \n\n\n\n The faba bean crop is facing different biotic and abiotic factors that ultimately lower its production. One of \nthe serious threats to faba beans production is the bruchid beetle (Bruchu rufimanus). The crop acts as a host \nplant, and the life cycle of bruchid beetles (Bruchus rufimanus) is based on faba bean crop. Studies reveal that \nthere is a significant impact of different organic extracts on faba bean (Vicia faba L.) Production against the \nbruchid beetle, furthermore, essential oils and organic extracts bearing insecticidal compounds that counter \nthe bruchid beetle infestation to an appropriate level. Globally, crops bear unstable yield which leads to an \ninadequate area under production and limited farmer expertise in relation to faba bean crop production. The \nultimate result is inadequate experience and expertise of its management practices. Use of chemical \ninsecticides has an adverse impact on the natural ecosystem and non-target insect pests. The review is based \non different biological approaches that may counter the bruchid beetle (Bruchus rufimanus) infestation. \nThere is a need to limit its attack at pre harvest stage to obtain quality seed and to boost the yield potential. \nThere is very less literature available on pre harvest control of bruchid beetles (Bruchus rufimanus). Different \nplant based essential oil and extracts having insect repellent tendency with insecticidal compounds. \nInnovative approaches like Semiochemical based insect trap and nanosilica coating will prove a breakthrough \nto counter bruchid beetle (Bruchus rufimanus) infestation. This review paper represents the summary and \noverview related to the improved quality of faba beans (Vicia faba L.) crop with bio-control against bruchid \nbeetle (Bruchus rufimanus). The data reveals the combined exposure of research papers and other literature \nthat is available in terms of related aspects. This may lead to literature overview from previous research and \npresent findings in a combined form. Further work is needed to counter bruchid beetle (Bruchus rufimanus) \nattack at pre harvest stage by using biological approaches. \n\n\n\nKEYWORDS \n\n\n\nFaba Bean, Bruchid Beetle, Bruchus rufimanus, Bio control. \n\n\n\n \n1.   INTRODUCTION \n\n\n\n1.1 Importance of Faba Bean (Vicia faba) \n\n\n\nFaba beans are characterized among the ancient crops worldwide. The \nimportance and popularity of this crop exist globally as third among the \nfeed grains. The crop is globally cultivated in around 58 countries. Faba \nbeans crop have the ability to withstand different climatic conditions that \nmakes it more popular, furthermore according to current global warming \nand adverse climatic conditions the crop have ability to bear severe \nclimatic conditions with multiple soil types (Singh et al., 2013). China is a \nworld leading country in terms of faba beans production. Due to dietary \nimportance as a food and feed the progress is made in nutritional value, \nimproved crop quality, and an increase in production. In terms of \nnutritional content the crop is the best source of lysine rich protein, a \nnumber of bioactive compounds, carbohydrates, vitamins and minerals \n(Dhull et al., 2021). Beans are enrich in protein with low oil content, having \n28 to 32% of protein as compared to field peas with 24% of protein. Faba \nbeans are processed at industrial scale into starch, fiber and protein. In \nrecent years there is an unusual difference in faba bean cultivation \nproduction and consumption. The crop has importance in crop rotation \n\n\n\nwith a hardy nature (Merga et al., 2019). Faba bean crop is leguminous in \nnature with ability to fix nitrogen, intercropping with other crops will \nincrease the soil productivity with an increase in yield resulting in an \nincrease in income of 50% (Isabirye et al., 2012). According to the current \nfood security and malnutrition scenario the researchers predict a rapid \nincrease in the production of faba beans over the coming five years. The \ncrop is a popular source of plant-based protein with an increase in demand \nglobally. According to a survey within ten years the crop has potential to \nreach 400,000 ha, and most of its consumption as an ingredient (Khazaei \net al., 2021).  \n\n\n\n1.2 Global Production of Faba Bean (Vicia faba) \n\n\n\nGlobal faba bean production during 2017 was 4.8 million metric tons. \nChina is among the largest producers with 1.8 metric tons followed by \nEthiopia with average production of 0.93 metric tons and Australia 0.37 \nmetric tons. As compared to other crops the area under production of faba \nbeans unfortunately has not increased, the only reason is fluctuation in its \nyield. Different biotic and abiotic stress and other a number of plant \nphenology and morphology factors lead to yield instability (Alharbi and \nAdhikari, 2020). Faba beans are used globally as livestock feed due to \nbeing rich in protein. In livestock production the better growth and \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 01-05 \n\n\n\n\n\n\n\n \nCite The Article: Umair Ali, Muhammad Ashfaq, Abdul Rafeeh Shakeel , Asad Ali (2023). Improved Quality of Faba Beans (Vicia Faba L.) \n\n\n\n Crop with Bio-Control Against Bruchid Beetle (Bruchus Rufimanus). Journal of Sustainable Agricultures, 7(1): 01-05. \n \n\n\n\nproduction of animals are achieved through raw faba beans seed \nconsumption (Meng et al., 2021). In terms of total production of faba beans \nthe majority of the portion is related to Asia, Africa and the European \nUnion (FAO, 2020). Annual production reached 5.43 million metric tons \naccording to statistics of 2019. Asia encompasses 33.55% of total world \nproduction of Faba beans followed by the European Union with 29.36% \nand Africa with 27.04%proportion (FAO, 2020). \n\n\n\nGraphical representation in the form of Figures 1, 2 and 3 illustrate the \nworld prominent countries of faba beans as producer, exporter and \nimporters. Around half of the world production of faba beans is \nencompassed by China and Ethiopia. During 2019 Australia ranked first in \nterms of top exporter with 265,543 Mt. Egypt ranked first in terms of \nmajor importers of faba beans with 309,355 Mt approximately 40.48% of \ntotal world production (FAO, 2020). \n\n\n\nTable 1: Top Leading Faba Beans Producing Countries Globally \n\n\n\nWorld leading Faba beans producing countries \n\n\n\nCountries Quantity (metric tons) \n\n\n\nChina 1,740,945 \n\n\n\nEthiopia 1,006,752 \n\n\n\nUnited Kingdom 547,800 \n\n\n\nAustralia 327,000 \n\n\n\nFrance 177,380 \n\n\n\nSource: FAO (2020) \n\n\n\n\n\n\n\nFigure 1: World Leading countries in Faba beans production (Source: \nFAO, 2020) \n\n\n\nTable 2: Leading Exporting Countries of Faba Beans Globally \n\n\n\nProminent Faba beans exporting countries \n\n\n\nCountries Quantity (metric tons) \n\n\n\nAustralia 265,543 \n\n\n\nUnited Kingdom 119,071 \n\n\n\nLithuania 92,445 \n\n\n\nEgypt 71,022 \n\n\n\nLatvia 66,860 \n\n\n\nSource: FAO, 2020 \n\n\n\n\n\n\n\nFigure 2: Prominent Faba beans exporting countries (Source: FAO, 2020) \n\n\n\nTable 3: Leading Importing Countries of Faba Beans Globally \n\n\n\nProminent Faba beans importing countries \n\n\n\nCountries Quantity (metric tons) \n\n\n\nEgypt 309,355 \n\n\n\nNorway 56,437 \n\n\n\nGermany 46,707 \n\n\n\nSaudi Arabia 43,397 \n\n\n\nFrance 30,396 \n\n\n\nSource: FAO, 2020 \n\n\n\n\n\n\n\nFigure 3: Prominent Faba beans importing countries (Source: FAO, \n2020) \n\n\n\n1.3 Bruchid Beetle (Bruchus Rufimanus) \n\n\n\nCultivation of faba bean crops within the European Union will facilitate \nsustainability in relation to providing an enriched source of protein as food \nand animal feed. Furthermore, there is a need to introduce the technology \nand techniques to counter the impact of Bruchid beetle against its \ndestructive impact. Bruchid beetles significantly devalue the faba bean \nseed by concentrating post embryonic growth inside the seed.  \nUnfortunately a lot of research work is taking place on different control \nmeasures of bruchid beetles but still there is an unavailability of proper \ncontrol strategy (Segers et al., 2021). Faba beans having multiple dietary \nimportances acts as a host plant of bruchid beetles. During the life cycle of \nbruchid beetles the adult sustains on pollen and when the pods are at \nvegetative stage the female lays eggs on pods and then larvae penetrate \ninside the seeds.  Sowing dates have a significant impact to counter the \nattack of bruchid beetles, in relation with delayed bloom the adult feed on \nother flowering plants. In view of sowing dates there should be less or \nmore effect of bruchid beetle on faba bean production (Hamidi et al., \n2021). The egg laying process lasts from 4 to 5 weeks during the vegetative \ndevelopment of pods.  \n\n\n\nIn case of serious infestation, the egg laying capacity increases and per pod \napproximately 34 eggs were estimated. Higher Yield losses would be \nestimated in relation to the average number of eggs (Gailis et al., 2022). \nStudies reveal that the attack of bruchid beetle on faba ban seeds resulting \nin poor seed germination beside this water also facilitates the weevil \nemergence after the sowing seed. In conclusion the seed infestation results \nin lowering the germination percentage with a prospect of future \ninfestation of the coming cropping cycle (Khelfane-Goucem and \nMedjdoub-Bensaad, 2016). The development rate of bruchid beetles \ndiffers in relation to the type of crop varieties. Mortality rates on different \nstages of development (Eggs, Larvae, and Pupas) are significantly higher \non commonly grown varieties. Studies reveal that about 64 to 99% \nmortality rates were estimated among larvae development within seed. \nSome of the faba bean varieties have pod formation that remains a hurdle \nfor larvae penetration. A bio control strategy is significantly successful of \nlowering the adult ratio by using the Triaspis thoracicus (Seidenglanz and \nHu\u0148ady, 2016). \n\n\n\n1.4   Life cycle of Bruchid Beetle (Bruchus rufimanus) \n\n\n\nDuring the month of February termination of bruchid beetle diapauses \ntakes place and the adults start to colonize. Male of bruchid beetles began \nto colonize during the February after diapause termination, furthermore \nduring the month of March the female ready to colonize after feeding of \npollen and nectar of faba bean flowers. The degree of abundance of adults \nis directly related to the availability of trophic sources at its earlier stage \nof development. Ovipositor takes place as the female of a bruchid beetle \nappeared on young pods until its maturation. Larvae maturation takes \nplace inside the mature seed of faba bean when the pods where green. \nPupae development takes place during the seed storage inside the dry \nseed (Medjdoub-Bensaad et al., 2007).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 01-05 \n\n\n\n\n\n\n\n \nCite The Article: Umair Ali, Muhammad Ashfaq, Abdul Rafeeh Shakeel , Asad Ali (2023). Improved Quality of Faba Beans (Vicia Faba L.) \n\n\n\n Crop with Bio-Control Against Bruchid Beetle (Bruchus Rufimanus). Journal of Sustainable Agricultures, 7(1): 01-05. \n \n\n\n\n\n\n\n\nFigure 4: Life Cycle of Bruchid Beetle (Bruchus rufimanus) \n\n\n\n2. USE OF CHEMICAL AGAINST BRUCHUS RUFIMANUS \n\n\n\nTo counter the destructive impact of bruchid beetles a large number of \nchemical pesticides have been used. The adverse impact of these chemical \npesticides is not only on plant health but it may also affect the environment \nas well as non target insect populations.  Many of the chemical pesticides \nhave been banned due to the hazard impact on the ecosystem.  Many of the \nhazardous chemicals leached down with rain water and polluted the \nunderwater reservoirs. Post harvest treatment is significantly lowering \nthe insect infestation but there is a need to limit the insect infestation at \nan earlier stage. Biological control is a key to achieve sustainability and eco \nfriendly approaches to counter the insect infestation.  \n\n\n\n3.  IMPACT OF BIO RATIONAL CONTROL STRATEGY AGAINST \n\n\n\nBRUCHUS RUFIMANUS \n\n\n\nStudies reveal that insecticidal impact of Eucalyptus and Artemisia, both \ngenera can be used as fumigant and insect repellant.  1,8 cineole, and \u03b1-\npinene the essential bio active compound derived from Eucalyptus and  \u03b2-\nthujone and Camphor derived from Artemisia significantly proven as bio \ninsecticides (Ben, 2014). Like bruchid beetles, many of the insects attack \non pods in leguminous crops. The studies stated that use of bio insecticides \nTephrosia, Tagetes and Tobacco extract have ability to decrease the insect \npopulation during pod development stages (Kawuki et al., 2005). \nArtemisia essential oil has insecticidal and repellent ability in combination \nwith essential oil derived from Cinnamomum camphora (L.) seeds against \nthe bruchid beetle. There is also an antagonistic impact of combined \nmixture of Artemisia essential oil and Cinnamomum camphora (L.) seeds \noil on seed germination of bean crop against the insect infestation (Liu et \nal., 2006).  \n\n\n\nApplication of microbial agents also counters the bruchid beetle losses in \nthe field in relation with application of some essential oils. A field \nexperiment was conducted to analyze the impact of microbial agents in \ncombination with essential oils. Results derived from the experiment \nstates that application of Beauveria bassiana, Metarhaizum anisopliae \nfungusand essential oil derived from Nigella and Mustard shows \nsignificant impact to lower the bruchid beetle infestation (Sabbour and E-\nAbd-El-Aziz, 2007). Beside the essential oil some plant based organic \nextracts also proven insect repellent ability. Application of Vernonia \nlasiopus and Tithonia diversifolia leaf extract have repellency tendency. \nThe extract is analyzed using GC-MS technique and it is concluded that \nboth extract have  bio active compound against the weevils repellency \n(Gitahi et al., 2021).  \n\n\n\nStudies reveal that Azadirachta indica oil significantly manifest a powerful \ninsecticide with 100% mortality rate within two day against \nCallosobruchus maculatus F. Beside this Acorus calamus, rice husk and \nmustard oil also has insect killing properties against pulse beetle (Paneru \nand Shivakoti, 1970). Essential oil derived from Zea mays, Arachis \nhypogaea, Helianthus annuus, and Sesamum indicum were tested against \nthe three species of pulse beetle i.e Callosobruchus rhodesianus, \nCallosobruchus chinensis, Callosobruchus maculatus. Findings illustrate \nthat there was a significant reduction in ovipositor of all species tested \nwhen applied at a concentration of 10ml/kg. It is also concluded that a \nsignificant decrease in longevity of adults of Callosobruchus maculatus and \nCallosobruchus chinensis may also be recorded (Rajapakse and Van Emden, \n1997). \n\n\n\n4.   SEMIOCHEMICAL BASED TRAPS AGAINST BRUCHUS RUFIMANUS \n\n\n\nSemiochemical based traps are emerging technology to counter the insect \nattack. They consist of a volatile compound that attracts the insects and \ncaptures them. Bruchus rufimanus mature male are attracted by the bloom \nof faba beans. Meanwhile the female of Bruchus rufimanus may also be \nattracted by bloom volatile in the presence of mature male. Volatile \ncompounds extracted from faba bean bloom are used as semiochemical \ntraps during the colonization of female and male adults. These traps are \nsignificantly beneficial to lowering the insect population when used before \novipositor by targeting fertilized females (Bruce et al., 2011). \n\n\n\n5.  IMPACT OF DIFFERENT SOWING DATES AGAINST BRUCHUS \n\n\n\nRUFIMANUS \n\n\n\nSowing dates may also be a strong counter strategy against the Bruchus \nrufimanus. Bruchus rufimanus population significantly affects female \nbiobehavioral synchrony to the young pods. It is estimated that a crop with \nearly flowering and pods long diversification distinct the Bruchus \nrufimanus to winter sown crops. In case of delayed flowering it will \nfacilitate the beetle to feed on other crops instead of the main crop. \nElimination of bloom availability by surrounding crops in relation with \npods long diversification varieties will facilitate the low infestation \n(Hamidi et al., 2021). In the view of the life cycle of Bruchus rufimanus, it is \nstated that faba bean crop sown during late March and Early April results \nin lower insect infestation. Delayed sowing may also lower the yield. \nStudies should be needed to improve the yield by delayed sowing (Ward, \n2018).  \n\n\n\n6.   POST-HARVEST TREATMENT AGAINST BRUCHUS RUFIMANUS \n\n\n\nIn order to protect stored grain the post harvest treatment is essential. \nEssential oil derived from plants in combination with parasitoids \nsignificantly reduces the insect infestation during storage period. For this \npurpose essential oil derived from Artemisia absinthium, Artemisia herba-\nalba, and Artemisia campestris were analysed using GCeMS and GC \ntechniques and applied in combination with parasitoids to manifest the \nimpact against Bruchus rufimanus. Significant reductions in insect\u2019s \ninfestation were recorded with the application of Artemisia campestris \nalong with natural enemies. Triaspis luteipes and Dinarmus basalis used as \nnatural enemies on target insect.  \n\n\n\nApplication of essential oil of Artemisia campestris along with release of \nabove mentioned parasitoids, were results in significantly decrease in \ninsect infestation in stored grain (Titouhi et al., 2017). Moringa oleifera is \ncharacterized as a powerful insecticide with insect repellent abilities \nwidely utilized. In order to analyze its bio-insecticidal properties, powder \nderived from different plant parts (flower, Leaf, Stem, Root) were analyzed \nagainst Bruchus rufimanus infestation in cowpea seed. The impacts were \nanalyzed by targeting virgin adults, and findings reveal that moringa \nbloom powder when applied at a concentration of 0.5 g results in a \nsignificant reduction in egg laying capacity. Finding manifest that the \nMoringa oleifera bloom have insecticidal properties and used as bio \ninsecticide against bruchid beetle (Adenekan, et al., 2013).  \n\n\n\nStorage grain of leguminous crops is susceptible to bruchid beetle \ninfestation, a physical method of post harvest treatment using hot and cold \nSalt water, application of essential oil derived from Sesamum indicum and \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 01-05 \n\n\n\n\n\n\n\n \nCite The Article: Umair Ali, Muhammad Ashfaq, Abdul Rafeeh Shakeel , Asad Ali (2023). Improved Quality of Faba Beans (Vicia Faba L.) \n\n\n\n Crop with Bio-Control Against Bruchid Beetle (Bruchus Rufimanus). Journal of Sustainable Agricultures, 7(1): 01-05. \n \n\n\n\nAzadirachta indica seed powder were tested. During the analyses period \nof five months a significant decrease is manifest in insect infestation. Most \nappropriate treatment was application of essential oil of Sesamum indicum \nwhen using 5 drop/250g grain followed by Azadirachta indica seed \npowder 10g/250 g grain and hot salt water 50g/250g grain (Ahmad et al., \n2015). A limited but emerging technology is nano silica coating. Limited \nwork is undergone but the potential is wide. The current literature states \nthat it\u2019s the first ever attempt to establish nano based silica coating \nmaterials to protect stored grain from insect and pest infestation.  \n\n\n\nFurthermore, it is stated that a significant dosage of nano silica is used by \nutilizing its minimum concentration as a coating material against pulses \nseed. Results manifest the efficiency any accuracy of nano silica based \ncoating against insect infestation (Arumugam et al., 2016). Much of the \nliterature demonstrates the insecticidal properties of rice husk ash. It is \nstated that before grain storage the application of rice husk ash at a \nconcentration of 1% thoroughly mixed with grains will results  an effective \ncontrol against insect infestation (Naito, 1988). A significant cultural \npractice to control bruchid beetles during storage is neglected nowadays. \nSunning and sieving S&S is a strong technique to eliminate the egg, larvae \nand adults from beans (Huis 1991). There are a number of advantages by \nadopting this approach, seed germination and grain appearance would not \nbe affected. It is concluded that the application of this technique is \nanalyzed on farms and results declared that it is suitable and beneficial as \nthe application of a substance having insecticidal properties. The approach \nis familiar to farmers because of its low cast, and well grain maintenance \n(Songa and Rono, 1998).  \n\n\n\n7.   CONCLUSION \n\n\n\nPlant proteins are the prominent alternatives in the current scenario of \nfood security and malnutrition. Faba beans crop is a significant source of \nnitrogen fixation among the leguminous crops. The dietary importance of \ncrops is well known and also withstand in a diverse type of climatic region \nand soil type.  There is a need to boost the production and yield of faba \nbeans to meet future generation dietary needs. Use of plant derived \nessential oil and extract as biocontrol against bruchid beetle infestation is \nsignificantly improving the crop productivity. \n\n\n\nThe essential oil and extract have not only insect repellant ability but may \nalso act as insecticides. It is more significant to limit the infestation before \nlarvae penetrates into young pods; because once it penetrates into pod it\u2019s \nvery hard to counter its attack at pre harvest stage. Post-harvest treatment \nmay also effective against bruchid beetle infestation but it\u2019s more difficult \nand not economical. To limit bruchid beetle attack it is crucial to \nunderstand its life cycle. Sowing dates may also a significant impact to \ncounter its attack but ultimately late or pre sowing may have adverse \neffect on crop yield. Chemical insecticides are dominant to control bruchid \nbeetle but they have hazard impact on crop and environment. There is a \nneed to more focus on pre harvest control of bruchid beetle with more \ninnovative and sustainable approaches. Very less work has been published \nin terms of biological control of faba beans at pre harvest stage.  \n\n\n\nREFERENCES \n\n\n\nAdenekan, M.O., Okpeze, V.E., Ogundipe, W.F and Oguntade, M., 2013. \nEvaluation of Moringa Oleifera Powders for Control of Bruchid \nBeetles during Storage. International Journal of Agricultural \nPolicy and Research, 1 (10), Pp. 306\u201310. \n\n\n\nAhmad, T., Haile, A., Ermias, A., Etbarek, R., Habteab, S., and Teklai, S., 2015. \nEco-Friendly Approaches for Management of Bruchid Beetle \nCallosobruchus Chinensis (Coleoptera: Bruchidae) Infesting \nFaba Bean and Cowpea under Laboratory Conditions. Journal of \nStored Products and Postharvest Research, 6 (3), Pp. 25\u201329. \n\n\n\nAlharbi, N.H., and Kedar, N.A., 2020. Factors of Yield Determination in Faba \nBean (Vicia Faba). Crop and Pasture Science, 71 (4), Pp. 305\u201321. \n\n\n\nArumugam, G., Veeramani, V., Sakthivelkumar, S., and Janarthanan, S., \n2016. Efficacy of Nanostructured Silica as a Stored Pulse \nProtector against the Infestation of Bruchid Beetle, \nCallosobruchus Maculatus (Coleoptera: Bruchidae). Applied \nNanoscience (Switzerland), 6 (3), Pp. 445\u201350. \n\n\n\nBen, J., and Jouda, M., 2014. Essential Oil as a Source of Bioactive \nConstituents for the Control of Insect Pests of Economic \nImportance in Tunisia. Medicinal and Aromatic Plants, 03 (02). \n\n\n\nBruce, T.J., Janet, L.M., Lesley, E.S., and John, A.P., 2011. Development of \nSemiochemical Attractants for Monitoring Bean Seed Beetle, \n\n\n\nBruchus Rufimanus. Pest Management Science, 67 (10), Pp. \n1303\u20131308. \n\n\n\nDhull, S.B., Kidwai, M.K., Noor, R., Chawla, P., and Rose, P.K., 2021. A Review \nof Nutritional Profile and Processing of Faba Bean (Vicia Faba L.). \nLegume Science, Pp. 1\u201313. \n\n\n\nFAO (Food and Agriculture Organization). 2020. Crop Production and \nTrade Data. http://www.fao.org/faostat/en/#data (accessed \nFebruary 4, 2022). \n\n\n\nGailis, J\u0101nis, Asta\u0161ova, N., J\u0101kobsone, E., and Ozoli\u0146a-Pole, L., 2022. Biology \nof Broadbean Seed Beetle (Bruchus Rufimanus; Coleoptera: \nChrysomelidae) in Latvia. Acta Agriculturae Scandinavica Section \nB: Soil and Plant Science, 72 (1) Pp. 4\u201316. \n\n\n\nGitahi, Maina, S., Ngugi, M.P., Mburu, D.N., and Machocho, A.K., 2021. \nContact Toxicity Effects of Selected Organic Leaf Extracts of \nTithonia Diversifolia (Hemsl.) A. Gray and Vernonia Lasiopus (O. \nHoffman) against Sitophilus Zeamais Motschulsky (Coleoptera: \nCurculionidae). International Journal of Zoology. \n\n\n\nHamidi, Rachid, Taupin, P., and Fr\u00e9rot, B., 2021. Physiological Synchrony \nof the Broad Bean Weevil, Bruchus Rufimanus Boh., to the Host \nPlant Phenology, Vicia Faba L. Frontiers in Insect Science, 1, Pp. \n1\u201310. \n\n\n\nHuis, A.V., 1991. Biological Methods of Bruchid Control in The Tropics: A \nReview. Insect Sci. Applic., 12 (1), Pp. 87\u2013102. \n\n\n\nIsabirye, Moses, D.V., Kitutu, R.M., Yemeline, Y., Deckers, J., and Poesen, J., \n2012. We Are IntechOpen, the World\u2019 s Leading Publisher of \nOpen Access Books Built by Scientists, for Scientists TOP 1 %.\u201d \nIntech 13. \n\n\n\nKawuki, R.S., Agona, A., Nampala, P., and Adipala, E., 2005. A Comparison \nof Effectiveness of Plant-Based and Synthetic Insecticides in the \nField Management of Pod and Storage Pests of Cowpea. Crop \nProtection, 24 (5), Pp. 473\u201378. \n\n\n\nKhazaei, H., Hawkins, G., and Vandenberg, A., 2021. Historical Review of \nFaba Bean Improvement in Western Canada. Legume Science, 3 \n(4), Pp. 1\u20139. \n\n\n\nKhelfane-Goucem, K., and Ferroudja, M.B., 2016. Impact of Bruchus \nRufimanus Infestation upon Broad Bean Seeds Germination. \nAdvances in Environmental Biology, 10 (5), Pp. 144. \n\n\n\nLiu, C.H., Mishra, A.K., Tan, R.X., Tang, C., Yang, H., and Shen, Y.F., 2006. \nRepellent and Insecticidal Activities of Essential Oils from \nArtemisia Princeps and Cinnamomum Camphora and Their \nEffect on Seed Germination of Wheat and Broad Bean. \nBioresource Technology, 97 (15), Pp. 1969\u20131973. \n\n\n\nMedjdoub-Bensaad, F., Khelil, M.A., and Huignard, J., 2007. Bioecology of \nBroad Bean Bruchid Bruchus Rufimanus Boh. (Coleoptera: \nBruchidae) in a Region of Kabylia in Algeria. African Journal of \nAgricultural Research, 2 (9), Pp. 412\u2013417. \n\n\n\nMeng, Z., Qingqing, L., Yan, Z., Jiahong, C., Zhipeng, S., Chunhuan, R., Zijun, \nZ., Xiao C., and Yafeng, H., 2021. Nutritive Value of Faba Bean \n(Vicia Faba L.) as a Feedstuff Resource in Livestock Nutrition: A \nReview. Food Science and Nutrition, 9 (9), Pp. 5244\u20135262. \n\n\n\nMerga, B., Egigu, M.C., and Wakgari, M., 2019. Reconsidering the Economic \nand Nutritional Importance of Faba Bean in Ethiopian Context. \nCogent Food and Agriculture, 5 (1). \n\n\n\nNaito, A., 1988. Low-Cost Technology for Controlling Soybean Insect Pests \nin Indonesia. Food and Fertilizer Technology Center, Pp. 1\u201314. \n\n\n\nPaneru, R.B., and Gopal, P.S., 1970. Use of Botanicals for the Management \nof Pulse Beetle (Callosobruchus Maculatus F.) in Lentil. Nepal \nAgriculture Research Journal, 4 (1989), Pp. 27\u201330. \n\n\n\nRajapakse, R., and Van Emden, H.F., 1997. Potential of Four Vegetable Oils \nand Ten Botanical Powders for Reducing Infestation of Cowpeas \nby Callosobruchus Maculatus, C. Chinesis and C. Rhodesianus. \nJournal of Stored Products Research, 33 (1), Pp. 59\u201368.   \n\n\n\nSabbour, M.M., and E-Abd-El-Aziz, S., 2007. Efficiency of Some \nBioinsecticides against Broad Bean Beetle, Bruchus Rufimanus \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 01-05 \n\n\n\n\n\n\n\n \nCite The Article: Umair Ali, Muhammad Ashfaq, Abdul Rafeeh Shakeel , Asad Ali (2023). Improved Quality of Faba Beans (Vicia Faba L.) \n\n\n\n Crop with Bio-Control Against Bruchid Beetle (Bruchus Rufimanus). Journal of Sustainable Agricultures, 7(1): 01-05. \n \n\n\n\n(Coleoptera: Bruchidae). Research Journal of Agriculture and \nBiological Sciences, 3 (2), Pp. 67\u201372. \n\n\n\nSegers, A., Megido, R.C., Lognay, G., and Francis, F., 2021. Overview of \nBruchus Rufimanus Boheman 1833 (Coleoptera: \nChrysomelidae): Biology, Chemical Ecology and Semiochemical \nOpportunities in Integrated Pest Management Programs. Crop \nProtection, Pp. 140. \n\n\n\nSeidenglanz, M., and Igor, H., 2016. Effects of Faba Bean (Vicia Faba) \nVarieties on the Development of Bruchus Rufimanus. Czech \nJournal of Genetics and Plant Breeding, 52 (1), Pp. 22\u201329. \n\n\n\nSingh, A.K., Bharati, R.C., Manibhushan, N.C., and Pedpati, A., 2013. An \nAssessment of Faba Bean (Vicia Faba L.) Current Status and \nFuture Prospect. 8 (50), Pp. 6634\u201341. \n\n\n\nSonga, J.M., and Wilson, R., 1998. Indigenous Methods for Bruchid Beetle \n(Coleoptera: Bruchidae) Control in Stored Beans (Phaseolus \nVulgaris L.). International Journal of Pest Management, 44 (1), \nPp. 1\u20134. \n\n\n\nTitouhi, F., Amri, M., Messaoud, C., Haouel, S., Youssfi, S., Cherif, A., and \nJem\u00e2a, J.M.B., 2017. Protective Effects of Three Artemisia \nEssential Oils against Callosobruchus Maculatus and Bruchus \nRufimanus (Coleoptera: Chrysomelidae) and the Extended Side-\nEffects on Their Natural Enemies. Journal of Stored Products \nResearch, 72, Pp. 11\u201320. \n\n\n\nWard, R.L., 2018. The Biology and Ecology of Bruchus Rufimanus (Bean \nSeed Beetle). Thesis. School of Natural and Environmental \nSciences. Newcastle University.\u201d. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 05-09 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.05.09 \n\n\n\nCite the Article: Ahukaemere CM, Okoli NH, Aririguzo BN and Onwudike SU (2020). Tropical Soil Carbon Stocks In Relation To Fallow Age And Soil Depth.  \nMalaysian Journal of Sustainable Agriculture, 4(1): 05-09. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.01.2020.05.09 \n\n\n\nTROPICAL SOIL CARBON STOCKS IN RELATION TO FALLOW AGE AND SOIL \nDEPTH  \n\n\n\nAhukaemere CM*, Okoli NH, Aririguzo BN and Onwudike SU  \n\n\n\nDepartment of Soil Science, Federal University of Technology, Owerri Nigeria \n\n\n\n*Corresponding author e-mail: mildredshine@yahoo.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 18 November 2019 \nAccepted 23 December 2019 \nAvailable online 24 January 2020\n\n\n\nThe interest in Soil carbon has risen significantly in the science community due to the potential of climate \n\n\n\nchange mitigation through soil carbon sequestration.  Changes in fallow periods influence how much and at \n\n\n\nwhat rate carbon is sequestered in or released from the soil. Carbon sequestration in soils under three \n\n\n\ndifferent fallow ages (7, 14 and 21) at varying sampling depths (0-20, 20-40, 40-60, 60-80, 80-100 cm) was \n\n\n\ninvestigated using the method of Batjes and data obtained were subjected to analysis of variance. Organic \n\n\n\ncarbon content was generally low ranging from 3.99 \u2013 5.67g kg-1. Soil carbon sequestered under the three \n\n\n\nvarying fallow ages ranged from 1295 \u2013 1611g cm-2. Though no significant variation was observed in the \n\n\n\namount of C sequestered by the varying ages of vegetation, results showed that 14 years fallow sequestered \n\n\n\nthe highest quantity of carbon (1611g cm-2) while the least (1295 g cm-2) was obtained in 7 year fallow. On \n\n\n\nthe other hand, sampling depth had a significant influence on soil carbon content. In 7 years fallow period, 0-\n\n\n\n20, 20-40 and 40-60 cm sampling depths contained significantly highest carbon stock values. In 14 and 21 \n\n\n\nyears fallow ages, 0-20 cm sampling depth sequestered significantly highest carbon (3147.04 g cm-2, 2247 g \n\n\n\ncm-2) compared to other sampling depths. Conclusively, more carbon is sequestered at the soil surface than \n\n\n\nin the sub-soil and prolonged fallow age up to 21 years may not be beneficial to soil carbon sequestration. \n\n\n\nKEYWORDS \n\n\n\nCarbon Sequestration, Fallow Period, Tropical Soil, Sampling Depth. \n\n\n\n1. INTRODUCTION \n\n\n\nQuantification of the impacts of fallow periods on carbon stocks in Sub-\n\n\n\nSahara Africa is challenging because of the spatial and vertical \n\n\n\nheterogeneity of soil, climate, management conditions and due to lack of \n\n\n\ndata on carbon pools of most common agro-ecosystems. Soil is important \n\n\n\nreservoir of active organic carbon and is major player in the global cycle \n\n\n\nof this element. The soil serves as a source or sink of atmospheric carbon \n\n\n\ndioxide (CO2), depending on land use and management of soil and \n\n\n\nvegetation (Lal 2005).  Over 60% of the world's carbon is held in both soils \n\n\n\n(more than 40%) and the atmosphere (as carbon dioxide; 20%) \n\n\n\n(Alexander et al. 2015). The conversion of native ecosystems such as \n\n\n\nforests, grasslands and wetlands to agricultural uses, and the continuous \n\n\n\nharvesting of plant materials, have led to significant losses of plant \n\n\n\nbiomass and carbon, thereby increasing the carbon dioxide (CO2) level in \n\n\n\nthe atmosphere (Ahukaemere 2015). \n\n\n\nCarbon sequestration involves the process of transferring atmospheric \n\n\n\nCO2 into the soil through crop residues and other organic solids and \n\n\n\nstoring it securely so it is not immediately re-emitted into the atmosphere \n\n\n\n(Lal 2004). Thus, soil carbon sequestration means increasing soil organic \n\n\n\ncarbon (SOC) and soil inorganic carbon (SIC) stocks through judicious land \n\n\n\nuse, adequate fallow period and other management practices (Akamigbo \n\n\n\n2010). This transfer or \"sequestering\" of carbon helps off-set emission \n\n\n\nfrom fossil fuel combustion and other carbon-emitting activities while \n\n\n\nenhancing soil quality and productivity. Globally, scientists and \n\n\n\npolicymakers are facing challenges on how to increase the amount of \n\n\n\ncarbon sequestered in the soil in order to mitigate climate change. \n\n\n\nUnderstanding how fallow period affects carbon stored in the ecosystems \n\n\n\nand how canopy cover influence atmospheric concentrations of CO2 will \n\n\n\nbe important in understanding the long-term sequestration capacity of the \n\n\n\nplants. In general, the amount of carbon in soil is determined by the \n\n\n\nbalance between carbon input from vegetation cover, in the form of dead \n\n\n\nplant litter (roots and shoots) and root exudates, and output via \n\n\n\ndecomposition processes, burning and soil erosion. Another possible way \n\n\n\nto enhance soil carbon sequestration involves the manipulation of plant-\n\n\n\nsoil feedbacks, especially in grassland. For example, recent studies showed \n\n\n\nthat increase in plant diversity and the introduction of certain plant \n\n\n\nspecies such as legumes into mixed grasslands can reap benefits for soil \n\n\n\ncarbon sequestration (Anikwe 2010; Ahukaemere et al. 2016). Generally, \n\n\n\na research effort aiming at the influence of soil depth, fallow duration and \n\n\n\ntype, offers a potential way forward for understanding how vegetation-soil \n\n\n\ninteractions might be manipulated to enhance soil carbon storage. \n\n\n\nTherefore, a good knowledge of carbon sequestration and storage in soils \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 05-09 \n\n\n\n\n\n\n\n \nCite the Article: Ahukaemere CM, Okoli NH, Aririguzo BN and Onwudike SU (2020). Tropical Soil Carbon Stocks In Relation To Fallow Age And Soil Depth.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 05-09. \n \n\n\n\n\n\n\n\nis important for the development of healthy soil and reducing its adverse \n\n\n\neffects. \n\n\n\nCarbon sequestered in soils, was not given enough attention due to \n\n\n\nunavailability of data to demonstrate any major changes in soil carbon \n\n\n\nstock resulting from variation in fallow periods. As a result, the dynamics \n\n\n\nof soil carbon stock under the fallow lands has not been adequately \n\n\n\nevaluated, particularly in relation to the Carbon status of soils under \n\n\n\ndifferent ages of fallow. Therefore, the objective of the study was to assess \n\n\n\nthe effects of fallow periods and soil sampling depths on soil carbon \n\n\n\nsequestration. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Description of the Study Area \n\n\n\nThe study sites are located at Ezinihitte Mbaise, in Owerri, South-eastern \n\n\n\nregion of Nigeria. The geology of the study area is characterized mainly by \n\n\n\ncoastal plain sands. The area lies between latitudes 5o28' and 5o41'N and \n\n\n\nlongitudes 7o20' and 7o48'E   with elevation ranging from 120 to 143 m \n\n\n\nabove sea level. The climate is a humid tropic with annual rainfall average \n\n\n\nof 2500 mm. Mean annual temperature varies between 27 \u2013 30oC with a \n\n\n\nrelative humidity of 75-80% (NIMET 2016). The vegetation is rainforest \n\n\n\ncharacterized by a variety of plant species. Low technology agriculture is \n\n\n\ncommon, and farmers concentrate their activities on an arable form of \n\n\n\nfarming and small ruminant animals. Land preparation is by slashing, \n\n\n\nfollowed by burning and packing un-burnt debris. Short duration \n\n\n\nfollowing has resulted to use of inorganic fertilizers in the area. \n\n\n\n2.2 Soil Sampling and Laboratory Analyses \n\n\n\nThree representative sampling sites were chosen based on the fallow \n\n\n\nduration using the random survey approach. The fallow lands comprised \n\n\n\nof mixed vegetation involving different plant species. Information on land \n\n\n\nuse history and fallow duration was obtained from the farmers and \n\n\n\nlandowners through an oral interview. One profile pit was dug at each site. \n\n\n\nWith a measuring tape, in order to obtain a uniform depth, each profile pit \n\n\n\nwas demarcated into 5 layers namely 0-20, 20-40, 40-60, 60-80, 80-100 \n\n\n\ncm respectively. These uniform sampling depths were carefully selected \n\n\n\nto eliminate error caused by inherent horizon thickness. Four \n\n\n\nrepresentative soil samples were collected from each layer of the pits.  \n\n\n\nCore samples were collected using open-faced coring tube for bulk density \n\n\n\ndetermination. Soil samples were air dried and subsequently ground and \n\n\n\nsieved with 2 mm sieve. Soil samples were subjected to routine analyses. \n\n\n\nParticle size distribution was determined by hydrometer method, bulk \n\n\n\ndensity was by core sampler method [10], moisture content was by \n\n\n\ngravimetric method, soil pH was determined using pH meter and organic \n\n\n\ncarbon was by chromic wet oxidation method (Gee and Or, 2002; \n\n\n\nGrossman and Reinch 2002; Obi 1992; Thomas 1982; Nelson and \n\n\n\nSommers 1996). Total Nitrogen was estimated by micro-Kjeldahl method \n\n\n\nwhile available phosphorus was determined using Bray II solution \n\n\n\n(Bremner and Mulvaney 1982; Olsen and Sommers 1982). \n\n\n\nCarbon sequestration (g cm-2) was evaluated using the equation of Batjes \n\n\n\n(Batjes 1996). The equation is outlined as follows; \n\n\n\nAmount of carbon sequestered= Ci x Bi x Di \u00d710 \n\n\n\nWhere Bi is the bulk density of individual layer (g cm-3); Ci is organic \n\n\n\ncarbon content of the layer (g kg-1); Di is the thickness of the layer (cm). \n\n\n\n2.3 Data analysis \n\n\n\nAnalysis of variance (ANOVA) was used to evaluate the differences in soil \n\n\n\nphysicochemical properties and soil carbon sequestration across soils of \n\n\n\ndifferent fallow ages. For statistically different parameters (p<0.05), \n\n\n\nmeans were separated using Least Significant Difference (LSD). \n\n\n\nCorrelation analysis was conducted to detect the functional relationship \n\n\n\nbetween soil variables while the vertical variations of soil properties were \n\n\n\nevaluated using coefficient of variation analysis and ranking done using \n\n\n\nthe procedure of Wilding where CV < 15% = low variation, CV >15 < 35% \n\n\n\n= moderate variation, CV > 35 % = high variation (Wilding 1985).  GenStat \n\n\n\nstatistical software was used for statistical analyses (Payne et al. 2007). \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1 Soil Physical and Chemical Properties \n\n\n\nThe average sand, silt and clay contents of the soils ranged from 835.2 \u2013 \n\n\n\n889.2 g kg-1; 18.2 \u2013 57.2g kg-1 and 92.6 \u2013 122.6 g kg-1 respectively The Sand \n\n\n\ncontent of the 7 years fallow was significantly (p<0.05) highest across the \n\n\n\nthree fallow periods. The average silt and clay contents of the three fallow \n\n\n\nages did not vary statistically (Table 1). Considering the sampling depth, \n\n\n\nsand content varied significantly across the soil depths under the 7 years \n\n\n\nfallow period while no significant variation was obtained among the \n\n\n\nsampling depths of the 14 years fallow period. In 21 years fallow, the sand \n\n\n\ncontent (775.2 g kg-1) of the 60-80 cm sampling depth was significantly \n\n\n\n(p<0.05) lowest across the five sampling depths.   The clay content of the \n\n\n\n20-40 cm sampling depth under the 7 years fallow was significantly \n\n\n\nhighest (127.6 g kg-1) across the sampling depths. In 14 years fallow \n\n\n\nperiod, 40 -60 cm depth contained significantly highest clay fraction \n\n\n\n(142.6 g kg-1) while the 80-100 cm sampling depth under the 21 years \n\n\n\nfallow period had significantly highest clay fraction (157.6 g kg-1).  This \n\n\n\nfinding indicated the significant influence of soil depth on soil texture \n\n\n\nother than the effects of management practice.  \n\n\n\nSoil texture has earlier been defined as a near-permanent attribute of the \n\n\n\nsoil and hardly does it easily change due to land use, management or \n\n\n\nconservation (Salley et al. 2017). Soil bulk density values differed \n\n\n\nsignificantly across the three fallow ages.14 years fallow had the least bulk \n\n\n\ndensity value (1.44 g cm-3) while the 21 years fallow had the highest value \n\n\n\n(1.64 g cm-3). Also, significant differences were found among the individual \n\n\n\nsampling depths.  Bulk density influences availability and flow (lateral or \n\n\n\nvertical) of soil water and the growth of the plant roots. The results \n\n\n\nindicated that the soils had values that stood at its optimality (Powlson et \n\n\n\nal. 2011). The soil reactions were moderately acidic (mean pH values = \n\n\n\n5.11 \u2013 5.70) and differed significantly across the contrasting fallow \n\n\n\nperiods.  From the results, significantly highest pH value was obtained in \n\n\n\n14 years fallow while the lowest was recorded in 7 years fallow \n\n\n\nrespectively. The acidic condition of the soils could be attributed to the \n\n\n\nuptake of base-forming cations by plants. The uptake of nutrients by \n\n\n\nplants, differences in quantity and quality of biomass returned to the soil \n\n\n\naffect soil reaction (Ahukaemere et al. 2013). \n\n\n\nTable 1 showed no significant differences in cation exchange capacity of \n\n\n\nthe soils under the different fallow ages. However, the effective cation \n\n\n\nexchange capacity (ECEC) of soils of the study area was generally low \n\n\n\n(2.80 \u2013 3.44 cmol+kg-1). Soils of the coastal plain sands origin had earlier \n\n\n\nbeen reported to be made of low ECEC, base cations and base saturation \n\n\n\n(Soil Survey Staff, 2003; Offiong et al. 2009). The mean total nitrogen (TN) \n\n\n\nand available phosphorus contents ranged from 0.37-0.49 g kg-1 and \n\n\n\n12.74-20.30 mg kg-1. Significant differences were not found in the TN and \n\n\n\navailable phosphorus contents of the soils under the three fallow ages. \n\n\n\nSampling depths significantly (p<0.05) affected the TN contents of the \n\n\n\nsoils. The soil TN was significantly lowest at 60-80 and 80-100 cm \n\n\n\nsampling depths due to lower organic carbon content as compared to the \n\n\n\nother sampling depths. 0-20 cm sampling depth under the 14 and 21 years \n\n\n\nfallow contained significantly highest quantity of TN (1.01, 0.62 g kg-1)\n\n\n\n\n\n\n\nTable 1: Soil physical and chemical properties \n\n\n\nDEPTH \n(cm) \n\n\n\nSand   \ng kg-1 \n\n\n\nSilt     \ng kg-1 \n\n\n\nClay       \ng kg-1 \n\n\n\nBD          \ngcm-3 \n\n\n\nMC    \n(%) SCR pH \n\n\n\nOC   g \nkg-1 \n\n\n\nTN        g \nkg-1 \n\n\n\nAVP    mg \nkg-1 C/N TEB \n\n\n\nTEA \ncmol+kg-1 \n\n\n\nECEC \n\n\n\ncmol+kg-1 \nBS \n(%) \n\n\n\n7 years fallow period \n\n\n\n0-20 895.2    32.2 72.6 1.42 7.94 0.44 4.80 5.39 0.47 24.50 11.5 2.25 1.00 3.25 69.2 \n\n\n\n20-40 835.2 37.2 127.6 1.61 9.01 0.29 4.92 4.59 0.38 10.51 12.1 4.98 1.36 6.84 80.1 \n\n\n\n40-60 915.2 7.2 77.6 1.58 8.32 0.09 5.57 6.38 0.57 12.00 11.2 1.55 1.36 2.41 43.5 \n\n\n\n60-80 905.2 7.2 87.6 1.58 9.09 0.08 5.18 2.39 0.22 13.30 10.9 1.28 0.6 1.44 58.4 \n\n\n\n80-100 895.2 7.2 97.6 1.59 7.11 0.07 5.11 2.19 0.19 11.21 11.6 1.55 0.56 2.11 73.4 \n\n\n\nMean 889.2 18.2 92.6 1.56 8.29 0.19 5.11 4.19 0.37 14.30 11.46 2.32 0.98 3.21 64.92 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 05-09 \n\n\n\nCite the Article: Ahukaemere CM, Okoli NH, Aririguzo BN and Onwudike SU (2020). Tropical Soil Carbon Stocks In Relation To Fallow Age And Soil Depth.  \nMalaysian Journal of Sustainable Agriculture, 4(1): 05-09. \n\n\n\n14 years fallow period \n\n\n\n0-20 865.2 17.2 117.6 1.36 10.69 0.15 5.64 11.57 1.01 16.10 11.5 3.32 1.16 4.48 74.1 \n\n\n\n20-40 855.2 7.2 137.6 1.48 9.07 0.05 5.40 7.38 0.64 12.60 11.5 3.36 1.24 4.59 73 \n\n\n\n40-60 835.2 22.2 142.6 1.47 9.56 0.16 5.76 3.39 0.29 9.10 11.7 1.7 1.00 2.7 63 \n\n\n\n60-80 835.2 27.2 137.6 1.47 10.61 0.2 5.74 2.79 0.25 12.60 11.2 1.61 1.24 2.85 56.5 \n\n\n\n80-100 835.2 87.2 77.6 1.42 9.82 0.12 5.98 3.19 0.28 13.30 11.4 1.49 1.08 2.56 57.9 \n\n\n\nMean 845.2 32.2 122.6 1,44 9.95 0.14 5.70 5.66 0.49 12.74 11.46 2.30 2.30 3.44 64.9 \n\n\n\n21 years fallow period \n\n\n\n0-20 855.2 67.2 77.6 1.61 7.36 0.87 5.52 6.98 0.62 12.60 11.3 2.46 0.44 2.9 74.1 \n\n\n\n20-40 895.2 17.2 87.6 1.82 8.34 0.2 5.18 3.79 0.34 17.50 11.2 1.97 0.2 2.17 92.1 \n\n\n\n40-60 835.2 57.2 107.6 1.69 8.35 0.53 5.86 2.39 0.21 35.00 11.4 2.52 0.56 3.08 81.8 \n\n\n\n60-80 775.2 117.2 107.6 1.61 12.43 1.09 5.11 4.59 0.41 19.60 11.2 2.42 0.8 3.22 75.2 \n\n\n\n80-100 815.2 27.2 157.6 1.49 13.07 0.17 5.51 2.19 0.2 16.80 11 1.76 0.88 2.64 66.7 \n\n\n\nMean 835.2 57.2 107.2 1.64 9.91 0.52 5.44 3.99 0.36 20.30 11.22 2.23 0.58 2.80 77.98 \n\n\n\nLSD(0.05 53.29 50.34 19.00 0.10 2.43 0.35 0.29 2.89 0.26 10.18 0.41 1.31 0.49 1.89 13.63 \n\n\n\nBD = Bulk density, MC = Moisture content, SCR = Silt clay ratio, OC = Organic carbon, TN = Total nitrogen, AVP = Available phosphorus, C/N = Carbon \n\n\n\nnitrogen ratio, TEB = Total exchangeable bases, TEA = Total exchangeable acidity, ECEC = Effective cation exchange capacity \n\n\n\n3.2 Quantity of carbon in soils under different fallow ages and soil \n\n\n\ndepths \n\n\n\nFigures 1 and 2 show the average organic carbon contents and carbon \n\n\n\nsequestration values in soils under the three fallow ages. The mean \n\n\n\norganic carbon content of the soils ranged from 3.99-5.66 g kg-1 while the \n\n\n\naverage carbon sequestration value ranged from 1295.00-1611 g cm-2. \n\n\n\nThough not statistically different, 14 years fallow had higher organic \n\n\n\ncarbon (5.66 g kg-1) and carbon stock (1611 g cm-2) than other fallow ages \n\n\n\n(Figs.1 and 2). However, high C sequestration observed in 14 years fallow \n\n\n\ncould be attributed to the plant's ability to capture and store atmospheric \n\n\n\ncarbon since old plants sequester lesser carbon than young ones (Ogban \n\n\n\nand Ekerette 2001; Poulton et al. 2003).  The results obtained from this \n\n\n\nstudy also indicated that prolonged fallow period on degraded tropical \n\n\n\nsoils may not be beneficial to soil carbon sequestration. \n\n\n\nFigure 1: The average organic carbon content in soils under different \n\n\n\nfallow ages. LSD (0.05) =2.83   \n\n\n\nFigure 2: The average carbon stock in soils under different fallow ages. \n\n\n\nLSD (0.05) = 818.3 \n\n\n\nOn the other hand, sampling depth had a significant influence on soil \n\n\n\ncarbon content. In 7 years fallow period, 40-60 cm sampling depth \n\n\n\ncontained significantly highest organic carbon (Table 1) while in 14 and \n\n\n\n21 years fallow ages, 0-20 cm sampling depth contained significantly \n\n\n\nhighest organic carbon content (11.57 g kg-1, 6.98 g kg-1). Also, 20-40 cm \n\n\n\ndepth under 14 years fallow had significantly higher quantity of organic \n\n\n\ncarbon than 40-60, 60-80 and 80-100 cm sampling depths. High coefficient \n\n\n\nof variation (>35%) between the SOC content of the different sampling \n\n\n\ndepths confirmed these variations. Results of the study show that soil \n\n\n\ndepth affected the quantity of carbon sequestered in the soil obtained \n\n\n\nsimilar result (Mbah and Idike 2011). Soil carbon sequestration followed \n\n\n\na similar trend with organic carbon. Across the three fallow periods, 0-20, \n\n\n\n20-40, 40-60, 60-80 and 80-100 cm sampling depths stored 2308.3, 1681, \n\n\n\n1274, 998.7 and 752.7 g cm-2 (Table 2). \n\n\n\nTable 2: Effect of sampling depth on soil carbon \n\n\n\nDepth (cm) OC (g kg-1) CS (g cm-2) \n\n\n\n0-20 7.98 2308.30 \n\n\n\n20-40 5.25 1681.00 \n\n\n\n40-60 4.05 1274.00 \n\n\n\n60-80 3.25 998.70 \n\n\n\n80-100 2.53 752.70 \n\n\n\nLSD (0.05) 3.73 1056.40 \n\n\n\nCS = carbon sequestration, OC = organic carbon \n\n\n\nGenerally, from the results, soil carbon reduced with sampling depth at all \n\n\n\nsites used for the study. In 7 years fallow period, 40-60 cm sampling \n\n\n\ndepths contained significantly highest carbon sequestration value. In 14 \n\n\n\nand 21 years fallow ages, 0-20 cm sampling depth sequestered \n\n\n\nsignificantly highest carbon (3147.04 g cm-2, 2247 g cm-2) compared to \n\n\n\nother depths (Fig. 3). The quantity of carbon stored in the uppermost layer \n\n\n\n(0-20 cm) of soil under the different fallow ages was greater than the \n\n\n\ndeepest layer by 25-28% probably because most of the litters and other \n\n\n\nplant residues are incorporated or deposited on the soil surface. Dick and \n\n\n\nGregorich reported high carbon concentration on the soil surface. \n\n\n\nHowever, the upper part of the soil happened to be the first beneficiary of \n\n\n\nthe photosynthetic extraction of carbon into the terrestrial environment \n\n\n\nfrom the atmosphere through phyto-mechanisms (Dick and Greorich \n\n\n\n2004). The coefficient of variation results showed high variation (43, 64 \n\n\n\nand 48%) among the varying soil depths. This was further explained by \n\n\n\nthe values of the standard deviation obtained from the study (Table 3). \n\n\n\nFigure 3: Carbon stocks (g C m2) of the varying soil sampling depths (cm) \n\n\n\nunder the three different fallow periods. LSD (0.05) = 818.3  \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 years 14 years 21 years\n\n\n\nOrg. C (g kg-1)\n\n\n\nOrg. C (g/kg)\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\nD\nE\n\n\n\nP\nT\n\n\n\nH\n\n\n\n0\n-2\n\n\n\n0\n\n\n\n2\n0\n\n\n\n-4\n0\n\n\n\n4\n0\n\n\n\n-6\n0\n\n\n\n6\n0\n\n\n\n-8\n0\n\n\n\n8\n0\n\n\n\n-1\n0\n\n\n\n0\n\n\n\n0\n-2\n\n\n\n0\n\n\n\n2\n0\n\n\n\n-4\n0\n\n\n\n4\n0\n\n\n\n-6\n0\n\n\n\n6\n0\n\n\n\n-8\n0\n\n\n\n8\n0\n\n\n\n-1\n0\n\n\n\n0\n\n\n\n0\n-2\n\n\n\n0\n\n\n\n2\n0\n\n\n\n-4\n0\n\n\n\n4\n0\n\n\n\n-6\n0\n\n\n\n6\n0\n\n\n\n-8\n0\n\n\n\n8\n0\n\n\n\n-1\n0\n\n\n\n0\n\n\n\nF A L L O W  A G E 7  Y R S 1 4  Y R S 2 1  Y R S\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\n7 years 14 years 21 years\n\n\n\nCarbon Seq. (g cm-2)\n\n\n\nCarbon Seq. (g/m2\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 05-09 \n\n\n\nCite the Article: Ahukaemere CM, Okoli NH, Aririguzo BN and Onwudike SU (2020). Tropical Soil Carbon Stocks In Relation To Fallow Age And Soil Depth.  \nMalaysian Journal of Sustainable Agriculture, 4(1): 05-09. \n\n\n\nTable 3: The Mean, standard deviation and coefficient of variation \nresults of soil carbon \n\n\n\nFallow Age Organic Carbon \n\n\n\n(g kg-1) \n\n\n\nCarbon stock \n\n\n\ng cm-2 \n\n\n\n7 years \n\n\n\nMean  4.19 1295.30 \n\n\n\nSDV 1.846 560.9499 \n\n\n\nCV 44.07 43.31 \n\n\n\n14 years \n\n\n\nMean  5.67 1610.88 \n\n\n\nSDV 3.787 1023.238 \n\n\n\nCV 66.91 63.52 \n\n\n\n21 years \n\n\n\nMean  3.99 1313.11 \n\n\n\nSDV 1.945 631.7356 \n\n\n\nCV 48.78 48.11 \n\n\n\nSDV = Standard deviation, CV = Coefficient of variation, CV < 15% = Low \nvariation, CV > 15 < 35% = Moderate variation, CV > 35 = High variation \n(Wilding, 1985). \n\n\n\nResults of the correlation analysis show that carbon sequestration \n\n\n\ncorrelated positively with organic carbon, total nitrogen and total \n\n\n\nexchangeable bases (r = 0.988**, r = 0.999**, r = 0.502*) (Table 4). These \n\n\n\nrelationships indicate the potential influence of these soil parameters on \n\n\n\ncarbon sequestration. Also, soil moisture contents correlated positively \n\n\n\nwith clay (r = 0.602*). Generally, clay has a high rate of adsorption and can \n\n\n\nretain water easily. Odunze reported that moisture content increased with \n\n\n\nincreasing clay content as clay would cause impaired drainage, especially \n\n\n\nat the subsurface horizons (Odunze 2003). Coarse-textured soils are \n\n\n\ncharacterized by low moisture content and high drainage, thus resulting \n\n\n\nto moisture stress.\n\n\n\nTable 4: Correlation matrix of soil properties \n\n\n\nsoil prop. CS ECEC MC OC PH TEB TN Clay Sand Silt  \n\n\n\nCS 1 \n\n\n\nECEC 0.453 1 \n\n\n\nMC -0.105 0.084 1 \n\n\n\nOC 0.988** 0.458 -0.037 1 \n\n\n\nPh -0.035 -0.23 0.1960 0.009 1 \n\n\n\nTEB 0.502* 0.965** -0.011 0.484 -0.311 1 \n\n\n\nTN 0.989** 0.428 -0.032 0.999** 0.008 0.458 1 \n\n\n\nclay -0.140 0.369 0.603* -0.089 0.201 0.275 -0.103 1 \n\n\n\nsand 0.170 -0.316 -0.705 0.141 -0.258 -0.2531 0.148 -0.526* \n\n\n\nSilt -0.081 0.060 0.321 -0.091 0.135 0.066 -0.088 -0.192  -0.787** 1 \n*and** = significant at 0.05 and 0.01 probability levels respectively, CS = Carbon sequestration, ECEC = Effective cation exchange capacity, OC = organic\ncarbon, MC = Moisture content, TN = Total nitrogen, TEB = Total exchangeable bases. \n\n\n\n4. CONCLUSIONS \n\n\n\nThe results of this study revealed that fallow age and soil depth influence \n\n\n\nthe amount of carbon sequestered in the tropical soils. 14 years fallow \n\n\n\nsequestered the highest quantity of carbon while the least carbon stock \n\n\n\nwas observed in 7 years fallow. In 7 years fallow, carbon stock was \n\n\n\ndominant at 40-60 cm depth while in 14 and 21 years fallow ages, 0-20 cm \n\n\n\nsampling depth sequestered significantly highest carbon compared to \n\n\n\nother sampling depths. Conclusively, in tropical soils, carbon is more often \n\n\n\nsequestered in the top-soil layer than in the sub-soil layer. Prolonged \n\n\n\nfallow age up to 21 years may not be beneficial to soil carbon \n\n\n\nsequestration. Soil properties such as total exchangeable bases, organic \n\n\n\ncarbon and total nitrogen significantly affected soil carbon sequestration \n\n\n\nin the soils. In order to maintain this carbon sequestration, soils are to be \n\n\n\nkept covered for certain period of time; and other management practices \n\n\n\nthat encourage the deposition of organic residues on the soil surface \n\n\n\nshould be adopted. \n\n\n\nREFERENCES \n\n\n\nAhukaemere, C.M. 2015. Sequestration and Dynamics of Carbon and \n\n\n\nNitrogen in Soils of Dissimilar Lithologies under Different Land Use \n\n\n\nTypes in South-east Nigeria. A Ph.D Thesis of the Department of Soil \n\n\n\nScience and Technology, Federal University of Technology, Owerri \n\n\n\nNigeria, 266. \n\n\n\nAhukaemere, C.M., Akamigbo, F.O.R., Onweremadu, E.U. 2013. Soil colour \n\n\n\nand soil organic matter as indicators of soil quality in falsebedded \n\n\n\nsandstones soils in Southeastern Nigeria. Nige. J. Agri. Fo. Enviro., \n\n\n\n9(3), 6-11. \n\n\n\nhttp://njafe.org/njafe2003V9No3/2_Ahukae_ere_et_al.pdf \n\n\n\nAhukaemere, C.M., Obi, C.I., Ndukwuand, B.N., Nwamadi, N.J. 2016. \n\n\n\nCharacterization and classification of soils of Egbema in Imo State, \n\n\n\nSouth-eastern Nigeria. Fu. J. Seri, 2(1), 41-47. \n\n\n\nfile:///C:/Users/USER/Downloads/1471514211characterization_a\n\n\n\nnd_classification_of_soils_of_egbema_in_imo_state_south_eastern_ni\n\n\n\ngeria%20(1).pdf \n\n\n\nAkamigbo, F.O.R. 2010. The role of soil science in combating \n\n\n\nenvironmental and climate change hazards. A solicited paper \n\n\n\ndelivered at HRH EzeEgesie complex, Michael Okpara University of \n\n\n\nAgric. Umudike, UmuahiaAbia State, Nigeria, 22. \n\n\n\nAlexander, P., Paustian, K., Smith, P., Moran, D. 2015. The economics of soil \n\n\n\ncarbon sequestration and agricultural emissions abatement. Soil, 1, \n\n\n\n331-339. \n\n\n\nAnikwe, M.A.N. 2010. Carbon storage in soils of Southeastern Nigeria \n\n\n\nunder different management practices. Carb. Bala. andManag., 5(5), \n\n\n\n1-7. DOI: https://doi.org/10.1186/1750-0680-5-5 \n\n\n\nBatjes, N.H. 1996. Total carbon and nitrogen in the soils of the world. \n\n\n\nEuropean Journal of Soil Science, 47, 151-163. \n\n\n\nhttps://library.wur.nl/isric/fulltext/isricu_t47d6414d_001.pdf \n\n\n\nBremner, J.M., Mulvaney, C.S. 1982.  Total-Nitrogen.  In:  Methods of Soil \n\n\n\nanalysis Part 2. A.L. Page, R.H. Mille and D.R. Keeney (Eds.).  American \n\n\n\nSociety of Agronomy, 595-624. \n\n\n\nDick, W.A., Greorich, E.G. 2004. Development and Maintaining Soil Organic \n\n\n\nMatter Levels. In: Managing Soil Quality Challenges in Modern \n\n\n\nAgriculture. schjonning, P., S. Elmholt and B.T. Christen (Eds.). ABI \n\n\n\nPublishing, 103-120. \n\n\n\nGee, G.W., Or, D. 2002. Particle Size Analysis.  In: Methods of Soil Analysis \n\n\n\nPart 4-Physical Methods, J.H. Dane and G.C. Topp, (Eds.).  Soil Science \n\n\n\nSociety of America, (5), 255-293. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 05-09 \n\n\n\n\n\n\n\n \nCite the Article: Ahukaemere CM, Okoli NH, Aririguzo BN and Onwudike SU (2020). Tropical Soil Carbon Stocks In Relation To Fallow Age And Soil Depth.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(1): 05-09. \n \n\n\n\n\n\n\n\nGrossman, R.B., Reinch, T.G. 2002. Bulk Density and Linear Extensibility. \nIn: Methods of Soil Analysis Part 4-Physical methods. Dane, J.H and \nG.C. Topp (Eds.). Soil Science Society of America. Book series, (5), \n201-228. \n\n\n\nLal, R. 2004. Soil carbon sequestration impacts on global climate change \n\n\n\nand food security. Sci., 304, 1623-1627.https://www.c-agg.org/wp-\n\n\n\ncontent/uploads/1623.pdf \n\n\n\nLal, R. 2005. No-till farming and environment quality. In: symposio sobre \n\n\n\nplantiodireto eMeioambient; sequestro de carbon e qualidade da \n\n\n\nagua.Anais. Foz do Iguacu,18-20 de Maio, 29-37. \n\n\n\nMbah, C.N., Idike, F.I. 2011. Carbon storage in tropical agricultural soils of \n\n\n\nSoutheastern Nigeria under different management practices.Inter. \n\n\n\nAgricultural Science Research Journal, 12, 53-57. \n\n\n\nhttp://www.interesjournals.org/IRJAS \n\n\n\nNelson, D.W., Sommers, L.E. 1996. Total Carbon, Organic Carbon and \n\n\n\nOrganic Matter. In: Methods of Soil Analysis Part 3-Chemical \n\n\n\nMethods. D. L. Sparks et al. (Eds.). Soil Science Society of America. \n\n\n\nBook series, (5), 961-1010. \n\n\n\nNIMET (Nigerian Meteorological Agency), Nigeria. 2016. Climate Weather \n\n\n\nand Water Information, for sustainable development and safety. \n\n\n\nObi, M.E. 1990. Soil physics: A compendium of lectures of University of \nNigeria on human-induced soil degradation.2ndEdn., Wageningen: \nInternational Soil Reference and Information Center. \n\n\n\n \nOdunze, A.C. 2003. Northern Guinea Savanna Soils and Rainfall Properties \n\n\n\nfor Erosion Control and   Fertility Improvement. Sci. Tech. and Res. \nComm., 33, 73-82.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 85-91 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.85.91 \n\n\n\nCite The Article: Ayari Mohamed Saleh, Douh Boutheina, Mguidiche Amelc (2022). Behavior and Water Use Efficiency of Eight Tunisian Varieties of Chickpea (Cicer \nArietinum L.) in Two Bioclimatic Stages. Journal of Sustainable Agricultures, 6(2): 85-91. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.85.91 \n\n\n\nBEHAVIOR AND WATER USE EFFICIENCY OF EIGHT TUNISIAN VARIETIES OF \nCHICKPEA (CICER ARIETINUM L.) IN TWO BIOCLIMATIC STAGES \n\n\n\nAyari Mohamed Saleha, Douh Boutheinab*, Mguidiche Amelc3 \n\n\n\naHigher Institute of Biotechnology of Beja, Avenue Habib Bourguiba BP 382 Beja 9000, Tunisia. \nbDepartment of Horticultural Systems Engineering and Natural Environments, Higher Institute of Agronomy of Chott Meriem, University of \nSousse, BP 47, 4042 Sousse, Tunisia. \ncOlive Tree Institute of Sousse, Ibn Khaldoun 14, Sousse 4061, Tunisia; University of Sfax, Tunisia. \n*Corresponding Author Email: boutheina_douh@yahoo.fr \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 02 December 2021 \nAccepted 06 January 2022 \nAvailable online 13 January 2022\n\n\n\nThis work was carried out from December 2016 to June 2017 in two different regions on the north of Tunisia \nbelong to the Sub-humid bioclimatic stage on Beja and the Semi-arid bioclimatic stage on Oued Mliz. It aims \nto identify the varieties of chickpea adaptable on each bioclimatic stage and to evaluate the efficiency of water \nuse for some varieties of chickpea. Indeed, analysis of yield parameters such as biological yields, weight of \nhundred seeds, seeds yield, number of seeds. All varieties were grown in rainfed conditions. For the sub-\nhumid and semi-arid bioclimatic sites plant have received respectively an amount of water of 346 and \n261mm. The results show that there is a significant correlation between these parameters. The cultivation of \nthe collection of eight varieties of chickpea in rainfed soil showed an important adaptation to drought. The \nnumber of pods marked in Beja1 and Nayer varieties are the highest, because of the ability to fill the pods \nduring the year. While other varieties have a lower number of pods indicating that spring drought could be \nthe cause of high flower abortion, pericarp development and empty pod formation. This research revealed \nthat in the sub-humid bioclimatic stage, all varieties adapt and produce better than on the semi-arid. The \nsemi-arid Tunisian is characterized by the final drought which causes the hydrous stress at chickpea. The \nconduit of this last in these zones is dependent on the selection of the varieties early and resistant to the \nwater deficit. \n\n\n\nKEYWORDS \n\n\n\nwater use efficiency, harvest index, chickpeas, rainfed condition, bioclimatic stage. \n\n\n\n1. INTRODUCTION\n\n\n\nWorldwide, 90% of chickpea (Cicer arietinum L.) crops are rainfed. Spring \n\n\n\ndrought represents the main abiotic constraint for increasing production \n\n\n\n(Rani et al., 2020; Arif et al, 2021). In Tunisia, most of the area cultivated \n\n\n\nwith chickpea is concentrated in the north of the country, particularly in \n\n\n\nthe regions of Beja, Jendouba, Nabeul, Mateur and Bizerte, which are \n\n\n\ncharacterized by a humid to sub-humid climate. In Tunisia, the chickpea \n\n\n\noccupies the second place after the bean. It is grown on an average annual \n\n\n\narea of 19 650 ha which represents 1.1% of the area sown to field crops. \n\n\n\nThe production is about 13 520 t with an average yield of 670 kg ha-1 \n\n\n\n(Bouhdida et al, 2013, Nefzi et al., 2016, Ouji et al., 2016). To meet the \n\n\n\nneeds of the concept, the Tunisian government resorts to imports of about \n\n\n\n19,000 t year-1 (AAC, 2006) which represent 141% of national production. \n\n\n\nThe chickpea suffers from many difficulties, apart from the environmental \n\n\n\nconditions and the bad management of the crop techniques which are not \n\n\n\nnegligible causes of the weakness of the production, it seems that the \n\n\n\nmajor problem remains that of abiotic factor as the deficiency in \n\n\n\nphosphorus, the salinity, and the drought (Hichri et al., 2014; Kaashyap et \n\n\n\nal., 2017). The latter is a major factor, which in case of low availability, \n\n\n\nconstrains the production of legume crops. Two types of droughts affect \n\n\n\nthe chickpea crop in Tunisia, a spring drought caused by a break in rainfall \n\n\n\nand a terminal drought occurring at the end of the crop's growth cycle due \n\n\n\nto a lack of rainfall and a drying up of the water reserves in the soil (Wery \n\n\n\net al., 1994). The amount of water available for agriculture in the \n\n\n\nMediterranean is decreasing due to increasing population pressure and \n\n\n\ngreater frequency of drought. Therefore, the efficiency of water use for \n\n\n\nagricultural production must be maximized (Douh et al., 2021; Khila et al., \n\n\n\n2021). \n\n\n\nThis study has set as main objectives: agro-physiological characterization \n\n\n\nof eight local varieties of chickpea to select the best genotypes that adapt \n\n\n\nto the edapho-climatic conditions of different bioclimatic stages: the sub-\n\n\n\nhumid represented by the region of Beja, the upper semi-arid represented \n\n\n\nby the region of Oued Mliz. Indeed, the evaluation of varieties from the \n\n\n\nnational program of improvement of chickpea is of particular interest to \n\n\n\nensure food security and help small farmers to cope with climate change. \n\n\n\n2. MATERIAL AND METHODS \n\n\n\n2.1 Study sites \n\n\n\nOued Beja- Beja (S1): The trial was conducted in spring cultivation on a \n\n\n\nplot of the experimental unit of Oued Beja of the CRRGC (Regional Field \n\n\n\nCrop Research Center of Beja), located in the north-west of Tunisia \n\n\n\n\nmailto:boutheina_douh@yahoo.fr\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 85-91 \n\n\n\nCite The Article: Ayari Mohamed Saleh, Douh Boutheina, Mguidiche Amelc (2022). Behavior and Water Use Efficiency of Eight Tunisian Varieties of Chickpea (Cicer \nArietinum L.) in Two Bioclimatic Stages. Journal of Sustainable Agricultures, 6(2): 85-91. \n\n\n\ngovernorate of Beja which is part of the bioclimatic semi-arid stage at \n\n\n\nLatitude 36\u00b044\u201905\u2019\u2019N, Longitude 9\u00b013\u201935\u2019\u2019E. This area is characterized by \n\n\n\nan average annual rainfall of 500 to 600 mm. Figure 1 shows that during \n\n\n\nthe trial, the rainfall recorded from December to June was 261 mm \n\n\n\nrecorded by a meteorological station installed within the center. The soil \n\n\n\nhas a clay-silt texture with a pH of 7.2. \n\n\n\nOued Mliz- Jendouba (S2): The trial was conducted in spring cultivation \n\n\n\non a plot of the research station of Oued Mliz, located in the north-west of \n\n\n\nTunisia, governorate of Jendouba, which is part of the upper sub-humid \n\n\n\nbioclimatic stage at a latitude of 36\u00b026\u201954\u2019\u2019N, Longitude 8\u00b032\u201955\u2019\u2019N. During \n\n\n\nthe trial, the rainfall recorded was about 346 mm (Figure 1). The soil has \n\n\n\na clay-silt texture with a pH of 7.4. \nFigure 1: Rainfall for the period experimental growing season in the two \n\n\n\nsites \n\n\n\nTable 1: Characteristics of chickpea varieties studied (Sivapalan et al., 2003) \nVarieties Inscription Date  Breeder Main Characteristic Photo \nB\u00e9ja1 2003 INRAT/ICARDA Productive medium seeded  \n\n\n\nRecommended for winter sowing \n\n\n\nBouchra  2003 INRAT/ICARDA Productive medium seeded  \nRecommended for winter sowing \n\n\n\nNayer  2003 INRAT/ICARDA Productive medium seeded  \nRecommended for winter sowing \n\n\n\nNour 2011 INRAT/ICARDA Productive, large seed (40-\n44g/100seeds) Recommended for \nwinter sowing \n\n\n\nChetoui \n1987 INRAT/ICARDA \n\n\n\nProductive, small seed (30-\n32g/100 seeds) Recommended \nfor winter seeding \n\n\n\nKasseb 1987 INRAT/ICARDA Small seed,  \nrecommended for winter sowing \n\n\n\nAmdoun 1987 INRAT/ICARDA Big seed (45-48g/100 seeds). \nRecommended for spring sowing. \n\n\n\nRabha  2017 INRAT/ICARDA High yielding, large seed size (46-\n47g/100 seeds). Recommended \nfor winter and spring planting \n\n\n\nINRAT: National Institute of Agronomic Research of Tunisia \n\n\n\nICARDA: International Center for Agricultural Research in the Dry Areas \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 85-91 \n\n\n\nCite The Article: Ayari Mohamed Saleh, Douh Boutheina, Mguidiche Amelc (2022). Behavior and Water Use Efficiency of Eight Tunisian Varieties of Chickpea (Cicer \nArietinum L.) in Two Bioclimatic Stages. Journal of Sustainable Agricultures, 6(2): 85-91. \n\n\n\n2.2 Plant material \n\n\n\nThe plant material used consists of a population of chickpea (Cicer \n\n\n\narietinum L.) composed of eight varieties registered in Tunisia from the \n\n\n\nnational program of improvement of chickpea at the CRRGC of Beja. Table \n\n\n\n1 presents the morphological, physiological, and technological \n\n\n\ncharacteristics of the chickpeas cultivated in Tunisia. \n\n\n\n2.3 Experimental design and management \n\n\n\nThe trial was conducted in randomized block replicate three times for each \n\n\n\ntreatment. A total of 24 blocks was used. Each block was divided into six \n\n\n\nlines of 4 m of length spaced 0.5 m apart. Seeding was carried out on \n\n\n\nDecember 20, 2017, for a 60seed/row seeding rate. Planting density was \n\n\n\n30 plant/m\u00b2 (2 lines/m * 15 plant/linear meter) The previous crop was a \n\n\n\nworked fallow. Sowing was done manually and took place on December 20 \n\n\n\nand harvest took place on June 18. \n\n\n\nFigure 2: Experimental design respectively at Oued Mliz and Beja \n\n\n\n2.4 Soil parameters \n\n\n\nSoil analysis was made in the laboratory of rural engineering belongs to \nthe Higher Institute of Agronomy of Chott Meriem, Sousse. the soil texture \nwas determined by the Robinson pipette method, water content at the \npermanent wilting point and at the field capacity with a pressure cooker, \nsoil permeability by Muntz method, salinity, and pH respectively by a \nconductivity meter and a pH meter. \n\n\n\n2.5 Agronomic parameters  \n\n\n\nThe impact of the climatic stage had been tested on eight chickpea \nvarieties. For that, six plants were selected at random for each variety and \neach region to show the relationship between parameters. Just before \nharvesting the height of the plant has been determined. At maturity, all the \nplants were cut at ground level. The pods were manually removed from all \nthe harvested plants and counted then number of pods per plant was \ndetermined. All the pods were threshed by hand, and number of seeds per \npod determined. The seeds were air-dried, cleaned and weighed to \ndetermine grain yield, from each plot after the color of the plant and pod \nturned yellow, is extrapolated to the hectare (kg ha-1). Sub-samples of the \nseeds were used to determine 100 seed weight (100-SW) recorded from \neach plot and expressed in gram (g). Harvest index (HI) was determined \n\n\n\nas the ratio of grain yield to biological yield. It is calculated according to \nthe formula of Yoshida (1981) as follows: \n\n\n\n\ud835\udc6f\ud835\udc70 =\n\ud835\udc6e\ud835\udc80\n\n\n\n\ud835\udc69\ud835\udc80\n\n\n\nGY: Seed Yield (kg ha-1); \n\n\n\nBY: Biological Yield (kg ha-1). \n\n\n\n2.6 Water use efficiency (WUE) \n\n\n\nIt defines the quantity of production obtained by a unit of water used. It is \ncalculated considering organic and seed yields. From this, we can \ndistinguish the efficiency of use of dry matter and seed water. This notion \nconsiders the need to maximize production per unit of available water in \nthe context of increasing food demand and limited water resources \n(Molden et al., 2010). A study clarified that the WUE could be determined \naccording to the dry matter yield according to the formula (Siddique et al., \n2001): \n\n\n\n\ud835\udc7e\ud835\udc7c\ud835\udc6c \ud835\udc83\ud835\udc8a\ud835\udc90 = \ud835\udc6b\ud835\udc7e \ud835\udc6c\ud835\udc7b\ud835\udc84\u2044  \n\n\n\nWUEbio: Biological Water Use Efficiency (g mm-1) \n\n\n\nDW: Dry Weight (g) \n\n\n\nETc: Crop evapotranspiration (mm) \n\n\n\nWUE can be determined according to the biological yield or according to \nthe seed yield according to the formula: \n\n\n\n\ud835\udc7e\ud835\udc7c\ud835\udc6c\ud835\udc94  =\n\ud835\udc7a\ud835\udc80\n\n\n\n\ud835\udc6c\ud835\udc7b\ud835\udc84\n\n\n\nWUEs: Seed Water Use Efficiency (kg ha-1 mm-1) \n\n\n\nSY: Seed Yield (kg ha-1) \n\n\n\nETc: Crop evapotranspiration (mm) \n\n\n\n2.7 Data analysis \n\n\n\nThe data was subject to obtained underwent analysis of variance (ANOVA) \nwith the procedure (GLM), for General Linear Model was conducted using \nSPSS software (version 23). The means fitted to the model (LSMEANS) \nwere calculated for each treatment through the Student-Newman-Keuls \ntest (SNK) at the 5% threshold for the comparison of the means between \nvarieties. The model used is of the form: \n\n\n\n\ud835\udc80\ud835\udc8a = \u03bc+\ud835\udc7f\ud835\udc8a + \ud835\udc86\ud835\udc8a \n\n\n\nYi: Variable to be explained \n\n\n\n\u03bc: Average factor for each variable to be explained \n\n\n\nXi: Fixed effects of explanatory variables \n\n\n\nei: Residual error \n\n\n\nTo test whether the differences between semi-arid and sub-humid \nbioclimatic stage were significant, independent samples t-test were used. \nIf the observed value of t for testing whether the two sites are different \nwas greater than the 5% significant point it was concluded that the \nobserved differences between the two sites was significant at 5%. \n\n\n\nFor relationship between parameters Pearson Correlation was used to \nevaluate whether there is statistical evidence for a linear relationship \namong the same pairs of chickpea parameters, represented by a \npopulation correlation coefficient 5% and 1% \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Edaphic and climatic characterization  \n\n\n\nTable 2 show that the soil texture of the two sites Oued Mliz and Beja are \nclay silt with approximately a hydraulic conductivity of water through the \nsoil of 1.4 cm/h. Soil moisture is a main property of the soil which acts as \na water reservoir, making it available for crops as it is required. Soil water \nis very important to the complete soil system because both it\u2019s necessary \nfor plant growth, and it contain nutrients essential for plant growth. Table \n1 indicated that water availability on the two site is about 15,5%. \n\n\n\nTo determine the bioclimatic stage, we used the Emberger climatic index:  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 85-91 \n\n\n\nCite The Article: Ayari Mohamed Saleh, Douh Boutheina, Mguidiche Amelc (2022). Behavior and Water Use Efficiency of Eight Tunisian Varieties of Chickpea (Cicer \nArietinum L.) in Two Bioclimatic Stages. Journal of Sustainable Agricultures, 6(2): 85-91. \n\n\n\nQ= 2000P/ (M\u00b2-m\u00b2) \n\n\n\nP : Average annual precipitation (mm). \n\n\n\nm : Average of the maxima of the warmest month (K\u00b0). \n\n\n\nM : Average of the minima of the coldest month (K\u00b0). \n\n\n\nfollowing the calculation of the Emberger index we have classified the \nexperimental site \"Oued Mliz\" belonging to the Governorate of Jendouba \nto the Sub-humid and that of \"Beja\" to the Semi-arid superior. \n\n\n\nTable 2: Physical and hydrodynamic soil characteristics of Chott \n\n\n\nMeriem \n\n\n\nSite  Bioclimatic \n\n\n\nstage \n\n\n\nSoil \n\n\n\ntexture \n\n\n\n\u0275fc  \n\n\n\n(%) \n\n\n\n\u0275pwp  \n\n\n\n(%) \n\n\n\nKs \n\n\n\n(cm \n\n\n\nh-1) \n\n\n\npH \n\n\n\n[-] \n\n\n\nEC \n\n\n\n(mS \n\n\n\ncm-\n\n\n\n1) \n\n\n\nBeja \n\n\n\n(S1) \n\n\n\nSemi-arid  clay-silt  25.15 9.74 1.42 7.2 0.55 \n\n\n\nOued \n\n\n\nMliz \n\n\n\n(S2) \n\n\n\nSub-humid clay-silt  25.25 9.84 1.36 7.4 0.63 \n\n\n\n3.2 Agronomic parameters \n\n\n\n3.2.1 Plant height  \n\n\n\nFigure 3 shows the variation in the height of chickpea varieties depending \n\n\n\non the bioclimatic stage. We notice that there is no significant difference \n\n\n\nbetween the average heights of the different varieties nor between the \n\n\n\nresults of the two sites for each variety. The values fluctuate between \n\n\n\n46.5\u00b12.8 and 50.8\u00b12.6 cm at the sub-humid bioclimatic stage and between \n\n\n\n40\u00b13.55 and 48.6\u00b11.23 cm. \n\n\n\nFigure 3: Mean values of the plant height as a function of the \n\n\n\ninteraction\u2019s varieties \u00d7 bioclimatic stage \n\n\n\n3.2.2 Number of pods and seeds per plant  \n\n\n\nSite did not affect the number of pods per plant (figure 4). In contrast, the \neffects of cultivar (P<0.05) on the number of pods per plant were \nsignificant in sub-humid and semi-arid bioclimatic stage. Number of pods \nper plant was greater in S2 with an average of 34.5\u00b18.9, the higher value \n47.2\u00b113.7 was recorded for Nayer and the lowest value 24.1\u00b13.9 for \nAmdoun and Rebha. In the semi-arid site, the pods number per plant was \nabout 32.4\u00b18.8 the higher value 44.3\u00b112.4 was recorded for Nayer and the \nlowest value 20.1\u00b14.6 for Amdoun. On average, the number of pods per \nplant on S2 is higher than in S2 of about 6,2%.  \n\n\n\nThangwana and Ogola (2012) showed that the highest number of pods per \nplant was recorded in the desi types (ICCV201 & ICCV37) and the lowest \nin kabuli type (ICCV92337). On average, desi cultivars produced greater \n(27.6) number of pods per plant compared with kabuli cultivars (17.7) in \nthe winter sowing.  \n\n\n\nThe effect of cultivar on the number of seeds per plant was significant in \nboth sites (P<0.05); it ranged from a minimum of 23.4\u00b12.3 (Amdoun and \nRabha) to a maximum of 42.1\u00b119.1 (Beja1) in Sub-humid site, and a \nminimum of 16.4\u00b13.7 (Amdoun) to a maximum of 38.1\u00b118.4 (Beja1) in \nsemi-arid site (figure 5). Site did not affect number of seeds per plant for \nthe eight varieties. \n\n\n\nThe effect of cultivar on 100-SW was significant (P<0.05) in both sites; \n100-SW ranged from 27.6\u00b12.3 g (Nayer) to 40.9\u00b11.9 g (Amdoun) in S1, and \n27.4\u00b12.317.6 g (Nayer) to 42.2\u00b11.6 g (Amdoun) in S2 (figure 6). Despite \nthat the number of seeds is higher on the sub-humid site, statistically, \nthere\u2019s no significant difference on the weight of one hundred seeds (100-\nSW) which are similar on the two sites. Amdoun cultivar recorded the \nhighest 100-SD in the two sites with 42.2\u00b11.6 and 40.9\u00b11.9g respectively \nS2 and S1 recording an improvement of 3%. The site did not affect the \nquality as it\u2019s related to cultivar while it influences the quantity which \nrelated to biotic and abiotic stress and agricultural practices. \n\n\n\nFigure 4: Mean values of the pods number as a function of the \n\n\n\ninteraction\u2019s varieties \u00d7 bioclimatic stage \n\n\n\nFigure 5: Mean values of number of seeds per plant as a function of the \n\n\n\ninteraction\u2019s cultivar \u00d7 bioclimatic stage \n\n\n\nFigure 6: Mean values of 100 seeds weight as a function of the \n\n\n\ninteraction\u2019s cultivar \u00d7 bioclimatic stage \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 85-91 \n\n\n\nCite The Article: Ayari Mohamed Saleh, Douh Boutheina, Mguidiche Amelc (2022). Behavior and Water Use Efficiency of Eight Tunisian Varieties of Chickpea (Cicer \nArietinum L.) in Two Bioclimatic Stages. Journal of Sustainable Agricultures, 6(2): 85-91. \n\n\n\n3.2.3 Grain and biological yield \n\n\n\nTable 3 presents the average grain and biological yield of the eight \n\n\n\nvarieties studied of chickpeas, conducted in two different sites Beja and \n\n\n\nOued Miliz. The experimental site had a significant difference at (P<0.05) \n\n\n\non grain yield only for rabha cultivar, in which an improvement of 21.7% \n\n\n\nwas recorded on the subhumid compared to semi aid bioclimatic stage. \n\n\n\nThe lowest grain yield was produced in Rabha cultivar on both sites with \n\n\n\n6.64\u00b11.1 g/plant on S1 (1991 Kg/ha) and 9.04\u00b10.4 g/plant on S2 (2711 \n\n\n\nkg/ha); and the highest values 10.58\u00b13.4 g/plant for Nour on S1 (3174 \n\n\n\nkg/ha) and 15.53\u00b15.2g/plant for Beja1 on S2 (4059 Kg/ha). On average, \n\n\n\nBeja1 cultivar produces 1.5 times greater than Rabha on S2 and Nour \n\n\n\nproduces 1.6 times more than Rabha on S1. \n\n\n\nSeveral factors can affect the grain yield such as climate, variety, cultural \n\n\n\npractices, and planting density. Thangwana and Ogola (2012), conducted \n\n\n\nresearch on a chickpea crop to study the impact of planting density, \n\n\n\nvariety, and sowing date on crop yield. For a planting density of 33 \n\n\n\nplants/m\u00b2, the lowest grain yield was produced in the Desi cultivar \n\n\n\nICCV201(950 kg ha-1); and the highest was obtained in the Kabuli cultivar \n\n\n\nICCV92337 (2435 kg ha-1). \n\n\n\nIn agreement with the results of grain yield, biological yield of all varieties \n\n\n\nwas higher at the Beja site than those of Oued Miliz. Statistically, there is \n\n\n\nno significant difference at the 5% threshold neither between varieties nor \n\n\n\nbetween sites. However, Nayer cultivar recorded the highest biological \n\n\n\nyield with 30.23\u00b16.8 and 29.17\u00b16.3 kg/m\u00b2respectively on S2 and S1. \n\n\n\nGenetic improvement is one approach to remove these limitations. Since \n\n\n\nthe green revolution, improvement in wheat grain yield in many \n\n\n\nenvironments has been due to an increase in the number of grains per unit \n\n\n\narea and harvest index (Siddique et al., 1989; Shearman et al., 2005; Flohr \n\n\n\net al., 2018). With the exceptions of environments that were characterized \n\n\n\nby severe stem frost, the management factors presented here have shown \n\n\n\nlimited scope to improve HI and yield in early sown crops. Nonetheless, \n\n\n\nthe responses of the cultivars in these series of experiments do suggest \n\n\n\nfuture yield gain may be able to be achieved through further increases in \n\n\n\npartitioning of assimilates to the growing spike that lead to increased grain \n\n\n\nnumber (Slafer et al., 2015). Yield responses were still small and variable \n\n\n\n(including yield reductions) and interacted with environment and cultivar \n\n\n\n(Porker et al., 2020). \n\n\n\nTable 3: Mean values of grain yield and biological yield as a function of the interaction\u2019s cultivar \u00d7 bioclimatic stage \n\n\n\nGrain yield per plant (g) Grain yield (kg/ha) Biological yield (kg/m\u00b2) \n\n\n\nVarieties sub-humid  semi-arid sub-humid  semi-arid sub-humid  semi-arid \n\n\n\nRabha 9.03\u00b10.4 6.64\u00b11.1 2711 1991 23.37\u00b10.2 22.73\u00b11.2 \n\n\n\nNour 10.63\u00b12.9 10.58\u00b13.4 3189 3174 27.64\u00b16.3 27.07\u00b16.9 \n\n\n\nB\u00e9ja 1 13.53\u00b15.2 9.82\u00b12.3 4059 2947 29.33\u00b110.2 28.33\u00b110.3 \n\n\n\nBochra 10.24\u00b12.4 9.94\u00b12.6 3072 2982 24.87\u00b17.2 22.50\u00b18.2 \n\n\n\nNayer 10.51\u00b11.3 8.61\u00b11.3 3153 2583 30.23\u00b16.8 29.17\u00b16.3 \n\n\n\nChetwi 10.42\u00b12.5 10.33\u00b12.3 3126 3099 23.23\u00b15.5 22.7\u00b14.6 \n\n\n\nKasseb 9.09\u00b13.3 8.68\u00b13.4 2726 2604 22.43\u00b10.9 21.63\u00b11.3 \n\n\n\nAmdoun 9.25\u00b10.7 8.09\u00b11.5 2774 2427 19.7\u00b10.4 18.90\u00b11.3 \n\n\n\n3.2.4 Harvest Index \n\n\n\nCultivar did not affect harvest index in both bioclimatic stage (figure 7). In \ncontrast, the highest average value of harvest index was recorded for \nAmdoun on S2 and for Chitwi on S1 respectively with 0.47\u00b10.04 and \n0.45\u00b10.01. Harvest index average on sub-humid and semi-arid were \nrespectively of 41.6\u00b14 and 38.6\u00b16%. harvest index was greater in Oued \nMliz compared with Beja site with 7%. This result may be due to the root \nwater uptake which is higher in Oued Mliz (346 mm) compared to Beja \n(261 mm). Environmental factors are important for HI and include \nseasonal pattern of precipitation and temperatures during crop \nreproductive development.  \n\n\n\nThe HI results have shown of early sown slow developing cultivars \napproaching 0.5, which is nearing the values always reported in well \nmanaged fast developing spring cultivars sown later in autumn such as \n0.45 and the maximum of 0.56. (Porker et al., 2020; Flohr et al., 2018; \nUnkovich et al., 2010). \n\n\n\nFigure 7: Mean values of harvest index as a function of the interaction\u2019s \n\n\n\ncultivar \u00d7 bioclimatic stage \n\n\n\n3.2.5 Biological and seeds Water use efficiency  \n\n\n\nVariability of biological WUE within the same variety and site is less in the \n\n\n\nsub-humid bioclimatic stage than the semi-arid. WUEbio of the eight \n\n\n\nchickpea cultivars shows a significant difference between the two studied \n\n\n\nsites Beja and Oued Miliz especially for Amdoun, Kasseb and rabha (Figure \n\n\n\n8). It seems that the biological WUE in semi-arid region, is higher \n\n\n\ncompared to Sub-humid. It ranged between 0.072\u00b10.01 Kg/m3 for \n\n\n\nAmdoun and 0.11\u00b10.02 Kg/m3 for Nayer in Beja site while it was between \n\n\n\n0.05 \u00b10.01 and 0.09\u00b10.02 Kg/m3. \n\n\n\nFigure 9 presents the water use efficiency of chickpea seeds for eight \n\n\n\nvarieties in the region of Beja and Oued Miliz. Despite that at p<0.05 no \n\n\n\nsignificant difference had been recorded between Seeds WUE on S1 and \n\n\n\nS2, the highest values are revealed in the semi-arid region. Seeds WUE \n\n\n\nranged between 0.025 and 0, 041 Kg/m3 in S2 and between 0.026 and \n\n\n\n0.039 Kg/m3 in S1. Seeds WUE average of the two sites S1 and S2 are \n\n\n\nrespectively of 0.035\u00b10.005 and 0.030\u00b10.004 Kg/m3. \n\n\n\nEven though in the same site, all varieties received the same amount of \n\n\n\nwater, the WUE recorded for Beja1 is 32.8% compared to Kasseb in the \n\n\n\nsub-humid stage. Thus, in the semi-arid stage a highly significant \n\n\n\ndifference between the WUES of the different varieties was recorded. \n\n\n\nindeed, an improvement of 37.3% of Nour compared to Rabha. Thus, the \n\n\n\nmost efficient varieties in semi-arid environment are Nour, Beja1, Bochra, \n\n\n\nKasseb and Chetwi, while in the sub-humid environment are Nour, Beja1, \n\n\n\nBochra and Chetwi. \n\n\n\n\nhttps://www.frontiersin.org/articles/10.3389/fpls.2020.00994/full#B55\n\n\nhttps://www.frontiersin.org/articles/10.3389/fpls.2020.00994/full#B59\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 85-91 \n\n\n\nCite The Article: Ayari Mohamed Saleh, Douh Boutheina, Mguidiche Amelc (2022). Behavior and Water Use Efficiency of Eight Tunisian Varieties of Chickpea (Cicer \nArietinum L.) in Two Bioclimatic Stages. Journal of Sustainable Agricultures, 6(2): 85-91. \n\n\n\nFigure 8: Mean values of biological water use efficiency as a function of \n\n\n\nthe interaction\u2019s cultivar \u00d7 bioclimatic stage \n\n\n\nFigure 9: Mean values of seeds water use efficiency as a function of the \n\n\n\ninteraction\u2019s cultivar \u00d7 bioclimatic stage \n\n\n\nTable 4: Relationship between agronomic parameters, yield, and water use efficiency \n\n\n\n Bio yield Pods number Seed number 100 SW Grain yield HI WUEbio WUES \n\n\n\nBio yield Pearson \nCorrelation \n\n\n\n,928** ,860** -,635** ,659** -,022 ,845** ,587** \n\n\n\nSig. (bilat\u00e9rale) ,000 ,000 ,000 ,000 ,883 ,000 ,000 \n\n\n\nPods number Pearson \nCorrelation \n\n\n\n,947** -,665** ,771** ,037 ,781** ,708** \n\n\n\nSig. (bilat\u00e9rale) ,000 ,000 ,000 ,801 ,000 ,000 \n\n\n\nSeed number Pearson \nCorrelation \n\n\n\n-,623** ,832** ,128 ,695** ,738** \n\n\n\nSig. (bilat\u00e9rale) ,000 ,000 ,386 ,000 ,000 \n\n\n\n100 SW Pearson \nCorrelation \n\n\n\n-,378** ,011 -,509** -,330* \n\n\n\nSig. (bilat\u00e9rale) ,008 ,939 ,000 ,022 \n\n\n\nGrain yield Pearson \nCorrelation \n\n\n\n,286* ,443** ,845** \n\n\n\nSig. (bilat\u00e9rale) ,049 ,002 ,000 \n\n\n\nHI Pearson \nCorrelation \n\n\n\n-,431** -,126 \n\n\n\nSig. (bilat\u00e9rale) ,002 ,393 \n\n\n\nWUEbio Pearson \nCorrelation \n\n\n\n,668** \n\n\n\nSig. (bilat\u00e9rale) ,000 \n\n\n\nWUES  Pearson \nCorrelation \n\n\n\nSig. (bilat\u00e9rale) \n\n\n\n3.2.6 Relationship between agronomic parameters  \n\n\n\nThere was a strong positive relationship between all parameters indicated \n\n\n\nin table 4. Harvest Index and grain yield explaining up to 28.6% of the \n\n\n\nvariation in yield. At other sites, HI was not correlated with other \n\n\n\nparameters.  Majority of the parameters were positively associated at all \n\n\n\nsites except HI. There was significant variation in biological yield, and \n\n\n\nnumber of pods, grain number, 100-SW, WUES and WUEbio with Pearson \n\n\n\ncorrelation coefficient respectively, 92.8, 86, -63.5, 65.9, 84.5 and 58.7% \n\n\n\nacross experiments. The largest amount of variation and effect size was \n\n\n\ndue to cultivar. While the interaction environment with management \n\n\n\npractices like density of plantation, amendments, sowing date were not \n\n\n\nstudied to explain the variance combined and to justify the relationship as \n\n\n\none management factor for subsequent analysis. \n\n\n\nA recent research shows the strong relationship between parameters \n\n\n\nsuggest that total biomass can be improved along with maintenance of a \n\n\n\nhigh HI using Genotype\u00d7 density of plantation strategies to improve crop \n\n\n\nyield. The lack of relationship at Temora Site suggests other factors may \n\n\n\nbe driving yield responses (Porker et al., 2020). \n\n\n\n4. CONCLUSIONS \n\n\n\nTo increase chickpea production of and fill the national deficit in this \nessential food, it would be necessary to resort to a second alternative \nwhich consists in the extension of the culture of this species to the semi-\narid Tunisian area. However, it is essential to determine the factors that \ncould generate a high grain yield and a higher water use efficiency. This \nwork aimed to evaluate the impact of the interaction Cultivar-bioclimatic \nstage on the morphological parameters, yield, harvest index and water use \nefficiency. Eight cultivars were conducted at the same condition in each \nsite (S1: Beja site belongs to semi-arid stage and S2: Oued Mliz belongs to \nsub-humid stage).  \n\n\n\nExperimental site had a significant difference at (P<0.05) on grain yield \nonly for rabha cultivar, in which an improvement of 21.7% was recorded \non the subhumid compared to semi aid bioclimatic stage. While there\u2019s no \nsignificant difference for the seven other cultivars. The lowest grain yield \nwas produced in Rabha cultivar on both sites with 1991 Kg ha-1 on S1 and \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 85-91 \n\n\n\nCite The Article: Ayari Mohamed Saleh, Douh Boutheina, Mguidiche Amelc (2022). Behavior and Water Use Efficiency of Eight Tunisian Varieties of Chickpea (Cicer \nArietinum L.) in Two Bioclimatic Stages. Journal of Sustainable Agricultures, 6(2): 85-91. \n\n\n\n2711 Kg ha-1 on S2: and the highest values 3174 Kg ha-1 for Nour on S1 and \n4059 Kg ha-1 for Beja1 on S2. On average, Beja1 cultivar produces 150% \ncompared to Rabha on S2 and Nour produces 160% compared to Rabha \non S1. Grain yield recorded in the current study was greater than the \naverage yield of the other chickpea grain yield in the same regions which \ncan be suggested to plantation density (30 plant/m\u00b2) of the present study \nwhich may be suitable for chickpea production. Improvement grain \nproduction allows at the same time to increase farmer\u2019s income and to \nguarantees food security. Moreover, the increase in grain yield with \nplanting density suggests that the planting density used in the current \nstudy may be below the optimum for this site and the cultivars. All \nvarieties received the same amount of water at the same site, the grain \nWUE recorded for Beja1 is 32.8% compared to Kasseb in the sub-humid \nstage. Thus, in the semi-arid stage a highly significant difference between \nthe WUES of the different varieties was recorded. indeed, an improvement \nof 37.3% of Nour compared to Rabha. Thus, the most efficient varieties in \nsemi-arid environment are Nour, Beja1, Bochra, Kasseb and Chetwi, while \nin the sub-humid environment are Nour, Beja1, Bochra and Chetwi. \nClearly, these preliminary findings show the huge potential of chickpea in \nthis arid to semi-arid environment of Tunisia. However, further research, \nincluding a wide range of cultivars, planting densities, seasons, and test \nsites, is recommended before definite conclusions can be drawn. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThis project was supported by the Regional Center for Field Crop Research \n\n\n\nin Beja (CRRGCB), IRESA Tunisia for kindly supplying seeds of chickpea. \n\n\n\nREFERENCES \n\n\n\nAAC. 2006. Pois chiche : Situation et perspectives. Le Bulletin bimensuel. \n19(13), 4. \n\n\n\nArif, A., Parveen, N., Waheed, M. Q., Atif, R. M., Waqar, I., & Shah, T. M. 2021. \nA comparative study for assessing the drought-tolerance of chickpea \nunder varying natural growth environments. Frontiers in plant science, \n11, 2228. \n\n\n\nBamouh, A. 1998. Gestion de la contrainte pluviom\u00e9trique pour \nl\u2019am\u00e9lioration de la production v\u00e9g\u00e9tale et de l\u2019efficience d\u2019utilisation de \nl\u2019eau. Bulletin Mensuel de Liaison du Programme National de Transfert \nde Technologie en Agriculture, 50, 1-4. \n\n\n\nBouhadida, M., Benjannet, R., Madrid, E., Amri, M., & Kharrat, M. 2013. \nEfficiency of marker-assisted selection in detection of ascochyta blight \nresistance in Tunisian chickpea breeding lines. Phytopathologia \nMediterranea, 202-211. \n\n\n\nDouh, B., Mguidiche, A., Zouari, I., Gargouri, K., & Ayachi, M. 2021. Impact \nof different organic amendments on: hydrodynamic soil properties and \nolive tree behavior conducted under deficit irrigation. Communications \nin Soil Science and Plant Analysis, 1-15. \n\n\n\nFlohr, B. M., Hunt, J. R., Kirkegaard, J. A., Evans, J. R., Swan, A., Rheinheimer, \nB. 2018a. Genetic gains in NSW wheat cultivars from 1901 to 2014 as \nrevealed from synchronous flowering during the optimum period. Eur. \nJ. Agron. 98, 1\u201313. doi: 10.1016/j.eja.2018.03.009 \n\n\n\nHirich, A., El Omari, H., Jacobsen, S. E., Lamaddalena, N., Hamdy, A., Ragab, \nR., ... & Choukr-Allah, R. 2014. Chickpea ('Cicer arietinum'L.) \nphysiological, chemical and growth responses to irrigation with saline \nwater. Australian Journal of Crop Science, 8(5), 646-654. \n\n\n\nKaashyap, M., Ford, R., Bohra, A., Kuvalekar, A., & Mantri, N. 2017. \nImproving salt tolerance of chickpea using modern genomics tools and \nmolecular breeding. Current genomics, 18(6), 557-567. \n\n\n\nKhila, S. B., Douh, B., Mguidiche, A., & Boujelben, A. 2021. Assessing the \nWater Productivity of Durum Wheat in Tunisian Semi-Arid Conditions. \nIn Agriculture Productivity in Tunisia Under Stressed Environment (pp. \n195-211). Springer, Cham. \n\n\n\nNefzi, F., Trabelsi, I., Amri, M., Triki, E., Kharrat, M., & Abbes, Z. 2016. \nResponse of some chickpea (Cicer arietinum L.) genotypes to Orobanche \nfoetida Poir. parasitism. Chilean journal of agricultural research, 76(2), \n170-178. \n\n\n\nOuji, A., El-Bok, S., Mouelhi, M., Ben Younes, M., & Kharrat, M. 2016. Yield \nand yield components of chickpea (Cicer arietinum L.) as influenced by \nsupplemental irrigation under semi-arid region of Tunisia. World \nJournal of Agricultural Research, 4(5), 153-157. \n\n\n\nPorker, K., Straight, M., & Hunt, J. R. 2020. Evaluation of G\u00d7 E\u00d7 M \nInteractions to increase harvest index and yield of early sown wheat. \nFrontiers in plant science, 11, 994. doi.org/10.3389/fpls.2020.00994.  \n\n\n\nRani, A., Devi, P., Jha, U. C., Sharma, K. D., Siddique, K. H., & Nayyar, H. 2020. \nDeveloping climate-resilient chickpea involving physiological and \nmolecular approaches with a focus on temperature and drought \nstresses. Frontiers in plant science, 10, 1759. \n\n\n\nShearman, V. J., Sylvester-Bradley, R., Scott, R. K., Foulkes, M. J. 2005. \nPhysiological Processes Associated with Wheat Yield Progress in the UK. \nCrop Sci. 45, 175\u2013185. doi: 10.2135/cropsci2005.0175a \n\n\n\nSiddique, K. H. M., Kirby, E. J. M., Perry, M. W. 1989. Ear: Stem ratio in old \nand modern wheat varieties; relationship with improvement in number \nof grains per ear and yield. Field Crops Res. 21, 59\u201378. doi: \n10.1016/0378-4290(89)90041-5 \n\n\n\nSivapalan, S., O'Brien, L., Ortiz-Ferrara, G., Hollamby, G. J., Barclay, I., & \nMartin, P. J. 2003. Corrigendum to: A comparative study for yield \nperformance and adaptation of some Australian and CIMMYT/ICARDA \nwheat genotypes grown at selected locations in Australia and the WANA \nregion. Australian journal of agricultural research, 54(3), 331-331. \n\n\n\nSlafer, G. A., Elia, M., Savin, R., Garc\u00eda, G. A., Terrile, I. I., Ferrante, A., et al. \n2015. Fruiting efficiency: an alternative trait to further rise wheat yield. \nFood Energy Secur. 4, 92\u2013109. doi: 10.1002/fes3.59 \n\n\n\nThangwana, N. M., & Ogola, J. B. O. 2012. Yield and yield components of \nchickpea (Cicer arietinum): Response to genotype and planting density \nin summer and winter sowings. Journal of Food, Agriculture & \nEnvironment, 10(2), 710-715. \n\n\n\nUnkovich, M., Baldock, J., Forbes, M. 2010. \u201cChapter 5 - Variability in \nHarvest Index of Grain Crops and Potential Significance for Carbon \nAccounting: Examples from Australian Agriculture,\u201d in Advances in \nAgronomy (Academic Press), 173\u2013219. \n\n\n\nWery, J., Silim, S. N., Knights, E. J., Malhotra, R. S., & Cousin, R. 1994. \nScreening techniques and sources of tolerance to extremes of moisture \nand air temperature in cool season food legumes. In Expanding the \nProduction and Use of Cool Season Food Legumes (pp. 439-456). \nSpringer, Dordrecht. \n\n\n\n\nhttps://doi.org/10.3389/fpls.2020.00994\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 22-25 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2020.22.25 \n\n\n\nCite the Article: Md. Amirul Islam, Shah Md. Ariful Islam, Maria Akter Sathi (2020). Identification Of Lentil Varieties/Lines Resistant To Stemphyl ium Blight Considering \nDisease Reaction And Yield. Malaysian Journal of Sustainable Agriculture, 4(1): 22-25.\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2020.22.25\n\n\n\nIDENTIFICATION OF LENTIL VARIETIES/LINES RESISTANT TO STEMPHYLIUM \nBLIGHT CONSIDERING DISEASE REACTION AND YIELD \n\n\n\nMd. Amirul Islama*, Shah Md. Ariful Islamb, Maria Akter Sathic  \n\n\n\na Sustainable Intensification Program, International Maize and Wheat Improvement Center (CIMMYT)- Bangladesh. \nb Upazilla Agriculture Office, Babuganj, Barishal, Bangladesh. \nc Department of Horticulture, Patuakhali Science and Technology University, Dumki, Patuakhali. \n*Corresponding Author Email: musa_amirul@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 01 December 2019 \nAccepted 06 January 2020 \nAvailable online 05 February 2020\n\n\n\nA piece of study was carried out to identify the resistant varieties to stemphylium blight disease of lentil at \nPlant Pathology Division, Bangladesh Agriculture Research Institute, Joydebpur, Gazipur during the period of \nSeptember 2013 to April 2014. The experimental design was RCB in field condition having three replications. \nEleven lentil test entries along with 2 check variety BARI masur-1 and BARI masur-7 were evaluated. At \nmaturity 4 lines showed Moderately Resistant (MR) and 7 lines showed Moderately Susceptible (MS) types \nof reaction.  The line BLX-06004-12 gave the highest yield (1456 kg ha-1) followed by BLX-06004-2 (1113.30 \nkg ha-1 ) and  BLX-05001-6 (1106.30 kg ha-1) which were designated as moderately resistant to stemphylium \nblight disease. The lowest yield (987.30 kg ha-1) was recorded in BLX-05008-21 which was designated as \nmoderately susceptible to stemphylium blight disease.  \n\n\n\nKEYWORDS \n\n\n\nLentil, Disease, Stemphylium, Resistant, Yield.\n\n\n\n1. INTRODUCTION \n\n\n\nLentil (Lens culinaris Medik) is one of the most important food legumes \ncrops of Bangladesh. It ranks second in respect of acreage (162 thousands \nhectares) and production (211 thousands metric tons) in the country \n(Anonymous, 2012). Greater Faridpur, Jessore, Kushtia, Pabna and \nRajshahi are the major lentil growing areas in Bangladesh. The disease is \na serious constraint in lentil cultivation and is widespread throughout the \ncountry with the highest severity in Jessore, Pabna, Kushtia,Faridpur, \nMadaripur and Dhaka (Bakr and Ahmed, 1992). In the recent years, the \ndisease is also a threat to lentil cultivation in the southern parts of the \ncountry like Barisal and Bhola districts. The most important diseases of \nlentil in Bangladesh are stemphylium blight, rust and foot and root rot. \nAmong them stemphylium blight caused by Stemphylium botryosum has \ncreated panic in the lentil growers as well as researchers in the country. \nStemphylium blight, a damaging major disease of lentil, attacked the crop \nat any growing stage of damage depending upon how early the disease was \nappeared.  \n\n\n\nThe disease is a serious concern not only in Bangladesh but also in \nnortheast India and Nepal causing up to 100% yield losses under epidemic \nconditions. The climatic conditions in Bangladesh are favorable for the \nrapid development and growth of various plant pathogens. Although some \nfungicides are available to manage the stemphylium blight disease but it is \nnecessary to develop alternate and more effective control measures with \nfungicides. Chemical control measure of this disease was to some extent \ncostly and cumbersome. Among the alternation means of disease \nmanagement, development of resistant variety is the most widely \npreferred method. Growing of resistant cultivar like \u201cUtfala\u201d the first \nimproved Lens culinaris varity in Bangladesh was therefore, easy, cheap \n\n\n\nand environment friendly (Sarker et al., 1992).  \n\n\n\nIt showed consistently higher yield over years across locations and \nexhibited yield potential of up to 3.45 ton/ha in favorable climatic \nconditions at ishurdi during 1983-84. Beare reported that resistant \nvarieties provide a more effective and more consistent method of \nstemphylium blight control (Beare, 2002; FRG, 2012; Bakr and Ahmed, \n1993; Rashid et al., 2009; Podder, 2012). On the other hand, BARI Masur-\n4 was selected from the cross ILL5888 \uf0b4 FLIP84-112L in 1995 and \nproduced an average seed yield of 2300 kg/ha. BARI masur-4  has an erect \ngrowth habit and was suitable for intercropping with sugarcane and mixed \ncropping with mastard. It had combined resistance to rust and \nStemphylium blight (Stemphylium botryosum) (Sarker and Erskine, 1998). \nBesides, there were 21 lentil lines that were blight resistant with higher \nyield (Rashid et al., 2009). In view of the above facts research works have \nbeen undertaken to screen out the resistant/tolerant source(s) of lentil \ngermplasms against Stemphylium blight disease. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2 . 1  E x p e r i m e n t  s i t e  d e t a i l s  \n\n\n\nThe experiment was conducted at the research field of Plant Pathology \nDivision, Bangladesh Agricultural Research Institute (BARI), Gazipur \nduring September 2013 to April 2014.The experimental field was high \nland with highly sandy loam texture belonging to the Madhupur tract \nunder AEZ-28. \n\n\n\n2.2 Collection and preservation of Seeds \n\n\n\nSeeds of 11 lentil lines i.e. BLX-05001-6, BLX-05002-3, BLX-05002-6, BLX-\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 22-25 \n\n\n\n05008-2, BLX-05008-5, BLX-05008-15, BLX-05008-21, BLX-05009-7, \nBLX-06004-2, BLX-06004-12, FLIP-95-12 and two check varieties i.e BARI \nmasur-1 (Susceptible check) and BARI masur-7 (Resistant check) were \ncollected from Pulses Research Sub Station (PRSS), Gazipur for conducting \nthis experiment. The collected seeds received from PRSS were \nimmediately stored in a well-ventilated room at room temperature. \nSpecial care was taken and the seeds were duly registered. After \nregistration, seeds were preserved in a deep fridge in the Plant Pathology \nlaboratory of BARI till they were used for field experiment. \n\n\n\n2.3 Experimental design and layout \n\n\n\nThe experimental plot was nicely prepared mechanically in late October \n2013. Weeds and other rubbishes were removed. Fertilizers were applied \nat the time of final land preparation as per recommendation (FRG, 2012). \nThe experiment was conducted in Randomized Complete Block (RCB) \ndesign with three replications. The row length was 5m and width was 30 \ncm. The distance between the block was 1m. Susceptible check variety \nBARI masur-1 was sown after every two test lines. Resistant check variety \nBARI masur-7 was sown at the beginning and end of each \nblock/replication. \n\n\n\n2 . 4  Seed sowing and management \n\n\n\nFurrows were made with power tiller driven furrows maintaining a \n\n\n\ndistance of 30 cm. The required amounts of seeds for each row were sown \n\n\n\nin the furrow. The furrows were covered with soil soon after sowing. The \n\n\n\nlength of line was 5m with continuous sowing of seed in the lines. The seed \n\n\n\nwere sown in the morning on 21 November, 2013. To minimize the seed \n\n\n\nborne pathogen, seeds were treated with Provax-200 @ 2.5g/kg seed.  \n\n\n\nIntercultural operation was done in order to maintain the normal hygienic \n\n\n\ncondition of crop growth. Weeding was done three times during the \n\n\n\ngrowing period of the crop at 20, 35 and 50 days after sowing. Insecticide \n\n\n\n\u2018Karate (0.2%) was applied for controlling pod borer and aphid of lentil. \n\n\n\n2 . 5  Data recording on yield and yield contributing parameters \n\n\n\nDays to 1st flowering was recorded when 1st flowering is open. Days to \n50% Flowering (DFLR) was recorded in number of days after sowing \nwhen 50% plants in the row sets the first flower. Days to maturity (DMAT) \nwas recorded in number of days after sowing when 90% of the row is \nready for harvest. Ten plants of each unit plots were randomly selected for \nrecording the data on plant height, number of branch per plant, number of \npods per plant, seed per pod after harvest. Thousand seeds were counted \nby a seed counter and weight taken through digital high precision balance \n(0.001g). Grain yield of lentil kg ha-1 was calculated by converting the \nweight of row yield into hectare and was expressed in kg. \n\n\n\n2.6 Analysis of data \n\n\n\nThe collected data were analyzed statistically. The experimental data were \nanalyzed by Statistix 10.0 software at 5% level of significance. Treatment \nmeans were compared by DMRT. \n\n\n\n3. RESULT \n\n\n\n3.1 Disease Reaction to Stemphylium Blight of lentil at three \n\n\n\ndifferent stages during rabi season of 2013-2014 \n\n\n\nThe lentil lines were evaluated for their reaction to Stemphylium blight \nunder natural epiphytotic condition during the winter season of 2013-14 \nand showed significant difference in reaction to Stemphylium botryosum. \nIn flowering stage i.e. at 45 days after sowing out of 11 lines 3, 6 and 2 lines \nshowed Highly Resistant (HR), Resistant (R) and Moderately Resistant \n(MR) reaction, respectively. The 3 highly resistance lines were BLX-\n05002-3, BLX-06004-12 and BLX-06004-2 in the flowering stage (Figure \n1). In flowering stage, out of 11 lines 2, 7 and 2 lines were showed \nResistant (R), Moderately Resistant (MR) and Moderately Susceptible \n(MS) reaction, respectively in the pod setting stage (Figure 1). The \nresistant 2 lines were BLX-06004-12 and BLX-06004-2. The data was \ntaken 60 days after sowing. At maturity stage, 4 lines showed Moderately \nResistant (MR) and 7 lines showed Moderately Susceptible (MS) type of \nreaction (Figure 1). None of the 11 entries showed either resistant or \nsusceptible reaction against the disease. The Moderately Resistant (MR) \nlines were BLX-06004-12, BLX-06004-2, BLX-05002-3 and BLX-05001-6. \nThe data was taken 75 days after sowing.  \n\n\n\n3.2 Performance of test lines / varieties in yield and yield \n\n\n\ncontributing characteristics during Rabi season of 2013-14 \n\n\n\n3.2.1 Days to 1st Flowering, Days to 50% Flowering (DFLR), Days to \n\n\n\nmaturity (DMAT), Plant height and No. of branch plant-1 \n\n\n\nFigure 1: Reaction on Stemphylium blight disease of 11 lentil lines at \ndifferent stages \n\n\n\nThere was no variation regarding disease reaction at 1st flowering among \ndifferent lentil lines/varieties. Days to the 1st flowering ranged from 49.93 \ndays to 47.77 days. It was observed that BARI masur-7 started flowering 2 \ndays later than the other lines. The highest (49.33) and the lowest (47.77) \ndays to start flowering were recorded in BARI masur-7 and BLX-05001-6 \nline (Table 1). DFLR showed variation among the lines/varieties. It ranged \nfrom 55.63 to 53.17 while the highest was recorded in BLX-05008-5 and \nthe lowest was recorded in BARI masur-1. DMAT of all the test and check \nlines/varieties ranged from 99.03-95.97. The highest days of maturity was \nobserved in FLIP-95-12 followed by BLX-05002-3 (98.37), BLX-05001-\n6(97.70) and the  lowest (95.97) in BLX-05009-7 followed by BARI masur-\n7 (96.40), BLX-05008-21(96.40), BARI masur-1(96.53). The tested 11 \nlines and two check varieties showed significant difference to each other \nin the field condition. The plant height ranged from 33.40 cm to 43.40 cm \nwhile the tallest plant (43.40 cm) was found in BLX-06004-12 line and the \nshortest plant (33.40 cm) was recorded in BLX-06004-2 line. Plants in \nsome of the lines were taller than the check variety. Number of branch per \nplant was counted as the primary branch of plant that is the first branching \nof the plant. It was found that most of the lines/varieties gave two primary \nbranches yet only a few lines gave about three primary branches. The \nhighest (2.98) Number of branch per plant was observed in BLX-06004-\n12 and lowest (2.53) in BLX-05008-5 (Table 1). \n\n\n\n3.2.2  Number of pod plant-1, Number of seed pod-1 and 1000-seed \n\n\n\nweight \n\n\n\nNumber of pod per plant was recorded after harvesting of plants. Pods \n\n\n\nwere counted from ten (10) plants in every lines/varieties and showed \n\n\n\nsignificant difference from one to others (Table 2). Number of pod ranged \n\n\n\nfrom 58.23 to 86.57 while minimum number of pod was recorded in BLX-\n\n\n\n05008-21(58.23) followed by BLX-05009-7(58.33) and maximum number \n\n\n\nof pod was recorded in BLX-06004-12(86.57) followed by BARI masur-\n\n\n\n7(85.37). The number of seed per pod was recorded after harvesting of the \n\n\n\nplant by counting seed in each pod from ten (10) plants in every \n\n\n\nlines/varieties. Number of seed per pod ranged from 1.80 to 2.00 (Table \n\n\n\n2). The highest number of seed per pod was recorded in BARI masur-7 and \n\n\n\nBLX-06004-12 and lowest in BLX-05009-7. In respect of 1000 seed weight \n\n\n\nmarked variation has been found. The thousand seed weight under \n\n\n\ndifferent lines/ verities ranged from 15.50 to 21.10 g (Table 2). The \n\n\n\nhighest weight was recorded in BLX-06004-12 followed by BARI masur-\n\n\n\n7(20.73 g) and lowest (15.5 g) 1000 seed weight was recorded in BLX-\n\n\n\n05008-2 followed by BLX-05002-6 (15.53 g). \n\n\n\n3.2.3    Grain yield \n\n\n\nThe grain yield per hectare differed significantly among the test entries \n\n\n\nunder field condition (Table 2). The yield ranged from 987.30 to 1456.00 \n\n\n\nkg ha-1 whiles the highest (1456.00 kg ha-1) grain yield was recorded in \n\n\n\nBLX-06004-12 followed by BARI masur-7 (1451.00 kg ha-1) and BARI \n\n\n\nmasur-1 (1125.00 kg ha-1) and lowest (987.3 kg ha-1) yield was recorded \n\n\n\nin BLX-05008-21 followed by BLX-05008-2 (995.00 kg ha-1) and BLX-\n\n\n\n05008-5 (996.30 kg ha-1)\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\nHR R MR MS S\n\n\n\nFlowering stage Pod setting stage Maturity stage\n\n\n\nCite the Article: Md. Amirul Islam, Shah Md. Ariful Islam, Maria Akter Sathi (2020). Identification Of Lentil Varieties/Lines Resistant To Stemphyl ium Blight Considering \nDisease Reaction And Yield. Malaysian Journal of Sustainable Agriculture, 4(1): 22-25.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 22-25 \n\n\n\nTable 1: Days to 1st flowering, 50% flowering, maturity, Plant height and No. of branch plant-1 of 11 selected lentil germplasms and two check varieties \n\n\n\nSl. No. Name of \n\n\n\nlines/varieties \n\n\n\nDays to 1st  \n\n\n\nFlowering \n\n\n\nDays to 50% \n\n\n\nFlowering \n\n\n\nDays to \n\n\n\nMaturity \n\n\n\nPlant height (cm) No. of branch plant-1 \n\n\n\n1 FLIP-95-12 48.80 ab 55.07 ab 99.03 a 37.80 c 2.77 abc \n\n\n\n2 BLX-05002-3 47.80 b 55.10 ab 98.37 ab 35.63 def 2.63 bcd \n\n\n\n3 BLX-06004-12 47.93 b 54.87 ab 96.63 bc 43.40 a 2.98 a \n\n\n\n4 BLX-05009-7 49.20 ab 54.83 ab 95.97 c 34.10 gh 2.57 cd \n\n\n\n5 BLX-06004-2 48.10 b 54.33 bc 97.03 abc 33.40 h 2.60 bcd \n\n\n\n6 BLX-05001-6 47.77 b 54.60 ab 97.70 abc 35.30 efg 2.63 bcd \n\n\n\n7 BLX-05002-6 48.30 ab 55.10 ab 97.60 abc 34.90 fg 2.80 ab \n\n\n\n8 BLX-05008-21 47.83 b 54.57 ab 96.40 bc 36.93 cd 2.60 bcd \n\n\n\n9 BLX-05008-15 48.10 b 54.80 ab 97.07 abc 35.00 fg 2.57 cd \n\n\n\n10 BLX-05008-2 47.97 b 55.03 ab 97. 33 abc 36.63 cde 2.57 cb \n\n\n\n11 BLX-05008-5 49.37 ab 55.63 a 97.10 abc 34.30 fgh 2.53 d \n\n\n\n12 BARI masur-1 48.54 ab 53.17 c 96.53 bc 34.57 fgh 2.67 bcd \n\n\n\n13 BARI masur-7 49.93 a 53.87 bc 96.40  bc 39.87 b 2.93 a \n\n\n\nCV (%) 2.03 1.37 1.34 2.43 5.05 \n\n\n\nLSD (0.05) 1.65 1.25 2.20 1.49 0.23 \n\n\n\n4. DISCUSSION\n\n\n\nIn the present investigation stemphylium blight of lentil caused by \nStemphylium botryosum showed typical symptoms on lentil plants. This \nstudy was carried out to investigate the performance of different lentil \nlines/varieties under natural condition. The lentil lines/varieties were \nevaluated for their resistance to stemphylium blight disease caused by \nstemphylium botryosum. The tested lentil lines/varieties showed wide \nvariation in reaction to stemphylium blight under field condition at \ndifferent growth stages. The sensitivity of the tested lentil lines/varieties \nincreased with the increase in age of the plants. The prevalence of \nstemphylium was as follows: vegetative stage > flowering stage > pod \nsetting stage. But this tendency may not be always a regular pattern to all \nthe lines/varieties. From this research, the tested lentil variety/genotypes \ndiffered significantly from one to another in respect of disease, yield and \nyield contributing characters under field condition.  \n\n\n\nIn flowering stage, out of 11 lines 3, 6 and 2 lines showed Highly Resistant \n(HR), Resistant (R) and Moderately Resistant (MR) type of reaction \nrespectively. In the pod setting stage the scenario was changed and 2, 7 \nand 2 lines showed Resistant (R), Moderately Resistant (MR) and \nModerately Susceptible (MS) reaction respectively. At maturity stage \nactual scenario was observed, only 4 and 7 lines showed Moderately \nResistant (MR) and Moderately Susceptible (MS) type of reaction \nrespectively. The Moderately Resistant (MR) lines were BLX-06004-12, \nBLX-06004-2, BLX-05002-3 and B LX-05001-6. None of the lines showed \nresistant type of reaction against the disease. Bakr and Ahmed studied on \n\n\n\n110 genotypes and found only one genotype resistant to Stemphylium \nblight and 11 genotypes were tolerant (Bakr and Ahmed, 1993). Beare \nscreened lentil lines/verities against stemplylium blight under natural \ncondition and obtained some lines/varieties as moderately resistant and \nsusceptible (Beare, 2002).  \n\n\n\nThe finding of the present study revealed that the tested lentil \nlines/verities showed different types of reaction to stemphylium blight \nunder field condition. Some researcher screened lentil lines and found that \n21 entries were Resistant (R) to stemphylium blight (Rashid et al., 2009). \nFrom this research, it was observed that the tested lentil lines/verities \ndiffered significantly in respect of plant height, number of pod per plant, \nnumber of branch per plant and yield. Plant height was found maximum in \nthe BLX-06004-12 lines followed by BARI masur-7, FLIP-95-12, BLX-\n05008-21 and BLX-05008-2 and the minimum in the BLX-06004-2. \nNumber of pod per plant was maximum in the BLX-06004-12 lines \nfollowed by BARI masur-7, BARI masur-1, BLX-05002-6 and FLIP-95-12 \nand the minimum in the BLX-05008-21. The highest grain yield was \nrecorded from the BLX-06004-12 lines followed by BARI masur-7 and \nboth were statistically identical.  \n\n\n\nConsidering the yield performance, it was observed seven lines/varieties \nproduced more than 1 ton yield with a range from 1456 to 1020.30 (kg ha-\n\n\n\n1). The lines are: BLX-06004-12, BARI masur-7, BARI masur-1, BLX-06004-\n2, BLX-05001-6, BLX-05002-6 and BLX-05002-3. The findings of the study \nis closely related with the study (Sarker et al., 1992; Rashid et al., 2009; \nPodder, 2012; Sarker and Erskine, 1998). They reported that the lentil \nlines differed significantly in respect of agronomic traits and yield \nparameters. The variation in yield of lentil was mainly due to Stemphylium \n\n\n\nTable 2: Number of pod plant-1, Number of seed/pod, 1000 seed weight and grain yield of selected 11 lentil germplasms and two check varieties \n\n\n\nSl. No. Name of lines/varieties No. of pod plant-1 Number of seed/pod 1000 seed weight (g) Grain yield (kg ha-1) \n\n\n\n1 FLIP-95-12 68.47 b 1.82 bc 16.27 cd 1011.00 ef \n\n\n\n2 BLX-05002-3 64.07 c 1.81 c 15.67 de 1020.30 e \n\n\n\n3 BLX-06004-12 86.57 a 2.00 a 21.10 a 1456.00 a \n\n\n\n4 BLX-05009-7 58.33 e 1.80 c 15.80 de 1005.00 fg \n\n\n\n5 BLX-06004-2 58.53 de 1.83 bc 18.27 b 1113.30 bc \n\n\n\n6 BLX-05001-6 59.87 de 1.82 bc 16.33 cd 1106.30 cd \n\n\n\n7 BLX-05002-6 69.10 b 1.83 bc 15.53 e 1101.00 d \n\n\n\n8 BLX-05008-21 58.23 e 1.82 bc 16.50 c 987.30 h \n\n\n\n9 BLX-05008-15 59.60 de 1.85 bc 15.90 cde 1006.00 fg \n\n\n\n10 BLX-05008-2 61.27 cd 1.84 bc 15.50 e 995.00 gh \n\n\n\n11 BLX-05008-5 59.03 de 1.82 bc 16.33 cd 996.30 gh \n\n\n\n12 BARI masur-1 71.30 b 1.88 b 20.73 a 1125.00 b \n\n\n\n13 BARI masur-7 85.37 a 2.00 a 20.47 a 1451.00 a \n\n\n\nCV (%) 2.55 2.17 2.30 0.64 \n\n\n\nLSD (0.05) 2.84 0.07 0.67 11.20 \n\n\n\nCite the Article: Md. Amirul Islam, Shah Md. Ariful Islam, Maria Akter Sathi (2020). Identification Of Lentil Varieties/Lines Resistant To Stemphyl ium Blight Considering \nDisease Reaction And Yield. Malaysian Journal of Sustainable Agriculture, 4(1): 22-25.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(1) (2020) 22-25 \n\n\n\nblight disease. This variation may be due to i) the effect of Stemphylium \nbotryosum on formation of pod. ii) variations of genetic make up of lentil \nlines/verities and iii) growing conditions of plants. A group researchers \nreported yield reduction of lentil due to Stemphylium blight (Bakr, 1993; \nMwakutuya et al., 2002; Neubauer, 1998). They were described that yield \nreduction of lentil increased with the increasing of Stemphylium blight \ndisease severity. With the findings of the present study it may be \nconcluded that the lentil lines showed appreciable difference reaction to S. \nbotryosum which need to be tested further for more confirmation of the \nresult of this study. \n\n\n\n5. CONCLUSION \n\n\n\nThis piece of experiment was designed to screen resistant/tolerant lentil \n\n\n\nvarieties/lines against stemphylium blight disease. Eleven lentil lines \n\n\n\nalong with a susceptible and a resistant check was evaluated and it was \n\n\n\nobserved that the line BLX-06004-12 gave the highest yield (1456 kg ha-\n\n\n\n1) followed  by BLX-06004-2 (1113.30  kg ha-1)  and  BLX-05001-6 \n\n\n\n(1106.30 kg ha-1) and were moderately resistant to stemphylium blight \n\n\n\ndisease. The lowest yield (987.30 kg ha-1) was recorded in BLX-05008-21 \n\n\n\nfollowed by BLX-05008-2 and BLX-05008-5. So, the line BLX-06004-12, \n\n\n\nBLX-06004-2 and BLX-05001-6 may be selected for next year up-scaling \n\n\n\ndue to synchronous maturity, higher yield and low disease severity. \n\n\n\nFrom the above study it can be concluded that \n\n\n\n\u2022 Three lentil lines viz.  BLX-06004-12, BLX-06004-2 and BLX-\n\n\n\n05001-6 showed moderately resistant reaction against \n\n\n\nstemphylium blight disease \n\n\n\n\u2022 These three lines can be used in the resistant breeding program \n\n\n\nas a stemphylium blight disease resistant source \n\n\n\nREFERENCES \n\n\n\nAnonymous. 2012., Krishi Diary, Agricultural Information Service, DAE, \n\n\n\nMinistry of Agriculture. \n\n\n\nBakr, M.A., 1993. Plant protection of lentil in Bangladesh; Lentil in south \n\n\n\nAsia. Edited by Erskine, W and Saxena, M. C. Proceedings of the \n\n\n\nSeminar on lentil in South Asia, 11-15 March 1991, New Delhi, India, \n\n\n\nICARDA, Aleppo, Syria, Pp. 177-186. \n\n\n\nBakr, M.A., Ahmed, F., 1992. Development of Stemphylium blight on lentil \n\n\n\ngenotypes and its chemical control. Bangladesh J. Plant Pathology, 8 (1 \n\n\n\nand 2), Pp. 39-40.  \n\n\n\nBakr, M.A., Ahmed, F., 1993. Intigrated management of Stemphylium blight \n\n\n\nof lentil, Abst. No. 3.5.47. presented in the 6th Intl. Congress of Plant \n\n\n\nPathology held in Montreal, Canada, 28, Pp.361 \n\n\n\nBeare, M., 2002. Investigation into Stemphylium botryosum resistance in \n\n\n\nlentil, Undergraduate thesis, University of Saskatchewan, Saskatoon, \n\n\n\nSaskatchewan. \n\n\n\nFRG., 2012. Fertilizer Recommendation Guide, Bangladesh Agricultural \n\n\n\nResearch Council (BARC), Farmgate Dhaka 1215, Pp. 274.  \n\n\n\nMwakutuya, E., Vandenberg, B., Banniza, S., 2002. Effect of culture age, \n\n\n\ntemperature, incubation time and light regime on conidial germination \n\n\n\nof Stemphylium botryosum on Lentil, University of Saskatchewan. \n\n\n\nDepartment of Plant Science, 51, Campus Drive, Saskatoon, S7N5A8, \n\n\n\nCanada. \n\n\n\nNeubauer, C., 1998. Epidemiology and damage potential of Stemphylium \n\n\n\nbotryosum Wallr. On asparagus. Gesunde Pflanzen, 50 (8), 251-256. \n\n\n\nPodder, R., 2012. The Potential for Breeding for stemphylium blight \n\n\n\nResistance in the Genus Lens., Master of Science (M.Sc.), University of \n\n\n\nSaskatchewan, http://hdl.handle.net/10388/ETD-2012-10-739. \n\n\n\nRashid, M.H., Uddin, M.J., Islam, Q.M.S., 2009. Development of Integrated \n\n\n\nManagement Package for Stemphylium blight and Rust Disease of Lentil \n\n\n\nProject, PRC, BARI, Ishurdi, Pabna, Report submitted to the Ministry of \n\n\n\nScience and Information & Communication Technology Government of \n\n\n\nthe People\u2019s Republic of Bangladesh. Bangladesh Secretariat. Dhaka-1000, \n\n\n\nPp. 46. \n\n\n\nSarker, A., Erskine, W., 1998. High yielding lentil (Lens culinaris Medikus) \n\n\n\nvarieties for Bangladesh: an outcome of ICARDA\u2019s decentral. \n\n\n\nSarker, A., Rahman, M.A., Rahman, A., Zaman, W., 1992. Utfala: a \nlentil variety for Bangladesh. Lens, 19, 14-15.\n\n\n\nCite the Article: Md. Amirul Islam, Shah Md. Ariful Islam, Maria Akter Sathi (2020). Identification Of Lentil Varieties/Lines Resistant To Stemphyl ium Blight Considering \nDisease Reaction And Yield. Malaysian Journal of Sustainable Agriculture, 4(1): 22-25.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 23-27 \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Onyemekara NN, Ofoegbu J, Mike-Anosike E, Emeakaraoha M, Adeleye S, Chinakwe PO (2019). \nMicrobial Population Changes In The Rhizosphere Of Tomato Solanum Lycopersicum Varieties During Early Growth In Greenhouse. \n\n\n\nMalaysian Journal of Sustainable Agriculture, 3(1): 23-27. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 15 November 2018  \nAccepted 17 December 2018 \nAvailable online 11 January 2019 \n\n\n\nABSTRACT\n\n\n\nThe microbial population changes in the rhizosphere of two varieties of tomato: cherry and plum were studied. \n\n\n\nThey were grown in a greenhouse for five weeks. Standard microbiological procedures were applied. Biochemical \n\n\n\nand cultural characteristics revealed the presence of Bacillus, Enterococcus, Staphylococcus, Rhizobium as bacterial \n\n\n\nspecies and Penicillium, Mucor and Saccharomyces as fungal species. Total Heterotrophic Bacterial Counts (THBC) \n\n\n\nranged from 1.0 x 106 to 4.8 x 107 cfu/g; 7.0 x 107 to 4.5 x 109 cfu/g and 5.4 x 107 to 3.0 x 109 cfu/g for bare soil, \n\n\n\nrhizosphere soil of cherry tomato and rhizosphere soil of plum tomato respectively. Total Fungal Counts (TFC) were \n\n\n\nlower and ranged from 1.3 x 106 to 6.5 x 106 cfu/g, 1.2 x 106 to 8.7 x 106 cfu/g and 1.0 x 106 to 1.2 x 106 cfu/g for \n\n\n\nbare soil, rhizosphere soil of cherry tomato and rhizosphere soil of plum tomato respectively. The microbial \n\n\n\nsuccession pattern further revealed that Bacillus sp, Enterococcus sp, Rhizobium sp, Mucor sp and Saccharomyces \n\n\n\nsp were the predominant microorganisms present in bare soil and rhizosphere soils of cherry and plum tomatoes. \n\n\n\nThe presence of plant growth promoting rhizobacteria e.g. Bacillus sp and Rhizobium sp, is of great advantage to the \n\n\n\nearly growth of tomato plants as they play important roles in increasing soil fertility, plant growth , and suppression \n\n\n\nof phytopathogens for healthy plant development and sustainable agriculture. \n\n\n\nKEYWORDS \n\n\n\nMicrobial succession, plant growth promoting rhizobacteria. rhizosphere, total fungal count, total heterotrophic \n\n\n\nbacterial count\n\n\n\n1. INTRODUCTION \n\n\n\nSoil is the uppermost layer of the earth\u2019s surface on which plants grow. It \n\n\n\nis made up of decomposed rock materials and minerals, organic matter, \n\n\n\nwater, gases and living organisms that combine effectively to support life \n\n\n\non earth [1]. Plant growth and productivity depends largely on the nature \n\n\n\nof the soil and microorganisms form a greater percentage of living \n\n\n\norganisms found in the soil, amongst other soil organisms such as worms \n\n\n\nand insects. Microbial communities play a pivotal role in the functioning \n\n\n\nof plants by influencing their physiology and development [2]. Soil \n\n\n\nmicroorganisms affect soil fertility positively and also make nutrients \n\n\n\navailable to plants. They do this by several mechanisms of plant \u2013 microbe \n\n\n\ninteractions helping in such processes as carbon sequestration and \n\n\n\nnutrient cycling [3]. Microorganisms in the soil also help in organic matter \n\n\n\ndecomposition, soil degradation, and bioremediation of polluted soils \n\n\n\n[4,5]. \n\n\n\nThe rhizosphere is that region of the soil, including plant roots and tissues \n\n\n\ncolonized by microorganisms. Plant roots provide the soil microorganisms \n\n\n\nwith exudates that serve as substrates (e.g. carbohydrate sources) and \n\n\n\nsignaling molecules. These exudates initiate rhizospheric interactions \n\n\n\n(plant-microbe and microbe-microbe) and influence the soil microbial \n\n\n\ncommunity. Rhizosphere interactions involving plant roots, soil, and \n\n\n\nmicrobes obviously change the physical and chemical properties of the soil  \n\n\n\nand in turn, the entire microbial population of the rhizosphere \n\n\n\nenvironment [6-8]. The microbial community structure in the rhizosphere \n\n\n\nis usually different from that in bulk soil (i.e. non-rhizosphere soil) \n\n\n\nbecause of the nutrients available to the soil as a result of biological \n\n\n\ninteractions between the roots and the microbial community of the soil \n\n\n\n[9]. \n\n\n\nAmongst rhizosphere microorganisms, bacteria have been found to \n\n\n\nstrongly influence plant growth. This has been attributed to the \n\n\n\npredominance of amino acids and other growth factors required by \n\n\n\nbacteria that are readily available in the root exudates secreted by plants \n\n\n\nin the rhizosphere. During the early stage of plant growth, the roots grow \n\n\n\ninto the soil and release organic materials in the rhizosphere leading to \n\n\n\ndevelopment of reasonable microbial population in the area and creates \n\n\n\nopportunity for plant-microbe interactions. Several factors affect the \n\n\n\nmicrobial flora of the rhizosphere which includes soil type and its \n\n\n\nmoisture, soil pH, soil amendments, proximity of roots to the soil, the plant \n\n\n\nspecies, age of the plant, and root exudates [10]. \n\n\n\nThis study was aimed at evaluating the microbial population changes \n\n\n\noccurring in the rhizosphere of two varieties of tomatoes: cherry and \n\n\n\nplum, during early growth of their seedlings. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \nDOI : http://doi.org/10.26480/mjsa.01.2019.23.27 \n\n\n\n   RESEARCH ARTICLE \n\n\n\nMICROBIAL POPULATION CHANGES IN THE RHIZOSPHERE OF TOMATO \nSOLANUM LYCOPERSICUM VARIETIES DURING EARLY GROWTH IN \nGREENHOUSE \n\n\n\nChinakwe EC1, Ibekwe VI1, Nwogwugwu UN1, Onyemekara NN1, Ofoegbu J2, Mike-Anosike E1, Emeakaraoha M1, Adeleye S1, Chinakwe PO3 \n\n\n\n1Department of Microbiology, Federal University of Technology, Owerri, Imo State, Nigeria \n2Department of Science Laboratory Technology, Federal University of Technology, Owerri, Imo State, Nigeria \n3Department of Crop Science, Federal University of Technology, Owerri, Imo State, Nigeria \n\n\n\n*Corresponding Author Email: eti_chukwumaeze@yahoo.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:eti_chukwumaeze@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 23-27 \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Onyemekara NN, Ofoegbu J, Mike -Anosike E, Emeakaraoha M, Adeleye S, Chinakwe PO (2019). \nMicrobial Population Changes In The Rhizosphere Of Tomato Solanum Lycopersicum Varieties During Early Growth In Greenhouse. \n\n\n\nMalaysian Journal of Sustainable Agriculture, 3(1): 23-27. \n\n\n\n2.1 Study Area \n\n\n\nThe greenhouse of the School of Agriculture and Agricultural Technology, \n\n\n\nFederal University of Technology, Owerri, Imo State, Nigeria (5.3905oN, \n\n\n\n6.9907oE). \n\n\n\n2.2 Collection of Samples \n\n\n\n2.2.1 Soil Sample \n\n\n\nSoil samples were randomly collected from uncultivated portion of the \n\n\n\nfarmland using a spade at a depth of about 20cm and bulked. Five (5) kg \n\n\n\neach of the bulked soil were packed into separate 5- litre pots for use in \n\n\n\nplanting of the tomato seeds. \n\n\n\n2.2.2 Tomato Seeds \n\n\n\nPlum and cherry seed varieties were sourced from the Imo State \n\n\n\nAgricultural Development Programme (ADP) Office, Owerri, Imo State, \n\n\n\nNigeria. \n\n\n\n2.2.3 Seed Planting and Rhizospheric Soil Sample Collection \n\n\n\nNine pots were set up containing 5kg of the farmland soil, three each for \n\n\n\nthe bare soil, plum tomato, and cherry tomato and placed inside a green \n\n\n\nhouse. \n\n\n\nFive seeds, each of plum and cherry tomatoes, were planted in the pots \n\n\n\nlabeled for plum and cherry tomato. Bare soil without these plants served \n\n\n\nas a control in this study. \n\n\n\nThe soils containing these seeds were watered daily to encourage \n\n\n\ngermination. The seeds on germination were left to grow for five weeks \n\n\n\nand the microbial populations as well as the microbial succession patterns \n\n\n\nwere determined. \n\n\n\nThe bare soil and rhizospheric soils from the roots of the tomato plants \n\n\n\nwere collected using sterile container bottles and taken to the laboratory \n\n\n\nfor analysis, weekly for five weeks. Bulk soil, defined a soil that does not \n\n\n\nadhere to plant roots, was obtained at least 20 cm from the plants. Bulk \n\n\n\nsoils from five different spots were combined into one sample. \n\n\n\nRhizosphere soil, defined as soil that adheres to the plant root after gentle \n\n\n\nshaking was obtained from five plants, using sterile brushes, and \n\n\n\ncombined into one sample. Both rhizosphere and bulk soil samples were \n\n\n\nimmediately transferred to the laboratory in a cool container (0\u201310\u00b0C) \n\n\n\nwithin 2 hours. \n\n\n\n2.3 Determination of Microbial Population and Succession Patterns \n\n\n\nOne (1) gram each of dry samples of the bare soil and the rhizosphere soils \n\n\n\nfrom plum and cherry tomatoes were analyzed weekly after germination \n\n\n\nfor five weeks to determine the microbial populations and microbial \n\n\n\nsuccession patterns in the samples. \n\n\n\nMicrobiological analysis was carried out using the dilution method and \n\n\n\ncultured on specific media [16]. Total heterotrophic count for bacteria was \n\n\n\ndone on Nutrient Agar, Total Fungal Count on Potato Dextrose Agar and \n\n\n\nTotal Rhizobium Count on Congo Red Yeast Extract Mannitol Agar \n\n\n\n(CREYEMA). Nutrient agar was incubated for 24 hours while Potato \n\n\n\nDextrose Agar and Rhizobium Agar (CREYEMA) were incubated for 48-96 \n\n\n\nhours and at 28oC. \n\n\n\n3. RESULTS \n\n\n\n3.1 Microbiological Analysis \n\n\n\nMicrobiological analysis of bare soil and rhizospheric soils of plum and \n\n\n\ncherry tomatoes gave results as shown in Table 1. The bacteria isolated \n\n\n\nincluded Bacillus sp, Enterococcus sp, Staphylococcus sp and Rhizobium sp. \n\n\n\nFungal species isolated from the soil samples included Mucor, Penicillium \n\n\n\nand Saccharomyces. \n\n\n\nThe Total Heterotrophic Bacterial Counts (THBC), Total Fungal Counts \n\n\n\n(TFC) and Total Rhizobium Counts (TRC) are shown in Tables 2, 3 and 4 \n\n\n\nrespectively. There were fluctuations in these counts for the five- week \n\n\n\nperiod. \n\n\n\nTable 1: Bacterial and Fungal Isolates from Bare Soil and Rhizospheric Soils of Plum and Cherry Tomatoes \n\n\n\nSoil Type Bacteria Fungi \n\n\n\nBare Soil Bacillus sp \n\n\n\nEnterococcus sp \n\n\n\nStaphylococcus sp  \n\n\n\nRhizobium sp \n\n\n\nMucor sp \n\n\n\nPenicillium sp \n\n\n\nSaccharomyces sp \n\n\n\nRhizosphere Soil from Cherry Tomato Bacillus sp \n\n\n\nEnterococcus sp \n\n\n\nStaphylococcus sp \n\n\n\nRhizobium sp \n\n\n\nMucor sp \n\n\n\nPenicillium sp \n\n\n\nSaccharomyces sp \n\n\n\nRhizosphere Soil  \n\n\n\nfrom Plum Tomato \n\n\n\nBacillus sp \n\n\n\nEnterococcus sp \n\n\n\nStaphylococcus sp \n\n\n\nRhizobium sp \n\n\n\nMucor sp \n\n\n\nPenicillium sp \n\n\n\nSaccharomyces sp \n\n\n\nTable 2: Total Heterotrophic Bacterial Count (THBC) of Bare Soil and Rhizosphere Soils of Plum and Cherry Tomatoes during the Five- week Period of \n\n\n\nGrowth. \n\n\n\nSample Week 1 \n\n\n\n(CFU/g) \n\n\n\nWeek 2 \n\n\n\n(CFU/g) \n\n\n\nWeek 3 \n\n\n\n(CFU/g) \n\n\n\nWeek 4 \n\n\n\n(CFU/g) \n\n\n\nWeek 5 \n\n\n\n(CFU/g) \n\n\n\nBare Soil 9.8 x 107 1.0 x 106 1.2 x 107 3.2 x 107 4.8 x 107 \n\n\n\nSoil + Cherry Tomato ND 7.0 x 107 1.1 x 108 3.8 x 108 4.5 x 109 \n\n\n\nSoil + Plum Tomato ND 5.4 x 107 9.0 x 108 3.5 x 108 3.0 x 109 \n\n\n\nKey: CFU/g = Colony forming unit per gram of Soil; ND = Not Determined\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 23-27 \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Onyemekara NN, Ofoegbu J, Mike-Anosike E, Emeakaraoha M, Adeleye S, Chinakwe PO (2019). \nMicrobial Population Changes In The Rhizosphere Of Tomato Solanum Lycopersicum Varieties During Early Growth In Greenhouse. \n\n\n\nMalaysian Journal of Sustainable Agriculture, 3(1): 23-27. \n\n\n\nTable 3: Total Fungal Count (TFC) of Bare Soil and Rhizosphere Soils during the Five -Week Period of Growth. \n\n\n\nSample Week 1 \n\n\n\n(CFU/g) \n\n\n\nWeek 2 \n\n\n\n(CFU/g) \n\n\n\nWeek 3 \n\n\n\n(CFU/g) \n\n\n\nWeek 4 \n\n\n\n(CFU/g) \n\n\n\nWeek 5 \n\n\n\n(CFU/g) \n\n\n\nBare Soil 2.6 x 106 7.8 x 106 5.8 x 106 6.5 x 106 1.3 x 106 \n\n\n\nSoil + Cherry Tomato ND 3.1 x 106 4.4 x 106 1.2 x 106 8.7 x 106 \n\n\n\nSoil + Plum Tomato ND 6.5 x 106 7.6 x 106 1.0 x 106 1.2 x 107 \n\n\n\nKey: CFU/g = Colony forming unit per gram of Soil \n\n\n\nND = Not Determined \n\n\n\nTable 4: Total Rhizobial Count of Bare Soil and Rhizosphere Soils during the Five- Week Period of Growth. \n\n\n\nSample Week 1 \n\n\n\n(CFU/g) \n\n\n\nWeek 2 \n\n\n\n(CFU/g) \n\n\n\nWeek 3 \n\n\n\n(CFU/g) \n\n\n\nWeek 4 \n\n\n\n(CFU/g) \n\n\n\nWeek 5 \n\n\n\n(CFU/g) \n\n\n\nBare Soil 1.2 x 106 1.8 x 107 3.6 x 106 2.0 x 106 9.8 x 105 \n\n\n\nSoil + Cherry Tomato ND 2.6 x 107 6.9 x 107 4.1 x 107 7.0 x 107 \n\n\n\nSoil + Plum Tomato ND 1.7 x 107 3.7 x 107 9.7 x 107 9.3 x 107 \n\n\n\nKey: CFU/g = Colony forming unit per gram of Soil     ND = Not Determined\n\n\n\n3.2 Microbial Succession Pattern of the Isolates during Early Growth \n\n\n\nof Plum and Cherry Tomatoes  \n\n\n\nThe microbial succession patterns for bacteria and fungi isolated from the \n\n\n\nsoil samples are shown in Tables 5 and 6 respectively. Bacillus sp and \n\n\n\nSaccharomyces sp were present in all the soil samples all through the study \n\n\n\nperiod. Rhizobium sp was also present in the rhizosphere soils of cherry \n\n\n\nand plum tomatoes all through the study period. There were fluctuations \n\n\n\nin the occurrence of the other bacteria (Enterococcus sp and \n\n\n\nStaphylococcus sp) and fungi (Penicillium sp and Mucor sp) from weeks 3 \n\n\n\n\u2013 5 isolated during the study period.\n\n\n\nTable 5:  Succession Pattern of Bacteria and Rhizobium during the Five Weeks Period of Growth \n\n\n\nSamples Bacillus sp Enterococcus sp Staphylococcus sp Rhizobium sp \n\n\n\nWeek 1 \n\n\n\nBare Soil + + + + \n\n\n\nWeek 2 \n\n\n\nBare Soil + + + - \n\n\n\nSoil + Cherry Tomato + + + + \n\n\n\nSoil + Plum Tomato + + - + \n\n\n\nWeek 3 \n\n\n\nBare Soil + + + - \n\n\n\nSoil + Cherry tomato + - - + \n\n\n\nSoil + Plum Tomato + - - + \n\n\n\nWeek 4 \n\n\n\nBare Soil + - + - \n\n\n\nSoil + Cherry tomato + - + + \n\n\n\nSoil + Plum Tomato + - - + \n\n\n\nWeek 5 \n\n\n\nBare Soil + + - + \n\n\n\nSoil + Cherry tomato + - - + \n\n\n\nSoil + Plum Tomato + + - + \n\n\n\nKey:  - = Absent \n\n\n\n+ = Present \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 23-27 \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Onyemekara NN, Ofoegbu J, Mike-Anosike E, Emeakaraoha M, Adeleye S, Chinakwe PO (2019). \nMicrobial Population Changes In The Rhizosphere Of Tomato Solanum Lycopersicum Varieties During Early Growth In Greenhouse. \n\n\n\nMalaysian Journal of Sustainable Agriculture, 3(1): 23-27. \n\n\n\nTable 6: Succession Pattern of Fungi during the Five-Week Period of Growth \n\n\n\nSamples Mucor sp Saccharomyces sp Penicillium sp \n\n\n\nWeek 1 \n\n\n\nBare Soil + + - \n\n\n\nWeek 2 \n\n\n\nBare Soil + + + \n\n\n\nSoil + Cherry Tomato + + - \n\n\n\nSoil + Plum Tomato + + - \n\n\n\nWeek 3 \n\n\n\nBare Soil - + + \n\n\n\nSoil + Cherry Tomato - + - \n\n\n\nSoil + Plum Tomato + + - \n\n\n\nWeek 4 \n\n\n\nBare Soil - + - \n\n\n\nSoil + Cherry Tomato - + - \n\n\n\nSoil + Plum Tomato - + - \n\n\n\nWeek 5 \n\n\n\nBare Soil - + - \n\n\n\nSoil + Cherry Tomato - + + \n\n\n\nSoil + Plum Tomato + + + \n\n\n\nKey: - = Absent \n\n\n\n+ = Present \n\n\n\n4. DISCUSSION\n\n\n\nMicrobial diversity in soil is considered important for maintaining the \n\n\n\nsustainability of agriculture production systems. The quantity and type of \n\n\n\nmicroorganisms are determining factors of the productivity of any kind of \n\n\n\nsoil [11]. The isolation of Bacillus sp, Enterococcus sp, Staphylococcus sp, \n\n\n\nRhizobium sp, and Penicillium sp were in line with reports from studies by \n\n\n\na scholar [12]. They isolated Bacillus sp, Rhizobium sp, Enterococcus sp, \n\n\n\nand Streptomyces as part of the microbial community present in a study to \n\n\n\ndetermine the endophytic community of roots of Phaseoulus vulgaris. A \n\n\n\nscholar also reported the presence of Bacillus sp, Fusarium, and Penicillium \n\n\n\nsp in rhizosphere of bean plant [13]. Penicillium and Mucor, which are also \n\n\n\nfilamentous fungi were isolated from bare soil and rhizosphere soils of \n\n\n\ncherry and plum tomatoes. The presence of these filamentous fungi may \n\n\n\nbe useful in absorption of nutrients and water thereby helping in early \n\n\n\ngrowth of the tomato plants [14]. \n\n\n\nEnterobacter and Bacillus species had been identified as plant growth \n\n\n\npromoting rhizobacteria (PGPR). The isolation of these organisms in the \n\n\n\nrhizosphere of growing tomato is also very significant as they help \n\n\n\nimprove plant growth and health. These bacteria are however provided \n\n\n\nwith amino acids and growth factors by the root exudates of the plants. \n\n\n\nSaccharomyces sp isolated could be as a result of soluble sugars in the plant \n\n\n\nroot exudates which act as chemo attractants for the microorganisms. \n\n\n\nThe succession pattern of the population of microorganisms in the bare \n\n\n\nsoil and rhizosphere soils of plum and cherry tomatoes (Tables 5 and 6) \n\n\n\nshowed that Bacillus sp and Rhizobium sp were more abundant during the \n\n\n\nfive-week period of study. There were fluctuations in the occurrences of \n\n\n\nEnterococcus sp and Staphylococcus sp in the bare soil as well as \n\n\n\nrhizosphere soils studied. Bacillus sp was present in the soil samples \n\n\n\nstudied; and this could be as a result of their  ability to resist biotic and \n\n\n\nabiotic stresses (as in the case of being present in the bare soil and \n\n\n\nthrough), or availability of nutrients (from interactions with plant root \n\n\n\nexudates, which contain compounds such as malic acid, that recruit such \n\n\n\nbacteria as Bacillus sp; as well as sugars and amino acids which the \n\n\n\nbacteria require for growth). Rhizobium sp are nitrogen fixing bacteria; \n\n\n\nand their present in the rhizosphere soils of growing plum and cherry \n\n\n\ntomatoes could be as a result of plant-microbe symbiosis, or benefits from \n\n\n\nplant root exudates. These plant-microbe interactions generally affect the \n\n\n\nrhizosphere microbial community and plant productivity generally. The \n\n\n\npresence of Saccharomyces sp all through the five-weeks of the study; both \n\n\n\nin the bare soil, and the rhizosphere soils could also be attributed to \n\n\n\navailability of nutrients from the root exudates (soluble sugars) and its \n\n\n\nphysiological attributes. \n\n\n\nThe population study of the bacterial isolates for five-weeks is in \n\n\n\nagreement with reports from a scholar that bacteria population in the \n\n\n\nrhizosphere soil ranges from 108 to 109 CFU/g of soil [15]. The total \n\n\n\nheterotrophic bacterial count (THBC) ranged from 1.0 x 106 to 4.5 x 109 \n\n\n\nCFU/g of soil, for the bare soil and rhizosphere soils of cherry and plum \n\n\n\ntomatoes. There were fluctuations in the THBC for bare soil for the five-\n\n\n\nweek period (Table 2.0). There was however, a progressive increase in \n\n\n\nTHBC for the rhizosphere soils of cherry and plum tomatoes, for the five-\n\n\n\nweek period [12]. There were fluctuations in the Total rhizobial counts \n\n\n\n(TRC) for bare soil and the rhizosphere soils studied (Table 4.0); and also, \n\n\n\nthe total fungal counts (TFC) of the soil samples studied (Table 3.0). The \n\n\n\nTHBC and TRC from this study were higher than TFC, and this helps \n\n\n\nbuttress the point and report from previous studies that amongst \n\n\n\nrhizosphere microorganisms, bacterial influence plant growth more than \n\n\n\nothers. \n\n\n\n5. CONCLUSION \n\n\n\nIn conclusion, our result suggested that the interactions in the rhizosphere \n\n\n\nbetween plant roots, soil and microorganisms are necessary for plant \n\n\n\nhealth and productivity. The study revealed the presence of notable plant \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 23-27 \n\n\n\nCite The Article: Chinakwe EC, Ibekwe VI, Nwogwugwu UN, Onyemekara NN, Ofoegbu J, Mike-Anosike E, Emeakaraoha M, Adeleye S, Chinakwe PO (2019). \nMicrobial Population Changes In The Rhizosphere Of Tomato Solanum Lycopersicum Varieties During Early Growth In Greenhouse. \n\n\n\nMalaysian Journal of Sustainable Agriculture, 3(1): 23-27. \n\n\n\ngrowth promoting rhizobacteria (Bacillus and Enterococcus sp), as well as \n\n\n\nnitrogen fixing bacteria (Rhizobium sp).  This is desirable, considering the \n\n\n\neffect it will have on the growth of tomato, a highly nutritious and \n\n\n\neconomic crop; they can be applied as biofertilizers, to encourage \n\n\n\nsustainable growth of tomato plants. Further investigations like efficiency \n\n\n\ntest under greenhouse and field conditions are needed to evaluate the role \n\n\n\nof the isolated PGPR. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWe thank Mr. Ononiwu Nnaemeka of Imo State Agricultural development \n\n\n\nprogramme office, Owerri, Nigeria for providing the tomato seeds and Dr. \n\n\n\nWesley Braide of Federal University of Technology, Owerri - Nigeria, for \n\n\n\nhis valuable advice \n\n\n\nCompeting interests: None \n\n\n\nREFERENCES \n\n\n\n[1] Chinakwe, E.C., Egbadon, E.O., Ofoh, M.C., Ojibe, O., Onyeji-Jarret, C., \n\n\n\nEmeakaroha, M.C., Nwogwugwu, N.U., Chinakwe, P.O. 2017. In vitro \n\n\n\nEvaluation of the Effect of Inorganic Fertilizer on Rhizosphere Soil \n\n\n\nMicrobial Populations during Early Growth of Zea mays and Phaseolus \n\n\n\nvulgaris. Biotechnology Journal International, 18(1), 1-9. \n\n\n\n[2] Rodrigo, M., Paolina, G., Raaijmakers, J.M. 2013. The rhizosphere \n\n\n\nmicrobiome: significance of plant beneficial, plant pathogenic, and human \n\n\n\npathogenic microorganisms FEMS Microbiology Reviews, 37(5), 634\u2013663.  \n\n\n\n[3] Huang, X., Jacqueline, M.G., Kenneth, F.R., Ruifu, Z., Qirong, S., Jorge, \n\n\n\nM.V. 2014. Rhizosphere Interactions: Root Exudates, Microbes and \n\n\n\nMicrobial Communities. NRC Research Press, Botany, 92, 267-275. \n\n\n\n[4] Berendsen, R.L., Pieterse, C.M.J., Bakker, P.A. 2012. The rhizosphere \n\n\n\nmicrobiome and plant health. Trends in Plant Science, 17, 478\u2013486. \n\n\n\n[5] Li, Y., Chen, Y.L., Li, M., Lin, X.G., Liu, R.J. 2012. Effects of Arbuscular \n\n\n\nMycorrhizal Fungi Communities on Soil Quality and the Growth of \n\n\n\nCucumber Seedlings in a Greenhouse Soil of Continuously Planting \n\n\n\nCucumber. Pedosphere, 22, 79-87. \n\n\n\n[6] Innes, L., Hobbs, P.J., Bardgett, R.D. 2004. The impacts of individual \n\n\n\nplant species on rhizosphere microbial communities in soils of different \n\n\n\nfertility. Biology and Fertility of Soils, 40, 7\u201313. \n\n\n\n[7] Garbeva, P., Van-Elsas, J.D., Van-Veen, J.A. 2008. Rhizosphere microbial \n\n\n\ncommunity and its response to plant species and soil history. Plant Soil, \n\n\n\n302, 19\u201332. \n\n\n\n[8] Bakker, M.G., Manter, D.K., Sheflin, A.M., Weir, T.L., Vivanco, J.M. 2012. \n\n\n\nHarnessing the rhizosphere microbiome through plant breeding and \n\n\n\nagricultural management. Plant Soil, 360, 1\u201313. \n\n\n\n[9] Paterson, E., Gebbing, T., Abel, C., Sim, A., Telfer, G. 2007. \n\n\n\nRhizodeposition shapes rhizosphere microbial community structure in \n\n\n\norganic soil. New Phytologist. 173, 600\u2013610. \n\n\n\n[10] Nihorimbere, V., Ongena, M., Smaraiassi, M. and Thonart, P. 2011. \n\n\n\nBeneficial Effect of the Rhizosphere Microbial Community for Plant \n\n\n\nGrowth and Health. Biotechnology, Agronomy, Society and Environment, \n\n\n\n15, 327-337. \n\n\n\n[11] Ribeiro, C.M., Cardoso, E.J. 2011. Isolation, selection and \n\n\n\ncharacterization of root associated growth promoting bacteria in Brazil \n\n\n\nPine (Araucaria angustifolia). Microbiological Research, 167, 69-78.  \n\n\n\n[12] Lopez-Lopez, A., Rogel, M.A., Ormeno Orrillo, E., Martinex-Romero, \n\n\n\nJ., Martinez-Romero, E. 2010. Phaseolus vulgaris Seed-Borne Endophytic \n\n\n\nCommunity with Novel Bacterial Species such as Rhizobium endophyticum \n\n\n\nsp. nov. Systematic and Applied Microbiology, 33, 322-327. \n\n\n\n[13] Patkowska, E. 2009. Effect of Bio-Products on Bean Yield and \n\n\n\nBacterial and Fungal Communities in the Rhizosphere and Non-\n\n\n\nRhizosphere. Polish Journal of Environmental Studies, 18(2), 255-263. \n\n\n\n[14] Bokati, D., Herrera, J., Poudel, R. 2016. Soil Influences Colonization \n\n\n\nof Root-Associated Fungal Endophyte Communities of Maize, Wheat and \n\n\n\ntheir Progenitors Corporation. Journal of Mycology, 9, 1-9. \n\n\n\n[15] Semenov, A.M., van Bruggen, A.H.S., Zelenev, V.V. 1999. Moving \n\n\n\nWaves of Bacterial Populations and Total Organic Carbon along Roots of \n\n\n\nwheat. Microbial. Encol., 37, 116-128. \n\n\n\n[16] Pochon, J., Tardieux, P. 1962. Analytical Techniques of Soil \n\n\n\nMicrobiology St-Mande.  Edition de la Tourtourelle, 111.  \n\n\n\n\njavascript:;\n\n\njavascript:;\n\n\njavascript:;\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 21-23 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.21.23 \n\n\n\nCite the Article: Biplov Sapkota, Shristi Upadhyaya, Anuj Lamichhane, Rajendra Regmi, Kuldip Ghimire, Raj Kumar Adhikari (2021). First Record of Hermetia Illucens \n(Linnaeus, 1758) \u2013 Black Soldier Fly, From Nepal. Malaysian Journal of Sustainable Agriculture, 5(1): 21-23. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2021.21.23\n\n\n\nFIRST RECORD OF HERMETIA ILLUCENS (LINNAEUS, 1758) \u2013 BLACK SOLDIER FLY, \nFROM NEPAL  \n\n\n\nBiplov Sapkotaa*, Shristi Upadhyayaa, Anuj Lamichhanea, Rajendra Regmib, Kuldip Ghimirec, Raj Kumar Adhikarid \n\n\n\naAgriculture and Forestry University \nbDepartment of Entomology, Agriculture and Forestry University \ncPlant Quarantine Office, Ministry of Agriculture and Cooperatives, Government of Nepal \ndValue Chain Development Project, Government of Nepal \n*Corresponding Author Email: bplv624@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 21 October 2020 \nAccepted 25 November 2020 \nAvailable online 15 December 2020\n\n\n\nHermetia illucens (Linnaeus, 1758)- Black soldier fly is a beneficial insect which has been used in simple \n\n\n\nsystems, to treat organic waste efficiently and rapidly, and to produce animal feed ingredient and fertilizer as \n\n\n\nend products. These flies are naturally found in warmer parts of the globe. The incidence of Black soldier fly \n\n\n\nwas recorded for the first time in Nepal in between April and May 2020 in the sub urban area of Chitwan \n\n\n\nDistrict, Nepal. Identification of the insect was done in the Laboratory of Department of Entomology, Faculty \n\n\n\nof Agriculture, Agriculture and Forestry University, Nepal. Both adult and larval forms of the insect were \n\n\n\nidentified based on the study of morphological characteristics of captured specimens using simple \n\n\n\nmicroscope and stereomicroscope. The record of this insect in Nepal opens up a new dimension for its use in \n\n\n\nbio-systems to treat organic waste and produce more sustainable ingredient for animal feeding, and rich \n\n\n\nfertilizer to be used in agriculture.  \n\n\n\nKEYWORDS \n\n\n\nInsects, Nepal, New Record, Black soldier fly.\n\n\n\n1. INTRODUCTION \n\n\n\nHermetia illucens (Linnaeus, 1758), commonly called Black soldier fly \n(BSF), belongs to the family Stratiomyidae. They have a wasp like \nappearance and are sleek and glossy looking with a blackish coloration. \nBlack soldier flies have two wings and does not possess a stringer (Diclaro \n& Kaufman, 2012). The fly is native to the Neotropics, but at present found \nthroughout the warmer parts of the globe following decades of spread \n(Marshall et.al., 2015).  Adults of the fly inhabit and mate near larval \nhabitat. It is not recognized as a pest since the adult of the fly is not \nattracted to human habitation or foods (Furman et.al., 1959). The adults \ndo not need to eat as they rely on the fat stored from the larval stage. The \nlarva of this fly is a voracious consumer of decaying organic matter \nincluding kitchen waste, spoiled feed, decaying fruits and vegetables, \nanimal manure and human excreta (Newton et.al., 1977; Diener et al., \n2011).  In the last few decades, there has been considerable interest in \nusing larvae of H. illucens for organic waste control, composting, and as \nanimal food supplements (Marshall et al., 2015). The larva of Black soldier \nfly can be used as an alternative source of protein ingredient in the making \nof poultry, fish and other livestock feed. Feeding studies with chickens, \npigs, catfish and tilapia have shown that the larvae or larval meal of this fly \nwas a suitable replacement for a high proportion of conventional protein \nand fat sources (Hale, 1973; Newton et.al., 1977; Bondari & Sheppard, \n1987). Black soldier fly has not been reported in any places of Nepal. The \nmajor objective of this study is to report the incidence of Hermetia illucens \n(Linnaeus, 1758) in Nepal. \n\n\n\n2. METHODOLOGY \n\n\n\nThe first record of Black soldier fly was an incidental occurrence. It was \nmade in afternoon hours in the semi-urban setting of Chitwan District, \nNepal. The insect was seen near the organic waste collection and \ncomposting pit of a local resident\u2019s house. The major flora around the \nrecord site were Mangifera indica, Musa spp., Zea mays, and Citrus limon. \nThe first sighting was recorded using a mobile camera device (Redmi Note \n8 Pro, 64 MP Camera Sensor). The Global Positioning System (GPS) data of \nlocation, time and date were recorded on the picture. Following this \nincidence, another incidence was recorded nine days after the first record \nduring which two black soldier flies were sighted on the same place again. \nThree days after the second incidence, a Black soldier fly was found \nperforming oviposition on the wall of the waste collection and composting \nbin. This observation developed a suspense of previous egg-laying by \nother flies and subsequent larval growth in the waste collection and \ncomposting bin. The upper layer of the waste in the bin was removed and \nnumerous larvae of Black soldier flies along with few Housefly (Musca \nDomestica) larvae were seen. Some larvae were collected and rinsed with \nwater for further observation. Two adult flies were captured for detailed \nstudy, which was performed under LEICA GZ6 stereomicroscope and \nsimple microscope under 10x and 20x at the Department of Entomology, \nAgriculture and Forestry University, Nepal. All specimens were preserved \nand stored in the Department\u2019s laboratory for future references. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 21-23 \n\n\n\nCite the Article: Biplov Sapkota, Shristi Upadhyaya, Anuj Lamichhane, Rajendra Regmi, Kuldip Ghimire, Raj Kumar Adhikari (2021). First Record of Hermetia Illucens \n(Linnaeus, 1758) \u2013 Black Soldier Fly, From Nepal. Malaysian Journal of Sustainable Agriculture, 5(1): 21-23. \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\nAs larvae, Hermetia illucens eat decaying organic material, like food scares \nand kitchen waste due to which adults of the flies are seen near to places \nwhere such waste is ample. The observed insect was identified as Black \nsoldier fly adults on the basis of morphological descriptions given by \nSheppard et. al ( 2002). The adult flies had a wasp-like appearance and \nwere blackish blue in coloration. They also had two typical translucent \n\"windows\" located on the first abdominal segment. The flies were \nobserved to be 1cm to 1.5 cm in length. The adult's antennae were \nelongated, and legs had typical white coloration near the end.  \n\n\n\nThe larvae of the Black soldier fly were also identified on the basis of their \nmorphology. They were dull-whitish in color, with a small projecting head \ncontaining chewing mouthparts and had segmentations throughout their \nbody. These observations were in line with descriptions given by Hall and \nGerhardt (2002). \n\n\n\nTable 1: Details of Incidence of Black soldier fly \n\n\n\nLocation Elevation Date Time Stage Number Sex Activity \n\n\n\nApril \n\n\n\n25, \n\n\n\n2020 \n\n\n\n3:27 \n\n\n\nPM \n\n\n\nAdult 1 M/F Sitting \n\n\n\n27.6887, \n\n\n\n84.45133 \n\n\n\n212 \n\n\n\nmasl \n\n\n\nMay \n\n\n\n4, \n\n\n\n2020 \n\n\n\n2:38 \n\n\n\nPM \n\n\n\nAdult 2 M/F Sitting \n\n\n\nMay \n\n\n\n7, \n\n\n\n2020 \n\n\n\n1:52 \n\n\n\nPM \n\n\n\nAdult 1 F Ovipositing \n\n\n\nMay \n\n\n\n7, \n\n\n\n2020 \n\n\n\n4:03 \n\n\n\nPM \n\n\n\nLarva Many M/F Feeding \n\n\n\nFigure 1: An adult  \n\n\n\nBlack soldier fly (dorsal aspects) \n\n\n\nfound near the organic waste bin \n\n\n\nFigure 2: An adult Black soldier \n\n\n\nfly (lateral aspects) found \n\n\n\novipositing on the crevices of \n\n\n\nwaste-bin wall \n\n\n\nFigure 3: An adult Black soldier \n\n\n\nfly being held in hand where its \n\n\n\nwhite-edged feet and reproductive \n\n\n\nprominence can be seen. \n\n\n\nFigure 4: Organic waste \n\n\n\ncollection pit where Black \n\n\n\nsoldier fly and larva were \n\n\n\nrecorded. \n\n\n\nFigure 5: Numerous Black soldier \n\n\n\nfly larvae feeding on organic waste \n\n\n\nas seen on the waste bin \n\n\n\nFigure 6: Few larvae of \n\n\n\nHousefly seen along with \n\n\n\nnumerous Black soldier fly larva \n\n\n\ninside an eggshell \n\n\n\nFigure 7: Larvae and prepupae of \n\n\n\nBlack soldier fly extracted from \n\n\n\nwaste bin (after cleaning) \n\n\n\nFigure 8: Prepupae of Black \n\n\n\nsoldier fly extracted from waste \n\n\n\nbin (after cleaning) \n\n\n\nFigure 9: Ventral view of Black \n\n\n\nsoldier fly captured from the site \n\n\n\nof incidence \n\n\n\nFigure 10: Dorsal view of Black \n\n\n\nsoldier fly captured from the \n\n\n\nsite of incidence \n\n\n\n3.1 Basis of identification \n\n\n\nOne of the captured flies (Female, 13mm) preserved in 70% ethanol \nsolution. It was dissected and the parts were studied under \nstereomicroscope and simple microscope (10x and 20x). Wings were \nobserved under 10x lens, where peculiar venation with a small discal cell \nwas seen (Fig. 11). The antenna had a spatulated apical flagellum (Fig. 12). \nPulvillus, empodium and claw were observed under 20x lens in the limbs \nof the fly (Fig. 13). All observations made were in accordance with the \nBlack soldier fly identification key (De Carvalho & De Mello-Patiu, 2008). \n\n\n\nFigure 11: Wings of the \n\n\n\ncaptured fly where \n\n\n\ndiscal cell can be seen \n\n\n\nin the middle \n\n\n\nFigure 12: Antenna \n\n\n\nof the captured fly \n\n\n\nwith spatulated \n\n\n\napical flagellum \n\n\n\nFigure 13: Tip of \n\n\n\nlimb of the \n\n\n\ncaptured insect \n\n\n\nwith its parts \n\n\n\nFigure 14: Cephalic region of the captured larva observed under \nstereomicroscope \n\n\n\n.  \n\n\n\n\n\n\n\n\n\n\n\nDiscal Cell \nApical Flagellum \n\n\n\nClaw \n\n\n\nEmpodium \n\n\n\nPulvillus \n\n\n\n.  \n\n\n\n\n\n\n\n\n\n\n\nDiscal Cell \nApical Flagellum \n\n\n\nClaw \n\n\n\nEmpodium \n\n\n\nPulvillus \n\n\n\n.  \n\n\n\n\n\n\n\n\n\n\n\nDiscal Cell \nApical Flagellum \n\n\n\nClaw \n\n\n\nEmpodium \n\n\n\nPulvillus \n\n\n\nSimilarly, larva preserved in 70% ethanol solution (23mm) was observed \nunder a stereomicroscope. The larva had a flattened and roughened body \nwith setose teguments (Fig. 8 and 14) and had a sclerotized cephalic \nregion. The observations made were in line with the Black soldier fly larva \nidentification key (Vel\u00e1squez et.al., 2010).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 21-23 \n\n\n\nCite the Article: Biplov Sapkota, Shristi Upadhyaya, Anuj Lamichhane, Rajendra Regmi, Kuldip Ghimire, Raj Kumar Adhikari (2021). First Record of Hermetia Illucens \n(Linnaeus, 1758) \u2013 Black Soldier Fly, From Nepal. Malaysian Journal of Sustainable Agriculture, 5(1): 21-23. \n\n\n\n4. CONCLUSIONS \n\n\n\nThe captured fly and larval specimens were confirmed to be the adult and \nlarval stages of Black soldier fly respectively. The presence of Black soldier \nfly population in Nepal was previously not reported or mentioned in any \nliteratures. This record can be a milestone for further studies in relation to \ndistribution of the fly in the country. The presence of Black soldier fly \ncould be an indication of the benefits Nepal can get, through deployment \nof this fly species for organic waste management and its subsequent \nbioconversion to protein feed ingredient and organic fertilizer. This \nevidence justifying the presence of wild Black soldier fly population may \nvalidate the feasibility of commercial insect rearing protocols in the \nregion. \n\n\n\nCONFLICT OF INTEREST \n\n\n\nThe authors declare no conflict of interest.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors are thankful to Department of Entomology, Agriculture and \nForestry University, Rampur for the valuable inputs offered for correct \nidentification of the insect specimens. \n\n\n\nREFERENCES \n\n\n\nBondari, K., & Sheppard, D. C. 1987. Soldier fly, Hermetia illucens L., larvae \nas feed for channel catfish, Ictalurus punctatus (Rafinesque), and blue \ntilapia, Oreochromis aureus (Steindachner). Aquaculture Research, \n18(3), 209\u2013220. https://doi.org/10.1111/j.1365-2109.1987.tb00141.x \n\n\n\nDe Carvalho, C. J. B., & De Mello-Patiu, C. A. 2008. Key to the adults of the \nmost common forensic species of Diptera in South America. Revista \nBrasileira de Entomologia, 52(3), 390\u2013406. \nhttps://doi.org/10.1590/S0085-56262008000300012 \n\n\n\nDiclaro, J. W., & Kaufman, P. E. 2012. Black soldier fly Hermetia illucens \nLinnaeus Insecta\u202f: Diptera\u202f: Stratiomyidae). IFAS Extension, 5. \nRetrieved from http://entomology.ifas.ufl.edu/creatures. \n\n\n\nDiener, S., Zurbr\u00fcgg, C., Roa Guti\u00e9rrez, F., Hong Nguyen, D., Morel, A., \nKoottatep, T., & Tockner, K. 2011. Black Soldier Fly Larvae for Organic \n\n\n\nWaste Treatment-Prospects And Constraints. In eawag.ch. Retrieved \nfrom \nhttps://www.eawag.ch/fileadmin/Domain1/Abteilungen/sandec/pub\nlikationen/SWM/BSF/Black_soldier_fly_larvae_for_organic_waste_trea\ntment.pdf \n\n\n\nFurman, D. P., Young, R. D., & Catts, P. E. 1959. Hermetia illucens \n(Linnaeus) as a Factor in the Natural Control of Musca domestica \nLinnaeus. Journal of Economic Entomology, 52(5), 917\u2013921. \nhttps://doi.org/10.1093/jee/52.5.917 \n\n\n\nHale, O. M. 1973. Dried Hermetia illucens larvae (Diptera: Stratiomyidae) \nas a feed additive for poultry. Ga Entomol Soc J. Retrieved from \nhttps://agris.fao.org/agris-\nsearch/search.do?recordID=US201303264936 \n\n\n\nHall, R., & Gerhadrt, R. 2002. Flies (Diptera). In Medical and veterinary \nentomology (pp. 127\u2013145). Academic Press. \n\n\n\nMarshall, S. A., Woodley, N. E., & Hauser, M. 2015. The Historical Spread of \nThe Black Soldier Fly, Hermetia illucens (l.) (Diptera, Stratiomyidae, \nHermetiinae), and its Establishment in Canada. Journal of the \nEntomological Society of Ontario, 146. \nhttps://doi.org/10.1093/jee/52.5.917 \n\n\n\nNewton, G. L., Booram, C. V., Barker, R. W., & Hale, O. M. 1977. Dried \nHermetia Illucens Larvae Meal as a Supplement for Swine. Journal of \nAnimal Science, 44(3), 395\u2013400. \nhttps://doi.org/10.2527/jas1977.443395x \n\n\n\nSheppard, D. C., Tomberlin, J. K., Joyce, J. A., Kiser, B. C., & Sumner, S. M. \n2002. Rearing Methods for the Black Soldier Fly (Diptera: \nStratiomyidae): Table 1. Journal of Medical Entomology, 39(4), 695\u2013\n698. https://doi.org/10.1603/0022-2585-39.4.695 \n\n\n\nVel\u00e1squez, Y., Maga\u00f1a, C., Mart\u00ednez-S\u00e1nchez, A., & Rojo, S. 2010. Diptera of \n\n\n\nforensic importance in the Iberian Peninsula: Larval identification key. \n\n\n\nMedical and Veterinary Entomology, 24(3), 293\u2013308. \n\n\n\nhttps://doi.org/10.1111/j.1365-2915.2010.00879.x \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 95-98 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.95.98 \n\n\n\nCite The Article: Saroj Regmi, Ishwar Chandra Prakash Tiwari, Naba Raj Devkota,  Ramashish Sah,  Ritesh Kumar Yadav, Naveen Pant, Utsa v Lamichhane(2021). \nEffect Of Dietary Supplementation Of Garlic And Ginger In Different Combination On Feed Intake And Rowth Performance In Comme rcial Broilers.  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 5(2): 95-98\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\nDOI: http://doi.org/10.26480/mjsa.02.2021.95.98\n\n\n\nRESEARCH ARTICLE \n\n\n\nEFFECT OF DIETARY SUPPLEMENTATION OF GARLIC AND GINGER IN DIFFERENT \nCOMBINATION ON FEED INTAKE AND GROWTH PERFORMANCE IN\nCOMMERCIAL BROILERS \n\n\n\nSaroj Regmia*, Ishwar Chandra Prakash Tiwaria, Naba Raj Devkotaa,  Ramashish Sahb,  Ritesh Kumar Yadavc, Naveen Pantd, Utsav Lamichhaned\n\n\n\na Department of Animal Nutrition, Faculty of Animal Science, Veterinary Science and Fisheries (FAVF), Agriculture and Forestry University, \nRampur, Chitwan, Nepal. \nb Department of Livestock Production and Management, Faculty of Animal Science, Veterinary Science and Fisheries (FAVF), Agriculture and \nForestry University, Rampur, Chitwan, Nepal. \n3Faculty of Agriculture, Agriculture and Forestry University, Rampur, Chitwan, Nepal. \n4Faculty of Animal Science, Veterinary Science and Fisheries (FAVF), Agriculture and Forestry University, Rampur, Chitwan, Nepal. \n*Corresponding author email: regmisaroj645@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited.\n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 10 January 2021 \nAccepted 11 February 2021 \nAvailable online 18 March 2021\n\n\n\nA study was conducted at AFU livestock farm, Rampur, Chitwan in 2017-18 to determine the effect of dietary \nsupplementation of garlic and ginger in different combination on feed intake, growth performance and \neconomics by using commercial Cobb 500 broilers. A total of two hundred, 11-day-old chicks were allocated \nrandomly to five different treatments. The experiment was designed in a Completely Randomized Design, \neach treatment with four replication and each replication had 10 birds. They were fed isoproteinous and \nisocaloric Basal diet (BD) and BD supplemented with four different levels of garlic and ginger such as T1 (BD \nonly), T2 (BD + 1% garlic); T3 (BD + 1.0% ginger); T4 (BD + 0.5% garlic and 0.5% ginger) and T5 (BD + 1% \ngarlic and 1% ginger). Weekly average body weight, weight gain, feed consumption, and feed efficiency were \nrecorded up to sixth week of age. All data were statistical analyzed using Completely Randomized Design. The \nresults showed that overall feed consumption was significantly (P\u22640.05) higher for broiler fed diet \nsupplemented with 0.5% garlic and 0.5% ginger (T4) followed by T5 (basal diet with 1% garlic and 1% ginger). \nOn the other hand significantly higher (P\u22640.01) cumulative weekly live body weight and body weight gain \n(g/bird) was found for the treatment with supplemented 1% garlic powder (T2), followed by broiler fed diet \nsupplemented with 0.5% garlic and 0.5% ginger powder (T4). Similarly, feed conversion ratio was \nsignificantly (P\u22640.01) better in broiler fed basal diet with supplementation of 1% garlic (T2) followed by basal \ndiet with supplementation of 0.5% garlic and 0.5% ginger (T4). The maximum benefit was obtained from the \nbroiler fed basal diet with supplementation of 1% garlic (T2). The findings revealed that broiler fed basal diet \nwith supplementation of 1% garlic powder had helped as a growth promoter contributing to the better \ngrowth performance, feed efficiency and higher benefit: cost ratio. Thus, addition of 1% garlic powder can be \nsafely recommended as a growth promoter in broilers.  \n\n\n\nKEYWORDS \n\n\n\nbroiler, feed conversion ratio, garlic, ginger, growth performance. \n\n\n\n1. INTRODUCTION\n\n\n\nPoultry industry is the epitome of economy in Nepal in the recent decades. \nIt has become one of the major national industries (Bhattarai, 2005). \nAccording to FAOSTAT 2014, GDP contribution by the poultry industry is \n3.5% and the investment in the industry is NRs 22 billion with the growth \nrate of 17-18%. Poultry meat is very good and cheap source of protein \nwhich is essential for the growth and maintenance of body. Increasing \nawareness about the nutritional value of meat among the consumer, \nincrease income level of people, change in food habits, population growth, \ninflow of tourists and easy access to market, the demand of poultry meat \nis increasing every year. Poultry meat is very good and cheap source of \nprotein which is essential for the growth and maintenance of body. \n\n\n\nIncreasing awareness about the nutritional value of meat among the \nconsumer, increase income level of people, change in food habits, \npopulation growth, inflow of tourists and easy access to market, the \ndemand of poultry meat is increasing every year. 80% of the total cost of \nproduction of the poultry industry is contributed by the poultry feed \n(Borazjanizadeh et al., 2011).  \n\n\n\nAs the poultry industry is booming, there is always a challenge to maintain \nthe surplus supply of feed. To soar the efficiency of the feed, different \norganic and inorganic substances are added to the feed, which are feed \nadditives. Feed additives are the non-nutritional substance that accelerate \ngrowth and efficiency of the feed utilization (Church and Pond, 1998). The \nuse of additive in poultry feed to attain the properties like antibiotic, \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 95-98 \n\n\n\nCite The Article: Saroj Regmi, Ishwar Chandra Prakash Tiwari, Naba Raj Devkota,  Ramashish Sah,  Ritesh Kumar Yadav, Naveen Pant, Utsav Lamichhane(2021). \nEffect Of Dietary Supplementation Of Garlic And Ginger In Different Combination On Feed Intake And Rowth Performance In Comme rcial Broilers.  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 5(2): 95-98.\n\n\n\nantioxidant, and growth promoter. Implementation of such properties of \nthe additives eventually minimize the effective cost of the feed. In the \ncommercial scale most of the feed additive used are the synthetic feed \nadditives. There are various questionable effect of the synthetic product in \nthe poultry health. And the use of antibiotics in the commercial scale are \nquestionable due to the antimicrobial resistance. Due to various factors \nlike antibiotic resistance, phasing out of antibiotic compound as growth \npromoter from animal feed has been reported (William et al 2001). To \nreplace synthetic additives without adversely affecting the performance of \nbirds, natural growth promoters such as prebiotics, probiotics, synbiotics, \nenzymes, plant extracts, etc., can be used to feed the broilers \n(Borazjanizadeh et al.,2011).  \n\n\n\nDue to the growth promoting effect, antibiotic effect, and immunological \neffect of the garlic and ginger, in the present scenario garlic and ginger are \nbroadly used as feed additives in the poultry feed. The use of garlic as food \nsupplement has been reported for great health benefits (Sallam 2004, \nStomacev et al 2011). The chemical properties of the substances in the \ngarlic and ginger shows antibacterial effects, antifungal and antiparasitic \neffects (Ankri and Mirelman 1999; Konjufea et al 1977). Report showed \nthat dietary immunomodulator (like garlic and ginger) that enhance \nhumoral immunity and immunological stress will affect growth \nperformance most positively Garlic shows immune enhancing activities \nthat include the promotion of lymphocyte formation (Kyo et al 2001; \nHumphrey, 2012). The inhibitory properties of garlic on growth of \nmicroorganism including bacteria, yeast, viruses and fungi has been \ndocumented (Kivanc and Kunduhoglu, 1997). \n\n\n\n2. MATERIALS AND METHODS\n\n\n\nThe experiment was conducted from 15th November 2017 to 24th \nDecember 2017 at AFU Livestock, Rampur Chitwan. A total of 200, day old \nCobb500 broiler chicks were grouped brooded in deep litter for 10 days \nand were fed commercial broiler starter ration. After 10 days, birds were \nshifted to deep litter housing system for experimental trial in Completely \nRandomized Design (CRD) with five treatment and four replications. The \nstarter ration was feed for 0-4 weeks and finisher ration was fed for 5-6 \nweeks. Garlic and Ginger was cut into smaller pieces and dried sufficiently \nin the sunlight. After drying, required amount of ginger and garlic was \nprepared by fine grinding and passing through 1 mm sieve and stored in \nthe airtight plastic container till incorporated in feed. Other feed \ningredients were also purchased from local market was grounded at feed \nmeal of AFU. Then grounded garlic and ginger were mixed in the prepared \nfeed in the proportion of 0.5% and 1% on weight basis for required \ntreatment groups. Thus, experimental diets consisted of standard broiler \nfeed (Basal diet) supplemented with different levels of garlic and ginger. \nDiets were formulated in such a way that each diet contained at least the \nminimum recommended levels of protein and energy, recommended for \nCobb 500 broilers. Different dietary treatments used in experiment were \nas follows. \n\n\n\nT1 : Control diet (Basal diet) \nT2 : Basal diet + 1% garlic  \nT 3 : Basal diet + 1% ginger \nT4 : Basal diet + 0.5% garlic + 0.5% ginger \nT5 : Basal diet + 1% garlic + 1% ginger \n\n\n\nPoultry are very sensitive to the environmental condition in which they \nare raised.  \n\n\n\nFloors, interiors of the walls as well as the roof were scrubbed, and the \nentire previous litter and undesirable materials were removed from the \npoultry house. All pens in experimental shed were washed with clean \nwater, phenol and coated with lime. Before the start of experiment, the \nexperimental unit was disinfected by using 5% phenol solution, followed \nby spraying of 3% solution of Kohrsolin inside and outside of the \nexperimental house. A thin layer of dust lime was broadcasted as \ndisinfectant on the floor and rice husks was used as litter. Filament bulbs \nand gas brooder were used during brooding period. Experiment groups \nwere separated using concrete partitioning. All groups were provided \nwith individual feeder and waterer. Manual turning and mixing of feed was \ndone frequently 4-5 times in a day. All the groups were provided similar \nenvironmental and management condition during entire experimental \nperiod. \n\n\n\nThe feed intake was calculated weekly by subtracting the feed residue over \nfrom each feed offered. The average weekly body weight gain was \ncalculated by subtracting previous live weight of the birds from their \ncorresponding body weight. The weekly cumulative feed conversion ratio \n\n\n\nof the birds in each replication was determined by dividing the weekly \ncumulative feed intake by their respective average total body weight. \nWhile calculating the economy, the gross expenditure was calculated by \nthe sum of cost of chicks, brooding, medicines, herbs, feeds, labors etc. at \nthe end of the experiment. Gross income was obtained by selling of final \nbody weight of birds and litters. The data collected were analyzed \nstatistically using MSTAT. The data were subjected to analysis of variance. \nDifferences between the treatments were tested for significance by Least \nSignificance difference (LSD) by using MSTAT. Where needed descriptive \nanalysis was done by using MS Excel 2007. \n\n\n\n3. RESULT AND DISCUSSION\n\n\n\nThe overall weekly feed consumption was recorded significantly (P<0.05) \nhighest (1.302 kg) in treatment fed basal ration supplemented with 0.5% \ngarlic and 0.5% ginger (T4) which was statistically similar with T5 (basal \ndiet with 1% garlic and 1% ginger). Similarly, significantly (P<0.05) \nminimum weekly feed consumption (1.208 kg) was recorded in control \ngroup (T1) whereas treatments T2 (basal diet with 1% garlic) and T3 (basal \ndiet with 1% ginger) were in between T4 and T1.Similar result was also \nobserved in overall mean daily feed consumption. Some researchers has \nreported the similar results to this study who had reported that feed \nintake was higher in garlic supplemented broilers as compare to control \ngroup (Oladele et al., 2012; Isa, 2011; Mansoub and Myandoab, 2011). In \n4th week of experiment, significantly (P<0.01) maximum body weight \n(2645.75 g) was recorded for T2 (Basal ration supplemented with 1% \ngarlic) which was statistically similar with T3, T4 and T5. Significantly \n(P<0.05) minimum body weight (2170.00 g) was observed in T1 (control \ngroup).  \n\n\n\nSimilar significant result on total body weight of broiler with garlic \nsupplementation was reported by stated that effect of garlic \nsupplementation was found non-significant on its total body weight (Aji et \nal., 2011; Rahimi et al., 2011). In contrast, a group researcher had reported \nthat there is no any significant (P>0.05) effect in weight gain of broiler by \ninclusion of raw garlic paste and sun-dried garlic powder in the broiler \ndiet (Ologhobo et al., 2008).  \n\n\n\nMean weekly body gain (2606.32 g) was significantly (P<0.01) higher with \nfed basal supplemented with 1% garlic (T2) at 4th week of experimental \nperiod which was similar with T3 (basal ration with 1% ginger), T4 (basal \nration with 0.5% garlic and 0.5% ginger) and T5 (basal ration with 1% \ngarlic and 1% ginger). While fed basal ration without supplementation \ngarlic and ginger (T1) has significantly (P<0.01) minimum weekly body \nweight gain (2132.786 g).The overall daily weight gain was significantly \n(P<0.01) higher (85.089 g) in broiler fed diet supplemented with 1% garlic \n(T2) which was statistically similar with T4 (basal ration with 0.5% garlic \nand 0.5% ginger) and T5 (basal diet with 1% garlic and 1% ginger) \nfollowed by weight gain (78.268 g) in broiler fed diet with supplemented \nwith 1% ginger (T3). Similarly, overall daily weight gain was significantly \n(P<0.05) minimum (67.777g) in T1 (control group).  \n\n\n\nThe findings are consistent with the result of who found a positive and \nsignificant effect on the body weight gain with addition of garlic in broiler \ndiet (Pourali et al., 2010; Mansoub, 2011; Stanacev et al., 2011; Suriya et \nal., 2012). These results are similar with the findings of had also the same \nfindings with this results who reported the significant (P<0.05) \nimprovement on body weight gain  was found in the broilers \nsupplemented with garlic in their diet as compared to control and to those \nof mixture of garlic and ginger (Kumar et al., 2010; Songsang et al., 2008; \nAhmad, 2005; Mahmood et al., 2009).  \n\n\n\nThe overall mean weekly feed conversion ratio showed significantly \n(P<0.01) poor feed conversion ratio (2.545) in control group (T1) whereas \nbasal diet supplemented with 1% garlic (T2) has significantly (P<0.01) \nbetter feed conversion ratio. By the supplementation use of garlic in \nbroiler diet results the better feed conversion ratio, greater feed efficiency \nand utilization which was also reported by reported that there is better \nFCR in the broilers supplemented with garlic in their basal diet (Esmail, \n2012; Mahmood et al., 2009; Onu, 2010; Fadlalla et al., 2010; Prasad et al., \n2009;  Raeesi et al., 2010; Mansoub, 2011; Suriya et al., 2012).  \n\n\n\nWhile comparing the benefit: cost ratio over feed cost from each bird with \ncontrol and different treatment groups, it is clear that maximum benefit \nwas obtained in garlic group (T2) followed by T4 (basal ration with 0.5% \ngarlic and 0.5% ginger), T5 (basal ration with 1% garlic and 1% ginger), T3 \n(basal ration with 1% ginger). Diet without supplementation of garlic and \nginger (T1) had lowest benefit: cost ratio. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 95-98. \n\n\n\nCite The Article: Saroj Regmi, Ishwar Chandra Prakash Tiwari, Naba Raj Devkota,  Ramashish Sah,  Ritesh Kumar Yadav, Naveen Pant, Utsav Lamichhane(2021). \nEffect Of Dietary Supplementation Of Garlic And Ginger In Different Combination On Feed Intake And Rowth Performance In Comme rcial Broilers.  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 5(2): 95-98.\n\n\n\nFigure 1: Overall mean weekly feed consumption (Kg/bird) \n\n\n\nFigure 2: Mean cumulative body weight (g/bird) \n\n\n\nFigure 3: Mean weekly body weight gain (g/bird) \n\n\n\nFigure 4: Mean overall daily body weight gain(g/bird) \n\n\n\nFigure 5: Mean overall FCR \n\n\n\n4. CONCLUSION\n\n\n\nThe study was planned to generate more information about the effect of \nfeeding dietary supplementation of garlic and ginger and their \ncombination on the growth performance of broiler chicken. The results \nobtained revealed that supplementation of garlic at the rate of 1% had \nsignificantly improved body weight, body weight gain and feed conversion \nratio. From the results of this experiment, it can be concluded that \nsupplementation of certain proportion of garlic improves performance of \nbroiler when added as feed additives, an alternative to antibiotic growth \npromoter in commercial broiler farms. In addition, basal feed \nsupplementation of 1% garlic was superior in terms of Benefit: Cost ratio \nand thus could increase farmer\u2019s profitability. However, these results need \nto be verified in terms of critical level of addition in the farmer\u2019s \nmanagement conditions before making any recommendations. \n\n\n\nFarmers/poultry growers can utilize added Garlic powder to increase \noverall growth performances (AWFI, AWWG, Relative LW and FCR) in \nbroiler production. Also they can utilize it to obtain overall economic \nprofits (Net income/bird and B:C Ratio) in broiler production. \n\n\n\nACKNOWLEDGEMENT\n\n\n\nThe authors acknowledge the facilities and financial support provided for \nthe present study from Agriculture and Forestry University, Rampur, \nChitwan, Nepal. \n\n\n\nREFERENCES\n\n\n\nAhmad, S., 2005. Comparative efficiency of garlic, turmeric and kalongi as \ngrowth promoter in broiler. M. Sc. (Hons.) Thesis, Department Poultry \nSciences, University of Agriculture, Faisalabad, Pakistan. \n\n\n\nAji, S.B., Ignatius, K., Asha'Adatu, Y., Nuhu, J.B., Abdulkarim, A., Aliyu, U., \nNuman, P.T., 2011. Effects of Feeding Onion (Allium \u0441\u0435\u0440\u0430) and Garlic \n(Allium sativum) on Some Performance Characteristics of Broiler \nChickens. Research Journal of Poultry Sciences, 4 (2), Pp. 22-27. \n\n\n\nAl-Amin, Z.M., Thomson, M., Al-Qattan, K.K., Peltonen-Shalaby, R., Ali, M., \n2006. Anti-diabetic and hypolipidaemic properties of ginger (Zingiber \nofficinale) in streptozotocin-induced diabetic rats. British journal of \nnutrition, 96 (4), Pp. 660-666. \n\n\n\nAl-Shuwaili, M.A., Ibrahim, E.I., Naqi Al-Bayati, M.T., 2015. Effect of dietary \nherbal plants supplement in turkey diet on performance and some \nblood biochemical parameters. Glob. J. Biosci. Biotechnol, 4 (2), Pp. \n153-157.\n\n\n\nAnkri, S., Mirelman, D., 1999. Antimicrobial properties of allicin from \ngarlic. Microbes and infection, 1 (2), Pp. 125-129. \n\n\n\nAsghar, A., Farooq, M., Mian, M.A., Khurshid, A., 2000. Economics of broiler \nproduction in Mardan division. Journal of Rural Development and \nAdministration, 32 (3), Pp. 56-65. \n\n\n\nBhattarai, T.C., 2005. Nepalese poultry industries and strategies for \nsustainable  development. Proceedings: National Poultry Expo, Pp. \n166-171.\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 95-98 \n\n\n\nCite The Article: Saroj Regmi, Ishwar Chandra Prakash Tiwari, Naba Raj Devkota,  Ramashish Sah,  Ritesh Kumar Yadav, Naveen Pant, Utsav Lamichhane(2021). \nEffect Of Dietary Supplementation Of Garlic And Ginger In Different Combination On Feed Intake And Rowth Performance In Comme rcial Broilers.  \n\n\n\nMalaysian Journal Of Sustainable Agriculture, 5(2): 95-98.\n\n\n\nBorazjanizadeh, M., Eslami, M., Bojarpour, M., Chaji, M., Fayazi, J., 2011. The \nEffect of Clove and Oregano on Economic Value of Broiler. Journal of \nAnimal and Veterinary Advances, 10 (2), Pp. 169-173. \n\n\n\nChinnah, A.D., Baig, M.A., Tizard, I.R., Kemp, M.C., 1992. Antigen dependent \nadjuvant activity of a polydispersed \u03b2-(1, 4)-linked acetylated mannan \n(acemannan). Vaccine, 10 (8), Pp. 551-557. \n\n\n\nDemir, E., Sarica, \u015e., \u00d6zcan, M.A., Sui, M.M., 2003. The use of natural feed \nadditives as alternatives for an antibiotic growth promoter in broiler \ndiets. British Poultry Science, 44 (S1), Pp. 44-45. \n\n\n\nEsmail, S.H., 2012. Black cumin and garlic powder in poultry diets. \n\n\n\nFadlalla, I.M.T., Mohammed, B. H., Bakhiet, A.O., 2010. Effect of feeding \ngarlic on the performance and immunity of broilers. Asian Journal of \nPoultry Science, 4 (4), Pp. 182-189. \n\n\n\nHanieh, H., Narabara, K., Piao, M., Gerile, C., Abe, A., Kondo, Y., 2010. \nModulatory effects of two levels of dietary Alliums on immune \nresponse and certain immunological variables, following \nimmunization, in White Leghorn chickens. Animal Science Journal, 81 \n(6), Pp. 673-680. \n\n\n\nIsa, K.J.J., 2011. Performance and lipid profile of broilers fed two medicinal \nplants (Doctoral dissertation). \n\n\n\nKivanc, M., Kunduhoglu, B., 1997. Antimicrobial activity of fresh plant juice \non the growth of bacteria and yeasts. Journal of Qafqaz University, \n1(1), Pp. 27-35. \n\n\n\nKonjufca, V.H., Pesti, G.M., Bakalli, R.I., 1997. Modulation of cholesterol \nlevels in broiler meat by dietary garlic and copper. Poultry science, 76 \n(9), Pp. 1264-1271. \n\n\n\nKumar, S., Sharadamma, K.C., Radhakrishna, P.M., 2010. Effects of a garlic \nactive based growth promoter on growth performance and specific \npathogenic intestinal microbial counts of broiler chicks. International \nJournal of Poultry Science, 9 (3), Pp. 244-246. \n\n\n\nKyo, E., Uda, N., Kasuga, S., Itakura, Y., 2001. Immunomodulatory effects of \naged garlic extract. The Journal of nutrition, 131 (3), Pp. 1075S-1079S. \n\n\n\nMahmood, S., Mushtaq-Ul-Hassan, M., Alam, M., Ahmad, F., 2009. \nComparative efficacy of Nigella sativa and Allium sativum as growth \npromoters in broilers. International Journal of Agriculture and \nBiology (Pakistan). \n\n\n\nMansoub, N.H., Nezhady, M.\u0410.M., 2011. The effect of using thyme, garlic \nand nettle on performance, carcass quality and blood \nparameters. Annals of Biological Research, 2 (4), Pp. 315-320. \n\n\n\nMohamed, A.B., Al-Rubaee, M.A., Jalil, A.Q., 2012. Effect of ginger (Zingiber \nofficinale) on performance and blood serum parameters of \nbroiler. International Journal of Poultry Science, 11 (2), Pp. 143-146. \n\n\n\nMorakinyo, A.O., Akindele, A.J., Ahmed, Z., 2011. Modulation of antioxidant \nenzymes and inflammatory cytokines: Possible mechanism of anti-\n\n\n\ndiabetic effect of ginger extracts. African Journal of Biomedical \nResearch, 14 (3), Pp. 195-202. \n\n\n\nOladele, O.A., Emikpe, B.O., Bakare, H., 2012. Effects of dietary garlic \n(Allium sativum L.) supplementation on body weight and gut \nmorphometry of commercial broilers. Int. J. Morphol, 30 (1), Pp. 238-\n240. \n\n\n\nOloghobo, A.D., Adebiyi, F.G., Adebiyi, O.A. 2008. Effect of long-term \nfeeding of raw and sun-dried garlic (Allium sativum) on performance \nand lipid metabolism of broiler chicks. In Proceedings of the \nConference on International Research on Food Security, National Res. \nManage. Rural Dev. Univ. Hohenheim. Pp. 7-9. \n\n\n\nOnu, P.N., 2010. Evaluation of two herbal spices as feed additives for \nfinisher broilers. Biotechnology in Animal Husbandry, 26 (5-6), Pp. \n383-392.\n\n\n\nPourali, M., Mirghelenj, S.A., Kermanshahi, H., 2010. Effect of garlic powder \non productive performance and immune response of broiler chickens \nchallenged with newcastledisiease virus. Global Veterineria, 4. \n\n\n\nPrasad, R., Rose, M.K., Virmani, M., Garg, S.L., Puri, J.P., 2009. Lipid profile \nof chicken (Gallus domesticus) in response to dietary \nsupplementation of garlic (Allium sativum). International Journal of \nPoultry Science, 8 (3), Pp. 270-276. \n\n\n\nRaeesi, M., Hoeini-Aliabad, S.A., Roofchaee, A., ZareShahneh, A., Pirali, S., \n2010. Effect of periodically use of garlic (Allium sativum) powder on \nperformance and carcass characteristics in broiler chickens. World \nAcademy of Science, Engineering and Technology, 68, Pp. 1213-1219. \n\n\n\nRahimi, S., TeymoriZadeh, Z., Torshizi, K., Omidbaigi, R., Rokni, H., 2011. \nEffect of the three herbal extracts on growth performance, immune \nsystem, blood factors and intestinal selected bacterial population in \nbroiler chickens. Journal of Agricultural Science and Technology, 13, \nPp. 527-539. \n\n\n\nSallam, K.I., Ishioroshi, M., Samejima, K., 2004. Antioxidant and \nantimicrobial effects of garlic in chicken sausage. LWT-Food Science \nand Technology, 37 (8), Pp. 849-855. \n\n\n\nSongsang, A., Suwanpugdee, A., Onthong, U., Sompong, R., Pimpontong, P., \nChotipun, S., &Promgerd, W., 2008. Effect of garlic (Allium sativum) \nsupplementation in diets of broilers on productive performance, meat \ncholesterol and sensory quality. In Conference on International \nResearch on Food Security, Natural Resource Management and Rural \nDevelopment, University of Hohenheim, Tropentag. \n\n\n\nStana\u0107ev, V., Glamo\u010di\u0107, D., Miloscaron, N., Puva\u010da, N., Stana\u0107ev, V., \nPlavscaron, N., 2011. Effect of garlic (Allium sativum L.) in fattening \nchicks nutrition. African Journal of Agricultural Research, 6 (4), Pp. \n943-948.\n\n\n\nSuriya, R., Zulkifli, I., Alimon, A.R., 2012. The effect of dietary inclusion of \nherbs as growth promoter in broiler chickens. J. Anim. Vet. Adv., 11 \n(3), Pp. 346-350. \n\n\n\n\n\n"
"\n\n Malaysian Journal of Sustainable Agriculture (MJSA)1(2) (2017) 12-14\n\n\n\nRUSSIA FOREST RESOURCE MANAGEMENT\nChen Qu1, Dai Wen-Bin1, Gao Yun2*\n\n\n\n1School of Land and Food, Department of Environmental Study and Geography, Tasmania University, \nTasmania 7005, Australia.\n2Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, \nChina\n*Corresponding author email: gaoyun0526@163.com\n\n\n\nARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 September 2017 \nAccepted 19 October 2017 \nAvailable online 30 October 2017 \n\n\n\nKeywords: \n\n\n\nForest resource, Russia, Resource \nmanagement, Conservation of \nbiodiversity\n\n\n\nABSTRACT\n\n\n\nThe forest resource is the material basis of forestry production. The status of forest resources is the most important \nsymbol to measure the effectiveness of forestry work. Due to the impact of human activities and natural factors, forest \nresources are always in dynamic change. There is a worldwide forest decrease, which has devastating effects on the \nenvironment ecosystem although regulatory measures have been taken to accentuate the significance of environment \necosystem preservation. So, management of forest resource becomes an important researching topic. Therefore, it is \nnecessary to strengthen the management and supervision of forest resources and the establishment of a scientific and \neffective management system. Management of forest resource is decision-making and organized activities in forest \nprotection, cultivating, updating and application of forest resources through planning, control, adjustment, inspection \nand supervision. The main purpose of this report is to identify and survey the management of forest resource in \nRussia where lies large areas of forest resource, introduce the components of forest resources, probe into the specific \nmeans of management of forest resource, evaluate the measure and point out effective management solutions and \nfailing reasons. At last potential efforts for better management of forest resource is discussed.\n\n\n\nCite this article as: Chen Qu, Dai Wen-Bin, Gao Yun  (2017).  Russia Forest Resource \nManagement.  Malaysian Journal of Sustainable Agriculture,  1(2):12-14.\n\n\n\n1. INTRODUCTION\n\n\n\nForest management has been high-profile since ancient times. However, low \nlevel of technology and inadequate management do not have access to \neffective development and use of forest resources although there is an \nabundance of forest resources. Also, improper processing and utilization of \nforest resources to some extent damages the forest ecosystem on the \ngrounds that different ways of forest resources management should be \napplied in different regions, nation\u2019s even countries. So precise analysis of \nforest resource management requires that we first recognize the \nclassification of forest resources as well as the characteristics of different \ncategories then applies different management methods to different places.\n\n\n\n2. CURRENT STATUS OF FOREST RESOURCE IN RUSSIA\n\n\n\n764 million hectares of forest lies in Russia, which is indicated in FAO\u2019s State \nof the world\u2019s forest 1999. However, deforestation happens and becomes \nworse. There is a trend of decrease of forest rate from 1650 to 2000 \nalthough an increase existed from 1930 to 2000. Almost 55 percent of land \nwas covered in 1680. Since then, cover rate of forest declined sharply and \ndecreased to 25 percent, the lowest percent in history. From 1930 to 2000, \ncover rate increased to almost 40 percent. It is distinct that large areas of \nforest have been cleared, a showcase of temporary upswing of over rate fails \nto compensate the damaged forest [1].\n\n\n\n3. THE NATURE OF MANAGEMENT OF FOREST RESOURCES\n\n\n\nThe reasonable management of forest resources means total adaptability \nand effectives. It is conducive to forest ecological balance with the economic, \necological and cultural interests taken into account and also it does services \nto contemporary people and future generations [2]. In spatial view, a \nsuitable scenario to mitigate deforestation, a perennial problem throughout \nthe whole world should be accommodated with a certain country because \ndifference exists and one-sizes-fits all model should be avoided. In temporal \nview, management style should combine the national background, for \ninstance, political situation, economic policy and leaders in helm of the \ngovernment because maximum benefits entail different management \npolicies in different period. \n\n\n\n3.1 Factors and Stakeholders of Management of Forest Resources\n\n\n\nWood-processing industries also accounts a lot in forest management \nbecause the lumber processing industry process lumbers as raw materials \nintensively, carries them to demanders, which speeds up lumber shipping \nindustry since timbers are massive and not easy to transport and at last \nconforms to consumers and satisfy the market demand.  \n\n\n\nMiners and oil operators also cause direct damage to forest management.  \n\n\n\nContents List available at RAZI Publishing \nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nJournal Homepage:  : http://www.razipublishing.com/journals/malaysian- journal- \nofsustainable-agriculture-mjsa/\n\n\n\nISSN: 2521-2931 (Print)\nISSN: 2521-294X (Online)\n\n\n\nThey have to cut trees and dig holes to check the existence of mine and oil \n[3]. As the core of the management, governments and officials' policies \nguarantee the effective operation of forest management as well as national \norganizations and non-governmental organizations that advocate open and \nfriendly circumstance for forest management. NGOs contribute a lot to the \nforest and other natural resources conservation and almost 200 NGOs keep \nclose contact mutually with the assistance of Socio-Ecological Union [4]. \nTaiga Rescue Network in Russia, the national-wide non-governmental-\norganization, aimed at northern forest protection. Also, it exerted itself to \nadvice Russian Karelia, Karelia Greens, Russian Greenpeace and other \nenvironmental organizations in Russia [5].\n\n\n\nA rational statement is made that those factors and stakeholder are not \nseparate from each other. Instead, they exist in a chain of causation. The \nforest management add incentive to every field of forest industry with a \nmyriad of profits and the satisfaction of all stakeholder\u2019s guarantee that \nforest management operates smoothly \n\n\n\n3.2 The System of Forest Resources Management\n\n\n\n3.2.1. The Governance Structure \n\n\n\nGovernance structure refers to a management system of a given country at \nall levels including state regulation, inter-governmental system, regional \ngovernment, local government and self-government even international \ninstitutions on a certain issue. In Russia, state regulation sets the tone for \ninter-governmental system, regional government and so on. For example, \nFederal Forest Service of Russia accentuates the value of centralization of \nthe national-wide forest management. The Karelian Forest Campaign was an \noverriding organization movement initiated by Taiga Rescue Network in \nmid-1990s. Also, it cooperated with foreign environmental organization \nsuch as Finnish environmental institution and Finnish companies importing \nwoods from Karelian Republic [6]. \n\n\n\nRussia's forests are mainly located in four regions\uff0cEurope, West Siberia, \nEast Siberia and Far East. Main part of forest land lies in Far East with 312.9 \nmillion hectares. East Siberia is ranked second with 224.8 million hectares. \nAnd Europe has 139.1 million hectare and West Siberia 78.4 million hectare. \nWe conclude that uneven distribution of forest land in Russia requires \nsupervision and management departments in different regions. Also, \nmanagement system should be tailored to the region and cooperation and \nexchange between regions should be strengthened [7]. \n\n\n\nIn March 2000, when in power, Putin started to carry on the reform of \ninstitutions of centralization administrative. The presidential order in May \n17 abolished independent federation forestry bureau responsible for the \nnational forestry work and transferred forest management function into \n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.12.14\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.12.14\n\n\n\n\n\n\n13  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 12-14  \n\n\n\nRussia, it was possible to connect forest organizations in different \ncountries and regions due to the access to electrical communication \nfacilities. \u201cElectronic Russia\u201d was introduced in 2001 and its revised \nessential target in 2006 is to improve the government staff\u2019s ability by \ntelecommunication technology application and promote the serve quality \nand administration of the government for the citizens. Now, the advanced \nelectrical communication technology facilitates dates and information \ntransmission and measure forest area, growth proportion, destruction \ndegree to forest resources of natural disasters and human factors and \neven the frequency as well as the high incidence of forest fires. \n\n\n\n4. THE OBJECTIVE AND EVALUATION OF FOREST MANAGEMENTS \n\n\n\n4.1 Economic prosperity of forest industry\n\n\n\nBooming forest industry, the commercial goal of forest management, is a \nprognosis of sustainability, which include an upswing of wood and non-\nwood forest products, growing timber available and so on.  The forest \nindustry in the Russia is an important social economy domain with almost \n23,700 lumber industry enterprises and more than 100 thousand \nworkers. It is pillar industry in Russia of 45 federation main body. Forest \nindustry and lumber processing industry are the vital role as the local \nspecialization production department. So forest resources management \npromotes the prosperity of lumber processing industry and furniture \nindustry development, maximizes their profits and also simultaneously \ncreated the harmonious ecological environment other industry \ndevelopment.\n\n\n\n4.2 Awareness of Forest Management\n\n\n\nOne of the social influences is to deepen the public's impression of forest \nresources protection and profound understanding so as to increase \nawareness of forest resource management and encourage people to join in \nthe management of forest resources voluntarily. Therefore, civil society \norganizations are particularly important. It is a lot to recommend \ncombining public activities such as media, exhibitions, and fairs with \nforestry subjects.  \n\n\n\n4.3 Conservation of Biodiversity \n\n\n\nBiodiversity emerged as early as in 1980s considered as an important \nenvironment-related issue (L\u00e9v\u00eaque and Claude Mounolou) due to the \nreduction of species caused by human activity and even extinction of rare \nbiological ones. Biological diversity is a total of ecological processes \nrelated with organisms (animals, plants, microbes\uff09and the environment \nincluding ecosystems, species and genetics. Biological diversity is the base, \nupon which the survival of mankind, sustainable economic, social \ndevelopment and ecological security and food safety depend.\nTo start with, soil and water conservation is a significant measure to \nevaluate forest management along with carbon dioxide reduction. In a \nmicrocosmic perspective, management also needs soil moisture, level of \nwater table and sediment load evaluation.\n\n\n\n5. OPPORTUNITIES FOR BETTER FOREST MANAGEMENT\n\n\n\n5.1 Problems in Forest Management \n\n\n\nIt can predicate that forest resource management in Russia has \ncontributed a lot. However, problems still exist. In Russia specific \ngeographical situation requires different laws and systems, which leads to \nthe collision between state legislation and local ones. What is more, \ninformation is not exposed to the public transparently, which renders \nvindication of government actions inaccessible. For instance, 40 percent of \nland is reforested after clear-cutting while roughly 2% is showed in \nGreenpeace Russia. As the biggest forest country in the world, the Russia \nlumber profound processing falls behind other developed countries. The \nRussia forest industry is confronted with the default of fund, modernized \ninfrastructures such as lumber industry equipment. Ministry of Forestry's \ncancellation has caused three kinds of adverse consequences directly: The \nforest management department's cadres drain massively; the forestry \ndomain lacks the unified management and production disorder stirs; \nCriminalities on forestry domain timber become rampant [7, 10].\n\n\n\nAnother issue warrants attention is forest fires, which occurs nearly in \n400 million hectares each year, namely10% of forest area in the world, for \ninstance, Russia according to statistics and 9 billion tons of biomass \nduring a raging forest fire reduced to ashes. It has been a chronic problem \nalthough successful fire management strategies have been made. \nThe number of forest fires in Russia in 2009 and 19,600 in 2008 with \nmore than 291,000 hectares effected, up to more than 25,000 in 2006 \nwith total affected area of 1.3 million hectares. In Russia, the number of \nfire increased by1.8 times and fire area increased 7 times. \n\n\n\nnatural resource department. At the end of February 2005, the \nforestry management body was set as follows: Federal Forestry \nBureau under the Ministry of natural resources; local forest \nauthority as the Federal Forest Service Agency in all federal bodies. \nIn Khabarovsk, Khabarovsk branch was established. Supervision \nBureau was set as follows: Natural resource utilization supervision \nbureau under the Ministry of natural resources, inspector general \nbureau in the Far East area.\n\n\n\n3.2.2. The Value of Forest Management \n\n\n\n3.2.2.1. Decentralization \n\n\n\nDecentralization management refers to the power transfer from \nnational government to regional officials, organization and experts \nand so on, which causes positive effects, for example full use of \nforest resources and negative effects, namely disorder in forest \nindustry market for maximum profits [8]. There is a trend of \ndecentralization of forest management in Russia history.\n\n\n\nAs one of the overriding forestry institution the Federal Forest \nService of Russia takes control of almost 94% of the forest land, \nwhich is a prognosis of the priority of center government control \nover forestry in accordance with the political situation at that time. \nThe Basic Law for Forest indicated market was injected into the \nforestry management and leasing and auctions were allowed. It \ninaugurated a new area on the grounds that shift from government-\ncontrol to market-control renders a myriad of opportunities despite \nthe competition. The Federal Forest Service also changed into a \ncompetitive forestry company from an independent controller [9]. \n\n\n\nIn 1995 the whole Russian logging system accelerated the \nestablishment of market condition work, actually completed the \nconversion of all kinds of enterprises to industry-wide private \nownership. For example, 90% percent of National transport \nenterprises were \u2018joint stock\u2019. Currently, a total of 47 forest industry \nholding company existed throughout the whole Russia with more \nthan 600 enterprises. Financial and industrial group is to be built \nand National Forest Bank has been established, whose branches have \nbegun operated. As for the investment in forestry, with the advance \nof privatization, forestry investment structure has shifted from state-\noverride into private investment with appropriate investment \nsubsidies given by nation according to the actual situation. \nProduction performance is evaluated mainly by market economic \neffects. \n\n\n\nAnother major concept in Russia is forest fund, which is closely \nlinked with forest resource management. It was once restricted to \nforest management while now it comprises more than forest and \nrefers to the real estate in Russia [8]. In 2011 funds in Russian \nforest sector amounted to 33,200,000,000 rubles with an increase of \n50%. 10 billion rubles was used for forest fire prevention in various \nregions including the acquisition of private fire-fighting equipment. \nThe financial plan of Russian forest sector in 2011 also includes the \ncosts for forest surveying and cartography, forest management and \nforest records management.\n\n\n\n3.2.2.2.  Law Protection\n\n\n\nRussia officials revised stringent laws to dwindle unlawful acts and \npenalize them severely. The First Code issued in 1977 is considered \nas the most significant forest management law in Russia. And it is \napplied to all the Federal forests. In 1995, amendments and \nsupplementing for the forest law was drafted in accordance with the \nspirit of the times to protect the orderly, rational use of forces and \nregeneration, which provided legal basis for forest protection. There \nwas no certain form of ownership of forest resources, resulting in a \ndeadlock in forest use and forest protection in former Russia forest \nlaw while according to the Russian Constitution, possession, use and \ndisposal of natural resources of forest were subjected to Russian \nFederation. Therefore, \u201crevised draft\u201d stipulated explicitly that the \nforest resources belong to the federation country, but the forest \nproduct (lumber, mushroom, wild fruit and so on) obtained in forest \nresources process may have private, state-owned and other many \nkinds of forms.\n\n\n\n3.2.2.3.  Electrical Communication Application\n\n\n\nAs is known, sound and fast development of forestry entails \ntechnical innovation. With the development of science and \ntechnology, electrical communication technology is also widely used \nin the management of forest resources. As early as in 1990s in \n\n\n\nCite this article as: Chen Qu, Dai Wen-Bin, Gao Yun  (2017).  Russia Forest Resource \nManagement.  Malaysian Journal of Sustainable Agriculture,  1(2):12-14.\n\n\n\n\n\n\n\n\n14  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 12-14  \n\n\n\n5.2 Opportunities for Better Management \n\n\n\nRussia has abundance of forest resources and overwhelming potential for \nforest industry. And that entails more efforts and endeavor. First, law and \nregulation perfection should be accentuated persistently, which lay \nunderground for policy performance. Also, penalties and rewards should be \ncombined [11]. Second, transform of business practice and management \nmode should be accelerated. Intensive processing of limber should be \nencouraged. Third, monitor and supervision of forest fire should be \nstrengthened and forest rate minimized.\n\n\n\n6. CONCLUSIONS\n\n\n\nThis study shed light on forest resource management including the nature, \nsystem, value and so on in Russia. It concludes that forest management \nshould be tailored to the specific situation of a given country. This essay \nputs emphasis on the sustainable forest management from its origin, it \ndevelopment and its application in Russia. Also, significance and prospect \nof forest management is illustrated.\nAs is known, the whole world is faced with forest exploitation and damage \nso forest management draws more attention. However, complicated factors \nexist in different countries so the best solution to ameliorate and eliminate \nit entails the meticulous investigation of the previous forest management. \nConclusion is arrived based on limited information and dates then further \nsurvey is necessary. \n\n\n\nREFERENCES    \n\n\n\n[1] Colfer, C.J.P. 2005. The complex forest: communities, uncertainty, and \nadaptive collaborative management. Resources for the Future.\n\n\n\n[2] Contreras-Hermosilla, A. 2000. The underlying causes of forest decline \n(No. CIFOR Occasional Paper no. 30, p. 25p). CIFOR, Bogor, Indonesia.\n\n\n\n[3] Duinker, P. N., and Trevisan, L. M. 2003. Adaptive management: \n\n\n\nprogress and prospects for Canadian forests. Towards sustainable \nmanagement of the boreal forest. NRC Research Press, Ottawa, 857-892.\n\n\n\n[4] Forest Fund of Russia. 1995. Statistical Collection. Moscow: State \nForestry Committee,\n\n\n\n[5] Isaev, A. S., and Korovin, G. N. 2009. Forest management in Russia. \nLesnoe Khozya\u0131s\u0306tvo, (4), 33-34.\n\n\n\n[6] Lakehead University. 2017. Forest Management in Russia. http://\nwww.borealforest.org/world/rus_mgmt.htm\n\n\n\n[7] Lehtinen, A., Donner-Amnell, J., and S\u00e6ther, B. 2004. Introduction: \nNorthern forest regimes and the challenge of internationalization. Politics \nof Forests\u2014Northern Forest\u2014Industrial Regimes in the Age of \nGlobalization, 3-30.\n\n\n\n[8] Tsvetkov, V. N. 1957. Dynamic flow birefringence, optical anisotropy, \nand shape of macromolecules in solutions. Journal of Polymer Science Part \nA: Polymer Chemistry, 23 (103), 151-166.\n\n\n\n[9] United Nations and FAO/ECE Trade Development and Timber Division, \nR. 2005. European Forest Sector Outlook Study: Main Report. pp.210-215\n\n\n\n[10] Vorobiov, D. 1999. Transboundary movement toward the saving of the \nKarelian \n\n\n\nforests. Towards a Sustainable Future: Environmental Activism in Russia \nand the United States. St. Petersburg: Institute of Chemistry of St. \nPetersburg State University.\n\n\n\n[11] World Bank. 1997. Russia: Forest Policy During Transition. Library of \nCongress Cataloging in Publication Date. Washington, DC: World Bank.\n\n\n\nCite this article as: Chen Qu, Dai Wen-Bin, Gao Yun  (2017).  Russia Forest Resource \nManagement.  Malaysian Journal of Sustainable Agriculture,  1(2):12-14.\n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 44-48 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.44.48 \n\n\n\nCite the Article: Mohammad Sharif Sarker, K. M. Mohiuddin, Laith Khalil Tawfeeq Al Ani, Mohamad Nazmul Hassan, Rojina Akter, Md. Sakhawat Hossain, \nMd. Niuz Morshed Khan (2020). Effect Of Bio-Nematicide And Bau-Biofungicide Against Root-Knot (Meloidogyne Spp.) Of Soybean.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 44-48. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.44.48 \n\n\n\nEFFECT OF BIO-NEMATICIDE AND BAU-BIOFUNGICIDE AGAINST ROOT-KNOT \n(MELOIDOGYNE SPP.) OF SOYBEAN  \n\n\n\nMohammad Sharif Sarkera, K. M. Mohiuddinb, Laith Khalil Tawfeeq Al Anic, Mohamad Nazmul Hassana, Rojina Aktera, Md. Sakhawat Hossaina, \nMd. Niuz Morshed Khand* \n\n\n\na Department of plant pathology, Bangladesh Agricultural University, Mymensingh, Bangladesh. \nb Department of Agricultural Chemistry, Bangladesh Agricultural University, Mymensingh, Bangladesh. \nc College of Agricultural, University of Bagdad, Baghdad, Iraq.  \nd Department of Biotechnology Bangladesh Agricultural University. \n\n\n\n*Corresponding author email: rumman899@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 26 February 2020\n\n\n\nMeloidogyne spp. considered highly dangerous on soybeans. It is very difficult to find the suitable method for \ncontrolling without affecting on the environment. Therefore, in this study we used Four treatments with a \nnewly developed Bio-nematicide, BAU-Biofungicide, Bio-nematicide + BAU-Biofungicide including control \nwere tested against root-knot (Meloidogyne incognita) of two soybean varities (Sohag and BARI Soybean-5). \nThe bio-agents were used as side dressing. Bio-nematicide in combination with BAU-Bio-fungicide showed \nthe best performance with the highest length of shoot and root, fresh weight of shoot and root with nodules, \nweight of seeds and number of nodules per plant correspondingly with decreased number of galls and adult \nfemales of the nematode. Bio-nematicide and BAU-Biofungicide showed better performance in plant growth \ncharacters, yield of seeds and nodulation resulting in reduced galling and nematode development. BARI \nsoybean-5 appeared with higher plant growth characters, nodulation and yield with reduced galling \ncompared to variety Sohag. Positive response was observed with Bio-nematicide interacting with all the \nvarieties of soybean. Negative correlation was found between gall numbers and all plants growth, nodulation \nand yield components. The combination between biological control agents is useful for the supporting and \nsucceeding the biocontrol of Meloidogyne incognita. Thus, it is leading to save the environment from the \nresidue of pesticides.  \n\n\n\nKEYWORDS \n\n\n\nsoybean, Bio-nematicide, biocontrol, Trichoderma, Meloidogyne.\n\n\n\n1. INTRODUCTION \n\n\n\nThe soybean [Glycine max (L.)] is a species of legume native to East Asia \n\n\n\nwhich is an important source of fats, minerals, proteins, vitamins and \n\n\n\nenergy for human and livestock (Tamagno et al., 2018; Aslam et al., 2019). \n\n\n\nLike many other legumes it can fix atmospheric nitrogen symbiotically. \n\n\n\nYield of soybean in Bangladesh is unexpectedly lower than other soybean \n\n\n\nproducing countries of the world. Root-knot diseases is a major constraint \n\n\n\nfor higher production of soybean. Root knot nematode Meloidogyne genus \n\n\n\nwidely occurs all over the world with a large host range which is one of the \n\n\n\nmost important pests limiting the productivity in agricultural field \n\n\n\nproductivity (Ramzan et al., 2019). The soil and climatic conditions of \n\n\n\nBangladesh has made her an ideal abode for nematodes. For controlling \n\n\n\nnematidic diseases different chemical and organic ingredients are used \n\n\n\nhaving problems like toxicity to wide range of soil organisms, and \n\n\n\nappearance of resistant strains among nematodes, though farmers are \n\n\n\nusing those to control plant-parasitic nematodes broadly (Lafta and \n\n\n\nKasim, 2019; El-Dabaa et al., 2019). Furthermore, chemical nematicides \n\n\n\nare so costly.  \n\n\n\nBAU-Biofungicide and Bio-nematicide using as nematode killing agents \n\n\n\nare new approaches as eco-friendly measures. Potentially antagonistic \n\n\n\nmicroorganisms in minimizing the crop damage by the soil borne \n\n\n\npathogens has been reported (Uikey et al., 2019). Trichoderma species \n\n\n\nwere evaluated for their efficiency in controlling soil borne plant \n\n\n\npathogenic nematodes like Meloidogyne spp (Lafta and Kasim, 2019; El-\n\n\n\nDabaa et al., 2019). It is reported that Trichoderma could decrease the \n\n\n\npathogenic factors associated with root-knot nematodes and could be \n\n\n\napplied as controlling agents for Meloidogyne spp. Researchers found that \n\n\n\nAll tested variants suppressed nematode reproduction and root galling \n\n\n\nand result in plant growth improvement compared to the control \n\n\n\n(Yankova et al., 2014). The lowest rate of infestation and the highest total \n\n\n\nyield were established in the combination BioAct WG and Trichoderma \n\n\n\nviride strain T6. Till now little attention has been given for controlling the \n\n\n\ndisease by biological means without any disturbance of the natural \n\n\n\nenvironment and beneficial microorganism. The main objective of this \n\n\n\nstudy was undertaken for observing the effect of bio-control agents BAU-\n\n\n\nBiofungicide and a newly developed Bio-nematicide for controlling root-\n\n\n\nknot (Meloidogyne spp.) of Soybean. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 44-48 \n\n\n\nCite the Article: Mohammad Sharif Sarker, K. M. Mohiuddin, Laith Khalil Tawfeeq Al Ani, Mohamad Nazmul Hassan, Rojina Akter, Md. Sakhawat Hossain, \nMd. Niuz Morshed Khan (2020). Effect Of Bio-Nematicide And Bau-Biofungicide Against Root-Knot (Meloidogyne Spp.) Of Soybean.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 44-48. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Site Selection  \n\n\n\nThe experiment was conducted in the Department of Seed Science and \n\n\n\nTechnology, Bangladesh Agricultural University, Mymensingh, \n\n\n\nBangladesh. Sandy loamy soil, sand and well decomposed cow dung were \n\n\n\ntaken at the ratio of 2:1:1 and mixed uniformly. At the rate of 3% (V/V) \n\n\n\nformalin was used to sterilize for per cubic feet of soil. Polythene sheet was \n\n\n\nused for covering the formalin treated soil and allowed to stay for 72 hours \n\n\n\nwithout any disturbance. The polythene sheet was removed after 72hours \n\n\n\nand the sterilized soil was exposed for air drying for 48hours to remove \n\n\n\nexcess vapor of formalin. Forty earthen pots (30cm diameter) were taken \n\n\n\nand each was provided with a small broken piece of brick on the bottom \n\n\n\nof the pore of earthen pot and filled with 5kg of sterilized and dried soil. \n\n\n\n2.2 Seed Collection \n\n\n\nSeeds of soybean variety BARI Soybean-5 were collected from Bangladesh \n\n\n\nAgricultural Research Institute (BARI), Joydebpur, Gazipur. \n\n\n\n2.3 Collection of Trichoderma harzianum \n\n\n\nBAU-Biofungicide was collected from the Disease Resistance laboratory, \n\n\n\nDepartment of Plant Pathology, BAU, Mymensingh.  \n\n\n\n2.4 Collection of Bio-nematicide \n\n\n\nBio-nematicide was collected from Department of Plant Pathology, BAU, \n\n\n\nMymensingh. \n\n\n\n2.5 Sowing of soybean seeds and after care of seedling  \n\n\n\nPot soil was loosened properly and five seeds of variety BARI soybean-5 \n\n\n\nwere directly sown in the respective pots of treatment. Equal number of \n\n\n\nsurface sterilized seeds was directly sown in ten pots designated as \n\n\n\ncontrol. One healthy seedling was allowed to grow in each pot by removing \n\n\n\nthe others after germination of seeds. Necessary weeding, irrigation etc. \n\n\n\nwere done when necessary.  \n\n\n\n2.6 Preparation of inoculum and inoculation of soybean plants \n\n\n\nFew brinjal plants were previously inoculated with single egg mass of \n\n\n\n(Meloidogyne incognita) growing in pots with sterilized soil. Severely \n\n\n\ngalled root system of brinjal was used for collecting mature egg masses of \n\n\n\nroot-knot nematode. Reddish brown mature egg masses were collected \n\n\n\nfrom infected roots of these plants with the help of fine forceps for \n\n\n\ninoculation. A moist petri dish was used for keeping egg masses. After 20 \n\n\n\ndays of planting, each soybean plant was inoculated with 6 egg masses \n\n\n\ncollected from infected brinjal plants. On each side of the plant, 3 egg \n\n\n\nmasses were placed on the exposed roots of the seedling by opening the \n\n\n\nsoil at the stem base. \n\n\n\n2.7 Preparation of Bio-nematicide \n\n\n\nNeem seeds, leaf and stem bark dried. After drying all plant parts were \n\n\n\nblended with blender. Neem Plant powder were mixed with talc powder \n\n\n\nas 1:1 ratio to use in the soybean pot experiments. \n\n\n\n2.8 Application of Bio-Nematicide and BAU-Biofungicide \n\n\n\nBoth Bio-nematicide and BAU-Biofungicide were used as seed coating \n\n\n\nduring seed sowing at the rate of 6g per kg of soybean seeds and side \n\n\n\ndressing as secondary dose at 20days after germination. Total three \n\n\n\nsecondary doses have been applied at the rate of 1g of Bio-nematicide and \n\n\n\nBAU-Biofungicide. Ten gram soybean seeds of BARI Soybean-5 variety \n\n\n\nmixed with Bio-nematicde, BAU-Biofungicide and Bio-nematicide + BAU-\n\n\n\nBiofungicide respectively. One milliliter (1ml) water added to mixing the \n\n\n\ninoculum with soybean seeds and left for 30minutes in cool and shady \n\n\n\nplace for proper coating. Control treatment seeds were treated with one \n\n\n\nmilliliter (1ml) water. \n\n\n\n2.9 Different parameters studied \n\n\n\nAfter 95 days of inoculation, the plants at mature stage were carefully \n\n\n\nuprooted from the pots and the following parameters in relation to plants \n\n\n\nand pathogen were studied: \n\n\n\n2.10 Measurement of length and fresh weigh of shoot and root \n\n\n\nLength of shoot was measured from the base of the stem up to the top most \n\n\n\nleaf. Similarly, length of root was measured from the starting point of the \n\n\n\nroot to the largest available lateral root apex. The shoot and root portion \n\n\n\nwere blotted with fine tissue paper and fresh weights were measured by \n\n\n\nelectrical balance before the materials could get desiccated. \n\n\n\n2.11 Counting number of galls g-1 of root \n\n\n\nRandomly 1g of fresh root was taken from the bulk to count the number of \n\n\n\ngalls formed. Average number of galls g-1 of root was counted from five \n\n\n\nreplicated plants. Then, the roots were preserved in 5% formalin solution. \n\n\n\n2.12 Number of adult females, J2, J3 and J4 juveniles in 5 \n\n\n\ngalls/treatment \n\n\n\nNumber of adult females, J2, J3 and J4 juveniles were counted and recorded \n\n\n\nafter proper staining of the galls.  \n\n\n\n2.13 Statistical analysis of data \n\n\n\nAll data were analyzed following standard procedures for analysis of \n\n\n\nvariance. Differences between means were evaluated for significant level \n\n\n\nfollowing a modified Duncan\u2019s Multiple Range Test (DMRT). Linear \n\n\n\ncorrelation co-efficient and determinations of the slope and intercept \n\n\n\nvalues of linear equations were also performed following standard \n\n\n\nstatistical methods. Except where otherwise stated, differences referred \n\n\n\nto, in the text were significant at P\u2265 0.05 level of probability. \n\n\n\n3. RESULTS \n\n\n\n3.1 Effect of different treatments on the growth, yield, nodulation, \n\n\n\ngalling incidence and egg mass development in soybean  \n\n\n\nIn the present study, four treatments with Bio-nematicide, BAU-\n\n\n\nBiofungicide, Bio-nematicide + BAU-Biofungicide including control were \n\n\n\nused to assess their effect on different plant growth characters, yield, \n\n\n\nnodulation, galling incidence and development of adult females and \n\n\n\njuveniles of root knot nematode (Meloidogyne incognita) in soybean. \n\n\n\n3.1.1 Length of shoot \n\n\n\nLengths of shoot were significantly influenced by the treatments. Mean \n\n\n\nlength of shoot ranged from 31.32cm to 49.20cm. The highest shoot length \n\n\n\nwas recorded with treatment T3 (Bio-nematicide + BAU-Biofungicide) \n\n\n\nhaving 49.20cm followed by treatment T2 (BAU-Fungicide) and T1 (Bio-\n\n\n\nnematicide) having 44.51cm and 37.24cm, respectively. The control \n\n\n\ntreatment T0 gave the lowest response with minimum shoot length \n\n\n\n31.20cm (Table 1; Plates 1a, 1b, 2a and 2b). \n\n\n\nTable 1: Effects of different treatment on the growth, yield, nodulation, galling, egg masses in soybean after 95 days of inoculation \n\n\n\nTreatments Length \nof shoot \n\n\n\n(cm) \n\n\n\nLength \nof root \n\n\n\n(cm) \n\n\n\nFresh \nweight \n\n\n\nof shoot \n(g) \n\n\n\nFresh \nweight of \nroot with \n\n\n\nnodule (g) \n\n\n\nNumber \nof pods \n\n\n\nper \nplant \n\n\n\nWeight \nof pods \n\n\n\nper \nplant \n\n\n\n(g) \n\n\n\nNumber \nof seeds \n\n\n\nper \nplant \n\n\n\nWeight \nof seeds \n\n\n\nper \nplant \n\n\n\n(g) \n\n\n\nNumber of \nnodules \n\n\n\nper plant \n\n\n\nNumber of \ngalls per g \n\n\n\nof root \n\n\n\nNumber of \negg \n\n\n\nmasses per \ng of root \n\n\n\nT0 31.32d 13.16c 9.29b 1.31b 14.70d 7.39d 26.50d 5.97c 2.60c 3.65a 2.59a \n\n\n\nT1 37.24c 16.69b 10.31b 1.49b 21.80c 9.89c 37.70c 7.97b 8.00b 1.75c 0.91b \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 44-48 \n\n\n\nCite the Article: Mohammad Sharif Sarker, K. M. Mohiuddin, Laith Khalil Tawfeeq Al Ani, Mohamad Nazmul Hassan, Rojina Akter, Md. Sakhawat Hossain, \nMd. Niuz Morshed Khan (2020). Effect Of Bio-Nematicide And Bau-Biofungicide Against Root-Knot (Meloidogyne Spp.) Of Soybean.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 44-48. \n\n\n\n3.1.2 Length of root \n\n\n\nMaximum length of root was recorded with treatments T3 followed by T2, \n\n\n\nT1 and T0 having 24.62cm, 17.11cm, 16.69cm and 13.16cm, respectively. \n\n\n\nT2 and T1 gave statistically identical response in root length. Control \n\n\n\ntreatment T0 appeared with minimum root length (Table 1; Plates 3a, 3b, \n\n\n\n4a and 4b). \n\n\n\n3.1.3 Fresh weight of shoot \n\n\n\nFresh weight of shoot ranged from 9.29g to 14.31g. Significantly, the \n\n\n\nhighest shoot weight 14.31g was found in plants treated with T3 followed \n\n\n\nby the plants treated with T1, T2 and T0 having 10.31g, 10.28g and 9.29g, \n\n\n\nrespectively. Statistically lower and significantly identical response was \n\n\n\nfound among the treatments T2, T1 and T0 (Table 1). \n\n\n\n3.1.4 Fresh weight of root with nodules \n\n\n\nMaximum fresh weight of root with nodules was noted with treatment T3 \n\n\n\nhaving 2.07g. Significantly lower and statistically similar fresh weight of \n\n\n\nroot with nodules were observed with the treatments T2, T1 and T0 having \n\n\n\n1.50g, 1.49g and 1.31g, respectively ( Table 1). \n\n\n\n3.1.5 Number of pods per plant \n\n\n\nMaximum number 36.00 of pods per plant was found with the treatment \n\n\n\nT3 followed by T2, T1 and T0 having 28.20, 21.80 and 14.70, respectively \n\n\n\n(Table 1). \n\n\n\n3.1.6 Weight of pods per plant \n\n\n\nMaximum weight of 13.26g of pods per plant was observed with the \n\n\n\ntreatment T3 followed by T2, T1 and T0 having 11.08g, 9.89g and 7.39g, \n\n\n\nrespectively. Control treatment gave the minimum weight of pods per \n\n\n\nplant (Table 1). \n\n\n\n3.1.7 Number of seeds per plant \n\n\n\nLike that of number of weight of pods per plant, treatment T3 gave the \n\n\n\nhighest number of seeds 66.30 per plant followed by T2, T1 and T0 having \n\n\n\n53.90, 37.70 and 26.50 number of seeds per plant, respectively. Control \n\n\n\ntreatment appeared with the minimum number of seeds (Table 1). \n\n\n\n3.1.8 Weight of seeds per plant \n\n\n\nThe highest significant seed weight 11.44g per plant was found with the \n\n\n\ntreatment T3 followed by 8.51g, 7.97g and 5.97 g in the treatments T2, T1 \n\n\n\nand T0, respectively. But the treatments T2 and T1 gave statistically \n\n\n\nidentical response in seed weight (Table 1). \n\n\n\n3.1.9 Number of nodules per plant \n\n\n\nMaximum and statistically identical numbers of 11.10 and 9.80 of nodules \n\n\n\nper plant were recorded with treatments T3 and T2 followed by 9.80, 8.00 \n\n\n\nand 2.60 nodules in the treatments T2, T1 and T0, respectively (Table 1). \n\n\n\n3.1.10 Number of galls per g of root \n\n\n\nThe control treatment T0 was found to have significantly the highest \n\n\n\nnumber of 3.65 of galls per g of root followed by T2 T1 and T3 having 2.99, \n\n\n\n1.75 and 0.76 galls/g of root, respectively. The minimum number of galls/g \n\n\n\nof root was recorded in the treatment T3 (Bio-nematicide + BaU-\n\n\n\nBiofungicide) having 0.76 (Table 1; 3a, 3b, 4a and 4b). \n\n\n\n3.1.11 Number of egg masses per g of root \n\n\n\nSignificantly higher and statistically identical numbers 2.71 and 2.59 of \n\n\n\neggmasses were observed with the treatments T2 and T0, respectively. \n\n\n\nLower significant and statistically similar numbers of 0.91 and 0.72 of \n\n\n\neggmasses were found with the treatments T1 and T3, respectively (Table \n\n\n\n1, plates 3a, 3b, 4a and 4b). \n\n\n\n3.2 Effect of different treatments on the development of \n\n\n\nMeloidogyne incognita in the inoculated soybean plants \n\n\n\nEffects of five different treatments on the development of adult females J2, \n\n\n\nJ3 and J4 juveniles of Meloidogyne incognita in the soybean varieties are \n\n\n\npresented in Table 2. \n\n\n\n3.2.1 Adult female \n\n\n\nMaximum number 3.20 of adult females of M. incognita was found with the \n\n\n\ntreatment T2 followed by T0, T1 and T3 having 3.00, 1.50 and 1.20, \n\n\n\nrespectively. Treatments T0 and T2 appeared to have statistically similar \n\n\n\nresponse on the growth of adult females. Same was true for treatments T1 \n\n\n\nand T3 (Table 2; Plates 7, 9 and 10). \n\n\n\n3.2.2 J2 juveniles \n\n\n\nMaximum number of 3.30 of J2 juveniles was found with the treatment T0 \n\n\n\nfollowed by T2, T1 and T3 having 3.20, 1.50 and 1.20, respectively. \n\n\n\nStatistically identical responded was found between the treatments T0 and \n\n\n\nT2 as well as between T1 and T3 (Table 2). \n\n\n\n3.2.3 J3 juveniles \n\n\n\nIn case of J3 the highest significant number 3.20 of J3 juveniles was \n\n\n\nrecorded with the treatment T0 followed 3.00, 2.30 and 1.60 in the \n\n\n\ntreatments T2, T1 and T3 respectively. But, there was no significant \n\n\n\ndifference between the treatments T1 and T3 as well as among the \n\n\n\ntreatments T0, T1 and T2 with respect to J3 juveniles (Table2). \n\n\n\n3.2.4 J4 juvenile \n\n\n\nIn case of J4 juveniles maximum number of 3.00 was recorded with the \n\n\n\ntreatment T0 followed by T2, T1 and T3 having 1.80, 1.70 and 1.70, \n\n\n\nrespectively. Significantly lower and identical response was found with \n\n\n\nrespect to J3 population among the treatments T1, T2 and T3 (Table 2). \n\n\n\nTable 2: Effect of different treatments on the development of adult \n\n\n\nand juveniles of Meloidogyne incognita on soybean after 95days of \n\n\n\ninoculation \n\n\n\nTreatments Number of \n\n\n\nadult \n\n\n\nfemales per \n\n\n\n5gals \n\n\n\nNumber of \n\n\n\nJ2 \n\n\n\njuveniles \n\n\n\nper 5galls \n\n\n\nNumber of \n\n\n\nJ3 juveniles \n\n\n\nper 5galls \n\n\n\nNumber of \n\n\n\nJ4 juveniles \n\n\n\nper 5galls \n\n\n\nT0 3.00a 3.30a 3.20a 3.00a \n\n\n\nT1 1.50b 1.30b 2.30ab 1.70b \n\n\n\nT2 3.20a 3.10a 3.00a 1.80b \n\n\n\nT3 1.20b 1.50b 1.60b 1.70b \n\n\n\nSx 0.343 0.324 0.504 0.361 \n\n\n\n4. DISCUSSION\n\n\n\nMaximum length of shoot and root, fresh weight of shoot and root with \n\n\n\nnodules, weight of seeds per plant were obtained with the treatment Bio-\n\n\n\nnematicide + BAU-Biofungicide. In case of number of egg masses per plant, \n\n\n\nthe treatment BAU-Biofungicide gave higher value than Bio-nematicide. In \n\n\n\nrespect of length of shoot and root, fresh weight of shoot and root with \n\n\n\nnodules, weight of seeds per plant, number of nodules per plant control \n\n\n\ntreatment with M. incognita alone was responsible for the significant \n\n\n\nreduction. In addition, the highest galling incidence correspondingly with \n\n\n\nthe lowest yield performance was observed with control treatment. \n\n\n\nT2 44.51b 17.11b 10.28b 1.50b 28.20b 11.04b 53.90b 8.50b 9.80b 2.99b 2.71a \n\n\n\nT3 49.20a 24.62a 14.31a 2.07a 36.00a 13.26a 66.30a 11.44a 11.10a 0.76d 0.72b \n\n\n\nSx 0.503 0.530 0.540 0.111 0.587 0.221 2.137 0.230 0.521 0.166 0.186 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 44-48 \n\n\n\nCite the Article: Mohammad Sharif Sarker, K. M. Mohiuddin, Laith Khalil Tawfeeq Al Ani, Mohamad Nazmul Hassan, Rojina Akter, Md. Sakhawat Hossain, \nMd. Niuz Morshed Khan (2020). Effect Of Bio-Nematicide And Bau-Biofungicide Against Root-Knot (Meloidogyne Spp.) Of Soybean.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 44-48. \n\n\n\nIncreased growth of plant yield and nematode controlling capability of \n\n\n\nBAU-Biofungicide treatments observed in this experiment could be \n\n\n\nbecause of release of growth promoting secondary metabolites and toxic \n\n\n\nmetabolites of Trichoderma. Some scientists also observed increased \n\n\n\ngrowth in tomato, soybean, tobacco and capsicum in pot and field \n\n\n\nexperiments after using Trichoderma inoculum (Goswami and Mittal, \n\n\n\n2004).  \n\n\n\nAs bio-agents Bio-nematicde and BAU-Biofungicide showed better \n\n\n\nresponses with higher growth of shoot and root as well as higher weights \n\n\n\nof shoot and root with nodules, number of nodules per plant \n\n\n\ncorrespondingly with higher yield per plant as evident with higher weight \n\n\n\nof seeds. There appeared significantly lower galling incidence in both Bio-\n\n\n\nnematicide and BAU-Biofungicide treated plants indicating their \n\n\n\nsuppressing effect on galling as observed with their combined application.  \n\n\n\nResearchers reported Trichoderma viride as egg parasitic against \n\n\n\nMeloidogyne incognita in the present study, reduced galling incidence \n\n\n\nalong with reduction of adult females might have been resulted from the \n\n\n\nadverse influence on hatching of inoculated eggs and eggs produced after \n\n\n\nfirst and second generation Meloidogyne incognita as similarly stated by \n\n\n\nthe above authors (Goswami and Mittal, 2004). In this present study Bio-\n\n\n\nnematicide showed better result than BAU-Biofungicide in controlling \n\n\n\nadult nematode. It also showed significant result in controlling J2, J3, and J4 \n\n\n\nnematode population. Furthermore, Bio-nematicide showed better \n\n\n\nperformance than BAU-Biofungicide in controlling egg masses. But it \n\n\n\nshowed lower responses than combined application of Bio-nematicide and \n\n\n\nBAU-Biofungicide in every sphere of investigation.  \n\n\n\nAmong the obtained results Bio-nematicide was most against eggmasses. \n\n\n\nNemato-toxic compounds of the neem plant, especially the azadirachtins, \n\n\n\nare released through volatilization, exudation, leaching and \n\n\n\ndecomposition. The modes of action of these compounds are complex, and \n\n\n\na number of mechanisms in relation to nematode management are yet to \n\n\n\nbe fully explored. Azadiractin is considered strong anti-feedant because of \n\n\n\nits effects on the insect\u2019s chemoreceptors, which deter the insects from \n\n\n\nconsuming the plant. Moreover, azadiractin not only blocks peptide \n\n\n\nhormone release that cause molting abnormalities but also cause damage \n\n\n\nin insect\u2019s tissues, including muscle, fat and gut cells. Researchers  \n\n\n\nreported inhibitions in most fungal plant pathogens by substances \n\n\n\nproduced by Trichoderma spp. and suggested that controlling other soil-\n\n\n\nborne pathogenic fungi Trichoderma harzianum will further provide a \n\n\n\nbetter, enabling environment where plants will develop more vigorously \n\n\n\nwithout being suppressed by other microorganisms, which would \n\n\n\notherwise affect or make it vulnerable to secondary infections pioneered \n\n\n\nby plant-parasitic nematodes (Izuogu et al., 2019).  \n\n\n\nThe findings of the present study can be correlated with of Akhtar and \n\n\n\nMalik, where they have reported that phenols, amino acids, aldehydes and \n\n\n\nfatty acids are release from neem which is antagonistic to root knot \n\n\n\nnematodes (Akhtar and Malik, 2000). Our results are supported by the \n\n\n\nstudy of previous work (Ganai et al., 2014; Lal and Rana, 2012).  They  \n\n\n\nstates that organic amendments of soil using dried poultry litter, \n\n\n\nmunicipal refuse, oil cakes of ground nut, neem mustard & neem products \n\n\n\nhave been found effective in the control of Meloidogyne incognita. In \n\n\n\npresent study thus it may be concluded that changes in protein after \n\n\n\ninfection are related to defence action, because abnormal metabolites are \n\n\n\nproduced in adjacent non-infected tissues. Such metabolites accumulated \n\n\n\nin infected tissues and are toxic to parasites and inhibit their growth and \n\n\n\npenetration.  \n\n\n\nThe metabolites released from the chemical constituents of neem \n\n\n\nstimulated the plant cells to release abnormal metabolites which repel the \n\n\n\nnematodes from the uninfected cells of plant. So, the use of neem products \n\n\n\nstimulated and changes the physiology of plant cells and tissue to repel the \n\n\n\nnematode parasites. In this study the combined effect of Bio-nematicide \n\n\n\nand BAU-Biofungicide gave higher significant growth of shoot and root, \n\n\n\nsignificant amount of fresh weight of shoot and root, higher number of \n\n\n\npods per plant and weight of pods per plant, weight of seeds per plant, \n\n\n\nhigher number of nodules with lower galling incidence and reduced \n\n\n\npopulation of adult females of M. incognita.  \n\n\n\nFrom the overall study, it was revealed that the highest plant growth \n\n\n\ncharacters of soybean in respect of length of shoot and root, fresh weight \n\n\n\nof shoot and root with nodules, weight of seeds per plant, number of \n\n\n\nnodules per plant and reduced incidence of galling with lower \n\n\n\ndevelopment of adult females of Meloidogyne incognita were achieved by \n\n\n\nthe side dressing with Bio-nematicide + BAU-Biofungicide as well as side \n\n\n\ndressing with Bio-nematicide compared to the control treatment. The \n\n\n\nefficacy of Bio-nematicide to control root-knot disease of soybean along \n\n\n\nwith improved plant growth characters, nodulation and yield components \n\n\n\nwere found almost equally good to combined application of BIO-\n\n\n\nnematicide with BAU-Biofungicide. It is also evident that control of \n\n\n\nMeloidogyne incognita with antagonistic bio-agents like Bio-nematicide \n\n\n\nand BAU-Biofungicide as side dressing components are quite effective.  \n\n\n\n5. CONCLUSION \n\n\n\nUsing chemical fungicide to control root-knot disease of soybean caused \n\n\n\nby M. incognita is so costly and harmful for our environment but this \n\n\n\ndiseases may be controlled through use of Bio-fungicide for eco-friendly \n\n\n\nmanagement. But, field trial is essential before any recommendation in \n\n\n\nmade to the farmers. Considering the importance of soybean as a valuable \n\n\n\ncrop and its greater yield loss due to the nemic root-knot disease, more \n\n\n\nattention has to be given for the control of the disease by biological means \n\n\n\nwithout disturbing the natural balance and the environment.  \n\n\n\nACKNOWLEDGMENTS  \n\n\n\nThanks to Bangladesh Agricultural University (BAU), Bangladesh for all \n\n\n\nexperimental support. Especially I want to thank Professor Dr. Ismail \n\n\n\nHossain and Professor Dr. Muyeen Uddin Ahmad for providing BAU-\n\n\n\nBiofungicide and Bio-nematicide respectively.   The authors would also \n\n\n\nlike to thank the reviewers for their comments on the manuscript. \n\n\n\nREFERENCES \n\n\n\nAkhtar, M., Malik, A., 2000. Roles of organic soil amendments and soil \n\n\n\norganisms in the biological control of plant-parasitic nematodes: a \n\n\n\nreview. Bioresource Technology, 74, 35-47. DOI: org/10.1016/S0960-\n\n\n\n8524(99)00154-6.  \n\n\n\nAslam, M.A., Javed, K., Javed, H., Mukhtar, T., Bashir, M.S., 2019. Infestation \n\n\n\nof Helicoverpa armigera H\u00fcbner (Noctuidae: Lepidoptera) on soybean \n\n\n\ncultivars in Pothwar region and relationship with physico-morphic \n\n\n\ncharacters. Pak. J. Agri. Sci., 56, 401-405. DOI: \n\n\n\n10.21162/PAKJAS/19.6979. \n\n\n\nEl-Dabaa, M.A., Abd-El-Khair, H., El-Nagdi, W., 2019. Field application of \n\n\n\nClethodim herbicide combined with Trichoderma spp. for controlling \n\n\n\nweeds, root knot nematodes and Rhizoctonia root rot disease in two \n\n\n\nfaba bean cultivars. Journal of Plant Protection Research, 59. DOI: \n\n\n\n10.24425/jppr.2019.129287   \n\n\n\nGanai, M.A., Rehman, B., Parihar, K., Asif, M., Siddiqui, M.A., 2014. \n\n\n\nPhytotherapeutic approach for the management of Meloidogyne \n\n\n\nincognita affecting Abelmoschus esculentus (L.) Moench. Archives of \n\n\n\nPhytopathology and Plant Protection, 47, 1797-1805. DOI: \n\n\n\norg/10.1080/03235408.2013.858425. \n\n\n\nGoswami, B.K., Mittal, A., 2004. Management of root-knot nematode \n\n\n\ninfecting tomato by Trichoderma viride and Paecilomyces \n\n\n\nlilacinus. Indian Phytopathology, 57, 235-236. \n\n\n\nIzuogu, N.B., Baba, H.S., Winjobi, E.O., 2019. Assessment of bio-agent \n\n\n\n(Trichoderma Harzianum) in the management of two pepper varieties \n\n\n\ninfected with root-knot nematode (Meloidogyne Incognita). Acta \n\n\n\nUniversitatis Sapientiae. Agriculture and Environment, 11, 16-22. DOI: \n\n\n\norg/10.2478/ausae-2019-0002  \n\n\n\nLafta, A.A., Kasim, A.A., 2019. Effect of nematode-trapping fungi, \n\n\n\ntrichoderma harzianumand pseudomonasfluorescens in controlling \n\n\n\nmeloidogyne spp. Plant Archives, 19, 1163-1168.  \n\n\n\nLal, B., Rana, B.P. 2012. Efficacy of neem products as seed treatment \n\n\n\nagainst Meloidogyne incognita in Okra. Annals of Plant Protection \n\n\n\nSciences, 20, 268-269. DOI: 20123143375. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 44-48 \n\n\n\nCite the Article: Mohammad Sharif Sarker, K. M. Mohiuddin, Laith Khalil Tawfeeq Al Ani, Mohamad Nazmul Hassan, Rojina Akter, Md. Sakhawat Hossain, \nMd. Niuz Morshed Khan (2020). Effect Of Bio-Nematicide And Bau-Biofungicide Against Root-Knot (Meloidogyne Spp.) Of Soybean.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 44-48. \n\n\n\nRamzan, M., Ahmed, R.Z., Khanum, T.A., Akram, S., Jabeen, S., 2019. Survey \n\n\n\nof root knot nematodes and RMi resistance to Meloidogyne incognita in \n\n\n\nsoybean from Khyber Pakhtunkhwa, Pakistan. European Journal of \n\n\n\nPlant Pathology, 1-13. DOI: org/10.1007/s10658-019-01740-z.   \n\n\n\nTamagno, S., Sadras, V.O., Haegele, J.W., Armstrong, P.R., Ciampitti, I.A., \n\n\n\n2018. Interplay between nitrogen fertilizer and biological nitrogen \n\n\n\nfixation in soybean: implications on seed yield and biomass \n\n\n\nallocation. Scientific reports, 8, 17502 DOI: org/10.1038/s41598-018-\n\n\n\n35672-1. \n\n\n\nUikey, K.W., Raghuwanshi, K.S., Uikey, D.W., 2019. In vitro evaluation of \n\n\n\ndifferent biocontrol agents against soil borne pathogens. International \n\n\n\nJournal of Chemical Studies, 7, 2621-2624 \n\n\n\nYankova, V., Markova, D., Naidenov, M., Arnaoudov, B., 2014. Management \n\n\n\nof root-knot nematodes (Meloidogyne spp.) in greenhouse cucumbers \n\n\n\nusing microbial products, T\u00fcrk Tar\u0131m ve Do\u011fa Bilimleri Dergisi, 1, \n\n\n\n1569-1573. DOI: org.tr/en/pub/turkjans/issue/13311/160947. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.67.76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2021.67.76 \n\n\n\nDROUGHT STRESS IMPACTS ON WHEAT AND ITS RESISTANCE MECHANISMS \n\n\n\nBipin Rijala*, Prakash Baduwala, Madhukar Chaudharya, Sandesh Chapagaina, Sushank Khanala, Saugat Khanalb, Padam Bahadur Poudela \n\n\n\na Institute of Agriculture and Animal Science, Paklihawa, Rupandehi, Nepal. \nb Faculty of Agriculture, Agriculture and Forestry University, Rampur, Chitwan, Nepal. \n\n\n\n*Corresponding Author email: rijalbpin@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 20 November 2020 \nAccepted 24 December 2020 \nAvailable online 06 January 2021 \n\n\n\nScarcity of water has been a serious agricultural hindrance to crop productivity since antiquity. Drought-\nstressed loss in wheat yield likely exceeds losses from all other causes, since both the severity and duration \nof the stress are censorious. Here, we have reviewed the effects of drought stress on the morphological, \nphysiological, and biochemical attributes along with the growth impacts, water relations, and photosynthesis \nimpacts in wheat. This review also illustrates the mechanism of drought resistance in wheat. Historical \ndrought years in Nepal have been identified and the yield losses were assessed. Wheat encounters a variety \nof morphological, physiological, biochemical responses at cellular and molecular levels towards prevailing \nwater stress, thus making it a complex phenomenon.  Drought stress affects leaf size, stems elongation and \nroot proliferation, imbalance plant-water relations and decline water-use efficiency. Different types of \nphysiological research are ongoing to find out the changes occurs in the wheat plant as a result of drought \nstress. Morphological changes can be looked through two ways: changes in root system and changes in shoot \nsystem such as effects on height, leaf senescence, flowering, and so on. Physiological changes involve changes \nin cell growth pattern, chlorophyll contents, photosynthetic disturbances, plant-water relations, etc. \nBiochemical changes occur in different chemical, biomolecules, and enzymes. Plants portray several \nmechanisms to withstand drought stress which can be classified as Drought escape, Drought avoidance, and \nDrought tolerance. Selection of wheat genotype that can tolerate water scarcity would be suitable for the \nbreeding program aiming to development of drought tolerant variety under water limited regions.  \n\n\n\nKEYWORDS \n\n\n\nAgronomic changes, Drought, Nepal, Resistance, Wheat.\n\n\n\n1. INTRODUCTION \n\n\n\nWheat, Triticum aestivum, is one of the most widely cultivated cereals, \n\n\n\nparticularly in the mediterranean region and other semi-arid regions from \n\n\n\ntemperate to subtropical areas of the world (Ahmed et al., 2019). Most of \n\n\n\nthe areas of land in which wheat is cultivated lie in arid and semiarid \n\n\n\nregions. A key determinant in the favorable outcome of wheat is its \n\n\n\nadaptation to a broad range of climatic conditions. Approximately, one-\n\n\n\nthird of the global population uses wheat as a staple crop and also the first \n\n\n\ncereal crop in majority of the developing countries (Bayoumi, 2009). It \n\n\n\nserves as an essential food source, as it contains carbohydrates, dietary \n\n\n\nproteins, fiber, calcium, zinc, fats, and energy. However, in many countries, \n\n\n\nincluding Nepal the attainable yield hasn\u2019t been achieved through there is \n\n\n\nhigh potential of enhancing the average yield.  \n\n\n\nWheat is mostly cultivated under rainfed conditions where fluctuations in \n\n\n\nrainfall pattern have caused water insufficiency to act as a determining \n\n\n\nfactor for declining the crop yield, especially when water deficit stress \n\n\n\noccurs during the flowering and grain filling period stages (Bassi et al., \n\n\n\n2017). The likeliness of drought stress in the coming days is high owing to \n\n\n\nglobal climate change and declines in availability of underground water \n\n\n\nresources for agriculture. It has been proved through many researches \n\n\n\nthat wheat production is drastically affected by abiotic stresses. A study \n\n\n\nreported that for every 1-degree centigrade increase in temperature, there \n\n\n\nis a yield loss of about 4.1% to 6% (Liu et al., 2016). Salinity contributes to \n\n\n\nthe reduction of wheat yield (Mujeeb-Kazi et al., 2019). On the other hand, \n\n\n\ndrought is considered as major menace to wheat yield and is gaining much \n\n\n\nattention nowadays. By 2025 it is anticipated that nearly 1.8 billion people \n\n\n\nwill face absolute water shortage and 65% of the world\u2019s population will \n\n\n\nface water-stressed environments (Nezhadahmadi et al., 2013b).  \n\n\n\nThe factor limiting the wheat production in many regions is primarily due \n\n\n\nto erratic rainfall, reducing average yield up to 50% and often over. Wheat \n\n\n\ncan be produced in a varied range of agro-climatic environments; \n\n\n\nnevertheless, most of these environments have drought stress as one of \n\n\n\nthe major constraints to their production and yield. The predicted global \n\n\n\nwarming and climatic fluctuations will increase the frequency of drought, \n\n\n\ntherefore leads to the losses of the wheat yield. The increase in annual \n\n\n\naverage temperature accompanied with fluctuations in rainfall patterns \n\n\n\nand arising drought risks in many regions have impacted agricultural \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\nproductions, globally, which has brought limitations on crop yield \n\n\n\npotential. Declining underground water availability and rising \n\n\n\ntemperature is assumed to worsen in coming decades (IPCC, 2013). \n\n\n\nDrought stress along with high temperature at reproductive stage \n\n\n\n(terminal growth phase of wheat crop) is prime contributing factor \n\n\n\ntowards low wheat yield in tropics and subtropics.  \n\n\n\nDrought is a leading environmental stress declining the global cereals \n\n\n\nproductivity, with up to half of the agricultural land prone to frequent \n\n\n\ndrought (Ashraf and Fooled 2007). A rising problem of global warming is \n\n\n\npredicted to amplify the frequency and severity of drought in the near \n\n\n\nfuture (Yu et al., 2017). It can bring to the lack of water resources which \n\n\n\ninfluences morphological, biochemical, physiological, and molecular \n\n\n\nattributes of the plants. All of these changes retard the plant growth and \n\n\n\nthe crop yield. Drought stress unfavorably alters the physiological and \n\n\n\nmorphological parameters of crops. Along with the complexity of the \n\n\n\ndrought itself, crop response to water shortage is even more complex \n\n\n\nbecause of unpredictable components in the environment and the \n\n\n\ninteraction among biotic and abiotic factors (Nevo and Chen, 2010). Such \n\n\n\nstress leads to significant reduction in photosynthetic efficiency, stomatal \n\n\n\nconductance, leaf area and water-use efficiency of wheat (Farooq et al., \n\n\n\n2019; Hussain et al., 2016).  \n\n\n\nDifferent researchers have performed study on drought resistance \n\n\n\nmechanism in many cereals, but the improvement of wheat for drought \n\n\n\ntolerance is limited for many constraints. There is a need to assess the \n\n\n\nresponse mechanism of the plants in response to drought stress. The \n\n\n\nobjectives of the current study are to assess the possible changes in the \n\n\n\nmorphological and physiological attributes of the wheat crop due to the \n\n\n\ndrought stress and plant tolerance mechanism to the stress.  \n\n\n\n2. DROUGHT STRESS \n\n\n\nFaced with inadequacy of water resources, drought is the distinct perilous \n\n\n\nhazard to global food security. It was the impetus of the great famines of \n\n\n\nthe past.  As the water supply is limiting worldwide, future food demand \n\n\n\nfor rapidly increasing population pressures is likely to further aggravate \n\n\n\nthe impacts of water stress. The severity of drought is uncertain as it relies \n\n\n\non various factors such as quantity and quality of precipitation, \n\n\n\nevaporative demands, and soil moisture contents (Wery et al., 1994). \n\n\n\nThere are various types of stress factors responsible for the reduction of \n\n\n\nwheat cultivation among which drought stress plays significant role for \n\n\n\nreducing the yield in wheat. About 50% of the cultivated land in the \n\n\n\ndeveloping country is under rainfed condition (Paulsen, 2002). The \n\n\n\nscarcity of water which induces gradual morphological, biochemical, \n\n\n\nphysiological and molecular changes is called as drought stress (Sallam et \n\n\n\nal., 2019). Due to drought stress, plant transpires less and in order to do \n\n\n\nso stomata of the wheat plant close to prevent water loss. And if stomata \n\n\n\nclose for a long time there occurs an oxidative damage to the plant leaves \n\n\n\ntissue which affects the different physiological and biochemical activity of \n\n\n\nwheat plant.  \n\n\n\nApproximately 65 million ha of wheat production was affected by drought \n\n\n\nstress in 2013 (Nations, 2020). Different development stages of plant from \n\n\n\ngermination, vegetative and reproductive stage to grain filling and \n\n\n\nmaturation of crop are disturbed when the plant suffers from drought \n\n\n\nstress. Drought reduces the nutrient uptake efficiency including nitrogen \n\n\n\nas a main factor and nutrient utilization by plants. The reduction in \n\n\n\nnutrient uptake capacity is due to impaired membrane permeability and \n\n\n\nactive transport and reduced transpiration rate resulting in decreased \n\n\n\nroot absorbing power (Ahmad et al., 2018). Different types of research \n\n\n\nshow that plant height, biomass and yield are more susceptible traits to \n\n\n\ndrought stress in comparison with number of spikes and 1000 grain \n\n\n\nweight (Nouri-Ganbalani et al., 2009). For the development of stress \n\n\n\ntolerant plants, we have to know the adaptation methods used by the plant \n\n\n\nfor surviving during drought stress. Knowing the importance of drought in \n\n\n\nwheat yield reduction, a number of researchers have been studying about \n\n\n\nthe effect of drought and the number of problems caused by it. They are \n\n\n\ncontinuously trying to develop new drought tolerant genotypes which can \n\n\n\nperform well under stress conditions.  \n\n\n\nDrought (water stress) is one of the most crucial environmental stresses \n\n\n\nand occurs for various reasons, including erratic rainfall, salinity, \n\n\n\nfluctuating temperatures, and high intensity of light. It is a \n\n\n\nmultidimensional stress and bring alteration in the physiological, \n\n\n\nmorphological, biochemical, and molecular attributes in plants. Prolonged \n\n\n\ndrought is a severe curb in landscape restoration in both arid and semiarid \n\n\n\nregions. There are numerous kinds of drought; meteorological, caused by \n\n\n\na prolonged lack of rainfall; hydrological, caused by a scarcity in river flow; \n\n\n\npedological, ascribed to a scarcity of water in the soil structure; agronomic, \n\n\n\ncaused by a insufficiency of water available to plants in order to balance \n\n\n\nthe physiological needs of evapotranspiration; and sociological, caused by \n\n\n\ncompeting consumptions to meet human and social needs. Landscape \n\n\n\nrestoration can be drastically affected by all kinds, but notably by \n\n\n\nagronomic droughts, which negatively affect seedling establishment and \n\n\n\ncrop stand establishment. Three major processes decline crop yield by soil \n\n\n\nwater-deficit; decreased canopy absorption of photosynthetically active \n\n\n\nradiation, reduced radiation-use efficiency, and decreased harvest index \n\n\n\n(Earl and Davis, 2003).  \n\n\n\nWheat has improved its tolerance mechanisms to withstand drought \n\n\n\nstress; however, these mechanisms are different and rely on the crop \n\n\n\nvarieties and the cultivars. It is important to enhance the drought \n\n\n\ntolerance of wheat crops under the changing climatic conditions. To date, \n\n\n\nthere are no efficient feasible technological mechanisms to promote crop \n\n\n\nproduction under drought stress environments. Yet, improvement of crop \n\n\n\nplants tolerant to drought stress might be a hopeful approach, which helps \n\n\n\nin maintaining the food security. Development of crops for increased \n\n\n\ndrought resistance needs the sound knowledge of physiological and \n\n\n\ngenetic mechanisms of the contributing traits at different plant \n\n\n\ndevelopmental stages. Relevant research has been carried out on drought \n\n\n\ntolerance in wheat crops.  Ingram and Bartels, more than a decade ago, \n\n\n\nexcellently reviewed those appreciable efforts (Bartels, 1996). Similar \n\n\n\nreviews have been done by which deal with specific aspects of plant \n\n\n\ndrought tolerance (Penna, 2003; Reddy et al., 2004; Agarwal et al., 2006).  \n\n\n\n3. DROUGHT IN NEPAL \n\n\n\nIn regards to irregular climate change and higher temperature in recent \n\n\n\nyears than that of global average, Nepal is considered to be among the \n\n\n\nmost vulnerable countries. From 1975 to 2005, the global mean surface \n\n\n\ntemperature rises by 0.6 0C while Nepal experienced a significantly higher \n\n\n\ntemperature rise of 1.5 0C during similar duration of time, from 1982 to \n\n\n\n2006 (Biwa et al., 2012). Likewise, rainfall pattern is also becoming more \n\n\n\nunpredictable (Wang et al., 2013). Consequently, average rainfall has been \n\n\n\ndeclining by 3.7 mm (\u22123.2%) monthly, per decade (Ministry of Education, \n\n\n\n2010). These ultimately created drought condition particularly for the \n\n\n\nrainfed farming, where farmers depend on monsoon rainfall for their \n\n\n\nmajor agricultural activities (Ghimire et al., 2010). Furthermore, the mean \n\n\n\nannual temperature is estimated to be rose between 1.3 0C to 3.8 0C by the \n\n\n\n2060s, and 1.8 0C to 5.8 0C by the 2090s and annual precipitation \n\n\n\ndeclination could be within the range of 10% to 20%, across the country \n\n\n\n(Ministry of Education, 2010).  \n\n\n\nIn Nepal, drought usually happens from March through June, which is the \n\n\n\nonset of monsoon and winter precipitation has almost declined to zero, \n\n\n\nalso groundwater has hardly been replenished (Joshi, 2018). Some areas \n\n\n\nof the trans-Himalayan regions are intensely dry tall through the year and \n\n\n\ndroughts occur often in the lowland of Terai and in the western hill of \n\n\n\nNepal. Nepal faced drought in 1972, 1977, 1982, and 1992. The country \n\n\n\nhas tackled incessant dry spells since 2002, particularly during the years \n\n\n\n2002, and from 2004 to 2006\u2014in monsoon (Joshi, 2018). Different \n\n\n\nincidences of drought were also noticed during 2012, 2013, and 2015. \n\n\n\nDrought in Nepal have created panic in the hill farming system, generally \n\n\n\nfor crop production and the livelihood support of farmers dependent on it. \n\n\n\nHowever, droughts can generate opportunities to learn different \n\n\n\nadaptations strategies that are appropriate in such changing \n\n\n\ncircumstances (Dulal et al., 2010).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\nTable 1: Major cereal loss in different drought years \n\n\n\nS.N \nDrought \n\n\n\nyears \nCauses of Drought \n\n\n\nMajor \n\n\n\ncereal \n\n\n\nloss (in \n\n\n\nMetric \n\n\n\nTon) \n\n\n\nAffected \n\n\n\nregions \n\n\n\n1 1972 \nLate onset of \n\n\n\nmonsoon/rainfall \n333,380 \n\n\n\nEastern and \n\n\n\nCentral \n\n\n\n2 1976 \nPoor distribution of \n\n\n\nrainfall  \n218,480 Western \n\n\n\n3 1977 Late onset of rainfall 322,320 \nEastern and \n\n\n\nCentral \n\n\n\n4 1979 Late onset of rainfall 544,820 Western \n\n\n\n5 1982 Late onset of rainfall 727,460 Eastern \n\n\n\n6 1986 \n\n\n\nPoor distribution of \n\n\n\nrainfall during August \n\n\n\nand September  \n\n\n\n377,410 Western \n\n\n\n7 1992 Late onset of rainfall 917,260 Eastern \n\n\n\n8 1994 \nPoor distribution of \n\n\n\nrainfall  \n595,976 All regions \n\n\n\n9 1997 \nPoor distribution of \n\n\n\nrainfall  \n69,790 Eastern \n\n\n\n10 2002 \nPoor distribution of \n\n\n\nrainfall  \n83,965 \n\n\n\nEastern and \n\n\n\nCentral \n\n\n\n11 2008 \n\n\n\nPoor distribution of \n\n\n\nrainfall during \n\n\n\nNovember 2008 to \n\n\n\nFebruary 2009 \n\n\n\n56,926 All regions \n\n\n\n12 2009 Late onset of rainfall 499,870 \nEastern and \n\n\n\nCentral \n\n\n\n13 2012 \nSummer monsoon late \n\n\n\nonset and long dry spell  \n797,629 \n\n\n\nEastern and \n\n\n\nCentral \n\n\n\n14 2013 \n\n\n\nInadequate rainfall that \n\n\n\naffected  wheat \n\n\n\nplantation \n\n\n\n56,000 \n\n\n\nEastern and \n\n\n\nCentral \n\n\n\nTerai \n\n\n\ndistricts \n\n\n\n15 2012 \n\n\n\nDelayed monsoon and \n\n\n\nweak at the onset, which \n\n\n\ndelayed paddy \n\n\n\ntransplantation  \n\n\n\nnot \n\n\n\navailable \n\n\n\nEastern \n\n\n\nTerai \n\n\n\n16 \n2015 to \n\n\n\n2016 \n\n\n\nPoor monsoon and \n\n\n\ndrought \n\n\n\n30,000 \n\n\n\npeople \n\n\n\nhighly \n\n\n\ninsecure \n\n\n\nMid and \n\n\n\nFar-\n\n\n\nWestern \n\n\n\nhills and \n\n\n\nmountains \n\n\n\nSource: (Joshi, 2018) \n\n\n\nTable 1 depicts the major drought years in Nepal, main causes of drought, \n\n\n\nand damages on chief cereal crops. But this is not the comprehensive data, \n\n\n\nand many of the database are still be missing. The major elements \n\n\n\ndetermined for the drought are delayed onsets of monsoon, irregular \n\n\n\nrainfall pattern, and decreased intensity of rainfall. For example, late onset \n\n\n\nof monsoon likely led to a delay in the sowing of rice, impacting the growth \n\n\n\nof wheat, along with reducing the volume of underground water (Joshi, \n\n\n\n2018).  \n\n\n\n4. EFFECTS OF DROUGHT ON WHEAT \n\n\n\nThe impacts of drought stress may range from morphological to molecular \n\n\n\nlevels and are detrimental to all physiological performances of plant. An \n\n\n\naccount of various drought stress effects and their extent is elaborated \n\n\n\nbelow. \n\n\n\n4.1 Morphological Changes \n\n\n\nAs a response of drought there occur various morphological changes in \n\n\n\nwheat crop which can be directly observed throughout the different stages \n\n\n\nof plant growth.  Generally, the morphological response of wheat can be \n\n\n\ncategorized into two parts i.e. shoot part and root part. The shoot part \n\n\n\ncontains changes in leaf shape, leaf expansion, leaf area, leaf size, leaf \n\n\n\nsenescence, leaf pubescence, leaf waxiness, cuticle tolerance and \n\n\n\nreduction in shoot length. And the lower root part includes changes in root \n\n\n\ndry weight, root density, and root length (Den\u010di\u0107 et al., 2000). Several \n\n\n\nstudies have shown that the correlation between morphological traits \n\n\n\nsuch as grain yield per plant, grain spike per plant, spike fertility and plant \n\n\n\nheight were considered as a reliable indicator for screening drought \n\n\n\ntolerant wheat cultivars. Researchers found the positive correlation \n\n\n\nbetween leaf area, plant height and grain yield.  In conclusion we came up \n\n\n\nwith the various morphological changes like decreased plant size, early \n\n\n\nmaturity, decreased leaf area, reduced yield, limited leaf extension, small \n\n\n\nleaf size, reduced number of tillers, reduced leaf longevity, reduced total \n\n\n\nshoot length, decreased plant height, increased in leaf rolling and \n\n\n\nreduction in plant biomass in wheat as a response to drought stress. \n\n\n\nAmong these various morphological responses some of major responses \n\n\n\nare discussed below. \n\n\n\n4.1.1 Changes in Plant Height \n\n\n\nThe most common effect of water stress is defective germination and poor \n\n\n\ncrop establishment (Harris et al., 2002). Drought has been proved to \n\n\n\nimmensely impaired germination and seedling stand. The quality and \n\n\n\nquantity of crop stand rely on these events, which are affected by water \n\n\n\ndeficit (Figure 2). Cell growth is an important drought-sensitive \n\n\n\nphysiological process due to the decline in turgor pressure \n\n\n\n(TaizandZeiger, 2006). Under serious water shortage, cell elongation of \n\n\n\nwheat can be altered by interruption of water flow from the xylem to the \n\n\n\nsurrounding elongating cells (Nonami, 1998). Drought affects the growth \n\n\n\nof wheat plant. Wheat is a plant which is very sensitive to water stress \n\n\n\ncondition and shows drastic change in growth of plant when exposed to \n\n\n\nthese types of situations.  \n\n\n\nHowever, the duration, time, magnitude of drought and stage of wheat \n\n\n\nplant also determines the effect of drought stress. Different experiments \n\n\n\nhave been carried out at different developmental stages like stem \n\n\n\nelongation, booting, grain filling of wheat plant and results shows that the \n\n\n\nplant facing drought stress start from stem elongation stage suffered more \n\n\n\nas compared to others. Plant height was reduced by 35% and 23% at stem \n\n\n\nelongation stage and booting stage respectively while the plant height was \n\n\n\nonly reduced by 7% at grain filling stage (Caverzan et al., 2016a). Similarly, \n\n\n\nreduction in the root and shoot growth of wheat when exposed to drought \n\n\n\nconditions is reported by many other researchers also (Azooz and Youssef, \n\n\n\n2010; Farooq et al., 2013). Hence drought is one of the major factors \n\n\n\nresponsible for overall decrease in growth of wheat plant. \n\n\n\nFigure 1: Drought effect on growth of wheat \n\n\n\nFigure 1 illustrates the mechanisms of growth impact under drought \n\n\n\nstress. Under water stress conditions, cell elongation in wheat is retarded \n\n\n\nby fall in turgor pressure. Less water uptake leads to a decrease in tissue \n\n\n\nwater contents. Consequently, turgor is lost. Similarly, drought stress also \n\n\n\nlowers down the metabolites required for cell division. As a result, \n\n\n\nimpaired mitosis, cell elongation and expansion result in reduced growth.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\n4.1.2 Leaf Senescence \n\n\n\nA study reported that if drought occurs during reproduction stage, the rate \n\n\n\nof senescence increases as a result of drought stress which causes the \n\n\n\nremarkable reduction of grain yield (Nawaz et al., 2013). There occurs a \n\n\n\nchange in leaf color due to breakdown of chlorophyll membrane and water \n\n\n\ncontent when the function of leaf deteriorates. Chlorosis leading to \n\n\n\ndecrease in photosynthesis is one of the vivid signs of leaf senescence (Ali \n\n\n\net al., 2020). Wheat growing in extreme drought conditions can cause \n\n\n\nsenescence to the whole plant but it also enhances the mobilization of \n\n\n\nstored carbohydrates during parenthesis from the stem and leaves to \n\n\n\ndeveloping grains and help in compensating the loss of yield caused by \n\n\n\nsenescence during drought stress (Farooq et al., 2014). The amount of \n\n\n\ntotal protein content, glutamine synthetase and rubisco (Ribulose Bi-\n\n\n\nPhosphate Carboxylase) was used to indicate the beginning and stages of \n\n\n\nsenescence in wheat. In most of the cases senescence occurs first in older \n\n\n\nleaves and then in younger leaves. But in some sensitive varieties, the \n\n\n\nsequence of senescence is disturbed due to drought and first occurs in flag \n\n\n\nleaves and later on older leaves. It is found that in younger leaves the \n\n\n\namount of glutamine synthetase isoenzyme declined considerably and the \n\n\n\nsequence of senescence were disturbed slightly as compared to plants \n\n\n\ngrowing in sufficient water conditions (Nagy et al., 2013). In conclusion, \n\n\n\nthe leaf senescence of wheat is correlated with drought and by monitoring \n\n\n\nthe carbon and nitrogen metabolism, we can achieve progress in making \n\n\n\ndrought sensitive genotype of wheat to make them tolerant. \n\n\n\nFigure 2: Impacts of drought on leaf (Source: Agrilife, 2015) \n\n\n\n4.1.3 Changes in Root System \n\n\n\nPlant root obtains nutrients and water from the ground and plays an \n\n\n\nimportant role during the condition of drought also. When there is scarcity \n\n\n\nof water resources plant root goes deep into the soil in order to absorb \n\n\n\nwater from the soil. Many researchers have reported that the volume, \n\n\n\nweight, length and density of root are interrelated with resistance of water \n\n\n\nscarcity in crops.  In order to survive against drought conditions, the \n\n\n\narchitecture of the root system is considered very important as the good \n\n\n\narchitecture of wheat extracts maximum soil water under drought stress \n\n\n\nand also improving the yield of the grain (Dodd et al., 2011).  The adaptive \n\n\n\nmechanism shown by wheat in order to fight against drought stress are \n\n\n\nosmotic adjustment of root, increase root penetration to the soil, increased \n\n\n\nroot density and root increase root to shoot ratio (Ali et al., 2020).   \n\n\n\nWhen there is scarcity of water root growth is favored over shoot growth. \n\n\n\nIf there is reduction in the water potential, osmotic adjustment in the root \n\n\n\nhelps to maintain level of turgidity up to some level and the water \n\n\n\npotential gradient is re-established for water uptake. The formation of \n\n\n\nlateral root also increases during drought stress in order to increase \n\n\n\nsurface area for water absorption.  Similarly, there is increase in cross-\n\n\n\nsection diameter which helps in maintaining water retention in vascular \n\n\n\nbundles of wheat. Also, there is increase in sclerenchyma cell diameter and \n\n\n\ndecrease in aerenchyma cell formation during drought stress (Henry et al., \n\n\n\n2012). So, during breeding programs, genotypes with improved root \n\n\n\nsystems are used for increasing yield because they can utilize the deep \n\n\n\nunderground water more properly to survive against drought stress.  \n\n\n\n4.2 Physiological Changes \n\n\n\nNumerous physiological responses have been determined in response to \n\n\n\ndrought stress. There are many physiological attributes that reduces the \n\n\n\neffect of drought stress on wheat crops. There is a direct relationship \n\n\n\nbetween the availability of water and performance of different \n\n\n\nphysiological processes of plant. When there is reduction in the water \n\n\n\navailability, these physiological processes are disturbed and plants are \n\n\n\nunable to produce sufficient amount of dry matter. Studies have shown \n\n\n\nthat during drought condition there is reduction in the plant nutrient \n\n\n\nuptake, plant growth rate and height as well as photosynthetic activities \n\n\n\nand dry matter production (Todaka et al., 2015; Barbeta et al., 2015; \n\n\n\nAshraf and Harris, 2013).  Deficiency of water also leads to decrease in \n\n\n\nchlorophyll contents, reduction in the water content and membrane \n\n\n\nstability (Sallam et al., 2019).  \n\n\n\nDue to the drought stress, there is a need to make some physiological \n\n\n\nchanges in the plant in order to alleviate the effect of drought stress \n\n\n\n(Vinocur and Altman, 2005). For surviving the drought situations, plant \n\n\n\nhave to adapt itself in this situation and for this there is a development of \n\n\n\nmany tolerant genotypes which helps to maintain the soluble sugars, \n\n\n\nproline content, amino acids, chlorophyll content, enzymatic and non-\n\n\n\nenzymatic antioxidant activities as well (Abid et al., 2016). The \n\n\n\nmodifications done during the breeding process of these tolerant variety \n\n\n\nhelp to alter the normal physiological process of wheat and performs its \n\n\n\nnormal functions on water deficit conditions. For obtaining this, wheat \n\n\n\nplant undergoes different adjustments like change in amount of \n\n\n\nantioxidant production, proline content, osmotic adjustment, hormone \n\n\n\ncomposition, opening and closing of stomata, cuticle thickness, root depth, \n\n\n\nloss of chlorophyll and decrease in transpiration (Rosenberg et al., 1990; \n\n\n\nZhu, 2002).  \n\n\n\nDifferent types of research have been done so far in order to understand \n\n\n\nthe physiological response of wheat to drought stress. A group researcher \n\n\n\nobserved that transpiration decreased significantly due to drought stress \n\n\n\nand then heat can slowly be lost from the leaves and leaf temperature can \n\n\n\nbe increased (Rosenberg et al., 1990). And due to this, there is increase in \n\n\n\nCO2 concentrations and photosynthesis which affects plant growth and \n\n\n\nfinally water use efficiency can be improved. These different types of \n\n\n\ndrought tolerance mechanism of plant help in understanding the \n\n\n\nphysiological response that helps to maintain the growth and productivity \n\n\n\nduring stress period. Similarly, these traits are also responsible in \n\n\n\nbreeding programs in order to develop drought tolerant varieties which \n\n\n\ncan perform well under those regions of the world which have scarcity of \n\n\n\nwater resources. \n\n\n\n4.2.1 Changes in Cell Growth Pattern \n\n\n\nWheat is very sensitive to water stress condition and shows drastic change \n\n\n\nin growth of plant when exposed to these types of situations. However, the \n\n\n\nduration, time, intensity of drought and stage of wheat crop also \n\n\n\ndetermines the effect of drought stress. Different experiments have been \n\n\n\ncarried out at different developmental stages like stem elongation, \n\n\n\nbooting, grain filling of wheat plant and results shows that the plant facing \n\n\n\ndrought stress start from stem elongation stage suffered more as \n\n\n\ncompared to others. Plant height was reduced by 35% and 23% at stem \n\n\n\nelongation stage and booting stage respectively while the plant height was \n\n\n\nonly reduced by 7% at grain filling stage (Caverzan et al., 2016a). Similarly, \n\n\n\nreduction in the root and shoot growth of wheat when exposed to drought \n\n\n\nconditions is reported by many other researchers also (Azooz and Youssef, \n\n\n\n2010; Farooq et al., 2013). Hence drought is one of the major factors \n\n\n\nresponsible for overall decrease in growth of wheat plant. \n\n\n\nMoreover, the duration, type, and magnitude of drought and the stage of plant \n\n\n\ngrowth also regulate the possible changes. A large body of literature is \n\n\n\navailable on the growth stage and tolerance level of wheat cultivars under \n\n\n\ndrought stress. Plant growth is also varied with duration and type of drought. \n\n\n\nShamsi and Kobraee conducted a two-factor experiment with three wheat \n\n\n\ncultivars and three different stages of wheat growth (Shamsi and Kobraee, \n\n\n\n2011). Drought stress was imposed at stem elongation, booting, and grain \n\n\n\nlling stages and continued up to harvest. \n\n\n\nResults showed that plants facing water stress from stem elongation stage \n\n\n\nsuffered more compared to other two stages of plant growth. Plant height was \n\n\n\nreduced by 35% and 23% in plants facing drought from stem elongation stage \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\nand booting stage, respectively, but only by 7% in plants exposed to drought \n\n\n\nat grain lling stage. Almost similar ndings were reported, by who initiated \n\n\n\ndrought at Moreover, the duration, type, and magnitude of drought and the \n\n\n\nstage of plant growth also regulate the possible changes (Akram, 2011). A \n\n\n\nlarge body of literature is available on the growth stage and tolerance level of \n\n\n\nwheat cultivars under drought stress. Plant growth is also varied with \n\n\n\nduration and type of drought. Some researchers conducted a two-factor \n\n\n\nexperiment with three wheat cultivars and three different stages of wheat \n\n\n\ngrowth (Shamsi and Kobraee, 2011). Drought stress was imposed at stem \n\n\n\nelongation, booting, and grain lling stages and continued up to harvest. \n\n\n\nResults showed that plants facing water stress from stem elongation stage \n\n\n\nsuffered more compared to other two stages of plant growth.  \n\n\n\nPlant height was reduced by 35% and 23% in plants facing drought from stem \n\n\n\nelongation stage and booting stage, respectively, but only by 7% in plants \n\n\n\nexposed to drought at grain lling stage. Almost similar findings were \n\n\n\nreported by who initiated drought at Moreover, the duration, type, and \n\n\n\nmagnitude of drought and the stage of plant growth also regulate the possible \n\n\n\nchanges (Akram, 2011). A large body of literature is available on the growth \n\n\n\nstage and tolerance level of wheat cultivars under drought stress. Plant \n\n\n\ngrowth is also varied with duration and type of drought. Some researchers \n\n\n\nconducted a two-factor experiment with three wheat cultivars and three \n\n\n\ndifferent stages of wheat growth (Shamsi and Kobraee, 2011). Drought stress \n\n\n\nwas imposed at stem elongation, booting, and grain lling stages and continued \n\n\n\nup to harvest. Results showed that plants facing water stress from stem \n\n\n\nelongation stage suffered more compared to other two stages of plant growth. \n\n\n\nPlant height was reduced by 35% and 23% in plants facing drought from stem \n\n\n\nelongation stage and booting stage, respectively, but only by 7% in plants \n\n\n\nexposed to drought at grain lling stage. Almost similar ndings were \n\n\n\nreported, who initiated drought aposed to drought (Akram, 2011). Moreover, \n\n\n\nthe duration, type, and magnitude of drought and the stag. \n\n\n\n4.2.2 Change in Chlorophyll Content and Photosynthetic Rate \n\n\n\nWith reduction in the volume of available water, plant closes their stomata \n\n\n\n(plausibly via ABA signaling), which reduces the CO2 influx. Reduction in \n\n\n\nCO2 not merely decreases the carboxylation directly but also directs more \n\n\n\nelectrons to form reactive O2 species. Serious drought stress restrict \n\n\n\nphotosynthesis due to the reduction in the activities of ribulose-1, 5-\n\n\n\nbisphosphate carboxylase/oxygenase (Rubisco), phosphoenolpyruvate \n\n\n\ncarboxylase (PEPCase), NADP-malic enzyme (NADP-ME), fructose-1, 6-\n\n\n\nbisphosphatase (FBPase) and pyruvate orthophosphate dikinase (PPDK). \n\n\n\nLowered tissue water contents also enhance the activity of Rubisco \n\n\n\nbinding inhibitors. Chlorophyll is a green pigment and is responsible for \n\n\n\nthe photosynthetic process. Mainly there are two types of chlorophyll \n\n\n\nfound in wheat i.e. chlorophyll a and chlorophyll b. The ratio between \n\n\n\nchlorophylls a and b is generally 3:1 depending upon cultivars, plant \n\n\n\ngrowth, and various environmental factors (Ahmad et al., 2018). Many \n\n\n\nresearchers and scientists have reported that whenever wheat plant goes \n\n\n\nthrough drought stress, there is significant reduction in the leaf \n\n\n\nchlorophyll content (Fotovat et al., 2007).  \n\n\n\nThe effect of this stress is more in the chlorophyll b and the number of \n\n\n\nchlorophyll b has decreased to more extend as compared to chlorophyll a. \n\n\n\nThis is explained by the fact that the part of the decrease in chlorophyll a \n\n\n\ncould be because of conversion to chlorophyll b (Fang et al., 1998). \n\n\n\nScientists have found that when wheat plant is exposed to light there \n\n\n\noccurs enzyme activation reaction of chlorophyll synthesis which \n\n\n\nincreases the chlorophyll content in young leaves but the chlorophyll \n\n\n\ncontent decrease by 13-15% in older leaves due to activation of \n\n\n\nchlorophyllase and inactivation of enzyme under drought condition \n\n\n\n(Nikolaeva et al., 2010). As the drought stress damages the chlorophyll \n\n\n\ncomponents there occurs change in the photosynthetic machinery which \n\n\n\nresists the photosynthesis. Different studies have shown that there is \n\n\n\ndecrease in the photosynthesis of cereal crops because of the drought \n\n\n\nstress. Electron transport chain is also affected by drought stress which \n\n\n\nultimately results for the production of ROS that are harmful for plant cells \n\n\n\nand organelles like mitochondria, chloroplast and perioxisomes (Farooqi \n\n\n\net al., 2020).  \n\n\n\nROS is also responsible for reduction of chlorophyll from the leaves. This \n\n\n\nchanges the inner structure of chloroplast, mitochondria, chlorophyll \n\n\n\ncontent and minerals. As a result of imbalance between the light capture \n\n\n\nand its utilization there is metabolic distortions of photosynthetic \n\n\n\nactivities, decrease in Rubisco activity, reduction of chloroplast \n\n\n\nmembranes, degradation of chloroplast structure and photosynthetic \n\n\n\napparatus, chlorophyll photo-oxidation, loss of chlorophyll substrate, \n\n\n\ninability of chlorophyll biosynthesis, and the increase of chlorophyllase \n\n\n\nactivity (Kabiri et al., 2014; Kingston-Smith and Foyer, 2000). Some of the \n\n\n\nmajor components limiting photosynthetic rate is CO2 diffusional \n\n\n\nlimitation due to early stomatal closure as a response to the drought \n\n\n\ninduced loss of turgor, reduced activity of different photosynthetic \n\n\n\nenzymes, decrease of biochemical components which help in the \n\n\n\nformation triose-phosphate and most of all there is reduction in the \n\n\n\nphotochemical efficiency of photosystem II (Pandey and Shukla, 2015). \n\n\n\nThe decrease in photosynthetic amount under drought condition is a \n\n\n\nresult of inhibition of RuBisCO (ribulose-1, 5-bisphosphate \n\n\n\ncarboxylase/oxygenase) enzyme activity and development of ATP (Dulai \n\n\n\net al., 2005). \n\n\n\nFigure 3: Effect of Drought on Photosynthetic activity. \n\n\n\n4.2.3 Membrane stability \n\n\n\nBiological membranes are the first and foremost target of different abiotic \n\n\n\nstresses. It is assumed that the maintenance membranes stability under \n\n\n\nwater stress is a major element of drought resistance in crops. The \n\n\n\nmembrane integrity is changed by drought stress. A plausible explanation \n\n\n\nof this is the rise of the cell permeability accompanied by electrolyte \n\n\n\nleakage from the cell. Drought has a huge effect on the plant cell, damaging \n\n\n\nthe selective permeability of the plasma membrane. As a result of drought \n\n\n\nstress cell membrane stability (CMS) depicts the ability of plant tissue to \n\n\n\nprevent electrolytes leakage by keeping the cell membrane in safe mood \n\n\n\n(Larson et al., 1971). Measurement of solute leakage from the plant tissue \n\n\n\nwas used to estimate the damage to the cell membrane caused by drought \n\n\n\nand heat under field conditions. The MSI (Membrane Stability Index) is \n\n\n\nhighest i.e. 82.1% in drought susceptible varieties compared to \n\n\n\nmoderately tolerant i.e. 79.4% and tolerant one i.e. 80.4% at vegetative \n\n\n\nstage, however no differences were recorded between tolerant and \n\n\n\nmoderately tolerant varieties but at anthesis stage moderately tolerant \n\n\n\nvarieties showed lowest MSI values i.e. 75.7% and the highest value were \n\n\n\nrecorded in drought tolerant varieties i.e. 78.8%, in general MSI decreased \n\n\n\nas plant advanced in age (Almeselmani et al., 2012). Due to the drought \n\n\n\nstress, there is loss of water from plant tissues which affects the both \n\n\n\nmembrane structure and function. A group researcher found that there is \n\n\n\na correlation between electrolyte leakage and drought and the leakage was \n\n\n\ncaused due to damage of cell membranes which becomes more permeable \n\n\n\n(Martin et al., 2006). Drought stress affects the plant more having lower \n\n\n\nCMS value than those genotypes which have higher CMS values (Mehraban \n\n\n\nand Miri, 2017). The genotypes having less than 50% values are highly \n\n\n\nsusceptible to drought while the genotypes with 71-80% values are \n\n\n\nconsidered to grow with full potential under drought condition (Mehraban \n\n\n\nand Miri, 2017). A group researcher reported that under drought \n\n\n\ncondition cell membrane stability (CMS) have positive relationship with \n\n\n\ntiller per plant, grain yield but negative relationship with 100 kernel \n\n\n\nweights (TGW) (Farshadfar et al., 2011). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\n4.2.4 Relative Water Content (RWC) \n\n\n\nRelative water content, leaf water potential, stomatal tolerance, \n\n\n\ntranspiration rate, and leaf temperature are important attributes that \n\n\n\ninfluence plant-water relation. Relative water content in leaves of wheat \n\n\n\nwas more initially during leaf development and reduced as the leaf \n\n\n\nmatured (Siddique et al., 2001). Undoubtedly, drought stressed wheat \n\n\n\ncrops had lesser relative water content than non-stressed ones. Exposure \n\n\n\nof these crops to drought stress significantly reduced the leaf water \n\n\n\npotential, relative water content and transpiration rate, with a substantial \n\n\n\nrise in leaf temperature (Siddique et al., 2001). Among the different types \n\n\n\nof water potential leaf RWC is considered as more important parameter \n\n\n\nunder water deficit conditions. Due to the drought stress, there is \n\n\n\nsignificant reduction in the RWC of wheat during its development stages.  \n\n\n\nThe effect of drought is more in later stage (after 6 weeks of emergence) \n\n\n\nand effects on water relations, nutrient uptake, growth, and yield than in \n\n\n\nearly stage (after 3 week of seedling emergence) in wheat (Nawaz et al., \n\n\n\n2014). As a result of drought stress there is reduction in water status \n\n\n\nduring crop growth, soil water potential and plant osmotic potential for \n\n\n\nwater and nutrient uptake which ultimately reduce leaf turgor pressure \n\n\n\nwhich results in upset of plant metabolic activities (Mehraban and Miri, \n\n\n\n2017). Excised leaf water retention (ELWR) is enhanced by drought stress \n\n\n\nwhich reflects the water retention mechanism in the leaf under stress that \n\n\n\nmay cause leaf rolling or decrease in exposed leaf surface area. Many \n\n\n\nresearchers have found that there is continuous variation in the relative \n\n\n\nwater content during drought stress because it is controlled by multiple \n\n\n\ngenes with additive effect.  \n\n\n\nHigh turgor potential and relative water content is maintained by drought \n\n\n\ntolerant genotypes to signify water had a little effect on their protoplasmic \n\n\n\nstructures as compared to sensitive genotypes which represent a highly \n\n\n\npositive correlation between water content and photosynthetic rate \n\n\n\n(Moayedi et al., 2010). The final impact of lower relative water content is \n\n\n\nreducing in water status and osmotic potential in plants. Under water \n\n\n\ndeficit conditions, maintenance of leaf turgor pressure is a crucial adaptive \n\n\n\nmechanism that plays a remarkable role in stomatal regulation and \n\n\n\nphotosynthetic activities. For the preservation of turgor pressure osmo-\n\n\n\nregulation plays an important part which help in the absorption of soil \n\n\n\nwater and helps in plant metabolic activities for its survival (Mehraban \n\n\n\nand Miri, 2017). Total grain yield per plant, biological yield per plant and \n\n\n\nharvest index of wheat has a positive correlation with relative water \n\n\n\ncontent (Abdul et al., 2010).  Hence relative water content is a useful \n\n\n\nparameter for selecting drought tolerant wheat genotypes (Hasheminasab \n\n\n\net al., 2012).  \n\n\n\n4.3 Biochemical changes \n\n\n\nWheat crops are provided with internal defense mechanism equipped with \n\n\n\nantioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT) \n\n\n\nand peroxidase (POX) for ROS scavenging under stressed conditions (Chen \n\n\n\net al., 2012). Therefore, water stress contributed to the drastic changes in \n\n\n\nthe biochemical attributes of the wheat plants as described below: \n\n\n\n4.3.1 Proline Content \n\n\n\nProline can be defined as one of the major amino acids which is used in the \n\n\n\nbiosynthesis of proteins. The response of wheat to water stress by \n\n\n\naccumulating proline is a useful tool to understand the mechanisms of \n\n\n\ndrought tolerance. The accumulation of some organic compatible solutes \n\n\n\nin wheat which adjust the intercellular osmotic potential is highly affected \n\n\n\nby drought stress. As there is accumulation of organic compatible solutes \n\n\n\nit increases the solute potential of plant which prevents loss of water \n\n\n\n(Naeem et al., 2015). Due to the lack of water, wheat plant accumulates \n\n\n\nproline content in larger extend than any other osmoregulators (Maralian \n\n\n\net al., 2010). It is reported that there is increase in the proline content of \n\n\n\nwheat plant after it had been subjected to drought stress (Vendruscolo et \n\n\n\nal., 2007). It is found that the maximum amount of proline increases in \n\n\n\nheading stage of wheat when it is under water stress condition (Maralian \n\n\n\net al., 2010). The genotypes of wheat which have more accumulation of \n\n\n\nproline under drought have the ability to bear drought stress and it is \n\n\n\ndifferent for different wheat genotypes because different genotypes have \n\n\n\nvariable water stress threshold. Hence the estimation of proline content of \n\n\n\nwheat can be a useful trait for selecting drought tolerant wheat genotype. \n\n\n\n4.3.2 Antioxidant Properties \n\n\n\nDue to the drought stress, there is accumulation of reactive oxygen species \n\n\n\n(ROS) in the cells, which can cause severe oxidative damage to the plants \n\n\n\nwhich inhibits the growth and grain yield of wheat plant. The equilibrium \n\n\n\nbetween the production and scavenging of ROS is knows as redox \n\n\n\nhomeostasis (Caverzan et al., 2016a). However, if the production of ROS \n\n\n\nexceeds the cellular scavenging capacity, it creates the unbalancing of the \n\n\n\nredox homeostasis which results in rapid and transient excess of ROS, \n\n\n\nknown as oxidative stress (Sharma et al., 2012). Therefore, plants have \n\n\n\nantioxidant mechanisms for scavenging the excess ROS and prevent \n\n\n\ndamage to cells. Enzymatic and non-enzymatic antioxidants are \n\n\n\nresponsible for maintaining the equilibrium between the production and \n\n\n\ndetoxification of ROS (Mittler, 2002). In wheat, several studies have \n\n\n\nshowed that there is change in the activity of the antioxidant defense \n\n\n\nsystem in plant to control the oxidative stress induced by many \n\n\n\nenvironmental factors like drought.  \n\n\n\nThere is activation of both enzymatic and non-enzymatic system which is \n\n\n\nused to detoxify the toxic levels of ROS which is harmful to plant produced \n\n\n\nas a result of drought stress (Caverzan et al., 2016b). Different studies have \n\n\n\nshowed that the responses of these enzymatic and non-enzymatic systems \n\n\n\nvary with different genotypes, Different genotypes shows different \n\n\n\nresponses under same condition. Generally tolerant genotypes show \n\n\n\nhigher antioxidant capacity resulting in lower oxidative damage to the \n\n\n\nplant. This response also depends upon several others factors like tissue \n\n\n\ntype, length, and intensity of the stress as well as on developmental stage \n\n\n\nproving the complexity of the mechanism of production and detoxification \n\n\n\nof ROS and the effect of ROS on antioxidant system (Caverzan et al., 2016a). \n\n\n\nHence having the information about the antioxidant response of wheat \n\n\n\nduring drought stress, it helps us to develop different improved genotypes \n\n\n\nhaving more antioxidant properties. \n\n\n\n5. RESISTANCE MECHANISM OF DROUGHT STRESS \n\n\n\nThrough the induction of different morphological, biochemical, and \n\n\n\nphysiological responses, wheat crops respond and adapt to and survive \n\n\n\nunder severe drought stress conditions. Drought stress disturbs the water \n\n\n\ncirculations at different levels, causing unwanted reactions and finally \n\n\n\nadaptation reactions (Beck et al., 2007). To survive under such conditions, \n\n\n\nsusceptible plants have defense mechanisms against drought stress, which \n\n\n\nneed to be studied comprehensively. As a result of drought stress, there \n\n\n\noccurs a rapid loss in the yield and yield performance of wheat. The \n\n\n\ndifferent process which occurs in plant in order to suppress the stress in \n\n\n\nthe given condition and producing the higher yield as compared to normal \n\n\n\nwater availability conditions is known as drought resistance. According to \n\n\n\nthe agriculture point of view drought resistance can be defined as the \n\n\n\nprocess in order to minimize the loss of economic yield under limited \n\n\n\nwater availability conditions (Bohnert et al., 1995). Generally, there are 3 \n\n\n\ndifferent forms of drought resistance i.e. drought escape, drought \n\n\n\navoidance and drought tolerance (Bohnert et al., 1995). \n\n\n\nFigure 4: Resistance mechanism of Drought Stress \n\n\n\nDrought \nstress\n\n\n\nDrough Escape\n\n\n\nEarly flowering\n\n\n\nEarly maturity\n\n\n\nDevelopmental \nPlasticity\n\n\n\nAssimilates \nRemobolization\n\n\n\nDrought \nAvoidance\n\n\n\nLess water \nloss\n\n\n\nClosing of stomata \n\n\n\nLeaf Rolling\n\n\n\nLeaf Firing\n\n\n\nGlaucousness\n\n\n\nMore water \nabsorption\n\n\n\nHigher Root Depth\n\n\n\nHigh root density\n\n\n\nHigh root-shoot ratio\n\n\n\nDrought \nTolerance\n\n\n\nOsmoregulation \n\n\n\nOsmotic \nadjustment\n\n\n\nAntioxidant \nEnzymes\n\n\n\nGenetic \nmodification\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\n5.1 Drought Escape \n\n\n\nThe process of shortening the life cycle or growing season of plant in order \n\n\n\nto avoid the dry environmental conditions is known as drought escape. \n\n\n\nThe condition required for drought escape is, when the phenological \n\n\n\ndevelopment is successfully matched with periods of soil moisture \n\n\n\navailability and it occurs when the growing season is reduced and terminal \n\n\n\ndrought stress prevails (Araus et al., 2002). In order to achieve drought, \n\n\n\nescape we can use different process like early maturity, developmental \n\n\n\nplasticity, assimilate remobilization (Vani et al., 2017). Among these \n\n\n\nvarious factors, early maturity of wheat is considered as one of the major \n\n\n\naspects for escaping the drought stress. For obtaining early maturity we \n\n\n\nhave to reduce the developmental time at different growing stages of \n\n\n\nwheat. Among different stages, shortening of time at flowering stage is \n\n\n\nconsidered as one of the most effective time for escaping drought \n\n\n\n(Shavrukov et al., 2017). Through selection of different genotypes of wheat \n\n\n\nhaving early flowering behavior, their vegetative growth can limit and \n\n\n\nenables the reproductive growth to occur before the terminal stress \n\n\n\n(Bodner et al., 2015). Early flowering and maturity can be considered as \n\n\n\nan effective mechanism for drought escaping however it has some \n\n\n\ndrawbacks and can limit the grain yield potential due to reduction in time \n\n\n\nfor photosynthesis and seed nutrient accumulation required for higher \n\n\n\ngrain yield (Bidinger and Witcombe, 1989; Radhika and Thind, 2014). \n\n\n\n5.2 Drought Avoidance \n\n\n\nThere are numerous ongoing physiological and metabolic activities on the \n\n\n\nplant which are not exposed to drought stress and are continuously \n\n\n\nperforming their normal functions even in the water deficit conditions. \n\n\n\nThe ability of plant to maintain relatively higher content of water in tissue \n\n\n\nof plant despite the fact of having lower water content in the soil is known \n\n\n\nas drought avoidance (Levitt and others, 1980). It helps to control the \n\n\n\nwater loss by controlling stomatal transpiration and also maintain water \n\n\n\nuptake through an extensive and deep root in the soil. During the drought \n\n\n\ncondition wheat maintains its water status by closing their stomata. In \n\n\n\ncontrast, there are some negative effects on photosynthesis and \n\n\n\nrespiration as a result of stomata closing. Moreover, there occurs a leaf \n\n\n\nrolling in response to drought in order to save water content in plant \n\n\n\nwhich later unrolls when the leaf-water relations of plant improve (Sirault \n\n\n\net al., 2015). It has been reported that the epi-cuticular wax layer of wheat \n\n\n\nalso called as glaucousnessis also responsible for maintaining the leaf-\n\n\n\nwater relations during the drought stress and is considered as important \n\n\n\ntrait for drought avoidance (Richards et al., 1986). Drought avoidance is \n\n\n\nalso influenced by several root characters like root length, root density and \n\n\n\nroot biomass (Kavar et al., 2008). Greater thickness of root, higher root \n\n\n\ndepth and higher root density are responsible for excessive water uptake \n\n\n\nduring drought stress (Aina and Fapohunda, 1986). Hence there are \n\n\n\nvarious characters of plant like stomatal transpiration, glaucousnessis, leaf \n\n\n\nrolling and different structural and functional aspects of root are \n\n\n\nresponsible for avoidance of drought under stress conditions. \n\n\n\n5.3 Drought Tolerance \n\n\n\nThe ability of plant to maintain their growth and development during \n\n\n\nwater deficit conditions is known as drought tolerance. Drought tolerance \n\n\n\nis a complex mechanism and plants modify its different physiological and \n\n\n\nbiochemical factors to fight against the water deficit conditions in order to \n\n\n\nmaintain its normal growth and yield capacity. Different studies have \n\n\n\nshown that osmoregulation, osmotic adjustment and activity of \n\n\n\nantioxidant enzymes plays a vital for dealing with the drought stress \n\n\n\nsituation in plants (Nemesk\u00e9ri and Helyes, 2019). In order to fulfill the \n\n\n\nincreasing demand of food for growing population it is must to develop the \n\n\n\nmore advanced wheat tolerant genotypes. Thus, the main aim of the \n\n\n\ndrought related research program is to identify such genes which can be \n\n\n\nused in the breeding program in order to develop new more drought \n\n\n\ntolerant genotypes of wheat. Drought tolerant variety of the wheat have \n\n\n\nsome modifications over the normal physiological and biochemical \n\n\n\nprocesses in order to survive in the water deficit conditions. Drought \n\n\n\ntolerance mechanism involves the activation of different physiological and \n\n\n\nbiochemical processes at cell, tissue, organ and whole plant level. Some of \n\n\n\nthe major fields for genetic modifications of wheat in order to obtain \n\n\n\ndrought tolerant variety are in table 2. \n\n\n\n  Table 2: Modification field for improving wheat resistance \n\n\n\nS.N Field for genetic modifications \n\n\n\n1 Drought-Induced Gene Expression/Single Action Gene \n\n\n\n2 Osmo-protectants, Metabolites and Protective Genes \n\n\n\n3 Transporter Genes \n\n\n\n4 Carbon Metabolism \n\n\n\n5 Transcription Factors \n\n\n\n6  Post-Translational Modification \n\n\n\n7 Protein Kinase \n\n\n\n8 Nuclear Factor \n\n\n\nSource: (Khan et al., 2019) \n\n\n\nUntil now, different studies have been done in order to exploit the genes \n\n\n\nthat are responsible for drought stress and have been categorized through \n\n\n\nRNA sequencing and the Affymetrix GeneChip technology (Dugas et al., \n\n\n\n2011). Researchers found that different types of kinase likeCDPKs \n\n\n\n(calcium-dependent protein kinases), CIPK (CBL interacting protein \n\n\n\nkinase), and SnRK2 (sucrose non-fermenting protein-related kinase 2) \n\n\n\nand MAPKs (mitogen-activated protein kinases) also shows some \n\n\n\nresponse to drought stress (Malone and Oliver, 2011). There is a \n\n\n\ncorrelation between the drought and antioxidant system and shows \n\n\n\npositive response towards it. Reports suggested that reactive oxygen \n\n\n\nspecies (ROS) like OH (Hydroxide), H2O2 (Hydrogen Peroxide), SOD (Super \n\n\n\nDioxide) and oxygen which is singlet are created in drought conditions \n\n\n\n(Nezhadahmadi et al., 2013a). Some of the studies shows that wheat \n\n\n\ngenotype having lower malondialdehyde (MDA) content and greater \n\n\n\nosmotic regulator has helpful for obtaining tolerance against drought \n\n\n\n(Nezhadahmadi et al., 2013a). All these parameters have important role in \n\n\n\ndrought tolerance and it can be useful for selecting drought tolerant \n\n\n\nvarieties and lines particularly at reproductive stage (M. Almeselmani, \n\n\n\n2012). \n\n\n\n6. CONCLUSION \n\n\n\nDifferent years of drought in Nepal have been identified and the impacts \n\n\n\nof those stresses on crop production have been assessed. Historical \n\n\n\nevidences have shown that there was a huge loss in crop yield in the past. \n\n\n\nDrought stress retards crop growth and development, leading to the \n\n\n\nchanges in morphological, physiological, and biochemical attributes of the \n\n\n\ncrop. Since majority of the global wheat cultivation area lies in arid and \n\n\n\nsemi-arid regions, drought is one of the major problems for obtaining the \n\n\n\npotential yield. It reduces the proper growth and development of plants \n\n\n\nhampering fruiting and grain filling which eventually leads to reduced size \n\n\n\nand number of wheat grains. Injury biochemical reactions under drought \n\n\n\nstress are among the major deterrents to growth. Due to this reason, there \n\n\n\nis great economic loss in the production of wheat all around the world. For \n\n\n\nimproving yield under drought condition, it is essential to understand the \n\n\n\nphysiological response of wheat under these situations. Various resistance \n\n\n\nmechanisms have been developed in the plants to cope with drought \n\n\n\nstress. CO2 assimilation by leaves is decreased primarily by stomatal \n\n\n\nclosure, membrane damage and disturbed activity of various enzymes, \n\n\n\nespecially those of CO2 fixation. By understanding the physiological, \n\n\n\nmorphological, and biochemical responses of wheat under this situation, \n\n\n\nit helps us to identify drought tolerance mechanism and develop drought \n\n\n\ntolerant varieties of wheat.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWe would like to express our sincere gratitude to Asst. Prof. Dr. Mukti Ram \n\n\n\nPoudel, Department of Agronomy, Plant breeding and Agri-statistics, \n\n\n\nPaklihawa Campus, Institute of Agriculture and Animal science for the \n\n\n\ncontinuous support during the manuscript preparation. Also, special \n\n\n\nthanks to the author\u2019s parents whose guidance and motivation are always \n\n\n\nwith us.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\nREFERENCES \n\n\n\nAbdul, A.K., Dennett, M., Munir, M., 2010. Drought tolerance screening of \n\n\n\nwheat varieties by inducing water stress conditions. Songklanakarin \n\n\n\nJournal of Science & Technology, 33. \n\n\n\nAbid, M., Tian, Z., Ata-Ul-Karim, S., Cui, Y., Liu, Y., Zahoor, R., Jiang, D., Li, X., \n\n\n\n2016. Nitrogen nutrition improves the potential of wheat (Triticum \n\n\n\naestivum L.) to alleviate the effects of drought stress during vegetative \n\n\n\ngrowth periods. Frontiers in Plant Science, 7. \n\n\n\nhttps://doi.org/10.3389/fpls.2016.00981 \n\n\n\nAgarwal, P.K., Agarwal, P., Reddy, M.K., Sopory, S.K., 2006. Role of DREB \n\n\n\ntranscription factors in abiotic and biotic stress tolerance in plants. Plant \n\n\n\nCell Rep, 25, Pp. 1263\u20131274.  \n\n\n\nAgrilife Extension. 2015. Insects, disease, and drought affecting texas \n\n\n\nwheat. https://agrilife.org/texasrowcrops/2015/04/07/insects-\n\n\n\ndisease-and-drought-affecting-texas-wheat/  \n\n\n\nAhmad, Z., Waraich, E.A., Akhtar, S., Anjum, S., Ahmad, T., Mahboob, W., \n\n\n\nHafeez, O.B.A., Tapera, T., Labuschagne, M., Rizwan, M., 2018. \n\n\n\nPhysiological responses of wheat to drought stress and its mitigation \n\n\n\napproaches. Acta Physiologiae Plantarum, 40 (4), \n\n\n\nhttps://doi.org/10.1007/s11738-018-2651-6 \n\n\n\nAhmed, H.G.M.D., Sajjad, M., Li, M., Azmat, M.A., Rizwan, M., Maqsood, R.H., \n\n\n\nKhan, S.H., 2019. Selection criteria for drought-tolerant bread wheat \n\n\n\ngenotypes at seedling stage. Sustainability, 11, Pp. 2584. \n\n\n\nAina, P.O., Fapohunda, H.O., 1986. Root distribution and water uptake \n\n\n\npatterns of maize cultivars field-grown under differential irrigation. \n\n\n\nPlant and Soil, 94 (2), Pp. 257\u2013265. \n\n\n\nAli, M., Gul, A., Hasan, H., Gul, S., Fareed, A., Nadeem, M., Siddique, R., Jan, \n\n\n\nS.U., Jamil, M., 2020. Cellular mechanisms of drought tolerance in wheat. \n\n\n\nClimate change and food security with emphasis on wheat, Pp. 155\u2013167. \n\n\n\nhttps://doi.org/10.1016/b978-0-12-819527-7.00009-1 \n\n\n\nAlmeselmani, B.M., Saud, A., Zubi, K.A., Hareri, F., Naaesan, M., Ammar, \n\n\n\nM.A., Kanbar, O.Z., 2012. Physiological performance of different durum \n\n\n\nwheat varieties grown under rainfed condition. Global Journal of Science \n\n\n\nFrontier Research Agriculture & Biology, 12 (1), Pp. 1\u201311. \n\n\n\nAlmeselmani, M., 2012. Physiological parameters for evaluating drought \n\n\n\ntolerance in durum wheat varieties grown in the fields in Syria. Journal \n\n\n\nof Biology and Today\u2019s World, 1 (2), Pp. 53\u201363. \n\n\n\nhttps://doi.org/10.15412/j.jbtw.01010205 \n\n\n\nAraus, J.L., Slafer, G.A., Royo, C., Serret, M.D., 2008. Breeding for yield \n\n\n\npotential and stress adaptation in cereals. Critical Reviews in Plant \n\n\n\nSciences, 27, Pp. 377\u2013412. doi:10.1080/07352680802467736 \n\n\n\nAshraf, M., Fooled, M.R., 2007. Roles of glycine betaine and proline in \n\n\n\nimproving plant abiotic stress resistance. Environmental and \n\n\n\nExperimental Botany, 59, Pp. 206\u2013216. \n\n\n\ndoi:10.1016/j.envexpbot.2005.12.006  \n\n\n\nAshraf, M., Harris, P.J.C., 2013. Photosynthesis under stressful \n\n\n\nenvironments: an overview. Photosynthetica, 51 (2), Pp. 163\u2013190. \n\n\n\nAzooz, M., Youssef, M., 2010. Evaluation of heat shock and salicylic acid \n\n\n\ntreatments as inducers of drought stress tolerance in hassawi wheat. \n\n\n\nAmerican Journal of Plant Physiology, 5. \n\n\n\nhttps://doi.org/10.3923/ajpp.2010.56.70 \n\n\n\nBarbeta, A., Mej\\\u2019\\ia-Chang, M., Ogaya, R., Voltas, J., Dawson, T.E., Pe\u00f1uelas, \n\n\n\nJ., 2015. The combined effects of a long-term experimental drought and \n\n\n\nan extreme drought on the use of plant-water sources in a \n\n\n\nMediterranean forest. Global Change Biology, 21 (3), Pp. 1213\u20131225. \n\n\n\nBassi, F., Sanchez-Garcia, M., 2017. Adaptation and stability analysis of \n\n\n\nICARDA durum wheat elites across 18 countries. Crop Sci, 57, Pp. 2419\u2013\n\n\n\n2430. \n\n\n\nBawa, K.S., Shrestha, U.B., Gautam, S.P., 2014. Widespread climate change \n\n\n\nin the himalayas and associated changes in local ecosystems. PLoS ONE, \n\n\n\n7, Pp. e36741. \n\n\n\nBayoumi, T., 2009. Agronomical traits and biochemical genetic markers \n\n\n\nassociated with salt tolerance in wheat cultivars (triticum aestivuml). \n\n\n\nIn6th International Plant Breeding Conference, Ismailia, Egypt May 3-5. \n\n\n\nBeck, E.H., Fettig, S., Knake, C., Hartig, K., Bhattarai, T., 2007. Specific and \n\n\n\nunspecific responses of plants to cold and drought stress. J. Biosci., 32, \n\n\n\nPp. 501\u2013510. \n\n\n\nBidinger, F.R., Witcombe, J.R., 1989. Evaluation of specific drought \n\n\n\navoidance traits as selection criteria for improvement of drought \n\n\n\nresistance. \n\n\n\nBodner, G., Nakhforoosh, A., Kaul, H.P., 2015. Management of crop water \n\n\n\nunder drought: a review. Agronomy for Sustainable Development, 35 \n\n\n\n(2), Pp. 401\u2013442. \n\n\n\nBohnert, H.J., Nelson, D.E., Jensen, R.G., 1995. Adaptations to \n\n\n\nenvironmental stresses. Plant Cell, 7 (7), Pp. 1099\u20131111. \n\n\n\nhttps://doi.org/10.1105/tpc.7.7.1099 \n\n\n\nCaverzan, A., Casassola, A., Brammer, S.P., 2016a. Antioxidant responses of \n\n\n\nwheat plants under stress. Genetics and Molecular Biology, 39 (1), Pp. \n\n\n\n1\u20136. https://doi.org/10.1590/1678-4685-GMB-2015-0109 \n\n\n\nCaverzan, A., Casassola, A., Brammer, S.P., 2016b. Antioxidant responses of \n\n\n\nwheat plants under stress. Genetics and Molecular Biology, 39 (1), Pp. \n\n\n\n1\u20136. https://doi.org/10.1590/1678-4685-GMB-2015-0109 \n\n\n\nChen, J., Xu, W., Velten, J., Xin, Z., Stout, J., 2012.Characterization of maize \n\n\n\ninbred linesfor drought and heat tolerance. Journal of Soil and Water \n\n\n\nConservation, 67, Pp. 354\u201364.  \n\n\n\nDen\u010di\u0107, S., Kastori, R., Kobiljski, B., Duggan, B., 2000. Evaluation of grain \n\n\n\nyield and its components in wheat cultivars and landraces under near \n\n\n\noptimal and drought conditions. Euphytica, 113 (1), Pp. 43\u201352. \n\n\n\nDodd, I.C., Whalley, W.R., Ober, E.S., Parry, M.A.J., 2011. Genetic and \n\n\n\nmanagement approaches to boost UK wheat yields by ameliorating \n\n\n\nwater deficits. Journal of Experimental Botany, 62 (15), Pp. 5241\u20135248. \n\n\n\nDugas, D.V., Monaco, M.K., Olson, A., Klein, R.R., Kumari, S., Ware, D., Klein, \n\n\n\nP.E., 2011. Functional annotation of the transcriptome of Sorghum \n\n\n\nbicolor in response to osmotic stress and abscisic acid. BMC Genomics, \n\n\n\n12 (1), Pp. 514. \n\n\n\nDulai, S., Istvan, M., Pr\u00f3nay, J., Csern\u00e1k, \u00c1., Tarnai, R., Molnar-Lang, M., \n\n\n\n2005. Effects of drought on photosynthetic parameters and heat stability \n\n\n\nof PSII in wheat and in Aegilops species originating from dry habitats. \n\n\n\nActa Biol Szeged, Pp. 50. \n\n\n\nDulal, H.B., Brodnig, G., Thakur, H.K., Green-Onoriose, C., 2010. Do the Poor \n\n\n\nhave What They Need to Adapt to Climate Change? A case study of Nepal. \n\n\n\nLocal Environ, 15, Pp. 621\u2013635 \n\n\n\nEarl, H., Davis, R.F., 2003.  Effect of drought stress on leaf and whole canopy \n\n\n\nradiation use efficiency and yield of maize. Agron.  J., 95, Pp. 688\u2013696. \n\n\n\nFang, Z., Bouwkamp, J., Solomos, T., 1998. Chlorophyllase activities and \n\n\n\nchlorophyll degradation during leaf senescence in non-yellowing \n\n\n\nmutant and wild type of Phaseolus vulgaris L. Journal of Experimental \n\n\n\nBotany, Pp. 49. https://doi.org/10.1093/jexbot/49.320.503 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\nFarooq, M., Hussain, M., Ul-Allah, S., Siddique, K.H., 2019. Physiological and \n\n\n\nagronomic approaches for improving water-use efficiency in crop \n\n\n\nplants. Agricultural Water Management, 219, Pp. 95\u2013108 \n\n\n\nFarooq, M., Hussain, M., Siddique, K.H.M., 2014. Drought stress in wheat \n\n\n\nduring flowering and grain-filling periods. Critical Reviews in Plant \n\n\n\nSciences, 33 (4), Pp. 331\u2013349. \n\n\n\nFarooq, M., Irfan, M., Aziz, T., Ahmad, I., Alam, S., 2013. Seed priming with \n\n\n\nascorbic acid improves drought resistance of wheat. In Journal of \n\n\n\nAgronomy and Crop Science (Vol. 199). \n\n\n\nhttps://doi.org/10.1111/j.1439-037X.2012.00521.x \n\n\n\nFarooqi, Z.U.R., Ayub, M.A., Zia ur Rehman, M., Sohail, M.I., Usman, M., \n\n\n\nKhalid, H., Naz, K., 2020. Regulation of drought stress in plants. In Plant \n\n\n\nLife Under Changing Environment. INC. https://doi.org/10.1016/b978-\n\n\n\n0-12-818204-8.00004-7 \n\n\n\nFarshadfar, E., Farshadfar, M., Moradi, F., 2011. Screening agronomic, \n\n\n\nphysiological and metabolite indicators of drought tolerance in bread \n\n\n\nwheat (Triticum aestivum L). American Journal of Scientific Research, 38, \n\n\n\nPp. 88\u201396. \n\n\n\nFotovat, R., Valizadeh, M., Toorchi, M., 2007. Association between water-\n\n\n\nuse efficiency components and total chlorophyll content (SPAD) in \n\n\n\nwheat (Triticum aestivum L.) under well-watered and drought stress \n\n\n\nconditions. J Food Agr Env., 5. \n\n\n\nGhimire, Y.N., Shivakoti, G.P., Perret, S.R., 2010. Household-level \n\n\n\nVulnerability to Drought in Hill Agriculture of Nepal: Implications for \n\n\n\nAdaptation Planning. Int. Sustain. Dev. World Ecol., 17, Pp. 225\u2013230 \n\n\n\nHasheminasab, H., Assad, M.T., Aliakbari, A., Sahhafi, S.R., 2012. Evaluation \n\n\n\nof some physiological traits associated with improved drought tolerance \n\n\n\nin Iranian whea t. 3 (4), Pp. 1719\u20131725. \n\n\n\nHenry, A., Cal, A.J., Batoto, T.C., Torres, R.O., Serraj, R., 2012. Root attributes \n\n\n\naffecting water uptake of rice (Oryza sativa) under drought. Journal of \n\n\n\nExperimental Botany, 63 (13), Pp. 4751\u20134763. \n\n\n\nHussain, M., Waqas-Ul-Haq, M., Farooq, S., Jabran, K., Farroq, M., 2016. The \n\n\n\nimpact of seed priming and row spacing on the productivity of different \n\n\n\ncultivars of irrigated wheat under early season drought. Experimental \n\n\n\nAgriculture, 52 (3), Pp. 477\u2013490 \n\n\n\nIngram, J., Bartels, D., 1996. The molecular basis of dehydration \n\n\n\ntolerancein plants, Annu. Rev. Plant Phys. Plant Mol. Biol., 47, Pp. 377\u2013\n\n\n\n403. \n\n\n\nIPCC. 2013.Climate change. The physical science basis. Working group-I, \n\n\n\ncontribution to the fifth assessment report of the intergovernmental \n\n\n\npanel on climate change. Pp. 1\u20131535 (Cambridge University Press \n\n\n\n2013).  \n\n\n\nJoshi, G.R., 2018. Agricultural economy of nepal: development challenges \n\n\n\nand opportunities; sustainable research and development centre: \n\n\n\nKathmandu, Nepal.  \n\n\n\nKabiri, R., Nasibi, F., Farahbakhsh, H., 2014. Effect of exogenous salicylic \n\n\n\nacid on some physiological parameters and alleviation of drought stress \n\n\n\nin Nigella sativa plant under hydroponic culture. Plant Protection \n\n\n\nScience, 50, Pp. 43\u201351. https://doi.org/10.17221/56/2012-PPS \n\n\n\nKhan, S., Anwar, S., Yu, S., Sun, M., Yang, Z., Gao, Z.Q., 2019. Development of \n\n\n\ndrought-tolerant transgenic wheat: achievements and limitations. \n\n\n\nInternational Journal of Molecular Sciences, 20 (13). \n\n\n\nhttps://doi.org/10.3390/ijms20133350 \n\n\n\nKingston-Smith, A.H., Foyer, C.H., 2000. Bundle sheath proteins are more \n\n\n\nsensitive to oxidative damage than those of the mesophyll in maize \n\n\n\nleaves exposed to paraquat or low temperatures. Journal of \n\n\n\nExperimental Botany, 51 (342), Pp. 123\u2013130. \n\n\n\nLarson, K., Eastin, J., Sullivan, C., 1971. Techniques for measuring plant \n\n\n\ndrought stress. https://doi.org/10.2135/cssaspecpub2.c1 \n\n\n\nLevitt, J., 1980. Responses of plants to environmental stresses. Volume II. \n\n\n\nWater, radiation, salt, and other stresses. (Issue Ed. 2). Academic Press. \n\n\n\nLiu, B., Asseng, S., M\u00fcller, C., Ewert, F., Elliott, J., Lobell, D.B., Martre, P., \n\n\n\nRuane, A.C., Wallach, D., Jones, J.W., 2016. Similar estimates of \n\n\n\ntemperature impacts on global wheat yield by three independent \n\n\n\nmethods. Nature Climate Change, 6 (12), Pp. 1130\u20131136. \n\n\n\nMalone, J.H., Oliver, B., 2011. Microarrays, deep sequencing and the true \n\n\n\nmeasure of the transcriptome. BMC Biology, 9 (1), Pp. 34. \n\n\n\nMaralian, H., Ebadi, A., Haji, B., 2010. Influence of water deficit stress on \n\n\n\nwheat grain yield and proline accumulation rate. African Journal of \n\n\n\nAgricultural Research, 5, Pp. 286\u2013289. \n\n\n\nMartin, U., Pallardy, S., Bahari, Z., 2006. Dehydration tolerance of leaf \n\n\n\ntissues of 6 woody angiosperm species. Physiologia Plantarum, 69, Pp. \n\n\n\n182\u2013186. https://doi.org/10.1111/j.1399-3054.1987.tb01964.x \n\n\n\nMehraban, A., Miri, M., 2017. Effect of drought stress on cell membrane \n\n\n\nstability, relative water content and some characteristics of crop pants, \n\n\n\n2 (3), Pp. 85\u201390. \n\n\n\nMinistry of Education. 2010. National Adaptation Programme of Action to \n\n\n\nClimate Change; Ministry of Environment: Kathmandu, Nepal. \n\n\n\nMittler, R., 2002. Oxidative stress, antioxidants and stress tolerance. \n\n\n\nTrends in Plant Science, 7 (9), Pp. 405\u2013410. \n\n\n\nhttps://doi.org/10.1016/S1360-1385(02)02312-9 \n\n\n\nMoayedi, A.A., Nasrulhaq, B.A., Barakbah, S., 2010. The Performance of \n\n\n\nDurum and Bread Wheat Genotypes Associated with Yield and Yield \n\n\n\nComponent under Different Water Deficit Conditions. Australian Journal \n\n\n\nof Basic and Applied Sciences, 4, Pp. 106\u2013113. \n\n\n\nMujeeb-Kazi, A., Munns, R., Rasheed, A., Ogbonnaya, F.C., Ali, N., Hollington, \n\n\n\nP., Dundas, I., Saeed, N., Wang, R., Rengasamy, P., 2019. Breeding \n\n\n\nstrategies for structuring salinity tolerance in wheat. Advances in \n\n\n\nAgronomy, 155, Pp. 121\u2013187.  \n\n\n\nNagy, Z., N\u00e9meth, E., Gu\u00f3th, A., Bona, L., Wodala, B., P\u00e9csv\u00e1radi, A., 2013. \n\n\n\nMetabolic indicators of drought stress tolerance in wheat: Glutamine \n\n\n\nsynthetase isoenzymes and Rubisco. Plant Physiology and Biochemistry, \n\n\n\n67, Pp. 48\u201354. \n\n\n\nNations, U., 2020. World Food and Agriculture - Statistical Pocketbook \n\n\n\n2019. In World Food and Agriculture - Statistical Pocketbook 2019. \n\n\n\nhttps://doi.org/10.4060/ca6463en \n\n\n\nNawaz, A., Farooq, M., Cheema, S.A., Yasmeen, A., Wahid, A., 2013. Stay \n\n\n\ngreen character at grain filling ensures resistance against terminal \n\n\n\ndrought in wheat. Int. J. Agric. Biol., 15 (6), Pp. 1272\u20131276. \n\n\n\nNawaz, F., Ashraf, M., Ahmad, R., Waraich, E., Shabbir, N., 2014. Selenium \n\n\n\n(Se) Regulates seedling growth in wheat under drought stress. Advances \n\n\n\nin Chemistry, 2014. https://doi.org/10.1155/2014/143567 \n\n\n\nNemesk\u00e9ri, E., Helyes, L., 2019. Physiological responses of selected \n\n\n\nvegetable crop species to water stress. Agronomy, 9 (8), Pp. 447. \n\n\n\nNevo, E., Chen, G.X., 2010. Drought and salt tolerances in wild relatives for \n\n\n\nwheat and barley improvement. Plant, Cell & Environment, 33, Pp. 670\u2013\n\n\n\n685. doi:10.1111/j.1365-3040.2009. 02107.x  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 67-76 \n\n\n\nCite the Article: Bipin Rijal, Prakash Baduwal, Madhukar Chaudhary, Sandesh Chapagain, Sushank Khanal, Saugat Khanal, Padam Bahadur Poudel (2021).  \nDrought Stress Impacts on Wheat And Its Resistance Mechanisms.  Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. \n\n\n\nNezhadahmadi, A., Prodhan, Z.H., Faruq, G., 2013a. Drought tolerance in \n\n\n\nwheat. The Scientific World Journal. \n\n\n\nNezhadahmadi, A., Prodhan, Z.H., Faruq, G., 2013b. Drought Tolerance in \n\n\n\nWheat. The Scientific World Journal, 610721. \n\n\n\nhttps://doi.org/10.1155/2013/610721 \n\n\n\nNikolaeva, M.K., Maevskaya, S.N., Shugaev, A.G., Bukhov, N.G., 2010. Effect \n\n\n\nof drought on chlorophyll content and antioxidant enzyme activities in \n\n\n\nleaves of three wheat cultivars varying in productivity. Russian Journal \n\n\n\nof Plant Physiology, 57 (1), Pp. 87\u201395. \n\n\n\nhttps://doi.org/10.1134/S1021443710010127 \n\n\n\nNouri-Ganbalani, A., Ganbalani, G., Hassanpanah, D., 2009. Effects of \n\n\n\ndrought stress condition on the yield and yield components of advanced \n\n\n\nwheat genotypes in Ardabil, Iran. Journal of Food, Agriculture and \n\n\n\nEnvironment, Pp. 7. \n\n\n\nPandey, V., Shukla, A., 2015. Acclimation and tolerance strategies of rice \n\n\n\n(oryza sativa l.) under drought stress. rice science, Pp. 22. \n\n\n\nhttps://doi.org/10.1016/S1672-6308(14)60289-4 \n\n\n\nPaulsen, G., 2002. Application of physiology in wheat breeding. Crop \n\n\n\nScience, 42 (6). https://doi.org/10.2135/cropsci2002.2228 \n\n\n\nPenna, S., 2003. Building stress tolerance through overproducing \n\n\n\ntrehalose in transgenic plants. Trends Plant Sci., 8, Pp. 355\u2013357. \n\n\n\nRadhika, Thind, S.K., 2014. Comparative yield responses of wheat \n\n\n\ngenotypes under sowing date mediated heat stress conditions on basis \n\n\n\nof different stress indices. Indian Journal of Ecology, 41, Pp. 339\u2013343. \n\n\n\nReddy, A.R., Chaitanya, K.V., Vivekanandan, M., 2004. Drought-induced \n\n\n\nresponses of photosynthesis and antioxidant metabolism in higher \n\n\n\nplants. J. Plant Physiol., 161, Pp. 1189\u20131202.  \n\n\n\nRichards, R., Rawson, H.M., Johnson, D.A., 1986. Glaucousness in Wheat: Its \n\n\n\nDevelopment and Effect on Water-use Efficiency, Gas Exchange and \n\n\n\nPhotosynthetic Tissue Temperatures. Functional Plant Biology, 13, Pp. \n\n\n\n465\u2013473. https://doi.org/10.1071/PP9860465 \n\n\n\nRosenberg, N.J., Kimball, B.A., Mart\u00edn, P., Cooper, C.F., Waggoner, P.E., 1990. \n\n\n\nFrom climate and CO2 enrichment to evapotranspiration. \n\n\n\nSallam, A., Alqudah, A.M., Dawood, M.F.A., Baenziger, P.S., B\u00f6rner, A., 2019. \n\n\n\nDrought stress tolerance in wheat and barley: Advances in physiology, \n\n\n\nbreeding and genetics research. International Journal of Molecular \n\n\n\nSciences, 20 (13). https://doi.org/10.3390/ijms20133137 \n\n\n\nSharma, P., Jha, A.B., Dubey, R.S., Pessarakli, M., 2012. Reactive oxygen \n\n\n\nspecies, oxidative damage, and antioxidative defense mechanism in \n\n\n\nplants under stressful conditions. Journal of Botany, Pp. 217037. \n\n\n\nhttps://doi.org/10.1155/2012/217037 \n\n\n\nShavrukov, Y., Kurishbayev, A., Jatayev, S., Shvidchenko, V., Zotova, L., \n\n\n\nKoekemoer, F., De Groot, S., Soole, K., Langridge, P., 2017. Early flowering \n\n\n\nas a drought escape mechanism in plants: How can it aid wheat \n\n\n\nproduction? Frontiers in Plant Science, 8(November), Pp. 1\u20138. \n\n\n\nhttps://doi.org/10.3389/fpls.2017.01950 \n\n\n\nSiddique, M.R.B., Hamid, A., Islam, M.S., 2001. Drought stress effects on \n\n\n\nwater relations of wheat, Bot. Bull. Acad. Sinica., 41, Pp. 35\u201339. \n\n\n\nSirault, X.R.R., Condon, A.G., Wood, J.T., Farquhar, G.D., Rebetzke, G.J., 2015. \n\n\n\nRolled-upness\u201d: phenotyping leaf rolling in cereals using computer \n\n\n\nvision and functional data analysis approaches. Plant Methods, 11, Pp. \n\n\n\n52. https://doi.org/10.1186/s13007-015-0095-1 \n\n\n\nVani, S., Dwivedi, D.K., Husain, R., Gyanendra, K., Yadav, G., Kumar, G., mitra, \n\n\n\nS., Singh, O.N., Vikram, N., Khan, N.A., 2017. Physiological, morphological \n\n\n\nand molecular mechanisms for drought tolerance in rice. International \n\n\n\nJournal of Current Microbiology and Applied Sciences, 6 (7), Pp. 4160\u2013\n\n\n\n4173. https://doi.org/10.20546/ijcmas.2017.607.431 \n\n\n\nVendruscolo, E.C.G., Schuster, I., Pileggi, M., Scapim, C.A., Molinari, H.B.C., \n\n\n\nMarur, C.J., Vieira, L.G.E., 2007. Stress-induced synthesis of proline \n\n\n\nconfers tolerance to water deficit in transgenic wheat. Journal of Plant \n\n\n\nPhysiology, 164 (10), Pp. 1367\u20131376. \n\n\n\nhttps://doi.org/10.1016/j.jplph.2007.05.001 \n\n\n\nVinocur, B., Altman, A., 2005. Recent advances in engineering plant \n\n\n\ntolerance to abiotic stress: achievements and limitations. Current \n\n\n\nOpinion in Biotechnology, 16 (2), Pp. 123\u2013132. \n\n\n\nhttps://doi.org/10.1016/j.copbio.2005.02.001 \n\n\n\nWang, S.Y., Yoon, J.H., Gillies, R.R., Cho, C., 2013. What caused the winter \n\n\n\ndrought in western nepal during recent years? J. Clim., 26, Pp. 8241\u2013\n\n\n\n8256. \n\n\n\nWery, J., Silim, S.N., Knights, E.J., Malhotra, R.S., Cousin, R., 1994. Screening  \n\n\n\ntechniques   and   sources   and   tolerance   to   extremes of moisture and \n\n\n\nair temperature in cool season food legumes, Euphytica, 73, Pp. 73\u201383.  \n\n\n\nYu, T., 2017. Improved drought tolerance in wheat plants overexpressing \n\n\n\na synthetic bacterial cold shock protein gene SeCspA. Nat. Publ. Gr., 7, \n\n\n\nPp. 44050.  \n\n\n\nZhu, J.K., 2002. Salt and drought stress signal transduction in plants. \nAnnual Review of Plant Biology, 53, Pp. 247\u2013273. \nhttps://doi.org/10.1146/annurev.arplant.53.091401.143329.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 59-65 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.56.65 \n\n\n\nCite the Article: Md. Mahfujur Rahman, Mahbuba Jahan, Khandakar Shariful Islam, Saleh Mohammad Adnan, Md. Salahuddin, Ahasanul Hoque , Majharul Islam (2020).  \nEco-Friendly Management Of Rice Yellow Stem Borer, Scirpophaga Incertulus (Pyralidae: Lepidoptera) Through Reducing Risk Of Insecticides.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 59-65. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.59.65 \n\n\n\nECO-FRIENDLY MANAGEMENT OF RICE YELLOW STEM BORER, SCIRPOPHAGA \nINCERTULUS (PYRALIDAE: LEPIDOPTERA) THROUGH REDUCING RISK OF \nINSECTICIDES \n\n\n\nMd. Mahfujur Rahmana*, Mahbuba Jahana, Khandakar Shariful Islama, Saleh Mohammad Adnana, Md. Salahuddinb, Ahasanul Hoquec, \nMajharul Islamd \n\n\n\naThe Department of Entomology, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh\nbThe Department of Horticulture, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh \ncThe Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh \ndSoil Science Division, Bangladesh Institute of Nuclear Agriculture (BINA), Bangladesh \n*Corresponding Author Email: mahfuj004@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 28 February 2020\n\n\n\nA study was conducted during the period of January to July, 2012 in the Entomology Field Laboratory, \nBangladesh Agricultural University, Mymensingh to manage the Yellow Stem Borer (YSB) of rice eco-friendly \nfollowing the Randomized Complete Block Design (RCBD) with four replications using the rice variety TN1. \nTo keep in view this point, three insecticides viz. Dursban 20 EC, Convoy 25 EC, Belt 24 WG and three \nbotanical extracts viz. Neem, Tobacco, Karanja extracts were used to compare their effectiveness against \nYellow Stem Borer (YSB),Scirpophaga incertulus and also against natural enemies of Yellow Stem Borer (YSB) \nas Yellow Stem Borer (YSB),Scirpophaga incertulus causes dead heart and white head symptoms at vegetative \nand reproductive stage of rice respectively, the number of dead heart and white head symptoms were \ncounted at different time interval viz. 7, 15, 21 days after spraying (DAS) to assess the effectiveness of the \ntreatments. The chemicals and botanicals caused significant difference in their effects against Yellow Stem \nBorer (YSB). Among the chemicals Dursban 20 EC caused highest reduction in dead heart and white head \nsymptoms and in case of botanicals Neem extracts caused highest reduction in the symptoms. The chemicals \nand botanicals were also evaluated for their side effects on natural enemies of rice. The botanicals were found \nless harmful than insecticides. Natural enemies like Lady Bird Beetle and Spider were abundant in the Neem \nextract sprayed rice field after several weeks of its application. The insecticides and botanicals reduced the \ninfestation of Yellow Stem Borer (YSB), Scirpophaga incertulus and thereby significantly influenced the yield \nperformance of rice. Dursban 20 EC treated plot showed highest yield (1.80 Kg/ plot) and Neem extract \ntreated plot showed the yield 1.40 Kg/ plot. Considering the efficacy and eco-friendly nature of Neem extracts \nit could be considered as an effective botanicals in successful management of the pest Yellow Stem Borer \n(YSB), Scirpophaga incertulus of rice.  \n\n\n\nKEYWORDS \n\n\n\nRice Yellow Stem Borer, Eco-friendly, Management.\n\n\n\n1. INTRODUCTION \n\n\n\nBangladesh, one of the smallest countries (area 57 K sq. miles) in South-\n\n\n\nEast Asia, has a predominantly farming-based economy. Agricultural land \n\n\n\nper capita is decreasing over the years in Bangladesh (BBS, 2012). \n\n\n\nAgriculture and environment are closely interlinked. Agricultural \n\n\n\nproduction system depends on the environment for utilization of land, \n\n\n\nrainfall, daylight duration, insect pests and diseases. Pest problem is one \n\n\n\nof the major constraints for achieving higher production in agriculture \n\n\n\ncrops. Bangladesh loses about 30% of its crops due to pests and diseases \n\n\n\neach year (BBS, 2012). Rice (Oryza sativa L.) is an important food crop \n\n\n\nwhich supplies staple food for nearly 50% of the global population (Fao, \n\n\n\n2011; Garris et al., 2005). Among the most cultivated cereals in the world, \n\n\n\nrice ranks as second to wheat. Stem borers (SBs) are key group of insect \n\n\n\npests of rice. Among the borers, yellow stem borer (YSB), Scircophaga \n\n\n\nincertulasis distributed throughout Indian sub-continent and is regarded \n\n\n\nas the most dominating and destructive pest species (Mahar et al., 1985). \n\n\n\nSevere infestation by YSB often results in complete crop failure \n\n\n\n(Kushwaha, 1995). Yellow stem borer S. incertulas usually comprised \n\n\n\nmore  than  90%  of  the borer  population  in  rice field, particularly in \n\n\n\nBangladesh. Farmers in Bangladesh depend on synthetic insecticides \n\n\n\nbecause they are readily available, highly promoted, inexpensive, easy to \n\n\n\napply and quick acting. However, applied insecticides also kill non-target \n\n\n\narthropods, typically insects involved in pollination and predators such as \n\n\n\nspiders and ground beetles. Insecticide residues find their way into water \n\n\n\nresources, particularly in rice cultivation, and affect the water we drink \n\n\n\nand food we eat (Cork and Krishnaiah, 2000; Cork et al., 2001). \n\n\n\nFurthermore, quite often the indiscriminate and unscientific use of \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 59-65 \n\n\n\nCite the Article: Md. Mahfujur Rahman, Mahbuba Jahan, Khandakar Shariful Islam, Saleh Mohammad Adnan, Md. Salahuddin, Ahasanul Hoque , Majharul Islam (2020).  \nEco-Friendly Management Of Rice Yellow Stem Borer, Scirpophaga Incertulus (Pyralidae: Lepidoptera) Through Reducing Risk Of Insecticides.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 59-65. \n\n\n\npesticides has led to many problems, such as pests developing resistance, \n\n\n\nresurgence of once minor pest into a major problem besides \n\n\n\nenvironmental and food safety hazards. In such a back drop bio-pesticides \n\n\n\nare reported to be safer to human health imparting no ecological toxicity \n\n\n\n(Ketkar, 1976). Though the efficacy of neem derivatives and a few other \n\n\n\nbio-pesticides on YSB incidence have been tested elsewhere, it has \n\n\n\nresulted only in a variable range of success (Ganguli and Ganguli, 1998). \n\n\n\nThe neem seed kernel extract (NSKE) is known to suppress the feeding, \n\n\n\ngrowth and reproduction of insects due to its biochemicals (Natarajan and \n\n\n\nSundaramurthy, 1990). Neem products can be recommended for many \n\n\n\nprogrammes on integrated pest management (Juan et al., 2000; Calvo and \n\n\n\nMolina, 2003). Vitexnegundo L. (Verbenaceae) has shown a promising \n\n\n\npesticidal activity against insects and is widely used for its pest control \n\n\n\nproperties (Hern\u2019Andez et al., 1999). Miranpuri and Kacharourian have \n\n\n\nalso reported the efficacy of some bio-pesticides for pest suppression \n\n\n\n(Miranpuri and Kachatourian, 1993). In this consideration efficacy of \n\n\n\ndifferent pesticide formulation on the YSB incidence in diverse Agro-\n\n\n\necological zone is needed to be explored (Kushwaha, 1995). In view of this \n\n\n\nand to evaluate the relative efficacy of 11 selected insecticide formulations \n\n\n\nagainst YSB incidence, a study was undertaken for three consecutive years \n\n\n\n(2007-2009) where no such experiment even of preliminary in nature was \n\n\n\ncarried out earlier. Considering the above facts the present research work \n\n\n\nis designed to manage the yellow stem borer eco-friendly by using bio-\n\n\n\npesticides.  \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nFor conducting this present research work, methods and procedures were \n\n\n\nfollowed that are described under the following the sub-heads: \n\n\n\n2.1 Location and Time of the Study \n\n\n\nThe experiment of the research was conducted in the Entomology Field \n\n\n\nLaboratory of Entomology Department, Bangladesh Agricultural \n\n\n\nUniversity, Mymensingh. The period of the study was from 10th January to \n\n\n\n17th July, 2012.  \n\n\n\n2.2 Characteristics of Soil \n\n\n\nThe soil of the experimental area was silty loam belonging to the Old \n\n\n\nBrahmaputra Floodplain Alluvial Tract under the Agro Ecological Zone 9 \n\n\n\n(FAO, 2011). The selected site was a well-drained medium high land \n\n\n\nhaving soil pH 6.8. The nutrient status of the soil under the experimental \n\n\n\nplot at depth of 0-30 cm was analyzed at the Humboldt Soil Testing \n\n\n\nLaboratory, Department of Soil Science, Bangladesh Agricultural \n\n\n\nUniversity, Mymensingh.  \n\n\n\n2.2.1 Weather \n\n\n\nThe experimental area was characterized by tropical rainfall during the \n\n\n\nmonth of March to June and scattered rainfall during the rest of the year. \n\n\n\nMonthly minimum and maximum temperature, relative humidity, total \n\n\n\nrainfall and total sunshine were recorded during the period of the present \n\n\n\nstudy (Appendix I). \n\n\n\n2.2.2 Planting Material \n\n\n\nFor testing the effectiveness of botanical extracts and chemical pesticides \n\n\n\nin the experimental plots for controlling yellow stem borer (YSB), TN1 rice \n\n\n\nvariety were used. After transplanting seedlings, recommended \n\n\n\nagronomic practices and fertilizer dose were applied. \n\n\n\n2.3 Treatments \n\n\n\nTable 1: List of chemicals and botanicals with doses \n\n\n\nTreatments  Doses \n\n\n\nT1- Dursban 20EC 4ml/litre of water \n\n\n\nT2 -Convoy 25 EC 3ml/litre of water \n\n\n\nT3 \u2013 Belt 24WG 500g/hectare \n\n\n\nT4-  Neem Extracts 15ml/ L \n\n\n\nT5 \u2013 Tobacco Extracts 15ml/ L \n\n\n\nT6 \u2013 Karanja Extracts 15ml/ L \n\n\n\nT7 \u2013 Control (untreated)  ---------------- \n\n\n\nThe test insecticides were applied thrice, first at the tillering stage and the \n\n\n\nsecond at the  panicle  initiation stage.  At each application, plants were \n\n\n\nsprayed to run-off point. Dead heart counts were taken 35 days after \n\n\n\ntransplanting by counting the number of tillers showing dead heart in ten \n\n\n\nalternate stands taken diagonally in each plot.  The total numbers of tillers \n\n\n\nin the same ten stands were also counted, a method used. White head \n\n\n\ncounts were taken 60 days after transplanting from ten alternate stands, \n\n\n\nwhich taken diagonally in the plots. The total numbers of productive tillers \n\n\n\nin the same ten stands were counted. The percentage dead hearts and \n\n\n\nwhite heads were computed by using formula (Abbott, 1925). \n\n\n\n2.4 Design of the Field Experiment \n\n\n\nIn field, the above 5 treatments were laid out in a Randomized Complete \n\n\n\nBlock Design (RCBD) with 4 replications arranged in field plots. Thus, \n\n\n\nthere were 20 (5\u00d74) unit plots altogether in the experiment. Distance \n\n\n\nbetween replication to replication was 0.60 m. Border between the plots \n\n\n\nwas 0.60 m to facilitate different intercultural operations (Figure 5).  \n\n\n\n2.5 Collection of Test Insecticides \n\n\n\n2.5.1 Dursban 20EC  \n\n\n\nCommon name: Chlorpyrifos. Properties: Chlorpyrifos is a broad-\n\n\n\nspectrum organophosphate insecticide. It is used as an insecticide on \n\n\n\ngrain, cotton, field, fruit, nut and vegetable crops, and as well as on lawns \n\n\n\nand ornamental plants. It is a systemic and contact insecticide. \n\n\n\nChlorpyrifos acts on pests primarily as a contact poison, with some action \n\n\n\nas a stomach poison. It is available as granules, wet table powder, dust and \n\n\n\nemulsifiable concentrate. It inhibits an enzyme of the nervous system \n\n\n\n(acetylcholine esterase). This causes convulsions and paralysis. \n\n\n\n2.5.2 Convoy 25 EC \n\n\n\nCommon name: Quinalphos \n\n\n\nProperties: Quinalphos effectively controls caterpillars on fruit trees, \n\n\n\ncotton, vegetables and peanuts; scale insect on fruit trees and pest \n\n\n\ncomplex on rice. Quinalphos also controls aphids, bollworms, borers, \n\n\n\nleafhoppers, mites, thrips, etc. on vines, ornamentals, potatoes, soya beans, \n\n\n\ntea, coffee, cocoa, and other crops. \n\n\n\n2.5.3 Belt 24 WG \n\n\n\nCommon name: Flubendiamide. Properties: Insecticide for the control of \n\n\n\nlepidopteran larvae in tomato, pepper greenhouse. The flubendiamide \n\n\n\nbelongs to a new chemical class of phthalic diamides and has a new mode \n\n\n\nof action at the biochemical level without showing cross resistance with \n\n\n\nany of the known groups of insecticides. Actsby activating receptors \n\n\n\nryanodine (ryanodine receptor modulator) thus preventing the operation \n\n\n\nof the muscular system, paralysis and death of insects.  \n\n\n\n2.6 Plant Extracts Preparation  \n\n\n\n2.6.1 Neem (Azadirachtaindica) extract \n\n\n\nLeaves and small branches of neem (5 kg) were cut into small pieces and \n\n\n\nmixed with 10 liter water. The water was boiled for 30-50 minutes. The \n\n\n\nsolution was kept to become cool for about 2 hours then filtered. \n\n\n\n2.6.2 Tobacco (Nicotianatabacum) extract \n\n\n\nThe tobacco leaf (3kg) was purchased from shop and mixed with 8 liters \n\n\n\nof water, which was boiled for 30-50 minutes, the solution was allowed to \n\n\n\ncool for about 2 hours then filtered. \n\n\n\n2.6.3 Karanja (Pongamiaglabra) extract  \n\n\n\nLeaves and small branches of Karanja (5 kg) were cut into small pieces and \n\n\n\nmixed with 10 liter water. The water was boiled for 30-50 minutes. The \n\n\n\nsolution was kept to become cool for about 2 hours then filtered. \n\n\n\n2.7 Methodology for Testing Botanicals and Chemical Insecticide \n\n\n\n2.7.1 Insecticides effectiveness of three selected insecticides in \n\n\n\ncontrolling yellow stem borer  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 59-65 \n\n\n\nCite the Article: Md. Mahfujur Rahman, Mahbuba Jahan, Khandakar Shariful Islam, Saleh Mohammad Adnan, Md. Salahuddin, Ahasanul Hoque , Majharul Islam (2020).  \nEco-Friendly Management Of Rice Yellow Stem Borer, Scirpophaga Incertulus (Pyralidae: Lepidoptera) Through Reducing Risk Of Insecticides.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 59-65. \n\n\n\nTest Insecticides were sprayed to control the yellow stem borer. The \n\n\n\neffectiveness of the insecticides on the yellow stem borer population was \n\n\n\nrecorded. The experiment was designed in a Randomized Complete Block \n\n\n\nDesign in the standing rice plant and was replicated 3 times. Each \n\n\n\ninsecticide was tested with a single dose and efficiency of the dose on \n\n\n\nyellow stem borer was compared. The spraying of insecticide doses was \n\n\n\ndone in March 2012 at 35 days after transplanting for dead heart counting \n\n\n\nand 60 days after transplanting for white head counting with the help of a \n\n\n\nhand-operated sprayer it was sprayed. Care was taken to avoid spray drift \n\n\n\non adjacent plots. The spraying was done in such a way that the spray \n\n\n\ndroplet did not coalesce and drain down in the soils and whole plant was \n\n\n\nthoroughly covered by spray material. After spraying each insecticide with \n\n\n\ndesigned dose the sprayer was washed and cleaned properly. Before, each \n\n\n\napplication, the sprayer was calibrated in order to use the right dose on \n\n\n\nthe plants without wastage of insecticides by determining the quantity of \n\n\n\nwater required for each plot. The control plots were not sprayed with \n\n\n\nanything. \n\n\n\n2.7.2 Efficacy of three selected botanical extracts in controlling yellow \n\n\n\nstem borer of rice   \n\n\n\nThe efficacy of three botanical extracts viz., neem extract, tobacco extract \n\n\n\nand karanja extract, each having single dose along with control was tested \n\n\n\nagainst yellow stem borer, Scirpophaga incertulus on standing rice plant at \n\n\n\nthe place of Bangladesh Agricultural University Campus, Mymensingh. The \n\n\n\ntrial was conducted in a Randomized Complete Block Design and was \n\n\n\nreplicated 3 times. Each botanical extract was tested at the dose of 15 ml/L \n\n\n\nand efficacy of the doses on yellow stem borer was compared. The \n\n\n\nspraying of botanical extracts was done in March 2012 at 35 days after \n\n\n\ntransplanting for dead heart counting and 60 days after transplanting for \n\n\n\nwhite head counting with the help of a hand-operated sprayer. Care was \n\n\n\ntaken to avoid spray drift on adjacent plants. The spraying was done in \n\n\n\nsuch a way that the spray droplet did not coalesce and drain down in the \n\n\n\nsoils and whole plot was thoroughly covered by spray material. After \n\n\n\nspraying each botanical extract with designed dose the sprayer was \n\n\n\nwashed and cleaned properly. Before, each application, the sprayer was \n\n\n\ncalibrated in order to use the right dose on the plants without wastage of \n\n\n\nbotanical extracts by determining the quantity of water required for rice \n\n\n\nplant. The control plots were not sprayed with anything. Pre-treatment  \n\n\n\ndata were recorded one day before application of botanical extract. For  \n\n\n\nrecording the data 10 hills were randomly selected from the plot for \n\n\n\nrespective botanical extracts treatment and 10 hills were observed from \n\n\n\neach plot. The data on the damage symptoms either dead heart or white \n\n\n\nhead per 10 hills were recorded after 7, 15, 21 days of spraying of chemical \n\n\n\nand botanical extracts. The presence of natural enemies was also observed \n\n\n\nat the time of recording the extent of damage. Yield of the treated plots \n\n\n\nwere recorded and compared for their difference. The data were analyzed \n\n\n\nstatistically and the mean values were separated using DMRT. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nExperiments were conducted in developing controlling methods for rice \n\n\n\nyellow stem borer, Scirpophagaincertulus under field condition. Efficacy of \n\n\n\ninsecticides as well as botanicals was evaluated against yellow stem borer, \n\n\n\nS. incertulus. The findings have been presented and discussed under the \n\n\n\nfollowing sub-heads. Pre-treatment data were recorded before the \n\n\n\napplication of chemical insecticides and botanicals. The availability of rice \n\n\n\nyellow stem borer, extent of damage caused by pest and effectiveness of \n\n\n\ntreatments to control the target insect was evaluated by counting the pre-\n\n\n\ntreatment dead heart and white head symptoms. \n\n\n\n3.1 Effect of Insecticides on Infestation of Rice Yellow Stem Borer \n\n\n\nafter Different Days after Spraying \n\n\n\n3.1.1 Data on dead heart and white head symptoms before and after \n\n\n\napplications of botanicals and chemical insecticides \n\n\n\nPre-treatment data for dead hearts and white heads revealed that all the \n\n\n\nplots of respective treatments were not significant (NS). The number of \n\n\n\ndead hearts observed in plots of dursban 20 EC, convoy 25 EC and belt 24 \n\n\n\nWG was1.74 (Table 2). The number of dead hearts observed in plots of \n\n\n\nneem extracts, tobacco extracts and karanja extract was 1.75 and (Table \n\n\n\n2). And that for the control was 1.73. The analysis of the data regarding \n\n\n\npretreatment effect for dead hearts revealed that all the plots of respective \n\n\n\ntreatments were not significant before treatment. After obtaining of pre-\n\n\n\ntreatment data plants which showed both symptoms were removed from \n\n\n\nthe plot and then the data of post-treatment were collected. \n\n\n\n\u25cf Means in a column followed by same letter(s) are not \n\n\n\nsignificantly different. \n\n\n\n\u25cf ** indicates significance at 1% level, * indicates 5% level of \n\n\n\nsignificance. \n\n\n\n\u25cf NS= non-significant \n\n\n\n3.1.2 Effect of Botanical Extracts and Chemical Insecticides on Dead \n\n\n\nheart Symptom of Rice after 7 DAS \n\n\n\nThe number of dead hearts was significantly influenced by the application \n\n\n\nof botanical extracts and insecticides after 7 days after spraying (Table 2). \n\n\n\nThe maximum dead heart symptom was observed in case of control (1.87) \n\n\n\nwhich was followed by tobacco extract and karanja extract application, \n\n\n\nwhereas the minimum was observed in case of dursban 20 EC (1.38) which \n\n\n\nwas followed by neem extract and Belt 24 WG application (Table 2). The \n\n\n\nmaximum reduction percent of dead heart was observed in dursban 20 EC \n\n\n\n(20.68%) which was followed by neem extract (18.85%) and belt 24 WG \n\n\n\n(12.64%) (Table-3). A similar result was found, neem extract showed a \n\n\n\nreduction of 15.59%, which was statistically similar with dursban 25 EC \n\n\n\n(Panda et al., 2004). The botanical Tobacco extract reduced only 2.28%, \n\n\n\nwhich was statistically similar with karanja extract 3.84%. In case of \n\n\n\ncontrol the dead heart percent was increased by 8.09%.  \n\n\n\n3.1.3 Effect of Botanical Extracts and Chemical Insecticides on Dead \n\n\n\nHeart Symptoms of Rice after 15 DAS \n\n\n\nEffect of botanical extracts and insecticides on dead hearts after 15 days \n\n\n\nafter spraying was significant at 5% level (Table 2). The maximum dead \n\n\n\nheart symptom was observed in case of control (2.44) which was followed \n\n\n\nby tobacco extract and karanja extract application, whereas the minimum \n\n\n\nwas observed in case of dursban 20 EC (1.00) which was followed by neem \n\n\n\nextract and belt 24 WG (Table 2). The maximum reduction percent of dead \n\n\n\nheart was observed in dursban 20 EC (27.53%) which was followed by \n\n\n\nneem extract (26.05%) and karanja extract (18.34%) (Table-3). A similar \n\n\n\nTable 2: Effect of different botanical extracts and chemical insecticides on damage of yellow stem borer at different days after spraying. \n\n\n\nTreatment Mean number of dead heart and white head at different time intervals \nPre-treatment 7 Days after spraying 15 Days after spraying 21 Days after spraying \n\n\n\nDead heart White head Dead heart White head Dead heart White head Dead heart White head \nDursban 20 EC 1.74 3.92 1.38b 2.51b 1.00b 1.89b 0.63b 1.69b \nConvoy 25 EC 1.74 4.20 1.58ab 2.92ab 1.33a 2.98a 1.22b 2.74ab \nBelt 24 WG 1.74 3.98 1.52ab 2.89ab 1.28ab 2.86ab 1.19b 2.71ab \nNeem extract 1.75 3.67 1.42b 2.98ab 1.05b 2.40ab 0.89ab 1.74b \nTobacco extract  1.75 3.19 1.71ab 3.83a 1.57a 3.96a 1.38ab 3.70ab \nKaranja extract 1.75 3.18 1.69ab 2.80ab 1.38a 2.97a 1.30ab 3.23ab \n\n\n\nControl 1.73 3.71 1.87a 4.80a 2.44a 3.92a 2.39a 4.77a \nLSD NS NS ** ** ** ** * * \nCV (%) 7.59 12.56 11.78 11.96 4.92 10.09 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 59-65 \n\n\n\nCite the Article: Md. Mahfujur Rahman, Mahbuba Jahan, Khandakar Shariful Islam, Saleh Mohammad Adnan, Md. Salahuddin, Ahasanul Hoque , Majharul Islam (2020).  \nEco-Friendly Management Of Rice Yellow Stem Borer, Scirpophaga Incertulus (Pyralidae: Lepidoptera) Through Reducing Risk Of Insecticides.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 59-65. \n\n\n\nresult was found (Mayabini, 2004). The effect of dursban 20 EC was \n\n\n\nstatistically similar with neem extracts 26.05%. Convoy 25 EC caused \n\n\n\n15.82% reduction which was identical with karanja extract 18.34%. In \n\n\n\ncase of control the dead heart percent was increased by 30.48%.  \n\n\n\nTable 3: Effect of different chemical insecticides and botanical \n\n\n\nextracts on reduction or increase of dead heart of rice at different \n\n\n\ndays after spraying (DAS) \n\n\n\nTreatment Reduction or increase of dead heart at different \n\n\n\ntime intervals \n\n\n\n% dead heart \n\n\n\nat 7 DAS \n\n\n\n% dead heart \n\n\n\nat 15 DAS \n\n\n\n% dead heart \n\n\n\nat 21 DAS \n\n\n\nDursban 20 \n\n\n\nEC \n\n\n\n-20.68a -27.53a -37.00a \n\n\n\nConvoy 25 EC -9.19bc -15.82bc -8.27c \n\n\n\nBelt 24 WG -12.64b -15.78bc -7.03cd \n\n\n\nNeem extract -18.85a -26.05a -15.23b \n\n\n\nTobacco \n\n\n\nextract  \n\n\n\n-2.28d -8.18d -12.10b \n\n\n\nKaranja \n\n\n\nextract \n\n\n\n-3.48d -18.34b -5.79d \n\n\n\nControl  8.09bc 30.48a -2.04e \n\n\n\nLSD ** ** * \n\n\n\n\u25cf % Reduction / increase were calculated using the pretreatment \n\n\n\nmean data of dead heart. \n\n\n\n\u25cf Negative sign (-) indicate % of reduction while positive sign (+)\n\n\n\nindicate % of increase in dead heart. \n\n\n\n\u25cf DAS = Days after spraying. \n\n\n\n\u25cf Means in a column followed by same letter (s) are not \n\n\n\nsignificantly different. \n\n\n\n\u25cf ** indicates significance at 1% level, * indicates 5% level of \n\n\n\nsignificance. \n\n\n\n3.1.4 Effect of Botanical Extracts and Chemical Insecticides on dead \n\n\n\nheart symptom of rice after 21 DAS \n\n\n\nEffect of botanical extracts and insecticides on dead hearts after 21 days \n\n\n\nafter spraying was significant at 5% level (Table 2). The maximum dead \n\n\n\nheart symptom was observed in case of control (2.39) which was followed \n\n\n\nby tobacco extract and karanja extract, whereas the minimum was \n\n\n\nobserved in case of dursban 20 EC (0.63) which was followed by neem \n\n\n\nextract and belt 24 WG application (Table 2). The maximum reduction \n\n\n\npercent of dead heart was observed in dursban 20 EC (37.00%) which was \n\n\n\nfollowed by neem extract (15.23%) and tobacco extract (12.10%) (Table-\n\n\n\n3). A similar result was found by Sheng-Cheng. Belt 24 WG reduced 7.03% \n\n\n\ndead heart symptom that was statistically similar with karanja extract \n\n\n\n(5.79%). In case of control the dead heart percent was reduced by 2.04%.  \n\n\n\n3.1.5 Effect of Botanical Extracts and Chemical Insecticides on White \n\n\n\nHead Symptoms of Rice after 7 DAS \n\n\n\nThe number of white head was significantly influenced by the application \n\n\n\nof botanical extracts and insecticides after 7days after spraying (Table 2). \n\n\n\nThe maximum white head symptom was observed in case of control (4.80) \n\n\n\nwhich was followed by tobacco extract and neem extract, whereas the \n\n\n\nminimum was observed in case of dursban 20 EC (2.51) which was \n\n\n\nfollowed by karanja extract and belt 24 WG (Table-2). The maximum \n\n\n\nreduction percent of white head was observed in dursban 20 EC (35.96%) \n\n\n\nwhich was followed by convoy 25 EC (30.47%) and belt 24 WG (27.38%) \n\n\n\n(Table-4). A similar result was found (Firake et al., 2010). In case of control \n\n\n\nthe white head percent was increased by 29.38%.  \n\n\n\n3.1.6 Effect of botanical extracts and chemical insecticides on white \n\n\n\nhead symptom of rice after 15 DAS \n\n\n\nEffect of botanical extracts and chemical insecticides on white head after \n\n\n\n15 days after spraying was significant at 5% level (Table 2). The maximum \n\n\n\nwhite head symptom was observed in case of tobacco extract (3.96) which \n\n\n\nwas followed by control and karanja extract application, whereas the \n\n\n\nminimum was observed in case of dursban 20 EC (1.89) which was \n\n\n\nfollowed by neem extract and belt 24 WG (Table-2). The maximum \n\n\n\nreduction percent of dead heart was observed in dursban 20 EC (24.70%) \n\n\n\nwhich was followed by neem extract (19.46%) and control (18.33%) \n\n\n\n(Table-4). A similar result was found (Mayabini, 2004). The effect of \n\n\n\ndursban 20 EC was statistically similar with neem extracts 19.46%.  \n\n\n\nTable 4: Effect of different chemical insecticides and botanical \n\n\n\nextracts on reduction or increase of white head symptoms at different \n\n\n\ndays after spraying (DAS) \n\n\n\nTreatment Reduction or increase of white head at different \n\n\n\ntime intervals \n\n\n\n% white head \n\n\n\nat 7 DAS \n\n\n\n% white head \n\n\n\nat 15 DAS \n\n\n\n% white head \n\n\n\nat 21 DAS \n\n\n\nDursban 20 \n\n\n\nEC \n\n\n\n-35.96a -24.70a -10.58b \n\n\n\nConvoy 25 EC -30.47a 2.05d -8.05b \n\n\n\nBelt 24 WG -27.38ab -1.03d -5.24c \n\n\n\nNeem extract -18.80cd -19.46ab -27.50a \n\n\n\nTobacco \n\n\n\nextract  \n\n\n\n20.06c 3.39cd -6.50c \n\n\n\nKaranja \n\n\n\nextract \n\n\n\n-11.94d 6.07c 8.75b \n\n\n\nControl 29.38a -18.33ab 21.68a \n\n\n\nLevel of \n\n\n\nsignificance \n\n\n\n** * * \n\n\n\n\u25cf % Reduction / increase were calculated using the pretreatment \n\n\n\nmean data of dead heart. \n\n\n\n\u25cf Negative sign (-) indicate % of reduction while positive sign (+)\n\n\n\nindicate % of increase in dead heart. \n\n\n\n\u25cf DAS = Days after spraying. \n\n\n\n\u25cf ** indicates significance at 1% level, * indicates 5% level of \n\n\n\nsignificance. \n\n\n\n\u25cf Means in a column followed by same letter(s) are not \n\n\n\nsignificantly different. \n\n\n\n3.1.7 Effect of botanical extracts and chemical insecticides on white \n\n\n\nhead symptom of rice after DAS \n\n\n\nEffect of botanical extracts and chemical insecticides on white head after \n\n\n\n21 days after spraying was significant at 1% level (Table 2). The maximum \n\n\n\nwhite head symptom was observed in case of control (4.77) which was \n\n\n\nfollowed by tobacco extract and karanja extract, whereas the minimum \n\n\n\nwas observed in case of dursban 20 EC (1.69) which was followed by neem \n\n\n\nextract and belt 24 WG (Table-2). The maximum reduction percent of \n\n\n\nwhite head was observed in neem extract (27.50%) which was followed \n\n\n\nby dursban 20 EC (10.58%) and convoy 25 EC (8.05%) (Table- 4). Similar \n\n\n\nresult was found by Sheng-Cheng. In case of control the white head percent \n\n\n\nwas increased by 21.68%.  \n\n\n\n3.2 Effect of Different Botanical Extracts and Chemical Insecticides \n\n\n\non Natural Enemies of Rice Yellow Stem Borer, S. incertulus \n\n\n\n3.2.1 Effect of different botanical extracts and chemical insecticides on \n\n\n\nlady bird beetle \n\n\n\nThe data on the number of lady bird beetle with different days after \n\n\n\nspraying (DAS) were presented in Table 5. Before application of botanicals \n\n\n\nand chemicals, the number of lady bird beetle among different plots was \n\n\n\nnot significant. Due to cause of applying chemicals viz. dursban 20 EC, \n\n\n\nconvoy 25 EC and belt 24 WG, the number of lady bird beetle decreased. \n\n\n\nIn case of neem extract application the number of lady bird beetle \n\n\n\nincreased with time interval. But it decreased in case of karanja and \n\n\n\ntobacco extracts. A similar result was found (Misra and Parida, 2004; \n\n\n\nAgrios, 1988; Brouwer, 2001; Catling, 1992; Catling et al., 1984; Fernando, \n\n\n\n1964; International Congress of Entomology, 2004). After 7 days after \n\n\n\nspraying of synthetic chemicals and botanicals, there was significant \n\n\n\nvariation in the number of lady bird beetle in different treatments. The \n\n\n\nhighest number of lady bird beetle was found in case of control (4.00), \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 59-65 \n\n\n\nCite the Article: Md. Mahfujur Rahman, Mahbuba Jahan, Khandakar Shariful Islam, Saleh Mohammad Adnan, Md. Salahuddin, Ahasanul Hoque , Majharul Islam (2020).  \nEco-Friendly Management Of Rice Yellow Stem Borer, Scirpophaga Incertulus (Pyralidae: Lepidoptera) Through Reducing Risk Of Insecticides.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 59-65. \n\n\n\nwhich were followed by tobacco extract and that was lowest in case of \n\n\n\ndursban 20 EC and karanja extract (3.00) application (Ishikura, 1967; \n\n\n\nIsrael and Abraham, 1967; Judenco, 1972; Kalode, 2005; Koehler, 1971). \n\n\n\nThe variation in the number of lady bird beetle due to various treatments \n\n\n\nwas significant at 1% level of probability at 15 days after application. The \n\n\n\nhighest number of lady bird beetle was found in case of convoy 25 EC and \n\n\n\ntobacco extract (4.33) which were followed by control (3.86) and that was \n\n\n\nlowest in case of dursban 20 EC (2.25) and belt 24 WG (2.39) application \n\n\n\n(Markham et al., 1991; Matteson, 2000). Again the variation in the number \n\n\n\nof lady bird beetle due to various treatments was significant at 1% level of \n\n\n\nprobability at 21 days after spraying. The highest number of lady bird \n\n\n\nbeetle was found in case of control (4.33) which was followed by neem \n\n\n\nextract (4.26) and that was lowest in case of karanjaextrct (2.15) which \n\n\n\nwas followed by dursban 20 EC (2.96) application (Mondal, 2010; Naqvi, \n\n\n\n1973; Pathak, 1970). \n\n\n\nTable 5: Effect of botanical extracts and chemical insecticides on lady \n\n\n\nbird beetle at different days after spraying (DAS) \n\n\n\nTreatment Number of Lady Bird Beetle \n\n\n\nBefore spray 7 DAS 15 DAS 21 DAS \n\n\n\nDursban 20 EC 3.69 3.00b 2.25b 2.96c \n\n\n\nConvoy 25 EC 4.67 3.67b 4.33a 3.33b \n\n\n\nBelt 24 WG 3.75 3.25b 2.39b 3.00bc \n\n\n\nNeem extract 3.00 3.45b 3.45ab 4.26a \n\n\n\nTobacco \n\n\n\nextract \n\n\n\n4.03 3.96ab 4.33a 3.25b \n\n\n\nKaranja \n\n\n\nextract \n\n\n\n3.50 3.00b 2.85b 2.15d \n\n\n\nControl 4.0 4.0a 3.86ab 4.33a \n\n\n\nLevel of \n\n\n\nsignificance \n\n\n\nNS ** ** ** \n\n\n\n\u25cf Means in a column followed by same letter(s) are not \n\n\n\nsignificantly different \n\n\n\n\u25cf ** indicates Significance at 1% level, * indicates 5% level of \n\n\n\nsignificance. \n\n\n\n\u25cf NS= Non-significant \n\n\n\n3.2.2 Effect of different botanical extracts and chemical insecticides \n\n\n\non spider \n\n\n\nThe data on the number of spider with different days after spraying (DAS) \n\n\n\nwere presented in Table 6. Before application of botanicals and chemicals, \n\n\n\nthe number of spider among different plots was on-significantly \n\n\n\ndifferentiated (Pedigo, 1991). After 7 days after spraying of chemicals and \n\n\n\nbotanicals, there was significant variation in the number of Spider due to \n\n\n\nvarious treatments. The highest number of spider was found in case of \n\n\n\ncontrol (4.67) which was followed by neem extract (4.10) and that was \n\n\n\nlowest in case of dursban 20 EC (1.33) and karanja extract (2.86) \n\n\n\napplication (Qunson, 2011; Ranasinghe, 1992). The variation in the \n\n\n\nnumber of spider due to various treatments was significant at 1% level of \n\n\n\nprobability at 15 days after application. The highest number of spider was \n\n\n\nfound in case of control (4.52) which was followed by neem extract (3.94) \n\n\n\nand that was lowest in case of dursban 20 EC (1.33) and karanja extract \n\n\n\n(2.17) application. Again the variation in the number of spider due to \n\n\n\nvarious treatments was significant at 1% level of probability at 21 days \n\n\n\nafter spraying (Salim et al., 2003). The highest number of Spider was found \n\n\n\nin case of control (4.52) which was followed by neem extract (3.50) and \n\n\n\nthat was lowest in case of dursban 20 EC (1.67) which was followed by \n\n\n\nkaranja extract (1.98) application. \n\n\n\nTable 6: Effect of botanical extracts and chemical insecticides on \n\n\n\nspider at different days after spraying (DAS) \n\n\n\nTreatment Number of Spider  \n\n\n\nBefore \n\n\n\nspray \n\n\n\n7 DAS 15 DAS 21 DAS \n\n\n\nDursban 20 EC 3.33 1.33d 1.33d 1.67d \n\n\n\nConvoy 25 EC 3.66 3.33b 3.75b 3.45b \n\n\n\nBelt 24 WG 3.25 3.00c 2.85bc 2.76c \n\n\n\nNeem extract 3.25 4.10a 3.94ab 3.50b \n\n\n\nTobacco extract 3.19 3.19b 3.75b 3.34b \n\n\n\nKaranja extract 3.12 2.86c 2.17c 1.98d \n\n\n\nControl 4.50 4.67a 4.52a 4.52a \n\n\n\nLevel of \n\n\n\nsignificance \n\n\n\nNS ** ** ** \n\n\n\n\u25cf Means in a column followed by same letter(s) are not \n\n\n\nsignificantly different \n\n\n\n\u25cf ** indicates Significance at 1% level, NS= non-significant \n\n\n\n3.3 Effect on yields by reducing the pest population  \n\n\n\nEffect on yields also observed at the end of the experiment, by reducing \n\n\n\ntest insect as yellow stem borer, S. incertulus of rice by the application of \n\n\n\nbotanical extracts and insecticides. The analysis showed significant \n\n\n\nvariation among the yield due to various treatments (Schwab, 1989). \n\n\n\nAmong the treatments dursban 20 EC showed the best result which was \n\n\n\nstatistically similar with belt 24 WG. The minimum yield was observed at \n\n\n\ncontrol (1.10) \n\n\n\nTable 7: Effects on yield of different treatments by reducing the \n\n\n\nyellow stem borer population \n\n\n\nTreatments Yield(kg) \n\n\n\nDursban 20EC 1.88a \n\n\n\nConvoy 25 EC 1.53b \n\n\n\nBelt 24WG 1.80a \n\n\n\nNeem extract 1.40bc \n\n\n\nTobacco extract  1.25c \n\n\n\nKaranja extract 1.25c \n\n\n\nControl 1.10d \n\n\n\nLevel of significance ** \n\n\n\n\u25cf Means in a column followed by same letter(s) are not \n\n\n\nsignificantly different \n\n\n\n\u25cf ** indicates Significance at 1% level, \n\n\n\n\u25cf NS= Not significant \n\n\n\n4. CONCLUSION \n\n\n\nThe experiments were conducted in the Field Laboratory, Department of \n\n\n\nEntomology, Bangladesh Agricultural University, Mymensingh during the \n\n\n\nperiod from 10th January to 17th July 2012. This experiment was conducted \n\n\n\nto find out the comparative efficacy of different botanical extracts and \n\n\n\nchemical insecticides against of yellow stem borer, S. incertulas. Three \n\n\n\nbotanical extracts viz., tobacco, neem and karanja extract at 15ml/L \n\n\n\nconcentration and three insecticides viz., dursban 20 EC @ 2g/L , convoy \n\n\n\n25 EC @ 2g/L and belt 24 WG @ 2 ml/L, and untreated control were \n\n\n\nincluded in  this field  test. The effect of those botanical extracts and \n\n\n\nchemical insecticides on natural enemies and yields performance was also \n\n\n\ndetermined.The reduction of dead heart and white head varied \n\n\n\nsignificantly with time interval due to various treatments. Cumulative \n\n\n\ntoxicity increased with the increase of time. Among the insecticides \n\n\n\ndursban 20 EC was most effective than convoy 25 EC and belt 24 WG in \n\n\n\ncontrolling yellow stem borer (YSB). In case of botanicals neem extracts \n\n\n\nwas more effective than tobacco and karanja extract. Efficacy of the \n\n\n\ninsecticides was high after first application but it reduced gradually in \n\n\n\ncourse of time.  \n\n\n\nIn case of botanicals contradictory incident was happened.The effect of \n\n\n\ninsecticides and botanicals on natural enemies of yellow stem borer (YSB) \n\n\n\nwas also examined to assess the treatments whether it was eco-friendly or \n\n\n\nnot. The number of natural enemies of yellow stem borer (YSB) such as \n\n\n\nlady bird beetle and spider was varied significantly with time interval due \n\n\n\nto different treatments. In case of insecticides, the number of lady bird \n\n\n\nbeetle and spider reduced gradually due to residual effect. Among the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 59-65 \n\n\n\nCite the Article: Md. Mahfujur Rahman, Mahbuba Jahan, Khandakar Shariful Islam, Saleh Mohammad Adnan, Md. Salahuddin, Ahasanul Hoque , Majharul Islam (2020).  \nEco-Friendly Management Of Rice Yellow Stem Borer, Scirpophaga Incertulus (Pyralidae: Lepidoptera) Through Reducing Risk Of Insecticides.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 59-65. \n\n\n\nchemicals dursban 20 EC was most destructive to lady bird beetle and \n\n\n\nspider. But in the observation of botanicals application the number of lady \n\n\n\nbird beetle reduced to a short extent. Among the botanicals karanja \n\n\n\nextracts was found most effective in reducing the number. Effect on yields \n\n\n\nalso observed at the end of the experiment, by reducing test insect as \n\n\n\nyellow stem borer of rice by the application of botanical extracts and \n\n\n\ninsecticides. The analysis showed significant level of variation in yield. \n\n\n\nAmong the treatments dursban 20 EC treated plot yielded highest amount \n\n\n\nof rice. On the other hand botanical extract treated plots yielded lower \n\n\n\namount of rice than that of the insecticides treated plots due to less \n\n\n\neffectiveness of botanicals than insecticides against yellow stem borer \n\n\n\n(YSB). Though the effectiveness of the botanicals was low than chemical \n\n\n\ninsecticides, the botanicals conserve the ecosystem by not hampering the \n\n\n\nlife of natural enemies of yellow stem borer (YSB). The results of the study \n\n\n\non the effectiveness of different botanical extracts and insecticides for the \n\n\n\ncontrolling of yellow stem borer of rice, S. incertulus revealed that dursban \n\n\n\n20 EC was the best to control yellow stem borer followed by convoy 25 EC, \n\n\n\nbelt 24 WG and neem extract. Farmer may use neem based insecticide to \n\n\n\nproduce rice which will ensure better yield and the conservation of \n\n\n\nbeneficial insect in rice field ecosystem.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThanks to the Department of Entomology, Bangladesh Agricultural \n\n\n\nUniversity, Bangladesh for all experimental support. \n\n\n\nREFERENCES  \n\n\n\nAbbott, W.S. 1925. A method for computing the effectiveness of an \n\n\n\ninsecticide. J. Econ. Entomol., 18, 265-676. \n\n\n\nAgrios, G.N. 1988. Plant pathology, 3rd edition. Academic Press, INC. San \n\n\n\nDiego, New York, Berkeley, Boston, London, Sydney, Tokyo, Toronto. \n\n\n\nBBS. 2012. The yearbook of agricultural statistics of Bangladesh. Stat. Div. \n\n\n\nMinistry. Plan., Govt. Peoples Rep. Bangladesh. Dhaka, pp.123-127. \n\n\n\nBrouwer, W. 2001. Costing in economic evaluations. In Economic \n\n\n\nevaluation in health care: merging theory and practice, Oxford \n\n\n\nUniversity Press. \n\n\n\nCalvo, D., Molina, J.M. 2003. Effects of a commercial neem \n\n\n\n(Azadirachtaindica) extract on Streblotepanda larvae. Phytoparasitica, \n\n\n\n31, 365-370.  \n\n\n\nCatling, D. 1992. Rice in Deep Water, International Rice Research Institute, \n\n\n\nThe Macmillan Press Ltd. Pp. 419-426. \n\n\n\nCatling, H.D., Islam, Z., Pattrasudhi, R. 1984.  Seasonal occurrence of the \n\n\n\nyellow stem borer Scirpophagaincertulas (Walker) on deep water rice \n\n\n\nin Bangladesh and Thailand. Agriculture, Ecosystems and Environ., 12 \n\n\n\n(1), 47-71. \n\n\n\nCork, A., Alam, S.N., Das, A., Das, C.S., Ghosh, G.C., Farman, D.I., Hall, D.R., \n\n\n\nMaslen, N.R., Vedham, K., Phythiam, S.J., Rouf, F.M.A., Srinivasan, K. \n\n\n\n2001. Female sex pheromone of brinjal fruit and shoot borer, \n\n\n\nLeucinodesorbonalis blend optimization. J. Chem. Ecol., 27 (9), 1867\u2013\n\n\n\n1877. \n\n\n\nCork, A., Krishnaiah, K. 2000. Pheromones for control of yellow stem borer, \n\n\n\nScirpophagaincertulas (Walker) (Lepidoptera: Pyralidae) in India. \n\n\n\nProceedings of XXI International Congress of Entomology, Iguassu, \n\n\n\nBrazil. pp. 132-141.  \n\n\n\nFAO. 2011. Food and agricultural commodities production. Food and \n\n\n\nAgriculture Organization of the United Nations. \n\n\n\nFernando, H.E. 1964. Major insect pests of the rice plant in Ceylon, John \n\n\n\nHopkins press, Baltimore, Maryland. 575P. \n\n\n\nFirake, D.M., Rachna, P., Karnatak, A.K. 2010. Evaluation of microbial and \n\n\n\nsome chemical insecticides against yellow stem borer and leaf folder of \n\n\n\nrice. Journal of Insect Science Ludhiana. Ludhiana, India: Indian Society \n\n\n\nfor the Advancement of Insect Science, 23 (2), 150-153. \n\n\n\nGanguli, R.N., Ganguli, J. 1998. Residual toxicity of insecticides and neem-\n\n\n\nbased formulations against Chilopartellus (Swin.) infestingmaize. \n\n\n\nIndian Journal Agricultural Research, 32, 227\u201332. \n\n\n\nGarris, A.J., Tai, T.H., Coburn, J., Kresovich, S., Mccouch, S. 2005. Genetic \n\n\n\nstructure and diversity in Oryza sativa L. Genetics, 169, 1631-1638.  \n\n\n\nHern\u00b4Andez, M.M., Heraso, C., Villarreal, M.L., Arispuro, I.V., Aranda, E. \n\n\n\n1999. Biological activities of crude plant extracts from Vitextrifolia L. \n\n\n\n(Verbenaceae). Journal of Ethnopharmacology, 67, 37-44. \n\n\n\nInternational Congress of Entomology, 1521 August 2004, Brisbane, \n\n\n\nAustralia. \n\n\n\nIshikura, H. 1967. Assessment of rice loss caused by rice stem borer. pp. \n\n\n\n169-179, in Major pests of rice plant. Proc. Sym. IRRI, Philippines, 1964. \n\n\n\nJohns Hopkins Press, Baltimore,\u2019 USA. \n\n\n\nIsrael, P., Abraham. T.P. 1967. Techniques of assessing crop losses caused \n\n\n\nby rice stem borers in the tropical areas, pp. 265-275. in the major \n\n\n\ninsect pests of rice plant. Proc. Sym. IRRI, Philippines, 1964. The John \n\n\n\nHopkins Press, Baltimore, USA. \n\n\n\nJuan, A., Sans, A., Riba, M. 2000.Antifeedant activity of fruit and seed \n\n\n\nextracts of Melia azedarach and Azadirachtaindica on larvae of \n\n\n\nSesamianonagrioides. Phytoparasitica, 28, 311-319. \n\n\n\nJudenco, E. 1972. The assessment of economic losses in yield of annual \n\n\n\ncrops caused by pests and the problem of economic threshold. PANS, \n\n\n\n18, 186-191. \n\n\n\nKALODE, M.B. 2005. Insect pest of rice and their management. In: Rice in \n\n\n\nIndian Perspective (Sharma, S.D. and  Nayak, B.C. eds.), Today and \n\n\n\nTomorrow Printers and Publishers, India, Pp. 819-854. \n\n\n\nKetkar, C.M. 1976. Utilization of neem (AzadirachtaindicaJuss) and its by-\n\n\n\nproducts. Directorate of Non-edible Oils & Soap Industry, Khadi& \n\n\n\nVillage Industries Commission, Bombay, India, 234P. \n\n\n\nKoehler, A.S. 1971. Stem borer problem in West Pakistan. Paper presented \n\n\n\nat International Rice Research Conf. IRRI, Philippines. \n\n\n\nKushwaha, K.S. 1995. Chemical control of rice stem borer, \n\n\n\nScirpaphagaincertulas (Walker) and leaf folder \n\n\n\nCnaphalocrocismedinalisGuenee on Basmati. Journal of Insect Science, \n\n\n\n8 (2), 225-226. \n\n\n\nMahar, M.M., Bhatti, I.M., Dhuyo, A.R. 1985. Stem borer infestation and \n\n\n\nyield loss relationship in rice and cost benefits of control. Fifth National \n\n\n\nSeminor on Rice and Production. Kalashakaku, April 23-25. \n\n\n\nMarkham, R.H., Wright, V.F., Rios Ibarra, R.M. 1991. Selective review of \n\n\n\nresearch on Prostephanustruncatus (Coleoptera: Bostrichidae) with in \n\n\n\nan noted and up dated bibliography.  J. CEIBA., 32 (1), 3 - 90. \n\n\n\nMatteson, P.C. 2000. Insect pest management in tropical Asian Irrigated \n\n\n\nrice. Annual Review of Entomology. 45, 549-574. \n\n\n\nMayabini, J. 2004. Efficacy of new insecticides as seedling root dip \n\n\n\ntreatment against yellow stem borer in Rabi rice. Indian Journal of Plant \n\n\n\nProtection, 32 (2), 37-39. \n\n\n\nMiranpuri, G.S., Kachatourian, G.G. 1993. Role of bioinsecticides in \n\n\n\nintegrated pest management and insect resistance management. \n\n\n\nJournal of Insect Science, 6, 161\u2013172. \n\n\n\nMisra, H.P., Parida, T.K. 2004. Field screening of combination insecticides \n\n\n\nagainst rice stem borer and leaf folder. Indian Journal of Plant \n\n\n\nProtection, 32 (2), 133-135. \n\n\n\nMondal, M.H. 2010. Crop Agriculture of Bangladesh: Challenges and \n\n\n\nopportunities. Bangladesh J. Agril. Res., 35 (2), 235-245. \n\n\n\nNaqvi, K.M. 1973. Insect pest situation of rice in Sindh, West Pakistan. \n\n\n\nSeminar held on rice research and production at Rice Research Station \n\n\n\nDokri (Larkana) on 14th-15th March under the Auspices of Agriculture \n\n\n\nResearch Council, PP291. \n\n\n\nNatarajan, K., Sundaramurthy, V.T. 1990. Effect of neem oil on cotton white \n\n\n\nfly (Bemisiatabaci) Indian Journal Agriculture Science, 60 (4), 290-291. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 59-65 \n\n\n\nCite the Article: Md. Mahfujur Rahman, Mahbuba Jahan, Khandakar Shariful Islam, Saleh Mohammad Adnan, Md. Salahuddin, Ahasanul Hoque , Majharul Islam (2020).  \nEco-Friendly Management Of Rice Yellow Stem Borer, Scirpophaga Incertulus (Pyralidae: Lepidoptera) Through Reducing Risk Of Insecticides.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 4(2): 59-65. \n\n\n\nPanda, B.M., Rath, L.K., Dash, D. 2004. Effect of fipronil on yellow stem \n\n\n\nborer Scirpophagaincertulas Walker and certain plant growth \n\n\n\nparameters in rice. Indian-Journal-of Entomology, 66 (1), 17-19. \n\n\n\nPathak, M.D. 1970. Insect pest and their control in Philippines Production \n\n\n\nManual. (Revised edition), University of Philippines and International \n\n\n\nRice Research Institute, Los Banos, Philippines. \n\n\n\nPedigo, P.D. 1991. Entomology and pest management, McMillan, New \n\n\n\nYork. pp 636. \n\n\n\nQunson, K. 2011. Yellow Rice Borer. \n\n\n\nhttp://www.kingquenson.com/en/News/ news_30.html.Accesed on \n\n\n\nSeptember 27th, 2012. \n\n\n\nRanasinghe, M.A.S.K. 1992. Paddy Pests in Sri Lanka, Science education \n\n\n\nseries No.30, Natural Resources energy & Science Authority of Colombo, \n\n\n\nSri Lanka. PP. 47-59. \n\n\n\nSalim, M., Akram, M., Ehsan A., Ashraf, M. 2003. Balance Fertilization for \n\n\n\nMaximizing Economic Crop Yield. RICE. A production handbook. \n\n\n\nPakistan Agricultural Research Councal, Islambad. \n\n\n\nSchwab, A. 1989. Pestizideinsatz in Entwicklungslandern; Gefahren und \nAlternativen. PAN, PestizidAktions - Netzwerke.v. - Weikersheim, \nMargraf, 274 p.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 29-33 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2021.29.33 \n\n\n\nCite the Article: A.B. Abdus Salam, M. Ashrafuzzaman, S. Sikder, Asif Mahmud, J.C. Joardar (2021). Influence Of Municipal Solid Waste Compost On Yield Of Tomato- \nApplied Solely And In Combination With Inorganic Fertilizer Where Nitrogen Is The Only Variable Factor . Malaysian Journal of Sustainable Agriculture, 5(1): 29-33. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.01.2021.29.33 \n\n\n\nINFLUENCE OF MUNICIPAL SOLID WASTE COMPOST ON YIELD OF TOMATO- \nAPPLIED SOLELY AND IN COMBINATION WITH INORGANIC FERTILIZER WHERE \nNITROGEN IS THE ONLY VARIABLE FACTOR\n\n\n\nA.B. Abdus Salam, M. Ashrafuzzaman, S. Sikder, Asif Mahmud, J.C. Joardar*\n\n\n\nSoil, Water and Environment Discipline, Khulna University, Khulna-9208, Bangladesh. \n\n\n\n*Corresponding Author E-mail: jcjoardar@swe.ku.ac.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 24 January 2020 \nAccepted 19 March 2020 \nAvailable online 21 December 2020\n\n\n\nMunicipal solid waste compost (MSWC) is considered as one of the prominent fertilizers that improve soil \n\n\n\nhealth and productivity. To evaluate the effects of MSWC on plant growth, an experiment was conducted by \n\n\n\nusing sole MSWC and with a combination of inorganic fertilizer. The sole MSWC was applied at the rate of 0, \n\n\n\n5, 10, 15 t ha-1. In case of MSWC with inorganic fertilizer, MSWC was applied equally (5 t ha-1) and phosphorus \n\n\n\nand potassium fertilizers were applied at 100 and 50 kg ha-1, respectively. Nitrogen was the only variable \n\n\n\nnutrient. Nitrogen was applied three different doses (25, 50 and 100 kg ha-1) along with control. Tomato \n\n\n\n(Solanum lycopersicum L.) was grown as experimental plant and maximum yield (72.7\u00b16.3 t ha-1) of tomato \n\n\n\nwas found when sole MSWC was applied at 15 t ha-1 and was significantly higher than other treatments. When \n\n\n\nMSWC was applied in combination with inorganic fertilizer, the combination 5 t ha-1 MSWC +100 kg N ha-1 \n\n\n\n+100 kg TSP ha-1 +50 kg MoP ha-1 produced maximum yield (79.0\u00b13.2 t ha-1). So, application of sole MSWC in \n\n\n\nsoil enhanced the productivity of soil and side by side, MSWC in combination with inorganic fertilizer reduced \n\n\n\nthe volume of MSWC application.  \n\n\n\nKEYWORDS \n\n\n\nGarbage composting, Organic fertilizer, Chemical fertilizer, Yield, Management.\n\n\n\n1. INTRODUCTION \n\n\n\nManagement of the growing amount of MSW, produced with the \n\n\n\ndevelopment of urbanization and industrialization, is a challenging \n\n\n\nproblem throughout the world. Improper management and disposal of the \n\n\n\nwaste led to pollution and serious health hazards that can be considered \n\n\n\nas a threat to the ecosystem and environmental sustainability (Adani et al., \n\n\n\n2000; Sharma and Shah, 2005; Srivastava et al., 2016). Current global \n\n\n\naverage generation of MSW is approximately 1.2 kg per person per day \n\n\n\nwhich will reach to 1.42 kg per person per day by 2025, reaching 2.2 billion \n\n\n\nton of waste per year, includes both domestic and commercial waste \n\n\n\n(White et al., 1995; Hoornweg and Bhada-Tata, 2012). \n\n\n\nMSW is largely incinerated or openly landfilled but the major fraction (> \n\n\n\n50%) of MSW constitutes organic wastes which is biodegradable under \n\n\n\nanaerobic conditions. Thus, the application of MSW in soil has become a \n\n\n\ngood choice for waste management and recycling (Barlaz et al., 2010; Lee \n\n\n\net al., 2018). Composting of MSW is gaining popularity to apply them as \n\n\n\nsoil amendments which represents an acceptable solution of landfill \n\n\n\ndisposal, volume reduction of MSW and at the same time increasing its \n\n\n\nvalue as it contributes soil organic matter restoration (Hargreaves et al., \n\n\n\n2008; Zhao et al., 2012). \n\n\n\nComposting is a widely accepted way of stabilizing solid organic wastes by \n\n\n\nbiological degradation of organic matter under aerobic conditions to \n\n\n\nproduce a safer and stable humus-like product (Baran et al., 2009; \n\n\n\nFern\u00e1ndez et al., 2014). At the time of composting of MSW, separation of \n\n\n\nthe waste into organic and inorganic fractions is the most important step \n\n\n\nto make high quality compost. Otherwise, inadequate separation of \n\n\n\nbiodegradable fractions from non-degradable materials results in high \n\n\n\nvalues of trace elements and heavy metals in compost (Barth and Kroeger, \n\n\n\n1998; Jodar et al., 2017). \n\n\n\nApplications of MSWC increase soil fertility by adding soil organic matter \n\n\n\nand plant nutrients. It also helps in enhancing water holding capacity, \n\n\n\ninfiltration, soil aeration, soil microbial response; reduce erosion; improve \n\n\n\nsoil structure (Bouzaiane et al., 2014; Weber et al., 2014; Lim et al., 2015). \n\n\n\nThus results in positive influence on plant growth (Rajaie and Tavakoly, \n\n\n\n2016). However, in some cases, the only MSWC application provide the \n\n\n\nnutrients for crops may not sufficient, hence, co-application of inorganic \n\n\n\nfertilizers with MSWC is helpful to improve soil nutrients and crops \n\n\n\nproductivity (Ramadass and Palaniyandi, 2007; Ghaly and Alkoaik, 2010; \n\n\n\nNigussie et al., 2015).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 29-33 \n\n\n\nCite the Article: A.B. Abdus Salam, M. Ashrafuzzaman, S. Sikder, Asif Mahmud, J.C. Joardar (2021). Influence Of Municipal Solid Waste Compost On Yield Of Tomato - \nApplied Solely And In Combination With Inorganic Fertilizer Where Nitrogen Is The Only Variable Factor . Malaysian Journal of Sustainable Agriculture, 5(1): 29-33. \n\n\n\nMoreover, N addition with MSWC enhanced positive effect of nutrients \n\n\n\nuptake because, the nitrogen contents in compost are generally low (0.5 to \n\n\n\n2%) and the stock of mineralized N is not enough to meet the crop \n\n\n\nrequirements for maximum yield (Rajaie and Tavakoly, 2016; Karlen et al., \n\n\n\n1995). The purpose of the study was to assess the effects of MSWC on \n\n\n\ntomato yield and to enhance the popularity of MSWC as organic fertilizer \n\n\n\nwhich will indirectly serve as a promising way for controlling the pollution \n\n\n\nof MSW by an effective and sustainable management approach. Side by \n\n\n\nside, it will be helpful to supply organic matter in soil and reduced the \n\n\n\nincreasing sole dependency on inorganic fertilizer by increasing the co-\n\n\n\napplication of MSWC and inorganic fertilizer. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Site description  \n\n\n\nThe experiment was conducted at the experimental field of Soil, Water and \n\n\n\nEnvironment Discipline, Khulna University, Bangladesh (N-22080.33\u00b4/E-\n\n\n\n89053.24\u00b4). The field was medium high land. The region is under hot humid \n\n\n\nsubtropical climate with plentiful rainfall during monsoon. \n\n\n\n2.2 Selection of experimental plant \n\n\n\nThe experimental plant was tomato (Solanum lycopersicum L.) which is an \n\n\n\nimportant commercial vegetable in Bangladesh due to its high production, \n\n\n\nconsumption and nutritional values (BARI, 2010). Tomato ranks seventh \n\n\n\nin worldwide production and in Bangladesh, among the winter vegetable \n\n\n\ncrops, tomato ranks second in respect of production and third in respect \n\n\n\nof area cultivated (FAOSTAT, 2011). Tomato covered 17037 ha of land and \n\n\n\nthe total production was approximately 131 thousand metric tons (BBS, \n\n\n\n2004, 2006). Tomato crop requires balanced fertilizer and water for \n\n\n\nobtaining economical yield. For proper growth of tomato organic and \n\n\n\ninorganic fertilizer both are needed. In this case, co-application of organic \n\n\n\ncompost and inorganic fertilizers (mainly N, P and K) play a significant role \n\n\n\nin tomato production rather than using separately (Ghaly and Alkoaik, \n\n\n\n2010; Kumar et al., 2013; Ogundare et al., 2015). Among the nutrients, N \n\n\n\nis very important for better quality and yield of tomato as it stimulates \n\n\n\nvegetative growth and flowering of tomato plant (Pascale et al., 2006; \n\n\n\nRashid et al., 2016). Different studies revealed that, the combined \n\n\n\napplication of MSWC and N fertilizer plays a significant role on the growth \n\n\n\nof tomato (Rajaie and Tavakoly, 2016; Abate et al., 2017).  \n\n\n\n2.3 Growing season  \n\n\n\nThe experiment was conducted in winter season. Tomato is usually \n\n\n\ncultivated in Bangladesh around the year but in this season tomato is \n\n\n\ncultivated intensively because this season is suitable for tomato. \n\n\n\n2.4 Collection of MSWC  \n\n\n\nTrucks of Khulna City Corporation (KCC), Bangladesh collects wastes for \n\n\n\nfinal disposal into Rajbandh landfill area which is located at a distance of \n\n\n\n9 kilometers from Khulna city center. About 70% of the total MSW in \n\n\n\nKhulna city is organic in nature and advantageous for preparation of \n\n\n\ncompost fertilizer. A popular NGO in Khulna named \u2018RUSTIC\u2019 is producing \n\n\n\nabout 30 tons MSWC per month using 46 tons (0.53%) solid waste (Roy et \n\n\n\nal., 2013). The MSWC was collected from RUSTIC (a NGO) for our \n\n\n\nexperiment.  \n\n\n\n2.5 Experimental setup and treatments combination \n\n\n\nThe experiment was carried out to assess the effects of sole MSWC and in \n\n\n\ncombination with inorganic fertilizers on tomato production. The whole \n\n\n\nexperiment was divided into two parts. In one part, MSWC was applied \n\n\n\nthree different doses (5, 10 and 15 t ha-1) along with control. In another \n\n\n\npart of the experiment, MSWC was also applied equally (5 t ha-1); P and K \n\n\n\nfertilizers were applied as recommended doses (100 and 50 kg ha-1, \n\n\n\nrespectively) to all plots at the beginning of the experiment. Nitrogen was \n\n\n\nthe only variable nutrient. Nitrogen was applied three different doses (25, \n\n\n\n50 and 100 kg ha-1) along with control. Urea, triple super phosphate (TSP) \n\n\n\nand muriate of potash (MoP) fertilizer were applied as the source of N, P \n\n\n\nand K where N is 46%, P2O5 is 52% and K is 60%. \n\n\n\n2.6 Experimental design \n\n\n\nA completely randomized design (CRD) was followed for the experiment \n\n\n\nand the plot size was 0.7m\u00d70.7m. \n\n\n\n2.7 Experimental plot preparation \n\n\n\nTotal 24 experimental plots were prepared. Traditional spade was used to \n\n\n\nplough the land. Weeds, stubble, and crop residues were removed \n\n\n\nmanually. The MSWC was mixed properly in every plot according to \n\n\n\nrequired amounts. In case of inorganic fertilizer, one-third of urea, total \n\n\n\nTSP and MoP were applied during field preparation. Another one-third of \n\n\n\nthe urea was applied at growth stage and rest of the urea was applied at \n\n\n\nflowering stage.    \n\n\n\n2.8 Tomato seedling collection \n\n\n\nSeedlings were collected from Gollamari bazar, Khulna, Bangladesh. The \n\n\n\nage of seedlings was 20 days. It was collected from the dealer in the \n\n\n\nafternoon to avoid the risk of dehydration.  \n\n\n\n2.9 Choice of varieties \n\n\n\nWe selected BARI tomato 3-variety that performs best under the local \n\n\n\nconditions. \n\n\n\n2.10 Seedling transplantation \n\n\n\nAfter collection, 20 days aged seedlings were transplanted in the \n\n\n\nexperimental field. There were 5 plants in each plot. An extensive care was \n\n\n\ntaken to keep enough moisture for the seedlings until the seedlings were \n\n\n\nstable. \n\n\n\n2.11 Irrigation  \n\n\n\nSame volume of normal tap water was used in every plot for irrigation \n\n\n\nwhen needed. \n\n\n\n2.12 Weeding and controlling pest \n\n\n\nWeeding was done regularly by manually uprooting. Early blight of tomato \n\n\n\ndisease was observed and fungicide (mancozeb group) was sprayed two \n\n\n\ntimes. \n\n\n\n2.13 Sampling and harvesting  \n\n\n\nMatured tomato fruits were collected at three different times. The fresh \n\n\n\nweight of tomato was recorded for each collection time. Tomato yield was \n\n\n\ncalculated in t ha-1 considering the area of the plot. \n\n\n\n2.14 Soil sample analysis  \n\n\n\nOrganic carbon, pH, electrical conductivity (EC) and all other nutrients \n\n\n\nwere determined into laboratory of Soil, Water and Environment \n\n\n\nDiscipline, Khulna University by following the procedures described \n\n\n\n(Imamul Huq and Alam, 2005).  \n\n\n\n2.15 Statistical analysis  \n\n\n\nStatistical analysis was done by following ANOVA technique using \n\n\n\nMINITAB 17.0 and DMRT test was applied to assess the differences \n\n\n\nbetween treatments (n=3). Graphs were prepared by using MS Excel 2010. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Soil properties \n\n\n\nAnalytical results of important soil properties are given in Table 1. Soil \n\n\n\nsample was mildly alkaline, non-saline in nature (Soil survey manual, \n\n\n\n1993). Nitrogen content was medium and OC, P, K and S contents were \n\n\n\nvery low, with regarding to the range of soil nutrients mentioned in \n\n\n\n(Imamul Huq and Alam, 2005). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 29-33 \n\n\n\nCite the Article: A.B. Abdus Salam, M. Ashrafuzzaman, S. Sikder, Asif Mahmud, J.C. Joardar (2021). Influence Of Municipal Solid Waste Compost On Yield Of Tomato - \nApplied Solely And In Combination With Inorganic Fertilizer Where Nitrogen Is The Only Variable Factor . Malaysian Journal of Sustainable Agriculture, 5(1): 29-33. \n\n\n\nTable 1: Some basic properties of soil sample \n\n\n\nSoil Properties Results \n\n\n\npH 7.44 \n\n\n\nElectric Conductivity (EC) 1.98 (dS/m) \n\n\n\nOrganic Carbon (OC) 0.56 (%) \n\n\n\nNitrogen (N) 0.25 (%) \n\n\n\nPhosphorus (P) 0.021 (%) \n\n\n\nPotassium (K) 0.11 (%) \n\n\n\nSulfur (S) 0.003 (%) \n\n\n\n3.2 Properties of MSWC \n\n\n\nThe MSWC used in this experiment was prepared by RUSTIC (a NGO) and \n\n\n\ndeclared some properties of prepared compost are presented in Table 2. \n\n\n\nThe pH of MSWC was neutral in nature (Soil survey manual, 1993) and the \n\n\n\nnutrient contents were low to medium according to the experimental \n\n\n\nresult of (NEERI, 2005, 2009).  \n\n\n\nTable 2: Some properties of MSWC \n\n\n\nProperties Results \n\n\n\nMoisture content 17.0 (%) \n\n\n\npH 7.0 \n\n\n\nOrganic Carbon 10.65 (%) \n\n\n\nNitrogen (N) 0.95 (%) \n\n\n\nPhosphorous (P) 0.70 (%) \n\n\n\nPotassium (K) 1.25 (%) \n\n\n\nSulfur (S) 0.29 (%) \n\n\n\nZink (Zn) 0.04 (%) \n\n\n\nCopper (Cu) 0.016 (%) \n\n\n\nChromium (Cr) 18.28 (mg kg-1) \n\n\n\nCadmium (Cd) 0.18 (mg kg-1) \n\n\n\nPresence of heavy metal in MSWC is a common problem because of the \n\n\n\npresence of plastics, electronic appliances, paint chips, batteries, motor \n\n\n\noils etc. (Hamdi et al., 2003). The result of heavy metal can be compared \n\n\n\nwith Spanish standard where the concentration of heavy metal in MSWC \n\n\n\nare categorized into three categories (Quality A, B and C) and quality C is \n\n\n\nconsidered as last permissive level of heavy metal (Real Decreto, 2013). \n\n\n\nThe contents of Zn and Cu in this MSWC were in B category, and Cr and Cd \n\n\n\nwere in A category. So, the heavy metal concentration in this compost, \n\n\n\nwhich is a big concern in case of MSWC application, is very low and there \n\n\n\nwas no risk of heavy metal contamination into soil as well as plant growth. \n\n\n\nSide by side, after comparing with the Indian standard threshold limit \n\n\n\nvalues of heavy metals in compost and soils also revealed that the heavy \n\n\n\nmetal content of this MSWC is under tolerance limit (Awashthi, 2000; ECN, \n\n\n\n2008). \n\n\n\n3.3 Yield of tomato \n\n\n\n3.3.1 Experiment-1 \n\n\n\nThe average fresh weight of tomato is presented in Table 3. The yield of \n\n\n\ntomato was significantly increased (p < 0.001) with increasing rate of sole \n\n\n\nMSWC application. The maximum yield (72.7\u00b16.3 t ha-1) was found where \n\n\n\n15 t ha-1 MSWC was applied whereas the minimum yield was observed in \n\n\n\ncontrol (33.6\u00b15.8 t ha-1). The results showed that the yield was \n\n\n\nsignificantly (p < 0.001) higher at the highest amount of MSWC application \n\n\n\n(15 t ha-1) but the production of tomato under control and 5 t ha-1 was \n\n\n\nstatistically same although tomato yield was significantly higher at 10 t ha-\n\n\n\n1 MSWC application as compared to control and 5 t ha-1. Similar findings \n\n\n\nwere reported on tomato plant (Rajaie and Tavakoly, 2016;  Maynard, \n\n\n\n2013). Other studied on potato, corn, wheat and poa revealed the similar \n\n\n\nresults that MSWC is an efficient source of available nutrients for \n\n\n\npromoting plant growth (Ghaly and Alkoaik, 2010; Horrocks et al., 2016; \n\n\n\nCiveira, 2010). The addition of MSWC increases the total N, OC, available \n\n\n\nP, pH, other microelements and improve soil physical status can be \n\n\n\nconsidered as the reasons of growth enhancement of plant (Amlinger et \n\n\n\nal., 2003; Bouzaiane et al., 2014; Weber et al., 2014). On the other hand, \n\n\n\napplication rate of MSWC is a very important factor because over \n\n\n\napplication results in a reduction of plant growth. Increased amount of \n\n\n\nMSWC application in lettuce and tomato inhibited plant growth and the \n\n\n\nreasons for growth inhibition were N immobilization or decreased N \n\n\n\nmineralization (Giannakis et al., 2014). \n\n\n\nTable 3: Response of MSWC with or without inorganic fertilizer on tomato \n\n\n\nyield \n\n\n\nExperiment-1 Experiment-2 \n\n\n\nTreatment \n\n\n\nTomato \n\n\n\nyield \n\n\n\n(t ha-1) \n\n\n\nTreatment \n\n\n\nTomato \n\n\n\nyield \n\n\n\n(t ha-1) \n\n\n\nControl 33.6\u00b15.8c Control 33.6\u00b15.8c \n\n\n\nMSWC \n\n\n\n5 t ha-1 40.0\u00b13.5c \n\n\n\n5 t ha-1 MSWC + \n\n\n\n100 kg TSP ha-1 + \n\n\n\n50 kg MoP ha-1 + \n\n\n\n0 kg \n\n\n\nN  \n\n\n\nha-1 \n\n\n\n39.7\u00b16.9c \n\n\n\n10 t ha-1 55.2\u00b13.1b \n\n\n\n25 \n\n\n\nkg N \n\n\n\nha-1 \n\n\n\n45.6\u00b16.8bc \n\n\n\n15 t ha-1 72.7\u00b16.3a \n\n\n\n50 \n\n\n\nkg N \n\n\n\nha-1 \n\n\n\n50.9\u00b11.0b \n\n\n\n100 \n\n\n\nkg N \n\n\n\nha-1 \n\n\n\n79.0\u00b13.2a \n\n\n\nData represent the average \u00b1 the standard deviation (n = 3) \n\n\n\n3.3.2 Experiment-2 \n\n\n\nResults of the average fresh weigh of tomato under different treatments \n\n\n\nfrom Experiment-2 where MSWC was applied in combination with \n\n\n\ninorganic fertilizer are also presented in Table 3. The maximum yield \n\n\n\n(79.0\u00b13.2 t ha-1) was observed under the combination of 100 kg N ha-1 + \n\n\n\n100 kg TSP ha-1 + 50 kg MoP ha-1 + 5 t ha-1 MSWC whereas the minimum \n\n\n\nyield was observed under control (33.6\u00b15.8 t ha-1). Statistical analysis of \n\n\n\nthe results showed that the maximum yield at treatment combination 100 \n\n\n\nkg N ha-1 + 100 kg TSP ha-1 + 50 kg MoP ha-1 + 5 t ha-1 MSWC was \n\n\n\nsignificantly (p < 0.001) higher as compared to the other combinations (50 \n\n\n\nkg N ha-1 + 100 kg TSP ha-1 + 50 kg MoP ha-1 + 5 t ha-1 MSWC; 25 kg N ha-1 + \n\n\n\n100 kg TSP ha-1 + 50 kg MoP ha-1 + 5 t ha-1 MSWC; 0 kg N ha-1 + 100 kg TSP \n\n\n\nha-1 + 50 kg MoP ha-1 + 5 t ha-1 MSWC and control). \n\n\n\nIntegrated use of MSWC and inorganic fertilizer especially N performed \n\n\n\nbest in tomato production (Ghaly and Alkoaik, 2010; Kumar et al., 2013).  \n\n\n\nRajaie and Tavakoly also reported that combined application of MSWC and \n\n\n\nN results improved growth of tomato than application of either MSWC or \n\n\n\nN fertilizer only because with N addition, the positive effect of MSWC on \n\n\n\nnutrients uptake become more prominent (Rajaie and Tavakoly, 2016). \n\n\n\nHowever, in our experiment, tomato yield significantly increased at 100 \n\n\n\nand 50 kg N ha-1. So, increased doses of N-fertilizer applications increased \n\n\n\nthe tomato yield. The total yield was not up to the maximum level this \n\n\n\nmight be due to pest attack. But the main point is that determination of \n\n\n\nproper application doses of fertilizer is the prerequisite to control the \n\n\n\nadverse effects on soil and plant growth (Hossain et al., 2017). \n\n\n\nIt was also found that the application of sole MSWC and combined \n\n\n\napplication of inorganic fertilizer (Urea, TSP, MoP) and MSWC with \n\n\n\nvariation of urea application, both the application of MSWC alone and in \n\n\n\ncombination with N-fertilizer increased tomato yield. The application of \n\n\n\nsole MSWC at 15 t ha-1 showed the maximum tomato production (72.7\u00b16.3 \n\n\n\nt ha-1) and approximately similar to (79.0\u00b13.2 t ha-1) the result obtained at \n\n\n\ntreatment combination 100 kg N ha-1 + 100 kg TSP ha-1 + 50 kg MoP ha-1 + \n\n\n\n5 t ha-1 MSWC. In some cases, mineralized N from compost is insufficient \n\n\n\nto meet the requirements for highest yield especially at the first growing \n\n\n\nseason (Eriksen et al., 1999). So, in that case, application of supplementary \n\n\n\ninorganic fertilizer is fruitful for better production (Okareh et al., 2014; \n\n\n\nScotti et al., 2016). Our experimental results revealed that MSWC itself a \n\n\n\ngood source of nutrients and potential alternatives to inorganic fertilizer \n\n\n\nbased on yield performance but the application rate is high (15 t ha-1). So, \n\n\n\nthis high amount of MSWC application could be reduced to 5 t ha-1 in \n\n\n\ncombination with inorganic fertilizer (100 kg N ha-1 + 100 kg TSP ha-1 + 50 \n\n\n\nkg MoP ha-1). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 29-33 \n\n\n\nCite the Article: A.B. Abdus Salam, M. Ashrafuzzaman, S. Sikder, Asif Mahmud, J.C. Joardar (2021). Influence Of Municipal Solid Waste Compost On Yield Of Tomato - \nApplied Solely And In Combination With Inorganic Fertilizer Where Nitrogen Is The Only Variable Factor . Malaysian Journal of Sustainable Agriculture, 5(1): 29-33. \n\n\n\n4. CONCLUSION \n\n\n\nPreparing MSWC from MSW is a very effective management practice of \n\n\n\norganic waste because the compost acts as a great nutrient source and it \n\n\n\npromotes plant growth. The results revealed that the fresh weight of \n\n\n\ntomato was significantly increased (p < 0.001) with higher rate of sole \n\n\n\nMSWC application and significantly higher at 15 t ha-1. Whereas, when \n\n\n\nMSWC was applied with inorganic fertilizer (Urea, TSP and MoP) it also \n\n\n\npromoted crop yield even in lower rate (5 t ha-1) of MSWC. Higher tomato \n\n\n\nyield was (p < 0.001) found at 5 t ha-1 MSWC + 100 kg N ha-1 + 100 kg TSP \n\n\n\nha-1 + 50 kg MoP ha-1 application. Though sole application of MSWC in soil \n\n\n\ncould improve soil fertility and crop productivity but MSWC in \n\n\n\ncombination with inorganic fertilizer can be recommended to reduce the \n\n\n\nvolume of MSWC application. But further research should be needed to \n\n\n\nfind the highest dose of MSWC and inorganic fertilizer especially N in the \n\n\n\ngrowth of tomato.  \n\n\n\nREFERENCES \n\n\n\nAbate, Z., Assefa, B., Negassa, W., 2017. Comparison of environmental \nperformance of municipal solid waste compost and chemical fertilizer. \nAm. J. Environ. Res. Econ. 2, Pp. 96\u2013101. doi: \n10.11648/j.ajere.20170203.11. \n\n\n\nAdani, F., Scatigna, L., Genevini, P., 2000. Biostabilization of mechanically \nseparated municipal solid waste fraction. Waste Manag. Res., 18, Pp. \n471\u2013477. https://doi.org/10.1177/0734242X0001800508. \n\n\n\nAmlinger, F., Gotz, B., Dreher, P., Geszti, J., Weissteiner, C., 2003. Nitrogen \nin bio waste and yard waste compost: dynamics of mobilization and \navailability\u2013a review. Eur. J. Soil Biol., 39, Pp. 107\u2013116. \n\n\n\nAwashthi, S.K., 2000. Prevention of food adulteration act no 37 of 1954, \nCentral and state rules as amended for 1999. Ashoka Law House, New \nDelhi. \n\n\n\nBaran, S., W\u00f3jcikowska-Kapusta, A., \u017bukowska, G., Bik, M., 2009. Changes \nof the phosphorus, potassium and magnesium content in compost of \ndifferent maturity. Adv. Agric. Sci. Probl. Issues., 537, Pp. 25\u201331. \n\n\n\nBARI, 2010. Krishi Projukti Hatboi, Bangladesh Agricultural Research \nInstitute, Joydevpur, Gazipur, Pp. 304. \n\n\n\nBarlaz, M.A., Staley, B.F., de los Reyes, F.L., 2010. Anaerobic biodegradation \nof solid waste, in: Mitchell, R., Gu, J., Wiley, B., Hoboken, N.J. (Eds.), \nEnviron. Microbiol. 12, Pp. 281\u2013299. \nhttps://doi.org/10.1002/9780470495117.ch12 \n\n\n\nBarth, J., Kroeger, B., 1998. Composting progress in Europe. Biocycle, 39, \nPp. 65\u201368. \n\n\n\nBBS, 2004. Statistical year book. Bangladesh bureau of statistics. Statistics \nDivision, Ministry of Planning, Govt. of the People\u2019s Republic of \nBangladesh, Pp. 135. \n\n\n\nBBS, 2006. Statistical year book. Bangladesh bureau of statistics. Statistics \nDivision, Ministry of Planning, Govt. of the People\u2019s Republic of \nBangladesh, Pp. 149.  \n\n\n\nBouzaiane, O., Jedidi, N., Hassen, A., 2014. Microbial biomass improvement \nfollowing municipal solid waste compost application in agricultural soil, \nin: Maheshwari, D.K. (Ed.), Composting for sustainable agriculture. \nSpringer, Berlin. Pp. 199\u2013208. https://doi.org/10.1007/978-3-319-\n08004-8_10. \n\n\n\nCiveira, G., 2010. Influence of municipal solid waste compost on soil \nproperties and plant reestablishment in peri-urban environments, Chil. \nJ. Agr. Res. 70, Pp. 446\u2013453. http://dx.doi.org/10.4067/S0718-\n58392010000300012. \n\n\n\nECN, 2008. European Compost Network. \nhttp://www.compostnetwork.info/?id=10. [Accessed 6 February \n2019]. \n\n\n\nEriksen, G.N., Coalea, F.J., Bollerob, G.A., 1999. Soil nitrogen dynamics and \nmaize production in municipal solid waste amended soil. Agron J., 91, \nPp. 1009\u20131016. DOI:10.2134/agronj1999.9161009x. \n\n\n\nFAOSTAT, 2011. Food and agriculture organization of the United Nations. \nhttp://faostat3.fao.org/faostat-gateway/go/to/down load/Q/QC/E. \n\n\n\nAccessed 27 January 2019. \n\n\n\nFern\u00e1ndez, J.M., L\u00f3pez-de-S\u00e1, E.G., Polo, A., Plaza, C., 2014. Short term \nstabilization of organic matter in physically, chemically, and \nbiochemically protected pools in soils amended with municipal wastes. \nClean-Soil Air Water., 42, Pp. 487\u2013493. \nhttps://doi.org/10.1002/clen.201300127. \n\n\n\nGhaly, A.E., Alkoaik, F.N., 2010. Effect of municipal solid waste compost on \nthe growth and production of vegetable crops. Am. J. Agric. Biol. Sci., 5, \nPp. 274\u2013281. \n\n\n\nGiannakis, G.V., Kourgialas, N.N., Paranychianakis, N.V., Nikolaidis, N.P., \nKalogerakis, N., 2014. Effects of municipal solid waste compost on soil \nproperties and vegetables growth. Compost Sci. Util., 22, Pp. 116\u2013131. \nhttps://doi.org/10.1080/1065657X.2014.899938. \n\n\n\nHamdi, H., Jedidi, N., Ayari, F., Yoshida, Ghrabi, M.A., 2003. Valuation of \nmunicipal solid waste compost of Tunis (Tunisia) \u2013 Agronomic aspect. \nProceedings of the 14th annual conference of the Japan society of waste \nmanagement experts, Vol. III, International Session, Pp. 62\u201364.  \n\n\n\nHargreaves, J.C., Adl, M.S., Warman, P.R., 2008. A review of the use of \ncomposted municipal solid waste in agriculture. Agric. Ecosyst. \nEnviron., 123, Pp. 1\u201314. https://doi.org/10.1016/j.agee.2007.07.004. \n\n\n\nHoornweg, D., Bhada-Tata, P., 2012. What a waste: A global review of solid \nwaste management, urban development series. World Bank, \nWashington, DC. \n\n\n\nHorrocks, A., Curtin, D., Tregurtha, C., Meenken, E., 2016. Municipal \ncompost as a nutrient source for organic crop production in New \nZealand. Agronomy, 6 (2), Pp. 35. \nhttps://doi.org/10.3390/agronomy6020035. \n\n\n\nHossain, M.Z., Niemsdorff, F.P.V.U., Heb, J., 2017. Effect of different organic \nwastes on soil properties and plant growth and yield: a review. Sci. \nAgric. Bohem., 48 (4), Pp. 224\u2013237. https://doi.org/10.1515/sab-2017-\n0030. \n\n\n\nImamul Huq, S.M., Alam, M.D., 2005. A handbook on analysis of soil, plant \nand water. BACER-DU, University of Dhaka, Bangladesh. \n\n\n\nJodar, J.R., Ramos, N., Carreira, J.A., Pacheco, R., Fern\u00e1ndez- Hern\u00e1ndez, A., \n2017. Quality assessment of compost prepared with municipal solid \nwaste. Open Engineering, 7, Pp. 221\u2013227. \nhttps://doi.org/10.1515/eng-2017-0028. \n\n\n\nKarlen, D.L., Wright, R.J., Kemp, W.O., 1995. Agriculture utilization of urban \nand industrial by-products. ASA Special Publ., 58, Pp. 19\u201372. \n\n\n\nKumar, M., Meena, M.L., Kumar, S., Maji, S., Kumar, D., 2013. Effect of \nnitrogen, phosphorus and potassium fertilizers on the growth, yield and \nquality of tomato var. Azad T-6, TAJH 8, Pp. 616\u2013619. \n\n\n\nLee, L.H., Wu, T.Y., Shak, K.P.Y., Lim, S.L., Ng, K.Y., Nguyen, M.N., Teoh, W.H., \n2018. Sustainable approach to biotransform industrial sludge into \norganic fertilizer via vermicomposting: a mini-review. J. Chem. Technol. \nBiotechnol., 93, Pp. 925\u2013935. https://doi.org/10.1002/jctb.5490. \n\n\n\nLim, S.L., Wu, T.Y., Lim, P.N., Shak, K.P.Y., 2015. The use of vermicompost \nin organic farming: overview, effects on soil and economics. J. Food. \nAgric. Sci., 95, Pp. 1143\u20131156. \n\n\n\nMaynard, A.A., 2013. Cumulative effect of annual additions of MSW \ncompost on the yield of field\u2013grown tomatoes, Compost Sci Util., 3, Pp. \n47\u201354. https://doi.org/10.1080/1065657X.1995.10701781 \n\n\n\nNEERI, 2005. Strategy paper on SWM in India. National Environmental \nEngineering Research Institute, Nagpur. \n\n\n\nNEERI, 2009. Assessment of quality of compost from waste processing \nplants of Calicut Delhi and Jalandhar. National Environmental \nEngineering Research Institute. \n\n\n\nNigussie, A., Kuyper, T.W., de Neergaard, A., 2015. Agricultural waste \nutilization strategies and demand for urban waste compost: evidence \nfrom smallholder farmers in Ethiopia. Waste Manage. 44, Pp. 82\u201393. \nDOI:10.1016/j.wasman.2015.07.038. \n\n\n\nOgundare, S.K., Babalola, T.S., Hinmikaiye, A.S., Oloniruha, J.A., 2015. \nGrowth and fruit yield of tomato as influenced by combined use of \norganic and inorganic fertilizer in Kabba. Nigeria. Eur. J. Agric. For. Res., \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(1) (2021) 29-33 \n\n\n\nCite the Article: A.B. Abdus Salam, M. Ashrafuzzaman, S. Sikder, Asif Mahmud, J.C. Joardar (2021). Influence Of Municipal Solid Waste Compost On Yield Of Tomato - \nApplied Solely And In Combination With Inorganic Fertilizer Where Nitrogen Is The Only Variable Factor . Malaysian Journal of Sustainable Agriculture, 5(1): 29-33. \n\n\n\n3, Pp. 48\u201356. \n\n\n\nOkareh, O.T., Oyewole, S.A., Taiwo, L.B., 2014. Conversion of food wastes \nto organic fertilizer: A strategy for promoting food security and \ninstitutional waste management in Nigeria. Res. Environ. Sci. Toxicol., 3, \nPp. 066\u2013072. http:/dx.doi.org/10.14303/jrest.2012.031 \n\n\n\nPascale, D.S., Tamburrino, R., Maggio, A., Barbieri, G., Fogliano, V., Pernice, \nR., 2006. Effect of nitrogen fertilization on the nutritional value of \norganically and conventionally grown tomatoes. Acta Hortic., 700, Pp. \n107\u2013110. DOI: 10.17660/ActaHortic.2006.700.14. \n\n\n\nRajaie, M., Tavakoly, A.R., 2016. Effects of municipal waste compost and \nnitrogen fertilizer on growth and mineral composition of tomato. Int. J. \nRecycl. Org. Waste Agric., 5, Pp. 339\u2013347. \nhttps://doi.org/10.1007/s40093-016-0144-4 \n\n\n\nRamadass, K., Palaniyandi, S., 2007. Effect of enriched municipal solid \nwaste compost application on soil available macronutrients in the rice \nfield. Arch. Agron. Soil Sci., 53, Pp. 497\u2013506. \nhttps://doi.org/10.1080/03650340701581479 \n\n\n\nRashid, A., Rab, A., Mohammad, H.M., Ali, J., Shahab Jamal, A., Rehman, A., \nAli, M., 2016. Effect of row spacing and nitrogen levels on the growth \nand yield of tomato under walk-in polythene tunnel condition. Pure \nAppl. Biol., 5, Pp. 426\u2013438. DOI: 10.19045/bspab.2016.50055. \n\n\n\nReal Decreto, 2013. e 28 de junio, sobre productos fertilizantes. Bolet\u00edn \nOficial del Estado, 164 (1), Pp. 51119\u201351207. \n\n\n\nRoy, T.K., Rahman, S., Dev, P.K., 2013. Compost fertilizer from municipal \nsolid wastes and its application in urban agro-forestry nurseries: A case \nstudy on Khulna City. Journal of Bangladesh Institute of Planners, 6, Pp. \n\n\n\n191\u2013199. \n\n\n\nScotti, R., Pane, C., Spaccini, R., Palese, A.M., Piccolo, A., Celano, G., \nZaccardelli, M., 2016. On-farm compost: a useful tool to improve soil \nquality under intensive farming systems. Appl. Soil Ecol. 107, Pp. 13\u201323. \nDOI: 10.1016/j.apsoil.2016.05.004 \n\n\n\nSharma, S., Shah, K.W., 2005. Generation and disposal of solid waste in \nHoshangabad, in: Book of proceedings of the second international \ncongress of chemistry and environment, Indore, India., Pp. 749\u2013751. \n\n\n\nSoil survey manual, 1993. Soil survey division staff, chapter 3, Soil \nconservation service. US Department of Agriculture, Washington DC. \n\n\n\nSrivastava, V., de Araujo, A.S.F., Vaish, B., Bartelt-Hunt, S., Singh, P., Singh, \nR.P., 2016. Biological response of using municipal solid waste compost \nin agriculture as fertilizer supplement. Rev. Environ. Sci. Biotechnol., 15, \nPp. 677\u2013696. https://doi.org/10.1007/s11157-016-9407-9. \n\n\n\nWeber, J., Kocowicz, A., Bekier, J., Jamroz, E., Tyszka, R., Debicka, M., \nParylak, D., Kordas, L., 2014. The effect of a sandy soil amendment with \nmunicipal solid waste (MSW) compost on nitrogen uptake efficiency by \nplants. Eur. J. Agron., 54, Pp. 54\u201360. DOI: \norg/10.1016/j.eja.2013.11.014 \n\n\n\nWhite, P.R., Franke, M., Hindle, P., 1995. Integrated solid waste \nmanagement: A lifecycle inventory, Chapman and Hall, London. \nDOI: 10.1007/978-1-4615-2369-7 \n\n\n\nZhao, S., Liu, X., Duo, L., 2012. Physical and chemical characterization of \nmunicipal solid waste compost in different particle size fractions. Pol. J. \nEnviron. Stud., 21, Pp. 509\u2013515\n\n\n\n\n\n"
"\n\nCite the article: Emmanuel Chidumayo (2018). Seed Scarification Reduces Seedling Survival And Tree Growth And Longevity In Senegalia Polyacantha At A Site In \nCentral Zambia, Southern Africa. Malaysian Journal of Sustainable Agriculture, 2(2) : 19-23. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nOne of the impediments to artificially regenerating forests is seed dormancy and seed scarification improves \ngermination rate. However, the majority of studies on seed treatment to break dormancy in dry tropical woody \nspecies have focussed on the seedling stage and little is known about the effects of seed treatment on saplings and \ntrees. This study, conducted at a permanent site in central Zambia, aimed at determining the effects of seed \nscarification on seedling emergence and survival and growth and longevity of Senegalia polyacantha, a fast \ngrowing and nitrogen-fixing species that is widely distributed in Sub-Saharan Africa. Seedling emergence from \nscarified and untreated seeds was monitored and first-year survival assessed. Enrichment planting with nursery \ntransplants and direct sowing of scarified seed was undertaken and the survival and growth of planted and non-\nplanted trees monitored for 17 years.  Seed scarification increased seedling emergence but seedling survival was \nsignificantly reduced. Planted trees from scarified seeds had lower radial growth (0.22 cm yr-1) compared to non-\nplanted trees (0.56 cm yr-1). Planted trees also had a shorter lifespan than non-planted trees. Seed scarification in \nS. polyacantha should be applied with caution to avoid significant negative effects on seedling survival and growth \nand longevity of trees.\n\n\n\nKEYWORDS \n\n\n\nAssisted forest regeneration, radial growth, seed dormancy, Senegalia polyacantha, survivorship\n\n\n\n1. INTRODUCTION\n\n\n\nReducing emissions from deforestation and forest degradation, and \n\n\n\nenhancing forest carbon stocks in developing countries (REDD+) is a \n\n\n\nmechanism for climate change mitigation and this can be done through \n\n\n\nreforestation, agroforestry and natural forest regeneration. The \n\n\n\ndistinction between agroforestry and assisted natural regeneration (ANR) \n\n\n\nis not always clear because agroforestry is a collective term that describes \n\n\n\nland use systems and practices that integrate trees with crops and/or \n\n\n\nanimals on the same land unit while ANR is natural forest regeneration \n\n\n\nwith human assistance intended to encourage the natural regeneration of \n\n\n\nnative species at a site. These restoration activities can be undertaken on \n\n\n\ndegraded landscapes and abandoned cropland (fallow).  \n\n\n\nOne of the common impediments to artificially regenerating trees on \n\n\n\ndegraded dry tropical forest sites is seed dormancy which occurs due to \n\n\n\nsome chemical, physical and/or physiological traits in seeds that cause \n\n\n\nregulated germination which is believed to be a survival mechanism that \n\n\n\nensures that germination occurs only when environmental conditions are \n\n\n\nfavorable [1]. Physical seed dormancy occurs in a number of species in the \n\n\n\nFabaceae family and seed scarification has been reported to give the \n\n\n\nhighest seedling emergence rate and the most vigorous seedlings [2]. The \n\n\n\nmajority of studies on seed dormancy in African dry tropical woody \n\n\n\nspecies have focussed on the seedling stage [3]. African dry forest tree \n\n\n\nspecies under go three post-seed phases during regeneration: seedling, \n\n\n\nsapling and tree phases. I define these life history stages as follows: (i) \n\n\n\nseedling as a plant that is less than 1-year old that is recognized by the \n\n\n\npresence of cotyledons, (ii) sapling as a plant that is >1.0 year old, <2.0 m \n\n\n\ntall and with a diameter at breast height (1.3 m above-ground, dbh) of <3.0 \n\n\n\ncm that originated from a seed, (iii) tree as a plant that is >1.0 year old,  \n\n\n\n>2.0 m tall and with a dbh of >3.0 cm. The transitions from seedling to \n\n\n\nsapling to tree have been a subject of many studies and there is growing \n\n\n\nsupport for the idea that demographic constraints in seedling recruitment \n\n\n\nand sapling release are responsible for much of the variability in tree \n\n\n\ndensity in savanna ecosystems [4]. \n\n\n\nWhereas seed treatment enhances seedling recruitment the long-term \n\n\n\neffects of this procedure on saplings and trees have rarely been \n\n\n\ninvestigated, especially in agroforestry, restoration and carbon \n\n\n\nsequestration projects [5]. This study aimed at investigating the effects of \n\n\n\nseed scarification on the demography and growth of planted Senegalia \n\n\n\n(formely Acacia) polyacantha Willd. trees in comparison to non-planted \n\n\n\ntrees over a period of 21 years (1996 \u2013 2017) at a site that was initially \n\n\n\ndegraded by charcoal production and later briefly cultivated in central \n\n\n\nZambia. The investigation aimed at assessing the effects of seed \n\n\n\nscarification on (i) seedling emergence and survival and (ii) growth and \n\n\n\nlongevity of S. polyacantha trees. The findings of the study have \n\n\n\nimplications for using this species in agroforestry, forest restoration \n\n\n\nthrough natural regeneration and carbon sequestration projects in Sub-\n\n\n\nSaharan Africa where this species occurs.  \n\n\n\n2. MATERIALS AND METHODS\n\n\n\n2.1 Study site \n\n\n\nThe 0.80-ha study plot is located at 15.467o S, 28.183o E, 1260 m altitude \n\n\n\nabove sea level (asl), about 15 km south of Lusaka city in central Zambia. \n\n\n\nPrint ISSN : 2521-2931 \n\n\n\nOnline ISSN : 2521-294X \n\n\n\nCODEN: MJSAEJ\n\n\n\nSEED SCARIFICATION REDUCES SEEDLING SURVIVAL AND TREE GROWTH AND LONGEVITY IN \nSENEGALIA POLYACANTHA AT A SITE IN CENTRAL ZAMBIA, SOUTHERN AFRICA\nEmmanuel Chidumayo\n\n\n\nMakeni Savanna Research Project, P.O. Box 50323, Lusaka, Zambia\n*Corresponding Author\u2019s E-mail: echidumayo@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and \nreproduction in any medium, provided  the original work is properly cited \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.02.2018.19.23 \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 19-23\n\n\n\n\nmailto:nwankwoala_ho@yahoo.com\n\n\n\n\n\n\nThe plot was established in 1996 when it was divided into two subplots: \n\n\n\ncontrol (0.31 ha) and experimental (0.49 ha). The control subplot is split \n\n\n\ninto three blocks while the experimental subplot is split into nine blocks \n\n\n\nof variable sizes (Figure 1) due to presence of termite mounds and the \n\n\n\nlayout required that each mound should not extend beyond a single block. \n\n\n\nThere are five termite mounds at the plot (one each in ES, EN, WCC, WC \n\n\n\nand WN). Trees in the area had been selectively cut for charcoal \n\n\n\nproduction during 1992 \u2013 1994. Charcoal production was by the earth-kiln \n\n\n\nmethod that removes about 93% of the aboveground wood biomass while \n\n\n\nthe rest remains in residual uncut trees (Chidumayo, 1991). At the time of \n\n\n\nplot establishment residual trees on the plot consisted of Piliostigma \n\n\n\nthonningii (Schumach.) Mile-Redhead, Senegalia polyacantha Willd. and \n\n\n\nVachellia sieberana DC. The plot was fenced off in 1994 to keep out \n\n\n\nlivestock and to minimize undesirable human disturbances in the re-\n\n\n\ngrowing woodland as part of measures for assisted natural regeneration.  \n\n\n\nFigure 1: Layout of blocks at the study plot. Blocks are named according \n\n\n\nto their location on the plot: W for west, E for east, S for south, N for \n\n\n\nnorth and C for center. Annually burnt blocks are indicated by grey \n\n\n\nshading. \n\n\n\nThe soil at the study plot is predominantly sand clay loam with 47% sand, \n\n\n\n34% clay and 19% silt and a pH of 5.4 [5]. The climate at Mt Makulu \n\n\n\n(15.550\u00b0S, 28.267E, 1240 m asl), 13 km south of the study plot, is \n\n\n\nsubtropical with alternating dry (May \u2013 October) and wet (November \u2013 \n\n\n\nApril) seasons with a long-term (1970 \u2013 2017) annual mean (\u00b11se) \n\n\n\nprecipitation of 836 \u00b134 mm with a coefficient of variation of 27.5%. \n\n\n\nAnnual mean minimum and maximum temperatures at Mt Makulu are \n\n\n\n14.7\u00b10.08oC and 27.6 \u00b10.12oC, respectively.  \n\n\n\n2.2 The study species \n\n\n\nSenegalia polyacantha occurs in Southern, Eastern and West Africa and \n\n\n\nproduces dehiscent pods that split when ripe and pod-valves, with their \n\n\n\nattached seeds, are dispersed by wind. The seeds weigh (mean \u00b1SE) \n\n\n\n0.096\u00b10.002 g. In Zambia S. polyacantha can reach a height of 18 m and a \n\n\n\nbreast height diameter of 40 cm [6]. Senegalia polyacantha is also a fast-\n\n\n\ngrowing nitrogen-fixing tree that has been used as an agroforestry and \n\n\n\nrestoration species in Sub-Saharan Africa [7-9]. Under natural conditions \n\n\n\nit is considered as a secondary succession species that invades disturbed \n\n\n\nlandscapes [10,11]. \n\n\n\n2.3 Senegalia polyacantha enrichment planting and crop cultivation \n\n\n\nThe experimental subplot was subjected to tree enrichment planting with \n\n\n\nS. polyacantha in December 1996. Tree enrichment planting involved \n\n\n\nnursery-raised transplants and seed sowing in north \u2013 south rows with an \n\n\n\ninter-row and inter-plant interval of 2 m, except on positions with already \n\n\n\nexisting naturally established woody plants where no planting was done. \n\n\n\nSown seeds and all nursery-raised seedlings were from scarified seeds, \n\n\n\nintended to reduce seed coat dormancy, that were collected from trees in \n\n\n\nthe neighbourhood of the study site. Enrichment planting rows were \n\n\n\nnumbered from west to east and rows 1 \u20133 were planted with 108 \n\n\n\ntransplants with row 1 covering blocks WS, WC and WN and rows 2 \u2013 3 \n\n\n\ncovered blocks WCS, WCC and WCN. Sixty seeds were sown in rows 4 \u2013 5 \n\n\n\nthat covered blocks WCS, WCC and WCN. The survival of plants from seed \n\n\n\nsowing and nursery transplants was monitored annually until 2017. The \n\n\n\nexperimental subplot was cultivated during three seasons (1996/97 to \n\n\n\n1998/99) by intercropping maize with pumpkin and beans and hand \n\n\n\nhoeing and weeding but care was taken when cultivating to ensure that no \n\n\n\ndamage was done to all established tree seedlings, saplings and live \n\n\n\nstumps with sprouts. All cultivation in the experimental (here after post-\n\n\n\ncultivation) blocks ceased in 1999 while blocks in the control subplot were \n\n\n\nnever cultivated following the initial disturbance caused by charcoal \n\n\n\nproduction.  \n\n\n\nDuring the 1997 \u2013 1999 period all blocks in the experimental subplot were \n\n\n\nprotected from fire, except for an accidental fire in July 1998 that affected \n\n\n\nblocks ECN, WCN, WN, WC, WCC and ECC. Blocks EN, ES, WS, WN and WCC \n\n\n\ntotally 0.38 ha were annually burnt from 2000 while the rest of the blocks \n\n\n\ntotally 0.42 ha were fire protected (see Figure 1). To ensure effective fire \n\n\n\ncontrol, a 3-m firebreak surrounding each block was annually cleared of \n\n\n\nany herbaceous material in April using hand hoes. Although the precise \n\n\n\ntiming of burning depended on the time when the seasonal rains ended, \n\n\n\nburning was done any time from mid-June to mid \u2013August that \n\n\n\ncorresponds to the early dry season burning [12].  \n\n\n\n2.4 Seedling germination and survival \n\n\n\nIn order to investigate the effect of seed scarification on seed germination \n\n\n\nand seedling mortality 385 untreated and 144 scarified S. polyacantha \n\n\n\nseeds, collected from trees in the neighbourhood of the study plot, were \n\n\n\nsown along the firebreak on the eastern perimeter of the plot in November \n\n\n\n2003. Untreated seeds were sown on 77 stations with five seeds per \n\n\n\nstation and scarified seeds were sown on 72 stations with two seeds per \n\n\n\nstation in two separate rows. The stations and rows were 1.0 m apart and \n\n\n\nthe planting stations were marked by aluminum stakes for easy re-\n\n\n\nsighting during subsequent inspections [13]. Seeds were sown in shallow \n\n\n\nholes (2 \u2013 3 cm deep) and seedling emergence (used as a proxy for seed \n\n\n\ngermination) was monitored at weekly intervals during the wet (rainy) \n\n\n\nseason for three years to obtain data on phased seed germination. A census \n\n\n\nof surviving seedlings was conducted at the end of the first year in \n\n\n\nDecember 2004 to determine seedling mortality.  \n\n\n\n2.5 Tree and sapling data \n\n\n\nCensuses of saplings, as defined above, among planted and non-planted \n\n\n\nplants were conducted biannually from 2006 to 2016 in all blocks. The \n\n\n\nlocation of each tree at the study plot was mapped for easy re-sighting and \n\n\n\nthe breast height (1.3 m above ground) diameter (dbh) measurement \n\n\n\npoint marked by permanent paint.  At the time of first measurement or \n\n\n\ntime of recruitment the source of each tree was recorded under three \n\n\n\ncategories: sown seed, nursery transplant and non-planted. The dbh (cm) \n\n\n\nof each tree, as defined above, was measured to the nearest mm and \n\n\n\nrecorded in March or April of each year until 2017. Tree measurements \n\n\n\nstarted in 2001 after adequate tree recruitment was evident.  Tree \n\n\n\nmortality was estimated from annual censuses data in the blocks that were \n\n\n\nconducted during tree measurements.  \n\n\n\n2.6 Data analysis \n\n\n\nSeedling emergence rate and mortality between untreated and scarified \n\n\n\nseeds were compared using the two proportions Fisher\u2019s exact test (Z) at \n\n\n\nP = 0.05 significance level. Census data for trees recruited in 2001were \n\n\n\nanalyzed using survival analysis and significance of differences between \n\n\n\nplanted and non-planted trees and between fire protection and annual \n\n\n\nburning treatments were determined by the logrank (L) test at P = 0.05. \n\n\n\nThe logrank is a non-parametric test for comparing two survival \n\n\n\ndistributions using censored data [14]. Tree growth rates were calculated \n\n\n\non an annual basis by subtracting previous year dbh from current year dbh \n\n\n\nof each tree and the non-parametric Kruskal-Wallis One-way Analysis of \n\n\n\nVariance (AOV) test (H) at P = 0.05 using ranks was applied to dbh \n\n\n\nincrement data to determine the significance of differences in tree growth \n\n\n\nrates between planted and non-planted trees and between fire protection \n\n\n\nand annual burning treatments.  \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 19-23 20 \n\n\n\nCite the article: Emmanuel Chidumayo (2018). Seed Scarification Reduces Seedling Survival And Tree Growth And Longevity In Senegalia Polyacantha At A Site In \nCentral Zambia, Southern Africa. Malaysian Journal of Sustainable Agriculture, 2(2) : 19-23. \n\n\n\n\n\n\n\n\nIn order to determine factors (explanatory or predictor variables) that \n\n\n\nmight explain variations in growth rates, tree dbh increment data were \n\n\n\nsubjected to best subset regression analysis in Statistix 9.0 [15]. using \n\n\n\nthree predictor variables: year, tree density and mean tree size. \n\n\n\nRegression analyses were carried out in two phases. Firstly, the best \n\n\n\nsubset regression analysis was carried out to select predictor variables \n\n\n\nthat explained the largest variation in tree growth rate [16,17]. When two \n\n\n\nindependent variables are highly correlated the analytical procedure used \n\n\n\nautomatically drops one of the predictor variables to avoid problems of \n\n\n\ncollinearity (Analytical Software, 1985\u20132008) [13,18]. Best subset \n\n\n\nregression analysis simultaneously compares models with single variables \n\n\n\nand all their possible combinations. The model with the lowest Akaike\u2019s \n\n\n\nInformation Criterion (AIC) for small samples (AICc) was selected as the \n\n\n\nbest model (Burnham & Anderson, 2002) [18]. However, after executing \n\n\n\nordinary linear regression analysis for the best model, any predictor \n\n\n\nvariable that had a high variance inflation factor (VIF >10.0) was excluded \n\n\n\nfrom the model because in a multiple regression this indicates a problem \n\n\n\nwith collinearity [19]. \n\n\n\n3. RESULTS\n\n\n\n3.1 Seedling emergence and survival \n\n\n\nOut of 144 scarified seeds a total of 74 seedlings were observed while \n\n\n\namong the 385 untreated seeds a total of 122 seedlings were realized. The \n\n\n\nseedling emergence rate of 51.4% among scarified seeds was significantly \n\n\n\nhigher than that of 30.2% among untreated seeds (Z = 4.07, P <0.0001). \n\n\n\nSeedling emergence from scarified seeds occurred within three weeks of \n\n\n\nsowing and no new seedlings were observed after this period. In contrast \n\n\n\nseedling emergence occurred over two wet seasons among untreated \n\n\n\nseeds. During the first season 91.8% of the 122 seedlings were recorded \n\n\n\nover a period of seven weeks while 3.3% emerged between 13 \u2013 14 weeks \n\n\n\nafter sowing. A further 4.9% of the seedlings emerged during the second \n\n\n\nwet season and no seedling emergence was observed in the third season.  \n\n\n\nSeedling emergence rate from the 60 scarified seeds sown in 1996 in \n\n\n\nblocks WCS, WCC and WCN was 63% (38 seedlings).   \n\n\n\nAmong the 2004 cohorts from seeds sown in November 2003 mortality \n\n\n\nduring the first year of 50% among seedlings from scarified seeds was \n\n\n\nsignificantly higher than that of 31% among seedlings from untreated \n\n\n\nseeds (Z = 2.47, P = 0.01). Proportionately significantly more seedlings \n\n\n\nfrom untreated seeds (69%) survived during the first year than the 50% \n\n\n\nfrom scarified seeds (Z = 2.47, P = 0.01). Mortality during the first year \n\n\n\namong seedlings from scarified seeds sown in 1996 in blocks WCS, WCC \n\n\n\nand WCN was also 50% as observed for the 2004 cohort. \n\n\n\n3.2 Tree recruitment and survivorship \n\n\n\nPlanted trees were recruited in 2001 but there was a punctuated but \n\n\n\ncontinuous recruitment among non-planted plants with recruitment \n\n\n\noccurring from 2001 to 2006 and 2008 to 2013 for annually burnt and fire \n\n\n\nprotected blocks, respectively (Figure 2). Of the 44 trees from the planted \n\n\n\nstock 16 originated from sown seeds and 28 from nursery transplants. \n\n\n\nThus among sown seeds 19 (50%) and three (8%) died as seedlings and \n\n\n\nsaplings, respectively, and 16 (42%) transitioned into the tree phase.  Out \n\n\n\nof the 108 nursery transplants 28 (26%) transitioned into the tree phase, \n\n\n\n42 (39%) died as saplings while 38 (35%) were still live saplings in 2017 \n\n\n\nwhen the study ended. There was no significant difference in the \n\n\n\nproportions of plants recruited into the planted tree population between \n\n\n\ntransplants and seedlings (Z = 1.6, P = 0.10). In spite of the punctuated tree \n\n\n\nrecruitment in the non-planted population, censuses of non-planted \n\n\n\nsaplings revealed that these were present in adequate numbers that \n\n\n\nexceeded the tree population throughout the study (Figure 2). \n\n\n\nFigure 2: Senegalia polyacantha non-planted tree population in fire \n\n\n\nprotected (empty circles) and annually burnt (filled circles) blocks and \n\n\n\nnon-planted sapling population (empty bars) at the study plot. \n\n\n\nIn 2001 a total of 14 and 44 trees were recruited into the population \n\n\n\namong non-planted and planted trees, respectively. The survival of these \n\n\n\nsub-cohorts was followed during the study (2001 \u2013 2017). Tree deaths \n\n\n\nwere first observed six to seven years after recruitment but over half of \n\n\n\nthe planted trees had died 12 years after recruitment compared to seven \n\n\n\npercent among non-planted trees (Figure 3).  \n\n\n\nFigure 3: Survival distribution patterns for planted (filled circles) and \n\n\n\nnon-planted (empty circles) Senegalia polyacantha trees recruited into \n\n\n\nthe population in 2001 at the study plot. Vertical lines show standard \n\n\n\nerror of mean and are shown in one direction for clarity. \n\n\n\nBy the end of the study in 2017, 86% of the planted trees had died \n\n\n\ncompared to 14% among non-planted trees. Survival analysis revealed \n\n\n\nsignificant differences in the survival distribution patterns between \n\n\n\nplanted and non-planted S. polyacantha trees (L = 4.74, P <0.0001). Among \n\n\n\nthe planted trees there was no significant difference in the survivorships \n\n\n\nof trees from transplants and seed (L = 1.57, P = 0.12). Similarly there was \n\n\n\nno significant difference in the survivorships of trees under fire protection \n\n\n\nand annual burning (L = -1.14, P = 0.25). \n\n\n\nTrees at time of death had a mean dbh of 7.3\u00b10.6 cm for the planted \n\n\n\npopulation and 13.9\u00b12.5 cm for the non-planted population. To determine \n\n\n\nif tree death was associated with a gradual decline in growth rate, changes \n\n\n\nin diameter increment for the previous five years prior to death were \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 19-23 21 \n\n\n\nCite the article: Emmanuel Chidumayo (2018). Seed Scarification Reduces Seedling Survival And Tree Growth And Longevity In Senegalia Polyacantha At A Site In \nCentral Zambia, Southern Africa. Malaysian Journal of Sustainable Agriculture, 2(2) : 19-23.\n\n\n\n\n\n\n\n\nanalyzed. There was a weak but significant negative linear relationship \n\n\n\nbetween year before death and annual dbh increment: y = -0.105 \u2013 0.11x, \n\n\n\nr2 = 0.05, P = 0.007 (Table 1). This suggests that tree senescence was \n\n\n\ngradual with growth rate declining over several years prior to death.  \n\n\n\nTable 1: Pattern in diameter increment of Senegalia polyacantha trees \n\n\n\nprior to death at the study plot. \n\n\n\nSample trees Year before death Diameter increment \n\n\n\n(mean\u00b11se) \n\n\n\n28 -5 0.46\u00b10.14 \n\n\n\n29 -4 0.43\u00b10.17 \n\n\n\n30 -3 0.12\u00b10.12 \n\n\n\n30 -2 0.06\u00b10.10 \n\n\n\n30 -1 0.08\u00b10.10 \n\n\n\n3.3 Tree growth rates \n\n\n\nIn 2017 planted trees had a mean dbh of 13.7\u00b11.1 cm compared to \n\n\n\n17.5\u00b11.5 cm among non-planted trees and the difference was significant (t \n\n\n\n= 2.03, P = 0.05). There was a significant difference in the annual growth \n\n\n\nrate of planted and non-planted S. polyacantha trees (H = 44.17, P <0.0001; \n\n\n\nFigure 4a). Overall planted trees had a dbh increment of 0.22\u00b10.03 cm yr-\n\n\n\n1 compared to 0.56\u00b10.05 cm yr-1 for non-planted trees. However, two \n\n\n\ndistinct periods of differential growth for each group of trees were \n\n\n\nevident: (ii) pre-2010 (2002 \u2013 2009) and (ii) post-2009 (2010 \u2013 2017). \n\n\n\nAmong planted trees annual dbh increment was 0.29\u00b10.04 cm yr-1 for the \n\n\n\npre-2010 period compared to 0.04\u00b10.06 cm yr-1 for the post-2009 period \n\n\n\nand the difference was significant (H = 19.47, P <0.0001). For non-planted \n\n\n\ntrees the dbh increment of 1.01\u00b10.07 cm yr-1 for the pre-2010 period and \n\n\n\n0.30\u00b10.05 cm yr-1 for the post-2009 period were also significantly \n\n\n\ndifferent (H = 53.39, P<0.0001). There was no significant difference in \n\n\n\nannual dbh increment for the non-planted trees between the control and \n\n\n\npost-cultivation blocks (H = 1.80, P = 0.18); similarly the difference in \n\n\n\nannual dbh increments of non-planted trees between trees under fire \n\n\n\nprotection and annual burning was not significant (H = 0.72, P = 0.40). \n\n\n\nHowever, planted trees under fire protection grew at a higher rate of \n\n\n\n0.31\u00b10.05 cm yr-1 than that of 0.14\u00b10.05 cm yr-1 under annual burning (H \n\n\n\n= 3.78, P = 0.05; Figure 4b). \n\n\n\nFigure 4: Trends in Senegalia polyacantha tree diameter increment in (a) \n\n\n\nplanted (filled circles) and non-planted (empty circles) trees and (b) \n\n\n\nplanted trees under fire protection (empty triangles) and annual burning \n\n\n\n(filled triangles). Vertical lines show standard error of mean and are \n\n\n\nshown in one direction for clarity. \n\n\n\nAs expected, the most important predictor of tree dbh increment was year \n\n\n\n(i.e., time after recruitment) for both non-planted (y = 213.47 \u2013 0.106x, r2 \n\n\n\n= 0.42, P <0.0001) and planted (y = 106.42 \u2013 0.053x, r2 = 0.33, P <0.0001) \n\n\n\ntrees. For planted trees the addition of tree density to year in a two-\n\n\n\nvariable model increased the explanatory power of the model by eight \n\n\n\npercent to 41% but this procedure had no effect on the single variable \n\n\n\nmodel for non-planted trees. \n\n\n\n4. DISCUSSION\n\n\n\n4.1 Seedling emergence and survival \n\n\n\nStudies of seed dormancy in dry tropical forest trees has been limited to \n\n\n\nthe seedling phase with most of these showing that mechanical \n\n\n\nscarification is the most effective method of increasing seed germination \n\n\n\nrates [20]. The findings of this study on S. polyacantha confirm these \n\n\n\nobservations but I am not aware of studies dealing with the post-seedling \n\n\n\neffects of seed scarification. The effects of seed scarification in S. \n\n\n\npolyacantha on seed germination and seedlings were both positive and \n\n\n\nnegative. Scarification increased the speed and rate of seedling emergence \n\n\n\nin S. polyacantha. The fact that a very low proportion of untreated seeds \n\n\n\ngerminated after the initial wave of germination during the first season \n\n\n\nand in the second wet season indicates low levels of seed dormancy in this \n\n\n\nspecies and scarification appears to increase the probability of \n\n\n\ngermination of seeds that would have naturally failed to germinate. The \n\n\n\nsignificantly higher levels of mortality among seedlings from scarified \n\n\n\nseeds than those from untreated seeds suggests that seeds that are \n\n\n\nselected against germination under natural conditions are induced to \n\n\n\ngerminate but probably did not survive by the end of the first year. In fact \n\n\n\nthe benefit gained by higher seed germination due to scarification is \n\n\n\ndwarfed by the higher survival among seedlings from untreated seeds. As \n\n\n\na consequence of this, survivors among seedlings from untreated seeds are \n\n\n\nproportionately more than those from scarified seeds by the end of the \n\n\n\nfirst year. The benefits of seed scarification in S. polyacantha are therefore \n\n\n\nrestricted to the germination stage only and the higher seedling mortality \n\n\n\ndoes not warrant the use of scarification.  \n\n\n\n4.2 Tree recruitment and survivorship \n\n\n\nTree recruitment among planted S. polyacantha occurred in a single event \n\n\n\nwhen plants were 5 \u2013 6 years old. The proportion transitioning into the \n\n\n\ntree phase was 30% while 26% were still alive as saplings in 2017. \n\n\n\nHowever, tree recruitment from the non-planted population was \n\n\n\ncontinuous and recruitment represented a very small proportion of the \n\n\n\nnon-planted sapling population (see Figure 2). These observations \n\n\n\nindicate that there are other constraints to tree recruitment in S. \n\n\n\npolyacantha, other than seedling mortality. It is apparent that S. \n\n\n\npolyacantha saplings consist of both fast and slow growers in the sense of \nwakeling et al (2011) who also pointed out that it takes 4 \u2013 5 years for \n\n\n\nthe top 20% fastest growing plants to reach the tree phase in Vachellia \n\n\n\n(formely Acacia) karroo in Hluhluwe iMfolozi Park in South Africa and \n\n\n\nthat slow growing saplings probably never transit to trees and are \n\n\n\ntherefore doomed to die as saplings [21].  Observations in this study \n\n\n\nsupports this proposition. It is also apparent that the low rate of sapling \n\n\n\ntransition to the tree phase was a constraint to tree recruitment in both \n\n\n\nplanted and non-planted populations. \n\n\n\nThe survival rate of S. polyacantha planted trees was significantly lower \n\n\n\nthan that of non-planted trees to the extent that the former had a projected \n\n\n\ntree life span of less than 18 years (see Figure 2). Further, given the much \n\n\n\nlower mortality among non-planted trees, the longevity of these trees is \n\n\n\nlikely to be much higher than that of planted trees. There was some \n\n\n\nindication that dead trees experienced lower growth rates in the two years \n\n\n\npreceding death which suggests that senescence was gradual.  Senegalia \n\n\n\npolyacantha is regarded as a secondary succession species and the \n\n\n\nfindings in this study confirmed this but also indicate that planted trees \n\n\n\nappear to be more short-lived than non-planted trees which should be \n\n\n\nconsidered when using this species in agroforestry and restoration \n\n\n\nprojects [9,10,15]. In the short term S. polyacantha can be used  for soil \n\n\n\nfertility improvement as the species is known to have symbiotic \n\n\n\nrelationship with nitrogen-fixing bacteria, especially in fallows following \n\n\n\nabandonment of crop cultivation [6-8]. However, planted trees should be \n\n\n\nharvested within 10 \u2013 12 years for fuel biomass before natural mortality \n\n\n\ndrastically thins out the population and growth rates decline to very low \n\n\n\nlevels [21]. \n\n\n\n4. CONCLUSION\n\n\n\nSeed scarification in S. polyacantha to enhance germination should be \n\n\n\napplied with great caution because the benefit gained through higher \n\n\n\ngermination is surpassed by the higher seedling mortality. Plants raised \n\n\n\nfrom scarified seeds were also less vigorous, grew at a lower rate and died \n\n\n\nvery early in life than plants from untreated seed. If S. polyacantha seed \n\n\n\nscarification has to be used in agroforestry or restoration projects, planted \n\n\n\ntrees should be harvested within 10 \u2013 12 years for fuel biomass before \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 19-23 22 \n\n\n\nCite the article: Emmanuel Chidumayo (2018). Seed Scarification Reduces Seedling Survival And Tree Growth And Longevity In Senegalia Polyacantha At A Site In \nCentral Zambia, Southern Africa. Malaysian Journal of Sustainable Agriculture, 2(2) : 19-23. \n\n\n\n\n\n\n\n\nnatural mortality drastically thins out the population and growth rates \n\n\n\ndecline to very low levels. \n\n\n\nConflict of Interest: The author declares that I have no conflict of \n\n\n\ninterest. \n\n\n\nGeolocation information: The study plot is located at 15.467o S, 28.183o \n\n\n\nE, 1260 m altitude above sea level. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nI wish to thank Nsama Saboi, Joel Lwambo, Cynthia Kambole, Nancy \n\n\n\nSerenje, Kayo Chipeta, Phelire Phiri and Tom Chisenga for their \n\n\n\nparticipation in the inventorying and measurement of trees at the study \n\n\n\nplot at different times during the study. \n\n\n\nREFERENCES \n\n\n\n[1] Analytical Software. 1985\u20132008. Statistix 9. Analytical Software, \n\n\n\nTallahassee. \n\n\n\n[2] Burnham, K., Anderson, D.R. 2002. Model Selection and Multimodel \n\n\n\nInference: A Practical Information-Theoretic Approach, 2nd edition. \n\n\n\nSpringer-Verlag, NewYork. \n\n\n\n[3] Chidumayo, E.N. 1988. Integration and Role of Planted Trees in a \n\n\n\nBush-Fallow Cultivation System in Central Zambia. Agroforestry Systems, \n\n\n\n7, 63 \u2013 76. \n\n\n\n[4] Chidumayo, E.N. 1991. Woody Biomass Structure and Utilisation for \n\n\n\nCharcoal Production in a Zambian Miombo Woodland. Bioresource \n\n\n\nTechnology, 37, 43-52. \n\n\n\n[5] Chidumayo, E.N. 2008. Demographic Implications of Life-History \n\n\n\nStage Characteristics in Two African Acacias at a Makeni Savanna Plot in \n\n\n\nZambia. Journal of Plant Ecology, 1, 217-225. \n\n\n\n[6] Fanshawe, D.B. 1971. The Vegetation of Zambia. Government Printer, \n\n\n\nLusaka, Zambia.  \n\n\n\n[7] Gashaw, M., Michelsen, A. 2002. Influence of Heat Shock on Seed \n\n\n\nGermination of Plants    from Regularly Burnt Savanna Woodlands and \n\n\n\nGrasslands in Ethiopia. Plant Ecology, 159, 83\u201393. \n\n\n\n[8] Harmand, J.M., Njiti, C.F., Bernhard-Reversat, F., Puig, H. 2004. \n\n\n\nAboveground and Belowground Biomass, Productivity and Nutrient \n\n\n\nAccumulation in Tree Improved Fallows in the Dry Tropics of Cameroon. \n\n\n\nForest Ecology and Management, 188, 249 \u2013 265.  \n\n\n\n[9] Higgins, S.I., Bond, W.J., Trollope, W.S.W. 2000. Fire, Resprouting and \n\n\n\nVariability: A Recipe for Grass-Tree Coexistence in Savanna. Journal of \n\n\n\nEcology, 88, 213\u2013229. \n\n\n\n[10] Hoffmann, W.A. 1999. Fire and Population Dynamics of Woody \n\n\n\nPlants in a Neotropical    Savanna: Matrix Model Projections. Ecology, 80, \n\n\n\n1354\u20131369. \n\n\n\n[11] Lee, E.T. 1992. Statistical Methods for Survival Data Analysis, 2nd \n\n\n\nEdition. Wiley, New York. \n\n\n\n[12] Masvodza, D.R., Dzomba, P., Mhandu, F., Masamha, B. 2013. Heavy \n\n\n\nMetal Content in Acacia Saligna and Acacia Polyacantha on Slime Dams: \n\n\n\n[15] Mulizane, M., Katsvanga, C.A.T., Nyakudya, I.W., Mupangwa, J.F. \n\n\n\n2005. The Growth Performance of Exotic and Indigenous Tree Species in \n\n\n\nRehabilitating Active Gold Mine Tailings Dump at Shamva Mine in \n\n\n\nZimbabwe. Journal of Applied Science and Environmental Management, 9, \n\n\n\n57\u20139. \n\n\n\n[16] Pahla, I., Muziri, T., Chinyise, T., Muzemu, S., Chitamba, J. 2014. \n\n\n\nEffects of Soil Type and Different Pre-Sowing Treatments on Seedling \n\n\n\nEmergence and Vigour of Acacia Sieberana. International Journal of Plant \n\n\n\nResearch, 4 (2), 51\u201355. \n\n\n\n[17] Sharam, G.J., Sinclair, A.R.E, Turkington, R., Jacob, A.L. 2009. The \n\n\n\nSavanna Tree Acacia Polyacantha Facilitates the Establishment of Riparian \n\n\n\nForests in Serengeti National Park, Tanzania. Journal of Tropical Ecology, \n\n\n\n25, 31\u201340. \n\n\n\n[18] Trapnell, C.G. 1959. Ecological Results of Woodland Burning \n\n\n\nExperiment in Northern Rhodesia. Journal of Ecology, 47, 129\u201368. \n\n\n\n[19] Wakeling, J.L., Staver, A.C., Bond, W.J. 2011. Simply the Best: The \n\n\n\nTransition of  Savanna Saplings to Trees. Oikos, 120, 1448 \u2013 1451. \n\n\n\n[20] Werner, P.A. 2012. Growth of Juvenile and Sapling Trees Differs with \n\n\n\nBoth Fire Season and Understorey Types: Trade-offs and Transitions out \n\n\n\nof the Fire Trap in an Australian Savanna.      Austral Ecology, 37, 644\u2013657. \n\n\n\n[21] White, F. 1983. The Vegetation of Africa. Natural Resources \n\n\n\nResearch 20, UNESCO Paris, France, 356. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 19-23 23 \n\n\n\nCite the article: Emmanuel Chidumayo (2018). Seed Scarification Reduces Seedling Survival And Tree Growth And Longevity In Senegalia Polyacantha At A Site In \nCentral Zambia, Southern Africa. Malaysian Journal of Sustainable Agriculture, 2(2) : 19-23. \n\n\n\nImplications for Phytoremediation. American Journal of Experimental \n\n\n\nAgriculture, 3 (4), 871- 883.   \n\n\n\n[13] Missanjo, E., Chioza, A., Kulapani, C. 2014. Effects of Different \n\n\n\nPretreatments to the Seed on Seedling Emergence and Growth of Acacia \n\n\n\nPolyacantha.  International Journal of Forest Research, 6. Article ID \n\n\n\n583069. Available: http://dx.doi.org/10.1155/2014/58 3069. \n\n\n\n[14] Mwase, W.F., Mvula, T. 2011. Effect of Seed Size and Pre-Treatment \n\n\n\nMethods of Bauhinia Thonningii Schum. on Germination and Seedling \n\n\n\nGrowth. African Journal of Biotechnology, 10 (13), 5143-5148. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 14-19 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2023.14.19 \n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Modeling Factors Influencing Barley Yield in Ethiopia:  \n\n\n\nAugmented Cobb-Douglas Production Approach. Journal of Sustainable Agricultures, 7(1): 14-19. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.14.19 \n\n\n\n\n\n\n\nMODELING FACTORS INFLUENCING BARLEY YIELD IN ETHIOPIA: AUGMENTED \nCOBB-DOUGLAS PRODUCTION APPROACH \n\n\n\nAbera Gayesa Tirfi \n\n\n\nAbera Gayesa M&D Consult Addis Ababa, ETHIOPIA. \n*Corresponding Author Email: aberagayesa@yahoo.com  \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 28 October 2022 \nRevised 12 November 2022 \nAccepted 20 December 2022 \nAvailable online 23 December 2022 \n\n\n\n The purpose of this study was to examine the climate and non-climatic inputs influencing barley yield in \n\n\n\nEthiopia. The study employed an augmented Cobb-Douglas production functional approach to model factors \n\n\n\ninfluencing barley yield. The results revealed that short-season rainfall and temperature variables showed a \npositive relationship with barley yield, having minimal impact on barley yield. Conversely, long-season \n\n\n\nrainfall showed negative impact on barley yield, mainly due to extreme rain events such as high rainfall above \n\n\n\noptimum requirement of the crop as well as scarcity of rainfall in some pocket areas. The result infers that \ncultivation of barley moderately depends on rainfall. Subsequently, irrigated land, fertilizer and barley seed \n\n\n\nquantities used exhibited positive impact on barley yield. Fertilizer and barley seed inputs demonstrated \n\n\n\npositively significant influence on barley yield, implying that barley yield is highly responsive to application \nof fertilizer and barley seed inputs and moderately responsive to irrigation input.  \n\n\n\nKEYWORDS \n\n\n\nDeterminant Parameters, Cereal Crops, Econometric Method \n\n\n\n1.   INTRODUCTION \n\n\n\nBarley (Hordeum vulgare L.) is one of the most important food crops in the \nworld in general and in Ethiopia in particular for a long period of time. \nWhile the crop is the fourth important cereal crop in the world production \nwise, following wheat, rice and maize, it accounts for both 9% in terms \ntotal area under cereal crops (0.95 million hectares) and total cereal \nproduction in Ethiopia (Yawson, et al., 2020; CSA, 2020; Tuttolomondo, et \nal., 2009). According to CSA, barley is considered as one of the staple cereal \ncrops after teff, maize, wheat and sorghum (CSA, 2018). In terms of \nutilization, barley is largely consumed as staple food and used for \npreparing the popular traditional drink called Tella (Araya et al., 2021).  \n\n\n\nHistorically, Ethiopia is considered as the center of origin and barley \ndiversity in the world having high level of morphological variations \nbetween landraces that has developed over time, through adaptation to \nvaried climatic and soil conditions (Lakew et al., 1997). This diversity may \nhave been likely contributed through long- term geographic isolation since \nbarley is considered as a founder crop of Old-World agriculture and may \nhave been cultivated in Ethiopia for the last 5,000 years (Mekonnon, et al., \n2014; Bekele, et al., 2005). Currently, producers cultivate the crop from \naltitudes ranging from 1,400 to over 4,000 meters above sea level (m.a.s.l) \nunder highly variable climatic and edaphic conditions (Asfaw, 2000). \nEvidences indicate that the crop is grown in all regions of the country \n(Wosen et al., 2015). The major barley growing regions of the country \ninclude former Shewa, Arsi, Bale, Gojam, Gonder, Wollo, and Tigray. \nAdditionally, short season/Belg season barley primarily grown from \nFebruary to May is mainly produced in Wollo, Shewa and Bale areas. It was \nestimated that about 1.08 and 2.38 million tons of barley were produced \nbetween the period 1981 and 2020 respectively, showing an increase of \nabout 220% over the years.  \n\n\n\nHowever, the production of barley crop in the country has been hampered \n\n\n\nby several factors which include climatic factors (rainfall, temperature, \nand carbon dioxide); erratic drought strain; potentially low yield of \navailable cultivars; and invasion of crop diseases and insect pests and \nweeds (Wosene, et al., 2015). Among these factors, change in climate \nsignificantly affects crop yields and production. The Intergovernmental \nPanel on Climate Change (IPCC) confirmed that anthropogenic activities \nare the main factors changing the climate system globally and will \ncontinue to do the same (IPCC, 2014). In the previous last century, the \neffects of changes in surface temperatures and precipitation on physical \nand biological systems are progressively being observed.  \n\n\n\nMany of the countries located in the African continent, including Ethiopia \nare reported to be highly vulnerable to the elaborated effects of changes \nin climate factors as these have poor access to mitigation and adaptive \nresources. Some researchers have measured the impacts these factors \nimpose on barley yield over different regions and locations and reported \nthat climatic parameters have adverse impact on barley yield. A group of \nresearchers in their modeling of climate change and its impact on food \nbarley explored an overall increasing trend in temperature and significant \nvariation of seasonal rainfall from the historical period which adversely \naffected barley yield (Bekele et al., 2019). A group of researchers modeled \ncrop management and sensitivity of food barley to effects exerted by \nchanges in climate parameters in northern parts of the country and \nreported a rise in temperature alone by 2, 4, 6 and 80C from the baseline \nwhich considerably and significantly reduced barley yield (Araya, et al., \n2021).  \n\n\n\nGinbo in his heterogeneous impacts of climate change on crop yields \nacross altitudes in Ethiopia discovered that climate change reduces barley, \nmaize, and wheat yield by 22.7%, 48%, and 10%, respectively, at high \naltitudes (Ginbo, 2022). Equally, simulated the effect of climate change in \nbarley yield in Italy and reported that yield variability increases slightly \nwith a rise in variability of both temperature and rainfall levels \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 14-19 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Modeling Factors Influencing Barley Yield in Ethiopia:  \n\n\n\nAugmented Cobb-Douglas Production Approach. Journal of Sustainable Agricultures, 7(1): 14-19. \n\n\n\n\n\n\n\n(Tuttolomondo et al., 2008). These findings inform us that changes in \nclimatic parameters, such as sea level rise, rising atmospheric \ntemperatures and altering rainfall patterns will pose crop yield reduction \nincluding barley. In view of the sensitiveness of barley crop changes in \nclimate factors, the attempts made to quantify the possible effects exerted \nby climatic variables on barley yield as well as production are limited. Few \nstudies have examined the impact of changes in climate variables on yield \nof barley (Bekele et al., 2019; Araya et al., 2021; Ginbo, 2022).  \n\n\n\nHowever, these studies covered limited and few pocket areas and \nlocations and did not cover the main barley growing belts of the country. \nIt was agreed that there is scarcity of such empirical studies having \nnational scope and focus on the impact imposed by climate change on \nbarley production in the country. Furthermore, unless researches focus on \naggregated national level impact analysis of climatic parameters and \nincorporate mitigation and adaption strategies, future sensitivity of barley \nyield to climate change will be more damaging. Hence, it would be realistic \nand meaningful to study the impacts posed by changes in climate factors \non the yield of barley aggregately at national level covering the main \nbarley growing belts. The main objective of this study was to examine the \ninfluence of climate change and related inputs on barley yield and provide \ninformation that could help to design strategies that may guide future \nmitigation and adaptation responses. \n\n\n\n2.    MATERIAL AND METHODS \n\n\n\n2.1   Description of the Study Area \n\n\n\nEvidences show that Ethiopia is located in the Horn of Africa, with \nlatitudinal and longitudinal locations lying between 3o to 15o N and 33o to \n48oE, respectively (Ethiopia, 2014). The country borders with Sudan in the \nwest, Eritrea in the north, Djibouti in the east, Somalia in the Southeast, \nKenya in the south, and South Sudan in the southwest (World Bank, 2021). \nAdministratively, the country is divided into four levels: regions or city \nadministrations, zones, woredas, and the kebeles; kebele being the lowest \ngrassroots administrative unit. According to the UN Population Funds \npopulation projection (2021), the population of Ethiopian has reached \n117.90 million with an annual growth rate of 2.6 percent. Evidences show \nthat barley is among the most important food crops grown in the country. \nAccording to CSA, the major barley growing belts in the country are \nlocated in Oromia, Amhara, Tigray, and Southern Nation Nationality and \nRegional State, supplying about 99.9% of the total national barley \nproduction (CSA, 2018). Zone-wise, barley is mainly grown in the zones of \nArssi, Bale, former Shewas, Wollo, Gojam, and Gonder; see Figure 1 for the \nmap (Gashaw and Tura, 2015).  \n\n\n\nFurther evidence show that barley grows best at higher altitudes with an \noptimum range of 2000-3500 meters above sea levels. Barley is mainly \ngrown as a 'meher' (main season) crop at higher elevation of Dega \nregions and also widely cultivated as a 'belg' crop in many areas. It is \ngrown mainly in Arssi, Bale, Shoa, Welo, Gojam and Gonder as a \nbelg/short-season crop (Assamere et al., 2021). In general, barley crops \nare grown during two consecutive seasons: the short/belg-season and \nlong/ meher-season at the higher elevations of Dega agroecologies \n(Muluken and Jemal, 2011). It has been reported that barley crops are \nconsiderably cultivated and supplied by the smallholder subsistence \nfarmers, who mostly use local seed varieties with either little or no \napplication of fertilizers, pesticides, and herbicides. \n\n\n\n\n\n\n\nFigure 1: Major Barley Growing Belts of Ethiopia \n\n\n\nThe amount of crop growing period rainfall required by barley crop ranges \nbetween 180 to 400mm depending on altitude and geographic locations \n(Cammarano et al., 2019). Although the crop mainly grows in the \nhighlands as specified above, it can also moderately be grown in a \n\n\n\nsubtropical climate characterized by hot, humid main-seasons and cool to \nmild bega seasons (October \u2013 December). It has been reported that barley \nbest suits a temperature of 12-15 0C over crop growing period and about \n300C temperature at maturity time. According to one study barley has no \ntolerance capacity to frost at all stages of plant growth, particularly at the \nflowering stage (Cammarano, et al., 2019). It has been reported that frost \nhighly affects the yield of barley crops during crop flowering stage since \nthis stage represents crop shift from vegetative to reproductive growth \n(Wiegmann, et al., 2019).  \n\n\n\nAlthough suitable agro-ecologies are available for barley crop production, \navailable evidence show that the yield of barley was fluctuating over time. \nData compiled from CSA show that the yield of barley declined from 130 \nkilogram/ hectare in 1981 to 93.4 kilograms/hectare in 2000 and \nincreased by 108.1 kilogram/hectare from 2001 to 198.2 \nkilograms/hectare in 2017; and then again started declining from 2018 \nonwards.  The trend of barley yield gives clues for the effect posed by \nclimate change and related factors. See Figure 2 for details.   \n\n\n\n\n\n\n\nFigure 2: Trend of barley yield over the period 1981 \u2013 2020 (Source: \nComputed using raw data compiled from CSA) \n\n\n\n2.2   Data Type and Source \n\n\n\nThis study used time series secondary data for the selected explanatory \nand dependent variables covering the period from 1981 to 2020.  The \nstudy used one independent variable (yield of barley expressed in \nkgs/hectare); and explanatory variables (crop growing period seasonal \nrainfalls expressed in millimeters (mm), crop growing period \ntemperatures expressed in (\u00baC), barley cultivated area expressed in \nmillion hectares, fertilizer and barley seed quantities applied on barley \ncrop cultivation). Data on production and yield of barley crop as well as \nsorghum cultivated area were compiled from Agricultural Sample Survey \nReports of Ethiopian Central Statistical Agency (CSA) covering the period \nfrom 1981 to 2020. Data on weather variables, i.e., short-season/belg and \nlong-season/meher rainfalls were purchased from the Ethiopian National \nMeteorological Agency (NMA) for 12 representative weather stations \nfalling within the major barley growing belts of the country. The \npurchased weather was then nationally aggregated (pooled) for crop \ngrowing period by taking average of weather stations selected for the \nstudy over the period 1981 to 2020.   \n\n\n\n2.3   Empirical Model Specification \n\n\n\nResearchers have employed Cobb-Douglas Production model to examine \nthe impacts exerted by climatic factors on cereal crops productivity and \nproduction (Gupta, et al., 2012; Shumatie, et al., 2017). This study has \nemployed an augmented Cobb-Douglas Production model to examine \nclimatic and non-climatic factors influencing the yield of barley. The model \nassumes that agricultural production is a function of many input variables \nsuch as cultivated area, fertilizers, seeds, oxen power, labors, working \ncapital, rainfall and temperature. In production theory, the relationship \nbetween explanatory variables (climate and related inputs) and crop yield \nnormally takes non-linear form (Chen, et al., 2004; Just and Pope, 1979). \nIn this context, the Cobb-Douglas Production model, in its stochastic form, \ncan be expressed as (Gujarati, 2004): \n\n\n\nYt = AX1\u03b21X2\u03b22\u2026 Xn\u03b2n e\u025b                                                        (1) \n\n\n\nWhere, Yt represents yield of barley, Xs\u2019 represent a set of explanatory \nvariables, and \u03b2s\u2019 represent parameters to be estimated. A represents \nconstant term, e is the base of natural logarithm, and \u025b is the disturbance \nterm with zero mean and constant variance. This non-linear form of Cobb \nDouglas Production model can be estimated through ordinary least \nsquares (OLS) by adding natural log on both sides of equation (1), which \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 14-19 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Modeling Factors Influencing Barley Yield in Ethiopia:  \n\n\n\nAugmented Cobb-Douglas Production Approach. Journal of Sustainable Agricultures, 7(1): 14-19. \n\n\n\n\n\n\n\nbecomes log-linear form. Estimates of this form of production function \ngive direct elasticity coefficients for the variables. The log-linear form of \nCobb Douglas Production model in this regard can be expressed as: \n\n\n\nlnYt = \u03b2 + \u03b2i\u2211 lnXn\ni=1 i + \u025bi                                     (2) \n\n\n\nWhere lnYt shows barley yield at time t, lnXi represents various farm \ninputs such as cropped land area, fertilizer and barley seed applied, and \nirrigated land area. Although farm inputs like farm machinery, oxen \npower, and labors are required to be included in the model, they were not \nincluded due to unavailability of time series data. In its functional form, \nthe augmented Cobb-Douglas Production model specified under equation \n(2) can be specified as: \n\n\n\nlnYt = \u03b10 + \u03b21lnBLat + \u03b22lnFertt + \u03b23lnBSt+ \u03b24lnIrrgArit + \u03b5t              (3) \n\n\n\nwhere, lnYt represent the natural log of barley yield, lnBLat represent the \nnatural log of cropped land area, lnFertt shows natural log of fertilizer \ninput used on barley crop production, lnBSt is natural log of barley seed \nconsumed, and lnIrrgArt is natural log of irrigated area under barley crop \nproduction at time t. \u03b5 is the disturbance term independently and \nidentically distributed. \n\n\n\nThe Cobb-Douglas Production model further assumes climatic factors as \nthe main influential input factors for the yield of crops. Climatic variables \nconsidered in this study as explained above were incorporated in the crop \nyield model. After incorporating climatic variables into the model, \nequation (3) in its log-linear form has been specified as follows:   \n\n\n\nlnYt = \u03b10 + \u03b21lnBLat + \u03b22lnFertt + \u03b23lnBSt+ \u03b24lnIrrgArt + \u03b25lnSSRt + \u03b26lnLSRt \n+ \u03b27lnMinTempt + \u03b28lnMaxTempt + \u03b5t                (4) \n\n\n\nWhere: lnSSRt is natural log of short/Belg-season rainfall, lnLSRt is natural \nlog of long/Meher-season rainfall, lnMinTempt is natural log of crop \ngrowing period (CGP) average minimum temperature, lnMaxTempt is \nnatural log of CGP average maximum temperature, and the other variables \ntake earlier definitions. Furthermore, t = time period from 1981 \u2013 2018, \n\u03b10, \u03b21, \u03b22, \u03b23, \u03b24, \u03b25, \u03b26, \u03b27, and \u03b28 are unknown parameters to be estimated, \nand \u03b5t is the disturbance. To estimate the Cobb-Douglas Production model \nspecified by equation 4, MedCal- Version 19.1 software and SPSS 24 \nStatistical packages were used. \n\n\n\n2.4   Method of Estimation \n\n\n\nThe barley yield models have been estimated using Ordinary Least \nSquares (OLS).  Prior to estimation of the model, the data series must be \nsubjected to various tests to confirm that the various properties of OLS \napproach and the various time series properties confirmed to give results \nthat are efficient and consistent. Since this study uses time series data, it \nwas necessary test the data series for stationarity/ unit root and \ndiagnostic test for checking presence of serial autocorrelation using \nappropriate methods and tools. In this study, two widely used methods, \ni.e. Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test were \nconducted to check the presence of unit roots in the data series (Dickey \nand Fuller, 1979; Phillips and Perron, 1988). The ADF test for stationarity \nin a series y can be estimated using the equation: \n\n\n\n\u2206yt = \u03bc + \u03b2t + \u03b3 yt-i + \u2211 \u2205\n\ud835\udc5d\n\ud835\udc56=1 i\u2206yt-i + \u025bt                (5) \n\n\n\nWhere \u03bc is the constant term, t is the time trend, i is equal the lag length in \n\u0394yt\u2212i, p is the maximum lag determined using Akaike Information Criterion \n(AIC) and Schwartz Criterion (SC) and \u03b5t is the disturbance term.  \n\n\n\nTime series were also subjected to a Phillips \u2013Perron (PP) test which has \na higher power.  The PP test took the form: \n\n\n\n\u2206Yt = \ud835\udf030 +\u2211 \ud835\udeff\ud835\udc5a\n\ud835\udc56=1 i\u2206Yt-i + \u025bt                 (6) \n\n\n\nWhere \u2206Yt was the first difference of the dependent variable; i is the \nnumber of truncation lags, where i=1, 2,\u2026, m; \ud835\udf03 and \ud835\udeff are coefficients and \n\u025bt is the error term. The null hypothesis of, \ud835\udc3b0: \ud835\udeff\ud835\udc56 = 0 (unit root) was tested \nagainst the alternative, \ud835\udc3b\ud835\udc34: \ud835\udeff\ud835\udc56< 0 (no unit root). If the computed test \nstatistic was found greater than the critical value at 5% level of \nsignificance, then the null hypothesis could not be rejected. If \ud835\udc3b\ud835\udc42 could not \nbe rejected, then the time series variable contained a unit root and hence \nnonstationary, otherwise it was stationary. \n\n\n\nIn addition to the unit root and diagnostic tests, the experimental design \nand scheme to be followed needs to be explained clearly to lead the \nprocess of carrying out research as per defined objective of the study and \nreach results intended. Any experiment design process should start by \ndefining the objective and variables to be included in the experiment. The \nplanning and designing of the study will be explained. Next, the hypothesis \nof the investigation should be defined which may state \u2018climate change has \nno impact on yields of barley\u2019\u2019 against the alternative hypothesis of \n\u2018\u2018climatic and non-climatic factors have impacts on barley yield\u2019\u2019.  Data \ncollection and modeling of the study will be conducted. The collected data \nwill be analyzed, and results interpreted with focus on whether the \nhypothesis confirmed the expected results or not. Finally, conclusions will \nbe presented explaining the main results of the study and proposed future \nresearch direction. Figure 3 presents an experimental flowchart to plan, \nconduct and complete the investigation under consideration.  \n\n\n\n\n\n\n\nFigure 1: Experimental Flowchart to plan, conduct and complete the \ninvestigation under consideration \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1   Results of Unit Root Tests \n\n\n\nThe unit root test results are presented in Table 1 below. According to the \ntest results, the following variables are stationary at level or order I(0): \nlnBaY, lnBaAr, lnBaIrrgAr, LnFert, LnBSeed, and LnMaxTemp. Conversely, \nthe following variables were found to be integrated of order I (1): LnSSR, \nLnLSR and LnMinTemp. The unit root result exhibited a mixture of I(0) \nand I (1). Whenever, the unit root test demonstrated a mixture of I (0) and \nI (1) among the data series, most researchers and econometricians \nrecommend use of Cobb-Douglas Production or ARDL modeling as the best \napproach (Sharma and Singh, 2019; Dushko, et al., 2011).  \n\n\n\nTable 1: Results of the Unit Root Tests \n\n\n\nVariable \nTable 1: \n\n\n\nADF PP \n\n\n\nResult \nLevel First Difference Level First Difference \n\n\n\nComputed t-\nStatistic \n\n\n\nCritical \nValue \n\n\n\nComputed t-\nStatistic \n\n\n\nCritical  \nValue \n\n\n\nComputed t-\nStatistic \n\n\n\nCritical  \nValue \n\n\n\nComputed \nt-Statistic \n\n\n\nCritical \nValue \n\n\n\nLnBaY -0.2272*** -4.25288 -6.19942 -4.2436 -2.1793*** -4.2119 -25.041 -4.21913 I(0) \n\n\n\nlnBaAr -3.6455*** -4.21187 -8.5007 -4.21913 -3.6395*** -4.21187 -19.2650 -3.19831 I(0) \n\n\n\nLnBaIrrgAr -3.6975*** -4.21187 -6.9341 -3.20032 -3.7260*** -4.21187 -11.7046 -3.19831 I(0) \n\n\n\nLnFert -2.9416*** -4.21187 -7.2393 -4.21913 -2.9228*** -4.21187 -13.18991 -3.19831 I(0) \n\n\n\nLnBSeed -1.9332*** -4.21187 -4.9361 -4.23497 -1.7293*** -4.21187 -7.30698 -4.21913 I(0) \n\n\n\nLnSSR -6.41428 -4.21914 3.8001*** -3.2003 -8.47373 -4.21188 -23.27511 -4.21913 I(1) \n\n\n\nLnLSR -4.91008 -3.52976 4.0254*** -4.24364 -4.88583 -3.19641 -20.97917 -4.21913 I(1) \n\n\n\nLnMinTemp -6.35686 -3.19641 -2.50206* -2.89000 -6.12426 -3.19641 -13.78382 -3.19831 I(1) \n\n\n\nLnMaxTemp -0.97548* -3.77000 -6.82005 -3.20245 -31.0864 -3.19641 -122.4843 -3.19831 I(0) \n\n\n\n*, ** and *** indicates significance level at 10%, 5% and 1%, respectively \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 14-19 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Modeling Factors Influencing Barley Yield in Ethiopia:  \n\n\n\nAugmented Cobb-Douglas Production Approach. Journal of Sustainable Agricultures, 7(1): 14-19. \n\n\n\n\n\n\n\n3.2   Diagnostic Tests \n\n\n\nNext to unit root test, diagnostic tests were conducted to detect the \n\n\n\npresence of serial correlation and multicollinearity in the data series. The \n\n\n\ntests demonstrated presence of no serial correlation in the regression \n\n\n\nmodels as observed residuals are significant at 5% level and the Durbin \n\n\n\nWatson statistic is almost close to 2 in most cases. The test also indicates \n\n\n\nthat there is no effect of multicollinearity as the p-values associated with \n\n\n\nthe test statistic is greater than 0.05 for the barley crop yield model. See \n\n\n\nTable 2 for details. \n\n\n\nTable 2: Diagnostic Test Results for Barley Yield Equation \n\n\n\nType of test Test statistic \nTest statistic \n\n\n\nvalue \nProbability \n\n\n\nNormality test \nD'Agostino-\nPearson test \n\n\n\n4.1545 0.4995 \n\n\n\nSerial Correlation \nLM Test \n\n\n\nObs*R-\nsquared \n\n\n\n0.7480 0.1060 \n\n\n\nHeteroskedasticity \nTest: ARCH \n\n\n\nObs*R-\nsquared \n\n\n\n0.7220 0.0760 \n\n\n\n3.3   Modeling Effects Exerted by Climate and Related Inputs on \nBarley Yield  \n\n\n\nThe Cobb-Douglas Production model was estimated using MedCal-Version \n\n\n\n19.1 software and SPSS 24 Statistical Packages. The model was estimated \n\n\n\nusing OLS technique. The elasticity coefficients estimated for the Cobb-\n\n\n\nDouglas Production model was found significant since the F-value \n\n\n\n(11.4996) indicates an overall regression model was fitted good following \n\n\n\nnormal distribution for the present data. The D'Agostino-Pearson test \n\n\n\nconducted to check distribution properties of the model proposed to \n\n\n\naccept normality at (P=0.4995). Furthermore, the adjusted R2 was found \n\n\n\nto be 0.683 implying that 68.3% of the variations in the model have been \n\n\n\nexplained by the input variables included in the analysis, implying good \n\n\n\nfitness of the estimated model to the data series. \n\n\n\nThe explanatory variables considered in the model were transformed into \n\n\n\ntheir logarithmic form so as to provide convenient interpretations of the \n\n\n\nelasticity coefficients and to reduce heterogeneity of the variance. The \n\n\n\nclimatic parameters considered in the estimation of Cobb-Douglas \n\n\n\nProduction model included: short-season rainfall, long-season rainfall, \n\n\n\nCGP average minimum and maximum temperatures (Feb-Sept). \n\n\n\nFurthermore, land area cultivated under barley crop, barley crop irrigated \n\n\n\narea, quantity of fertilizer and barley seed applied on barley production \n\n\n\nwere selected and incorporated in the barley yield model.  \n\n\n\nThe elasticity coefficients estimated for the variables included in the \n\n\n\nbarley yield model analysis are presented in Table 3. The elasticity \n\n\n\ncoefficients estimated demonstrated that the climatic factors included in \n\n\n\nthe model, except long-season rainfall, had positive relationship with the \n\n\n\nyield of barley, although statistically insignificant. The result implies that \n\n\n\nshort-season rainfall, nationally known as belg season and CGP average \n\n\n\nminimum and maximum temperatures have minimal positive impact on \n\n\n\nyield of barley. The positive elasticity of short-season rainfall is justified \n\n\n\nby the fact that short duration barley crops are mainly grown in the mid-\n\n\n\nhighlands of Bale, North Central Shewa, and North and South Wollo zones \n\n\n\nfrom February to May season. According MoA report, short/belg-season \n\n\n\ncontributes less than 10% of the total grain production in the country; it is \n\n\n\nalso crucially important for seed-bed preparation for both the short and \n\n\n\nlong-cycle meher crops, and for planting long-cycle cereal crops such as \n\n\n\nmaize, sorghum, and millet MoA (2001).  \n\n\n\nConversely, the long-season rainfall demonstrated negative impact on \n\n\n\nyield of barley, although statistically insignificant. The negative impact \n\n\n\nregistered on yield of barley during main season can be due to extreme \n\n\n\nrain events such as high rainfall above optimum requirements of the crop \n\n\n\nand scarcity of rainfall in some pocket areas. High rainfall above optimum \n\n\n\nrequirement can cause flooding, logging of crops and landslides which also \n\n\n\naffects yield of barley. Scarcity of rainfall during critical crop growth \n\n\n\nperiods can lead to wilting of the stalk of the crop; inhibit proper \n\n\n\nvegetative growth of the crop; and shrink grain filling. This infers that \n\n\n\ncultivation of barley in Ethiopia moderately depends on rainfall. The \n\n\n\nfinding of this study is analogous to that of (Kim and Pang, 2009). They \n\n\n\nconducted a study on the impacts exerted by climatic factors on rice yield \n\n\n\nin Korea and reported that precipitation has negative impact on the \n\n\n\naverage yield of rice.  \n\n\n\nAccording to them, the elasticity coefficients estimated for precipitation \n\n\n\nwere in the range of -0.14 ~ -0.05, which are relatively small. The study \n\n\n\nresults of Singh and Sharma also support the current study (Singh and \n\n\n\nSharma, 2018). Singh and Sharma in their study of measuring the \n\n\n\nproductivity of food-grain crops in different climate change scenarios in \n\n\n\nIndia found that actual rainfall in Rabi season has negatively associated \n\n\n\nwith barley yield while average minimum and maximum temperatures \n\n\n\nhad positive impact on barley yield, which implies that average minimum \n\n\n\nand maximum temperatures are beneficial for yield of barley during Rabi \n\n\n\nseason (Singh and Sharma, 2018). Conversely, they reported that yield of \n\n\n\nbarley is negatively and adversely affected due to increased actual rainfall \n\n\n\nduring crop growth period.  \n\n\n\nConversely, both CGP average maximum and minimum temperatures have \n\n\n\nexhibited a positive relationship with the yield of barley over the \n\n\n\nobservation period, which negates the theory and expected results. The \n\n\n\nresults indicate that a 1% increase in temperature parameters will \n\n\n\nincrease yield of barley by 0.81% and 0.035% respectively. Some studies \n\n\n\nindicate that the effect of increased temperature will depend on the crop\u2019s \n\n\n\noptimum temperature requirement for growth and production for a \n\n\n\nparticular crop like barley (USGCRP, 2014). In this context, warming may \n\n\n\nbenefit the types of crops that are typically planted in some areas or allow \n\n\n\nfarmers to shift to crops that are currently grown in warmer areas within \n\n\n\ncrop\u2019s optimum temperature requirement. However, if the higher \n\n\n\ntemperature exceeds a crop's optimum temperature, yields will decline.  \n\n\n\nAccording to Jacobs, barley requires a mild climate and grows better in \n\n\n\ndry, cool climates than in hot, moist areas (Jacobs, 2016). It is well adapted \n\n\n\nto high altitudes with cold, short season areas. The species possesses \n\n\n\nmoderate resistance to cold, but winter barleys are less winter hardy than \n\n\n\nwinter wheat, triticale or cereal ryes. In Ethiopia, barley well grows in mid-\n\n\n\nhighland and highland climates where temperature is cool. Some cultivars \n\n\n\nof barley also adapt to dryland areas where temperature is cold dry \n\n\n\ncondition. The finding of this study is analogous to that of (Kim and Pang, \n\n\n\n2009). In their study on the impact of climate change on rice yield in Korea, \n\n\n\nthey reported that temperature is positively related to average rice yield. \n\n\n\nThe elasticity for temperature is calculated as 0.82-0.89; thus a 1% rise in \n\n\n\ntemperature increases the average rice yield by 0.8 \u2013 0.9%. \n\n\n\nSimilarly, elasticity coefficients for non-climatic inputs such as irrigated \n\n\n\nbarley area, fertilizer and barley seed quantity consumed over the \n\n\n\nobservation period showed positive impact on barley yield while land area \n\n\n\nallocated for barley crop cultivation had negative impact on barley yield, \n\n\n\nalthough statistically not significant. Fertilizer and barley seed inputs had \n\n\n\npositive and significant (at 1% and 5% level) impact on barley yield. The \n\n\n\nresult indicated that a 1% increase in use of fertilizer and barley seed per \n\n\n\nunit area will increase barley yield by 0.41% and 0.06% respectively. The \n\n\n\nresult implies that barley yield is highly responsive to use of fertilizer and \n\n\n\nimproved seed inputs. Estimates of this study are similar to those of \n\n\n\n(Kumar and Sharma, 2013; Singh and Sharma, 2018).  \n\n\n\nKumar and Sharma conducted a study examine the impacts exerted by \n\n\n\nclimatic factors on agricultural productivity in India and reported that \n\n\n\nirrigated area and total fertilizer consumption positively affected barley \n\n\n\nyield, fertilizer consumed being significant at 1% level (Kumar and \n\n\n\nSharma, 2013). The result indicates that a 1% increase in fertilizer use led \n\n\n\nto an increase of barley yield by 0.12%.  Equally, Singh and Sharma in their \n\n\n\nstudy on productivity of food grain in India during Rabi season found that \n\n\n\ncropped area and irrigated area under barley crop had positive impact on \n\n\n\nbarley yield, the elasticity coefficients being 0.7356 and 0.0569 (Singh and \n\n\n\nSharma, 2018). These coefficients, however, are statistically insignificant. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 14-19 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Modeling Factors Influencing Barley Yield in Ethiopia:  \n\n\n\nAugmented Cobb-Douglas Production Approach. Journal of Sustainable Agricultures, 7(1): 14-19. \n\n\n\n\n\n\n\nTable 3: Estimates of Cobb-Douglas Production Function from Barley Yield Model \n\n\n\nIndependent variables Coefficient Std. Error t-stat P-value VIF \n\n\n\n(Constant) 2.6724     \n\n\n\nlnBaArea -0.01023 0.2162 -0.0473 0.9626 1.549 \n\n\n\nlnBaIrrigarea 0.008872 0.05726 0.155 0.8779 1.282 \n\n\n\nlnFertQ 0.4076*** 0.06314 6.455 <0.0001 1.526 \n\n\n\nlnBSeed 0.0757** 0.02818 2.687 0.0115 1.646 \n\n\n\nlnSSR 0.08750 0.1579 0.554 0.5835 1.399 \n\n\n\nlnLSR -0.06383 0.2709 -0.236 0.8153 1.533 \n\n\n\nlnMinTemp 0.8118 0.6591 1.232 0.2273 2.056 \n\n\n\nlnMaxTemp 0.03473 0.3529 0.0984 0.9222 1.890 \n\n\n\nSample size 40 \n\n\n\nCoefficient of determination R2 0.7480 \n\n\n\nR2-adjusted 0.6829 \n\n\n\nMultiple correlation coefficient 0.8648 \n\n\n\nResidual standard deviation 0.1526 \n\n\n\nF-Statistic 11.4996 \n\n\n\nD'Agostino-Pearson test for Normal distribution accept Normality (P=0.4995) \n\n\n\n** & *** indicate significance level at 5% and 1% respectively\n\n\n\n4.   CONCLUSION \n\n\n\nAmong the climate factors analyzed in this study, short/belg-season \nrainfall and temperature variables revealed a positive relationship with \nthe barley yield, although statistically insignificant. The result implies that \nshort/belg-season rainfall and average minimum and maximum \ntemperatures have minimal positive impact on the yield of barley. The \npositive elasticity coefficient of short-season/belg rainfall is justified by \nthe fact that short duration barley crops are grown in the mid-highlands \nof Bale, North Central Shewa, and North and South Wollo zones from \nFebruary to May season. The short/belg-season which contribute less than \n10% of the total grain production in the country, is crucially important for \nseed-bed preparation for both short and long-cycle meher crops as well as \nplanting long-cycle cereal crops such as maize, sorghum, and millet. \nConversely, long/meher-season rainfall demonstrated negative impact on \nthe yield of barley, although statistically insignificant. The negative impact \nregistered on yield of barley during main/meher-season can be due to \nextreme rain events such as high rainfall above optimum requirement of \nthe crop and scarcity of rainfall in some pocket areas. High rainfall above \noptimum requirement can cause flooding, logging of crops and landslides \nwhich also affects barley yield. Scarcity of rainfall during critical crop \ngrowth periods can lead to wilting of the stalk of the crop; inhibit proper \nvegetative growth; and shrinks grain filling. This infers that cultivation of \nbarley in Ethiopia moderately depends on rainfall.  \n\n\n\nSubsequently, the elasticity coefficients of irrigated barley area, fertilizer \nand barley seed quantities applied on barley production over the \nobservation period demonstrated positive impact on barley yield \nperformance.  Fertilizer and barley seed inputs had positive and \nsignificant (at 1% and 5% level) impact on barley yield during the \nobservation period. The result implies that barley yield is highly \nresponsive to use of fertilizer and barley seed inputs and moderately \nresponsive to irrigation input. Conversely, barley land area had negative \nimpact on barley yield, although statistically insignificant. In view of the \nfindings of this study and its limitation on impacts imposed by climate \nfactors, the direction of future research should focus on impact analysis as \nwell as mitigation and adaptation strategies to reduce the impacts exerted \non barley crop by climatic and related factors. The current study focused \non impacts of climatic factors imposed on barley yield and neglected \ninclusion of mitigation and adaptation strategies that reduce the impacts \nexerted by climate and related factors on yield of barley.  \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThe author is grateful to the different Institutions that provided the \nvarious dataset used in this study. \n\n\n\nDATA AVAILABILITY \n\n\n\nThe data used for this study can be made available upon request provided \nthere is going to be compliance with the owners\u2019 policy concerning \nsharing. \n\n\n\nFUNDING \n\n\n\nThis work was fully covered by the author; no external funding supported \nthe research work.  \n\n\n\nAUTHOR\u2019S CONTRIBUTIONS \n\n\n\nThe author has contributed to the study of conception and design. The \nauthor (Abera Gayesa Tirfi) has also performed all the material \npreparation, data collection and analysis, and writing up of the \nmanuscript.  \n\n\n\nDECLARATION OF COMPETING INTEREST \n\n\n\nThe author declares that there have been no competing interests. \n\n\n\nREFERENCES \n\n\n\nAlemu, G., and Haji, J., 2016. Economic Efficiency of Sorghum Production \nfor Smallholder Farmers in Eastern Ethiopia: The Case of Habro \nDistrict; Journal of Economics and Sustainable Development, 7 (15), \nPp. 44 \u2013 51. \n\n\n\nAragie, A.E., 2013. Climate change, growth, and poverty in Ethiopia. \nVolume-3. \n\n\n\nAraya, A., 2021. Modeling the effects of crop management on food barley \nproduction under a midcentury changing climate in northern \nEthiopia; Climate Risk Management, 32, Pp. 1 \u2013 15. \n\n\n\nAsfaw, Z., 2000. The barleys of Ethiopia; In: S. B. Brush (edit), 2000; GENES \nin the FIELD: On-Farm Conservation of Crop Diversity, International \nDevelopment Research Centre and International Plant Genetic \nResources Institute, Lewis Publishers, New York.  \n\n\n\nAssamere, A., Mamuye, A., Amare, K., and Fiseha, M., 2021. Diversity, \ndistribution, agronomic and post-harvest management of local \nbarley (Hordeum vulgare L.) variety in South Wolli, Ethiopia. PLOS \nONE, 16 (5): https://doi.org/10.1371/journal.pone.0250899.       \n\n\n\nBekele, K., 2019. Modeling Climate Change and its Impacts on Food Barley \n(HorduemvulgareL.) Production using Different Climate Change \nScenarios in Lemubilbilo District, Oromia Regional State, Ethiopia, \nInternational Journal of Research in Environmental Science, 5 (3), Pp. \n33 \u2013 40. \n\n\n\nCammarano, 2019. Rainfall and temperature impacts on barley (Hordeum \nvulgare L.) yield and malting quality in Scotland; Field Crop Research, \n241, https://doi.org/10.1016/j.fcr.2019.107559. \n\n\n\nChen, C., 2004. Yield variability as influenced by climate: A statistical \ninvestigation. Climate Change, 66, Pp. 239-261. \n\n\n\nCSA, 2018. Report on area and production of major crops in Ethiopia; \nagricultural sample survey 2017/18, Addis Ababa. \n\n\n\nCSA, 2020. Report on area and production of major crops, Agricultural \nSample Survey 2019/20, Bulletin 587, Addis Ababa, Ethiopia. \n\n\n\nDickey, D.A., and Fuller, W.A., 1979. Distributions of the estimators for \nautoregressive time series with a unit root, Journal of American \nStatistical Association, 74, Pp. 427- 431. \n\n\n\n\nhttps://doi.org/10.1371/journal.pone.0250899\n\n\nhttps://doi.org/10.1016/j.fcr.2019.107559\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 14-19 \n\n\n\n\n\n\n\n \nCite The Article: Abera Gayesa Tirfi (2023). Modeling Factors Influencing Barley Yield in Ethiopia:  \n\n\n\nAugmented Cobb-Douglas Production Approach. Journal of Sustainable Agricultures, 7(1): 14-19. \n\n\n\n\n\n\n\nDushko, J., Darko, L., and Cane, K., 2011. Cobb-Douglas production function \nrevisited, VAR and VECM analysis and a note on Fischer/Cobb-\nDouglass paradox, Munich Personal RePEc Archive, available at: \nhttps://mpra.ub.uni-muenchen.de/33576/.  \n\n\n\nEthiopia, 2014. Geography: Ethiopia location and size; available at: \nhttps://cpb-us-w2.wpmucdn.com \u203a files \n\n\n\nFAO (Food and Agriculture Organization), 2015. Adaptation to climate risk \nand food security: Evidence from smallholder farmers in Ethiopia. \nFood and Agriculture Organization, Rome, Italy. \n\n\n\nGashaw, G., and Tura, K., 2015. Review of Barley Value Chain Management \nin Ethiopia. Journal of Biology, Agriculture and Healthcare, 5 (10), Pp. \n84 - 98. \n\n\n\nGujrati, D., 2004. Basic Econometrics, fourth edition. McGraw-Hill, New \nYork, USA.  \n\n\n\nGupta, S., Sen, P., and Srinivasan, S., 2012. Impact of climate change on \nIndian economy: Evidence from food grain yields. Centre for \nDevelopment Economics Working Paper 218, New Delhi. \n\n\n\nJacobs, A.A., 2016. Plant guide for common barley (Hordeum vulgare L.), \nUSDA-Natural Resources Conservation Service, Jamie L. Whitten \nPlant Materials Center. Coffeeville, Mississippi. \n\n\n\nKim, M.K., and Pang, A., 2009. Climate Change Impact on Rice Yield and \nProduction Risk. Journal of Rural Development, 32 (2), Pp. 17 \u2013 29. \n\n\n\nKumar, A., and Sharma, P., 2013. Impact of Climate Variation on \nAgricultural Productivity and Food Security in Rural India. \nEconomics Discussion Papers, Kiel Institute for the World Economy. \nhttp://www.economics-\nejournal.org/economics/discussionpapers/2013-43.   \n\n\n\nMoA, 2001. 2001 Belg Season in North-Central Ethiopia, Report of a joint \nUN-EUE/MoA/FAO mission undertaken from 19 \u2013 29 March 2001; \nhttps://www.africa.upenn.edu-belg.  \n\n\n\nMojapelo, M.C., 2019. Estimation of sorghum supply elasticity in South \nAfrica. Journal of Agribusiness and Rural Development, 2 (52), Pp. \n131 \u2013 138. \n\n\n\nMuluken, B., and Jemal, E., 2011. Achievements in food barley breeding \nresearch in the early production systems of northwest, Ethiopia. In: \nMulatu, B. and Grando, S. (eds), 2011; Review of Barley Research and \nDevelopment in Ethiopia, Proceedings of the 2nd National Barley \nResearch and Development Review Workshop, 28-30 November \n2006, HARC, Holetta, Ethiopia. \n\n\n\nPhillips, P.C., and Perron, P., 1988. Testing for a Unit Root in Time Series \nRegression. Biometrika, 75 (2), Pp. 335-46. \n\n\n\nSharma, S., and Singh, S., 2019. The Validity of Wagner\u2019s Law in India: A \nPost-liberalization Analysis, M.J.P. Rohilkhand University, Uttar \nPradesh, India. \n\n\n\nSingh, A.K., and Sharma, P., 2018. Measuring the productivity of food-grain \ncrops in different climate change scenarios in India: Evidence from \ntime series investigation; Research, Vol. 4 (16): pp 661 \u2013 673. \n\n\n\nUnited Nations Population Funds. 2021. World population dashboard \nEthiopia. Retrieved from: https://www.unfpa.org/data/world-\npopulation/ET. \n\n\n\nUSGCRP, 2014. Hatfield, J., G. Takle, R. Grotjahn, P. Holden, R. C. Izaurralde, \nT. Mader, E. Marshall, and D. Liverman, 2014: Ch. 6: Agriculture. \nClimate Change Impacts in the United States: The Third National \nClimate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. \nYohe, (Eds.), U.S. Global Change Research Program, Pp. 150-174.  \n\n\n\nWiegmann, M., Maurer, A., Pham, A., March, T.J., Al-Abdallat, A., Thomas, \nW.T.B., 2019. Barley yield formation under biotic stress depends on \nthe interplay between flowering time genes and environmental cues, \nScientific Reports, No. 6397. \nhttps://www.nature.com/articles/s41598-019-42673-1.  \n\n\n\nWorld Bank, 2021. Overview: Retrieved from: \nhttps://www.worldbank.org/en/country/ethiopia/overview. \n\n\n\nYawson, D.O., Adu, M.O., Armah, F.A., 2020. Impacts of climate change and \nmitigation policies on malt barley supplies and associated virtual \nwater flows in the UK; Scientific Reports: \nhttp://www.nature.com/scientificreports\n\n\n\n \n\n\n\n\nhttps://mpra.ub.uni-muenchen.de/33576/\n\n\nhttp://www.economics-ejournal.org/economics/discussionpapers/2013-43\n\n\nhttp://www.economics-ejournal.org/economics/discussionpapers/2013-43\n\n\nhttps://www.africa.upenn.edu-belg/\n\n\nhttps://www.researchgate.net/institution/MJP_Rohilkhand_University\n\n\nhttps://www.unfpa.org/data/world-population/ET\n\n\nhttps://www.unfpa.org/data/world-population/ET\n\n\nhttp://nca2014.globalchange.gov/report/sectors/agriculture\n\n\nhttp://nca2014.globalchange.gov/report/sectors/agriculture\n\n\nhttp://nca2014.globalchange.gov/report/sectors/agriculture\n\n\nhttps://www.nature.com/articles/s41598-019-42673-1\n\n\nhttps://www.worldbank.org/en/country/ethiopia/overview\n\n\nhttp://www.nature.com/scientificreports\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 May 2019 \nAccepted 11 June 2019 \nAvailable online 14 June 2019 \n\n\n\nABSTRACT\n\n\n\nThis paper focuses on the impact of climate change on the lives of rural farmers in the Pwalugu and Balungu \n\n\n\ncommunities of the Upper East Region of Ghana since farmers all over the country have been exposed to various \n\n\n\nadaptation strategies to climate change. From the study which was conducted in 2017, it was revealed that \n\n\n\nclimate change affected respondents negatively resulting in reduced income level, inability to afford three \n\n\n\nsquare meals daily, inability to meet their health needs, inability to meet the educational needs of their children \n\n\n\nas well as inability to save at bank. Also, lack of finance, land tenure, norms/customs, lack of storage facilities, \n\n\n\nlack of ready markets, damage to crops by Fulani cattle and difficulty in obtaining seeds for farming were some \n\n\n\nchallenges militating against the adoption of other adaptive strategies to climate change. The farmers therefore \n\n\n\npracticed crop diversification, adjustment in planting date of their crops, irrigation, change method of \n\n\n\nproduction, migration to the southern part of the country during the dry season to work, trading, fishing, among \n\n\n\nothers as their specific adaptive strategies to climate change. The study recommends that, education should be \n\n\n\none of the areas for policy intervention by government/stakeholders since access to education is vital in \n\n\n\ndeveloping specific strategies of rural farmers to the diverse drivers and impacts of climate change on their \n\n\n\nlives. \n\n\n\nKEYWORDS \n\n\n\nspecific adaptive strategies, climate change, impacts, adaptation strategies, challenges militating adopting \n\n\n\nspecific strategies, barriers, Balungu, Pwalugu\n\n\n\n1. INTRODUCTION \n\n\n\nClimate is a renewable resource which varies at all-time scales, from year \n\n\n\nto year, as well as from one decade, century or millennium to the next [1]. \n\n\n\nClimate change is defined as the gradual change in the weather pattern of \n\n\n\nthe world over a long period of time mainly as a result of human activities \n\n\n\nwith respect to the environment [2]. It has become a developmental issue \n\n\n\nacross the world because of its effects on human lives and the future of the \n\n\n\nworld. Climate change is exacerbated by the increase of greenhouse gas \n\n\n\nemission caused by human behaviour [3-6]. \n\n\n\nClimate change is considered one of the most challenging global issues. \n\n\n\nThe most devastating adverse impacts of climate change in most \n\n\n\nsubtropical countries includes frequent drought, increased environmental \n\n\n\ndamage, increased infestation of crop by pests and diseases, depletion of \n\n\n\nhousehold assets, increased rural urban migration, increased biodiversity \n\n\n\nloss, depletion of wildlife and other natural resource base, changes in the \n\n\n\nvegetation type, decline in forest resources, decline in soil conditions (soil \n\n\n\nmoisture and nutrients), increased health risks and the spread of \n\n\n\ninfectious diseases, changing livelihood systems, among others [7,8]. \n\n\n\nScientific evidence also suggests that climate change has long term \n\n\n\nnegative impacts on agricultural productivity globally [9].  Resource-\n\n\n\ndependent livelihoods, such as farming, fishing, herding and hunting, face \n\n\n\nquite diverse and distinct climate change risks including directly from \n\n\n\nheavy rains, high winds, drought, fires, invasive species, glacier retreat,  \n\n\n\nocean acidification and sea level rise [10,11]. The severity of these impacts  \n\n\n\nwill ultimately be experienced differently, depending on location. For \n\n\n\ninstance, in the tropics and subtropics, crop yields are likely to fall by 10% \n\n\n\nto 20% because of increased climate variability [12]. \n\n\n\nInternational studies suggest that Africa is particularly vulnerable to \n\n\n\nclimate change and variability [13]. Climate change therefore particularly \n\n\n\naffects food security, livelihoods and social safety adversely and in so \n\n\n\nmany ways [14]. This is because climate change directly affects \n\n\n\nagricultural production and food availability. This is primarily due to the \n\n\n\nfact that agriculture is inherently sensitive to climate conditions and is one \n\n\n\nof the most vulnerable sectors to the risks and impacts of global climate \n\n\n\nchange [15]. In order to achieve food self-sufficiency and security, impacts \n\n\n\nof climate change on crop production and food availability should be a \n\n\n\npriority area for governments around the world [16,17]. \n\n\n\nHuman reactions to climate change are generally classified into two major \n\n\n\ncategories: mitigation through reducing greenhouse gas emissions, and \n\n\n\nadaptation to the changes posed by climate change. A researcher explains \n\n\n\nclimate change adaptation as the actions that people take in response to, \n\n\n\nor in anticipation of, projected or actual changes in climate, to reduce or \n\n\n\neliminate adverse impacts or to take advantage of the opportunities \n\n\n\ncreated by climate change. Studies carried out by IPCC, suggested that \n\n\n\nadaptation will be necessary to address impact resulting from the \n\n\n\nwarming which is already unavoidable due to past emissions. \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.02.2019.35.45 \n\n\n\n  RESEARCH ARTICLE \n\n\n\nIMPACT OF CLIMATE CHANGE ON FARMERS IN THE TALENSI DISTRICT OF THE \nUPPER EAST REGION OF GHANA \n\n\n\nDamian Felladam Tangonyire \n\n\n\nDepartment of Agriculture for Social Change, Regentropfen College of Applied Sciences, Kansoe-Bongo, Upper East Region, Ghana \n\n\n\n*Corresponding Author Email: damian.tangonyire@recas-ghana.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:damian.tangonyire@recas-ghana.com\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nMany of the African nation\u2019s economies are dependent on sectors that are \n\n\n\nvulnerable to climate conditions, such as agriculture, fisheries, forestry, \n\n\n\nand tourism. A group of researchers reported that agriculture and natural \n\n\n\nresources provide livelihoods for 70% to 80% of the population, and \n\n\n\naccount for 30% of GDP and 40% of export revenue in Sub-Saharan Africa \n\n\n\n[18]. Agriculture employs about 60% to 90% of the total labor force in Sub-\n\n\n\nSaharan Africa [19]. \n\n\n\nIn Ghana, agriculture is primarily small-scale and is the backbone of the \n\n\n\neconomy. It contributes about 35% of Ghana\u2019s GDP, generates about 30-\n\n\n\n40% of the foreign exchange earnings, and employs about 55% of the \n\n\n\npopulation [20]. Despite its high contribution to the overall economy, this \n\n\n\nsector is challenged by many climate-related disasters like prolonged dry \n\n\n\nspells and floods which are most often severe in Northern Ghana. As a \n\n\n\nresult, individuals, households and communities therefore engage in a \n\n\n\nnumber of activities and strategies in order to earn a living. Prominent \n\n\n\namong these livelihood activities and strategies in rural areas is farming \n\n\n\nwhich incidentally is the worse hit by climate change. Extensive research \n\n\n\ncarried out on the impacts of climate change on agriculture revealed \n\n\n\nepisodes of late rains for planting, variability in the pattern and levels of \n\n\n\nrainfall, and intermittent droughts and floods to be the fundamental \n\n\n\nproblems for farmers in Northern Ghana [21-23]. This has become a threat \n\n\n\nto the livelihoods of farmers in this particular zone. \n\n\n\nA researcher observed that, the dry season is increasing in length and \n\n\n\nbecoming more severe [24]. Temperatures are increasing and everyone, \n\n\n\nincluding farmers and rural dwellers, are conscious of this fact. Rainfall \n\n\n\nhas become increasingly erratic resulting in frequent droughts and floods \n\n\n\nwithin the same seasons.  The effect of all these is increase in food and \n\n\n\nnutrition insecurity. For instance, in 2007, the delayed rains in Northern \n\n\n\nGhana were followed by heavy rains resulting in farmers planting the same \n\n\n\ncrop fields several times during the season. Many farmers ran short of seed \n\n\n\nto plant. There was also extensive flooding that destroyed farms, livestock \n\n\n\nand poultry. The resultant effect was serious food and nutrition insecurity \n\n\n\nin almost all farming households in Northern Ghana, particularly the \n\n\n\nUpper East and Upper West Regions and in other regions of the country in \n\n\n\ngeneral. \n\n\n\nIn view of these fluctuations in the rainfall pattern and corresponding \n\n\n\nchanges in food availability, farmers in Northern Ghana have developed \n\n\n\nintricate strategies to adapt to climate change. For instance, some farmers \n\n\n\nuse traditional methods of weather forecast like behaviour of plants and \n\n\n\nanimals to predict weather conditions and decide when to prepare lands \n\n\n\nand sow seeds [25-27]. This indigenous knowledge makes it possible for \n\n\n\nfarmers to adequately prepare in advance for any climatic catastrophe. \n\n\n\nSome farmers in the Talensi District have adopted the dry season farming \n\n\n\nalong the White Volta as a major farming approach to address the rainfall \n\n\n\nissues. Crop diversification is also practiced by some farmers as a viable \n\n\n\nstrategy to resist shocks, decrease the risk of crop failure and in so doing \n\n\n\nreduce their vulnerability of livelihood to climate change [28]. This \n\n\n\ntherefore means that adaptation to climate change is not new to farming \n\n\n\nhouseholds and communities [29]. This study therefore seeks to \n\n\n\ndetermine the impact of climate change on farmers particularly in the \n\n\n\nTalensi district which is located in the Upper East Region of Ghana. \n\n\n\nSpecifically, the study aims to determine the specific adaptive strategies \n\n\n\nused by farmers in response to climate change and the reasons behind \n\n\n\nthose adaptive strategies as well as determine barriers militating against \n\n\n\nthe adoption of other adaptive strategies to climate change. \n\n\n\n2. METHODOLOGY \n\n\n\n2.1 Profile of study area \n\n\n\n2.1.1 Location and demographic characteristics of the study area \n\n\n\nBalungu and Pwalugu are the two communities located in the Talensi \n\n\n\ndistrict which were used for the study from March to July 2017. The \n\n\n\nTalensi district came into existence after the Nabdam district was created \n\n\n\nout of the then Talensi-Nabdam district in 2012. (Establishment \n\n\n\ninstrument 2012) [30]. It is located in the Upper East Region and has \n\n\n\nTongo as its capital. It is bordered to the North by the Bolgatanga \n\n\n\nMunicipal, South by the West and East Mamprusi districts (both in the \n\n\n\nNorthern Region), Kassena-Nankana district to the West and Nabdam \n\n\n\ndistrict to the East. The district lies between latitude 10\u00ba 15' and 10\u00ba 60' \n\n\n\nnorth of the equator and longitude 0\u00ba 31' and 1\u00ba 05' and west of the \n\n\n\nGreenwich meridian. It has a total land area of 838.4 km\u00b2. The population \n\n\n\nof Talensi district as indicated by the 2000 population and housing census \n\n\n\nwas 77,007.00 made up of 38,658 male and 38,349 females representing \n\n\n\n50.20 % male and 49.80% female respectively. \n\n\n\n2.1.2 Vegetation and climate \n\n\n\nThe vegetation is Guinea Savannah woodland consisting of sparse short \n\n\n\ndeciduous trees and a ground flora of grass. The most common economic \n\n\n\ntrees are sheanuts, dawadawa, baobab and acacia. \n\n\n\nThe climate is tropical with two distinct seasons: a rainy season, which is \n\n\n\nerratic and runs from May to October, and a dry season that stretches from \n\n\n\nOctober to April.   The mean annual rainfall for the district is 95mm and \n\n\n\nranges between 88mm-110mm. The area experiences a maximum \n\n\n\ntemperature of 45 degrees Celsius in March and April and a minimum of \n\n\n\n32 degrees Celsius in December. \n\n\n\n2.2.1 Study approach and design \n\n\n\nA combination of participatory methods, including key informant \n\n\n\ninterviews, household questionnaire surveys and focus-group discussions \n\n\n\nwere employed, allowing local farmers the opportunity to participate by \n\n\n\nsharing their experiences and knowledge to outline possible solutions to \n\n\n\nthe problem at hand. Multiple methods are good at reducing the \n\n\n\ninadequacies of a single method [31]. \n\n\n\nCross sectional study was used in designing the research. Variables were \n\n\n\nmeasured or determined at the same time in a given population in a cross-\n\n\n\nsectional study. This method allowed the assessment of practices, \n\n\n\nattitudes, knowledge and beliefs of a population in relation to a particular \n\n\n\nevent or phenomenon [32]. \n\n\n\n2.2.2 Selection of communities and sampling techniques \n\n\n\nTalensi district was used as the study area. Farmers in this area are \n\n\n\nactively involved in crop production during both the rainy and dry \n\n\n\nseasons. The communities in this district were purposely selected based \n\n\n\non the following criteria; communities highly vulnerable to the impacts of \n\n\n\nclimate change due to the unimodal rainfall pattern and communities \n\n\n\nwhose major sources of livelihoods are highly climate dependent. Two \n\n\n\ncommunities namely Pwalugu and Balungu were therefore purposively \n\n\n\nselected for the study. \n\n\n\nThis study also employed simple random sampling techniques to identify \n\n\n\nthe respondents in the two communities since a complete list of active \n\n\n\nfarmers in each community was obtained. Numbers were assigned to the \n\n\n\nnames of the farmers and the numbers which were randomly selected \n\n\n\nwere the respondents that the questionnaires were administered to. The \n\n\n\ntotal respondents in each community was determined using Yamane table \n\n\n\n(1967).  A sample size of hundred (100), fifty (50) from each community \n\n\n\nwas used for the study which was proportional to the size of their \n\n\n\npopulations (Balungu total population = 754, Pwalugu total population = \n\n\n\n1001 \n\n\n\n2.2.3 Data analysis and presentation \n\n\n\nThe data obtained from the respondents were coded in Statistical Package \n\n\n\nfor Social Sciences (SPSS) version 20 to enable appropriate statistical \n\n\n\nanalysis to be made. Chi-square tests were also performed at 5% level on \n\n\n\nall the variables to determine significant differences between the male and \n\n\n\nfemale respondents in each community as well as between the two \n\n\n\ncommunities. \n\n\n\nQuantitative data were analyzed using descriptive statistics whiles \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nqualitative data from interviews and focus groups were coded and indexed \n\n\n\nthrough intensive content analysis in order to identify major themes. Due \n\n\n\nto the difficulty involved in understanding and interpreting raw data, \n\n\n\nMicrosoft Excel was then used to generate frequency tables, cross \n\n\n\ntabulations and bar graphs to facilitate easy understanding and \n\n\n\ninterpretations. \n\n\n\n3. RESULTS \n\n\n\n3.1. Impacts of climate change on respondents in the two \n\n\n\ncommunities \n\n\n\nFigure 1 describes the impact of climate change on respondents. Changes \n\n\n\nin climate have affected respondents greatly and in diverse ways. In \n\n\n\nBalungu community, reduced income level resulting from crop failure was \n\n\n\nthe major impact of climate change (80.0%). This was followed by inability \n\n\n\nto meet the educational needs of their children (46.0%). In Pwalugu, the \n\n\n\nimpacts of climate change on respondents are the same as in Balungu with \n\n\n\nreduced income level as the greatest impact of climate change on \n\n\n\nrespondents representing 98.0% and inability to diversify as the least \n\n\n\nimpact of climate change representing 8.0%.  Reduced income level (p \n\n\n\nvalue=0.004, df = 1, x2 = 8.274) was the only impact of climate change \n\n\n\nidentified to be statistically significant between the male and female \n\n\n\nrespondents in the two communities. \n\n\n\nFigure 1: Impacts of climate change on respondents in the two communities \n\n\n\n3.2 Adaptation strategies employed by respondents and the reasons \n\n\n\nbehind those adaptive strategies \n\n\n\n3.2.1 Adaptation strategies of respondents \n\n\n\nVarious adaptation strategies have been used by farmers to reduce the \n\n\n\nimpact of climate change on them. Crop diversification (representing \n\n\n\n99%) was the major adaptation strategy embraced by the two \n\n\n\ncommunities with migration (representing 2%) being the least adopted \n\n\n\nstrategy. Similar adaptation strategies were employed by respondents in \n\n\n\nboth communities. In Balungu, crop diversification was the most adapted \n\n\n\nstrategy (98%), followed by trading (74%), with the least been migration \n\n\n\n(2%) and 100%, 84% and 2% respectively in Pwalugu. \n\n\n\nIrrigation (p value=0.015, df = 1, x2 = 5.894), trading (p value=0.024, df = \n\n\n\n1, x2 = 5.109) and fishing (p value=0.001, df = 1, x2 = 10.212) were the \n\n\n\nadaptation strategies that were statistically significant between the male \n\n\n\nand female respondents in Balungu whiles only fishing (p value=0.009, df \n\n\n\n= 1, x2 = 6.832) was statistically significant between the male and female \n\n\n\nrespondents in Pwalugu. The details of the various adaptation strategies \n\n\n\nare shown in Table 1 \u201cOthers\u201d as in adaptation strategies in the Table 1 \n\n\n\nrefers to hunting, driving, fishing, menial jobs, collection of fruits, \n\n\n\nestablishment of tree plantations and other construction works. \n\n\n\nFocus group discussions and key informant interviews from both \n\n\n\ncommunities also revealed that, the men are into the establishment of tree \n\n\n\nplantations whiles the women are into the collections of fruits such as \n\n\n\nguava, black berries, among others and tree products as long-term \n\n\n\nstrategies to climate change. \n\n\n\nTable 1: Adaptation strategies of respondents \n\n\n\nCommunity  Sex \n\n\n\nAdaptation strategies \n\n\n\nAdjustment \n\n\n\nin planting \n\n\n\ndate \n\n\n\nCrop \n\n\n\ndiversification Irrigation \n\n\n\nChange \n\n\n\nmethod of \n\n\n\nproduction Migration Trading Fishing Others \n\n\n\n N    (%)  N    (%)   N      (%)    N    (%) N      (%)   N    (%)  N    (%)   N    (%) \n\n\n\nBalungu Male \n 14    (28.0)  29     (58.0)   7      (14.0)   13     (26.0) 1    (2.0)   18    (36.0)  11    (22.0)   10   (20.0) \n\n\n\nFemale \n 11    (22.0)  20     (40.0)   0      (0.0)   4    (8.0) 0    (0.0)   19    (38.0)  0      (0.0)   3     (6.0) \n\n\n\n N=50 Total \n 25    (50.0)  49     (98.0)   7      (14.0)   17     (34.0) 1    (2.0)   37    (74.0)  11    (22.0)   13   (26.0) \n\n\n\nP value \n 0.774   0.235    0.015*   0.058 0.390   0.024* 0.001**   0.108 \n\n\n\nPwalugu Male \n 19    (38.0)  36     (72.0)   28    (56.0)   5    (10.0) 1    (2.0)   30   (60.0)  13    (26.0)   5     (10.0) \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\n N=50 Female \n 4    (8.0)  14     (28.0)   11    (22.0)   1    (2.0) 0    (0.0)   12   (24.0)  0      (0.0)   2     (4.0) \n\n\n\nTotal  23    (46.0)  50     (100.0)   39    (78.0)   6    (12.0) 1    (2.0)   42   (84.0)  13    (26.0)   7     (14.0) \n\n\n\nP value \n 0.123  -   0.951   0.510 0.529   0.837   0.009**   0.971  \n\n\n\nTotal  \nMale  \n\n\n\n 33    (33.0)  65     (65.0)  35     (35.0)   18     (18.0) 2    (2.0)   48   (48.0)  24    (24.0)   15   (15.0) \n\n\n\nN=100 \nFemale  \n\n\n\n 15    (15.0)  34     (34.0)  11     (11.0)   5    (5.0) 0    (0.0)   31   (31.0)  0      (0.0)   5     (5.0) \n\n\n\nTotal  \n 48    (48.0)  99     (99.0)  46     (46.0)   23     (23.0) 2    (2.0)   79   (79.0)  24    (24.0)   20   (20.0) \n\n\n\nP value \n 0.450   0.171   0.032*   0.129  0.295   0.085   0.000***   0.295  \n\n\n\n (N= sample size, % = sample percentage, *= significant at 0.01, **= \n\n\n\nsignificant at 0.001, ***= significant at 0.0001)  \n\n\n\n3.2.2 Reasons for adaptation strategies \n\n\n\nDifferent reasons were given by respondents for their choice of adaptation \n\n\n\nstrategies. Avoiding crop failure (80.0%) and getting different food for the \n\n\n\nfamily (82.0%) were the major reasons respondents adapted specific \n\n\n\nadaptation strategies to climate change in Pwalugu and Balungu \n\n\n\ncommunities respectively. Avoiding crop failure (p value=0.171, df = 1,  x2 \n\n\n\n= 1.871) as well as some plants supply nutrients (p value=0.153, df = 1, x2 \n\n\n\n= 2.041) were the only two reasons for adapting specific strategies to \n\n\n\nclimate change by respondents that were statistically insignificant \n\n\n\nbetween the male and female respondents in the two communities. Table \n\n\n\n2 gives a summary of the reasons given by respondents on the specific \n\n\n\nadaptation strategies they have adapted as well as the significant \n\n\n\ndifferences (p value) that exist between the two communities. \n\n\n\nTable 2: Reasons for adapting specific adaptation strategies \n\n\n\nReasons for adapting specific strategies \nBalungu  \n\n\n\nN=50 \n\n\n\nPwalugu  \n\n\n\nN = 50 \n\n\n\nTotal  \n\n\n\nN = 100 P value \n\n\n\nN    (%)  N    (%)  N    (%) \n\n\n\nTo avoid crop failure \n34    (68.0)  40    (80.0)  74    (74.0)    0.171  \n\n\n\nGet different food for the family \n\n\n\n41    (82.0)  30    (60.0)  71    (71.0)    0.043* \n\n\n\nAvoid buying foodstuffs outside \n\n\n\n8    (16.0)  0    (0.0)  8    (8.0)    0.003** \n\n\n\nIncome for family \n23    (46.0)  36    (72.0)  59    (59.0)    0.008** \n\n\n\nGet seeds for the next season \n\n\n\n7    (14.0)  0    (0.0)  7    (7.0)    0.006** \n\n\n\nSome plants supply nutrients \n\n\n\n2    (4.0)  0    (0.0)  2    (2.0)    0.153  \n\n\n\nAvoid problems with no market \n\n\n\n0    (0.0)  4    (8.0)  4    (4.0)    0.041* \n\n\n\n (*= significant at 0.01, **= significant at 0.001, ***= significant at 0.0001). \n\n\n\n3.3 Barriers militating against the adoption of other specific adaptive \n\n\n\nstrategies \n\n\n\n3.3.1 Challenges faced in adapting to climate change \n\n\n\nThe challenges faced by respondents in adapting to climate change are \n\n\n\ncommon or vary among communities (Table 3). Uncertainties in Table 3 \n\n\n\nrefers to floods, droughts, insect invasion and wildfires. In Balungu \n\n\n\ncommunity, the major challenge faced by respondents was lack of finance \n\n\n\n(representing 92%), with the least challenge been difficulty in obtaining \n\n\n\nseeds (2%). In Pwalugu, lack of finance (constituting 82%) is the most \n\n\n\ndifficult challenge in adapting to climate change, whiles lack of storage \n\n\n\nfacilities (representing 18%) was identified as being their least challenge \n\n\n\nin adapting to climate change. \n\n\n\nThere was statistical difference in norms/customs (p value=0.000, df = 1, \n\n\n\nx2 = 12.702) of the male and female respondents in Balungu. In Pwalugu, \n\n\n\nland tenure, (p value=0.016, df = 1, x2 = 5.821) lack of storage facilities (p \n\n\n\nvalue=0.000, df = 1, x2 = 20.184) and lack of ready markets (p value=0.006, \n\n\n\ndf = 1, x2 = 7.562) were identified to be significantly different between the \n\n\n\nmale and female respondents. When the two communities were combined, \n\n\n\nthere was significant difference between the male and female respondents \n\n\n\nin norms/customs (p value=0.016, df = 1, x2 = 5.801), land tenure (p \n\n\n\nvalue=0.025, df = 1, x2 = 5.022), lack of storage facilities (p value=0.002, \n\n\n\ndf = 1, x2 = 9.495) and lack of ready markets (p value=0.013, df = 1, x2 = \n\n\n\n6.176).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nTable 3: Challenges faced by respondents in adapting to climate change \n\n\n\nCommunity Sex \n\n\n\nChallenges faced by respondents in adapting to climate change \n\n\n\nTotal Finance \n\n\n\nLand \n\n\n\ntenure Uncertainties \n\n\n\nNorms/ \n\n\n\nCustoms \n\n\n\nLack of \n\n\n\nstorage \n\n\n\nfacilities \n\n\n\nLack of \n\n\n\nready \n\n\n\nmarkets \n\n\n\nDamage \n\n\n\nto crops \n\n\n\nby fulani \n\n\n\ncattle \n\n\n\nDifficulty \n\n\n\nobtaining \n\n\n\nseeds for \n\n\n\nfarming \n\n\n\nBalungu \n\n\n\nN=50 \n\n\n\nN     (%) N      (%) N      (%) N      (%) N      (%) N     (%)  N    (%)  N    (%) N      (%) \n\n\n\nMale 26  \n\n\n\n(52.0) \n\n\n\n21  \n\n\n\n(42.0) \n17     (34.0) 3      (6.0) 2    (4.0) 3     (6.0) \n\n\n\n 12  \n\n\n\n(24.0) \n 1      (2.0) 29     (58.0) \n\n\n\nFemale \n20   (40.0) 13  (26.0) 9    (18.0) 12  (24.0) 1    (2.0) 2     (4.0) \n\n\n\n 11  \n\n\n\n(22.0) \n 0      (0.0) 21     (42.0) \n\n\n\nTotal \n46   (92.0) 34   (68.0) 26     (52.0) \n\n\n\n15  \n\n\n\n(30.0) \n3    (6.0) 5   (10.0) \n\n\n\n 23  \n\n\n\n(46.0) \n 1      (2.0) 50   (100.0) \n\n\n\nP \n\n\n\nvalue \n0.473  0.432  0.271  0.000*** 0.754  0.924   0.441   0.390  \n\n\n\nPwalugu Male \n28   (56.0) 30   (60.0) 17     (34.0) \n\n\n\n18  \n\n\n\n(36.0) \n1    (2.0) \n\n\n\n14  \n\n\n\n(28.0) \n 9      (18.0)  10  (20.0) 36     (72.0) \n\n\n\n N=50 Female \n13   (26.0) 7     (14.0) 9    (18.0) 8     (16.0) \n\n\n\n8  \n\n\n\n(16.0) \n0     (0.0)  1    (2.0) \n\n\n\n 5  \n\n\n\n(10.0) \n14     (28.0) \n\n\n\nTotal \n41   (82.0) 37   (74.0) 26     (52.0) \n\n\n\n26  \n\n\n\n(52.0) \n9    (18.0) \n\n\n\n14  \n\n\n\n(28.0) \n\n\n\n 10  \n\n\n\n(20.0) \n 15  (30.0) \n\n\n\n50  \n\n\n\n(100.0) \n\n\n\nP \n\n\n\nvalue \n0.213  0.016* 0.278  0.650 0.000*** 0.006**  0.156   0.582  \n\n\n\nTotal  Male \n54   (54.0) 51   (51.0) 34     (34.0) \n\n\n\n21  \n\n\n\n(21.0) \n3    (3.0) \n\n\n\n17  \n\n\n\n(17.0) \n\n\n\n 21  \n\n\n\n(21.0) \n 11  (11.0) \n\n\n\n65     (65.0) \n\n\n\nN=100 Female \n33   (33.0) 20   (20.0) 18     (18.0) \n\n\n\n20  \n\n\n\n(20.0) \n9    (9.0) 2     (2.0) \n\n\n\n 12  \n\n\n\n(12.0) \n 5      (5.0) \n\n\n\n35     (35.0) \n\n\n\nTotal \n87   (87.0) 71   (71.0) 52     (52.0) \n\n\n\n41  \n\n\n\n(41.0) \n12  (12.0) \n\n\n\n19  \n\n\n\n(19.0) \n\n\n\n 33  \n\n\n\n(33.0) \n 16  (16.0) \n\n\n\n100 (100.0) \n\n\n\nP \n\n\n\nvalue \n0.112  0.025* 0.933  0.016* 0.002** 0.013*  0.841   0.731  \n\n\n\n (N= sample size, % = sample percentage, *= significant at 0.01, **= \n\n\n\nsignificant at 0.001, ***= significant at 0.0001). \n\n\n\nRespondents who are into fruit and tree product collection are also faced \n\n\n\nwith numerous challenges. The major challenge faced by respondents in \n\n\n\nboth communities is wild animals (about 94.0% and 86.0% in Balungu and \n\n\n\nPwalugu respectively). The least challenge cited by respondents in \n\n\n\nBalungu is accidents (constituting 4.0%) whiles beaten by heavy rains \n\n\n\n(constituting 6.0%) is the least challenge faced by respondents in Pwalugu. \n\n\n\nThese major (wild animals, p value= 0.034, df = 1, x2 = 4.504) and least \n\n\n\n(accidents, p value=0.007, df = 1, x2 = 7.162) challenges faced by \n\n\n\nrespondents were statistically significant of the male and female \n\n\n\nrespondents in the two communities. Focus group discussions revealed \n\n\n\nother challenges such as been lost in the bush/forest due to its thick and \n\n\n\nvast nature, heavy rains flooding foot paths and sometimes carrying \n\n\n\npeople away. Table 4 gives a summary of the various challenges affecting \n\n\n\nfruit and tree product collectors in adapting to climate change. \n\n\n\nTable 4: Challenges faced by respondents in assessing fruit and tree products \n\n\n\nChallenges faced by respondents in \ncollecting fruits and tree products \n\n\n\n   Balungu  \n    N=50 \n\n\n\nPwalugu  \nN = 50 \n\n\n\nTotal  \nN = 100 \n\n\n\nChi-square  \nP value \n\n\n\n  N      (%) N      (%) N      (%) \n\n\n\nWild animals \n  47     (94.0) 39    (78.0) 86     (86.0)   0.034* \n\n\n\nStorage problems \n  8    (16.0) 11    (22.0) 19     (19.0)   0.444  \n\n\n\nEating of fruits by animals \n  12     (24.0) 17    (34.0) 29     (29.0)   0.271  \n\n\n\nLow prices of products \n  7    (14.0) 6      (12.0) 13     (13.0)   0.766 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nLack of transport/bad roads   13     (26.0) 14    (28.0) 27     (27.0)   0.822 \n\n\n\nBeaten by heavy rains   7    (14.0) 3      (6.0) 10     (10.0)   0.182  \n\n\n\nAccidents \n  2    (4.0) 11    (22.0) 13     (13.0)   0.007** \n\n\n\n(*= significant at 0.01, **= significant at 0.001, ***= significant at 0.0001).  \n\n\n\n3.3.3 Suggested solutions by respondents in mitigating climate change \n\n\n\nSuggested solutions presented by respondents in mitigating climate \n\n\n\nchange and improving their livelihoods are presented in Table 5. Majority \n\n\n\nof the respondents suggested the provision of financial assistance (94%) \n\n\n\nas a way of responding to climate change in both communities. Avoidance \n\n\n\nof tree felling (representing 2%) and discouraging farming in waterlog \n\n\n\nareas (representing 4%) were the least solutions by respondents in \n\n\n\nBalungu and Pwalugu communities respectively. \n\n\n\nChi-square tests also revealed that, in Balungu, provision of financial \n\n\n\nassistance (p value=0.036, df = 1, x2 = 4.504) and supporting women who \n\n\n\nare into farming (p value=0.008, df = 1, x2 = 7.059) were statistically \n\n\n\nsignificant between the male and female respondents. There was also \n\n\n\nsignificant difference in provision of financial assistance (p value=0.004, \n\n\n\ndf = 1, x2 = 8.207) and supporting women who are into farming (p \n\n\n\nvalue=0.000, df = 1, x2 = 24.490) between the male and female \n\n\n\nrespondents in Pwalugu. \n\n\n\nWhen respondents from both communities were combined (N=100), \n\n\n\nprovision of financial assistance (p value=0.001, df = 1, x2 = 11.854), \n\n\n\nsupporting women who are into farming (p value=0.000, df = 1, x2 = \n\n\n\n27.275) and when lands are easily accessible (p value=0.016, df = 1, x2 = \n\n\n\n5.801) were the only suggested solutions found to be significantly \n\n\n\ndifferent between the male and female respondents. \n\n\n\nTable 5: Suggested solutions by respondents in mitigating climate change \n\n\n\nCommunity  Sex  \n\n\n\nWays of mitigating climate change by respondents \n\n\n\nTotal \n\n\n\nControlling of \n\n\n\nfulani \n\n\n\nherdsmen \n\n\n\nProvision of \n\n\n\nfinance \n\n\n\nDiscourage \n\n\n\nfarming in \n\n\n\nwaterlog areas \n\n\n\nSupport \n\n\n\nwomen who \n\n\n\nare into \n\n\n\nfarming \n\n\n\nWhen \n\n\n\nlands are \n\n\n\neasily \n\n\n\naccessible \n\n\n\nAvoid tree \n\n\n\nfelling \n\n\n\nBalungu \n\n\n\nN=50 \n\n\n\nN    (%) N    (%) N      (%) N     (%) N      (%)  N    (%) N      (%) \n\n\n\nMale 13     (26.0) 29     (58.0) 4    (8.0) 3    (6.0) 11    (22.0)  1    (2.0) 29    (58.0) \n\n\n\nFemale 10     (20.0) 18     (36.0) 1    (2.0) 9    (18.0) 13    (26.0)  0    (0.0) 21    (42.0) \n\n\n\nTotal 23     (46.0) 47     (94.0) 5    (10.0) 12    (24.0) 24    (48.0)  1    (2.0) 50    (100.0) \n\n\n\nP value 0.845 0.036* 0.293 0.008** 0.094 0.390 \n\n\n\nPwalugu Male 12     (24.0) 36     (72.0) 2    (4.0) 0    (0.0) 10    (20.0)  6    (12.0) 36    (72.0) \n\n\n\n N=50 Female 6    (12.0) 11     (22.0) 0    (0.0) 8    (16.0)  7      (14.0)  5    (10.0) 14    (28.0) \n\n\n\nTotal \n18     (36.0) 47     (94.0) 2    (4.0) 8    (16.0) 17    (34.0)  11    (22.0) 50    (100.0) \n\n\n\nP value 0.529 0.004** 0.368 0.000*** 0.136 0.106 \n\n\n\nTotal  Male 25     (25.0) 65     (65.0) 6    (6.0) 3    (3.0) 21    (21.0)  7    (7.0) 65    (65.0) \n\n\n\nN=100 Female \n16     (16.0) 29     (29.0) 1    (1.0) 17    (17.0)  20    (20.0)   5    (5.0) 35    (35.0) \n\n\n\nTotal 41     (41.0) 94     (94.0) 7    (7.0) 20    (20.0) 41    (41.0)  12    (12.0) 100    (100.0) \n\n\n\nP value \n0.482 0.001** 0.233 0.000*** 0.016* 0.569 \n\n\n\n (N= sample size, % = sample percentage, *= significant at 0.01, **= \n\n\n\nsignificant at 0.001, ***= significant at 0.0001).  \n\n\n\n4. DISCUSSION\n\n\n\n4.1.2 Impacts of climate change on respondents in the two \n\n\n\ncommunities \n\n\n\nChanges in climate poses a lot of problems to farmers. A researcher stated \n\n\n\nthat, resource-dependent livelihoods, especially farming face quite diverse \n\n\n\nand distinct climate change risks. In both communities, reduced income \n\n\n\nlevel by respondents was found to be the most devastating impact of \n\n\n\nclimate change on them. Other impacts cited by them include; inability to \n\n\n\nmeet the educational needs of their children, inability to save at a bank and \n\n\n\ninability to afford three square meals in a day leading to food insecurity. \n\n\n\nFrom the focus group discussion, it was realised that majority of \n\n\n\nrespondents\u2019 everyday meals come from millet, groundnut and maize. For \n\n\n\ninstance, both millet and maize can be used to prepare porridge in the \n\n\n\nmorning and Tuo Zaafi (T.Z) in the afternoon and evening. However, the \n\n\n\nimpacts of climate change have resulted in low yields of these valuable \n\n\n\ncrops hence making it difficult to afford three square meals in a day. For \n\n\n\nmost households in Northern Ghana, the attainment of food and nutrition \n\n\n\nsecurity has been a mirage, and climate change has exacerbated the \n\n\n\nsituation. This is consistent with the finding by a scholar who described \n\n\n\nreduced food security as the impacts of climate change in Northern Ghana, \n\n\n\nand another scholar who observed serious food and nutrition insecurity in \n\n\n\nNorthern Ghana when they were hit by floods and droughts [33]. Also, the \n\n\n\nimpacts of climate change on respondents justifies the conclusion by a \n\n\n\nresearcher and another researcher that poor, natural resource-dependent \n\n\n\nrural households will bear a disproportionate burden of adverse impacts \n\n\n\nof climate change [34,35]. Consistent with all focus groups discussions, it \n\n\n\nwas observed that climate change has adversely affected the livelihoods of \n\n\n\nrespondents and this goes a long way to support the suggestion by \n\n\n\nNellemann et al. [9] that climate change has long term negative impacts on \n\n\n\nagricultural productivity globally. It also agrees with the generalization by \n\n\n\na researcher that the effects of climate change are already in play with \n\n\n\npotentially disastrous consequences on farmers [36]. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\n4.2.1 Adaptation strategies employed by respondents and the reasons \n\n\n\nbehind those adaptive strategies \n\n\n\nAdaptation strategy is defined as a situation in which farmers address the \n\n\n\nadverse effects of climate change. In order to reduce the impacts of climate \n\n\n\nchange, respondents in both communities have various adaptive \n\n\n\nstrategies in which they undertake, and these strategies differ from one \n\n\n\ncommunity to another. Among these strategies is crop diversification \n\n\n\nwhich is seen as the most adapted strategy to climate change in both \n\n\n\ncommunities and the reason for adapting this strategy is simply to avoid \n\n\n\ncrop failure. This main strategy (crop diversification) has replaced \n\n\n\nmonoculture farming in the two communities. Farmers are now planting \n\n\n\ndifferent varieties of crops such as maize, rice, millet, groundnuts, rice, \n\n\n\ncalabash, water/yellow melon, among others as a way of addressing the \n\n\n\nadverse impacts of climate change. Crop diversification is the addition of \n\n\n\nnew crops on a farm taking into account the different returns from value-\n\n\n\nadded crops with complementary marketing opportunities. Thus, it is a \n\n\n\nconcept which is opposite to crop specialization [37]. It enhances plant \n\n\n\nproductivity, quality and health as well as builds crop resilience to \n\n\n\ndiseases and environmental stresses. Crop diversification as adopted by \n\n\n\nfarmers in the two communities is consistent with the findings by a group \n\n\n\nof researchers where they observed that farmers in Ethiopia adapted \n\n\n\nextensive crop diversification by planting different varieties [38,39]. A \n\n\n\nprevious researcher also observed planting different varieties as an \n\n\n\nadaptive strategy to climate change [40]. \n\n\n\nIn Pwalugu, irrigation is seen as the third extensively adapted practice by \n\n\n\nfarmers in response to climate change than in Balungu. This supports the \n\n\n\nfindings by a researcher that crop irrigation has seen some success and \n\n\n\nshould be more widely implemented. Also, it does not deviate from the \n\n\n\nfindings by several scholars that, irrigation has the potential to improve \n\n\n\nagricultural productivity through supplementing rainwater during dry \n\n\n\nspells and lengthening the growing season [41,42]. The main reason \n\n\n\naccounting for the variation in the two communities in adapting irrigation \n\n\n\nas an adaptive strategy is the availability of constant supply of water in \n\n\n\nPwalugu as compared to Balungu where the river dries up sometimes. The \n\n\n\navailability of constant supply of water in Pwalugu is due to its closeness \n\n\n\nto the White Volta. \n\n\n\nMigration is the least adapted strategy in both communities. None of the \n\n\n\nfemale respondents adapted migration as an adaptive strategy. This is \n\n\n\nconsistent with a researcher who noticed an increase in men migrating to \n\n\n\nSouth Africa and other places in search of jobs due to successive droughts \n\n\n\nin that country [43]. From the focus group discussion, migration is seen by \n\n\n\nrespondents as the last resort to adapting to climate change and those who \n\n\n\nmigrate are mostly young farmers who can do any manual or hard work. \n\n\n\nAccording to them, migrating to other places is very lucrative as they make \n\n\n\nmoney from it and are able to adapt to climate change. This supports the \n\n\n\nfindings by a recent researcher that farmers embark on migration to \n\n\n\nescape the adverse effects of climate change [44,45]. It is also consistent \n\n\n\nwith the conclusion by a researcher that, migration serve as a means of \n\n\n\nalleviating predicted challenges to agricultural livelihoods such as declines \n\n\n\nin harvest yields brought on by climate change [46]. \n\n\n\nChange method of crop and animal production as an adaptive strategy to \n\n\n\nclimate change was practiced more in Balungu than in Pwalugu. Also, the \n\n\n\nnumber of women practicing this strategy were more in Balungu than in \n\n\n\nPwalugu. With regards to change method of crop production, this finding \n\n\n\nis consistent with a researcher who identified regular weeding and crop \n\n\n\nrotation as indigenous adaptation strategies in northern Ghana [47]. A \n\n\n\nresearcher in their study observed change in method of animal production \n\n\n\nas a viable strategy to successful adaptation in the Northern Savanna \n\n\n\nzones [48]. \n\n\n\nFrom the focus group discussion, it was realized that establishment of tree \n\n\n\nplantations, collection of fruits and tree products, among others were \n\n\n\nidentified as long-term strategies in adapting to climate change. These \n\n\n\nstrategies are captured as \u201cothers\u201d in Table 1. Collection of fruits especially \n\n\n\nthe shea nuts, mangoes, guava, cashew, berries, among others is a good \n\n\n\nstrategy to climate change since they provide women with food especially \n\n\n\nin the dry season. Collection of fruits traditionally is women\u2019s business and \n\n\n\na source of income for many families in rural areas [49]. It is therefore, one \n\n\n\nof the greatest potentials that can be exploited to improve their adaptive \n\n\n\nstrategies to climate change. The nuts from the shea tree are used in \n\n\n\npreparing shea oil and butter which are used in almost every house for \n\n\n\ncooking. Shea butter and the other fruits gathered are also traded in the \n\n\n\nmarket for income because of their numerous benefits. This is consistent \n\n\n\nwith findings by a researcher who observed that, fruit collection especially \n\n\n\nshea in some cases contributes more than half of annual income of \n\n\n\nhouseholds in the Upper West region. They added that shea has a \n\n\n\nsignificant demand on the international market which is important for \n\n\n\nincome generation. \n\n\n\nThe male respondents were also into plantation establishments especially \n\n\n\nmoringa (Moringa oleifera) and mango (Mangifera indica). This finding is \n\n\n\nconsistent with a scholar who observed that, farmers in south Asia were \n\n\n\nengaged in plantation establishment as an adaptation strategy to climate \n\n\n\nchange [50]. According to the respondents, these trees serve as food for \n\n\n\nboth man and animals. In the dry season where forage is scarce, they rely \n\n\n\non the leaves of moringa to feed their animals. Firewood and a variety of \n\n\n\nfruits are also gotten from these trees which are sold to make money \n\n\n\nsufficient enough to sustain them in the dry season. This buttresses the \n\n\n\nfindings by a previous scholar who observed that, the income accumulated \n\n\n\nfrom tree products exceeded the accumulated value of crop yield in Kenya \n\n\n\n[51]. \n\n\n\n4.3 Barriers militating against the adoption of other specific adaptive \n\n\n\nstrategies \n\n\n\n4.3.1 Challenges faced in adapting to climate change \n\n\n\nRespondents faced various challenges in adapting to climate change. \n\n\n\nThese challenges become barriers thereby preventing respondents from \n\n\n\nfully adapting specific strategies to climate change. Barriers are defined as \n\n\n\nfactors, conditions or obstacles that reduce the effectiveness of adaptation \n\n\n\nstrategies [52,53]. \n\n\n\nAmong all the challenges listed by respondents, lack of financial \n\n\n\nassistance/support was the most important challenge affecting farmers' \n\n\n\nadaptation strategies to climate change. This is consistent with findings by \n\n\n\na previous scholar that, lack of adequate financial resources is an \n\n\n\nimportant factor constraining farmers\u2019 use of adaptation measures [54]. It \n\n\n\nalso goes to buttress the findings of several scholars who reported that \n\n\n\nfinancial constraints are major setbacks for farmers and institutions to \n\n\n\nadapt to climate change [55-57]. In Ethiopia, a researcher identified \n\n\n\nfinancial barriers due to lack of credit facilities as one of the most \n\n\n\nimportant obstacles hindering the implementation of climate adaptation \n\n\n\nstrategies by farmers [58]. \n\n\n\nLand tenure was also cited by respondents as the second important factor \n\n\n\nmilitating against the adoption of specific strategies to climate change. \n\n\n\nThis condition may prevail when they have insecure rights to land. Tenure \n\n\n\nsecurity can contribute to adoption of technologies linked to land such as \n\n\n\nirrigation equipment or soil conservation practices. Access to land is \n\n\n\ninfluenced by a number of factors which includes the behavior of tenants, \n\n\n\nhigh demands by landowners, lack of money, limited fertile lands, number \n\n\n\nof acres required, among others as presented in Table 3 Access to fertile \n\n\n\nland, among others are factors identified by a researcher to influence \n\n\n\nadaptation at the farm level. \n\n\n\nAlso, lands are mostly owned by family heads and this societal \n\n\n\nnorms/custom prevent women from owning their own lands/animals and \n\n\n\nadapting specific strategies to climate change. This was cited by \n\n\n\nrespondents as the fourth most important challenge impeding their \n\n\n\nadaptation. This societal norm results in women having fewer capabilities \n\n\n\nand resources than men [59-61]. This therefore goes a long way to affect \n\n\n\ntheir access to land for farming hence making them more vulnerable to the \n\n\n\nimpacts of climate change. This is consistent with the observation made by \n\n\n\na scholar, societal norms and values act as a major barrier to successful \n\n\n\nclimate adaptation in Western Nepal [62]. Several studies by a researcher \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nalso highlighted the constraints imposed by socio-cultural barriers on \n\n\n\nadaptation actions in several communities. Also, norms in the community \n\n\n\nthat inhibit men from collecting fruits in the community pose another \n\n\n\nchallenge to men in adapting successfully to climate change. This actually \n\n\n\nimpedes the capacity of men who want to go into shea butter and shea \n\n\n\npomade production. \n\n\n\nFulani cattle are a big challenge to farmers adapting to climate change. \n\n\n\nFrom the focus group discussion, Fulani herdsmen deliberately bring their \n\n\n\ncattle to feed on their crops especially maize crops at night when they go \n\n\n\nto sleep. These animals can destroy a whole farm. Because of these \n\n\n\nanimals, farmers can no longer leave their produce on the farm like they \n\n\n\nused to do some time ago. Some farmers even tried sleeping on the farm \n\n\n\nto avoid this problem, but the enormous difficulties associated with \n\n\n\nsleeping at a lonely farm has discouraged them. Been faced by heavy rains \n\n\n\nand wild animals have prevented them from sleeping there at night. The \n\n\n\nmost painful problem is, farmers do not always know the particular Fulani \n\n\n\nherdsmen who do that since they are many in the community. When \n\n\n\nfarmers report the problem to the appropriate authorities, these \n\n\n\nherdsmen easily bribe the authorities with one or two cows and that ends \n\n\n\nthe matter. The presence of these Fulani cattle in the communities are \n\n\n\nreducing the potential of farmers to adapt to climate change. \n\n\n\nLack of ready markets is seen as another challenge to farmers in \n\n\n\ndeveloping their capacity to adapt to climate change. This is most severe \n\n\n\nfor farmers who are not into any farming corporative within the \n\n\n\ncommunities. This is supported by a researcher who acknowledged that \n\n\n\ncertain factors such as market conditions influence farmers\u2019 response to \n\n\n\nclimate change [63]. According to a previous researcher, for effective \n\n\n\nadaptation to climate change to take place, access to marketing facilities is \n\n\n\nimportant [64]. Lack of ready markets especially for tomato has resulted \n\n\n\nin farmers diversifying. Tomato according to the farmers, cannot be stored \n\n\n\nfor longer periods and easily gets rotten. Lack of ready markets can be \n\n\n\nattributed to the availability and abundance of tomato in the rainy season \n\n\n\nthereby accounting for the low prices. This observation is not different \n\n\n\nfrom the view of a researcher when they noticed that, commodity prices, \n\n\n\nfinancial markets, among other factors affect the adaptive capacity of \n\n\n\nfarmers [65]. The NGOs supporting farmers in the communities like \n\n\n\nWienco is interested in onions. Since this NGO buys from the farmers, \n\n\n\nthere is always an assurance of ready markets hence the reason most of \n\n\n\nthem are diversifying in onions and other crops that can be stored for \n\n\n\nlonger periods. \n\n\n\nWomen who are into fruit and tree product collection are also faced with \n\n\n\na number of challenges. From the focus group discussion, women who \n\n\n\ncollect shea nut travel long distances into the forest in search of these \n\n\n\nfruits in the rainy season. The major challenge encountered by \n\n\n\nrespondents in both communities is wild animals such as snakes and \n\n\n\nscorpions. Snakes especially, are very common in the morning and is \n\n\n\ndifficult seeing them especially when the weather is not clear making them \n\n\n\nvulnerable to snake and scorpion bites. Their vulnerability is attributed to \n\n\n\nthe fact that, they do not have protective cloths such as raincoats, \n\n\n\nwellington boots and hand gloves to protect themselves during collection. \n\n\n\nThis was indicated by all the respondents that they do not have such \n\n\n\nprotective clothes. Therefore, cases of snake and scorpion bites during \n\n\n\nfruit collection are often common and this produces some fear in many of \n\n\n\nthe women. \n\n\n\nEating of shea nut fruits by animals is another major problem. According \n\n\n\nto the women, these animals eat the fruits and sometimes destroy the seed. \n\n\n\nThis is always very painful when they travel long distances and do not get \n\n\n\nany fruits in return. Some respondents also narrated how bad roads/lack \n\n\n\nof transport to and fro the forest is affecting them negatively. These roads \n\n\n\nare not accessible by vehicles (motor king). This makes it difficult for them \n\n\n\nto transport their fruits back home in larger quantities. \n\n\n\nLow prices were also a challenge confronting woman in fruit collection. \n\n\n\nThus, the price at which the shea products (processed nuts and butter) are \n\n\n\nusually being sold is too small as compared to their expectations. Related \n\n\n\nto this was the issue of fluctuation in the prices of the processed nuts and \n\n\n\nbutter which could sometimes result in losses as well as reduction in profit \n\n\n\nmargins of the butter processors, nut traders and butter traders.  Beaten \n\n\n\nby heavy rains, lack of storage facilities, felling of trees for charcoal, \n\n\n\nscarcity of fruits and accidents such as tree branches falling on you, \n\n\n\nclimbing and falling from the trees were cited by respondents as other \n\n\n\nchallenges faced in collecting fruits and tree products. Bushfires, cutting of \n\n\n\ntrees for firewood and destructive farming methods are all factors that \n\n\n\naffect the availability of shea nuts [66]. This reduces the ability of these \n\n\n\nwomen to successfully adapt to climate change. \n\n\n\n4.3.2 Suggested solutions by respondents in mitigating climate change \n\n\n\nA recent study emphasized that successful climate change adaptation \n\n\n\nhinges on the availability of effective adaptation strategies and the extent \n\n\n\nto which those strategies are adoptable. Whether a farmer takes adaptive \n\n\n\nactions will depend on his willingness to adopt, as well as to the system's \n\n\n\nadaptive capacity [67,68]. \n\n\n\nIn response to how these challenges could be resolved, majority of the \n\n\n\nrespondents in both communities cited financial assistance as a way of \n\n\n\nmitigating climate change. Access to finance will enable them get access to \n\n\n\nlands as well as the materials needed in production. \n\n\n\nControlling of Fulani herdsmen was the second important way of adapting \n\n\n\nto climate change. According to respondents, the devastating effects of \n\n\n\nthese animals are unbearable. When appropriate punishments are meted \n\n\n\nout to them, it will serve as a deterrent to the other herdsmen in the \n\n\n\ncommunity. This will prevent the destruction of their crops and will lead \n\n\n\nto high productivity. \n\n\n\nOther respondents were of the view that supporting women who are into \n\n\n\nfarming, avoiding tree felling, among others are ways of mitigating climate \n\n\n\nchange. \n\n\n\nFrom the focus group discussion, prohibition of the felling of shea trees for \n\n\n\ncharcoal was the major solution suggested by respondents who were into \n\n\n\nfruit and tree product collection as a way of mitigating climate change. \n\n\n\nAccording to them, most charcoal producers utilize the shea trees in the \n\n\n\nbush for their businesses. This intend reduces the availability of shea trees \n\n\n\nhence leading to a reduction in the quantity of shea fruits collected. Their \n\n\n\n(fruit collectors) adaptive capacity will be enhanced when there is a \n\n\n\nprohibition of the felling of shea trees. \n\n\n\nProvision of protective clothing was also suggested by respondents as a \n\n\n\nway of improving their adaptive capacity. According to them, provision of \n\n\n\nprotective clothes will prevent them from been bitten by snakes and \n\n\n\nscorpions as well as protect them from the dangers of other wild animals. \n\n\n\nIt will also eliminate fear in them. \n\n\n\nA few of the respondents also suggested that, a fixed/ standard price for \n\n\n\ntheir products like shea nuts and butter will impact positively on their \n\n\n\nadaptive capacity. According to them, this fixed price will aid prevent them \n\n\n\nfrom selling their products at a relatively cheaper prices just to make ends \n\n\n\nmeet. \n\n\n\nProvision of storage facilities and training on better preservation and \n\n\n\nprocessing skills were also cited by respondents as a way of mitigating \n\n\n\nclimate change. \n\n\n\nAmong all the non-farm activities, trading was the most viable non-farm \n\n\n\nadaptation strategy employed by respondents in both communities. This \n\n\n\nobservation is consistent with findings by a researcher who observed \n\n\n\npetty trading including (shea butter and dawadawa), charcoal production, \n\n\n\npito brewing, among others as non- farm strategies in Northern Ghana. \n\n\n\nTrading was however more intensive in Pwalugu than in Balungu. Access \n\n\n\nto markets in Pwalugu is more accessible than in Balungu due to the \n\n\n\npresence of the toll booth which creates conducive opportunity for \n\n\n\nrespondents to go into trading. This is justified by a group of researchers \n\n\n\nwho explained that, communities closer to markets adapt intensive \n\n\n\nactivities such as trading as adoption options to climate change whiles the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nlikelihood of communities farther away from markets adapting climate \n\n\n\nchange adaptation options decreases [69]. Non-farm income earning \n\n\n\nactivities such as trading therefore offers opportunities for diversification \n\n\n\nin the midst of challenges faced in agriculture. This is confirmed by a \n\n\n\nresearcher who reported that 40% of rural household income originated \n\n\n\nfrom non- farm activities in 11 Latin American countries [70]. This \n\n\n\nindicates how essential income generated from this source is to farmers. \n\n\n\nAccording to a study, non-farm activities help people to cope with \n\n\n\ntemporary adversity in the agricultural sector and also represent a longer-\n\n\n\nterm adaptation strategy when another options fail [71]. \n\n\n\nGenerally, most of the adaptation strategies employed by the respondents \n\n\n\nare consistent with findings of other researchers.  Crop diversification, \n\n\n\nirrigation, migration, change method of production, trading, adjustment in \n\n\n\nplanting dates have all been observed by several researchers as strategies \n\n\n\nthat have positively impacted on farmers in adapting to climate change \n\n\n\n[72,73]. \n\n\n\nAvoiding crop failure, getting different food for the family, income, getting \n\n\n\nseeds for the next planting season, among others were the reasons farmers \n\n\n\nadapted the various adaptation strategies in the two communities. \n\n\n\n5  CONCLUSION \n\n\n\nThe findings of the study revealed that, climate change affected \n\n\n\nrespondents negatively resulting in reduced income level, inability to \n\n\n\nafford three square meals daily,  inability to meet their health needs, \n\n\n\ninability to meet their educational needs of their children, inability to  save \n\n\n\nat bank as well as their inability to diversify. Respondents have therefore \n\n\n\ndeveloped specific strategies to reduce/cope with these negative impacts. \n\n\n\nThe adaptation strategies include adjustment in planting time, crop \n\n\n\ndiversification, irrigation, change method of crop/animal production, \n\n\n\nmigration, trading and \u201cothers\u201d such as fruit collection, plantation \n\n\n\nestablishment, driving, hunting and construction work were the specific \n\n\n\nadaptation strategies employed by respondents. \n\n\n\nIncome for the family, avoiding crop failure, avoiding buying foodstuffs \n\n\n\noutside the family, among others were the reasons respondents employed \n\n\n\nthe various adaptation strategies. Lack of finance, land tenure, \n\n\n\nnorms/customs, lack of storage facilities, lack of ready markets, damage to \n\n\n\ncrops by Fulani cattle and difficulty in obtaining seeds for farming were \n\n\n\nthe challenges militating against the adoption of other adaptive strategies \n\n\n\nto climate change. \n\n\n\nFruit and tree product collectors also faced challenges such as; wild \n\n\n\nanimals, storage problems, eating of fruits by animals, low prices of \n\n\n\nproducts, lack of transport/bad roads, been beaten by heavy rains as well \n\n\n\nas accidents. \n\n\n\n5.1 Recommendation \n\n\n\nSince lack of financial assistance/support was seen as the most important \n\n\n\nchallenge affecting farmers' adaptation strategies to climate change in the \n\n\n\ntwo communities. Thus, it is recommended that government and relevant \n\n\n\nstakeholders should collaborate with financial institutions to ensure that \n\n\n\nfunds are readily available to farmers to enable them effectively adapt to \n\n\n\nclimate change as well as provide training/workshop programmes to \n\n\n\nfarmers in order to enhance their capacity in planning and implementing \n\n\n\neffective strategies to climate change.  \n\n\n\nEducation should be one of the areas for policy intervention by \n\n\n\ngovernment/stakeholders since access to education is vital in developing \n\n\n\nspecific adaptive strategies to the impacts of climate change. \n\n\n\nREFERENCES \n\n\n\n[1] Ghil, M. 2002. Climate variability. The earth system: physical and \n\n\n\nchemical dimensions of global environmental change, Encyclopedia of \n\n\n\nGlobal Environmental Change, 1, 544-549. \n\n\n\n[2] IPCC. 2007. Climate change 2007 in, Solomon, S., Qin, D., Manning, M., \n\n\n\nChen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), The \n\n\n\nPhysical Science Basis. Contribution of Working Group I to the Fourth \n\n\n\nAssessment Report of the Intergovernmental Panel on Climate Change \n\n\n\nCambridge University Press, Cambridge, United Kingdom and New York, \n\n\n\nNY, USA, 83-996. \n\n\n\n[3] Ford, J.D., Berrang-Ford, L., Paterson, J. 2011. A systematic review of \n\n\n\nobserved climate change adaptation in developed nations. Climatic \n\n\n\nChange, 106(2), 327\u2013336. \n\n\n\n[4] Fussel, H.M. 2009. An updated assessment of the risks from climate \n\n\n\nchange based on research published since the IPCC Fourth Assessment \n\n\n\nReport. Climatic Change, 97, 469-482. \n\n\n\n[5] Parry, M., Canziani, O., Palutikof, J. 2008. Key IPCC conclusions on \n\n\n\nclimate change impacts and adaptations. WMO Bulletin, 57. \n\n\n\n[6] Adger, W.N., Agrawala, S., Mirza, M.M.Q., Conde, C., O\u2018Brien, K., Pulhin, \n\n\n\nJ., Takahashi, K. 2007. Assessment of adaptation practices, options, \n\n\n\nconstraints and capacity. In Parry, M.L., Canziani, O.F., Palutikof, J.P., Van \n\n\n\nDer Linden, P.J., Hanson, C.E. (Eds.), Climate change 2007: Impacts,\n\n\n\nadaptation and vulnerability. Contribution of working group II to the \n\n\n\nfourth assessment report of the IPCC, 717-743. \n\n\n\n[7] Abaje, I.B., Giwa, P.N. 2007. Urban Flooding and Environmental Safety: \n\n\n\nA Case Study of Kafanchan Town in Kaduna State. A Paper Presented at the \n\n\n\nGolden Jubilee (50th Anniversary) and 49th Annual Conference of the \n\n\n\nAssociation of Nigerian Geographers (ANG) Scheduled for 15th \u201319th \n\n\n\nOctober 2007 at the Department of Geography, University of Abuja, \n\n\n\nGwagwalada-Abuja. \n\n\n\n[8] Reilly, J. 1999. What does climate change mean for agriculture in \n\n\n\ndeveloping countries? A comment on Mendelsohn and Dinar. World Bank \n\n\n\nResearch Observer, 14, 295\u2013305. \n\n\n\n[9] Nellemann, C., MacDevette, M., Manders, T., Eickhout, B., Svihus, B., \n\n\n\nPrins, A., Kaltenborn, B. 2009. The environmental food crisis: The \n\n\n\nenvironments role in averting future food crises. A UNEP rapid response \n\n\n\nassessment. Arendal, UNDP. NEPAD Comprehensive Africa Agriculture \n\n\n\nDevelopment Programme. \n\n\n\n[10] Cinner, J., McClanahan, T., Graham, N., Daw, T., Maina, J., Stead, S.M., \n\n\n\nWamukota, A., Brown, K. and Bodin, O. 2012. Vulnerability of coastal \n\n\n\ncommunities to key impacts of climate change on coral reef fisheries. \n\n\n\nGlobal Environ. Change 22, 12\u201320. \n\n\n\n[11] Salick, J., Byg, A. 2007. Indigenous Peoples and Climate Change. \n\n\n\nTyndall Centre for Climate Change Research. University of Oxford, Oxford. \n\n\n\n[12] Thornton, P.K., Jones, P.G., Alagarswamy, A., Andresen, K. 2007. The \n\n\n\ntemporal dynamics of crop yield responses to climate change in East \n\n\n\nAfrica. Global Environmental Change, 4(5), 165-181. \n\n\n\n[13] Boko, M., Niang, I., Nyong, A., Vogel, C., Githeko, A., Medany, M., \n\n\n\nYanda, P. 2007. Africa. In M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. Van \n\n\n\nDer Linden, & C.E. Hanson (Eds.). Climate change (2007): Impacts, \n\n\n\nadaptation and vulnerability. Contribution of working group II to the \n\n\n\nfourth assessment report of the IPCC (pp. 433\u2013467). Cambridge: \n\n\n\nCambridge University Press. \n\n\n\n[14] Food and Agriculture Organization (FAO). 2008. Climate change and \n\n\n\nfood security: a framework document. Available at\n\n\n\nwww.fao.org/forestry/15538-079b31d45081fe9c \n\n\n\n3dbc6ff34de4807e4.pdf \n\n\n\n[15] Parry, M., Rosenzweig, C., Iglesias, A., Fisher, G., Livermore, M. 1999. \n\n\n\nClimate change and world food security. Global Environment Change, 9, \n\n\n\nS51\u2013S67. \n\n\n\n[16] Bryant, C.R., Smit, B., Brklacich, M., Johnston, T., Smithers, J., Chiotti, \n\n\n\nQ., Singh, B. 2000. Adaptation in Canadian agriculture to climatic \n\n\n\n\nhttp://www.fao.org/forestry/15538-079b31d45081fe9c%203dbc6ff34de4807e4.pdf\n\n\nhttp://www.fao.org/forestry/15538-079b31d45081fe9c%203dbc6ff34de4807e4.pdf\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nvariability and change. Climatic Change, 45, 181\u2013201. Cambridge: \n\n\n\nCambridge University Press. \n\n\n\n[17] Smit, B., Harvey, E., Smithers, C. 2000. How is climate change \n\n\n\nrelevant to farmers? In: Climate Change Communication: Proceedings of \n\n\n\nan international conference. \n\n\n\n[18] Toulmin, C., Huq, S. 2006. Africa and climate change. In Sustainable \n\n\n\nDevelopment Opinion, ed. International Institute for Environment and \n\n\n\nDevelopment. London: IIED \n\n\n\n[19] Thornton, P., Jones, P., Owiyo, T., Kruska, R., Herrero, M., Kristjanson, \n\n\n\nP., Notenbaert, A., Bekele, N., Omolo, A. 2006. Mapping climate \n\n\n\nvulnerability and poverty in Africa. Nairobi: International Livestock \n\n\n\nResearch Institute. \n\n\n\n[20] Ghana Fact Sheet. 2010. cited on the 4th of August 2010.\n\n\n\n[21] Nyantakyi-Frimpong, H. 2013. Indigenous knowledge and climate \n\n\n\nadaptation policy in northern Ghana, A project of the Africa initiative \n\n\n\nbackgrounder No 48 January 2013. \n\n\n\n[22] Amikuzuno, J., Donkoh, S.A. 2012. Climate variability and yields of \n\n\n\nmajor staple food crops in northern Ghana, African Crop Science Journal, \n\n\n\n20(2), 349-360. \n\n\n\n[23] Acquah, D.H. 2011. Farmers perception and adaptation to climate \n\n\n\nchange: a willingness to pay. Analysis, Journal of Sustainable Development \n\n\n\nin Africa, 13(5), 150-161. \n\n\n\n[24] Akudugu, M.A., Dittoh, S., Mahama, E.S. 2012. The implications of \n\n\n\nclimate change on food security and rural livelihoods: Experiences from \n\n\n\nnorthern Ghana. Journal of Environment and Earth Science, 2(3), 21-29. \n\n\n\n[25] Gyampoh and Asante. 2011. Mapping and Documenting Indigenous \n\n\n\nKnowledge in Climate Change Adaptation in Ghana. \n\n\n\n[26] Newsham, A.J., Thomas, D.S.G. 2011. Knowing, farming and climate \n\n\n\nchange adaptation in north-central Namibia. Global Environmental \n\n\n\nChange, 21(2), 761770. doi: 10.1016/j.gloenvcha.2010.12.003 \n\n\n\n[27] Roncoli, C., Ingram, K., Kirshen, P. 2002. Reading the rains: Local \n\n\n\nknowledge and rainfall forecasting in Burkina Faso. Society & Natural \n\n\n\nResources, 15, 409\u2013427.doi:10.1080/08941920252866774 \n\n\n\n[28] Thornton, P.K., Lipper, L. 2013. \u2015How does climate change alter \n\n\n\nagricultural strategies to support food security? Background paper for the \n\n\n\nconference, Food Security Futures: Research Priorities for the 21st \n\n\n\nCentury, Dublin. April 2013 pp. 11-12. Available at \n\n\n\nhttp://www.pim.cgiar.org/files/2013/03/ClimateChangeAnd Food \n\n\n\nSecurity_Priorities.Pdf \n\n\n\n[29] Dixon, J.L., Stringer, L.C., Challinor, A.J. 2014. Farming system \n\n\n\nevolution and adaptive capacity: Insights for adaptation support. \n\n\n\nResources, 3, 182\u2013214. \n\n\n\n[30] Ghana Statistical Service. 2010. Population and housing census: \n\n\n\nSummary report of final result. \n\n\n\n[31] Yeasmin, S., Rahman, K.F. 2012. Triangulation research method as \n\n\n\nthe tool of social science research. Bangladesh University of Professionals \n\n\n\n(BUP) Journal, 1(1), 154-163. \n\n\n\n[32] Olsen, C., George, D.M.M. 2004. Cross sectional study design and data \n\n\n\nanalysis. \n\n\n\n[33] Dietz, M. 2012. Value Chain Governance that Benefits the Poor\u2016. \n\n\n\nWorking Paper. Helvetas Swiss Inter cooperation. http://www.sdc\u2010\n\n\n\nemployment\u2010 \n\n\n\n[34] Kates, R. 2000. Cautionary tales: Adaptation and the global poor, \n\n\n\nClimatic Change, 45(1), 5-17. \n\n\n\n[35] Mendelsohn, R., Basist, A., Kurukulasuriya, P., Dinar. A. 2007. \n\n\n\nClimate and rural income. Climatic Change, 81(1), 101-18. \n\n\n\n[36] Adger, W.N., Vincent, K. 2005. Uncertainty in adaptive capacity. \n\n\n\nComptes Rendus Geoscience, 337, 399-410. \n\n\n\n[37] Singh, J. 1976. Agricultural Geography, Tata McGraw Hill Publishing \n\n\n\nCo. Ltd. New Delhi, 221. \n\n\n\n[38] Deressa, T., Hassan R.M., Alemu, T., Yesuf, M., Ringler, C. 2008. \n\n\n\nAnalyzing the determinants of farmers\u2019 choice of adaptation methods and \n\n\n\nperceptions of climate change in the Nile Basin of Ethiopia. Discussion \n\n\n\nPaper 00798 Washington DC: International Food Policy Research Institute \n\n\n\n(IFPRI) Retrieved July 02, 2012, from www.ifpri. Org \n\n\n\n[39] Young, K.R., Lipton, J.K. 2006. Adaptive governance and climate \n\n\n\nchange in the tropical highlands of Western South America. Climate \n\n\n\nChange, 78, 63\u2013102. \n\n\n\n[40] Maddison, D. 2007. The perception of and adaptation to climate \n\n\n\nchange in Africa. Policy research working paper no 4308. World Bank. \n\n\n\nRetrieved March 02, 2012, from https://openknowledge.worldbank. \n\n\n\norg/bitstream/ \n\n\n\n[41] Nhemachena, C., Hassan, R. 2007. Micro-Level Analysis of Farmers\u2019 \n\n\n\nAdaptation to Climate Change in Southern Africa. International Food \n\n\n\nPolicy Research Institute/ \n\n\n\n[42] Laube, W., Schreven, B., Awo, M. 2011. Small holder adaptation to \n\n\n\nclimate: dynamics and limits in Northern Ghana. Climatic Change, 111, \n\n\n\n753\u2013774. \n\n\n\n[43] Ribeiro, N., Cha\u00faque, A. 2010. Gender and Climate change: \n\n\n\nMozambique Case Study. Cape Town: Heinrich-B\u00f6ll-Stiftung. \n\n\n\n[44] Batterbury, S.P.J. 2002. Meeting Report. Access to Resources: Land \n\n\n\nTenure and Governance in Africa. 5 Mar 2001, University of Manchester, \n\n\n\nUK. Transformations in African Agriculture series. Cultural and Political \n\n\n\nEcology Newsletter, 37. \n\n\n\n[45] Mortimore, M.J., Adams, W.M. 2001. Farmer adaptation change and \n\n\n\ncrisis in the Sahel. Global Environmental Change-Human and Policy \n\n\n\nDimensions 11, 49\u201357. \n\n\n\n[46] Adger, W.N., Dessai, S., Goulden, M., Hulme, M., Lorenzoni, I., Nelson, \n\n\n\nD.R., Wreford, A. 2009. Are there social limits to adaptation to climate \n\n\n\nchange? Climatic Change, 93(3), 335-354. doi:10.1007/s10584-008-9520-\n\n\n\nz \n\n\n\n[47] Armah, R.N.A., Ramatu, M.A., Kuwornu, J.K.M., Osei-Owusu, Y. 2013. \n\n\n\nWhat influences farmers choice of indigenous adaptation strategies for \n\n\n\nagrobiodiversity loss in northern Ghana. British Journal of Applied Science \n\n\n\n& Technology, 3(4), 1162-1176. \n\n\n\n[48] Stanturf, A.J., Warren, M.L.J., Charnley, S., Polasky, S.C., Goodrick, S.L., \n\n\n\nArmah, F., Nyako, Y.A. 2011. Ghana climate change vulnerability and \n\n\n\nadaptation assessment. Available at http: //www.encapafrica. \n\n\n\norg/documents/ biofor/Climate% 20Change%20Assessment \n\n\n\n_Ghana_%20FINAL.pdf \n\n\n\n[49] Carette, C., Malotaux, M., Leeuwen, M.V., Tolkamp, M. 2009. Shea nut \n\n\n\nand butter in Ghana: Opportunities and constraints for local processing. \n\n\n\nWegeningen: Wageningen University. \n\n\n\n[50] Sterrett, C. 2011. Review of climate change adaptation practices in \n\n\n\nSouth Asia, Oxfam Research Report. Climate Concern, Melbourne, \n\n\n\nAustralia Retrieved March 02, 2012, from http://www.oxfam.org/ \n\n\n\n\nhttp://www.pim.cgiar.org/files/2013/03/ClimateChangeAnd%20Food%20Security_Priorities.Pdf\n\n\nhttp://www.pim.cgiar.org/files/2013/03/ClimateChangeAnd%20Food%20Security_Priorities.Pdf\n\n\nhttp://www.sdc\u2010employment\u2010/\n\n\nhttp://www.sdc\u2010employment\u2010/\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(2) (2019) 35-45 \n\n\n\nCite The Article: Damian Felladam Tangonyire (2019). Impact Of Climate Change On Farmers In The Talensi District Of The Upper East Region Of Gh ana.  \n\n\n\nMalaysian Journal of Sustainable Agriculture, 3(2): 35-45. \n\n\n\nsites/www.oxfam. org/ \n\n\n\n[51] Ong, C.K., Wilson, J., Deans, J.D., Mulatya, J., Raussen, T., Wajja-\n\n\n\nMusukwe, N. 2002. Tree-crop interactions: manipulation of water use and \n\n\n\nroot function. Agricultural Water Management, 53, 171\u2013186. \n\n\n\n[52] Huang, C., Vaneckova, P., Wang, X., Fitzgerald, G., Guo, Y., Tong, S. \n\n\n\n2011. Constraints and barriers to public health adaptation to climate \n\n\n\nchange. American Journal of Preventive Medicine, 40(2), 183\u2013190. doi:\n\n\n\n10.1016/j.amepre.2010.10.025 \n\n\n\n[53] Moser, S.C., Ekstrom, J.A. 2010. A framework to diagnose barriers to \n\n\n\nclimate change adaptation. Proceedings of the National Academy of \n\n\n\nSciences of the United States of America, 107(51), 22026\u201322031. \n\n\n\ndoi:10.1073/pnas. 1007887107 \n\n\n\n[54] Smit, B., Skinner, M.W. 2002. Adaptation options in agriculture to \n\n\n\nclimate change: A typology. Mitigation and Adaptation Strategies for \n\n\n\nGlobal Change, 7, 85-114. \n\n\n\n[55] Vidal, J. 2009. Rich nations failing to meet climate aid pledges, The \n\n\n\nGuardian, 1, 3-4. \n\n\n\n[56] Kandji, S.T., Verchot, L., Mackensen, J. 2006. Climate change and \n\n\n\nvariability in the Sahel region: impacts and adaptation strategies in the \n\n\n\nagricultural sector\u2016, United Nations Environment Programme Batterbury, \n\n\n\nS.P.J. (2004). Development, planning, and agricultural knowledge on the \n\n\n\nCentral Plateau of Burkina Faso, in Robson, E. and Cline-Cole, R. (Eds) West \n\n\n\nAfrican Worlds. Local and Regional Paths through Development, \n\n\n\nModernity and Globalisation. Pearson Education, New Jersey, 259-279. \n\n\n\n[57] Bryan, E., Deressa, T., Gbetibouo, G.A., Ringler, C. 2009. Adaptation \n\n\n\nto climate change in Ethiopia and South Africa: Options and constraints. \n\n\n\nEnvironmental Science and Policy, 12(4), 413-426. Retrieved June 12, \n\n\n\n2012 from http://www.sciencedirect.comhdl.handle.net/ \n\n\n\n[58] Gbegeh, B.D., Akubuilo, C.J.C. 2012. Socioeconomic determinants of \n\n\n\nadoption of yamminisett by farmers in Rivers state, Nigeria. Wudpecker \n\n\n\nJournal of Agricultural Research, 2(1), 033-038. \n\n\n\n[59] OECD. 2009. Integrating Climate Change Adaptation into \n\n\n\nDevelopment Co-operation: Policy Guidance, OECD Publishing, \n\n\n\nhttp://dx.doi.org/10.1787/9789264054950-en \n\n\n\n[60] Marenya, P.P., Barrett, C.B. 2007. Household-level Determinants of \n\n\n\nAdoption of Improved Natural Resources Management Practices among \n\n\n\nSmallholder Farmers in Western Kenya. Food Policy, 32, pp. 515 - 536. \n\n\n\n[61] Jones, L., Boyd, E. 2011. Exploring social barriers to adaptation: \n\n\n\nInsights from western Nepal. \n\n\n\n[62] Global Environmental Change, 21(4), 1262\u20131274. doi: \n\n\n\n10.1016/j.gloenvcha.2011. 06.002 \n\n\n\n[63] Mertz, O., Mbow, C., Reenberg, A., Diouf, A. 2009. Farmers \n\n\n\nperceptions of climate change and agricultural adaptation strategies in \n\n\n\nRural Sahel. Environ. Manag. 43, 804\u2013816. \n\n\n\n[64] Antwi-Agyei, P., Andrew, J.D., Lindsay, C.S. 2013. Barriers to climate \n\n\n\nchange adaptation in Sub-Saharan Africa: evidence from northeast Ghana \n\n\n\n& Systematic literature review. In Sustainability Research Institute. Paper, \n\n\n\n(52). \n\n\n\n[65] Antle, J.M., Capalbo, S.M. 2010. Adaptation of Agriculture and Food \n\n\n\nSystems to Climate Change: An Economic and Policy Perspective. Applied \n\n\n\nEconomic Perspectives & Policy, 32(3), 386-416. \n\n\n\n[66] McNally, D. 2008. Shea Butter: A guide to Production and Marketing. \n\n\n\nPeace Corps Ghana, version 1. \n\n\n\n[67] Howden, S.M., Soussana, J.F., Tubiello, F.N., Chhetri, N., Dunlop, M., \n\n\n\nMeinke, H. 2007. Adapting agriculture to climate change. Proceedings of \n\n\n\nthe National Academy of Sciences. 104, 19691\u201319696. \n\n\n\n[68] Crimp, S.J., Stokes, C.J., Howden, S.M., Moore, A.D., Jacobs, B., Brown, \n\n\n\nP.R., Ash, A.J., Kokic, P., Leith, P. 2010. Managing Murray-Darling Basin \n\n\n\nlivestock systems in a variable and changing climate: challenges and \n\n\n\nopportunities. Rangelands Journal, 32, 293\u2013304. \n\n\n\n[69] Zegeye, T., Haileye, A. 2001. Adoption of improved maize \n\n\n\ntechnologies and inorganic fertilizer in Northwestern Ethiopia. Ethiopian \n\n\n\nAgricultural Research Organization (EARO). Research report No.40, Addis \n\n\n\nAbaba, Ethiopia. 51. \n\n\n\n[70] Barrett, C.B., Reardon, T., Webb, P. 2001. Nonfarm income \n\n\n\ndiversification and household livelihood strategies in rural Africa: \n\n\n\nconcepts, dynamics, and policy implications. Food Policy 26, 315-331. \n\n\n\n[71] Scoones, I. 2009. Livelihoods perspectives and rural development\u2016, \n\n\n\nThe Journal of Peasant Studies, 36(1), 171-196. \n\n\n\n[72] Simbarashe, G. 2013. Climate change, variability and sustainable \n\n\n\nagriculture in Zimbabwe \u2018s rural communities, Russian Journal of \n\n\n\nAgricultural and Socio-Economic Sciences, 2(14), 89-100. \n\n\n\n[73] Khan, S. A., Kumar, S., Hussain, M.Z., Kalra, N. 2009. Climate change, \n\n\n\nclimate variability and Indian agriculture: Impacts Vulnerability and \n\n\n\nAdaptation Strategies\u2016, in, Singh, S.N. (Ed.), Environmental Science and \n\n\n\nEngineering, 2009. Springer-Verlag Berlin Heidelberg, 19-38. \n\n\n\n\nhttp://dx.doi.org/10.1787/9789264054950-en\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 72-78 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.72.78 \n\n\n\nCite The Article: Bimal Chandra Kundu, Sauda Naznin, Md. Abu Kawochar, Md. Mazadul Islam, Abdullah Al Mahmud, Md Nurul Amin, Md. Nasir Uddin, K.M. Delowar \n\n\n\nHossain (2022). Selection of Processing Potato Varieties Through Multi-Location Trials. Malaysian Journal of Sustainable Agricultures, 6(2): 72-78. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.72.78\n\n\n\nSELECTION OF PROCESSING POTATO VARIETIES THROUGH MULTI-LOCATION \nTRIALS \n\n\n\nBimal Chandra Kundua*, Sauda Naznina, Md. Abu Kawochara, Md. Mazadul Islama, Abdullah Al Mahmudb, Md Nurul Aminc, Md. Nasir Uddina, \nK.M. Delowar Hossaind \n\n\n\naTuber Crops Research Centre, Bangladesh Agricultural Research Institute (BARI), Joydebpur, Gazipur-1701, Bangladesh \nbOn-Farm Research Division (OFRD), BARI, Gaibandha 5700, Bangladesh \ncBreeder Seed Production Centre, BARI, Debiganj, Panchagarh-5020, Bangladesh \ndDepartment of Environmental Science and Technology, Jashore University of Science and Technology, Jashore-7408, Bangladesh. \n* Corresponding author email: kundubc@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 November 2021 \nAccepted 26 December 2021 \nAvailable online 05 January 2022\n\n\n\nThirteen exotic potato varieties along with four checks were evaluated at six agro-ecological locations of \nBangladesh for three generations during 2015-16 to 2017-18 in order to identify table purpose and \nprocessing quality varieties. Results indicated significant variation among the varieties. Based on the results \nof the 1st year multi-location trial, seven superior ones were selected for further testing in AYT and RYT in \nnext two years. In the SYT, varieties Farida and 7four7 were the highest yielders. In the AYT, the highest \naverage yield over location was also produced by 7four7 (38.70 t/ha). Varieties Cimega and Memphis also \ngave comparable yields to that of 7four7 (37.33 and 36.67 t/ha, respectively). Considering the yield of the \nthree generations, the above four varieties were significantly better than the checks. Considering the specific \nqualities, Farida was found most suitable for table purpose because of its high yield, medium-sized oval and \nsmooth tubers with good eating quality. The variety Taisiya produced tubers with good size and shape, but \nlow in dry matter content; so not suitable for processing. Memphis might be selected for French fry as it \nproduces maximum large sized tubers with good long oval shape. On the other hand, variety Panamera is a \nhigh yielder but its plant type was undesirable. On the whole, varieties Cimega, 7four7 and Farida are suitable \nfor table purpose, and Memphis may be selected for French fry under Bangladesh condition. None was found \nquite suitable for Chips preparation.  \n\n\n\nKEYWORDS \n\n\n\nPotato variety, french fry, table potato, processing potato, multi-location trial. \n\n\n\n1. INTRODUCTION \n\n\n\nPotato (Solanum tuberosum L.) is the world\u2019s largest non-cereal food crop \nand ranked 4th most important food crop after wheat, corn and rice (De \nHaan and Rodriguez, 2016). It is a vital crop to safeguard food security \nbecause of its growing demand and nutritional value. Considering the high \nyield potential, it can be a good substitute of cereal crops that have a high \nharvesting index above 75% (Scott et al., 2020; Thiele et al., 2010). In \nBangladesh, it is grown all over the country only in the winter season for \nits high demand to feed the hungry people.  According to Food and \nAgriculture Organization, Bangladesh ranks 7th position in potato \nproduction among the potato producing countries of the world and is \nconsidered as 2nd most important food crop in the country (FAOSTAT, \n2020). The country\u2019s average yield of potato is 20.61t/ha with a total \nproduction of 9.65 million tons from 0.47 million hectares of land. The \npotato production has increased from 1.55 to 9.65 Mil. tons and the \nproduction area has increased from 0.136 to 0.47 Mil. hectares over the \nlast two decades which improved the per unit area production 1.80-fold \n(FAOSTAT, 2020). However, this yield of potato is still low compared to \nother potato growing countries.  To ensure the country\u2019s food and \nnutritional security, per unit area of potato production should be 41.50 \nt/ha by the year 2030 (Al-Mahmud et al., 2021).  \n\n\n\nThere are two important factors for the low yield of potato in Bangladesh, \none is the lack of improved cultivars and the second factor is the low \ninvestment from the potato farmers (Kundu et al., 2020). During the \nharvesting time, the price of potato goes down due to glut of potato in the \nmarket and farmers experience financial loss, which influences low \ninvestment. The only way to overcome this situation is to increase the \nexport and processing of potato (Hussain, 2012). To enhance the export \nand processing of potato, it is necessary to improve the tuber quality \nthrough suitable varieties and improved management practices. \n\n\n\nFor varietal improvement, hybridization and selection is the common \npractice, but in Bangladesh, conventional breeding is very lengthy and \ncumbersome due to climatic constraint. So introduction and selection is \nthe common practice for variety development in Bangladesh which began \nin the 1960s (Siddique et al., 2015). Introduced varieties mostly from \nEuropean countries are tested here for several years and several locations \nto check the yield stability, degeneration rate, disease susceptibility, and \nspecific qualities like Chips making, French fry preparation, flakes making, \nand export quality. This study was a part of the variety identification \nprogram through which desirable varieties can be selected under \nBangladesh condition \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 72-78 \n\n\n\nCite The Article: Bimal Chandra Kundu, Sauda Naznin, Md. Abu Kawochar, Md. Mazadul Islam, Abdullah Al Mahmud, Md Nurul Amin, Md. Nasir Uddin, K.M. Delowar \n\n\n\nHossain (2022). Selection of Processing Potato Varieties Through Multi-Location Trials. Malaysian Journal of Sustainable Agricultures, 6(2): 72-78. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Planting materials and location \n\n\n\nThe experiment was conducted during the Rabi (winter) season of three \nconsecutive years, 2015-16 to 2017-18 in six agro-ecological \nenvironments across the country, namely Gazipur, Bogura, Jamalpur, \nJashore, Debiganj and Munshiganj. The secondary yield trial (SYT) was the \nfirst multi-location trial, and it included thirteen exotic varieties, namely \nCanberra, Cimega, Coronada, Farida, Granada, Jelly, Memphis, Montreal, \nNavigator, Panamera, Rosi, Taisiya and 7 four 7 along with four check \nvarieties, BARI Alu-7 (Diamant), BARI Alu-13 (Granola), BARI Alu-25 \n(Asterix) and BARI Alu-28 (Lady Rosetta). The next year trial named \nadvance yield trial (AYT) was conducted with seven exotic varieties \nnamely, Cimega, Farida, Memphis, Jelly, Panamera, Taisiya and 7 four 7 \nalong with the same check varieties at five locations. The final year \nevaluation, regional yield trial (RYT), was conducted with Cimega, Farida, \nMemphis, Taisiya, and 7 four 7, along with the three check varieties in six \nlocations. Participatory variety trial (PVS) was done at five locations, but \nnumber of farmers per location varied from 3 to six. All the seed materials \nwere supplied from the Breeder Seed Production Centre (BSPC), Debiganj, \nPanchagarh. \n\n\n\n2.2   Experimental procedure and management \n\n\n\nAll the trials were conducted on 3 x 3 m plot with 3 replications. Spacing \nwas 60cm x 25 cm. seeds were planted during mid-November, and \nharvesting was done at 95 DAP. Manure and fertilizers were applied @ \nCowdung 10 t/ha, Urea-350 kg/ha, TSP-220 kg/ha, MP-260 kg/ha, \nGypsum-120 kg/ha, Boric acid-15 kg/ha and ZnSO4 12 kg/ha (Kundu et \nal., 2013) where half of Urea and a complete dose of other fertilizers were \nthoroughly mixed with the soil before planting. At 35 days after planting, \nthe remaining Urea was added as side-dressing. Necessary intercultural \noperations such as weeding, earthing up, irrigation and plant protection \nmeasures were carried out according to the recommendation of TCRC, \nBARI (Kundu et al., 2013). The meteorological data of all the locations are \npresented in Figures 1 and 2 (BAMIS, 2021). \n\n\n\nFigure 1: Monthly mean rainfall (mm) of the experimental sites during \nthe potato growing season (average of 2015\u20132016, 2016-2017 and \n\n\n\n2017\u20132018 crop season). \n\n\n\nFigure 2: Monthly mean minimum and maximum temperature (0C) the \nexperimental sites during the potato growing season (average of 2015\u2013\n\n\n\n2016, 2016-2017 and 2017\u20132018 crop season) \n\n\n\n2.2 Data collection and analysis \n\n\n\nData were collected on several parameters, but only important ones are \n\n\n\npresented in tables in the Results and Discussion with proper analysis. \n\n\n\nTuber yield data were collected whole plot basis. The data were \n\n\n\nstatistically analyzed, and the means were separated by LSD (least \n\n\n\nsignificant difference) tested at 5% level of probability using statistical \n\n\n\nsoftware R x 64-program version 3.3.2 (Core Team, 2013). \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n2.3 SYT (Secondary Yield Trial): Cropping Season 2015-16 \n\n\n\nThe mean performances of the first-year multi-location trial with 13 exotic \n\n\n\nvarieties (average of six locations) for all the characters are presented in \n\n\n\ntable 1. The range of days to start of emergence (12.94 \u2013 16.83), emergence \n\n\n\n% at 30 DAP (64.41 \u2013 96.32), plant vigour (7.06 \u2013 9.00), plant height (54.27 \n\n\n\n\u201374.31 cm), number of stems/hill (3.42\u2013 6.95) and foliage coverage\n\n\n\npercentage (57.33 \u2013 88.22) were in standard scale and desirable though \n\n\n\nthey showed statistically significant differences among the tested \n\n\n\ngenotypes. The highest mean emergence % was found (96.3) in Rosi, \n\n\n\nwhereas the lowest mean (64.4) was in 7four7. Early emergence is a \n\n\n\ndesirable trait in a variety because it promotes early foliar production and \n\n\n\nreduces seed tuber pre-emergence damage. Plant vigour is the sign of \n\n\n\nplants good health which was 9.00 in Rosi on a scale of 1-10, where 1 \n\n\n\nmeans the poorest and 10 means the best (Lobato et al., 2008). The \n\n\n\nnumber of stems hill-1 was also good for the exotic plant materials along \n\n\n\nwith the check varieties and this character is important because it \n\n\n\nincreases the soil coverage and photosynthetic area which ultimately \n\n\n\nincreases the tubers yield per plant (Knowles and Knowles, 2006). Field \n\n\n\nresistance to virus diseases is an important character in respect of \n\n\n\ndegeneration of potato. The varieties Montreal, Panamera and Rosi were \n\n\n\nhighly susceptible to virus diseases; that\u2019s why they were rejected from \n\n\n\nthe next year trial even though they have the high yielding ability. Farida \n\n\n\ngave the significantly highest yield (32.53 t/ha) at 65 DAP which was \n\n\n\nstatistically identical with Cimega (32.05 t/ha). All the tested genotypes \n\n\n\ngave a higher yield than the checks at 65 DAP except Coronada. Farida and \n\n\n\nCimega were selected for further trials based on potential varieties for \n\n\n\nearly cultivation. Varieties Farida and 7four7 were the highest average \n\n\n\nyielder at maturity, followed by Cimega, Panamera, Memphis and Taisiya. \n\n\n\nThe performance of these varieties was also satisfactory in all six locations \n\n\n\n(Table 2). In the case of dry matter content, none of the variety was better \n\n\n\nthan the check varieties in the first-year trial which are in agreement with \n\n\n\nthe report of (Patel et al., 2000). Finally, based on the tuber yield and other \n\n\n\neconomic characters, varieties Cimega, Farida, Jelly, Memphis, Panamera, \n\n\n\nTaisiya, 7four7 were selected for the next year trial.  \n\n\n\n3.2 AYT (Advanced Yield Trial): Cropping Season 2016-17 \n\n\n\n3.2.1 Tuber Yield \n\n\n\nThe second year\u2019s results are presented in Table 3 which showed that the \n\n\n\nmean yields varied from almost 30 to 38.7 t/ha in the imported varieties, \n\n\n\nwhile the check varieties produced 24.8 to 27.5 t/ha. When location-wise \n\n\n\nyield was considered, Farida topped in the list yielding 57.37 t/ha at \n\n\n\nDebiganj, followed by 7four7, Cimega and Memphis at the same location. \n\n\n\nThese varieties were also performed better at most of the locations. The \n\n\n\nmean yields of these varieties were also significantly higher than the check \n\n\n\nvarieties. When the location averages were considered, Debiganj was the \n\n\n\nbest, followed by Munshiganj and Bogura. The performances at Jamalpur \n\n\n\nwere very poor, and that of Jashore was medium. Usually, Jamalpur yields \n\n\n\nare better than those of the other locations, but this year, all the varieties \n\n\n\nincluding checks performed very poorly at this station. It might be due to \n\n\n\nsome stress conditions, either from the fertilizer or water or from any \n\n\n\nother management practices. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 72-78 \n\n\n\nCite The Article: Bimal Chandra Kundu, Sauda Naznin, Md. Abu Kawochar, Md. Mazadul Islam, Abdullah Al Mahmud, Md Nurul Amin, Md. Nasir Uddin, K.M. Delowar \n\n\n\nHossain (2022). Selection of Processing Potato Varieties Through Multi-Location Trials. Malaysian Journal of Sustainable Agricultures, 6(2): 72-78. \n\n\n\nTable 1: Performances of the exotic potato varieties in SYT (Av. of six locations), 2015-16 \n\n\n\nVariety Days to first \nemergence \n\n\n\nEmergence at 30 DAP \n(%) \n\n\n\nPlant vigour at 45 \nDAP (1-10 score) \n\n\n\nPlant \nheight \n(cm) \n\n\n\nNo. of \nstems \n/hill \n\n\n\nFoliage \ncoverage at 60 \nDAP (%) \n\n\n\nPercentage of \nvirus \ninfection \n\n\n\nCanberra 14.11 93.2 8.58 68.2 4.58 85.2 8.3 \n\n\n\nCimega 14.28 90.3 8.61 61.3 4.32 85.2 1.7 \n\n\n\nCoronada 16.11 65.5 7.06 54.2 3.42 57.3 1.3 \n\n\n\nFarida 12.94 86.9 8.56 60.8 4.37 87.0 2.4 \n\n\n\nGranada 16.83 85.9 8.03 54.9 3.71 78.1 1.7 \n\n\n\nJelly 15.72 91.0 8.78 67.9 5.38 88.2 3.3 \n\n\n\nMemphis 16.06 89.8 8.83 66.9 3.66 86.8 3.7 \n\n\n\nMontreal 14.11 87.3 8.47 69.3 4.94 84.4 20.7 \n\n\n\nNavigator 14.83 90.4 8.39 58.4 4.69 84.0 7.2 \n\n\n\nPanamera 14.61 94.9 8.14 73.6 4.34 81.5 19.7 \n\n\n\nRosi 13.83 96.3 9.00 74.3 4.16 86.5 17.9 \n\n\n\nTaisiya 15.06 89.4 8.22 61.4 4.95 80.7 2.3 \n\n\n\n7 four 7 14.94 64.4 8.11 55.9 4.26 68.8 2.7 \n\n\n\nBARI Alu-7 \n(Diamant) \n\n\n\n14.00 93.8 8.86 63.5 6.95 87.4 12.7 \n\n\n\nBARI Alu-13 \n(Granola) \n\n\n\n14.28 93.3 8.17 55.4 4.87 83.8 2.3 \n\n\n\nBARI Alu-25 \n(Asterix) \n\n\n\n14.11 93.9 8.25 65.3 6.01 83.6 6.7 \n\n\n\nBARI Alu-28 \n(L. Rosetta) \n\n\n\n13.06 91.6 8.36 58.9 5.12 86.9 9.7 \n\n\n\nRange 12.94 \u2013 \n16.83 \n\n\n\n64.40 \u2013 96.32 7.06 \u2013 9.00 \n54.27 \u2013\n74.31 \n\n\n\n3.42\u2013 \n6.95 \n\n\n\n57.33 \u2013 88.22 1.3-19.7 \n\n\n\nLSD 0.59 2.64 0.344 1.73 0.262 1.65 \n\n\n\nTable 2: Tuber yields (t/ha) of the varieties at 95 DAP at different locations in SYT, 2015-16 \n\n\n\nVariety \nLocation \n\n\n\nBogura Debiganj Gazipur Jamalpur Jashore Munshiganj Mean \n\n\n\nCanberra 31.75 43.71 28.12 34.90 24.89 32.03 32.57fg \n\n\n\nCimega 39.77 61.96 36.26 43.54 34.58 36.11 42.04 b \n\n\n\nCoronada 41.71 40.44 23.11 23.79 24.54 38.18 31.96 g \n\n\n\nFarida 51.47 64.69 35.40 38.73 33.42 42.78 44.42 a \n\n\n\nGranada 36.68 43.53 24.27 34.38 27.87 35.98 33.79 ef \n\n\n\nJelly 35.58 60.06 30.01 38.14 33.64 38.34 39.30 c \n\n\n\nMemphis 39.17 58.46 34.49 41.03 35.65 42.35 41.86 b \n\n\n\nMontreal 36.73 49.79 39.00 31.66 37.58 35.74 38.42 c \n\n\n\nNavigator 33.45 47.88 27.55 35.70 30.99 30.47 34.34 de \n\n\n\nPanamera 44.34 61.06 35.22 43.48 32.41 37.61 42.35 b \n\n\n\nRosi 35.94 49.18 20.52 42.24 30.12 34.49 35.42 d \n\n\n\nTaisiya 42.64 57.44 30.12 41.96 35.35 41.98 41.58 b \n\n\n\n7 four 7 49.48 67.56 49.11 33.02 28.13 47.58 45.81 a \n\n\n\nBARI Alu-7 \n(Diamant) \n\n\n\n30.12 41.88 27.48 29.53 26.62 41.83 32.91 efg \n\n\n\nBARI Alu-13 \n(Granola) \n\n\n\n28.55 46.29 26.44 33.52 20.54 37.52 32.14 g \n\n\n\nBARI Alu-25 \n(Asterix) \n\n\n\n25.97 45.54 26.24 30.25 30.87 40.64 33.25 efg \n\n\n\nBARI Alu-28 (L. \nRosetta) \n\n\n\n24.69 47.87 25.78 26.51 26.81 39.18 31.81 g \n\n\n\nLSD 1.499 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 72-78 \n\n\n\nCite The Article: Bimal Chandra Kundu, Sauda Naznin, Md. Abu Kawochar, Md. Mazadul Islam, Abdullah Al Mahmud, Md Nurul Amin, Md. Nasir Uddin, K.M. Delowar \n\n\n\nHossain (2022). Selection of Processing Potato Varieties Through Multi-Location Trials. Malaysian Journal of Sustainable Agricultures, 6(2): 72-78. \n\n\n\n3.2.2  Tuber grading by weight \n\n\n\nThe tuber grade by weight is an important character of a variety to find \nout its suitability for export and processing. The average grades of tubers \nby weight are presented in table 4. Variety Memphis produced the \n\n\n\nmaximum large sized tubers. Cimega and 7four7 also produced large-sized \ntubers, but at a lower proportion. All the check varieties and Taisiya \nproduced a higher proportion of small-sized tubers. Farida and Jelly \nproduced maximum medium-sized tubers. So these two varieties are most \nsuitable for table purpose, while Memphis is most suited for processing. \n\n\n\nTable 4: Size grades (%) of tubers by weight (Average of five locations) in AYT, 2016-17 \n\n\n\nVariety <15 mm 15-28 mm 28-40 mm 40-55 mm >55mm \n\n\n\nCimega 0.65 5.21 30.29 47.85 16.0 \n\n\n\nFarida 1.24 5.18 33.94 47.19 12.4 \n\n\n\nJelly 1.28 4.97 32.01 51.67 10.1 \n\n\n\nMemphis 0.42 6.81 28.42 46.21 18.1 \n\n\n\nPanamera 0.72 7.70 35.89 45.79 9.9 \n\n\n\nTaisiya 1.23 10.33 35.40 49.05 4.0 \n\n\n\n7 four7 0.70 5.48 31.48 45.07 17.2 \n\n\n\nBARI Alu-7 (Diamant) 1.40 10.82 36.77 44.14 6.8 \n\n\n\nBARI Alu-13 (Granola) 1.02 13.09 33.30 48.72 3.9 \n\n\n\nBARI Alu-25 (Asterix) 1.07 7.23 36.24 47.81 7.6 \n\n\n\nBARI Alu-28 (L. Rosetta) 2.78 11.34 42.41 40.99 2.5 \n\n\n\nMean 1.14 8.01 34.20 46.77 9.8 \n\n\n\n3.2.2 Dry matter content (%) \n\n\n\nThe dry matter content of a tuber is an important characteristic for the \nprocessing quality of a variety (Leonel et al., 2017). It is also a good \nindicator for the keeping and storage quality of potatoes (Lisinska and \nLeszczynski, 1989). Tuber dry matter must be greater than 20% for \nprocessing of a variety and Tuber dry matter content varies greatly \nbetween cultivars and is a highly genetically determined trait (Ezekiel et  \n\n\n\nal., 1999; Kellock, 1995). The dry matter content of the tested varieties is \npresented in table 5. All the varieties did not behave similar from location \nto location, might be due to the microclimatic effect of different location or \npartially sampling error. The known high dry matter containing variety \nBARI Alu-28 produced the highest dry matter at all the locations, and the \naverage is 21.22%, while Taisiya produced the lowest (16.83%). All other \nvarieties including the checks were medium in dry matter content. \n\n\n\nTable 5: Dry matter (%) of the tested varieties at 95 DAP at different locations in AYT, 2016-17 \n\n\n\nVariety \nLocations \n\n\n\nBogura Debiganj Jamalpur Jashore Munshiganj Mean \n\n\n\nCimega 18.05 ab 18.90 cd 18.90 cd 20.32 bc 18.31c 18.90 b \n\n\n\nFarida 18.46 ab 20.33 abc 20.33 abc 18.13 de 19.22 bc 19.04 b \n\n\n\nJelly 18.05 ab 20.08 bc 20.08 bc 20.45 bc 18.46 c 19.26 b \n\n\n\nMemphis 16.80 b 21.56 ab 21.57 ab 19.52 cd 18.71 c 19.65 b \n\n\n\nPanamera 18.71 ab 20.23 bc 20.23 bc 18.67 cde 18.68 c 19.08 b \n\n\n\nTaisiya 17.30 b 14.90 e 14.90 e 15.77 f 19.37 bc 16.83 c \n\n\n\n7 four7 17.55 b 19.01 c 19.01 c 18.62 cde 19.15 bc 18.59 b \n\n\n\nBARI Alu 7 (Diamant) 19.71 a 19.16 c 19.17 c 21.7 1 18.65 c 19.81 b \n\n\n\nBARI Alu 13 (Granola) 17.76 b 17.07 d 17.07 d 17.42 ef 20.75 ab 18.25 b \n\n\n\nBARI Alu 25 (Asterix) 18.30 ab 22.21 a 22.21 a 20.34 bc 18.38 c 19.81 b \n\n\n\nBARI Alu 28 (L. Rosetta) 18.05 ab 22.01 ab 22.01 ab 22.60 a 22.22 a 21.22 a \n\n\n\nMean 18.07 19.59 19.59 17.44 19.26 18.79 \n\n\n\nCV (%) 3.76 \n\n\n\nTable 3: Tuber yields (t/ha) of the tested varieties at 95 DAP at different locations in AYT, 2016-17 \n\n\n\nVariety \nLocations \n\n\n\nBogura Debiganj Jamalpur Jashore Munshiganj Mean \n\n\n\nCimega 41.37 abc 53.62 ab 12.44 bc 34.61 a 44.59 a 37.33 a \n\n\n\nFarida 25.52 fg 57.37 a 14.82 abc 24.48 bc 43.08 a 33.06 b \n\n\n\nJelly 33.96 cde 41.66 c 10.94 bc 21.11 c 42.12 a 29.96 c \n\n\n\nMemphis 46.69 a 53.14 ab 18.68 ab 22.52 bc 42.31 a 36.67 ab \n\n\n\nPanamera 42.09 ab 42.99 c 15.30 abc 24.92 bc 42.52 a 33.57 b \n\n\n\nTaisiya 37.91 bcd 48.20 bc 12.71 bc 21.64 c 43.07 a 32.71 bc \n\n\n\n7four7 43.89 ab 54.97 ab 22.02 a 30.11 ab 42.50 a 38.70 a \n\n\n\nBARI Alu-7 \n(Diamant) \n\n\n\n32.15 def 26.91 d 13.20 bc 27.12 abc 38.15 ab 27.51 cd \n\n\n\nBARI Alu-13 \n(Granola) \n\n\n\n17.97 g 42.33 c 10.31 c 22.38 bc 30.94 b 24.79 e \n\n\n\nBARI Alu-25 \n(Asterix) \n\n\n\n29.29 ef 32.33 d 11.06 bc 23.75 bc 39.72 a 27.23 cd \n\n\n\nBARI Alu-28 (L. \nRosetta) \n\n\n\n30.33 def 31.59 d 10.20 c 21.72 c 37.78 ab 26.33 de \n\n\n\nMean 34.65 44.10 13.79 24.94 40.62 31.6 \n\n\n\nCV(%) 9.37 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 72-78 \n\n\n\nCite The Article: Bimal Chandra Kundu, Sauda Naznin, Md. Abu Kawochar, Md. Mazadul Islam, Abdullah Al Mahmud, Md Nurul Amin, Md. Nasir Uddin, K.M. Delowar \n\n\n\nHossain (2022). Selection of Processing Potato Varieties Through Multi-Location Trials. Malaysian Journal of Sustainable Agricultures, 6(2): 72-78. \n\n\n\n3.2.3 Processing qualities \n\n\n\nSeven exotic varieties of Advanced Yield Trial were tested to assess the \n\n\n\nquality of the chips (Figure 3) and French fries (Figure 4). No verities were \n\n\n\nfound super in chips quality. The varieties Panamera, Farida, Cimeha, \n\n\n\nTaisiya and 7 four 7 showed medium quality for chips, where Memphis \n\n\n\nand Jelly showed poor quality. While the French fry quality was \n\n\n\nconsidered, none was found to produce super, none was found to show \n\n\n\npoor results. Almost all varieties produced medium quality French fries. \n\n\n\nThe results indicated that these varieties may be utilized if other qualities \n\n\n\nlike tuber size, tuber shape, dry matter content, reducing sugar, etc. are \n\n\n\nfound suitable (Storey and Davies, 1992). Yield-wise all the imported \n\n\n\nvarieties were good, but for commercial feasibilities, these varieties \n\n\n\nshould be further checked for other qualities like pest resistance, \n\n\n\ndegeneration rate, and preservation and post-harvest losses. Based on the \n\n\n\nAYT results, five varieties Farida, 7four7, Cimega, Taisiya and Memphis \n\n\n\nwere selected for RYT (Regional Yield Trial). \n\n\n\nFigure 3: Chips quality of the seven exotic potato varieties. External \n\n\n\nappearance scale 1-20 (where 20=Excellent, 16=Very good, 12=Good, \n\n\n\n8=Fair, 4=Poor); External colour scale 1-10 (where 10=Light whitish, \n\n\n\n8=Light golden, 6=Golden, 4=slightly brown, 2=Dark); Texture \n\n\n\n(mealiness) scale 1-10 (where 20=Crispy, 16=moderately crispy, \n\n\n\n12=slightly crispy/soggy,8=soggy) \n\n\n\n3.3  RYT (Regional Yield Trial): Final year, Cropping Season 2017-18 \n\n\n\nRegional Yield Trial is mandatory before the release of a variety. The \n\n\n\nTechnical Committee of the National Seed Board (NSB) critically analyze \n\n\n\nthe results of the RYT before recommendation for release. Five best \n\n\n\nvarieties of the lot were placed in the RYT at six locations along with three \n\n\n\nvarieties as a check (Table 6). The economic worth of tuber production is \n\n\n\ndetermined by the marketable tuber yield (Kim et al., 2017). The yield \n\n\n\nresults showed that the imported varieties were better than the three \n\n\n\nchecks almost at all the locations. The variety 7four7 was the best one, \n\n\n\nfollowed by Cimega, Memphis, Taisiya and Farida. Among all the locations \n\n\n\nthe variety 7four7 yielded the highest (54.4 t/ha), followed by Memphis at \n\n\n\nMunshiganj. Among the stations, Jamalpur was the best, followed by \n\n\n\nMunshiganj. In the farmer\u2019s field, Cimega produced the highest, closely \n\n\n\nfollowed by 7four7 and Memphis (Table 7). When tuber grade was \n\n\n\nconsidered, Memphis, Cimega and 7 four 7 produced higherproportion of \n\n\n\nlarge sized truber, while Taisiya produced the highest percentage of \n\n\n\nmedium sized tubers (Table 7). \n\n\n\nFigure 4: French fries quality of the seven exotic potato varieties. \n\n\n\nExternal appearance scale 1-20 (where 20=Excellent, 16=Very good, \n\n\n\n12=Good, 8=Fair, 4=Poor); External colour scale 1-10 (where 10=Light \n\n\n\nwhitish, 8=Light golden, 6=Golden, 4=slightly brown, 2=Dark); Internal \n\n\n\ncolour scale 1-20 (where 20= Bright, white, crystalline, 16= Bright, white, \n\n\n\n12= Off-white, opaque, 8=Grayish, 4=Dark gray); Texture of outside \n\n\n\nstrips scale 1-20 (where 20=Crispy, 16=moderately crispy, 12=slightly \n\n\n\ncrispy/soggy,8= moderately soggy, 4=soggy); Texture of inside strips \n\n\n\nscale 1-30 (where 30=Mealy, 24= moderately mealy/soggy, 18= slightly \n\n\n\nmealy/soggy,12=soggy, 6= very soggy) \n\n\n\nTable 6: Tuber yield (t/ha) of potato varieties at 95 DAP at six locations in RYT, 2017-18 \n\n\n\nVariety Location \n\n\n\nBogura Debiganj Gazipur Jamalpur Jashore Munshiganj Mean \n\n\n\nCimega 41.4hij 42.0hi 46.4efg 50.5bc 38.2j-n 49.18b-f 44.6b \n\n\n\nFarida 34.8o-r 32.5rst 45.8fg 43.3gh 31.1stu 36.3m-q 37.3e \n\n\n\nMemphis 42.2hi 31.7rst 46.0efg 52.3ab 36.2n-q 50.6bc 43.2c \n\n\n\nTaisiya 38.1k-o 32.1rst 47.8c-f 49.9bcd 31.2stu 41.7hi 40.2d \n\n\n\n7 four 7 38.9i-n 46.6d-g 48.0c-f 54.4a 39.6i-m 49.2b-e 46.1a \n\n\n\nBARI Alu-7 \n(Diamant) \n\n\n\n37.9k-p 29.5 tuv 41.1h-k 39.7i-l 30.4tu 36.4l-q 35.9f \n\n\n\nBARI Alu-25 \n(Asterix) \n\n\n\n34.2qrs 23.7w 34.6pqr 44.1gh 29.3tuv 48.2c-f 35.7f \n\n\n\nBARI Alu-28 \n(L.Rosetta) \n\n\n\n28.1uv 26.4vw 30.3tu 31.5rst 24.5w 32.5rst 28.9g \n\n\n\nCV% 5.33 \n\n\n\n3.4 Participatory variety selection (RYT-PVS), 2017-18 \n\n\n\nSame five varieties along with checks were evaluated at farmers\u2019 fields of \n\n\n\nsix different agro ecological environments during 2017-18 cropping \n\n\n\nseason as PVS. Yield of all varieties varied significantly. The highest \n\n\n\naverage tuber yield (46.55 t/ha) was recorded in Cimega followed by 7 \n\n\n\nfour 7 (44.94 t/ha) and Memphis (43.22 t/ha) and lowest average yield \n\n\n\nwas found in check varieties BARI Alu-28 (Lady Rosetta) (30.52 t/ha).  \n\n\n\nConsidering size, shape, colour and yield, farmers of all locations liked all \n\n\n\nnew varieties, although it varied from farmer to farmer. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 72-78 \n\n\n\nCite The Article: Bimal Chandra Kundu, Sauda Naznin, Md. Abu Kawochar, Md. Mazadul Islam, Abdullah Al Mahmud, Md Nurul Amin, Md. Nasir Uddin, K.M. Delowar \n\n\n\nHossain (2022). Selection of Processing Potato Varieties Through Multi-Location Trials. Malaysian Journal of Sustainable Agricultures, 6(2): 72-78. \n\n\n\nTable 7. Tuber grade by weight (Average six locations in percentage) in RYT, 2017 -18 \n\n\n\nVariety % of Tuber Grading by Weight \n\n\n\n<15 mm 15-28 mm 28-40mm 40-55mm >55mm \n\n\n\nCimega 0.40 3.62 25.93 40.18 29.87 \n\n\n\nFarida 0.76 4.91 34.34 46.75 13.24 \n\n\n\nMemphis 0.35 2.56 19.53 46.70 30.86 \n\n\n\nTaisiya 0.83 5.88 49.45 38.43 5.41 \n\n\n\n7 four 7 0.58 3.46 22.92 46.27 26.77 \n\n\n\nBARI Alu-7 (Diamant) 0.91 6.28 40.05 41.27 11.48 \n\n\n\nBARI Alu-25 (Asterix) 1.09 5.27 32.61 50.57 10.46 \n\n\n\nBARI Alu-28 (L.Rosetta) 0.22 3.97 36.57 49.39 9.85 \n\n\n\nTable 8: Tuber yield (t/ha) of the exotic varieties at 95 DAP in PVS (farmers\u2019 fields), 2017-18 \n\n\n\nVariety Location \n\n\n\nBogura Gazipur Jamalpur Jashore Munshiganj Mean \n\n\n\nCimega 43.60 38.23 67.60 30.60 52.74 46.55 \n\n\n\nFarida 45.02 37.58 44.33 24.30 30.47 36.34 \n\n\n\nMemphis 44.64 30.42 55.00 36.80 49.24 43.22 \n\n\n\nTaisiya 34.05 28.94 58.66 27.80 44.56 38.80 \n\n\n\n7four7 48.77 32.00 73.33 26.35 44.27 44.94 \n\n\n\nBARI Alu-7 (Diamant) 33.67 26.79 54.13 24.65 33.64 34.58 \n\n\n\nBARI Alu-25 (Asterix) 30.19 26.25 48.16 28.40 40.54 34.71 \n\n\n\nBARI Alu-28 \n(L.Rosetta) \n\n\n\n26.53 28.95 38.58 22.90 35.66 30.52 \n\n\n\n4. CONCLUSION\n\n\n\nFrom the results of the three years of multi-location trials, and on-farm \nobservation, it can be concluded that all the five varieties (Cimega, Farida, \nMemphis, Taisiya, and 7four7) are suitable for commercial cultivation. So \nthese varieties may be recommended for release. Of those, the variety \n7four7 is the highest yielder for early as well as main crop cultivation; \nVariety Farida is most suitable for table purpose as because of its medium \nsized round-oval uniform tubers and palatability. Variety Cimega is also a \nhigh yielder but should be further checked for other specific qualities like \npests and diseases. While Memphis may be tried as a processing variety, \nas it produces large sized tubers, long-oval shape, smooth skin and good \ndry matter. For quality chips production, none was found comparable to \nthe check. \n\n\n\nAUTHORS' CONTRIBUTIONS \n\n\n\nBCK: Planning, Conceptualization, Supervision in all locations. SN, MAK, \nMMI, AAM, MNA, MNU and KMDH conducted experiments in different \nlocations and collected data. SN, MMI and MNU gathered and analysed \ndata, validation, writing and editing, and revised the whole manuscript. All \nauthors read and approved the final manuscript.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nWe earnestly give thanks to the Director-General of Bangladesh \nAgricultural Research Institute (BARI) for delivering the services for this \nstudy. \n\n\n\nCONFLICT OF INTEREST \n\n\n\nAll authors declare that there is no conflict of interest either financially or \notherwise. \n\n\n\nREFERENCES \n\n\n\nAl Mahmud, A., Alam, M.J., Kundu, B.C., Skalicky, M., Rahman, M.M., \nRahaman, E.H., Sultana, M., Molla, M., Hossain, A., El-Shehawi, A.M., \nBrestic, M., 2021. Selection of Suitable Potato Genotypes for Late-Sown \nHeat Stress Conditions Based on Field Performance and Stress \n\n\n\nTolerance Indices. Sustainability, 13 (5), Pp. 2770. \nhttps://doi.org/10.3390/su13052770 \n\n\n\nBAMIS, 2021. Bangladesh Agro-Meteorological Information Portal: Agro-\nMeteorological Information Systems Development Project: Department \nof Agricultural Extension. Available online: \nhttps://www.bamis.gov.bd/en/page/aezs-maps/. \n\n\n\nDe Haan, S., Rodriguez, F., 2016. Potato origin and production. Advances in \npotato chemistry and technology. Academic London, UK: Press, In J. \nSingh & L. Kaur (Eds.) Elsiever, 2, Pp. 1-32. \nhttps://doi.org/10.1016/B978-0-12-800002-1.00001-7 \n\n\n\nEzekiel, R., Verma, S.C., Sukumaran N.P., Shekhawat, G.S., 1999. A guide to \npotato processors in India. Technical Bulletin No. 48, CPRl, Shimla, PP. \n39. \n\n\n\nFAOSTAT, 2020. Statistical Database, Food and Agricultural Organization \nof United Nations [Internet]. Available from: \nhttp://www.fao.org/faostat/en/#data/QC. \n\n\n\nHussain, M.M., 2012. Potato Processing in Bangladesh: An Overview: \nWorkshop on potato processing in Bangladesh. Kabir et al. (eds). \nTCRC/AFE, Dhaka. \n\n\n\nKellock, T., 1995. Potatoes: factors affecting dry matter. Agriculture notes. \nAG0323. Victoria, Australia: State of Victoria, Department of Primary \nIndustries. \n\n\n\nKim, Y.U., Seo, B.S., Choi, D.H., Ban, H.Y., Lee, B.W., 2017. Impact of high \ntemperatures on the marketable tuber yield and related traits of potato. \nEuropean Journal of Agronomy, 89, Pp. 46-52. \nhttps://doi.org/10.1016/j.eja2017.06.005 \n\n\n\nKnowles, N.R., Knowles, L.O., 2006. Manipulating Stem Number, Tuber Set, \nand Yield Relationships for Northern\u2010and Southern\u2010Grown Potato Seed \nLots. Crop Science, 46 (1), PP. 284-96. \nhttps://doi.org/10.2135/cropsci2005.05-0078. \n\n\n\nKundu, B.C., Islam, M.S., Kawochar, M.A., Rashid, M.H., 2013. Potato \n(Solanum tuberosum L.) variety development through hybridization: a \n\n\n\n\nhttps://doi.org/10.3390/su13052770\n\n\nhttps://www.bamis.gov.bd/en/page/aezs-maps/\n\n\nhttps://doi.org/10.1016/B978-0-12-800002-1.00001-7\n\n\nhttp://www.fao.org/faostat/en/#data/QC\n\n\nhttps://doi.org/10.2135/cropsci2005.05-0078\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 72-78 \n\n\n\nCite The Article: Bimal Chandra Kundu, Sauda Naznin, Md. Abu Kawochar, Md. Mazadul Islam, Abdullah Al Mahmud, Md Nurul Amin, Md. Nasir Uddin, K.M. Delowar \n\n\n\nHossain (2022). Selection of Processing Potato Varieties Through Multi-Location Trials. Malaysian Journal of Sustainable Agricultures, 6(2): 72-78. \n\n\n\nnew era in Bangladesh. Bangladesh Journal of Agricultural Research, 38 \n(4), Pp. 637-46. https://doi.org/10.3329/bjar.v38i4.19019. \n\n\n\nKundu, B.C., Kawochar, M.A., Naznin, S., Ahmed, N.U., Halder, S.C., Mostofa, \nM., Delowar, H.K., 2020. Stability Analysis for Yield of Advanced Potato \nGenotypes for Commercial Cultivation in Bangladesh. SAARC Journal of \nAgriculture, 18 (1), Pp. 73-86. \nhttps://doi.org/10.3329/sja.v18i1.48383. \n\n\n\nLeonel, M., Do Carmo, E.L., Fernandes, A.M., Soratto, R.P., Eb\u00farneo, J.A., \nGarcia, \u00c9.L., Dos Santos, T.P., 2017. Chemical composition of potato \ntubers: the effect of cultivars and growth conditions. Journal of food \nscience and technology, 54 (8), Pp. 2372-8. \nhttps://doi.org/10.1007/s13197-017-2677-6. \n\n\n\nLisinska, G., Leszczynski, W., 1989. Potato science and technology. \nSpringer Science & Business Media. \n\n\n\nLobato, M.C., Olivieri, F.P., Altamiranda, E.G., Wolski, E.A., Daleo, G.R., \nCaldiz, D.O., Andreu, A.B., 2008. Phosphite compounds reduce disease \nseverity in potato seed tubers and foliage. European Journal of Plant \nPathology, 122 (3), Pp. 349-58. https://doi.org/10.1007/s10658-008-\n9299-9. \n\n\n\nPatel, H.R., Shekh, A.M., Pate, G.C., Mistry, D.S., 2000. Yield and quality of \npotato in relation to different dates of planting. Journal of the Indian \nPotato Association, 27 (3/4), Pp. 87-90. \n\n\n\nR Core Team, 2013. R. A Language and Environment for Statistical \nComputing; R Foundation for Statistical Computing: Vienna, Austria. \nAvailable online: http://www.R-project.org/ \n\n\n\nScott, G.J., Rosegrant, M.W., Ringler, C., 2020. Global projections for root \nand tuber crops to the year 2020. Food policy, 25 (5), Pp. 561-597. \nhttps://doi.org/10.1016/S0306-9192(99)00087-1 \n\n\n\nSiddique, M.N., Sultana, J., Huda, M.S., Abdullah, M.R., Chowdury, M.A., \n2015. Potato production and management with preference to seed \npotato supply chain, certification and actors involve in Bangladesh. \nInternational Journal of Business, Management and Social Research, 1 \n(1), PP. 01. https://doi.org/10.18801/ijbmsr.010115.01. \n\n\n\nStorey, R.M.J., Davies, H.V., 1992. Tuber quality. In The potato crop (pp.). \nSpringer, Dordrecht, Pp. 507-569. https://doi.org/ 10.1007/978-94-\n011-2340-2_12  \n\n\n\nThiele, G., Theisen, K., Bonierbale, M., Walker, T., 2010. Targeting the poor \nand hungry with potato science. Potato Journal, 37 (3/4), Pp. 75-86. \n\n\n\n\nhttps://doi.org/10.3329/bjar.v38i4.19019\n\n\nhttps://doi.org/10.3329/sja.v18i1.48383\n\n\nhttps://doi.org/10.1007/s13197-017-2677-6\n\n\nhttps://doi.org/10.1007/s10658-008-9299-9\n\n\nhttps://doi.org/10.1007/s10658-008-9299-9\n\n\nhttp://www.r-project.org/\n\n\nhttps://doi.org/10.1016/S0306-9192(99)00087-1\n\n\nhttps://doi.org/10.18801/ijbmsr.010115.01\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2023.06.13 \n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui (2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.06.13 \n\n\n\n\n\n\n\nIMPACT OF LEGUMES AND CEREALS ON OLIVE PRODUCTIVITY IN THE SOUTH \nMEDITERRANEAN \n\n\n\nAsmae Amassaghroua,b*, Karim Barkaoui c,d, Ahmed Bouaziza, Si Bennasseur Alaouia, Rachid Razoukb, Khalid Daouib \n\n\n\na Institute of Agriculture and Veterinary Medicine Hassan II, BP 6202 Rabat-Instituts10112, Rabat, Morocco \nb National Institute of Agricultural Research, Regional Center of Meknes, km10, Haj Kaddour Road, Meknes, Morocco \nc CIRAD, UMR ABSys, F-34398 Montpellier, France  \n\n\n\nd ABSys, Univ Montpellier, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Montpellier, France \n*Corresponding author email: a.amasaghrou@iav.ac.ma \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 10 September 2022 \nRevised 13 October 2022 \nAccepted 25 November 2022 \nAvailable online 07 December 2022 \n\n\n\n Intercropping of trees with crops on the same piece of land at a given time has been hypothesized to: enhance \n\n\n\ncrop yield, increase land-use and improve land equivalent ratio (LER). To address this hypothesis, we \n\n\n\nevaluated two legumes faba bean, lentil and three cereals durum wheat, soft wheat and barley grown in olive \n(Olea europea) agroforestry during two growing seasons (Y) with contrasting weather (Y1: 2015-2016 and \n\n\n\nY2: 2016-2017) under a Mediterranean climate of north western Morocco. We assessed the effect of annual \n\n\n\ncrops on olive growth and yield; the effect of trees on annual crop growth, yield components, and final yields; \n\n\n\nfinally, we calculated the land equivalent ratio (LER) of olive agroforestry to assess the productivity of the \nassociations. Legumes had no effect on olive growth and yield, while cereals negatively affected shoot \n\n\n\nelongation and olive yield compared to olive in sole crop. Olive limited crop growth and yield of all associated \n\n\n\ncrops and yield reduction was around 33 % for legumes and 47 % for cereals in agroforestry than sole crop. \nThe magnitude of reduction was higher in Y1 than Y2. Similar responses were found when comparing crops \n\n\n\nat different distances from trees. Annual crops generally had lower biomass and yield, near the trees \n\n\n\ncompared to the middle of tree inter-rows, causing significant spatial heterogeneity in crops. The LER reached \n1.36 with lentil and 1.33 with faba bean, the lowest LER was recorded with durum wheat in both years with \n\n\n\n1.01 in Y1 and 1.02 in Y2, and the highest LER with cereals was registered with soft wheat and reached 1.19 \n\n\n\nin Y1.  \n\n\n\nKEYWORDS \n\n\n\nFaba Bean, Lentil, Wheat, Barley, LER \n\n\n\n \n1.   INTRODUCTION \n\n\n\nIn the Mediterranean region, climate change is expected to result in \nincreased risk of heat stress and drought (Woetzel et al., \n2020). Agroforestry systems can be an effective means of stabilizing or \neven enhancing crop yields under climate change. Olive trees grown in \nrows or as scattered trees, have been intercropped with cereals and \nlegumes such as wheat, barley, faba bean\u2026 for centuries in the \nMediterranean area (Panozzo et al., 2019). A renewed interest in olive \nalley-cropping is currently emerging with the aim to improve the \nsustainability of both olive orchards and annual crop cultivation (Panozzo \net al., 2019). Crop growth and development strongly depends on local \nclimatic conditions. Each crop has different climatic and environmental \nrequirements for normal growth (e.g. temperature, light, slope \norientation, soil fertility, water availability, nutrients). These variables \nmay be affected by climate change, especially temperature, due to its \nimpact on the plant development (Tanasijevic et al., 2014). The mitigation \neffect that trees might have on these risks is uncertain, although trees can \nreduce air and crop temperature, the effect on crop phenology might lead \nto slower development and so a delay in the most sensitive stage, i.e. \nanthesis for cereals, which could happen when water stress occur and light \ninterception is low (Panozzo et al., 2019; Temani et al., 2021;  Zhang et al., \n2018).  \n\n\n\nSome Mediterranean alley cropping systems have been successful for \nthousands of years. e.g.. olive trees intercropped with cereals and legumes \n(Wolpert et al., 2020; Pantera et al., 2018; Torquebiau et al., \n2002). Intercrops of olive trees with cereals and legumes may increase the \nprofitability and sustainability of the farm by the production of biomass \nand grains from the understory crops and positively affect olive tree \nproductivity (Chehab et al., 2019; Panozzo et al., 2022). Besides, there is \nan increasing need to improve the resilience of annual crop especially \nwheat and legumes to climate change, while providing ecosystem services \n(Bedoussac et al., 2015; Jensen et al., 2012). \n\n\n\nIn Greece evaluate the productivity of barley and a mixture of barley and \ncommon vetch in an olive orchard, they found that the intercropping is a \nvery promising practice for Mediterranean areas with traditional olive \nagroforestry systems, and total grain yield of barley in agroforestry was \nclose to 80% of the average barley in monocultures (Mantzanas et al., \n2021). In another study in Portugal compared four management practices \n(ordinary tillage, cover crop with self-reseeding annual legume species, \nnatural vegetation fertilized, and natural vegetation unfertilized) in a \nrainfed olive orchard (Correia et al., 2015). They observed that the case \nwith legumes cover crops reached higher cumulative yield than in the \nother cases.  \n\n\n\nIn Morocco under Mediterranean condition, a study based on farmers \n\n\n\n\nmailto:a.amasaghrou@iav.ac.ma\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui ( 2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n\n\n\n\nestimation in mountainous areas of Morocco. Highlights that legume crops \ndo not affect negatively olive production, whilst cereals do (Daoui et al., \n2014). In a field survey conducted in northern Morocco which involved \nintercrops of olive trees with legumes (faba bean and chickpea (Cicer \narietinum)) and cereals (wheat and barley) found that legumes do not \naffect negatively olive production and the Land Equivalent Ratio with \nlegumes is higher than with cereals (Amassaghrou et al., 2021). A group \nresearcher in a field survey conducted along a rainfall gradient in northern \nMorocco which involved intercrops of olive trees and barley, found that \nbarley biomass production and grain yield at harvest was higher under the \nintermediate rainfall level compared to the wetter site (Temani et al., \n2021). Regarding the rainfed olive orchards, the main factors affecting the \nunderstory crops productivity are water availability, depending on local \nmeteorological conditions, and light intensity depending on tree age and \ncanopy cover.  \n\n\n\nAnother study conducted in Morocco compared the cultivation of wheat, \nfaba bean and coriander (Coriandrum sativum) in two distances from the \ntree (close to trunk and from the limit of olive tree canopy) (Razouk et al., \n2016). In rainfed olive orchards. The results showed that vegetative \ngrowth and yield of olive tree were reduced by sowing wheat even from \nthe canopy limit. In contrast, faba beans induced an improvement of olive \nproduction at the two tested sowing distances. The reduction in annual \ncrops biomass was recorded over an aureole around the tree canopy and \nshading induced a reduction of 70% in wheat yield and of 10% in grain \nweight. Other studies have demonstrated that the impact of trees on the \nunderstory vegetation could be negative (Noumi et al., 2011). For example, \nthe roots compete for water and nutrients with understory vegetation. The \nrole played by competition from tree roots is likely to be influential in the \nreduction of available soil moisture and hence in the reduction of plant \ngrowth  (Artru et al., 2017). Trees can also reduce light availability, which \ncan also limit plant production (Dufour et al., 2013; Xu et al., 2016; Noumi \net al., 2011).  \n\n\n\nThe aim of this study was to evaluate the growth and productivity of \nunderstory intercrops with olive trees: cereals and legumes, used grain \nproduction and their possible interactions, focusing on the spatial \nvariability of crop growth during two climatic years. Specifically, we asked \nwhether: (1) olive trees would have different growth and yield potential \nin association with legumes than with cereals; (2) legumes and cereals \nspecies would have different yield potential under agroforestry regarding \ntheir physiological requirements (especially light) and crop growth cycle \nduration; (3) olive agroforestry systems are more land-productive than \nsoles crops and trees and improve soil fertility under trees. \n\n\n\n2.   MATERIALS AND METHODS  \n\n\n\n2.1 Study Site \n\n\n\nThe experiments were carried out at the experimental station of the \nNational Institute for Agricultural Research (INRA Morocco) in Ain \nTaoujdate (33\u00b093\u201907.1\u201d N. 5\u00b027\u201935.7\u201d W. 550 m a.s.l) for two growing \nseasons (Y): Y1: 2015-2016 and Y2: 2016 - 2017. Based on historical data \n(30 years), the site has a sub-humid Mediterranean climate, with a yearly \naverage temperature of ~15 \u25e6C and a maximum daily temperature that \nincreased from 34 \u25e6C at the end of the last century up to 38 \u25e6C in 2017. The \nhistorical average annual precipitation is about 450 mm with high \nheterogeneity in the rainfall pattern across years (figure 1); 248 mm \nduring Y1 and 202 mm during Y2 (figure 1). \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Distribution of monthly precipitation (mm) for the two years of \nstudy (a: Y1 and b: Y2) (blue bars), as compared to the mean historical \nprecipitation for the last 30 years (1988\u20132017) at the study site (gray \n\n\n\nbars). \n\n\n\n2.2 Plant Material and Experimental Design \n\n\n\nWe compared two agroforestry systems, the first one with two widespread \nMediterranean food legume crop species, faba bean (Vicia faba, Cv. \n\u2018Aguadulce\u2019), and lentil (Lens culinaris, Cv.\u2019 Bakria\u2019) and the second system \nwith three cereals, durum wheat (Triticum durum. cv. 'Karim') soft wheat \n(Tritium eastevum. cv 'Arrehane') and barley (Hordeum vulgare. cv \n'population'), grown in an olive-based agroforestry (AFS). The \nexperimental design is shown in figure 2. The olive grove (Olea europaea. \nSubsp. Europaea. Cv. \u2019Picholine marocaine\u2019) was 50-years old with an \naverage height of 5.2 m (ranging from 4.5 to 6.0 m), average trunk \ndiameter of 0.4 m, average canopy diameter of 5.0 m. The density of olive \ntrees was 200 trees ha-1 with a regular 6\u00d78 m plantation design following \nan East-West orientation. \n\n\n\nThe trial was arranged as a randomized block design with four replicates, \ncrop sampling was distributed over three main \u2018Areas\u2019 to represent the \nspatial heterogeneity caused by trees in the AFS plot. Each sampling Area \nconsisted of 4 adjacent crop lines and had contrasting exposure to tree \nshade and belowground interactions (figure 2). The choice of the three \nAreas was made on the way in which shade is projected in the inter-row. \nAt the beginning of the growing season, Area 1 (A1) and Area 2 (A2) are \nmuch more in the shade than Area 3 (A3). At the end of the season, A1 and \nA3 are under tree shade. We can therefore say that A2 is an intermediate \nbetween A1 and A3 (figure 2) from the point of view of aerial influence. A1 \nreceives no shade at the beginning of the cycle but a little at the end, unlike \nA3 which is exposed to tree shade throughout the crop cycle. \n\n\n\n\n\n\n\nFigure 2: Experimental layout showing the plot of agroforestry (AFS) \nfaba bean, lentil, durum wheat, soft wheat and barley were sown in an \n\n\n\neast-west orientation leaving 1.5 m distance between the outer rows of \nthe crop strip and the tree line. (A) represent shade tree distribution \n\n\n\nuntil April and (B) represent shade tree distribution until July; A1, A2 and \nA3 represent the three area where measurements were done during \n\n\n\ngrowing season \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui ( 2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n\n\n\n\nA survey of six farmers in the study area under the same conditions (type \nof soil, fertilization) and same crops (variety) was set up to compare yields \nin SCS with AFS. Legumes and cereals were sown on the same date for the \ntwo years of the experiment, in January 2015 and December 2016. The \nsowing rates in AFS 180 kg. ha-1 for cereals, 100 kg.ha-1 for faba bean and \n50 kg.ha-1 for lentil. Crops were seeded in strips spaced at a distance of 0.3 \nm, for cereals and lentil and 0.5 m for faba bean, crop strips started 1.5 m \nfar from olive tree rows, every treatment covered an area of 120 m2 and \nthe total area was 600m2. \n\n\n\nFertilizer application rate for legumes was 48 kg P2O5 ha\u22121, and P was \napplied as triple superphosphate at the sowing date, while the fertilizer \napplication rate for cereals was 150 kg ha\u22121 of Diamomiun phosphate (18-\n46-00) and 360 kg ha\u22121 of ammonitrate (33%) in march. Technical \nmanagement (weed, disease, and pest control) has been performed to \nensure the safe growth of crops. In Y1, crops were harvested at maturity \non the 28th of June for faba bean and lentil and the 2 of July for cereals. In \nY2, crops were harvested at maturity on the 8th of May for lentil and 18th \nof May for faba bean and the 1st of June for cereals. Olive trees were \nmanually harvested in November Y1 and Y2, and all the fresh olive fruit \nwere weighed to measure the yield.  \n\n\n\nThe experimental plot is derived from alluvial soil. Soil samples were \ncollected from a depth of 0\u201330 cm near to tree and in the middle of inter-\nrow, and were mixed, to evaluate fertility and organic matter at the \nbeginning of the experiment (December Y1) Organic Matter in this soil \nlayer was 0.8 %, Olsen-P 21.9 mg kg\u22121, and K2O 331.2 mg kg\u22121. After \nharvesting annual crops in June Y1 and June Y2, soil samples were taken \nfrom 0-30 cm layer at three different distances from tree rows inside each \ntreatment with four repetitions. These soil samples were subjected to \nchemical analysis to determine their levels of fertility. Analyses were \nperformed by the following methods: organic matter by the Walkley and \nBlack method, available phosphorus by Olsen method and exchangeable \npotassium by ammonium acetate. \n\n\n\n2.3 Field Measurements and Sampling \n\n\n\nOn olive tree, the measurements included diameter at breast height, tree \ncanopy, tree height and the annual shoot elongation of all trees of the \nexperimental plot in AFS and in SCS. In each tree we selected two shoot \n(similar length) and measured their size every 15 days from February to \nJune in total 10 measurements were carried out. The olive yield per tree \nwas estimated at harvest in each sub-plot based on a total of 8 olive trees \nin each association and in SCS. Annual crop growth monitoring was \nperformed by three repeated measures at flowering, fructification, and \nmaturity stages in Y1 and Y2. At each stage, 60 plants/treatment (stratified \nsampling of 5 selected plants/row along 1 m line) were randomly selected \nto measure plant height.  \n\n\n\nOnce they reached maturity, crop plants were entirely harvested at ground \nlevel using hand-clippers. Plants were sorted by organs, oven-dried (70 \u25e6C. \n48 h) and weighed to determine the total aboveground biomass. Yield \ncomponents were also assessed (number of pods/spikes per unit area, \nnumber of grains per unit area, and hundred grain weight (HGW) for \nlegumes and thousand grain weight (TGW) for cereals). The harvest index \nwas calculated as the ratio between grain biomass and total aboveground \nbiomass. At harvest time, a survey of six farmers in the study area under \nthe same conditions (type of soil, fertilization, and variety) was conducted \nto identify the final grain yield of the different species studied in sole crop, \nand to be able to compare with the yields in agroforestry in our \nexperiment. \n\n\n\n2.4 Data Analysis  \n\n\n\nWe calculated the land equivalent ratio (LER), defined as the relative land \narea required for sole crops to achieve the same yield than intercropping, \nusing annual crops grain yields and olive yield over two years for each \nagroforestry system (Mead and Willey, 1980). The LER indicates a higher \n(or lower) productivity of agroforestry ('AFS') than the corresponding \norchard ('OR') and sole crops ('SCS') when the value is above or below 1, \nthe value is equal to 1 when agroforestry does not impact land \nproductivity.  \n\n\n\n\ud835\udc38\ud835\udc5e 1:           \ud835\udc3f\ud835\udc38\ud835\udc45\ud835\udc34\ud835\udc39\ud835\udc46 =  \ud835\udc3f\ud835\udc38\ud835\udc45\ud835\udc34\ud835\udc39\ud835\udc46 \ud835\udc42\ud835\udc59\ud835\udc56\ud835\udc63\ud835\udc52 + \ud835\udc3f\ud835\udc38\ud835\udc45\ud835\udc34\ud835\udc39\ud835\udc46 \ud835\udc36\ud835\udc5f\ud835\udc5c\ud835\udc5d \n\n\n\n\ud835\udc38\ud835\udc5e 2:              \ud835\udc3f\ud835\udc38\ud835\udc45\ud835\udc34\ud835\udc39\ud835\udc46  \ud835\udc42\ud835\udc59\ud835\udc56\ud835\udc63\ud835\udc52 = \ud835\udc42\ud835\udc59\ud835\udc56\ud835\udc63\ud835\udc52\n\ud835\udc4c\ud835\udc56\ud835\udc52\ud835\udc59\ud835\udc51 \ud835\udc34\ud835\udc39\ud835\udc46\n\n\n\n\ud835\udc4c\ud835\udc56\ud835\udc52\ud835\udc59\ud835\udc51 \ud835\udc46\ud835\udc36\ud835\udc46\n\n\n\n\n\n\n\n\ud835\udc38\ud835\udc5e 3:                \ud835\udc3f\ud835\udc38\ud835\udc45\ud835\udc34\ud835\udc39\ud835\udc46  \ud835\udc36\ud835\udc5f\ud835\udc5c\ud835\udc5d = \ud835\udc36\ud835\udc5f\ud835\udc5c\ud835\udc5d\n\ud835\udc4c\ud835\udc56\ud835\udc52\ud835\udc59\ud835\udc51 \ud835\udc34\ud835\udc39\ud835\udc46\n\n\n\n\ud835\udc4c\ud835\udc56\ud835\udc52\ud835\udc59\ud835\udc51 \ud835\udc46\ud835\udc36\ud835\udc46\n\n\n\n\n\n\n\nThe relative yield index (RY) were calculated as the ratio between olive \n\n\n\nyield in Agroforestry and sole crop for each association (de Wit and Van \nden Bergh, 1965):   \n\n\n\n\ud835\udc38\ud835\udc5e 4 \u2236                     \ud835\udc45\ud835\udc4c =  \ud835\udc4c\ud835\udc34\ud835\udc39\ud835\udc46 \ud835\udc4c\ud835\udc46\ud835\udc36\ud835\udc46\u2044        \n\n\n\nwhere \ud835\udc4c\ud835\udc34\ud835\udc39\ud835\udc46 and \ud835\udc4c\ud835\udc46\ud835\udc36\ud835\udc46  are respectively the yields of olive tree in agroforestry \nsystem and sole crop system. A ratio higher than 0.5 indicates that olive \ntree had higher yield in AFS than in SCS. while a ratio lower than 0.5 \nindicates that olive tree was less productive in AFS. We tested the \ndifferences in crop growth, yield components, final grain yield, and soil \nfertility using ANOVA with two factors: (1) type of system ('AFS', or, 'SCS'), \nand (2) year (2016, 2017). Each crop species (faba bean, lentil, durum \nwheat, soft wheat and barley) was tested separately. After significant \nANOVA (p < 0.05), the means were compared with Tukey multiple \ncomparison test. All statistical analyses were performed using IBM SPSS \nstatistics (version 26.0). \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1   Tree Growth and Yield are Enhanced by Legumes in Agroforestry \n\n\n\nAverage shoot elongation of olive tree was the lowest in association with \ncereal (P<0.001) which reduced shoot length of olive tree by 69 %, 75 % \nand 71% in association with durum wheat, soft wheat and barley \nrespectively comparatively to olive tree growing in monoculture (figure \n3). The depressive goods induced by wheat on vegetative growth of olive \ntree are substantially due to the competition for soil humidity and \nnutrients during the critical period of olive shoots growth. In \nthe study area this period occurs during June, that overlaps with wheat \ngrain filling. In fact, several studies indicated that  water stress during \nthe rapid shoots growth of olive \ntree induce a significant reduction on shoots growth, thereby affecting \ntheir final length (Razouk et al., 2016). \n\n\n\n\n\n\n\nFigure 3: Average shoot elongation in Y1 of olive tree in sole crop (olive) \nand olive tree grown in AFS with faba bean (Olive FB). lentil (Olive L), \nDurum wheat (Olive DW), Soft wheat (Olive SW) and Barley (Olive B). \nSignificance level (differences between olive in sole crop and olive in \n\n\n\nagroforestry): * p < 0.05; ** p < 0.01; *** p < 0.001. \n\n\n\n\n\n\n\nFigure 4: Olive yield (t.ha\u22121) in sole crop (olive) and olive tree grown in \nAFS with faba bean (Olive FB), lentil (OliveL), durum wheat (OliveDW), \n\n\n\nsoft wheat (OliveSW) and barley (OliveB). Significance level (differences \nbetween olive in sole crop and olive in agroforestry): * p < 0.05\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui ( 2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n\n\n\n\nIn association with legumes olive tree shoot elongation was higher and \nincreased with 53% and 14% in association with faba bean (p=0.61) and \nlentil (p=0.99) respectively comparatively to olive tree growing in sole \ncrop (figure 3), in contrast to the association with cereals, the association \nwith faba bean and lentil improved vegetative growth of olive same results \nwere found in another study at the same conditions (Razouk et al., 2016). \nThe favorable effect of legumes could be explained by the enrichment of \nsoil by nitrogen biologically fixed by this legume (Chehab et al., 2018).  \nAccording to previous studies carried out in the study region, this crop \nmay fix an important amount of nitrogen up to 300 kg/ha, that is largely \nsufficient to satisfy olive nitrogen requirements, particularly during the \nvegetative departure (Razouk et al., 2016). \n\n\n\nThe olive yields average did not vary between years (p= 0.95) and between \nolive in SCS and in AFS with faba bean (p =0.24) and lentil (p=0.29) (figure \n4), same results were found under Mediterranean condition confirming \nour hypothesis that olive yield may benefit from legumes cultivated in \ninterrow (Amassaghrou et al., 2021; Temani et al., 2021; Razouk et al., \n2016). A significative difference was recorded between olive in SCS and \nolive in AFS (figure 4) with  durum wheat (p=0.02), soft wheat (p=0.01) \nand barley (p=0.01) confirming that olive trees were under stress, and \ncereals  induced a depressive effect on olive yield (Amassaghrou et al., \n2021; Panozzo et al., 2020; Razouk et al., 2016; Daoui et al., 2014). \n\n\n\nTable 1: Relative yield index for olive yield (RY) in association with \nfaba bean (Olive FB), lentil (OliveL), durum wheat (OliveDW), soft wheat \n(OliveSW) and barley (OliveB). in Y1 and Y2. The highest values in each \n\n\n\nRow are shown in bold and the lowest values in italics. \n\n\n\n RY \n\n\n\nYear Olive FB Olive L Olive DW Olive SW Olive B \n\n\n\nY1 \n\n\n\nY2 \n\n\n\n0.75 \n\n\n\n0.56 \n\n\n\n0.70 \n\n\n\n0.52 \n\n\n\n0.46 \n\n\n\n0.42 \n\n\n\n0.46 \n\n\n\n0.53 \n\n\n\n0.60 \n\n\n\n0.52 \n\n\n\nRelative yield (RY) ranged from 0.60 to 0.94 in Y1 and ranged from 0.53 to \n0.70 in Y2 across different association with legumes and cereals (Table 1). \nIn general, RY were lower in year 2 than in year 1. and Olive FB had the \nhighest RY in both years. Compared to cereals, legumes performed better, \nopening possibilities for improving the productivity of the actual olive AFS \nwith more legumes. In a similar experiment, a group researchers found \nthat the relative yield was superior for faba bean (0.54) than for wheat \n(0.19). Other studies also confirmed that the relative yield in AFS was \nhigher for legumes than cereals (Temani et al., 2021; Amassaghrou et al., \n2021; Daoui and Fatemi, 2014). \n\n\n\n3.2   Olive Trees Reduce Cereals Yield Compared to Legumes \n\n\n\nIn opposition to our main hypothesis, olive trees negatively affected yield \nof all intercrops, during both years (figure 5), legumes and cereals grain \nyield was lower in AFS than in SCS, the reduction was around 35% for faba \nbean and 33% for lentil in Y2, the reduction was less pronounced in Y1 \nwith 30% for faba bean and 20% for lentil.  Same results were recorded \nwith cereals, when yields were lowest in AFS in Y2 than Y1 and the \nmagnitude of reduction was around 43%, 44% and 56% for Durum wheat, \nsoft wheat and barley respectively. Climatic conditions were mostly \nresponsible for low yields, During Y1 and Y2, very severe climatic \nconditions took place, due to an accentuated rainfall deficit (248 mm \nbetween December and June in Y1 and 202 mm between December and \nJune in Y2) with an irregular temporal distribution. The air temperature \nwas in continuous evolution especially during the period which extends \nfrom March until the harvest (early July). In another studies on the effect \nof trees on crop yield found similar results where olive tree reduced grain \nyields by around 20 %, 52% and 44 % for faba bean, wheat and barley \nrespectively (Panozzo et al., 2022; Amassaghrou et al., 2021; Temani et al., \n2021). \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Grain yield (t.ha-1) of faba bean, lentil, durum wheat, soft wheat \nand barley during Y1 (a) and Y 2(b) in AFS and SCS \n\n\n\n3.3   Olive Trees Reduce the Number of Grains Per Unit Area  \n\n\n\nFaba bean and lentil grain yield recorded significant difference between \narea (P<0.05, figure 6), and was higher in A2 in both years. Legumes yield \nwas negatively affected by olive tree and the lowest grain yield was under \nA1 and A3, the areas near to trees. Durum wheat (P<0.05) and soft wheat \n(p< 0.001) recorded a significative difference between areas in Y1 and no \ndifference in Y2 (figure 6), while no significant differences recorded for \nbarley (P=0.77) in both years. For all cereals, the highest yield was \nrecorded in A2, and the lowest yield in A3 (figure 6). Legumes and cereals \nplant height recorded a significant difference between areas (P<0.01), in \nY1 and Y2 the highest plant height was in A1. Faba bean pods number and \ngrains number (P < 0.001) varied significantly between areas; and no \nsignificant difference was recorded for aboveground biomass (P =1.24) \nand HGW (P=0.15) (table 2). For lentil, grains number (P<0.05), and HGW \n(P<0.01) varied significantly between Areas (table 2), in both years the \nhighest values were recorded in A2. \n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Grain yield (t.ha-1\u00b1 sd) at harvest in agroforestry (AFS) at the \nthree area (A1, A2, and A3) for legumes (a) and cereals (b) during Y1 and \n\n\n\nY2. Letter indicates significance difference between areas at the same \nyear \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui ( 2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n\n\n\n\nTable 2: Plant height(cm), number of pods/spikes and grains (m-2), weight of 100 grains for legumes (HGW) and 1000 grains for cereals (TGW) (g) Biomass (g) and the harvest index (HI) of faba bean, lentil, durum wheat, \nsoft wheat and barley in 2016 and 2017 in agroforestry (AFS). according to the three Areas (A1. A2 and A3). Lowercase letters indicate significant differences between among Areas (HSD Tukey test. P \u2264 0.05). \n\n\n\nYear Species \nPlant Height \n\n\n\n(cm) \nCV % \n\n\n\nPods/Spikes \n\n\n\nNumber.m-2 \n\n\n\nCV \n\n\n\n% \n\n\n\nGrains \n\n\n\nNumber.m-2 \n\n\n\nCV \n\n\n\n% \n\n\n\nHGW/TGW \n\n\n\n(g.m-2) \n\n\n\nCV \n\n\n\n% \n\n\n\nBiomass \n\n\n\n(g.m-2) \n\n\n\nCV \n\n\n\n% \n\n\n\n\n\n\n\n  A1 A2 A3  A1 A2 A3  A1 A2 A3  A1 A2 A3  A1 A2 A3  \n\n\n\nY1 \nFaba \nbean \n\n\n\n88.9a 75.2b 79.8c 8.6 102.0a 163.0b 75.5c 39.5 102.8a 129.0b 104.8c 13.0 662.4a 667.6a 654.1a 1.0 234.3a 351.8a 241.0a 23.9 \n\n\n\n Lentil 59.2a 45.9b 50.9c 12.9 2700.0a 2465.0b 2836.5a 7.0 1256.5a 1646.8b 1238.0c 16.7 5.0a 5.4b 4.6c 8.7 322.3a 387.3b 332.1c 10.1 \n\n\n\n DW 75.8a 58.1b 65.9c 13.3 510.3a 546.7a 363.9a 20.4 4268.1a 4496.1b 3480.0c 13.1 163.4a 187.7a 159.7a 6.9 358.3a 556.5b 538.5c 27.7 \n\n\n\n SW 84.4a 67.3b 74.7c 11.3 563.4a 745.3b 470.3c 23.6 3749.4a 5703.1b 3462.5c 28.3 161.2a 176.2a 139.4a 11.7 299.0a 548.8b 335.5c 34.2 \n\n\n\n Barley 79.5a 66.6b 75.0c 8.9 432.8a 563.1b 284.4c 32.7 3206.9a 3311.1a 2716.7a 10.3 137.2a 196.9b 135.1c 22.4 303.0a 451.8b 369.8c 19.9 \n\n\n\nY2 \nFaba \nbean \n\n\n\n99.2a 90.0b 90.6c 5.6 61.0a 101.0b 81.5c 24.6 167.5a 189.3b 161.5c 8.5 413.8a 537.8a 515.5a 13.5 358.8a 393.3a 387.0a 4.8 \n\n\n\n Lentil 62.5a 53.5b 56.1c 8.1 1362.5a 1419.5a 956.5a 20.3 968.6 a 1055.9b 818.2c 12.7 5.0a 5.4b 4.6c 8.7 105.1a 112.1a 85.7a 13.5 \n\n\n\n DW 75.7a 69.6b 70.7c 4.5 295.7a 419.2a 264.5a 25.0 28299a 2999.2a 2568.4a 7.7 116.3a 117.5a 116.6a 0.5 177.6a 230.2b 180.4c 15.1 \n\n\n\n SW 80.4a 70.6b 74.5c 6.6 272.8a 305.6b 246.1c 10.8 2567.2a 3435.8a 1890.0a 29.5 105.4a 127.1a 108.8a 10.2 188.2a 256.0b 212.6c 15.7 \n\n\n\n Barley 78.1a 70.8b 75.6c 5.0 132.6a 175.8b 194.4c 18.9 1627.1a 2016.4a 1730.6a 11.3 144.5a 145.4b 165.9c 8.0 245.4a 210.3b 262.4c 11.1 \n\n\n\n\n\n\n\nDurum wheat spikes number (P=0.766), grains number (0.181), and TGW (P=0.386) were higher in A2 in Y1 \nand Y2 and no significant difference was recorded between areas; only biomass (P<0.01) varied significantly \nbetween areas in both years (table 2). Soft wheat spikes number (P<0.05), grains number (P<0.01) and biomass \n(P < 0.001) varied significantly between areas. Like durum wheat highest values were registered in A2 in Y1 \nand Y2. Barley spikes number (P<0.01), TGW (P<0.05), biomass (P<0.01) varied significantly between areas \nand were higher in A2 in Y1 and A3 in Y2.  No significant difference was recorded for grains number (P=0.42) \n(table 2). \n\n\n\nThe reduction in final crop grain yield in agroforestry was a direct result of the reduction in the number of \n\n\n\ngrains per unit area. The number of grains is a major yield component for legumes (Lake et al., 2019). Since the \n\n\n\nnumber of grains per unit area is closely related to the number of pods, the impact of trees on crops was \n\n\n\nimportant for crop yield in agroforestry. Besides, drought and heat are considered major constraints in faba \n\n\n\nbean growth and production, and lentil is sensitive to shade, the two years were characterized by very severe \n\n\n\nclimatic conditions (figure 1) with an irregular temporal distribution; this condition makes crops more under \n\n\n\nstress and competition in agroforestry, and contrary to our hypothesis, tree shade had a negative effect on yield \n\n\n\n(Karkanis et al., 2018; Darabi et al., 2014). For cereals  the variations in grain number per unit area determine \n\n\n\nthe final yield more than other yield components (Temani et al., 2021; Li et al., 2020; Zhang et al., 2000). In \n\n\n\nboth years, final grain yield was highest under non shaded conditions and declined with increased shade near \n\n\n\nto trees. Other studies found that the yields in wheat were relatively low under trees, primarily due to shade \n\n\n\nand competition for nutrients and water (Razouk et al., 2016; Noumi et al., 2011). \n\n\n\nIn some cases, trait plasticity allows plants to adapt efficiently to shade, resulting in light uptake and biomass \n\n\n\nproduction at levels similar to single crops, especially during critical periods (Guadalupe Arenas-Corraliza et \n\n\n\nal., 2018). However, even if we noticed significant morphological changes in the plants (stem elongation, \n\n\n\nincreased leaf area, etc.), it was not enough to fully compensate for the reduced light under the olive trees. In \n\n\n\nagroforestry in China in a humid, semi-tropical climate and in Europe under Mediterranean climate, many other  \n\n\n\nstudies have been conducted on how trees affect cereal productivity and reveal that the main cause of cereal \n\n\n\nyield decreases is competition for light (Qiao et al., 2020; 2019; Pantera et al., 2018; Li et al., 2010). Shade \n\n\n\nusually limits biomass production and therefore decreases crop yields ( Li et al., 2010). Legumes differ in many \n\n\n\nphysiological aspects compared to cereals and potentially have many advantages in agroforestry, the \n\n\n\nindeterminate growth habit of legumes may improve the performance of agroforestry systems by valuing the \n\n\n\nshade under trees better than cereals  (Kato et al., 2019) . The capacity of legumes to fix the atmospheric \n\n\n\nnitrogen can enhance soil nitrogen available for the olive trees, improve soil fertility, and hence have positive \n\n\n\neffects on olive production (Dwivedi et al., 2015; Jensen et al., 2010). \n\n\n\n3.4   Soil Fertility is Increased Under Tree in Agroforestry  \n\n\n\nThe distance from the olive row had different effects on soil parameters measured in each plot of the \n\n\n\nintercropping to the olive tree. Soil organic matter content is the only parameter that was affected by this \n\n\n\ndistance (highly significant effect) and was higher in agroforestry with legumes than with cereals in Y1 and Y2. \n\n\n\nIndeed. the highest value was recorded near the olive tree in A1 (table 3) for durum wheat and in A3 for \n\n\n\nlegumes, soft wheat and barley (table 3), this is usually in the area close to the tree rows that the physical- \n\n\n\nchemical and biological parameters characterizing soil fertility are particularly improved and trees add organic \n\n\n\nmatter to the soil system in various manners, whether in the form of roots or litterfall or as root exudates in \n\n\n\nthe rhizosphere (Corbeels et al., 2018). A group researchers under the same conditions of our study found also \n\n\n\nthat soil fertility varied between the olive row in agroforestry with faba bean and the parameters measured (P-\n\n\n\nolsen, K and NO-3) were higher in the middle of plot, 3m away from olive row, this suggest that crop fertilization \n\n\n\nin agroforestry should take in to the count, the associated species and the distance from olive tree for an \n\n\n\nefficient use of mineral resources (Bouhafa et al., 2015).\n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui ( 2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n\n\n\n\n\n\n\n\nTable 3: OM%. K (mg.kg-1) and P (mg.kg-1) of the soil samples collected at (0-30 cm) depth in the three Area (A1, A2 and A3) after harvest during June \nin Y1 and Y2. The highest values in each Row are shown in bold. Lowercase letters indicate significant differences between among Areas (HSD) Tukey \n\n\n\ntest P \u2264 0.05 \n\n\n\nYear Species Organic matter % K (mg.kg-1) P (mg.kg-1) \n\n\n\n  A1 A2 A3 A1 A2 A3 A1 A2 A3 \n\n\n\nY1 Faba bean 1.8a 1.5b 2.4c 255.6a 263.4a 268.5a 20.4a 22.0a 19.9a \n\n\n\n Lentil 1.7a 1.3b 2.1c 277.2a 302.4a 289.3a 19.5a 21.5a 20.8a \n\n\n\n durum wheat 1.6a 1.1b 1.5c 298.8a 284.6a 305.4a 21.7a 23.0a 24.0a \n\n\n\n soft wheat 1.4a 1.2b 1.6c 2782a 283.5a 289.0a 24.5a 19.0a 22.0a \n\n\n\n Barley 1.6a 1.3b 1.8c 280.4a 279.1a 283.2a 21.0a 21.0a 22.0a \n\n\n\nY2 Faba bean 1.3a 1.1b 1.9c 266.4a 278.5a 263.4a 19.7a 21.0a 18.9a \n\n\n\n Lentil 1.6a 1.2b 2.2c 267.3a 278.9a 281.3a 18.5a 19.6a 18.0a \n\n\n\n durum wheat 1.4a 0.7b 1.3c 298.4a 287.9a 312.1a 17.0a 22.0a 22.5a \n\n\n\n soft wheat 1.1a 0.9b 1.7c 259.9a 267.3a 287.4a 19.7a 18.6a 15.9a \n\n\n\n Barley 1.2a 1.1b 1.5c 269.5a 279.2a 281.6a 22.0a 22.3a 18.4a \n\n\n\n3.5 Overall Yield of Agroforestry System in Mediterranean \nConditions \n\n\n\nLER represents the relative area of land required for a single crop to \nproduce the same yield per unit area as a cover crop (Mead and Willey, \n1980). In Y1, the land equivalent ratio (LER) was always > 1 and ranged \nfrom 1.01 with durum wheat to 1.36 with lentil without a clear distinction \nbetween legumes and cereals (table 4). In Y2, LER was lower and ranged \n\n\n\nfrom 1.02 with Durum wheat to 1.20 with faba bean. Despite some \nnegative effects on yields, the values of LER>1 confirm the hypothesis that \nagroforestry systems are more productive and efficient than pure \ncropping systems, and that olive agroforestry systems exhibit high LERs \nunder drier conditions than previous evaluations in Europe (Panozzo et \nal., 2019). Same results were found when olive in associated with annual \ncrop under Mediterranean condition (Amassaghrou et al., 2021; Temani et \nal., 2021; Panozzo et al., 2019).  \n\n\n\nTable 4: Land equivalent ratios of olive-faba bean, olive-lentil, olive-durum wheat, olive-soft wheat and olive-barley agroforestry systems in 2016 and \n2017. Partial LERs are indicated in brackets for crops and olive, respectively. \n\n\n\n LER  \n\n\n\nYear Faba bean + olive Lentil + olive Durum wheat + olive Soft wheat + olive Barley + olive \n\n\n\nY1 1.33 (0.58+0.75) 1.36 (0.66+0.69) 1.01(0.55+0.46) 1.19 (0.73+0.46) 1.16 (0.57+0.59) \n\n\n\nY2 1.20 (0.64+0.56) 1.18 (0.66+0.52) 1.02(0.61+0.41) 1.08 (0.56+0.52) 1.07 (0.55+0.52) \n\n\n\nAlthough tree effects on the crops varied between years, it was positive in \nthe years with extreme weather events such as high spring temperatures, \nwhich are expected to be more frequent in the coming years as a \nconsequence of climate change in Mediterranean areas ( Guadalupe \nArenas-Corraliza et al., 2018). Many other studies found an LER >1, found \nan average LER of 1.22 \u00b1 0.02 in a database of 100 mixed culture studies, \nwhile independently selected 126 papers from the literature and found an \naverage LER of 1.30 \u00b1 0.01 (Yu et al., 2015; Martin-Guay et al., 2018). \nCompared to cereals, legumes are less competitive for soil resources \n(nitrogen) and reach maturity earlier, leaving more resources available to \nolive trees, especially at the beginning of summer, when trees start to grow \nactively, and water availability sinks (Razouk et al., 2016). \n\n\n\n4.   CONCLUSION  \n\n\n\nRainfed cropping systems are critically affected by climate change in the \nSouth Mediterranean, especially by increasing rainfall scarcity and \nirregularity. Our study compared legumes with cereals in olive \nagroforestry systems, we showed that olive trees limited crop growth and \naffected grain yields of all crops by reducing pods/spikes number and \ngrains numbers in the rows near to trees. However, our results reveal that \nlegumes did not impact olive yields. Legumes in association with olive \ntrees are less competitive than cereals for soil resources and mature \nearlier, especially in early summer when trees begin to grow vigorously \nand water availability decreases, in addition, the ability of legumes to fix \natmospheric nitrogen can increase soil nitrogen availability to olive trees, \nimprove soil fertility, and have a positive impact on olive production. The \nindeterminate growth habits of legumes also may improve the \nperformance of agroforestry systems by valuing the shade under trees \nbetter than cereals.  \n\n\n\nThe tree competition for light is considered in several articles about \nagroforestry, for wheat. In our study cereals negatively affected olive yield, \nand olive trees on crop yields. Cereal yield in the agroforestry system is \nrelated to shade tolerance and the influence of trees at different \ndevelopmental stages (grain filling). Stress before crop flowering can \naffect carpel growth by decreasing the size of the ovaries, then reducing \npotential grain weight regardless of conditions during grain filling. Tree \nshape and pruning, tree row orientation and spacing and tree phenology \n\n\n\ncan reduce the effect of the tree on the crop. However, looking for crop \nvarieties with adequate physiological and morphological responses to \nshade will be promising to buffer the effects of trees on crop growth in \nMediterranean agroforestry. \n\n\n\nRegardless of the negative impact of agroforestry on final yield, LERs were \nhigher than 1 and revealed that an agroforestry system integrating \nlegumes and cereals between rows can improve the profitability of the \norchard and soil fertility. Therefore, we strongly recommend legumes to \nincrease and diversify the global productivity of olive groves and invite to \nconsider a greater diversity of legume species, because its precocity and \nrapid ripening have advantages in water use by avoiding the common \nterminal water stresses, particularly in dry Mediterranean conditions. \n\n\n\nCONFLICTS OF INTEREST \n\n\n\nThe authors declare that they have no known competing financial interests \nor personal relationships that could have appeared to influence the work \nreported in this paper. \n\n\n\nAUTHOR CONTRIBUTIONS \n\n\n\nAsmae Amassaghrou: Conceptualization, Methodology, Writing \u2013 review & \nediting. original draft preparation, Formal analysis. Karim Barkaoui: \nMethodology, Writing - review & editing. Ahmed Bouaziz: Methodology, \nreview & editing. Si Bennasseur Alaoui: Review & editing. Rachid Razouk: \nConceptualization, Methodology. Khalid Daoui: Conceptualization, \nMethodology, Writing review & editing. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nWe thank Dr. M Elmourid and ICRADA for their contribution to define the \ntheme of the project. This work was supported by the India - Morocco Food \nLegume Initiative (IMFLI) project sponsored by OCP - Foundation \n(Morocco), Swaminathan Research Foundation (India), INRA, IAV Hassan \nII (both Morocco), ICRISAT, and ICARDA between 2015 and 2017. KB has \nreceived the financial support provided by the ARIMNet call of the ERA-\nNET program (SEMIARID project, Grant agreement n\u25e6 618127), a program \nsupported by the European Union.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui ( 2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\nAmassaghrou, A., Bouaziz, A., Daoui, K., Belhouchette, H., Ezzahouani, A., \nBarkaoui, K., 2021. Productivit\u00e9 et efficience des syst\u00e8mes \nagroforestiers \u00e0 base d\u2019oliviers au Maroc\u202f: cas de Moulay Driss \nZerhoun. Cahiers Agricultures, 30, 2. \nhttps://doi.org/10.1051/cagri/2020041 \n\n\n\nArenas-Corraliza, M., Guadalupe, L\u00f3pez-D\u00edaz, M.L., Moreno, G., 2018. \nWinter cereal production in a Mediterranean silvoarable walnut \nsystem in the face of climate change. Agriculture, Ecosystems and \nEnvironment, 264, Pp. 111\u2013118. \nhttps://doi.org/10.1016/j.agee.2018.05.024 \n\n\n\nArtru, S., Garr\u00e9, S., Dupraz, C., Hiel, M.P., Blitz-Frayret, C., Lassois, L., 2017. \nImpact of spatio-temporal shade dynamics on wheat growth and \nyield, perspectives for temperate agroforestry. European Journal \nof Agronomy, 82, Pp. 60\u201370. \nhttps://doi.org/10.1016/j.eja.2016.10.004 \n\n\n\nBedoussac, L., Journet, E.P., Hauggaard-Nielsen, H., Naudin, C., Corre-\nHellou, G., Jensen, E.S., Prieur, L., Justes, E., 2015. Ecological \nprinciples underlying the increase of productivity achieved by \ncereal-grain legume intercrops in organic farming. A review. \nAgronomy for Sustainable Development, 35, Pp. 911\u2013935. \nhttps://doi.org/10.1007/s13593-014-0277-7 \n\n\n\nBouhafa, K., Moughli, L., Daoui, K., Douaik, A., 2015. Soil Properties at \nDifferent Distances of Intercropping in Three Olive Orchards in \nMorocco. International Journal of Plant & Soil Science, 7, Pp. 238\u2013\n245. https://doi.org/10.9734/IJPSS/2015/16807 \n\n\n\nChehab, H., Tekaya, M., Gouiaa, M., Mahjoub, Z., Laamari, S., Sfina, H., \nChihaoui, B., Boujnah, D., Mechri, B., 2018. The use of legume and \ngrass cover crops induced changes in ion accumulation, growth \nand physiological performance of young olive trees irrigated with \nhigh-salinity water. Scientia Horticulturae, 232, Pp. 170\u2013174. \nhttps://doi.org/10.1016/j.scienta.2018.01.012 \n\n\n\nChehab, H., Tekaya, M., Ouhibi, M., Gouiaa, M., Zakhama, H., Mahjoub, Z., \nLaamari, S., Sfina, H., Chihaoui, B., Boujnah, D., Mechri, B., 2019. \nEffects of compost, olive mill wastewater and legume cover \ncropson soil characteristics, tree performance and oil quality of \nolive trees cv.Chemlali grown under organic farming system. \nScientia Horticulturae. \nhttps://doi.org/10.1016/j.scienta.2019.04.039 \n\n\n\nCorbeels, M., Cardinael, R., Naudin, K., Guibert, H., Torquebiau, E., 2018. \nThe 4 per 1000 goal and soil carbon storage under agroforestry \nand conservation agriculture systems in sub-Saharan Africa. Soil \nand Tillage Research 0\u20131. \nhttps://doi.org/10.1016/j.still.2018.02.015 \n\n\n\nCorreia, C.M., Brito, C., Sampaio, A., Dias, A.A., Bacelar, E., Gon\u00e7alves, B., \nFerreira, H., Moutinho-Pereira, J., Rodrigues, M.A., 2015. \nLeguminous Cover Crops Improve the Profitability and the \nSustainability of Rainfed Olive (Olea europaea L.) Orchards: From \nSoil Biology to Physiology of Yield Determination. Procedia \nEnvironmental Sciences, 29, Pp. 282\u2013283. \nhttps://doi.org/10.1016/j.proenv.2015.07.213 \n\n\n\nDaoui, K., Fatemi, Z.A., Razouk, R., Bendidi, A., Chergaoui, A., Ramdani, A., \n2014. Olive tree and annual crops association \u2019 s productivities \nunder Moroccan conditions. \n\n\n\nDaoui, K., Fatemi, Z.E.A., 2014. Agroforestry systems in Morocco : the case \nof olive tree and annual crops association in Sa\u00efs region. Science, \nPolicy and Politics of Modern Agricultural System. Global Contexte \nto Local Dynamics of Substaminale Agriculture. \nhttps://doi.org/DOl 10.1007/978-94-007-7957-0_19 \n\n\n\nDarabi, F., Hatami, A., Javad, Z.M., 2014. Effect of artificial shading on yield \nand yield components of Lentil\u2019s cultivars. Advances in \nEnvironmental Biology, 8 (10), Pp. 87\u201392. \n\n\n\nde Wit, C., Van den Bergh, J., 1965. Competition between herbage plants. \nJournal of Agricultural Science, Pp. 1212\u2013221. \n\n\n\nDufour, L., Metay, A., Talbot, G., Dupraz, C., 2013. Assessing light \ncompetition for cereal production in temperate agroforestry \nsystems using experimentation and crop modelling. Journal of \n\n\n\nAgronomy and Crop Science, 199, Pp. 217\u2013227. \nhttps://doi.org/10.1111/jac.12008 \n\n\n\nDwivedi, A., Dev, I., Kumar, V., Yadav, R., Yadav, M., Gupta, D., Singh, A., \nTomar, S., 2015. Potential role of maize-legume intercropping \nsystems to improve soil fertility status under smallholder farming \nsystems for sustainable agriculture in India. International Journal \nof Life Sciences Biotechnology and Pharma Research, 4, Pp. 145. \n\n\n\nJensen, E.S., Peoples, M.B., Boddey, R.M., Gresshoff, P.M., Henrik, H.N., \nAlves, B.J.R., Morrison, M.J., 2012. Legumes for mitigation of \nclimate change and the provision of feedstock for biofuels and \nbiorefineries. A review, Agronomy for Sustainable Development. \nhttps://doi.org/10.1007/s13593-011-0056-7 \n\n\n\nJensen, E.S., Peoples, M.B., Hauggaard-Nielsen, H., 2010. Faba bean in \ncropping systems. Field Crops Research. \nhttps://doi.org/10.1016/j.fcr.2009.10.008 \n\n\n\nKarkanis, A., Ntatsi, G., Lepse, L., Fern\u00e1ndez, J.A., V\u00e5gen, I.M., Rewald, B., \nAlsi\u0146a, I., Kronberga, A., Balliu, A., Olle, M., Bodner, G., Dubova, L., \nRosa, E., Savvas, D., 2018. Faba bean cultivation \u2013 revealing novel \nmanaging practices for more sustainable and competitive \nEuropean cropping systems. Frontiers in Plant Science, 9, Pp. 1\u2013\n14. https://doi.org/10.3389/fpls.2018.01115 \n\n\n\nKato, S., Sayama, T., Taguchi-Shiobara, F., Kikuchi, A., Ishimoto, M., Cober, \nE., 2019. Effect of change from a determinate to a semi-\ndeterminate growth habit on the yield and lodging resistance of \nsoybeans in the northeast region of Japan. Breeding Science, 69, \nPp. 151\u2013159. https://doi.org/10.1270/jsbbs.18112 \n\n\n\nLake, L., Godoy-Kutchartt, D.E., Calderini, D.F., Verrell, A., Sadras, V.O., \n2019. Yield determination and the critical period of faba bean \n(Vicia faba L.). Field Crops Research, 241, Pp. 107575. \nhttps://doi.org/10.1016/j.fcr.2019.107575 \n\n\n\nLi, C., Hoffland, E., Kuyper, T.W., Yu, Y., Li, H., Zhang, C., Zhang, F., van der \nWerf, W., 2020. Yield gain, complementarity and competitive \ndominance in intercropping in China: A meta-analysis of drivers of \nyield gain using additive partitioning. European Journal of \nAgronomy, 113, Pp. 125987. \nhttps://doi.org/10.1016/j.eja.2019.125987 \n\n\n\nLi, H., Jiang, D., Wollenweber, B., Dai, T., Cao, W., 2010. Effects of shading \non morphology, physiology and grain yield of winter wheat. \nEuropean Journal of Agronomy, 33, Pp. 267\u2013275. \nhttps://doi.org/10.1016/j.eja.2010.07.002 \n\n\n\nLi, L., Gan, Y.T., Bueckert, R., Warkentin, T.D., 2010. Shading, defoliation \nand light enrichment effects on chickpea in northern latitudes. \nJournal of Agronomy and Crop Science, 196, Pp. 220\u2013230. \nhttps://doi.org/10.1111/j.1439-037X.2009.00409.x \n\n\n\nMantzanas, K., Pantera, A., Koutsoulis, D., Papadopoulos, A., Kapsalis, D., \nIspikoudis, S., Fotiadis, G., Sidiropoulou, A., Papanastasis, V.P., \n2021. Intercrop of olive trees with cereals and legumes in \nChalkidiki, Northern Greece. Agroforestry Systems, 95, Pp. 895\u2013\n905. https://doi.org/10.1007/s10457-021-00618-6 \n\n\n\nMartin-Guay, M.O., Paquette, A., Dupras, J., Rivest, D., 2018. The new Green \nR\u00e9volution : Substaminale intensification of agriculture by \nintercropping. Science of The Total Environment, 615, Pp. 767\u2013\n772. \nhttps://doi.org/https://doi.org/10.1016/j.scitotenv.2017.10.024 \n\n\n\nMead, R., and Willey, R.W., 1980. The Concept of a \u201cLand Equivalent Ratio\u201d \nand Avantages in Yields from Intercropping. Experimental \nAgriculture, 16, Pp. 217\u2013228. \n\n\n\nNoumi, Z., Abdallah, F., Torre, F., Michalet, R., Touzard, B., Chaieb, M., 2011. \nImpact of Acacia tortilis ssp. raddiana tree on wheat and barley \nyield in the south of Tunisia. Acta Oecologica, 37, Pp. 117\u2013123. \nhttps://doi.org/10.1016/j.actao.2011.01.004 \n\n\n\nPanozzo, A., Bernazeau, B., Desclaux, D., 2019. Durum wheat in organic \nolive orchard: good deal for the farmers? Agroforestry Systems, \n94, Pp. 707\u2013717. https://doi.org/10.1007/s10457-019-00441-0 \n\n\n\nPanozzo, A., Huang, H.Y., Bernazeau, B., Meunier, F., Turc, O., Duponnois, R., \nPrin, Y., Vamerali, T., Desclaux, D., 2022. Impact of Olive Trees on \nthe Microclimatic and Edaphic Environment of the Understorey \nDurum Wheat in an Alley Orchard of the Mediterranean Area. \nAgronomy, Pp. 12. https://doi.org/10.3390/agronomy12020527 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 06-13 \n\n\n\n\n\n\n\n \nCite The Article: Asmae Amassaghrou, Karim Barkaoui, Ahmed Bouaziz, Si Bennasseur Alaoui, Rachid Razouk, Khalid Daoui ( 2023).  \n\n\n\nImpact of Legumes and Cereals on Olive Productivity in The South Mediterranean. Malaysian Journal of Sustainable Agricultures, 7(1): 06-13. \n\n\n\n\n\n\n\nPantera, A., Burgess, P.J., Mosquera Losada, R., Moreno, G., L\u00f3pez-D\u00edaz, \nM.L., Corroyer, N., McAdam, J., Rosati, A., Papadopoulos, A.M., \nGraves, A., Rigueiro Rodr\u00edguez, A., Ferreiro-Dom\u00ednguez, N., \nFern\u00e1ndez Lorenzo, J.L., Gonz\u00e1lez-Hern\u00e1ndez, M.P., Papanastasis, \nV.P., Mantzanas, K., Van Lerberghe, P., Malignier, N., 2018. \nAgroforestry for high value tree systems in Europe. Agroforestry \nSystems, 92, Pp. 945\u2013959. https://doi.org/10.1007/s10457-017-\n0181-7 \n\n\n\nQiao, X., Chen, X., Lei, J., Sai, L., Xue, L., 2020. Apricot-based agroforestry \nsystem in Southern Xinjiang Province of China : influence on yield \nand quality of intercropping wheat. Agroforestry Systems, 94, Pp. \n477\u2013485. https://doi.org/10.1007/s10457-019-00412-5 \n\n\n\nQiao, X., Sai, L., Chen, X., Xue, L., Lei, J., 2019. Impact of fruit-tree shade \nintensity on the growth, yield, and quality of intercropped wheat. \nPLoS ONE, 14, Pp. 1\u201317. \nhttps://doi.org/10.1371/journal.pone.0203238 \n\n\n\nRazouk, R., Daoui, K., Ramdani, A.., Chergaoui, A., 2016. Optimal distance \nbetween olive trees and annual crops in rainfed intercropping \nsystem in northern Morocco. Journal of Crop Science Research, 1, \nPp. 23\u201332. \n\n\n\nTanasijevic, L., Todorovic, M., Pereira, L.S., Pizzigalli, C., Lionello, P., 2014. \nImpacts of climate change on olive crop evapotranspiration and \nirrigation requirements in the Mediterranean region. Agricultural \nWater Management, 144, Pp. 54\u201368. \nhttps://doi.org/10.1016/j.agwat.2014.05.019 \n\n\n\nTemani, F., Bouaziz, A., Daoui, K., Wery, J., Barkaoui, K., 2021. Olive \nagroforestry can improve land productivity even under low water \navailability in the South Mediterranean. Agriculture, Ecosystems \nand Environment, 307, Pp. 107234. \nhttps://doi.org/10.1016/j.agee.2020.107234 \n\n\n\nTorquebiau, E., Mary, F., Sibelet, N., 2002. Les associations agroforesti\u00e8res \net leurs multiples enjeux. Bois et for\u00eats des tropiques, 271, Pp. 23\u2013\n35. \n\n\n\nWoetzel, J., Pinner, D., Samandari, H., Engel, H., Krishnan, M., Vasmel, M., \nvon der Leyen, J., 2020. A Mediterranean basin without a \nMediterranean climate. McKinsey Global Institute, Pp. 24. \n\n\n\nWolpert, F., Quintas-Soriano, C., Plieninger, T., 2020. Exploring land-use \nhistories of tree-crop landscapes: a cross-site comparison in the \nMediterranean Basin. Sustainability Science. \nhttps://doi.org/10.1007/s11625-020-00806-w \n\n\n\nXu, C.L., Tao, H.B., Wang, P., Wang, Z.L., 2016. Slight shading after anthesis \nincreases photosynthetic productivity and grain yield of winter \nwheat (Triticum aestivum L.) due to the delaying of leaf \nsenescence. Journal of Integrative Agriculture, 15, Pp. 63\u201375. \nhttps://doi.org/10.1016/S2095-3119(15)61047-4 \n\n\n\nYu, Y., Stomph, T.J., Makowski, D., van der Werf, W., 2015. Temporal niche \ndifferentiation increases the land equivalent ratio of annual \nintercrops, A meta-analysis. Field Crops Research, 184, Pp. 133\u2013\n144. https://doi.org/https://doi.org/10.1016/j.fcr.2015.09.010 \n\n\n\nZhang, D., Du, G., Sun, Z., Bai, W., Wang, Q., Feng, L., Zheng, J., Zhang, Z., Liu, \nY., Yang, S., Yang, N., Feng, C., Cai, Q., Evers, J.B., van der Werf, W., \nZhang, L., 2018. Agroforestry enables high efficiency of light \ncapture, photosynthesis and dry matter production in a semi-arid \nclimate. European Journal of Agronomy 94, Pp. 1\u201311. \nhttps://doi.org/10.1016/j.eja.2018.01.001 \n\n\n\nZhang, H., Pala, M., Oweis, T., Harris, H., 2000. Water use and water-use \nefficiency of chickpea and lentil in a Mediterranean environment. \nAustralian Journal of Agricultural Research, 51, Pp. 295\u2013304. \nhttps://doi.org/10.1071/AR99059 \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1071/AR99059\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 110-116 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.110.116 \n\n\n\nCite The Article: Sultana Afroz, Md Shibly Noman, Yue Zhang, Md Yousuf Ali, Md Rubel Mahmud, Zhihong Li (2022). Species Identification of Economic \nImportant Adult Fruit Flies Based on DNA Barcoding (MT DNA COI) and Larvae Based on Species Specific Primers from Central and South \n\n\n\nParts of Bangladesh. Malaysian Journal of Sustainable Agricultures, 6(2): 110-116.\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-293X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.110.116\n\n\n\nSPECIES IDENTIFICATION OF ECONOMIC IMPORTANT ADULT FRUIT FLIES BASED \nON DNA BARCODING (MT DNA COI) AND LARVAE BASED ON SPECIES SPECIFIC \nPRIMERS FROM CENTRAL AND SOUTH PARTS OF BANGLADESH \n\n\n\nSultana Afroza,b, Md Shibly Nomana,c, Yue Zhanga, Md Yousuf Alid, Md Rubel Mahmude and Zhihong Lia, * \n\n\n\na Department of Entomology, MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, \nBeijing 100193, China. \nb Department of Agriculture Extension, Ministry of Agriculture, Dhaka, Bangladesh. \nc Department of Entomology, Bangladesh Jute Research Institute, Dhaka, Bangladesh. \nd Sublime Agro Ltd. Dhaka, Bangladesh. \ne Department of Plant pathology, Patuakhali Science and Technology University, Bangladesh. \n*Corresponding Author Email: lizh@cau.edu.cn\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 26 April 2022 \nAccepted 30 May 2022 \nAvailable online 02 June 2022\n\n\n\nBangladesh is an agro-based country. Several vegetables and fruits contribute greatly to the national \neconomy. Fruit fly species (Diptera: Tephritidae) have been a serious threat to agriculture in Bangladesh as \nwell as worldwide. Morphological identification sometimes creates misidentification in adult stages, while in \neggs, larvae and pupal stages are totally difficult. Nowadays, molecular identification based on DNA barcoding \nis an effective and rapid identification tool. However, this technique is very limited use in Bangladesh. In this \nstudy, adult samples were collected (trapping with ME and CUE) from three different geographic locations \n(Dhaka, Chittagong and Barisal) of Bangladesh. Adult flies were identified based on DNA barcoding (amplify \nthe sequences with COI gene ), and larvae were identified based on species-specific primers. Bactrocera \ndorsalis (Hendel), Zeugodacus tau (Walker) and Zeugodacus cucurbitae (Coquillett) were identified adult \nspecies found in all the locations, whereas B. dorsalis was found in a higher number. In case of host fly \nidentification on the basis of larvae, B. dorsalis was identified from guava in three locations, indicating guava \nfruit is the suitable host in Bangladesh. Proper management should be taken to control these pest species; \notherwise, they will become a great threat to the agriculture of Bangladesh. \n\n\n\nKEYWORDS \n\n\n\nBactrocera dorsalis, Zeugodacus tau, Zeugodacus cucurbitae, molecular identification, DNA barcodes, species-\nspecific primer. \n\n\n\n1. INTRODUCTION \n\n\n\nBangladesh has fertile land and a conducive climate for the growth of \nvarious agricultural products (Hoq et al., 2012). Agriculture is the most \ndominant sector of the economy of Bangladesh, which contributes more \nthan 15 percent of the GDP (BBS, 2018). Approximately 70% of the total \npopulation lives in rural areas and depends directly or indirectly on \nagriculture for their livelihoods.  Bangladesh has been blessed with \nvarious fruits and vegetables, including more than 90 vegetables and 60 \nfruits (Uddin et al., 2005). The main vegetables are tomato, brinjal, potato, \naroids, cabbage, cauliflower, pumpkin, bottle gourd, pointed gourd, bitter \ngourd, yard long bean, cucumber, and hyacinth bean, and fruits include \nbanana, guava, jack fruit, mango, papaya, lemons, pummelo, pineapple, \nlitchi and ber (jujube) are the most important (Hasanuzzaman, 2003). \n\n\n\nFruit flies (family: Tephritidae) are a group of agricultural pests that \ndamage a wide range of fruits and vegetables and pose tremendous threats \nworldwide, with both quantitative and qualitative losses (FAO/IAEA, \n2013). About 4000 described species from 500 genera of tephritids were \nidentified (Qin et al., 2015). Among them, more than 250 species are \nconsidered economically important (Li et al., 2013). Most of them belong \n\n\n\nto six: Anastrepha, Bactrocera, Ceratitis, Dacus, Zeugodacus, and Rhagoletis \n(White and Elson-Harris, 1992; Van Houdt et al., 2010). Moreover, 118 \nspecies have been known to occur on the Indian subcontinent as \ncultivation fruits and vegetable pests (David and Ramani, 2011; Drew and \nRomig, 2013; David et al., 2016, 2017; Leblanc et al., 2018). In Bangladesh, \nfruit fly also causes massive damage to both vegetables and fruit \nproduction (Alim et al., 2012). Recently, 29 fruit fly species (13 pests and \n16 non-pest species) were identified from the rural environment and \nforest areas of Bangladesh by six years surveys (2013-2018), of which \nBactrocera dorsalis (Hendel), B. zonata (Saunders), Zeugodacus tau \n(Walker) and Zeugodacus cucurbitae (Coquillett) was found as a major \nspecies (Leblanc et al., 2019).  \n\n\n\nIdentifying species is a key part of the recognition and description of \nbiodiversity. Traditional identification (morphological) needs experts like \ntaxonomists and trained technicians who can clearly identify taxa, as it \nneeds special skills and vast experience. But there has been a substantial \ndecrease in the number of taxonomists and other experts. Therefore, \nalternative and accurate methods of identification are necessary so that \nnon-experts can do it the very easy way. DNA-based identification \nmethods have been the most promising approaches instead of \nmorphological data for identifying the taxa (Busse et al., 1996; Blaxter, \n\n\n\n\nmailto:lizh@cau.edu.cn\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 110-116 \n\n\n\nCite The Article: Sultana Afroz, Md Shibly Noman, Yue Zhang, Md Yousuf Ali, Md Rubel Mahmud, Zhihong Li (2022). Species Identification of Economic \nImportant Adult Fruit Flies Based on DNA Barcoding (MT DNA COI) and Larvae Based on Species Specific Primers from Central and South \n\n\n\nParts of Bangladesh. Malaysian Journal of Sustainable Agricultures, 6(2): 110-116. \n\n\n\n2003). Nowadays, DNA barcoding has become a powerful tool for the \nsuccessful identification of fruit flies. On the basis of the COI gene, several \nrapid diagnostic approaches have been established, such as real-time PCR, \nPCR-RFLP (restriction fragment length polymorphisms), microfluid \ndynamic array techniques, and loop-mediated isothermal amplification \n(LAMP). Such methods were successfully implemented in identifying \nseveral economically important fruit fly species.  \n\n\n\nIn some studies, polymerase chain reaction (PCR) with species-specific \nprimers have also been used to resolve the need for post-amplification \ndigestion and DNA sequencing that enables quicker identification (Chua et \nal., 2010). The two pairs of species-specific primers were successfully \ndeveloped by based on 1517 bp of the mtDNA COI gene that could \ndistinguish B. carambolae and B. papayae under normal PCR testing \nconditions (Chua et al., 2010). A group researchers developed species \nprimers based on DNA barcode sequences for the identification of B. \ndorsalis and B. zonata, and this approach could accurately classify all life \nstages of the target species (Asokan et al., 2011). Some of researcher \n\n\n\ndeveloped two pairs of species-specific primers that can easily identify all \ndevelopmental stages of B. minax and B. tsuneonis, which is very cost-\neffective molecular method of identification (Zheng et al., 2019). \nNowadays, molecular identification based on DNA barcoding is an \neffective and rapid identification tool. However, this technique is very \nlimited use in Bangladesh. Hence, the present study was taken to identify \nthe economic important adult fruit fly species using DNA barcoding (mt \nDNA COI) and larvae using species specific primer. \n\n\n\n2. MATERIAL AND METHODS\n\n\n\n2.1  Experimental Area \n\n\n\nSamples were collected from three divisions Dhaka, Barisal and \nChittagong of Bangladesh during June and December 2018 (Figure 1). \nAdult fruit fly samples were collected from the commercial vegetable farm \n(Dhaka), guava orchard of Horticulture Centre, Madaripur, and vegetable \nfield (Barisal) and Commercial mango and citrus orchard (Chittagong). \n\n\n\nFigure 1: Map showing the different sampling locations of Bangladesh. Map was prepared based on the coordinate of location using ArcGIS 10.0 \n\n\n\n2.2   Study Species and Collection Design \n\n\n\nThe areas were surrounded by traps that were hung 10m apart from each \nother. Methyl Eugenol (ME) and Cue types of lure (CUE) were used for \ntrapping adult fruit flies. The lure was exchanged after five days. Adult fruit \nflies were removed from traps every day. Larvae from guava were \ncollected from three locations in two divisions. The collected samples were \ncounted and put into small tubes with 100% alcohol.  \n\n\n\n2.3  DNA Extraction, Barcodes Amplification by PCR and Sequencing \nof Adult Flies \n\n\n\nA total of 12 adult individuals from three locations were used for species \nidentification. DNA was extracted from the leg of adult fruit fly using the \ncommercial DNA mini kit, TIANGEN Total DNA Kit (China) followed by the \ninstructions of manufacturing company. DNA quality and concentration \nwas tested by a Quawell UV-Vis Q5000 spectrophotometer (Quawell \nTechnology Inc., San Jose, CA, USA). Only the good quality of DNA samples \nwas used for sequencing. The universal COI gene primers LCO-1490 (5\u2019-\nGGTCAACAAATCATAAAGATATTG-3\u2019) and HCO-2198 (5\u2019-\nTAAACTTCAGGGTGACCAAAAAATCA-3\u2019) were used to amply the DNA \n(Former, 1994).  Polymerase chain reaction (PCR) was completed in a final \nvolume of 50 \u00b5l containing 25 \u00b5l of 2\u00d7Taq Master Mix, 2 \u03bcl of forward and \n2 \u03bcl of reverse primers, 19 of \u00b5l sterilized distilled water (ddH2O) and 2 \u00b5l \nof DNA template. The reaction condition was described as follows:94\u02daC for \n3 minutes, followed by 35cycles of 94\u02daC for 1 minute, 53\u02daC for 1 minute, \n\n\n\nand 72\u02daC for 1 minute and then a final incubation at 72\u02daC for 10 minutes. \nThe reaction was performed on Veriti TM 96 \u2013well Thermal Cycler (ABI, \nUSA). The remaining DNA samples of all specimens were preserved at -\n80\u02daC in Plant Quarantine and Invasion Biology Lab (CAUPQL) in the \nDepartment of Entomology of China Agricultural University. The PCR \nproducts were purified and sequenced in both directions (forward and \nreverse) by BGI, Beijing, China. \n\n\n\n2.4   Sequences Analysis \n\n\n\nDNAMAN 5.2.2.0 was used for sequence assembly, and further analysis \nwas done for the low-quality sequence. The multiple alignments of \nsequences were done by MEGA 7. For consistency with the standard \nsequences for species identification, sequences of COI (barcoding region) \nwere submitted to the BOLD system. \n\n\n\n2.5 The Neighbour-Joining Tree \n\n\n\nThe neighbour-joining tree (NJ) was constructed for phylogenetic analysis \nof the sequence of samples of related species and some sequences derived \nfrom the Gene Bank database to verify the above identification. MEGA \n(version 7) was used to build the neighbor-joining (NJ) tree with NJ \nmethod and Kimura 2-Parameter (Hebert, 2003a). Kimura -2 parameter \nwas also determined using MEGA version 7 for computing the pairwise \ngenetic distance. The associations were calculated on the basis of genetic \ndistances. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 110-116 \n\n\n\nCite The Article: Sultana Afroz, Md Shibly Noman, Yue Zhang, Md Yousuf Ali, Md Rubel Mahmud, Zhihong Li (2022). Species Identification of Economic \nImportant Adult Fruit Flies Based on DNA Barcoding (MT DNA COI) and Larvae Based on Species Specific Primers from Central and South \n\n\n\nParts of Bangladesh. Malaysian Journal of Sustainable Agricultures, 6(2): 110-116. \n\n\n\n2.6  Species-Specific Gene Amplification and Gel Electrophoresis for \nLarvae \n\n\n\nThree larvae were used for identification. DNA was extracted from larvae \nusing the commercial tissue/cell DNA mini kit, TIANGEN Total DNA Kit \n(China). DNA samples of all specimens were deposited at -80\u02daC in Plant \nQuarantine Lab in the Department of Entomology of China Agricultural \nUniversity. Species-specific primer of 10 species was used for larvae \nidentification (Table 1), which were collected (Jiang, 2015). Primers were \nsynthesised by Tsingke Biotechnology Co, Ltd, Beijing, China. Polymerase \n\n\n\nchain reaction (PCR) was completed in a final volume of 50 \u00b5l containing \n25 \u00b5l of 2\u00d7Taq Master Mix, 2 \u03bcl of forward and 2 \u03bcl of reverse primers, 19 \nof \u00b5l sterilized distilled water (ddH2O) and 2 \u00b5l of DNA template. Reaction \ncondition was described as follows: 95\u02daC for 3 minutes, followed by 30 \ncycles of 95\u02daC for 15 seconds, 60\u02daC for 1 minute, and then a final incubation \nat 60\u02daC for 1 minute. A total of 6 \u00b5l PCR product was used for \nelectrophoretic analysis to get the specific band for species identification. \nD2000 marker and 6 \u00b5l PCR product was poured into 1.5 agarose gel with \na dye and was run 1x TAE buffer with 120V for 30 minutes. The result was \nobserved under the UV light of gel imaging system. \n\n\n\nTable 1: Species-Specific Primer List of Economically Important Fruit Flies in This Study \n\n\n\nSL No Fruit Fly Species Code Primer Sequence (5` -3`) Length (bp) Max TM \n\n\n\n1 B. correcta BBCOF TGACTTGTCCCCCTAATACTG 21 65.6 \n\n\n\nBBCOR GTCGATCGCATGTTAATAACG 21 \n\n\n\n2 B. dorsalis BBDF GCTATTTTTTCACTTCACTTAACG 24 60.2 \n\n\n\nBBDR AGTATTTAAGTTTCGGTCTGTTAG 24 \n\n\n\n3 B. latifrons BBLF CGAATAAACAATATAAGATTTTGG 24 61.4 \n\n\n\nBBLR GTGATGAAGTTAACTGCTCCTAAG 24 \n\n\n\n4 B. tryoni BBTF ATTAATCGGAGACGATCAG 19 61.7 \n\n\n\nBBTR AGCTAAATCAACTGAAACC 19 \n\n\n\n5 B. zonata BBZR ACTTGTTCCCCTAATATTAGGAACC 25 64.1 \n\n\n\nBBZF TGTTAATACAACTGCTCAGACGAAG 25 \n\n\n\n6 B(Z) bezziana BZBF CTCCTGATATAGCATTCACC 20 59.0 \n\n\n\nBZBR AAGTATAGTGATAGCTCCAACC 22 \n\n\n\n7 B(Z) cilifera BZCIF GGCTGTAAATTTTATCACTACAGTC 25 64.4 \n\n\n\nBZCIR CGGTCTGTCAAAAGTATAGTAATG 24 \n\n\n\n8 B(Z) cucurbitae BZCUF GGAGATGATCTAATCTATAATGTC 24 63.1 \n\n\n\nBZCUR GCTCAAACGAATAAAGGTAAC 21 \n\n\n\n9 B(Z) scutellata BZSF CTCGGAGCCCCAGATATAACC 21 65.3 \n\n\n\nBZSR GGGCTGTTAATACTACTGCTCAG 23 \n\n\n\n10 B(Z) tau BZTF GGAGCACCAGATATAGCG 18 63.0 \n\n\n\nBZTR GGTATTCGGTCAAATGTAATC 21 \n\n\n\n3. RESULTS \n\n\n\n3.1  Collection Information of Adult Samples in Different Locations of \nBangladesh \n\n\n\nFrom the three different locations of Bangladesh, a total of 1897 fruit fly  \n\n\n\nindividuals were collected (Table 2). Based on morphology, 1291 \nindividuals belonged to Bactrocera, and606 individuals were Zeugodacus. \nThe genus Bactrocera was found more in all regions (Table 2). \n\n\n\nTable 2: Collection Information of Adult Samples From Bangladesh In This Study \n\n\n\nDate Of Collection \nLocations \n\n\n\nLure Type Caught Genus \nNumber of \nSpecimens Name Latitude Longitude \n\n\n\nDecember/18 Dhaka 24.134 90.3035 \nME Bactrocera 287 \n\n\n\nCUE Zeugodacus 133 \n\n\n\nJune/2018 Barisal 22.128 90.423 \nME Bactrocera 452 \n\n\n\nCUE Zeugodacus 161 \n\n\n\nJune/2018 Chittagong 23.203 92.0443 \nME Bactrocera 552 \n\n\n\nCUE Zeugodacus 312 \n\n\n\nTable 3: Collection Information of Larvae Samples from Bangladesh in This Study \n\n\n\nDate of Collection \nLocation \n\n\n\nHost Number of Specimens \nLocation Latitude Longitude \n\n\n\nJune/2019 \nMadaripur 22.128 90.423 Guava 110 \n\n\n\nBakerjong -- -- Guava 91 \n\n\n\nJune/2019 Chittagong 23.203 92.0443 Guava 96 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 110-116 \n\n\n\nCite The Article: Sultana Afroz, Md Shibly Noman, Yue Zhang, Md Yousuf Ali, Md Rubel Mahmud, Zhihong Li (2022). Species Identification of Economic \nImportant Adult Fruit Flies Based on DNA Barcoding (MT DNA COI) and Larvae Based on Species Specific Primers from Central and South \n\n\n\nParts of Bangladesh. Malaysian Journal of Sustainable Agricultures, 6(2): 110-116. \n\n\n\n3.2 DNA Sequences and Analysis of DNA Sequences  \n\n\n\nEach individual provides a high level of DNA quality and a fragment of \n\n\n\n~700bp produced by PCR amplification with universal COI gene primers. \nPCR amplification was accomplished successfully, and 700bp COI gene \nsequence was obtained (Figure 2). \n\n\n\nFigure 2: Test of PCR result on Bactrocera and Zeugodacus by gel electrophoresis. Line 1-4, Bactrocera spp.; line 5-12, Zeugodacus and line M: D2000 \nMarker. \n\n\n\n3.3 Analysis of DNA Sequences \n\n\n\nSequences of 9 individuals (1 from each location) were obtained from the \nthree locations (Dhaka, Chittagong and Barisal) of Bangladesh, and their \nsimilarity was compared with standard sequences of the Life Data System \nBarcode (BOLD) for species identification. Three identified \nspecies/location was presented in Table 4. Compared with all the COI \nsequences in BOLD, sequence similarity between species of B. \ndorsalis100%, Z. cucurbitae 100% and Z. tau 100%. \n\n\n\nTable 4: Identified adult fruit fly species from different location of \nBangladesh \n\n\n\nSamples Id Location Identified Species \nSimilarity \n\n\n\n(%) \n\n\n\n1 BDK1 Chittagong Bactrocera dorsalis 100 \n\n\n\n2 BDK2 Chittagong Zeugodacus tau 100 \n\n\n\n3 BDK3 Chittagong \nZeugodacus \ncucurbitae \n\n\n\n100 \n\n\n\n4 BDD1 Dhaka Bactrocera dorsalis 100 \n\n\n\n5 BDD2 Dhaka Zeugodacus tau 100 \n\n\n\n6 BDD3 Dhaka \nZeugodacus \ncucurbitae \n\n\n\n100 \n\n\n\n7 BDB1 Barisal Bactrocera dorsalis 100 \n\n\n\n8 BDB2 Barisal Zeugodacus tau 100 \n\n\n\n9 BDB3 Barisal \nZeugodacus \ncucurbitae \n\n\n\n100 \n\n\n\nBDK= Chittagong (East part), BDD= Dhaka (Centre part), BDB= Barisal \n(South part), \n\n\n\n3.4 Neighbor-Joining (NJ) Tree \n\n\n\nThe data were presented in the neighbour-joining (NJ) tree only for the \nsole purpose of clustering species on a distance basis based on sequence \nsimilarity rather than character-based sequence clustering. From the NJ \ntree, it was calculated that the obtained species was similar to the species \nobtained from NCBI of the same species (Figure 3).  \n\n\n\nFigure 3: Neighbor Joining (NJ) tree developed from COI barcoding \nanalysis showing the phylogenetic position of 9 fruit fly species B. \n\n\n\ndorsalis, Z. cucurbitae, Z. tau and Anastrepha frateculus. Anastrepha \nfrateculus was out of the group. Percent bootstrap value of 1000 \n\n\n\nreplicates. \n\n\n\n3.5 Larvae Identification by Species-Specific Primers \n\n\n\nAccording to the presence or absence of a band in the gel profile, the \nspecificity and sensitivity of particular primer were tested. A total of 10 \nspecies-specific primers were used to identify the larvae from guava. 250 \nbp fragment was amplified inline - 2 indicated that B. dorsalis was found \nin the larvae, collected from guava in different locations (Figure 4. A-C). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 110-116 \n\n\n\nCite The Article: Sultana Afroz, Md Shibly Noman, Yue Zhang, Md Yousuf Ali, Md Rubel Mahmud, Zhihong Li (2022). Species Identification of Economic \nImportant Adult Fruit Flies Based on DNA Barcoding (MT DNA COI) and Larvae Based on Species Specific Primers from Central and South \n\n\n\nParts of Bangladesh. Malaysian Journal of Sustainable Agricultures, 6(2): 110-116. \n\n\n\nFigure 4: Diagnosis of larvaebased on species specific primer from guava fruit. A. Chittagong sample, B. Madaripur sample, 3. Bakergonj sample.  Line 1 is \nB. correcta, Line 2 is B. dorsalis, Line 3 is B. latifrons, Line 4 is B. tryoni, Line 5 is B. zonata , Line 6 is Z. bezziana, Line 7 is Z.cilifera, Line 8 is Z.cucurbitae, \n\n\n\nLine 9 is Z.scutellata, Line 10 is Z.tau and line M:D2000 Marker\n\n\n\n4. DISCUSSION\n\n\n\nThe morphological characteristics of adult insects may be difficult to \nseparate Bactrocera sibling species or the identification of arrogant stages \n(White et al., 1997). Molecular dedication of fruit flies would have been an \nadvantage in such instances (Greenstone, 2006). A group researcher \nproposed a technique for amplifying a 648 bp region of the mitochondrial \ncytochrome-c oxidase subunit 1 (COI) gene to ensure rapid and accurate \nidentification of a wide range of biological specimens (Hebert et al., 2003 \na, b). The Life Barcode project was then introduced to encourage DNA \nbarcoding as a global standard for sequence-based eukaryote \nidentification. In the present study for the identification of adult fruit fly \nspecies, the DNA barcoding technique (mt DNA COI) was used. Several \nstudies have been reported. A studied the COI sequences of fruit fly \nsamples collected from New Zealand ports, and the findings were \ncompatible with the previous results of the constraint fragment duration \npolymorphisms (RFLPs) (Armstrong et al., 2005). \n\n\n\nHowever, identification using DNA barcodes detected species recognized \nRFLP analysis could not remember (Armstrong et al., 2005). The DNA \nbarcoding (amplification of the sequence by COI gene) method was \napplied to identify the larvae collected from guava fruit of Thailand, \nresulting in the identification of B. correct (Buahom et al., 2011). Some \nresearchers only differentiated four species of Anastrepha (Anastrepha \ngrandis, A. serpentine, A. ludens, and A. striata) based on barcode data, \nthough they used 539 DNA sequence from 74 species (Barr et al., 2017). \nOut of 10 species of Bactrocera, eight species were easily distinguished by \n\n\n\nbased on the standard DNA barcoding region of the COI gene. Bangladesh \nis divided into eight divisions (Manger et al., 2018). The south part is \ncomprising of Khulna, Barisal and Chittagong division. Khulna division is \nfamous for mainly vegetables and some parts have guava orchard. The \nBarisal division is famous for guava production.  \n\n\n\nThe Chittagong division is the only hilly area of Bangladesh, famous for \nmango and citrus production. Vegetables also have been grown in some \nparts of this region. In the central part (Dhaka division), some commercial \nmixed fruits orchards and vegetable farms are also present. During the \nsample collection period of this study, only guava fruits and some summer \nvegetables were presented in the field. Only two genera of fruit fly were \ncollected, of which Bactrocera was found to bethe higher in number in all \nlocations.  Alam reported 10 tephritids fruit flies from Bangladesh (East \nPakistan) (Alam, 1967). A group researchers recorded 11 tephritids fruit \nflies from Bangladesh (Kapoor et al., 1980). In some study, they identified \n15 species of fruit fly belonging to Bactrocera (6), Daculus (1), \nHemigymnodacus (1), Parasinodacus (1), Sinodacus (1), Dacus (3)and \nZeugodacus (2) during one-year survey of different locations (field, \norchard, forest and rural areas) of Bangladesh.  \n\n\n\nLeblanc and his colleagues again did a six-year survey, including most of \nBangladesh and found 29 fruit fly species, of which 13 species were \nconsidered pests and 16 were non-pest species (Leblanc et al., 2019). In \nthis study three species, vizB. dorsalis, Z. tau, and Z. cucurbitae were \nidentified from different areas of Bangladesh in a short survey (June and \nDecember 2018). The report of the molecular identification of fruit flies in \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 110-116 \n\n\n\nCite The Article: Sultana Afroz, Md Shibly Noman, Yue Zhang, Md Yousuf Ali, Md Rubel Mahmud, Zhihong Li (2022). Species Identification of Economic \nImportant Adult Fruit Flies Based on DNA Barcoding (MT DNA COI) and Larvae Based on Species Specific Primers from Central and South \n\n\n\nParts of Bangladesh. Malaysian Journal of Sustainable Agricultures, 6(2): 110-116. \n\n\n\nBangladesh is limited. Recently, a six-year survey (2013-2018) in the \ndifferent locations of Bangladesh found 13 pest species of fruit flies, of \nwhich B. dorsalis found to be the dominant species followed by Z. tau, Z. \ncucurbitae and B. zonata (Leblanc et al., 2019). But in this study, B. zonata \ndid not collect. The lower number of identified species may be due to the \nshort period of the survey, seasonal variation, host unavailability in the \ncrop field or orchard and the locations.  \n\n\n\nSome researchers conducted a two-year survey (November 2017 to \nOctober 2019) in the different agricultural areas of Bangladesh and found \n13 pests and non-pests\u2019 species, of which three pest species, viz. B. dorsalis \n(58%), Z. tau (13.5%) and Z. cucurbitae (23.6%) were found to be \ndominant (Hossain et al., 2019). They also noted that B. dorsalis was higher \nin wet summer months (May to August), whereas Z. cucurbitae was higher \nin March and May. In this study, found only two genera may be due to the \nseasonal variation and host availability in the field and maybe the selection \nof farmers\u2019 field or commercial and research orchard and the short time of \nthe survey. For insect\u2019s host identification, the identification of larvae is \nthe best option.  \n\n\n\nBeing the higher production and demand of guava in Bangladesh, larvae \nfrom this fruit of three different areas were examined. B. dorsalis was \nidentified from the guava of all three locations, seems that guava is one of \nthe hosts of B. dorsalis in Bangladesh. Our finding is disagreed with the \nstudy (Leblanc et al., 2013). They found that the peach fruit fly, B. zonata \nis a serious pest of a variety of fruits especially in mango, carambola and \nguava in Bangladesh. But in our neighbor country India, the pest status of \nB. zonata is considered equal to or greater than that of the Oriental fruit \nfly (B. dorsalis) and they may overlap in the same crop (Kapoor, 1993). So, \nin near future B. dorsalis may be severe problem in our agriculture.  \n\n\n\n5. CONCLUSION \n\n\n\nBangladesh is very small but agro-based country in South Asia. Fruit fly \ncauses serious damage to several vegetables and fruit crops every year. \nSurvey is very important for identification of insects. Long time, cover the \nmost of locations and season wise survey is effective for studying insects \nin a country that will be included in future research. Molecular \nidentification is an effective method, as it can diagnosis very accurately \nand rapidly. Identification is very important to take proper and timely \ncontrol measures; however, molecular identification is limited in \nBangladesh. Therefore, this study could be the foundation of future insect \nidentification research in Bangladesh. In this study, B. dorsalis, Z. tauand Z. \ncucurbitae were found in the three locations. B. dorsalis was identified \nfrom guava in all three locations. So it needs to pay more attention to \nmanaging these harmful pest species and saving the crops; results will \nincrease the production. \n\n\n\nFUNDING \n\n\n\nThis work was supported by the Natural Science Foundation Project of \nChina (No. 31972341). \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nWe thank, S.M. Kamrul Hasan Chowdhury, Bangladesh Agricultural \nResearch Institute, Bangladesh. We also thank the other members of Plant \nQuarantine and Invasion Biology Laboratory, China Agricultural \nUniversity (CAUPQL). \n\n\n\nREFERENCES \n\n\n\nAlam, M.Z., 1967. A report on the survey of insect and mite fauna of East \nPakistan. East Pakistan Agricultural Research Institute, Dhaka, Pp. \n151. \n\n\n\nAlim, M.A., Hossain, M.A., Khan, M., Khan, S.A., Islam, M.S., Khalequzzaman, \nM., 2012. Seasonal variations of melon fly, Bactrocera cucurbitae \n(Coquillett) (Diptera: Tephritidae) in different agricultural habitats of \nBangladesh. ARPN J. Agric. Biol. Sci., 7, Pp. 905\u2013911. \n\n\n\nArmstrong, K.F., and Ball, S.L., 2005. DNA barcodes for biosecurity: \nInvasive species identification. Philos. Trans. R. Soc. B., 360, Pp. 1813\u2013\n1823. \n\n\n\nAsokan, R., Rebijith, K.B., Singh, S.K., Sidhu, A.S., Siddharthan, S., Karanth, \nP.K., Ellango, R., Ramamurthy, V.V., 2011. Molecular identification and \nphylogeny of Bactrocera species (Diptera: Tephritidae). Florida \nEntomologist, 94, Pp. 1026\u20131035. \n\n\n\nBangladesh Bureau of Statistics. 2018. Statistical Yearbook of Bangladesh, \nStatistics Division, Ministry of Planning, Government of the People\u2019s \nRepublic of Bangladesh, Dhaka. \n\n\n\nBarr, N.B., Ruiz-Arce, R., Farris, R.E., Silva, J.G., Lima, K.M., Dutra, V.S, \nThomas, D.B., 2017. Identifying Anastrepha (Diptera; Tephritidae) \nSpecies Using DNA Barcodes. J. Econ. Entomol., 111, Pp. 405\u2013421.  \n\n\n\nBlaxter, M., 2003. Counting angels with DNA. Nature, 421, PP. 122\u2013124. \n\n\n\nBuahom, N., Li, Z.H., Wu, J.J., and Liu, J.Q., 2011. Molecular identification of \nfruit fly larvae from Thailand based on DNA barcoding. Plant Quar., 25, \nPp. 49\u201352. \n\n\n\nBusse, H.J., Denner, E.B.M., and Lubitz, W., 1996. Classification and \nidentification of bacteria: current approaches to an old problem. \nOverview of methods used in bacterial systematics. J. Biotech., 47, Pp. \n3\u201338. \n\n\n\nChua, T.H., Song, B.K., Chong, Y.V., 2010. Development of allele-specific \nsingle-nucleotide polymorphism-based PCR markers in COI for the \ndifferentiation of B. papayae and B. carambolae (Diptera: Tephritidae). \nJ. Econ. Entomol., 103, Pp. 1994\u20131999. \n\n\n\nDavid, K.J., and Ramani, S., 2011. An illustrated key to fruit flies (Diptera: \nTephritidae) from Peninsular India and the Andaman and Nicobar \nIslands. Zootaxa, 3021, Pp. 1\u201331. https://doi. \norg/10.11646/zootaxa.3231.1.4  \n\n\n\nDavid, K.J., Hancock, D.L., Singh, S.K., Ramani, S., Behere, G.T., Salini, S., \n2017. New species, new records and updated sub-generic key of \nBactrocera Macquart (Diptera: Tephritidae: Dacinae: Dacini) from \nIndia. Zootaxa, 4272, Pp. 386\u2013400. https://doi.org/10. \n11646/zootaxa.4272.3.4 \n\n\n\nDavid, K.J., Ramani, S., Whitmore, D., and Ranganath, H.R., 2016. Two new \nspecies and a new record of Bactrocera Macquart (Diptera: \nTephritidae: Dacinae: Dacini) from India. Zootaxa, 4103, Pp. 25\u201334. \nhttps://doi.org/10.11646/zootaxa.4103.1.2 \n\n\n\nDrew, R.A.I. and Romig, M.C., 2013. Tropical fruit flies of South-East Asia. \nCABI, Wallingford, Pp. 655. \n\n\n\nFAO/IAEA. 2013. Trapping guidelines for area-wide fruit fly programmes. \nOnline at: http://www-pub.iaea.org/MTCD/publications/PDF/TG-\nFFP_web.pdf. 2013; (accessed 12 December 2013). \n\n\n\nGreenstone, M.H., 2006. Molecular methods for assessing insect \nparasitism. Bull. Entomol. Res., Pp. 1-13. \n\n\n\nHasanuzzaman, S.M., 2003. Plant Genetic resources in SAARC Countries: \nTheir Conservation and Management. In: Plant Genetic resources in \nSAARC Countries: Their Conservation and Management. SAARC \nAgricultural Information Centre (SAIC), BARC Complex, Farmgate, \nDhaka, Bangladesh. Pp. 1-239. \n\n\n\nHebert, P.D.N., Cywinska, A., Ball, S.L. and deWaard, J.R., 2003a. Biological \nidentifications through DNA barcodes. Proceedings of the Royal Society \nof London, Series B, 270, Pp. 313\u2013321. \n\n\n\nHebert, P.D.N., Ratnasingham, S., and deWaard, J.R., 2003b. Barcoding \nanimal life: cytochrome c oxidase subunit 1 divergences among closely \nrelated species. Proceedings of the Royal Society of London, Series B., \n270, Pp. S96\u2013S99. \n\n\n\nHoq, M.S., Raha, S.K., Sultana, N., 2012. Value addition in vegetable \nproduction, processing, and export from Bangladesh. Bangladesh J. \nAgri. Res., 37 (3), Pp. 377-388. \n\n\n\nHossain, M.A., Leblanc, L., Momen, M., Bari, M.A., Khan, S.A., 2019. Seasonal \nAbundance of Economically Important Fruit Flies (Diptera: \nTephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors \nand Host Plants.Proc. Hawaiian Entomol. Soci., 51 (2), Pp. 25\u201337. \n\n\n\nJiang, F., 2015. Technique system for molecular identification of \nquarantine fruit flies in China. PhD dissertation, China Agricultural \nUniversity, Beijing, China. \n\n\n\nKapoor, V.C., Hardy, D.E., Agarwal, M.L., and Grewal, J.S., 1980. Fruit fly \n(Diptera: Tephritidae) systematics of the Indian subcontinent. Export \nIndia Publications, Jullundur, India, Pp. 113. \n\n\n\n\nhttps://doi.org/10.%2011646/zootaxa.4272.3.4\n\n\nhttps://doi.org/10.%2011646/zootaxa.4272.3.4\n\n\nhttps://doi.org/10.11646/zootaxa.4103.1.2\n\n\nhttp://www-pub.iaea.org/MTCD/publications/PDF/TG-FFP_web.pdf\n\n\nhttp://www-pub.iaea.org/MTCD/publications/PDF/TG-FFP_web.pdf\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 110-116 \n\n\n\nCite The Article: Sultana Afroz, Md Shibly Noman, Yue Zhang, Md Yousuf Ali, Md Rubel Mahmud, Zhihong Li (2022). Species Identification of Economic \nImportant Adult Fruit Flies Based on DNA Barcoding (MT DNA COI) and Larvae Based on Species Specific Primers from Central and South \n\n\n\nParts of Bangladesh. Malaysian Journal of Sustainable Agricultures, 6(2): 110-116. \n\n\n\nLeblanc, L., Doorenweerd, C., San Jose, M., Sirisena, U.G.A.I., Hemachandra, \nK.S. and Rubinoff, D., 2018. Description of a new species of Dacus from \nSri Lanka (Diptera, Tephritidae, Dacinae), and new country \ndistribution records. Zookeys, 795, Pp. 105\u2013114. \nhttps://doi.org/10.3897/zookeys.795.29140 \n\n\n\nLeblanc, L., Hossain, M.A., Doorenweerd, C., Khan, S.A., Momen, M., San \nJose, M. and Rubinoff, D., 2019. Six years of fruit fly surveys in \nBangladesh: a new species, 33 new country records and recent \ndiscovery of the highly invasive Bactrocera carambolae (Diptera, \nTephritidae). Zookeys, 876, Pp. 87\u2013109. \n\n\n\nLeblanc, L., Hossain, M.A., Khan, S.A., San Jose, M. and Rubinoff, D., 2013. A \npreliminary survey of the fruit flies (Diptera: Tephritidae: Dacinae) of \nBangladesh. Pro. Hawaiian Entomol. Soci., 45, Pp. 51\u201358. \nhttp://hdl.handle.net/10125/31004 \n\n\n\nLi, Z.H., Jiang, F., Ma, X.L., Fang, Y., Sun, Z.Z., Qin, Y.J. and Wang, Q.L., 2013. \nReview on prevention and control techniques of Tephritidae invasion. \nPlant Quarantine, 27, Pp. 1\u201310. \n\n\n\nManger, A., Behere, G.T., Firake, D.M., Sharma, B., Deshmukh, N.A., Firake, \nP.D., Thakur, N.S.A. and Ngachan, S.V., 2018. Genetic characterization \nof Bactrocera fruit flies (Diptera: Tephritidae) from Northeastern \nIndia based on DNA barcodes. Mitochondrial DNA. A., 29, Pp. 792\u2013799.  \n\n\n\nQin, Y., Paini, D.R., Wang, C., Fang, Y., Li, Z.H., 2015.  Global establishment \nrisk of economically important fruit fly species (Tephritidae). PLoS \nONE, 10 (1), Pp. e0116424. Doi: 10.1371/journal. pone.0116424. \n\n\n\nUddin, M.S., Hossain, M.K., Huda, S.M.S., 2005. Status, distribution, and \nmarket prices of major fruits in Chittagong district, Bangladesh. Int. J. \nFor. Usuf. Mngt., 6 (2), Pp. 23-30. \n\n\n\nVan Houdt, J.K.J., Breman, F.C., Virgilio, M. and Meyer, M.D., 2010. \nRecovering full DNA barcodes from natural history collections of \nTephritids fruit flies (Tephritidae, Diptera) using mini barcodes. Mol. \nEco. Res., 10, Pp. 459\u2013465. \n\n\n\nWhite, I.M., and Elson-Harris, M., 1992. Fruit Flies of Economic \nSignificance: Their Identification and Bionomics. Inter. Inst. Entomol. \nLondon., Pp. 601. \n\n\n\nWhite, I.M. and Hancock, D.L., 1997. CABIKEY to the Dacini (Diptera: \nTephritidae) of Asian, Pacific and Australasia Regions. CAB \nInternational, Wallingford, Oxon, UK. \n\n\n\nZheng, L., Zhang, Y., Yang, W., Zeng, Y., Jiang, F., Qin, Y., Zhang, J., Jiang, Z., \nHu, W., Guo, D., Wan, J., Zhao, Z., Liu, L., Li, Z., 2019. New Species-\nSpecific Primers for Molecular Diagnosis of Bactrocera minax and \nBactrocera tsuneonis (Diptera: Tephritidae) in China Based on DNA \nBarcodes. Insects, 10, Pp. 447. Doi:10.3390/insects10120447 \n\n\n\n\nhttps://doi.org/10.3897/zookeys.795.29140\n\n\nhttp://hdl.handle.net/10125/31004\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 2(2) (2018) 24 \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (online)\n\n\n\nCODEN : MJSAEJ\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) \n\n\n\nDOI : http://doi.org/10.26480/mjsa.02.2018.24 \n\n\n\nUNEXPECTED ROOTING IN SHOOT-TIP CUTTINGS OF PONYTAIL PALM \n(BEAUCARNEA RECURVATA)\nShahram Sedaghathoor*, HabibRostami Shahrajil\n\n\n\nDepartment of Horticulture, Rasht branch, Islamic Azad University, Rasht, Iran, \n*Corresponding Author\u2019s E-mail: sedaghathoor@yahoo.com\n\n\n\nARTICLE DETAILS\n\n\n\nArticle History: \n\n\n\nReceived 12 November 2017 \nAccepted 12 December 2017 \nAvailable online 1 January 2018 \n\n\n\nABSTRACT \n\n\n\nCutting has mentioned one of Beaucarnea\u2019s propagation methodsin some scientific references, but it is hard-to-\nroot plant. This experiment carried out to try rooting of shoot cutting of this species. For this purpose, Ponytail \npalm tip shoot cuttings were treated by 4 concentrations of Indole butyric acid (IBA) including: 0, 1000, 2000 \nand 4000 mg/l. The rooted cutting of Beaucarnea recurvata was treated by 4000 mg/l IBA.\n\n\n\nKEYWORDS \n\n\n\nIBA, Nolina, rooting, foliage \n\n\n\nBased on a study, ponytail palm (Beaucarnea recurvataLem.) is one of the \nimportant foliage pot plants. It is endemic to America and it is commercially \npropagated by seed [1]. According to research, all 10 species of Beaucarnea \nare in the list of endangered plants [2]. Currently, commercial production of \nPonytail palm is mainly conducted by seed and this method results in \nintensification of Beaucarnea extinction. Cutting propagation is a desirable \noption to deal with this dilemma. Based on research, the main advantage of \nasexual propagation is offspring plants genetically identical to the parent \n[3]. Some researchers suggested micropropagation protocol for Beaucarnea \n[3,4]. Cutting is an important method for propagating of ornamental trees \nand shrubs [5]. \n\n\n\nAccording to a research, cutting has mentioned one of Beaucarnea\u2019s \npropagation methods in some scientific references (Brickell, 1994), but it is \nhard-to-root plant [6]. Auxins have the greatest effect on root formation in \ncuttings. Plants produce natural auxin (IAA) in their branches and leaves, \nbut exogenous auxin needs to be applied for improved rooting [7]. A \nresearcher said that the maximum success was achieved in plants such as \nkiwifruits, figs and apples through IBA treatment, which is necessary for \nhardwood and softwood cuttings [8]. \n\n\n\nThe objective of our investigation was to try rooting of shoot cutting of this \nspecies. For this purpose, Ponytail palm tip shoot cuttings were treated by 4 \nconcentrations of Indole butyric acid (IBA) including: 0, 1000, 2000 and \n4000 mg/l. This trial carried out in sand medium for six months. \nUnfortunately, the experiment was not successful after six months. But we \ncontinued experiment for 9 months and only two rooted cuttings were \nobtained (Figure 1).  \n\n\n\nFigure 1: Rooting of shoot cutting of Beaucarnea recurvata with 4000 mg/l \nIBA \n\n\n\nThe rooted cutting of Beaucarnea recurvata was treated by 4000 mg/l IBA. \nSince none of the producers have a reasonable report on the rooting of \nBeaucarnea, this report could be interesting and useful for propagators. It is \nrecommended that trials be conducted with higher IBA concentrations and \nother hormonal substances such as NAA. \n\n\n\nREFERENCES \n\n\n\n[1] McConnell, D.B., Henley, R.W., Biamonte, R.L. 1980. Commercial foliage \nplants. pp. 544-593. In: Joiner, J.N. (ed.) Foliage Plant Production. Prentice-\nHall, Inc. Englewood Cliffs, UK.\n\n\n\n[2] Osorio-Rosales, ML.Y., Mata-Rosas, M. 2005. Micropropagation of \nendemic and endangered Mexican species of ponytail palms. Hort Science \n40:1481-1\n\n\n\n[3] Deywane, L.I., Yeager, T.H. 2003. Propagation of \nlandscapeplants.Institute of Food and agricultural Sciences, University of \nFlorida, CIR. 579, 15 P.\n\n\n\n[4] Samyn, G.L.J. 1993. In vitro propagation of ponytail palm: producing \nmultiple-shoot plants. HortScience 28 (3): 225.\n\n\n\n[5] Reezi, S., Naderi, R., Khalighi, A., Zamani, Z., Etemad, V. 2006. Asexual \nPropagation of Piceapungens \u2018Koster\u2019 through Cuttings and grafting under \nVarious Hormonal Treat-ments. J Iranian Nat Res 59 (3): 589-601.484.\n\n\n\n[6] Brickell, C. 1994. Gardeners' Encyclopedia of Plants and Flowers (Royal \nHorticultural Society). Dorling Kindersley.\n\n\n\n[7] S\ufffd tefan\u010di\u010d, M., Vodnik, D., S\ufffd tampar F, Osterc, G. 2007. The effects of a \nfogging system on the physiological status and rooting capacity of leafy \ncuttings of woody species. Trees 21: 491-496. http://dx.doi.org/10.1007/\ns00468-007-0150-2\n\n\n\n[8] Ercisli, S., Esitken, A., Cangi, R., Sahin, F. 2003. Adventitious root \nformation of kiwifruit in relation to sampling date, IBA and Agrobacterium \nrubi inoculation. Plant Growth Regulation 41: 133-137.\n\n\n\nSHORT COMMUNICATION\n\n\n\nCite the article: Shahram Sedaghathoor, HabibRostami Shahrajil (2018). Unexpected Rooted In Shoot-Tip Cuttings Of Ponytail Palm (Beaucarnea Recurvata). \nMalaysian Journal of Sustainable Agriculture, 2(2) : 24.\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and \nreproduction in any medium, provided  the original work is properly cited \n\n\n\n\nhttp://doi.org/10.26480/mjsa.02.2018.06.08\n\n\nmailto:small.hearts@yahoo.com\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 104-110 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2021.104.110 \n\n\n\nCite the Article: Oduntan, O. B, Oluwayemi, B. J (2021). Optimization of A Clay-Slate Fluidized Bed Dryer for Production of Fish Feed.  \nMalaysian Journal of Sustainable Agriculture, 5(2): 104-110. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2021.104.110\n\n\n\nOPTIMIZATION OF A CLAY-SLATE FLUIDIZED BED DRYER FOR PRODUCTION OF \nFISH FEED \n\n\n\nOduntan, O. Ba, Oluwayemi, B. Jb \n\n\n\na Aquaculture and Fisheries Management, University of Ibadan, Ibadan, Nigeria.  \nb Agricultural and Environmental Engineering, University of Ibadan, Ibadan, Nigeria. \n\n\n\n*Corresponding author email: femkem03@yahoo.co.uk \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 19 November 2020 \nAccepted 23 December 2020 \nAvailable online 25 March 2021\n\n\n\nFor feed producers who suffer from high intolerance to production costs, the only way to cope with the \ncondition is to avoid devices that drive up costs. Extruded feed processed from a clay-slate dryer through a \nfluidized bed could be used to make fish feed.  The aim of the study was  optimise the process conditions on \nthe clay-slate fluidized bed dryer operating at a commercial production of fish feed using the response surface \nmethodology. The fish feed composition were processed at bed height (50-200 mm), drying air temperature \n(60\u2013120\u00b0C), airflow velocity (0.66-0.70 m/s), drying time (10\u201390 min) and extrudates size (4\u20138 mm). \nProduct quality parameters such as moisture ratio and dryer efficiency were determined and analyzed. \nSecond-order polynomial equations, containing all the process variables, were used to measured process. \nMoisture ratio was influenced mostly by linear relationship temperature and drying time. The temperature \nand the quadratic temperature conditions significantly affected the efficiency of the dryer. For the fluidized \nbed drying of extruded fish feed, optimal conditions were set for the bed height of 185.76 mm, a temperature \nof 97.2\u00b0C, an air flow rate of 0.67, a drying time of 65.36 min and an extrudate size of 7.40 mm recommended. \nAt these conditions the moisture ratio and efficiency were 0.86 and 74.39, respectively. The influence of the \nvarious components of the fluidized bed dryer on the drying rate must be better understood so that control \nsystems can be developed to take full advantage of this technology.  \n\n\n\nKEYWORDS \n\n\n\nFluidized bed drying, Fish feed, Extruder, Process optimization, Response surface, polynomial equations. \n\n\n\n1. INTRODUCTION \n\n\n\nFish feed refers to protein based feed products with proper amount of \n\n\n\namino acids, fatty acids, vitamins, minerals and/or other ingredients, \n\n\n\nwhich are only processed for fish energy-yielding macronutrients \n\n\n\n(protein, lipid and carbohydrate) by physical production method (Banrie, \n\n\n\n2013).  In order to facilitate storage and delivery, fish feed is often stored \n\n\n\nin pellet form.  Since fish fed in aquaculture are often completely reliant on \n\n\n\nthe nutrients in the feed to provide all the nutrients required for healthy \n\n\n\ngrowth and development. Manufacturing of pelletized or extruded fish \n\n\n\nfeed is one of the most difficult, regulated and high risk branches of feed \n\n\n\nmanufacturing requiring great care and attention to detail at all stages, \n\n\n\namong which drying is one of the most important ones.  \n\n\n\nVarious moisture removal devices have been used to dry agricultural \n\n\n\nproducts, including pelletized fish feed since ancient times. This \n\n\n\nequipment includes the use of heat to evaporate the water present in the \n\n\n\nfeed, as well as the removal of steam from the feed surface. The use of hot \n\n\n\nair flowing through food is the most common way of transferring heat to a \n\n\n\ndrying material, because it is mainly a convective process (Cruz et al., \n\n\n\n2015). The effectiveness of a drying process depends on different factors: \n\n\n\nmethod of heat transfer, continuity or discontinuity of the process, \n\n\n\ndirection of the heating fluids with respect to the product (pressure \n\n\n\natmospheric, low, deep vacuum). Drying process can be performed by \n\n\n\nusing different kinds of equipment such as: air cabinet, belt drier, tunnel \n\n\n\ndrier, spray dryer, drum dryer, foam dryer, freeze-dryer, oven (Severini et \n\n\n\nal., 2005).  \n\n\n\nHowever, the major disadvantage of some drying process of foods is the \n\n\n\nlong drying time required during the falling rate period which increase \n\n\n\nproduction cost. Fluidized bed drying is a drying process in which there is \n\n\n\nan intense heat and mass transfer between particles present in the liquid \n\n\n\nstate and the air flowing through the bed. The drying method is widely \n\n\n\nused in various industries (Mujumdar, 1995). Since, this technology in \n\n\n\ndrying application is characterized with large contact surface area \n\n\n\nbetween solids and gas, high thermal inertia of solids, good degree of \n\n\n\nsolids mixing, and rapid transfer of heat and moisture between solids and \n\n\n\ngas that shortens drying time considerably without damaging heat \n\n\n\nsensitive materials (Freitas and Freire, 2001; Malafronte et al., 2015).  \n\n\n\nHowever, a significant challenge of fluidized bed drying is flow of material, \n\n\n\nwhich greatly reduces productivity and product quality. The major factors \n\n\n\ninfluencing drying rate of feed material are heat and mass transfer \n\n\n\nbetween air and solids because it affects the efficiency and reduces the \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 104-110 \n\n\n\nCite the Article: Oduntan, O. B, Oluwayemi, B. J (2021). Optimization of A Clay-Slate Fluidized Bed Dryer for Production of Fish Feed.  \nMalaysian Journal of Sustainable Agriculture, 5(2): 104-110. \n\n\n\ndrying time of the process making the process exceptional (Maheswari et \n\n\n\nal., 2015). With increasing particles size and high density, the mass flow of \n\n\n\nthe material became even slower and non-uniform. Therefore, some \n\n\n\nresearcher suggested that materials with density between 1.4 and 4 g/cm3, \n\n\n\nincline a lot at the commencement of fluidization; nevertheless, it is good \n\n\n\nfor high flow rates (Hrdlicka et al., 2016). Increase in air velocity, results \n\n\n\nin increase in the pressure-drop across the particle layer until it \n\n\n\ncorresponds to the weight of the particles per area of the bed.  \n\n\n\nDrying conditions including inlet air temperature, outlet air temperature, \n\n\n\ndrying air flow rate and pressure, as well as structures of drying and \n\n\n\nheating chambers have also been studied to improve product functional \n\n\n\nproperties (Erbay et al., 2015; Anjali and Satya, 2015; Francia et al., 2014;  \n\n\n\nWawrzyniak et al., 2012). A group researchers also explored the particles \n\n\n\nmust have a relatively small range of particle sizes to minimize \n\n\n\nentrainment and to maximize the uniformity of moisture content \n\n\n\n(Hrdlicka et al., 2016). A group researchers optimized drying conditions of \n\n\n\nmaca tuber in a pilot spray drier using response methodology and found \n\n\n\nthat under optimum operating conditions (air inlet temperature was 65\u2103, \n\n\n\nair flow was 150 m\u00b3/h and material particle size was 3 mm), desirability \n\n\n\nfunctions with maximum glucosinolates content retained at a value \n\n\n\n3.192mg/g, as well as the better appearance such as saturation value, \n\n\n\nrehydration ratio and good sensory quality were achieved (Tu et al., 2014).  \n\n\n\nAmira and Ahlem investigated the effect of operating parameters of \n\n\n\nsucrose fluid bed drying on powder quality and on drying time and found \n\n\n\nthat gas flow and sugar particle size had significant effects (in absolute \n\n\n\nvalues) on the quality of the sugar dried (Amira and Ahlem, 2017). The \n\n\n\noptimal drying parameters obtained in this experiment were lower values \n\n\n\ntemperature of 40\u00b0C, compressed air flow rate of 3.5 N/m3, pressure of 2.5 \n\n\n\nbar and at higher level of particle size of 500 granulom.  A studied the effect \n\n\n\nof different drying conditions (inlet air temperature, feed flow rate and \n\n\n\ntotal solids) on potato dehydrated in microwave assisted fluidized bed \n\n\n\ndrying system and the optimum conditions obtained by the response \n\n\n\nsurface methodology for browning index were inlet air velocity 20 m/s \n\n\n\nand drying temperature 50\u00b0C with desirability 1.00 (Akhtar et al., 2015). \n\n\n\nAccording to previous researches, drying conditions had significant \n\n\n\ninfluences on the drying rate and quality of final products.  For commercial \n\n\n\nanimal feed manufacturers who suffer from high intolerance to production \n\n\n\ncosts, the only way to cope with the condition is to avoid devices that drive \n\n\n\nup costs. Extruded feed processed from a clay-slate dryer through a \n\n\n\nfluidized bed could be used to make fish feed.The objectives of this study \n\n\n\nwere to investigate the effect of drying conditions on the efficiency of the \n\n\n\nclay slated fluidizied dryer using experimental data of response surface \n\n\n\nexperiment.  \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Design of the clay slate screen  \n\n\n\nThe clay slate consists of clay soil (90%) and cement powder (10%), which \n\n\n\nis reinforced with acid-resistant stainless steel and is used to absorb heat \n\n\n\nfrom the heating elements and to dissipate the heat (Figure 1). The simple \n\n\n\ndesign consists of a work screen inclined at 30o, built under the one-third \n\n\n\nlength of the screen. The slate ensures efficient hot air flow and further \n\n\n\nimproves the air direction by reducing the backflow on the blower \n\n\n\nimpeller. \n\n\n\n2.2 Sample preparation \n\n\n\nPowdered formulated samples, with protein, fat fibre and ash content of \n\n\n\n36.8%, 8.8%, 4.7% and 12.6%, respectively, were purchased from a local \n\n\n\nmarket in Ibadan, Nigeria. The blend samples was extruded at different die \n\n\n\nsize (4, 6 and 8 mm), at screw speed (401\u2009rpm) and barrel temperature \n\n\n\n(80oC). \n\n\n\n2.3 Sample drying \n\n\n\nA commercial, fluidized bed dryer (FEDOX-645FD), which had a holding \n\n\n\ncapacity of 200 kg, was used in this study. The dryer consists of four sub-\n\n\n\nunits comprising of the heating, clay-slate block, drying and collecting unit \n\n\n\nof the fluidized bed dryer (Figure 1). The blower speed was alternated \n\n\n\nfrom between 1200, 1300, 1400 rpm by using the variable voltammeter \n\n\n\nacross drying runs. Airflow velocity on the shelf measured by an \n\n\n\nanemometer (model 24-6111, Kanomax, Inc., Japan) were 0.67, 0.68, 0.7 \n\n\n\nm/s respect to blower speed variations. The temperature of the drying air \n\n\n\nwas varied by the thermocouple placed inside the heating chamber to \n\n\n\nmeet the predetermined temperatures (60, 80, 100, 120oC).  \n\n\n\nRelative humidity (RH) inside the drier was maintained at 40%. The \n\n\n\nextruded feed was loaded into the fluidized bed dryer at varied thin layer \n\n\n\n(0.05, 0.10, 0.15, 0.20 m). Five samples each were collected between time \n\n\n\nintervals of 10\u201390 min were used to determine the initial moisture content \n\n\n\nin an air oven at 103oC for 25 h. To complete drying and attain equilibrium, \n\n\n\nthe test was terminated when the change of sample mass was less than \n\n\n\n0.01 g. After drying, moisture content of the sample was determined by the \n\n\n\nsame procedure used for measurement of initial moisture content.  \n\n\n\n2.4 Moisture content analysis  \n\n\n\nFor moisture content analysis, about 10 g of feed was weighed by an \nelectronic balance of \u00b10.001 g accuracy (PS-20, frtcraft, Germany).  The \nsamples were put into plastic bags and sealed for transport to the \nlaboratory. Gravimetric method was conducted according to AOAC \nmethods using a ventilated oven (ESP-400 Series, BLUE M, USA) at 130\u00b0C \nfor 16 h (Chen, 2003).  Moisture content (MC, wet basis) was computed \nas: \n\n\n\n\ud835\udc40\ud835\udc36= \n\ud835\udc40\ud835\udc56\u2212\ud835\udc40\ud835\udc37\ud835\udc40\n\n\n\n\ud835\udc40\ud835\udc3c\n. 100%    (1) \n\n\n\nwhere, Mi is the initial mass of the sample before oven-drying, g; MDM is the \n\n\n\nmass of dry matter, g. \n\n\n\n2.5 Moisture removed \n\n\n\nThe moisture content removed from the fish feed extrudates is then \n\n\n\ncalculated after the initial and final weight of the extrudates sample has \n\n\n\nbeen gotten or determined. The ease of migration depends on the porosity \n\n\n\nof the substance and the surface area available. This is calculated by using \n\n\n\nEq. (2) (Buchinger and Weiss, 2001) \n\n\n\n\ud835\udc40\ud835\udc36\ud835\udc5f\ud835\udc5a = \ud835\udc4a\ud835\udc56 \u2212 \ud835\udc4a\ud835\udc53                                                                                                 (2) \n\n\n\nWhere MCrm is Moisture content removed, Wi  is initial weight, and Wf  is \n\n\n\nfinal weight \n\n\n\nFigure 1: Description of the fluidized bed dryer system. \n\n\n\n1-Exit opening, 2-Chamber cover, 3-Drying bed, 4-Heating chamber, 5- \n\n\n\nClay slate, 6-Heater, 7-Blower. \n\n\n\n2.6 Mathematical modeling of drying curves \n\n\n\nThe Fick\u2019s equation for solid materials with thick geometry was applied to \n\n\n\nthe experimental data during the extrudates drying (Eq. 3). The \n\n\n\nassumption for the thick form of extrudates samples was that the moisture \n\n\n\nwas initially evenly distributed, with negligible external resistance, \n\n\n\ntemperature gradients and shrinkage during drying throughout the bulk \n\n\n\nof a sample. The surface moisture content of the sample immediately \n\n\n\nequilibrates with the state of the surrounding air. The resistance to mass \n\n\n\ntransfer at the surface is negligible compared to the internal resistance of \n\n\n\nthe sample. The equation is as follows (Tunde-Akintunde, 2011): \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 104-110 \n\n\n\nCite the Article: Oduntan, O. B, Oluwayemi, B. J (2021). Optimization of A Clay-Slate Fluidized Bed Dryer for Production of Fish Feed.  \nMalaysian Journal of Sustainable Agriculture, 5(2): 104-110. \n\n\n\n\ud835\udc40\ud835\udc45 = [\n\ud835\udc40\ud835\udc61\u2212\ud835\udc40\ud835\udc52\n\n\n\n\ud835\udc40\ud835\udc56\u2212\ud835\udc40\ud835\udc52\n] = \ud835\udc34\ud835\udc52\ud835\udc65\ud835\udc5d(\u2212\ud835\udc3e\ud835\udc61)  (3) \n\n\n\nWhere MR is the dimensionless moisture ratio, Wt, the average moisture \n\n\n\ncontent at time t, Wi, the initial moisture content, We, the equilibrium \n\n\n\nmoisture content respectively, on dry weight basis, K is the drying \n\n\n\nconstant (s -1 ), and A is a constant. It is believed that the rate of moisture \n\n\n\nremoval is proportional to the difference between the products to be dried \n\n\n\nand their equilibrium moisture content, and the total resistance to \n\n\n\nmoisture transfer is on the outer surface of the products. It is expressed \n\n\n\nmathematically as: \n\n\n\n\ud835\udc51\ud835\udc40\n\n\n\n\ud835\udc51\ud835\udc61\n=  \u2212\ud835\udc580(\ud835\udc40\ud835\udc61 \u2212 \ud835\udc40\ud835\udc52)   (4) \n\n\n\nwhere ko is the drying constant (s-1 ). The solution of this equation is:  \n\n\n\nFrom Saravacos and Charm, the drying constant ( Ko ) in Eq. 4 is related to \n\n\n\nthe diffusion coefficient by (Saravacos and Charm, 1962):  \n\n\n\n\ud835\udc3e\ud835\udc5c =  [\n\ud835\udf0b2\ud835\udc37\n\n\n\n4\ud835\udc3f2\n]  (5) \n\n\n\nwhere D is the diffusivity (m2/s), and L is the half-thickness of the sample \n\n\n\n(m).  \n\n\n\nThis semi-empirical model is generally used to express the relationships \n\n\n\nbetween the drying constant and the characteristic dimension of the \n\n\n\ndrying sample or the drying temperature, while at the same time \n\n\n\nsatisfactorily describing the drying behavior of the thin film (Madamba et \n\n\n\nal., 1996; Sarsavadia et al., 1999). Thus, the moisture ratio in Eq. (3) \n\n\n\naccording to Evin to Eq. (6) has been simplified (Evin, 2012). \n\n\n\nMR = Wt /Wi    (6) \n\n\n\nThe drying data were graphically analyzed in terms of reduction in \n\n\n\nmoisture content and moisture ratio. \n\n\n\n2.7 Efficiency of drying \n\n\n\nThe psychometric chart was used to calculate the efficiency of the fluidized \n\n\n\nbed dryer based on the resulted parameters determined. The drying \n\n\n\nefficiency was used for evaluation of dryer designs or comparison between \n\n\n\ndryers, since it is a measurement of the degree of utilization of the sensible \n\n\n\nheat in the drying air (Foster, 1973). \n\n\n\nThe Eq. (7) is used to calculate the efficiency of the dryer as stated by ASAE \n\n\n\n(Rev. Aug. 2005) \n\n\n\n\u03b7 =  \n\ud835\udc5a \u00d7 \ud835\udc40\ud835\udc53\ud835\udc54\n\n\n\n\u210e\ud835\udc53\ud835\udc54\n\u00d7 100%                                                                                                                  (7) \n\n\n\nwhere \u03b7 is drying efficiency (%), \u1e41 is mass of air flow (m3/s) = Specific \n\n\n\nvolume of input air \u00d7 air flow, Mfg  is mass of water to be evaporated, g, hfg \n\n\n\nis latent heat of evaporation of water kJ/Kg of H2O, Specific volume of input \n\n\n\nair (v) is 1.01 m3/kg and air flow = 0.7 kg/s. \n\n\n\n2.8 Experimental design and Statistical optimization of factors \n\n\n\nThe design required 38 independent experiments. Machine efficiency was \n\n\n\nmeasured in triplicate. The results which were obtained during tests were \n\n\n\nanalyzed with the use of the response surface experiment designed using \n\n\n\nI-optimal design module in Design Expert software (Version 11.0, Stat-\n\n\n\nEase, Inc., Minneapolis, USA) to investigate the effect of machine drying \n\n\n\nconditions. In I-optimal design module, the numeric factor, block and \n\n\n\nrunning times were set as 5, 1 and 38, respectively. The range of five \n\n\n\nfactors were based on the results of single factor experiment. This was \n\n\n\nused to reveal the effect of operating conditions on the dryer performance. \n\n\n\nThe experimental run for the fluidized bed drying operation is displayed \n\n\n\nin Table 1.  \n\n\n\nTable 1: Experimental Run \n\n\n\nRun \n\n\n\nBed \n\n\n\nHeight \n\n\n\n(mm) \n\n\n\nTemp. \n\n\n\n(oC) \n\n\n\nAirflow \n\n\n\nvelocity \n\n\n\n(m/s) \n\n\n\nDrying \n\n\n\nTime \n\n\n\n(min) \n\n\n\nExtrudates \n\n\n\nsize (mm) \n\n\n\n1 200 60 0.66 90 8 \n\n\n\n2 50 60 0.68 70 4 \n\n\n\n3 50 100 0.70 90 4 \n\n\n\n4 50 60 0.68 90 8 \n\n\n\n5 50 60 0.70 10 8 \n\n\n\n6 150 60 0.68 90 6 \n\n\n\n7 150 80 0.66 90 4 \n\n\n\n8 150 60 0.68 90 6 \n\n\n\n9 150 60 0.68 50 8 \n\n\n\n10 150 80 0.70 70 6 \n\n\n\n11 50 120 0.68 10 4 \n\n\n\n12 50 120 0.68 50 6 \n\n\n\n13 150 100 0.68 90 8 \n\n\n\n14 200 120 0.66 50 6 \n\n\n\n15 150 80 0.70 70 6 \n\n\n\n16 200 60 0.70 90 8 \n\n\n\n17 100 60 0.70 30 4 \n\n\n\n18 200 60 0.66 10 4 \n\n\n\n19 50 60 0.70 70 6 \n\n\n\n20 50 120 0.66 90 8 \n\n\n\n21 200 80 0.68 10 6 \n\n\n\n22 150 120 0.70 30 8 \n\n\n\n23 100 120 0.66 10 8 \n\n\n\n24 200 80 0.68 70 4 \n\n\n\n25 100 120 0.68 10 4 \n\n\n\n26 50 80 0.66 30 6 \n\n\n\n27 50 80 0.66 30 6 \n\n\n\n28 150 60 0.68 50 8 \n\n\n\n29 150 100 0.68 90 8 \n\n\n\n30 200 60 0.70 10 4 \n\n\n\n31 100 120 0.70 70 6 \n\n\n\n32 200 120 0.70 10 4 \n\n\n\n33 100 60 0.68 30 6 \n\n\n\n34 200 100 0.66 10 8 \n\n\n\n35 200 120 0.70 90 4 \n\n\n\n36 50 100 0.70 30 6 \n\n\n\n37 200 60 0.66 10 4 \n\n\n\n38 100 120 0.66 90 4 \n\n\n\nBased on the experiments design, the optimized drying condition was \n\n\n\nobtained on which the highest efficiency could be achieved.  Under this \n\n\n\noptimized drying condition, samples with different sizes were dried and \n\n\n\nthe influences of each on machine efficiency were analyzed. The response \n\n\n\n(efficiency) for various experimental conditions was related to coded \n\n\n\nvariables (xi, I = 1, 2, 3 and 4) by a second-degree polynomial (Eq. 8) as \n\n\n\ngiven below: \n\n\n\ny = b0 + b1x1+ b2x2+ b3x3+ b4x4 + b5x5 + b12x1x2 + b13x1x3 + b14x1x4 + b23 \n\n\n\nx2x3 + b24x2x4 + b34x3x4 + b11x12 + b22x22 + b33x32 + b44x42 +    (8) \n\n\n\nWhere, x1, x2, x3, x4 and x5 are the coded values of bed height (mm), \n\n\n\ntemperature (oC), airflow velocity (ms-1), drying time (min) and extrudates \n\n\n\nsize (mm) respectively. The coefficients of the polynomial were \n\n\n\nrepresented by b0 (constant); b1,b2,b3,b4,b5 (linear effects); \n\n\n\nb12,b13,b14,b23,b24,b34 (interaction effects); b11,b22,b33,b44,b55 (quadratic \n\n\n\neffects); and _ (random errors). The quality of the polynomial model is \n\n\n\nexpressed using the determination coefficient, namely R2 and Adj-R2. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 104-110 \n\n\n\nCite the Article: Oduntan, O. B, Oluwayemi, B. J (2021). Optimization of A Clay-Slate Fluidized Bed Dryer for Production of Fish Feed.  \nMalaysian Journal of Sustainable Agriculture, 5(2): 104-110. \n\n\n\nStatistical significance was verified using the appropriate precision factor \n\n\n\nand F test (Rauf et al., 2008). \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Optimization of Fluidized bed dryer using the RSM approach \n\n\n\nThe experimental result of testing a fluid bed dryer in Table 2, which \n\n\n\nshows the effect of extruded fish feed on the  moisture ratio. The lowest \n\n\n\nresult was obtained with a bed height of 100 mm when temperature, \n\n\n\nairflow velocity, time and extrudates size were 120\u00b0C, 0.66 ms-1, 90 \n\n\n\nminutes and 4 mm, respectively, with a moisture content of 0.646 g. The \n\n\n\nhighest moisture ratio of 0.986 was established at 50 mm bed height, \n\n\n\ntemperature (60\u00b0C), airflow velocity (0.7 ms-1 ), time (10 min) and size of \n\n\n\nextruded fish (8 mm). It can be seen that the high selected values for all \n\n\n\noperational factors led to a high moisture ratio value. Table 2 shows that \n\n\n\nlow productivity was observed at 38%, while high machine performance \n\n\n\nwas observed at 75%. It was observed that the operating temperature \n\n\n\nincreased with increasing machine performance. \n\n\n\nTable 2: Experimental result \n\n\n\nRun Moisture Ratio Efficiency (%) \n\n\n\n1 0.78 38 \n\n\n\n2 0.89 38 \n\n\n\n3 0.75 75 \n\n\n\n4 0.80 38 \n\n\n\n5 0.99 38 \n\n\n\n6 0.85 38 \n\n\n\n7 0.73 66 \n\n\n\n8 0.85 38 \n\n\n\n9 0.89 38 \n\n\n\n10 0.89 66 \n\n\n\n11 0.98 57.8 \n\n\n\n12 0.75 57.8 \n\n\n\n13 0.77 75 \n\n\n\n14 0.81 57.8 \n\n\n\n15 0.89 66 \n\n\n\n16 0.77 38 \n\n\n\n17 0.89 38 \n\n\n\n18 0.88 38 \n\n\n\n19 0.65 38 \n\n\n\n20 0.82 57.8 \n\n\n\n21 0.98 66 \n\n\n\n22 0.95 57.8 \n\n\n\n23 0.98 57.8 \n\n\n\n24 0.89 66 \n\n\n\n25 0.92 57.8 \n\n\n\n26 0.94 66 \n\n\n\n27 0.95 66 \n\n\n\n28 0.90 38 \n\n\n\n29 0.77 75 \n\n\n\n30 0.97 38 \n\n\n\n31 0.72 57.8 \n\n\n\n32 0.86 57.8 \n\n\n\n33 0.94 38 \n\n\n\n34 0.98 75 \n\n\n\n35 0.74 57.8 \n\n\n\n36 0.89 75 \n\n\n\n37 0.88 38 \n\n\n\n38 0.65 57.8 \n\n\n\nTable 3 summarizes the results of each dependent variable with their \ncoefficients of determination (R\u00b2). The statistical analysis indicates that \nthe proposed model was adequate, possessing significant lack of fit and \nwith very satisfactory values of the R\u00b2 for all the responses. \n\n\n\nThe Model F-value of 4.19 implies the model is significant (p<0.05). There \n\n\n\nis only a 0.01% chance that an F-value this large could occur due to noise. \n\n\n\nP-values less than 0.05 indicate model terms are significant (p<0.05). The \n\n\n\nLack of Fit F-value of 444.82 implies the Lack of Fit is significant (p<0.05). \n\n\n\nThere is only a 0.26% chance that a Lack of Fit F-value this large could \n\n\n\noccur due to noise.  R2 and adjusted R2 values of the model are 0.8313 and \n\n\n\n0.6329, respectively. A negative Predicted R\u00b2 (-0.1523) implies that the \n\n\n\noverall mean may be a better predictor of your response than the current \n\n\n\nmodel. Adequate Precision measures the signal to noise ratio. A ratio \n\n\n\ngreater than 4 is desirable (Montgomery, 2001). Therefore adequate \n\n\n\nprecision ratio of 8.15 indicates a good signal. This model can be used to \n\n\n\nnavigate the design space. \n\n\n\nAnalysis of variance for quadratic model (Table 3) showed that the \n\n\n\nmoisture ratio was significantly based on the linear conditions of \n\n\n\ntemperature [(T, p<0.05)] and drying time [(DT, p <0.05)]. By neglecting \n\n\n\nthe non significant terms in the predictive models in Eq. 1 and with the \n\n\n\ncoded values of independent variables, the following equation (Eq. 2) \n\n\n\ndescribes the effect of significant process variables on fluidized bed dryer \n\n\n\nfor the production of fish feed. \n\n\n\nYMR = 0.892 \u2013 0.03x2 - 0.1x4 \u2013 0.01x1x2 + 0.03x1x3 + 0.02x1x4 \u2013 0.01x2x3 \u2013 0.02 \n\n\n\nx2x4 - 0.02 x2x5                                                                                                            (9) \n\n\n\nFrom the regression equation presented in Eq. (9), the negative coefficient \n\n\n\nof temperature (x2) and drying time (x4), indicated a decrease in the \n\n\n\nfluidized bed moisture ratio as the temperature and drying time level \n\n\n\nincreased. The equation indicate a high value of coefficient  and significant \n\n\n\nlevel (p<0.05) from drying time considered in the study. The perturbation \n\n\n\nchart in Fig. 2 shows how the function of a given factor reacted as a level, \n\n\n\nthis factor changes when other factors are set at the optimal level (Oh et \n\n\n\nal., 1995). It shows that decrease in drying time, increase the moisture \n\n\n\nratio from 0.89 to 1.0 significantly; a slight quadratic rise in moisture ratio \n\n\n\nwas established when the bed height was rised. Rise in the extrudes size \n\n\n\nincrease the moisture ratio to 0.92. In addition, increase in airflow velocity \n\n\n\ndecrease  the moisture ratio to 0.84 and increase in temperature also \n\n\n\nindicated a quadratic decrease (0.80 ). \n\n\n\nTable 3: Model analysis data for the moisture ratio response \n\n\n\nvariables for the fluidized bed dryer. \n\n\n\nSource Sum of square Df \nMean \n\n\n\nsquare \nF value \n\n\n\nP value \n\n\n\nProb>F \n\n\n\nModel 0.2743 20 0.0137 4.19 0.0022 \n\n\n\nx1 0.0000 1 0.0000 0.0036 0.9529 \n\n\n\nx2 0.0196 1 0.0196 6.00 0.0255 \n\n\n\nx3 0.0002 1 0.0002 0.0529 0.8209 \n\n\n\nx4 0.2154 1 0.2154 65.82 < 0.0001 \n\n\n\nx5 0.0086 1 0.0086 2.64 0.1226 \n\n\n\nx1 x2 0.0006 1 0.0006 0.1717 0.6838 \n\n\n\nx1 x3 0.0139 1 0.0139 4.24 0.0551 \n\n\n\nx1 x4 0.0051 1 0.0051 1.57 0.2271 \n\n\n\nx1 x5 0.0000 1 0.0000 0.0132 0.9098 \n\n\n\nx2 x3 0.0004 1 0.0004 0.1313 0.7216 \n\n\n\nx2 x4 0.0022 1 0.0022 0.6744 0.4229 \n\n\n\nx2 x5 0.0073 1 0.0073 2.24 0.1530 \n\n\n\nx3 x4 0.0007 1 0.0007 0.2275 0.6395 \n\n\n\nx3 x5 0.0032 1 0.0032 0.9878 0.3342 \n\n\n\nx4 x5 0.0063 1 0.0063 1.92 0.1836 \n\n\n\nx12 0.0001 1 0.0001 0.0296 0.8655 \n\n\n\nx2\n2 0.0060 1 0.0060 1.84 0.1921 \n\n\n\nx3\n2 0.0095 1 0.0095 2.89 0.1072 \n\n\n\nx4\n2 0.0008 1 0.0008 0.2361 0.6333 \n\n\n\nx5\n2 0.0005 1 0.0005 0.1499 0.7034 \n\n\n\nLack of \n\n\n\nfit \n0.0556 11 0.0051 444.82 < 0.0001 \n\n\n\nR2 0.8313 \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 104-110 \n\n\n\nCite the Article: Oduntan, O. B, Oluwayemi, B. J (2021). Optimization of A Clay-Slate Fluidized Bed Dryer for Production of Fish Feed.  \nMalaysian Journal of Sustainable Agriculture, 5(2): 104-110. \n\n\n\nFigure 2: A perturbation plan for moisture ratio \n\n\n\nThe 3D surface plot in Figure 3 shows the effect of temperature and drying \n\n\n\ntime under constant bed height (125mm), airflow velocity (0.68 m/s) and \n\n\n\nextrudates size (4mm) on moisture ratio. An optimal temperature of 60oC \n\n\n\nand drying time of 10min was observed at which the extrudates start \n\n\n\ndrying upon getting to the temperature (90oC), and thus the maximum \n\n\n\nmoisture ratio was obtained. Increase in trend may be due to the high \n\n\n\nmoisture content in the extrudate capillary structure with low in the \n\n\n\nexhaust air temperature which was an indication of extremely low airflow \n\n\n\nvelocity and poor fluidization in the fluid bed. Similar findings were \n\n\n\nobserved in pharmaceutical industry for drying granulating powders and \n\n\n\nbambara groundnut (Badday et al., 2014; Gao et al., 2000). With the \n\n\n\nincrease of temperature above 119oC at 68 min drying time, less efficient \n\n\n\nmoisture ratio was established from drying chamber, so that the final \n\n\n\nproduct was dried enough before entering the collection bag, leading to \n\n\n\nhigh values of moisture removal. In addition, as the  increased from 103.9 \n\n\n\nto 120oC at all levels of drying time, more extrudates were moved toward \n\n\n\nthe entrance to the collection chamber and the end products were \n\n\n\nsufficiently dried as they entered the collection bag. The contour diagram \n\n\n\nshows that the moisture ratio is greatest when the bed height is in the \n\n\n\nrange of 122 to 200 mm, the air flow rate increases slightly and the \n\n\n\nmoisture ratio increases with increasing air flow rate.  \n\n\n\na. Plot of response surface\n\n\n\nb. Plot of contour\n\n\n\nFigure 3: 3-D representation of the interaction between temperatue and \n\n\n\ndrying time at 4mm extrudates size on moisture ratio. \n\n\n\nWith the increase of extrudates size (8mm), higher drying time could be \n\n\n\napplied for decreased optimal moisture ratio rate meanwhile obtaining \n\n\n\nhigher production efficiency (Figure 4). Increase in drying time resulted in \n\n\n\ndecrease in moisture ratio. The fish feed extrudates could be dried in in a \n\n\n\nlonger time. From thi study, 0.68 m/sec was still adopted in response \n\n\n\nsurface experiment even though the moisture ratio was pretty high at \n\n\n\ngiven drying conditions. The result of the experiment as shown that a high \n\n\n\nvalue of moisture ratio was established in a wider coverage at below \n\n\n\ndrying time less than 30min with all levels of temperature. Similar findings \n\n\n\nwere observed in rice paddy and  bambara groundnut seed (Caicedo et al., \n\n\n\n2002; Badday et al., 2014). The contour chart shows that the moisture \n\n\n\nratio is the optimum value of 1.0 when the drying time is in the region of \n\n\n\n10-30 min. the contour are ellipses, and decrease with increase drying \n\n\n\ntime. This implies that a small change in drying time could make a \n\n\n\nsignificant change in the drying pattern of the extrudates. \n\n\n\na. Plot of response surface \n\n\n\nb. Plot of contour \n\n\n\nFigure 4: 3-D representation of the interaction between temperatue and \n\n\n\ndrying time at 6mm extrudates size on moisture ratio. \n\n\n\n3.2 Interaction between temperatue and drying time in \n\n\n\ndetermining the efficiency \n\n\n\nThe experimental conditions and the corresponding machine efficiency \n\n\n\nwere shown in Table 2 and the analysis of variance was shown in Table 4. \n\n\n\nThe quadratic model F value of 1165.69 implies the model is significant. It \n\n\n\nwas observed from Table 4 and Eq. 10 that F values for x1, x3, x4 and x5 are \n\n\n\nless than 3.66 and p values greater than 0.0726, indicating no direct \n\n\n\nsignificance on machine efficiency. F-values for  temperature (x2) ; \n\n\n\ninteraction of bed height and airflow velocity,  square terms of \n\n\n\ntemperature (x22) were 7074.80, 9.02 and 10398.73 and p values less than \n\n\n\n0.0001, 0.008 and less than 0.0001 respectively (P < 0.05), indicating that \n\n\n\nboth terms are significant. Considering these criteria, following response \n\n\n\nmodel was selected for representing the variation of lateral expansion for \n\n\n\nfurther analysis. These insignificant terms were then deleted from the \n\n\n\nquadratic model to gain a modified quadratic model, which can describe \n\n\n\nthe relationship between efficiency and influencing factors more simply. \n\n\n\nThe obtained modified quadratic model was shown in Eq. (10) where Yeff \n\n\n\nrepresented efficiency in response surface experiment. \n\n\n\nYeff =  72.71 - 0.069x1 + 0.15x2 + 0.23x3 + 0.06x4 + 0.23x5 -0.13x1x2 - 0.46x1x3 \n\n\n\n\u2013 0.05x1x4 + +0.33x1x5 - 25.14 x2 2   (10) \n\n\n\nIt is evident from Eq. 10 that coefficients of x2, x3, x4 and x5 are positive, but \n\n\n\nthat of x1 is negative. Therefore, increase in temperature, airflow velocity, \n\n\n\ndrying time and extrudate size may increase the machine drying efficiency, \n\n\n\nwhereas increase in bed height may reduce the machine performance. \n\n\n\nSince coefficient of x22 is negative, a maximum efficiency will occur in the \n\n\n\nrange of temperature selected for the study.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 104-110 \n\n\n\nCite the Article: Oduntan, O. B, Oluwayemi, B. J (2021). Optimization of A Clay-Slate Fluidized Bed Dryer for Production of Fish Feed.  \nMalaysian Journal of Sustainable Agriculture, 5(2): 104-110. \n\n\n\nFigure 5: A perturbation plan for fluidizied bed efficiency \n\n\n\nThe pertubation chart shows how the function of a given factor reacted as \n\n\n\na level, this factor changes when other factors are set at the optimal level \n\n\n\n(Oh et al., 1995). The steep slope or curve in the graphs indicates the \n\n\n\nresponsiveness of the response rate (Anderson and Whitcomb, 2005). A \n\n\n\ngraph presenting the result of individual factor on machine's efficiency is \n\n\n\nshown in Figure 5 The combination of are the main factors influencing \n\n\n\nmachine efficiency. Figure 5 shows that efficiency decreases from 72.98 to \n\n\n\n57.78% because the factor (temperature) increases from the zero point to \n\n\n\nthe right. On the other hand, the machine's efficiency was almost constant \n\n\n\nas the other factors increases. It was observed that temperature is highly \n\n\n\nsignificant for all runs. This may be due to the design of clay slate \n\n\n\narrangement towards the drying of extrudates during the experimental \n\n\n\ntest.  \n\n\n\nThe effect of varying the temperature and drying time on dryer efficiency \n\n\n\nwhile bed heighr, airflow veocity and extrudates size are fixed at 125mm, \n\n\n\n0.68 m/s and 4 mm, respectively. From Figure 6, it is evident that the \n\n\n\nefficiency increased with the increase in temperature until at a \n\n\n\ntemperature above 108oC when the efficiency begin to fall, which may be \n\n\n\ndue to increase airflow with the decrease in moisture content. A high \n\n\n\noptimum efficiency (70%) was experienced at different operative \n\n\n\ncondition of the fluidized bed dryer. However, no significant change was \n\n\n\nobserved with the change in drying time in the drying processess. This was \n\n\n\ndue to the limiting factor caused by bulk density of extrudates, which was \n\n\n\nsignificant at the high bed height and hence reduced the efficiency \n\n\n\npercentage of the fluidized bed dryer.   \n\n\n\nTable 4: Model analysis data for the efficiency response variables for \n\n\n\nthe fluidized bed dryer. \n\n\n\nSource \nSum of \n\n\n\nsquare \ndf \n\n\n\nMean \n\n\n\nsquare \nF value \n\n\n\nP value \n\n\n\nProb>F \n\n\n\nModel 7204.36 20 360.22 1165.69 < 0.0001 \n\n\n\nx1 0.0884 1 0.0884 0.2859 0.5998 \n\n\n\nx2 2186.24 1 2186.24 7074.80 < 0.0001 \n\n\n\nx3 1.01 1 1.01 3.27 0.0884 \n\n\n\nx4 0.0709 1 0.0709 0.2294 0.6381 \n\n\n\nx5 1.13 1 1.13 3.66 0.0726 \n\n\n\nx1 x2 0.1861 1 0.1861 0.6023 0.4484 \n\n\n\nx1 x3 2.79 1 2.79 9.02 0.0080 \n\n\n\nx1 x4 0.0239 1 0.0239 0.0774 0.7842 \n\n\n\nx1 x5 1.05 1 1.05 3.40 0.0828 \n\n\n\nx2 x3 0.0273 1 0.0273 0.0883 0.7700 \n\n\n\nx2 x4 0.8553 1 0.8553 2.77 0.1145 \n\n\n\nx2 x5 0.2199 1 0.2199 0.7117 0.4106 \n\n\n\nx3 x4 0.0858 1 0.0858 0.2776 0.6051 \n\n\n\nx3 x5 0.4515 1 0.4515 1.46 0.2433 \n\n\n\nx4 x5 0.3346 1 0.3346 1.08 0.3126 \n\n\n\nx12 0.0043 1 0.0043 0.0138 0.9080 \n\n\n\nx22 3213.39 1 3213.39 10398.73 < 0.0001 \n\n\n\nx32 0.3488 1 0.3488 1.13 0.3029 \n\n\n\nx42 0.9235 1 0.9235 2.99 0.1020 \n\n\n\nx52 0.3607 1 0.3607 1.17 0.2951 \n\n\n\nLack of fit 6.29 14 0.4492 \n\n\n\nR2 0.9899 \n\n\n\nFigure 6. 3-D representation of the interaction between temperature and \n\n\n\ndrying time at 4mm extrudates size on dryer efficiency \n\n\n\n3.3 Optimization by Response Surface Methodology and Model \n\n\n\nValidation \n\n\n\n The next step in the present study was to determine the effects of five \n\n\n\nindependent variables (bed height, temperature, airflow velocity, drying \n\n\n\ntime and extrudates size) shown in Table 6, alongwith the mean predicted \n\n\n\nvalues for bed dryer. For this purpose, the response surface methodology, \n\n\n\nusing I-optimal design, was adopted for finding optimal conditions. \n\n\n\nExperiment was then carried out under the recommended conditions and \n\n\n\nresulting response was compared to the predicted values. On the \n\n\n\noptimized drying condition for our experimental facilities bed height of \n\n\n\n185.76 mm, temperature of 97.2\u2218C, airflow velocity of 0.67 m/s, drying \n\n\n\ntime of 65.36 min and extrudates size of 7.40 mm. Comparison between \n\n\n\nRSM and validation methods was then assessed in optimum conditions \n\n\n\npoint for drying machine at 1:1 scale. The reaction of experiment gave the \n\n\n\nreasonable percentage of moisture ratio and efficiency of 0.86% and \n\n\n\n74.39%, respectively. This result confirmed the validity of the model, and \n\n\n\nthe experimental value was determined to be quite close to the predicted \n\n\n\nvalue, implying that empirical model derived from RSM experimental \n\n\n\ndesign can be used to  adequately describe the relationship between the \n\n\n\nfactors and responses. \n\n\n\nTable 6: Optimum conditions derived by drying system \n\n\n\nMethod Bed \n\n\n\nhei\n\n\n\nght, \n\n\n\nmm \n\n\n\nTe\n\n\n\nmp.\n\n\n\n, \u00b0C \n\n\n\nAirfl\n\n\n\now \n\n\n\nvelo\n\n\n\ncity, \n\n\n\nm/s \n\n\n\nDry\n\n\n\ning \n\n\n\ntim\n\n\n\ne, \n\n\n\nmin\n\n\n\n. \n\n\n\nExtru\n\n\n\ndates \n\n\n\nsize, \n\n\n\nmm \n\n\n\nMois\n\n\n\nture \n\n\n\nratio \n\n\n\nEfficie\n\n\n\nncy,% \n\n\n\nPredict\n\n\n\ned \n\n\n\n185.\n\n\n\n76 \n\n\n\n97.2 0.67 65.3\n\n\n\n6 \n\n\n\n7.40 0.86 74.39 \n\n\n\nExperi\n\n\n\nmental \n\n\n\n187.\n\n\n\n40 \n\n\n\n93.3 0.67 64.8\n\n\n\n5 \n\n\n\n7.03 0.88 73.42 \n\n\n\n4. CONCLUSION \n\n\n\nThe drying rate phenomenon during fluid bed drying process can be \n\n\n\nreduced by optimizing drying condition. Moisture removal of the fish feed \n\n\n\ncould be improved by reducing temperature, interaction term of \n\n\n\ntemperature-airflow velocity and drying time-exdrudates size. Optimum \n\n\n\nconditions of facilities bed height of 185.76 mm, temperature of 97.2oC, \n\n\n\nairflow velocity of 0.67, drying time of 65.36 and extrudates size of 7.4 mm \n\n\n\nwere recommended for the fluidized bed drying of extruded fish feed. For \n\n\n\nthe efficiency, temperature and airflow rate are significant model terms \n\n\n\nwith quadratic terms. The method to improve the machine effeciency by \n\n\n\noptimizing drying condition can also be applied in larger scale fish feed \n\n\n\ndrying.  \n\n\n\nAUTHORS\u2019 CONTRIBUTIONS  \n\n\n\nAll authors contributed to the study conception and design. Material \n\n\n\npreparation, data collection and analysis were performed by Oduntan, O. \n\n\n\nB and Oluwayemi, B. J. The first draft of the manuscript was written by \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 5(2) (2021) 104-110 \n\n\n\nCite the Article: Oduntan, O. B, Oluwayemi, B. J (2021). Optimization of A Clay-Slate Fluidized Bed Dryer for Production of Fish Feed.  \nMalaysian Journal of Sustainable Agriculture, 5(2): 104-110. \n\n\n\nOduntan, O. B and all authors commented on previous version of the \n\n\n\nmanuscript. All authors read and approved the final. \n\n\n\nACKNOWLEDGMENTS  \n\n\n\nThe study was undertaken with the support of Fexod Fedek Ventures \n\n\n\n(Project No. FFVN1516)  \n\n\n\nREFERENCES \n\n\n\nAkhtara, J., Kumarb, J., Malik, S., 2015. Quality Parameters of Dehydrated \n\n\n\nPotato Under Combined Microwave-cum-Fluidized Bed Drying. Journal \n\n\n\nof Food Research and Technology, 3 (2), Pp. 71-82. \n\n\n\nAnderson, M.J., Whitcomb, P.J., 2005. RSM Simplified: Optimizing \n\n\n\nProcesses Using Response Surface Methods For Design of Experiments, \n\n\n\nProductivity Press. \n\n\n\nAnjali, V., Satya, V.S., 2015. Spray drying of fruit and vegetable juices-A \n\n\n\nreview.  Food Science and Nutrition, 55 (1), Pp. 701\u2013719. \n\n\n\nASAE. 2000. ASAE Standards D245.5: Moisture relationships of grains. \n\n\n\nASAE, St. Joseph, MI. \n\n\n\nBadday, B.A., Gupta, A., Beddai, A., Naik, M.T., 2014. Experimental study on \n\n\n\nthe bed voidage and minimum fluidization velocity of gas-solid \n\n\n\nfluidization under different conditions. International Journal of \n\n\n\nMechanical Engineering, 2 (7), Pp. 22-26. \n\n\n\nBanrie, 2013. Principles of fish nutrition, \n\n\n\n(http://www.thefishsite.com/articles/principles-of-fish-nutrition.) \n\n\n\nBuchinger, J., Weiss, W., 2001. Solar Drying. Austria: Institute for \n\n\n\nSustainable Technologies. \n\n\n\nChen, C., 2003. Evaluation of air oven moisture content determination \n\n\n\nmethods for rough rice. Biosystems Engineering, 86 (4), Pp. 447\u2013457. \n\n\n\nCruz, A.C., Guine, R.P.F., Goncalves, J.C., 2015. Drying kinetics and product \n\n\n\nquality for convective drying of apples (cvs. Golden Delicious and \n\n\n\nGranny Smith). International Journal of Fruit Science, 15 (1), Pp. 54\u201378. \n\n\n\nErbay, Z., Koca, N., Kaymak-Ertekin, F., Ucuncu, M., 2015. Optimization of \n\n\n\nspray drying process in cheese powder production.  Food and \n\n\n\nBioproducts Processing, 93, Pp. 156\u2013165.  \n\n\n\nEvin, D., 2012. Thin layer drying kinetics of Gundelia tournefortii L. Food \n\n\n\nand Bioproducts Processing, 90, Pp. 323\u2013332. \n\n\n\nhttps://doi.org/10.1016/j.fbp.2011.07.002 \n\n\n\nFoster, 1973. Grain storage techniques: evolution and trends in developing \n\n\n\ncountries, by food and agriculture organization of the united state \n\n\n\nnation. \n\n\n\nFrancia, V., Mart\u00edn, L., Bayly, A.E., Simmons, M.J.H., 2015. Particle \n\n\n\naggregation in large counter-current spray drying towers: Nozzle \n\n\n\nconfiguration, vortex momentum and temperature.  Procedia \n\n\n\nEngineering, 102, Pp. 668\u2013675.  \n\n\n\nFreitas, L.A.P., Freire, J.T., 2001. Heat transfer in a draft tube spouted bed \n\n\n\nwith bottom solids feed, Powder Technology, 114, Pp. 152-162. \n\n\n\nGao, J.Z., Gray, D.B.,  Motheram, R., Hussain, M.A., 2000. Importance of Inlet \n\n\n\nAir Velocity in Fluid Bed Drying of a Granulation Prepared in a High \n\n\n\nShear Granulator. AAPS PharmScience Technology, 1 (4), Pp. 1-4. \n\n\n\n(http://www.pharmscitech.com/) \n\n\n\nHrdlicka, J., Skopec, P., Opatril, J., Dlouhy, T., 2016. Oxyfuel combustion in \n\n\n\na bubbling fluidized bed combustor. Energy Procedia, 86, Pp. 116 \u2013 123.  \n\n\n\nMadamba, P.S., Driscoll, R.H., Buckle, K.A., 1996. The thin-layer drying \n\n\n\ncharacteristics of garlic slices. Journal of Food Engineering, 29 (1), Pp. \n\n\n\n75-97.  \n\n\n\nMaheswari, S.U., Kumaresan, R., Janet, A., 2015. Investigation of drying \n\n\n\nkinetics of finger millet in fluidized bed dryer. Journal of Chemical and \n\n\n\nPharmaceutical Research, 7 (3), Pp. 2040-2050. \n\n\n\nMalafronte, L., Ahrne, L., Schuster, E., Innings, F., Rasmuson, A., 2015. \n\n\n\nExploring drying kinetics and morphology of commercial dairy \n\n\n\npowders.  Journal of Food Engineering, 158, Pp. 58\u201365. \n\n\n\nMontgomery, D.C., 2001. Design and Analysis of Experiments, New York, \n\n\n\nWiley, Pp. 416-419. \n\n\n\nMujumdar, A.S., 2006. Handbook of Industrial Drying. 3rd Edition. Boca \n\n\n\nRaton, FL: CRC Press. \n\n\n\nOh, S., Rheem, J., Sim, J., Kim, L., Baek, Y., 1995. Optimizing conditions for \n\n\n\nthe growth of Lactobacillus casei YIT 9018 in tryptone-yeast extract-\n\n\n\nglucose medium by using response surface methodology. Applied and \n\n\n\nEnvironmental Microbiology, 61 (11), Pp. 3809\u20133814. \n\n\n\nRauf, M.A., Marzouki, N., K\u00f6rbahti, B.K., 2008. Photolytic decolorization of \n\n\n\nRose Bengal by UV/H2O2 and data optimization using response surface \n\n\n\nmethod. Journal Hazard Material, 159, Pp. 602\u2013609. \n\n\n\nSaravacos, C.D., Charm, S.E., 1962. A study of the mechanism of fruit and \n\n\n\nvegetable dehydration. Food Tech., 16 (1), Pp. 78-81.  \n\n\n\nSarsavadia, P.N., Sawhney, R.L., Pangavhane, D.R., Singh, S.P., 1999. Drying \n\n\n\nbehavior of brined onion slices. Journal Food Engineering, 40 (3), Pp. \n\n\n\n219-226.  \n\n\n\nSeverini, C., Baiano, A., Pilli, T.D., Carbone, B.F., Derossi, A., 2005. Combined \n\n\n\ntreatments of blanching and dehydration: study on potato cubes. Journal \n\n\n\nof Food Engineering, 68 (3), Pp. 289-296. \n\n\n\nTunde-Akintunde, T.Y., 2011. Mathematical modeling of sun and solar \n\n\n\ndrying of chilli pepper. Renewable Energy, 36, Pp. 2139\u20132145. \n\n\n\nhttps://doi.org/10.1016/j.renene.2011.01.017. \n\n\n\nWawrzyniak, P., Podyma, M., Zbicinski, I., Bartczak, Z., Rabaeva, J., 2012. \nModeling of air flow in an industrial countercurrent spray-drying \ntower.  Drying Technology, 30 (2), Pp. 217\u2013224.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 58-64 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.01.2023.58.64 \n\n\n\n \nCite The Article: Augustine Matthew Adinoyi, Ofuya Thomas Inomisan, Idoko, Joy Ejemen, Adesina Jacobs Mobolade  (2023). Response of Five Selected Stored Legume \n\n\n\nSeeds Species to Oviposition Deterrent, Ovicidal and Grain Protectant Activities of Some Botanicals Against Callosobruchus Maculatus (FAB.)  \n(Coleoptera: Chrysomelidae). Malaysian Journal of Sustainable Agricultures, 7(1): 58-64. \n\n\n\n \nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN:MJSAEJ \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture \n(MJSA) \n\n\n\n  \nDOI: http://doi.org/10.26480/mjsa.01.2023.58.64 \n\n\n\n\n\n\n\nRESPONSE OF FIVE SELECTED STORED LEGUME SEEDS SPECIES TO OVIPOSITION \nDETERRENT, OVICIDAL AND GRAIN PROTECTANT ACTIVITIES OF SOME \nBOTANICALS AGAINST Callosobruchus Maculatus (FAB.) (Coleoptera: \nChrysomelidae) \n\n\n\nAugustine Matthew Adinoyia, Ofuya Thomas Inomisanb, Idoko, Joy Ejemenb, Adesina Jacobs Moboladec* \n\n\n\na Department of Crop, Soil and Pest Management, University of Africa Toro-Orua, Bayelsa State, Nigeria \nb Department of Crop, Soil and Pest Management, Federal University of Technology, P. M. B. 704, Akure, Ondo State, Nigeria \nc Department of Crop Production Technology, Rufus Giwa Polytechnic, P. M. B. 1019, Owo, Ondo State, Nigeria. \n*Corresponding Author Email: moboladesina@rugipo.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 11 January 2023 \nRevised 18 February 2023 \nAccepted 28 March 2023 \nAvailable online 30 March 2023 \n\n\n\n Ten plant powders were tested at 2% on five legume seeds for their entomocidal effects against \n\n\n\nCallosobruchus maculatus. A completely randomized design (CRD) with five pairs of freshly emerged adult \n\n\n\nbruchids was used to treat 40g of disinfected legume seed types with 2g of the selected plant powders. An \n\n\n\nanalysis of variance was performed on the data on the percentage of oviposition deterrence, hatchability, pest \ntolerance, and weight loss. Zanthoxylum zanthoxyloides (87.02%) P. guineense (77.36%) and E. aromatica \n\n\n\n(73%) significantly deterred oviposition. The maximum percentage egg hatched was recorded in G. max \n\n\n\n(94.71%) and significantly lower on C. cajan with P. guineense (2.33).  Ife Brown (20.83%) recorded \nsignificantly lowest tolerance compared to M. pruriens (99.17%). Percentage weight loss was significantly \n\n\n\nreduced in G. max irrespective of treatment and M. pruriens suffer no weight loss. Incorporation of Z. \n\n\n\nzanthoxyloides, P. guineense and E. aromatica powder proved to be promising biopesticide. \n\n\n\nKEYWORDS \n\n\n\negg hatchability: egg laid; legume seed types; percentage pest tolerance; resistance and susceptibility; Vigna \nsubterranean. \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nGrain legumes serve as the most important sources of dietary protein, oil, \nand micronutrients like iron and zinc, that are lacking in the diets of the \nvulnerable groups (Adesina et al., 2019a; Ofuya, 1986). Grain legumes also \nfulfils the protein demand of vegetarian and low-income groups of the \npopulation (Viashali et al., 2018).  However, they become infested with \nBruchid Beetles Callosobruchus maculatus (Fab.) (Coleoptera: \nChrysomelidae), a cosmopolitan polyphagous stored legumes insect pest \nduring postharvest storage (Bushara, 1988, Kashiwaba, et al., 2003; Ofuya, \n2001; Adebayo et al., 2013). Within three to four months of storage, the \ninfestation rate on grains may reach 50%. (Oparaeke and Dike, 2005). \nDamage caused by the insect leads to quality and quantity deterioration of \nthe grains thus resulted in loss of weight, seed viability, nutritional and \neconomic value and makes the grains unfit for human consumption \n(Adesina and Idoko, 2013; Adesina et al., 2019b; Rawat and Srivastava, \n2011). \n\n\n\nChemical control using fumigants and synthetic insecticides has \ndominated efforts to stop insects and other storage pests from wreaking \nhavoc (Akinkurolere et al., 2006). Although it has been claimed that these \npesticides work well against pests on stored goods but with various \nattendant problems such as residual toxicity, widespread environmental \nand health hazards, genetic resistance by insect species which have \ndirected the need for the development of alternative strategies that are \neffective, ecofriendly and biodegradable pesticides (Isman, 2006; Dayan et \nal., 2009). Empirical evidence have shown that plant derivatives mixed \nwith stored food grains, reduced oviposition rate, suppressed adult \n\n\n\nbruchids emergence and also reduced seed damage rate (Golob and \nWebley, 1980; Prakash and Rao, 1997; Joey et al., 2001; Pugazhvendan et \nal., 2009; Khater, 2011).  \n\n\n\nIn order to prevent infestation among susceptible legume seed species \nwhen stored together, it is essential to be aware of host preference, the \nbiology of the insect pest, and how the environment interacts with it. This \nwill help prevent a large buildup of the C. maculatus population and their \npreference for less preferred host grains of the polyphagous insect pest. \n(Kosini and Nukenine, 2017). The physical appearances of legume seed \ntypes can also limit their tolerability by C. maculatus females for \noviposition. Oviposition suppression agents are crucial to the storage \nbeetle's life cycle because they establish the circumstances for progeny \ndevelopment from the egg to the adult stage in ensuring generational \ndevelopment (Mitchell, 1990). Hence, the need to investigate C. maculatus \ninfestation suppression through use of insecticidal active botanicals thus \nmitigate the undiscriminating usage of synthetic chemicals and their \nattendant shortcomings. \n\n\n\n2.   MATERIALS AND METHODS \n\n\n\n2.1   Experimental Conditions \n\n\n\nThe study was conducted in the Entomology Laboratory of the \nDepartment of Crop, Soil, and Pest Management at the Federal University \nof Technology in Akure, Ondo State, Nigeria (7\u00b0 16' N, 5\u00b0 12' E), under \nambient conditions of 28 \u00b1 2\u00b0C temperature, 70 \u00b1 5% relative humidity, \nand a 12L: 12D photo regime.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 58-64 \n\n\n\n\n\n\n\n \nCite The Article: Augustine Matthew Adinoyi, Ofuya Thomas Inomisan, Idoko, Joy Ejemen, Adesina Jacobs Mobolade  (2023). Response of Five Selected Stored Legume \n\n\n\nSeeds Species to Oviposition Deterrent, Ovicidal and Grain Protectant Activities of Some Botanicals Against Callosobruchus Maculatus (FAB.)  \n(Coleoptera: Chrysomelidae). Malaysian Journal of Sustainable Agricultures, 7(1): 58-64. \n\n\n\n\n\n\n\n2.2   Collection and Preparation of Legume Seed Types \n\n\n\nFive legume seed types were collected from the International Institute of \nTropical Agriculture (IITA), Ibadan, Nigeria. The seeds were hand-selected \nto guarantee that only entire, uninfected seeds were used, however, they \nwere nonetheless sterilized in a freezer at -5\u00b0 C for seven days to eradicate \nany undetected infestations (Adesina et al., 2012; Augustine et al., 2016). \n\n\n\nThe seeds were then removed and allowed to adjust and acclimate for 24 \nhours at room temperature in the open laboratory before being used to \nprevent mould growth. (Olotuah, et al., 2007; Idoko and Adesina, 2012). \nThe morphological characteristics of the legume\u2019s seeds examined were \ntexture and colour. The colour was visually observed, while texture was \nobserved by rolling the seeds between the thumb and index fingers.  \n\n\n\n\n\n\n\nTable 1: Morphological Characteristics of The Six Legumes Seeds. \n\n\n\nLegumes Common name Texture Colour \n\n\n\nCajanus cajan (L.) Mill- Pigeon pea Rough Brown \n\n\n\nGlycine max (L.) Merrill Soya bean Smooth Milky \n\n\n\nVigna subterranean L. Verdcourt Bambara Groundnut Smooth Brown \n\n\n\nSphenostylis stenocarpa Hochst. Ex A. R. African yam Bean Smooth Glossy milk \n\n\n\nMucuna pruriens (L.) Velvet Bean Smooth Black \n\n\n\n2.3   Preparation of Botanical Powders Used as Treatment: \n\n\n\nOryza sativa bran was obtained from a rice mill in Iju-Itaogbolu, Ondo \nState, Nigeria, the well-researched insecticidal plants indicated in Table 2 \nwere purchased from a local herb shop in Oja-Oba, Akure, Ondo State, \n\n\n\nNigeria. In the lab, the various plant materials were air dried before being \nindividually processed into fine powders. The powders were sieved \nthrough a 1mm2 screen and kept cool and dry until usage in an airtight \nnylon bag. Cypermethrin dust a synthetic insecticide used as standard \ncheck (control) was purchased from agro chemical store.  \n\n\n\nTable 2: Different Botanicals with The Part Used for The Study \n\n\n\nScientific Name Family Common name Part used \n\n\n\nEugenia aromatica (O. Berg) Myrtaceae Clove plant Pod \n\n\n\nZanthoxylum zanthoxyloides (Lam) Rubiaceae Candlewood Root bark \n\n\n\nPiper guineense (Schum and Thonn) Piperaceae West African black pepper Seed \n\n\n\nOryza sativa L. Poaceae Rice Bran \n\n\n\nAllium sativum L. Alliaceae Garlic Bulbs \n\n\n\nMomordica charantia Linn. Cucurbitaceae Bitter melon Leaf \n\n\n\nOcimum gratissimum L. Lamiaceae Clove Basil Leaf \n\n\n\nXylopia aethoipica (Dunal A. Rich) Annonceae Negro pepper Pod \n\n\n\nNicotiana tabacum (L.) Solanaceae Tobacco Leaf \n\n\n\nZingiber officinale (Rosc.) Zingiberaceae Ginger Rihzomes \n\n\n\n \n2.4   Insect Culture \n\n\n\nA well-known susceptible cowpea variety, Ife Brown, was obtained from \nthe International Institute of Tropical Agriculture (IITA) and was \nsubcultured on it in a 2-liter Kilner jar, covered with muslin cloth to allow \nfor adequate aeration. The original culture of C. maculatus used for the \nstudy was obtained from already infested cowpea grains purchased from \nOja-Oba market in Akure, Ondo State, Nigeria. About 400 g of thoroughly \ncleaned and sterilized cowpea seeds were placed in a subculture along \nwith 10 pairs (10 males and females each) of adult C. maculatus in order \nto facilitate oviposition and provide a consistent flow of emerging adults \nfor the study. \n\n\n\n2.5   Entomotoxic Test \n\n\n\nAbout 40g of the disinfected legumes seeds were weighed separately into \na Petri\u2010dish (9.0cm) and 2g of the chosen plant powders were added to a \npetri dish. The Petri dish was thoroughly shaken to guarantee equal \nmixing and coating of the powders with grains (Adesina and Ofuya, 2011). \nEach ten newly developed adult bruchids of both sexes were then \nintroduced into the petri dishes of each group. Based on the outline used \nand the color of the plate covering the end of the abdomen, the sex was \nidentified (Iloba et al., 2007). In contrast to the male, who has a smaller \nplate with no stripes, the female has a larger plate that is darkly colored \non both sides (Christopher and Lawrence, 2014). Five replicates of each \ntreatment and control were used, and they were set up in the lab in an \nundisturbed completely random design (CRD). At 5 and 7 days after \ninfestation, respectively, observations were taken on the number of eggs \nlaid and hatching eggs, and the percentage weight loss following the \nemergence of adults was noted at the conclusion of the study.  These data \nwere used to determined; percentage reduction of egg laid or oviposition \ndeterrent, hatchability, pest tolerance and weight loss respectively \n(Arivoli and Tennyson, 2013; Olakojo et al., 2007; Abdullahi et al., 2011; \nIleke and Oni, 2011).  \n\n\n\n2.6   Data Analysis \n\n\n\nUsing SPSS version 16.0, an analysis of variance (ANOVA) was performed \non the data obtained on the number of eggs laid and the number of eggs \nthat hatched. To guarantee data normalization and homogeneity prior to \nANOVA, data based on counts (numbers of egg laid and hatched eggs) were \nsquare root transformed and data based on percentages were arc-sine \ntransformed. The Abbott formula was also used to assess the extent to \nwhich the botanicals proved effective (Abbot, 1925). The formula for \nAbbot: (1-Ta/Ca) x 100 where Ca represents the number of eggs in the \ncontrol petri dish and Ta represents the number of eggs in the petri dish \nthat was treated. The Tukey test was used to differentiate the significant \ntreatment means of the investigated parameters at the 5% level of \nsignificance. \n\n\n\n3.   RESULTS  \n\n\n\n3.1   Oviposition Response of C. Maculatus to Active Botanicals on \nSome Legume\u2019s Seeds \n\n\n\nFigure 1 to 3 reveals an overview of C. maculatus oviposition response and \npercentage pest tolerance to the six varieties of legume seed. The result \nshows that legume types significantly (F = 1.36; df = 4; P < 0.05) influence \nC. maculatus oviposition preference in terms of percentage egg laid \nreduction and egg hatched and percentage pest tolerance. M. pruriens \n(85.33) had the highest mean number of eggs laid compared to the other \ntypes of legume seed, while S. stenocarpa came in second. (72.00) whereas \nthe lowest number of eggs were laid on C. cajan (46.67) and statistically \nlower than number of eggs laid on Ife brown (Figure 1). Meanwhile, \nnumber of hatched eggs was highest in G. max (59.67), trailed by V. \nsubterranean (44.00) and C. cajan (18.67) had the least (Figure 2). \nPercentage pest tolerance was significantly lowest (F = 3.47; df = 4; \nP<0.05) in Ife Brown (20.83) compared to M. pruriens (99.17%) that had \nthe significantly highest (F = 1.34; df = 4; P<0.05) percentage of pest \ntolerance, followed by S. stenocarpa (83.33) and C. cajan (Figure 3).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 58-64 \n\n\n\n\n\n\n\n \nCite The Article: Augustine Matthew Adinoyi, Ofuya Thomas Inomisan, Idoko, Joy Ejemen, Adesina Jacobs Mobolade  (2023). Response of Five Selected Stored Legume \n\n\n\nSeeds Species to Oviposition Deterrent, Ovicidal and Grain Protectant Activities of Some Botanicals Against Callosobruchus Maculatus (FAB.)  \n(Coleoptera: Chrysomelidae). Malaysian Journal of Sustainable Agricultures, 7(1): 58-64. \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Number of eggs laid and percentage reduction of eggs laid \n\n\n\n\n\n\n\nFigure 2: Number of hatched eggs and percentage hatched eggs \n\n\n\n\n\n\n\nFigure 3: Percentage pest tolerance of some legumes seeds preserved with some botanicals\n\n\n\n3.2   Oviposition Deterrent Potentials of Some Botanicals on Legumes \nSeeds  \n\n\n\nResults in Table 4 showed that C. maculatus egg laying capabilities was \nsignificantly influenced (F = 2.52; df = 4; P > 0.05) in relation to the various \nbotanicals and legumes seeds types. C. cajan preserved with P. guineense \n(4.33), E. aromatica (7.67) and Z. zanthoxyloides (17.00) recorded \n\n\n\nsignificantly lowest number of eggs laid compared to other plant powders \nand control but was not significantly different from those laid on \nCypermethrin (12.67).  The number of eggs laid ranged from 8.67 to \n151.00 in seeds treated with G. max; substantially more eggs were laid in \nseeds treated with O. gratissimum (151.00), M. charantia (145.00), A. \nsativum (144.00), Z. officinale (141.33), and X. aethiopica (141.33). \n(130.67) compared with P. guineense (8.67), E. aromatica (11.00) and \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 58-64 \n\n\n\n\n\n\n\n \nCite The Article: Augustine Matthew Adinoyi, Ofuya Thomas Inomisan, Idoko, Joy Ejemen, Adesina Jacobs Mobolade  (2023). Response of Five Selected Stored Legume \n\n\n\nSeeds Species to Oviposition Deterrent, Ovicidal and Grain Protectant Activities of Some Botanicals Against Callosobruchus Maculatus (FAB.)  \n(Coleoptera: Chrysomelidae). Malaysian Journal of Sustainable Agricultures, 7(1): 58-64. \n\n\n\n\n\n\n\nother botanicals treated seeds (Table 4). Additionally, it was uncovered \nthat the number of eggs was significantly higher on seeds treated with X. \naethiopica (114.67), O. gratissimum (106.33), and M. charantia (96.33) \ncompared to seeds treated with Piper guineense (24.00), Z. zanthoxyloides \n(24.00), and E. aromatica (28.67) (F = 3.13; df = 4; p0.05). \n\n\n\nS. stenocarpa seeds treated with O. sativa (17.33) and Z. zanthoxyloides \n(31.33) had expressively lesser amount of eggs laid compared to those \n\n\n\npreserved with Z. officinale (175.33), X. aethiopica (152.67), M. charantia \n(135.33) and A. sativum (105.67) that recorded significantly increased \nnumber of eggs laid (Table 4).  Meanwhile, Z. zanthoxyloides (3.67) \nfollowed by O. sativa (17.00) recorded significantly lower (F = 2.39; df = 4; \nP<0.05) quantity of eggs set on M. pruriens seeds. All the plant materials \nused, did not significantly suppressed oviposition by C. maculatus \nirrespective of the types of legume seeds in comparison with control and \nCypermethrin treated seeds (Table 4).  \n\n\n\nTable 4: Number of Eggs Laid by C. Maculatus on Different Types of Legume Seeds Treated with Botanical Powders \n\n\n\nPlant species Cajanus cajan Glycine max Vigna subterranea Sphenostylis stenocarpa Mucuna pueriens \n\n\n\nE. aromatic 7.67\u00b11.16d 11.00\u00b11.16c 28.67\u00b12.93cde 84.67\u00b13.41abcd 44.33\u00b12.85abc \n\n\n\nP. guineense 4.33\u00b10.46d 8.67\u00b12.22c 24.00\u00b11.78cde 82.00\u00b14.62abcd 26.00\u00b11.84abc \n\n\n\nX. aethiopica 141.00\u00b12.46ab 130.67\u00b15.54a 114.67\u00b16.99ab 152.67\u00b13.37ab 90.00\u00b13.01abc \n\n\n\nZ. officinale 139.00\u00b13.62ab 141.335.06a 83.33\u00b13.25abcd 175.333.46a 142.33\u00b15.45a \n\n\n\nM. charantia 93.00\u00b14.12abc 145.003.18a 96.33\u00b12.68abc 135.33\u00b16.56abc 129.67\u00b17.08a \n\n\n\nO. gratissimum 157.33\u00b13.72ab 151.006.52a 106.33\u00b15.32abc 98.00\u00b13.46abcd 139.33\u00b13.02a \n\n\n\nO. sativa 26.33\u00b12.55cd 15.67\u00b11.75c 53.00b\u00b13.01cde 17.33\u00b10.62cd 17.00\u00b11.89bc \n\n\n\nZ. zanthoxyloides 17.00\u00b12.27d 23.00\u00b10.85bc 24.00\u00b12.12de 31.33\u00b11.46bcd 3.67\u00b10.61c \n\n\n\nA. sativum 124.00\u00b13.52ab 144.00\u00b17.02a 64.00\u00b12.73bcde 105.67\u00b14.62abcd 57.33\u00b13.36abc \n\n\n\nN. tabacum 55.00\u00b14.26cd 45.67\u00b1abc 53.33\u00b16.34bcde 71.33\u00b14.92abcd 104.33\u00b16.12ab \n\n\n\nCypermethrin 12.67\u00b11.22d 15.67\u00b10.73c 9.00\u00b12.07e 14.67\u00b12.21d 16.33\u00b11.94bc \n\n\n\nControl 177.67\u00b15.53a 96.33\u00b15.68ab 201.33\u00b15.65a 160.33\u00b17.91a 62.67\u00b14.04abc \n\n\n\nThe Tukey test indicates that there is no statistically significant difference \nbetween the means in each column with the same letter at the 5% level of \nprobability. \n\n\n\n3.3   Ovicidal Activities of Some Botanicals Against Callosobruchus \nMaculatus of Different Legume Seeds  \n\n\n\nTable 5 shows number of hatched eggs on different types of legume seeds \ntreated with botanical powders. In C. cajan seeds treated with P. guineense \n(2.33), E. aromatica (5.00) and Z. zanthoxyloides (14.33) there were \nconsiderably fewer eggs that hatched compared to a substantially larger \n(F = 2.15; df = 4; P 0.05) number of hatching eggs in seeds treated with O. \ngratissimum (140.67), Z. officinale (124.33) and X. aethiopica (115.67). \n\n\n\nEgg hatchability was considerably decreased (F = 2.03; df = 4; P<0.05) on \nG. max seeds treated with P. guineense (5.00) and E. aromatica (8.33) \namong other treatments. Oryza sativa (11.67) and Z. zanthoxyloides \n(14.33) came after it. Meanwhile, seeds treated with A. sativum (130.67), \nM. charantia (98.67) and Z officinale (89.67) were substantially more \nprevalent in hatching eggs.  \n\n\n\nVigna subterranean seeds treated with Z. zanthoxyloides (17.00) recorded \nsignificantly (F = 2.17; df = 4; P<0.05) lowest hatched among the various \nbotanicals used. On the other hand, the number of hatched eggs was \nconsiderably more (F = 4.95; df = 4; P<0.05) in seeds treated with X. \naethiopica (96.67), M. charantia (89.33) and Z. officinale (70.67) \nineffectual in lowering the hatchability of eggs deposited by C. maculatus \n(Table 3). \n\n\n\nResults obtained from S. stenocarpa treated seeds showed that hatched \neggs was significantly (F = 3.05; df = 4; P<0.05) affected by the various \nplant powders (Table 4). Seeds treated with O. sativa and N. tabacum \n(12.67) recorded significantly lower hatched eggs compared to those \ntreated with X. aethiopica (119.67), Z. officinale (108.33) and M. charantia \n(104.00) that recorded significantly higher (F = 3.85; df = 4; P<0.05) \nhatched eggs. Meanwhile, hatched eggs from E. aromatica (2.33) and Z. \nzanthoxyloides (2.67) powders treated M. pruriens seeds were significantly \nlower compared to those preserved with Z. officinale (109.67) and M. \ncharantia (95.67) ensured considerably higher (F = 1.93; df = 4; P<0.05) \nmean hatched eggs. \n\n\n\nTable 5: Percentage of Eggs Hatched from Eggs Oviposited by C. Maculatus on Legume Seeds Treated with Different Botanical Powders and \nCypermethrin. \n\n\n\nPlant species Cajanus cajan Glycine max Vigna subterranea Sphenostylis stenocarpa Mucuna pueriens \n\n\n\nE. aromatic 5.00\u00b11.28d 8.33\u00b12.73d 23.33\u00b1bcd 57.67\u00b14.16ab 2.33\u00b10.38c \n\n\n\nP. guineense 2.33\u00b10.83d 5.00\u00b11.83d 22.67\u00b1bcd 27.00\u00b13.32ab 5.00\u00b11.15bc \n\n\n\nX. aethiopica 115.67\u00b15.57a 98.67\u00b14.21ab 96.67\u00b1ab 119.67\u00b16.13a 52.00\u00b15.03abc \n\n\n\nZ. officinale 124.33\u00b14.91a 89.67\u00b14.67ab 70.67\u00b1abc 108.33\u00b18.41ab 109.67\u00b18.45a \n\n\n\nM. charantia 84.33\u00b14.58abc 103.33\u00b16.52ab 89.33\u00b1ab 104.00\u00b16.65ab 95.67\u00b17.32a \n\n\n\nO. gratissimum 140.676.18a 82.67\u00b15.47ab 93.67\u00b16.53ab 78.33\u00b15.57ab 94.67\u00b15.64ab \n\n\n\nO. sativa 24.00\u00b13.05cd 11.67\u00b10.56cd 49.33\u00b15.12bcd 12.67\u00b13.82b 13.67\u00b13.13abc \n\n\n\nZ. zanthoxyloides 14.33\u00b10.66d 14.3319\u00b11cd 17.00\u00b11.46cd 18.33\u00b12.43ab 2.67\u00b11.22c \n\n\n\nA. sativum 103.00\u00b14.72ab 130.67\u00b17.02a 55.33\u00b13.85bcd 80.67\u00b14.01ab 54.00\u00b16.14abc \n\n\n\nN. tabacum 35.00\u00b12.12bcd 38.33\u00b13,52bcd 45.67\u00b12.25bcd 12.67\u00b11.59ab 70.00\u00b14.07abc \n\n\n\nCypermethrin 11.67\u00b12.04d 13.33\u00b12.25cd 6.00\u00b11.04d 11.67\u00b10.83b 11.33\u00b11.84abc \n\n\n\nControl 153.67\u00b14.54a 66.33\u00b13.94abc 171.33\u00b15.72a 119.00\u00b15.03a 39.67\u00b14.92abc \n\n\n\nThe Tukey test indicates that there is no statistically significant difference \nbetween the means in each column with the same letter at the 5% level of \nprobability. \n\n\n\n3.4   Legume Seeds Treated with Various Botanical Powders and \nTheir Percentage Weight Reduction  \n\n\n\nPercentage of weight loss in various C. maculatus infested legume seeds \n\n\n\nwas documented in Table 5. The result bared that M. pruriens seeds \npreserved with botanical powders do not suffered any weight loss. \nSimilarly, G. max seeds admixed with E. aromatica, P. guineense and X. \naethiopica did not suffered weight loss, also no weight loss was noted in V. \nsubterranean seeds preserved with E. aromatica. C. cajan seeds treated \nwith A. sativum (6.43%) and Z. officinale (5.30%) ensured maximum ratio \nof weight loss whereas seeds preserved with P. guineense (0.40%) and Z. \nzanthoxyloides (0.83%) offered the minimum percentage weight loss. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 58-64 \n\n\n\n\n\n\n\n \nCite The Article: Augustine Matthew Adinoyi, Ofuya Thomas Inomisan, Idoko, Joy Ejemen, Adesina Jacobs Mobolade  (2023). Response of Five Selected Stored Legume \n\n\n\nSeeds Species to Oviposition Deterrent, Ovicidal and Grain Protectant Activities of Some Botanicals Against Callosobruchus Maculatus (FAB.)  \n(Coleoptera: Chrysomelidae). Malaysian Journal of Sustainable Agricultures, 7(1): 58-64. \n\n\n\n\n\n\n\nPercentage weight loss was significantly higher (F = 4.59; df = 4; P<0.05) \non V. subterranean seeds treated with X. aethiopica (21.50%) and O. \ngratissimum (20.53%) respectively. Meanwhile, G. max, C. cajan and S. \n\n\n\nstenocarpa treated seeds recorded non-significant weight loss among all \nthe botanical treatments used except the control.  \n\n\n\n\n\n\n\nTable 5: Different Plant Powders' Effects on The % Weight Loss of Various Types of Legume Seeds \n\n\n\nPlant species Cajanus cajan Glycine max Vigna subterranea Sphenostylis stenocarpa Mucuna pueriens \n\n\n\nE. aromatica 1.3\u00b10.50a 0.00b\u00b10.0 0.00\u00b10.0b 1.67\u00b10.32a 0.00\u00b10.0 \n\n\n\nP. guineense 0.4\u00b12.70a 0.00b\u00b10.0 13.77\u00b12.34ab 1.87\u00b10,85a 0.00\u00b10.0 \n\n\n\nX. aethiopica 3.4\u00b11.50a 0.00b\u00b10.0 21.50\u00b12.68a 2.23\u00b11.70a 0.00\u00b10.0 \n\n\n\nZ. officinale 5.3\u00b12.48a 0.17\u00b11.67b 6.53\u00b12.07ab 2.60\u00b11.51a 0.00\u00b10.0 \n\n\n\nM. charantia 2.43\u00b11.18a 1.53\u00b10.96b 14.37\u00b11.94ab 1.87\u00b11.14a 0.00\u00b10.0 \n\n\n\nO. gratissimum 4.6\u00b10.93a 1.20\u00b12,54b 20.53\u00b13.32a 3.37\u00b10.63a 0.00\u00b10.0 \n\n\n\nO. sativa 1.03\u00b11.56a 0.00b\u00b10.0 6.67\u00b12.74ab 2.57\u00b11.35a 0.00\u00b10.0 \n\n\n\nZ. zanthoxyloides 0.83\u00b11.43a 1.23\u00b11.35b 6.77\u00b12.73ab 1.67\u00b10.96a 0.00\u00b10.0 \n\n\n\nA. sativum 6.43\u00b12.52a 2.17\u00b11.12b 5.30\u00b11.46ab 3.30\u00b12.07a 0.00\u00b10.0 \n\n\n\nN. tabacum 4.27\u00b12.92a 0.43\u00b11.26b 10.03\u00b10.62ab 1.63\u00b11.02a 0.00\u00b10.0 \n\n\n\nCypermethrin 1.27\u00b10.68a 0.50\u00b11.13b 3.17\u00b11.89ab 3.13\u00b10.11a 0.00\u00b10.0 \n\n\n\nControl 5.07\u00b11.19a 12.33\u00b13.25a 14.00\u00b12.77ab 2.83\u00b12.55a 0.00\u00b10.0 \n\n\n\nThe Tukey test indicates that there is no statistically significant difference \nbetween the means in each column with the same letter at the 5% level of \nprobability. \n\n\n\n5.   DISCUSSION \n\n\n\nAccording to numerous authors, the comparison of E. aromatica, P. \nguineense, Z. zanthoxyliodes, and O. sativa in the control of C. maculatus had \nbeen justified (Ivbijaro and Agbaje, 1986; Olaifa and Erhun, 1988; Lale, \n1994; Ogunwolu and Odunlami, 1996; Adedire and Lajide, 2001). The \npresent investigation has indicated that some of the bean varieties tested \nexhibited varied degrees of resistance and vulnerability to adult C. \nmaculatus infestation. The result obtained clearly reveals, there was a \nsubstantial difference in the levels of resistance and susceptibility among \nthe legume seed types to C. maculatus. It was discovered that Ife Brown \nand M. pruriens are very resistant and vulnerable to C. maculatus \ninfestation, respectively. This confirms the findings of Oke and Olajire \n2012, who found that different cowpea types varied in their susceptibility \nand resistance to C. maculatus. Seed characteristics such as seed coat \ntexture (smooth or rough), hardness could and nutritional variables might \nhave contributed to the ovipositional preference reported and minimize \nthe destruction caused by C. maculatus to infested cowpea seeds. \n\n\n\nAccording to the bruchid beetle favoured cowpea with a smooth seed \ncover less for oviposition (Messina and Renwick, 1985). While Ndakidemi \nand Dakora, 2003 noted that phenolics, alkaloids, and terpenes are \nabundant in V. unguiculata cultivars that are resistant to bruchids. It is \nclear that C. maculatus was susceptible to the insecticidal effects of the \npowders prepared from these plants. It was evident that Z. zanthoxyliodes, \nE. aromatica and P. guineense applied at 0.2g/10g of legume seed types \nperformed as the best botanicals to exhibit insecticidal and ovicidal action \nin suppressing egg hatching and ultimately preserved stored legumes \nseeds from weight loss and consequently damage caused by C. maculatus \ninfestation.  \n\n\n\nThese findings corroborated Mbata's, 1993 findings that susceptibility \nindex and weight reduction are typically substantially associated. The \ntoxic effect exhibited by these botanicals may possibly be associated to the \noccurrence of secondary metabolites in the plants. The observed \nbioactivities of the plants against C. maculatus may be influenced by the \nrelative concentrations of these compounds, their interactions, and the \nvolatility of their products. Zanthoxylum zanthoxyloides contain compound \nknown as zanthoxylol; P. guineense contains piperidine, piperine and \nchavicine, while E. aromatica contains bioactive compounds such as \nlimonene, eugenol and cineole as the major active components which are \novicidal and have a lethal effect on insect pests in stored product (Udo, \n2011; Akinneye and Ogungbite, 2013; Lale, 1995; Okonkwo and \nOkoye,1996). This could account for their identical response to the insects \nin the current investigation.  \n\n\n\nIn the present study, oviposition was found not to only vary significantly \nwith different plant materials but also with the legume types. C. maculatus \nlaid suggestively less eggs on seeds preserved with diverse types of \nmaterials than control sets. Mucuna pruriens seeds do not suffered weight \nloss, while G. max treated with E. aromatica, P. guineense and X. aethiopica \n\n\n\nalso do not recorded any weight loss indicating that M. pruriens and G. max \nmay be resistant to C. maculatus because, despite the insect laying \nnumerous eggs on the seed, it was unable to penetrate the seed in any way. \nAlternatively, it may be because the chemical components of the botanical \npowders have ovicidal properties that prevent C. maculatus females from \novipositing. Inhibition in oviposition, ovicidal effects, a larvicidal effect on \nneonate larvae before the penetration of the seeds or a larvicidal effect on \nlarvae settled within the seed have all been reported to inhibit the \nreproduction of stored insects at various stages of the cycle (Ofuya, 1990; \nRegnault-Roger and Hamraoui, 1995; Kim et al., 2003). As a result, insects \nwere unable to infest grain and and cause seed destruction/weight loss \n(Tapondjou et al., 2002; Pandey et al., 2011).  \n\n\n\nAnother result was that hormoligosis may be the cause of the major \nineffectiveness of botanical powders of O. gratissimum, X. eithopica, Z. \nofficinale, M. charantia, A. sativum, and N. tabacum in safeguarding \nlegumes. According to some studied sub-lethal dosages of botanical \npesticides promote female oviposition as well as behavioral hormoligosis \nin oviposition preference (Lale, 1991; Abdullah et al., 2006). The reduction \nin weight loss of stored food grains is directly proportional to larvae \nfeeding activities and insect population (Adesina and Mobolade-Adesina, \n2020). Neonatal larvae must break through the outer skin of the legume \nseeds because C. maculatus females lay their eggs on the seed surface in \norder to reach the endosperm, where they feed. The lower weight loss \nnoted in the study could be due to less oviposition, increased egg or larval \nmortality, or possibly decreased egg hatching. (Adesina and Mobolade-\nAdesina, 2020). The percentage of seed weight loss increased dramatically \nas larval density increased. \n\n\n\n6.   CONCLUSION  \n\n\n\nThe study shows that C. maculatus oviposition deterrent and pest \ntolerance capabilities were significantly influenced in relation to the \nvarious botanicals and legumes seeds types. Zanthoxylum zanthoxyloides, \nP. guineense and E. aromatica powders possessed insecticidal, ovicidal and \ngrains protectant properties. While G. max, C. cajan and M. pruriens \nexhibited low susceptibility to C. maculatus infestation. Therefore, \nincorporation of Z. zanthoxyloides, P. guineense and E. aromatica powder \nfor controlling C. maculatus proved to be an encouraging biopesticide \ngrain protectant for the safeguard of stored pulses seeds as replacements \nto synthetic insecticides. Conversely, mixture of pulse seed types in \nstorage should be avoided or minimized since all the seeds with the \nexception of M. pruriens exhibited susceptibility to C. maculatus \ninfestation. The tolerant seeds should be incorporated into grain legumes \nbreeding programmes against C. maculatus infestation to suppress grain \nlosses during storage. \n\n\n\nCONFLICT OF INTERESTS STATEMENT \n\n\n\nThe writers say they have no conflicting interests. \n\n\n\nAUTHORS CONTRIBUTION \n\n\n\nThe experiment was developed, planned, and carried out by AMA; it was \nsupervised by OTI; and its findings were analyzed and explained by IJE. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 58-64 \n\n\n\n\n\n\n\n \nCite The Article: Augustine Matthew Adinoyi, Ofuya Thomas Inomisan, Idoko, Joy Ejemen, Adesina Jacobs Mobolade  (2023). Response of Five Selected Stored Legume \n\n\n\nSeeds Species to Oviposition Deterrent, Ovicidal and Grain Protectant Activities of Some Botanicals Against Callosobruchus Maculatus (FAB.)  \n(Coleoptera: Chrysomelidae). Malaysian Journal of Sustainable Agricultures, 7(1): 58-64. \n\n\n\n\n\n\n\nJMA gathered relevant literature and developed the manuscript draft. The \npaper has been read and approved by all authors. \n\n\n\nREFERENCES \n\n\n\nAbbott, W.S., 1925. A method for computing the effectiveness of an \ninsecticide. Journal of Economic Entomology, 18, Pp. 265-267. \n\n\n\nAbdullah, N.M.M., Singh, J., Sohal, B., 2006. Behavioral hormoligosis in \noviposition preference of Bemisia tabaci on cotton. Pesticide \nBiochemistry and Physiology, 84 (1), Pp. 10-16.  \n\n\n\nAbdullahi, N., Mojeed, Q., Oyeyi, T.I., 2011. Studies on the efficacy of \nVittallaria paradoxa seed oil on the oviposition, hatchability of eggs \nand emergence of Callosobruchus maculatus (F) (Coleopteran: \nBruchidae) on treated cowpea seed. Journal of Entomology, 1 Pp. 1-\n7. \n\n\n\nAdebayo, R.A., Idoko, J.E., Adetuyi, R.O., 2013. Response of four local \nvarieties of cowpea to water extracts of Chromolaena odorata (King \nand Robinson) and Vernonia Amygdalina (L.). International Journal \nof Agricultural and Food Security, 4 (1&2), Pp. 437-444. \n\n\n\nAdedire, C.O., Lajide L., 2001. Efficacy of powders of some tropical plants \nin the control of the pulse beetle, Callosobruchus maculatus (F.) \n(Coleoptera: Bruchidae). Applied Tropical Agriculture, 6, Pp. 11-5. \n\n\n\nAdesina, J.M., Afolabi, L.A., Ofuya, T.I., 2012. Evaluation of insecticidal \nproperties of Momordica charantia in reducing oviposition and seed \ndamage by Callosobruchus maculatus (Fab.)  International Journal \nAgricultural Technology, 8 (2), Pp. 493-499.  \n\n\n\nAdesina, J.M., Idoko, J.E., 2013. Effects of initial infestation levels on \ndamage to resistant and susceptible cowpea by Callosobruchus \nmaculatus (Fabr.), Archieve of Phytopathology and Plant \nProtection, 47 (14), Pp. 1726-1736.  \nDOI:10.1080/03235408.2013.856078. \n\n\n\nAdesina, J.M., Ileke, K.D., Rajashekar, Y., 2019a.  Eugenia aromatica O. Berg \nand Afromomum melegueta K. Schum botanical entomocides as \npossible synergetic protectant against Callosobruchus maculatus \n(Fabricus) (Coleoptera: Chrysomelidae) infestation on stored \ncowpea (Vigna unguiculata (L.) Walp.). Herbal Polonica, 65 (2), Pp. \n1-13. \n\n\n\nAdesina, J.M., Mobolade-Adesina, T.E., 2020. Callosobruchus maculatus \n(Fab.) Coleoptera: Chrysomelidae) Infestation and Tolerance on \nStored Cowpea Seeds Protected with Anchomanes difformis (Blume) \nEngl. Extracts. Journal of Horticulture and Post-Harvest Research, 3 \n(2), Pp. 367-378.  \n\n\n\nAdesina, J.M., Ofuya, T.I., 2011. Evaluation of leaf and vine powders of \nSecamone afzelii (Schult) K. Schum for control of Callosobruchus \nmaculatus (Fab.) (Coleoptera: bruchidae) in stored cowpea Vigna \nunguiculata (L.) Walp. South Asian Journal of Experimental Biology, \n1 (3), Pp., 158 \u2013 163. \n\n\n\nAdesina, J.M., Raghavendra, A., Rajashekar, Y., Ofuya, T.I., 2019a. Potential \nuse of Clerodendrum capitatum extracts and its formulation for \ncontrol of three major stored-product beetles. Food Quality and \nSafety, 20, Pp. 1-7.  \n\n\n\nAkinkurolere, R.O., Adedire, C.O. Odeyemi, O.O., 2006. Laboratory \nevaluation of the toxic properties of forest anchomanes, Anhomanes \ndifformis, against pulse beetle, Callosobruchus maculatus \n(Coleoptera: Bruchidae). Insect Science, 13, Pp. 25-29.  \n\n\n\nAkinneye, J.O., Ogungbite, O.C., 2013. Insecticidal activities of some \nmedicinal planted against Sitophilus zeamais (Motschulsky) \n(Coleoptera: Curculionidae) on stored maize, Arch. Phytopathology \nand Plant Protection, 46 (10), Pp. 1206 \u20131213. \n\n\n\nArivoli, S., Tennyson, S., 2013. Screening of plant extracts for oviposition \nactivity against Spodoptera litura (Fab). (Lepidoptera: Noctuidae). \nInternational Journal of Fauna Biological Studies, 1 (1), Pp. 20-24. \n\n\n\nAugustine, A.M., Idoko, J.E., Adepoju, A.O., 2016. Ovipositional Response of \nSeed Beetle Callosobruchus Maculatus (F.) Coleoptra: \nChrysomelidae on Some Selected Varieties of Cowpea, Vigna \nUnguiculata (L.) Walp. International Journal of Research in \nAgriculture and Forestry, 3 (5), Pp. 28-34.  \n\n\n\nBushara, A.G., 1988. Insect Depredation during Storage. In World Crops: \nCool Season Food Legumes; Summerfield, R.J., Ed.; Kluwer: \nDordrecht, the Netherlands, Pp. 367-378. \n\n\n\nChristopher, W.B., Lawrence, S.B., 2014. A Handbook on Bean Bettles, \nCallosobruchus maculatus. National Science Foundation, USA. Pp. 1-\n17. \n\n\n\nDayan, F.E., Cantrell, C.L., Duke, S.O., 2009. Natural products in crop \nprotection. Bio-organic Medical and Chemistry, 17, Pp. 4022-4034.  \n\n\n\nGolob, P., Webley, D.J., 1980. The Use of Plants and Minerals as Protectants \nof Stored Product Tropical Product Institute G138. Post-Harvest \nPest and Quality Section Natural Resource Institute, Cathan, UK., Pp. \n32. \n\n\n\nIdoko, J.E., Adesina, J.M., 2012. Infestation Level of Callosobruchus \nmaculatus on Cowpea using Different Particle Sizes of Eugenia \naromatica and Piper guineense Powders. World Journal of \nEngineering, Pure and Applied Sciences, 2 (5), Pp. 156-60. \n\n\n\nIloba, B.N., Umoetok, S.B.A., Keita, S., 2007. The biological control of \nCallosobruchus maculatus (Fabricius) by Dinarmus basalis \n(Rendani) on stored cowpea (Vigna unguiculata Walp) seeds. \nResearch Journal of Applied Science, 2 (4), Pp. 397 \u2010399. \n\n\n\nIsman, M.B., 2006. Botanical insecticides, deterrence and repellents in \nmodern agriculture and an increasingly regulated world. Annual \nReview of Entomology, 51, Pp. 45 \u2013 66.  \n\n\n\nJoey, P.P., Thomas, J., Mathew, S., Skaria, B.P., 2001. Medicinal Plants. \nInternational Tropical Horticulture, Pp. 449-632.  \n\n\n\nKashiwaba, K., Tomooka, N., Kaga, A., Han, O.K., Vaughan, D.A., 2003. \nCharacterization of resistance to three bruchid species \n(Callosobruchus spp., Coleoptera, Bruchidae) in cultivated rice bean, \n(Vigna umbellata (Thunb.) Ohwi and Ohashi). Journal of Econonic \nEntomology, 96, Pp. 207\u2013213. \n\n\n\nKhater, H.F., 2011. Ecosmart Biorational Insecticides: Alternative Insect \nControl Strategies. In: Insecticides, Perveen, F. (Ed.). In Tech, Rijeka, \nCroatia. ISBN 979-953-307-667-5.  \n\n\n\nKim, S.I., Roh, J.Y., Kim, D.H., Lee, H.S., Ahn, Y.J., 2003. Insecticidal activities \nof aromatic plant extracts and essential oils against Sitophilus \noryzae and Callosobruchus chinensis. Journal of Stored Products \nResearch, 39, Pp. 293-303.  \n\n\n\nKosini, D., and Nukenine, E.N., 2017. Susceptibility of Three Legume \nSpecies to Callosobruchus maculatus (Coleoptera: Chrysomelidae) \nAttack and Impact of Rearing Medium on Female Oviposition Host \nPreference. Agricultural Research and Technology Open Access \nJournal, 8 (1), Pp. 10-14. \n\n\n\nLale, N.E.S., 1991. The biological effects of three essential oils \non Callosobruchus maculatus. Revue de Zoologie Africaine, 105, Pp. \n357-362. \n\n\n\nLale, N.E.S., 1994. Laboratory assessment of the effectiveness and \npersistence of powders of four spices on cowpea bruchid and maize \nweevil in airtight facilities. Samaru Journal of Agricultural Research, \n11, Pp. 79- 84. \n\n\n\nLale, N.E.S., 1995. An overview of the use of plant products in the \nmanagement of stored product Coleoptera in the tropics. Post-\nHarvest News Information, 6, Pp. 69\u201375.  \n\n\n\nMbata, G.N., 1993. Evaluation of susceptibility of varieties of cowpea to \nCallosobruchus maculatus (F.) and Callosobruchus subinnotatus \n(Pic.) (Coleoptera: Bruchidae). Journal of Stored Product Research, \n29, Pp.  207-213.  \n\n\n\nMessina, F.J., Barmore, J.L., Renwick, J.A.A.1985. Oviposition deterrent \nfrom eggs of Callosobruchus maculatus: Spacing mechanism or \nartifact? Journal of Chemical Ecology, 13, Pp. 219-226.  \n\n\n\nMitchell, R., 1990. Behavioral ecology of Callosobruchus maculatus. In: \nFujii, K., Gatehouse, M.R., Johnson, C.D., Mitchell, R., Yoshida, T. \n(Eds.), Bruchids and Legumes: Economics Ecology and Coevolution. \nKluwer Academic Publishers, Dordrecht, Pp. 317\u2013330. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 7(1) (2023) 58-64 \n\n\n\n\n\n\n\n \nCite The Article: Augustine Matthew Adinoyi, Ofuya Thomas Inomisan, Idoko, Joy Ejemen, Adesina Jacobs Mobolade  (2023). Response of Five Selected Stored Legume \n\n\n\nSeeds Species to Oviposition Deterrent, Ovicidal and Grain Protectant Activities of Some Botanicals Against Callosobruchus Maculatus (FAB.)  \n(Coleoptera: Chrysomelidae). Malaysian Journal of Sustainable Agricultures, 7(1): 58-64. \n\n\n\n\n\n\n\nNdakidemi, P.A., Dakora, F.D., 2003. Legume seed flavonoids and \nnitrogenous metabolites as signals and protectants in early seedling \ndevelopment. Functional Plant Biology, 30, Pp. 729-745.  \n\n\n\nOfuya, T.I., 1986. Use of wood ash, dry chilli pepper fruits and onion scale \nleaves for reducing Callosobruchus maculatus (F.) damage in \ncowpea seeds during storage. Journal of Agricultural Sciences \nCambridge, 107, Pp. 467- 468. \n\n\n\nOfuya, T.I., 1990. Oviposition deterrence and ovicidal properties of some \nplant powders against Callosobruchus maculatus in stored cowpea \n(Vigna unguiculata) seeds. Journal of Agricultural Sciences, 115 (3), \nPp. 343\u2013345. doi:10.1017/s0021859600075766. \n\n\n\nOfuya, T.I., 2001. Biology, Ecology and Control of Insect Pests of stored \nLegumes in Nigeria. In: Pests of Stored cereals and Pulses in Nigeria: \nBiology, Ecology and Control, Edited by T. I. Ofuya and N. E. S. Lale, \nDave Collins Publication, Nigeria, Pp. 24-58. \n\n\n\nOgunwolu, E.O., Idowu, O., 1994. Potential of powdered Zanthoxylum \nzanthoxyloides root bark and Azadirachta indica seed for the control \nof the of cowpea seed bruchid. Callosobruchus maculatus. Nig. \nJournal of African Zoology, 108, Pp. 521-528.  \n\n\n\nOke, O.A., Olajire, T.E., 2012. Determination of Insect Pests on Planted \nCowpea Varieties at Teaching and Research Farm of Federal \nUniversity of Agriculture, Abeokuta, Ogun State, Nigeria. Online \nInternational Journal of Microbiology Research, 1 (1), Pp.  1-7.  \n\n\n\nOkonkwo, E.U., Okoye, W.I., 1996. The efficacy of four seed powders and \nthe essential oils as protectants of cowpea and maize grains against \ninfestation by Callosobruchus maculatus (Fabricius) (Coleoptera: \nBruchidae) and Sitophilus zeamais (Motschulsky) (Coleoptera: \nCurculionidae) in Nigeria. International Journal Pest Management, \n42 (3), Pp. 143\u2013146.  \n\n\n\nOlaifa, J.I., Erhun, W.O., 1987. Insecticidal activity of some Nigerian plants. \nInsect Science and Application, 8, Pp. 221-224.  \n\n\n\nOlotuah, O.F., Ofuya, T.I., Aladesanwa, R.D., 2007 Comparison of four \nbotanical powders in the control of Callosobruchus maculatus (Fab.) \n(Coleoptera: Bruchidae) and Sitophilus zeamais (Mots) (Coleoptera: \nCrculionidae). Proceeding Akure Humbolt Kellong 3rd SAAT Annual \n\n\n\nConference, Fed. University of Tech., Akure, Nigeria 16th \u2013 19th, Pp. \n56\u201059. \n\n\n\nOparaeke, A.M., Dike, M.C., 2005. Mondora myristica (Gaertn), Dunal \n(Myristicaceae) and Allium cepa L. (Liliaceae) as Protectants \nAgainst Cowpea Seed Bruchid, Callosobruchus maculatus (Fab.) \ninfesting Stored Cowpea Seeds. Nigerian Journal of Entomology, 2, \nPp. 84 \u2013 92. \n\n\n\nPandey, A.K., Mishra, A., Singh, P., Tripathi, N., 2011. Effect of some \nEssential Oils on the Oviposition and Emergence of Callosobruchus \nSpp. Indian Journal of Entomology, 73 (1), Pp. 10\u201314. \n\n\n\nPrakash, A., Rao, J., 1997. Botanical Pesticides in Agriculture. CRC Press \nInc., USA. ISBN-13: 9780873718257, 480.  \n\n\n\nPugazhvendan, S.R., Elumalai, K., Ronald Ross, P., Soundararajan, M., 2009. \nRepellent activity of chosen plant species against Tribolium \ncastaneum. World Journal of Zoology, 4, Pp. 188-190. \n\n\n\nRawat, S., Srivastava, M., 2011. Evaluation of qualitative and quantitative \nlosses caused by Callosobruchus chinensis to some pulses. Journal of \nEntomological Research, 35 (2), Pp. 117-120.  \n\n\n\nRegnault-Roger, C., Hamraoui, A., 1995. Fumigant toxic activity and \nreproductive inhibition induced by monoterpenes on \nAcanthoscelides obtectus (Say) (Coleoptera), a bruchid of kidney \nbean (Phaseolus vulgaris L.). Journal of Stored Product Research, 31, \nPp. 291\u2013299.  \n\n\n\nTapondjou, L.A., Adler, C., Bouda, H., Fontem, D.A., 2002. Efficacy of powder \nand essential oil from Chenopodium ambrosioides leaves as post-\nharvest grain protectants against six-stored product beetles. \nJournal of Stored Product Research, 38 (4), Pp. 395\u2013402. \ndoi:10.1016/s0022-474x(01)00044-3. \n\n\n\nUdo, I.O., 2011. Potentials of Zanthoxylum zanthoxyloides Lam. (Rutaceae) \nfor the control of stored product insect pests. Journal of Stored \nProduct Postharvest Research, 2 (3), Pp. 40-44.  \n\n\n\nVaishali, P.S., Shyamkant, S.M., Yugal, K.Y., 2018. Interactive effect of \nrosemary oil and phosphine gas on pulse beetle, Callosobruchus \nchinensis L. (Coleoptera: Chysomelidae). Journal of Entomology and \nZoology Studies, 6 (5), Pp. 94-97.  \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 81-85 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mjsa.02.2020.81.85 \n\n\n\nCite the Article: Abishkar Khatiwada, Pragya Adhikari (2020). Effect Of Various Organic Fertilizers On Seedling Health And Vigour Of Different Varieties Of Cucumber In \nRautahat Condition. Malaysian Journal of Sustainable Agriculture, 4(2):81-85. \n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2020.81.85 \n\n\n\nEFFECT OF VARIOUS ORGANIC FERTILIZERS ON SEEDLING HEALTH AND VIGOUR \nOF DIFFERENT VARIETIES OF CUCUMBER IN RAUTAHAT CONDITION  \n\n\n\nAbishkar Khatiwada*, Pragya Adhikari \n\n\n\nB.Sc. Ag, Agriculture and Forestry University, Chitwan, Nepal. \n*Corresponding Author Email: abishkarkhatiwada11@gmail.com, pragya.adhikari20@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 08 January 2020 \nAccepted 10 February 2020 \nAvailable online 10 March 2020\n\n\n\nCucumber (Cucumis sativus L.) is one of the most popular vegetable crop of cucurbitaceae family. The \nexperiment was laid out as 4\u00d72 factorial Completely Randomized Block Design (RCBD) with three \nreplications. The treatment consisted of two cucumber varieties (Dynasty and Malini) and different types of \nOrganic Fertilizers namely (Peat moss, Vermicompost, Trichocompost and bare soil). Seedlings were raised \non plastic pots inside a plastic tunnel with respective treatment and randomization of replication was done. \nData analaysis was done using Duncan\u2019s Multiple Range Test (DMRT) using GENSTAT. The result of the \nexperiment indicated that trichocompost had significantly higher germination index(23.41) being \nstatistically at par with peatmoss(22.76), greater number of leaves(3.9), dry root weight(0.8857g), dry shoot \nweight(1.647g), and lowest damping off incidence at 7DAS(1.567%), 11DAS(3.75%) and 15 DAS(4.58%). \nPeatmoss had higher germination (92.64%) being statistically at par with trichocompost (91.74%), larger \nleaf width(6.152cm) being at par with vermicompost(6.160cm) and trichocompost(6.023cm), higher fresh \nweight (23.67g) being at par with vermicomost(22g) and trichocompost(21.67g), higher vigor index(2320) \nand longer shoot length(7.980cm) being at par with trichocompost(7.853cm). Similarly control had higher \nroot to shoot length ratio(2.977). Also, variety malini was found to be superior in all observed parameters \nexcept damping off. Thus it is suggested to use malini as a variety and trichocompost as a potting media to \nraise seedlings of cucumber in rautahat condition for better results.  \n\n\n\nKEYWORDS \n\n\n\nOrganic Fertilizer, Peatmoss, Vermicompost, Trichocompost, Variety.\n\n\n\n1. INTRODUCTION\n\n\n\nCucumis sativus L. one of the most popular crop plant of cucurbitaceae \nfamily (about 750 species) is used as an important vegetable (Malepszy \nand Niemirowicz-Szczytt, 1991). It is often eaten as a vegetable but \nscientifically considered as a fruit as they contain enclosed seeds and \ndevelop from a flower (FDA, 2016). Cucumbers are summer season plants \nand grow best between 65\u00b0F to 75\u00b0F. This crop cannot tolerate prolonged \nexposure to temperatures below 55\u00b0F or above 90\u00b0F (FDA, 2016). For the \nproper growth and development of plant, the daytime temperatures \nshould be 27-300 C and soil temperature should be at least 180C (Pishgar-\nKomleh et al., 2013). The new approaches to the use of organic \namendments in farming have become an excellent source of plant \navailable nutrients and also have proven to be effective means of \nimproving soil structure, enhancing soil fertility (Arancon et al., 2004).  \n\n\n\nVarious organic materials have been recognized as soil amendments and \ndisease controllers, including the control of brown spot disease and \nescalation of bacterial numbers by rice bran and the increase in plant \ngrowth and reduction of nematode population by oil cakes (Osunlaja, \n1989; Khan and Saxena, 1997). Organic fertilizer have a positive effect to \nsoil microbial community with increasing number of aerobic bacteria and \nactinomycetes (Hanada, 1991). Organic fertilizers like vermicompost, \ntrichocompost and peatmoss provide a favourable and eco-friendly \nenvironment for the healthy growth and development of vegetable \n\n\n\nseedlings (Wilson and Carlile, 1989; Edwards and Burrows, 1988; Gravel \net al., 2007; Raviv et al., 1986; Abad et al., 2002). Damping-off diseases in \nseedling preparation which are primarily caused by the ubiquitous \npathogen Pythium ultimum Trow and Rhizoctonia solani has been \nimproved by introduction of improved soil media.  An experiment showed \nthe efficiency of two antagonists glaucodium and trichoderma to contol \nseveral diseases including damping off (Papavizas, 1985). \n\n\n\nThe use of inorganic fertilizers are rather expensive for low income, small-\nscale farmers, and are often associated with increased acidity and nutrient \nimbalance (Ayoola and Makinde, 2009). Hence organic fertilizers can be \nan effective alternative to chemical fertilizers as they contain high levels \nof nutrients and organic matter required for healthy growth of seedlings. \nThis technique of potting amendment also facilitates the healthy growth \nof cucumber seedlings even under adverse environment condition as the \npots can be easily moved in safe places. The major objective of this \nexperiment was to evaluate the effect of organic fertilizers as potting \namendment on seed germination, vigor and seedling quality of two \ncucumber varieties. Varieties of cucumber Malini and dynasty was \npurposively selected for the experimental trial which was the \nrecommended domain for our experimental site i.e. terai region (Krishna \net al., 2017; AICC, 2018). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 81-85 \n\n\n\nCite the Article: Abishkar Khatiwada, Pragya Adhikari (2020). Effect Of Various Organic Fertilizers On Seedling Health And Vigour Of Different Varieties Of Cucumber In \nRautahat Condition. Malaysian Journal of Sustainable Agriculture, 4(2): 81-85. \n\n\n\n2. METHODS AND METHODOLOGY \n\n\n\n2.1  Site Selection \n\n\n\nThe experiment was conducted in Mohammadpur -5, Rautahat which is \nthe working area of Prime Minister Agriculture Modernization Project. \nRautahat district has been identified as potential district for vegetable \nproduction and as per PMAM Project guidelines, 16 wards of Garuda \nMunicipaity has been identified as vegetable zone. It lies in terai region of \nProvince No. 2 including sixteen municipalities and 2 rural municipality \nwith an elevation of 300 m from mean sea level. It is situated in \n27\u00b000'00.0\"N latitude and 85\u00b020'00.0\" E longitude, covering an area of \n1,126 km2.  \n\n\n\nFigure 1: Location map of Garuda, Rautahat \n\n\n\n2.2 Weather Condition \n\n\n\nThe site where experimental trial was done lies in the sub-tropical zone of \nNepal. It is characterized by three distinct seasons namely, rainy monsoon \n(June \u2013 October), cool winter (November \u2013 February), and hot summer \n(March \u2013 May). Experimental trial was conducted during the month of 29th \n\n\n\nof January to 20th of February. Increased amount of rainfall, relative \nhumidity, maximum and minimum temperature were observed during the \nexperimental period from January to February.  \n\n\n\nFigure 2: Maximum temperature, minimum temperature, Relative \nHumidity (RH) and rainfall at 30 days interval during the growing season \n\n\n\nat Garuda, Rautahat 2075/76 \n\n\n\n2.3 Experimental Design \n\n\n\nThe experiment was laid out as 4\u00d72 factorial Completely Randomized \nBlock Design (RCBD) with 3 replications. The first factor consisted of four \ntypes of Organic Fertilizers namely; Peat moss, Vermicompost, \nTrichocompost and soil as a control and second factor consists of two \nCucumber varieties namely Dynasty and Malini.  \n\n\n\n2.4 Experimental Procedure \n\n\n\nSeedlings were raised on plastic pots measuring 15 cm x 12cm x 7 cm \n(1260 cm3) inside a plastic tunnel on 15th of Magh, 2075. 36 plastic pots \nwere used for each of treatment and thus 288 pots for each replication. \nOne seed were sown in each pot.  \n\n\n\n2.5  Germination%  \n\n\n\n The criterion used for seed germination was taken as emergence of 2 mm \nradicle at the time of observation (Odoemena, 1988). Germination counts \nwere recorded until 23 days after sowing. The germination percentage of \nthe seeds was finally determined for each of the treatments (Abdul-Baki \nand Anderson, 1973). \n\n\n\nGermination%=  \n                                                         \n\n\n\n2.6 Germination Index \n\n\n\nGermination index was calculated by using the formula; \nGermination Index =  \n\n\n\n2.7 Vigour Index \n\n\n\nFor determination of seedling vigour, 5 seedlings were randomly selected \nfrom each treatment and their individual shoot and root length were \nmeasured. The vigour index of the seedlings was determined by following \nthe formula (Abdul-Baki and Anderson, 1973). \n\n\n\nVigor index = [mean of root length (cm) + mean of shoot length (cm)] \u00d7 \npercentage of seed germination (%)  \n\n\n\n2.8 Fresh weight  \n\n\n\nAfter 23 days, the growth parameters were estimated after uprooting and \ncleaning the seedlings. Different parameters were recorded with \nappropriate measures. The fresh weight (in grams per 5 seedlings) was \nmeasured with a digital weighing balance. \n\n\n\n2.9  Dry weight \n\n\n\nDry sample was obtained after oven drying the fresh weight of sample in \nan oven under 105 0C for 24 hrs. Dry weight was measured (in grams per \n5 seedlings) with a digital weighing balance. \n\n\n\n2.10 Root Length \n\n\n\nLength of roots was measured after uprooting and cleaning the seedlings. \nRoot length of five seedlings was measured and average was taken.  \n\n\n\n2.11 Shoot Length \n\n\n\nShoot length of five seedlings was measured and average was taken. \nLength was measured using scale just above the crown region.  \n\n\n\n2.12 Disease Incidence \n\n\n\n Damping off disease incidence of infected seedlings was recorded and \ncalculated by using the following formula,  \n% Disease incidence = (Number of infected seedling) x 100/Number of \ninspected seedling.  \n\n\n\n2.13 Data analysis  \n\n\n\nAll the recorded were arranged systematically on the basis of various \nobserved parameters. Different statistical tools GENSTAT, EXCEL and \nMSWORD were used for the analysis of variance and tabulation. \n\n\n\n3. RESULTS \n\n\n\n3.1 Germination%, Germination Index, No. of Leaves and leaf width \n\n\n\nFor organic fertilizers Germination%, germination index, number of \nleaves and leaf width were all found to be significantly different (P<0.01). \nGermination% was higher in peatmoss (92.64%) than vermicompost \n(84.54%) and control (70.00%) but at par with trichocompost (91.74%). \nSimilarly, peat moss had higher germination index (22.76) than \nvermicompost (19.72) followed by control (16.36) but at par with \ntrichocompost (23.41). Maximum number of leaves was found in \ntrichocompost (3.9) which was at par with vermicompost (3.767) and \nfollowed by peatmoss (3.7) while being lowest in control (3.233). Larger \nleaf width was found in vermicompost (6.160cm) than control(3.7cm) but \nat par with peatmoss (6.152cm) and trichocompost (6.023cm). For \nVarieties, germination% (P<0.05) and Leaf width (P<0.01) were \nsignificantly different, and non-significant for germination index and \nnumber of leaves. Malini variety (86.96%) had more germination than \ndynasty (82.50%). The germination index of malini variety was higher \nthan dynasty variety. Variety malini (5.791cm) had larger leaf width than \ndynasty (5.227cm). \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\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\nNov Dec Jan Feb March April\n\n\n\nM\nax\n\n\n\n a\nn\n\n\n\nd\n M\n\n\n\nin\n. t\n\n\n\nem\n (\n\u00baC\n\n\n\n)\n\n\n\nR\nai\n\n\n\nn\nfa\n\n\n\nll \n(m\n\n\n\nm\n) \n\n\n\nan\nd\n\n\n\n R\nel\n\n\n\nat\niv\n\n\n\ne\n H\n\n\n\nu\nm\n\n\n\nid\nit\n\n\n\ny\n\n\n\nRH rainfall max tem min tem\n\n\n\nNumber of seeds germinated \n\n\n\nTotal number of seeds planted \nX   100 \n\n\n\n +   \n\n\n\nNo. of germinated seeds  \n\n\n\n   (2      nd    co   u    n   t)          + \n\n\n\nNo. of germinated seeds \n(1st count) \nDays to 1st count Days to 2nd count \n\n\n\nNo. of germinated seeds \n(3rd count) \nDays to 3rd count \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 81-85 \n\n\n\nCite the Article: Abishkar Khatiwada, Pragya Adhikari (2020). Effect Of Various Organic Fertilizers On Seedling Health And Vigour Of Different Varieties Of Cucumber In \nRautahat Condition. Malaysian Journal of Sustainable Agriculture, 4(2): 81-85. \n\n\n\nTable 1: Influence of organic fertilizers and variety on germination%, \ngermination index, number of leaves and leaf width \n\n\n\nTreatments Germination% Germination \nIndex \n\n\n\nNo. of \nLeaves \n\n\n\nLeaf \nWidth \n\n\n\nOrganic \nFertilizers \n\n\n\nPeatmoss 92.64a (9.623a) 22.76a 3.700b 6.152a \n\n\n\nControl 70.00c (8.359c) 16.36c 3.233c 3.700b \n\n\n\nVermicompost 84.54b (9194b) 19.72b 3.767ab 6.160a \n\n\n\nTrichocompost 91.74a (9.578a) 23.41a 3.900a 6.023a \n\n\n\nSEm(\u00b1) 2.868 (0.0639) 0.528 0.0546 0.1172 \n\n\n\nLSD(=0.05) 3.552 (0.1940) 1.603 0.1655 0.3555 \n\n\n\nF test 1% 1% 1% 1% \n\n\n\nVarieties \n\n\n\nMalini  86.96a (9.316a) 21.40 3.617 5.791a \n\n\n\nDynasty 82.50b (9.061b) 19.73 3.683 5.227b \n\n\n\nSEm(\u00b1) 0.828 (0.0452) 0.374 0.0386 0.0829 \n\n\n\nLSD (=0.05) 2.511 (0.1371) 1.133 NS 0.2514 \n\n\n\nF test 5% NS NS 1% \n\n\n\nCV% 3.4 (1.7) 6.3 3.7 5.2 \n\n\n\nGrand Mean 84.73 (9.188) 20.56 3.65 5.509 \n\n\n\nNote: CV= Coefficient of Variance, SEm=Standard error of means, LSD= \nLeast Significant Difference. Treatments means followed by the common \nletter (s) are not significantly different from each other based on DMRT at \n5% level of significance. \n\n\n\n2.1 Fresh wt., Dry root wt, Dry Shoot Wt, Vigor Index \n\n\n\nFor organic fertilizers; Fresh weight, dry root weight, dry shoot weight and \nleaf width were all found to be significantly different (P<0.01). Fresh \nweight of seedlings was found to be higher in peatmoss (23.67g) than \ncontrol (11.33g) but at par with vermicompost (22.00g) and \ntrichocompost (21.67g). Dry root weight of trichocompost (0.8857g) was \nfound greater followed by peatmoss (0.8152g), vermicompost (0.5532g) \nand control (0.4264g) respectively. Dry shoot weight of trichocompost \n(1.647g) was found higher followed by peatmoss (1.608g), vermicompost \n(1.346g) and control (0.857g) respectively. Vigour Index of peatmoss \n(2320) was found to be higher followed by trichocompost (2118), \nvermicompost (1822) and control (1391) respectively. For varieties dry \nroot weight, dry shoot weight and leaf width were found to be significantly \ndifferent (P<0.01), and nonsignificant for fresh weight. Variety Malini \n(21.08g) had greater fresh weight than dynasty (18.25g). Variety Malini \nhad more dry root weight than Dynasty. Variety Malini (1.4424g) had \nlarger dry shoot weight than Dynasty (1.2864g). Variety Malini(2017g) \nhad greater vigour index than Dynasty(1809g). \n\n\n\nTable 2: Influence of organic fertilizers and variety on fresh weight, \ndry root weight, dry shoot weight and vigor index \n\n\n\nTreatments Fresh \nwt \n\n\n\nDry root \nwt \n\n\n\nDry Shoot \nWt \n\n\n\nVigor \nIndex \n\n\n\nOrganic \nFertilizers \n\n\n\nPeatmoss 23.67a 0.8152b 1.608b 2320a \n\n\n\nControl 11.33b 0.4264d 0.857d 1391d \n\n\n\nVermicompost 22.00a 0.5532c 1.346c 1822c \n\n\n\nTrichocompost 21.67a 0.8857a 1.647a 2118b \n\n\n\nSEm(\u00b1) 0.834 0.00898 0.01061 43.8 \n\n\n\nLSD(=0.05) 2.529 0.02724 0.03219 132.8 \n\n\n\nF test 1% 1% 1% 1% \n\n\n\nVarieties \n\n\n\nMalini  21.08a 0.7237a 1.4424a 2017a \n\n\n\nDynasty 18.25b 0.6166b 1.2864b 1809b \n\n\n\nSEm(\u00b1) 0.590 0.00635 0.0075 31 \n\n\n\nLSD (=0.05) 1.789 0.01926 0.02276 93.9 \n\n\n\nF test NS 1% 1% 1% \n\n\n\nCV% 10.4 3.3 1.9 5.6 \n\n\n\nGrand Mean 19.67 0.6701 1.3644 1913 \n\n\n\nNote: CV= Coefficient of Variance, SEm= Standard error of means, LSD= \nLeast Significant Difference. Treatments means followed by the common \nletter (s) are not significantly different from each other based on DMRT at \n5% level of significance. \n\n\n\n2.2 Root Length, shoot length, Root/Shoot length ratio, Damping \nOff. \n\n\n\nFor organic fertilizers shoot length and root to shoot length ratio were \nfound to be significantly different(P<0.01). Root length was insignificant \nwith organic fertilizers and variety. Shoot length was significantly greater \nin trichocompost (7.980cm) than vermicompost(7.263cm) and \ncontrol(5.167cm) but at par with peatmoss (7.853cm). Root and shoot \nlength ratio was found significantly higher in control (2.977) than other \norganic fertilizers. However, the three organic fertilizers, peatmoss \n(2.118), vermicompost (2.007) and trichocompost (1.962) were at par to \neach other. For varieties shoot length and root to shoot length ratio were \nfound to be significantly different(P<0.01). Variety Malini (7.413cm) had \nsignificantly longer shoot length than Dynasty(6.718cm). Root to shoot \nlegth ratio was found significantly higher in variety dynasty (2.394) than \nmalini (2.137).  \n\n\n\nTable 3: Influence of organic fertilizers and variety on root length, \nshoot length and root to shoot length ratio \n\n\n\nTreatments Root \nLength \n\n\n\nShoot \nlength \n\n\n\nRoot/Shoot length \nratio \n\n\n\nOrganic Fertilizers \n\n\n\nPeatmoss 16.89 7.980a 2.118b \n\n\n\nControl 15.21 5.167c 2.977a \n\n\n\nVermicompost 14.58 7.263b 2.007b \n\n\n\nTrichocompost 15.39 7.853a 1.962b \n\n\n\nSEm(\u00b1) 0.390 0.1157 0.0577 \n\n\n\nLSD(=0.05) NS 0.3510 0.1750 \n\n\n\nF test NS 1% 1% \n\n\n\nVarieties \n\n\n\nMalini  15.64 7.413a 2.137b \n\n\n\nDynasty 15.40 6.718b 2.394a \n\n\n\nSEm(\u00b1) 0.276 0.0818 0.0408 \n\n\n\nLSD (=0.05) NS 0.2482 0.1237 \n\n\n\nF test NS 1% 1% \n\n\n\nCV% 6.2 4 6.2 \n\n\n\nGrand Mean 15.52 7.066 2.266 \n\n\n\nNote: CV= Coefficient of Variance, SEm=Standard error of means, LSD= \nLeast Significant Difference. Treatments means followed by the common \nletter (s) are not significantly different from each other based on DMRT at \n5% level of significance. \n\n\n\nFor organic fertilizers; damping off 7DAS, 11DAS and 15 DAS were found \nto be significantly different(P<0.01). At 7DAS the highest effect against \ndamping off was recorded in trichocompost(1.567%) and was followed by \ncontrol(2.175%) being at par with vermicompost(2.686%) while poor \nperformance was found in peatmoss(3.006%). Similarly, at 11DAS also \ntrichocompost showed the best performance (3.750%) against damping \noff followed by control (7.083%) being at par with vermicompost \n(7.917%) and peatmoss with the highest damping off incidence (10.00%). \nAt 15DAS trichocompost was superior with lowest damping off incidence \n(4.58%) followed by control (10.42%) and vermicompost (13.75%) while \npeatmoss with the least performance (16.67%). For varieties; damping off \nat 7DAS, 11DAS and 15 DAS were found to be significantly \ndifferent(P<0.01). Irrespective of the days, variety dynasty showed the \nbest performance over malini against damping off incidence. \n\n\n\nTable 4: Influence of organic fertilizers and variety on disease \nincidence at 7DAS, 11DAS and 15DAS \n\n\n\nTreatments Disease Incidence \n\n\n\n7 DAS 11 DAS 15 DAS \n\n\n\nOrganic Fertilizers \n\n\n\nPeatmoss 3.006a 10.00a 16.67a \n\n\n\nControl 2.175b 7.083b 10.42c \n\n\n\nVermicompost 2.686ab 7.917b 13.75b \n\n\n\nTrichocompost 1.567c 3.750c 4.58d \n\n\n\nSEm(\u00b1) 0.2494 0.605 0.673 \n\n\n\nLSD(=0.05) 0.5348 1.835 2.041 \n\n\n\nF test 0.01 0.01 0.01 \n\n\n\nVarieties \n\n\n\nMalini  3.014 10.21a 14.79a \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 81-85 \n\n\n\nCite the Article: Abishkar Khatiwada, Pragya Adhikari (2020). Effect Of Various Organic Fertilizers On Seedling Health And Vigour Of Different Varieties Of Cucumber In \nRautahat Condition. Malaysian Journal of Sustainable Agriculture, 4(2): 81-85. \n\n\n\nDynasty 1.703 4.17b 7.92b \n\n\n\nSEm(\u00b1) 0.1763 0.428 0.476 \n\n\n\nLSD (=0.05) 0.3782 1.297 1.443 \n\n\n\nF test 0.01 0.01 0.01 \n\n\n\nCV% 18.3 20.6 14.5 \n\n\n\nGrand Mean 2.359 7.19 11.35 \n\n\n\nNote: CV= Coefficient of Variance, SEm=Standard error of means, LSD= \nLeast Significant Difference. Treatments means followed by the common \nletter (s) are not significantly different from each other based on DMRT at \n5% level of significance. \n\n\n\n4. DISCUSSION\n\n\n\nIn recent years, interest in the use of organic fertilizers as a potting media \nhas grown particularly with respect to their use as environment friendly \nefficient media for germination, increased vigour and healthy growth of \nseedlings. In this study, trichoderma inoculated trichocompost increased \ngermination percentage and germination index, which finally produced \nhealthier and vigorous seedlings. This could be due to interactions \nbetween Trichoderma, pathogen and plant that helped to maintain proper \nenvironment for the germination and growth of seedlings (Vinale et al., \n2008). Higher germination and germination index also could be due to the \nproduction of Indole Acetic Acid produced by the strain of Trichoderma \nharzianum. \n\n\n\nTrichocompost also had significant effect on root length(P<0.05), number \nof leaves(P<0.01) and leaf width(P<0.01) which was supported (Yedidia \net al., 1999). Their experiment demonstrated that Trichoderma harzianum \nincreased the chitinase and peroxidase activities in roots as well as leaves \nof treated plants where treated roots showed 6.4 fold increase and treated \nleaves showed 3.2 fold increased compared with the non treated plants at \npeak chitinase activity while treated plants showed 2-3 fold increase in \nboth activities in root and leaves at peak peroxidase activity. \nTrichocompost significantly increased the root length (P<0.05) and shoot \nlength(P<0.01) of seedlings. The result was similar to the experiment \nshown (Harman et al., 2004). Their experiment on 30 treated and 30 \nuntreated maize inbred line Mo17 resulted significant increase in root \nlength and shoot length on Trichoderma harzianum strain T22 treated \nplants. Seedlings grown in trichocompost had increased dry weight. \nSimilar results were shown in experiment done on tomato, pepper and \ncucumber on radish (Chang et al., 1986; Baker et al., 1984). Also, \ntrichocompost itself contained nutrients and organic matter available for \nseedling growth. \n\n\n\nSeedlings grown in trichocompost had the lowest disease incidence \n(P<0.01). This could be due to the antagonistic nature of Trichoderma on \nother pathogenic fungi (Vinale et al., 2008). It also colonizes the root and \nstimulates the plant defense system, leading to the production of \nbiochemical and structural compounds (Yedidia et al., 1999; Harman et al., \n2004; Harman, 2006; Ousley et al., 1994). Some researcehrs suggested \nthat peptaibols trichorzianin synthesized by Trichoderma inhibited \u03b2- 1,3 \nglucan synthatase activity in host fungus and preventing the \nreconstruction of pathogen cell wall, thus fascilitating the disruptive \naction of T. harzianum \u03b2- 1,3 glucanases (Lorito et al., 1996). This could \nhave caused the suppression of disease pathogens and helped to emerge \nseedling stronger and more vigorous. Trichoderma harzianum thus \nconsistently promoted the overall growth of plant (Ousley et al., 1994).  \n\n\n\nRoot length shoot length and vigor index was seen superior in peat moss. \nThis could be due to less or non-compaction in this potting media which \ncould have provided more aeration and helped in easy elongation of roots \nand development of root hairs. The organic matter in peatmoss also \nstimulated the root and shoot growth of seedlings to make it more \nvigorous. The greater water holding capacity of peat moss helps to provide \nmoisture persistently and thus water is available to the seed continuously. \nThis could have increased the germination% and germination index in \npeatmoss media. Root to shoot length ratio was seen significantly higher \nin controlled condition. Since the controlled condition had lower water \nholding capacity, the available moisture and soluble nutrients in the media \nwas comparatively lesser which caused slower growth of seedlings and \nthus shoot length was minimum. But the stress in root region caused plant \nto spread deeper for moisture and soluble nutrients and thus root length \nwas higher. Similarly, variety malini was found to be superior at all \nparameters except damping off which is supported (Rawat, 2013).  High \ndamping off incidence in malini could be due to its vigorous growth which \nmade it more succeptible to the disease. \n\n\n\n5. CONCLUSION \n\n\n\nThe experiment was conducted to find out the effect of various sources of \norganic fertilizers on seedling health and vigor of different varieties of \ncucumber. Trichocompost had significantly higher germination, \ngermination index, number of leaves, leaf width, fresh weight, dry weight, \nshoot length, and lowest damping off incidence. From this experiment it \ncan be concluded that variety Malini in trichocompost proved to be better \nfor raising seedlings of cucumber. Thus, it is suggested to use \ntrichocompost as a potting media for raising nursery seedlings as it \nproduced vigorous seedlings and suppressed the disease incidence. \nThough peatmoss also had positive results on seedling growth and vigour \nbut it has several downsides. It is more expensive than trichocompost, \nnon-renewable, and only commercially available while trichocompost is \ncost effective, easily prepared from locally available resource and can \nproduce healthy seedlings. \n\n\n\nREFERENCES \n\n\n\nAbad, M., Noguera, P., Puchades, R., Maquieira, A., Noguera, V., 2002. \nPhysico-chemical and chemical properties of some coconut coir dust for \n\n\n\nuse as a peat substitute for containerised ornamental plants, 82. \n\n\n\nAbdul-Baki, A.A., Anderson, J.D., 1973. Vigor Determination in Soybean \nSeed by Multiple Criteria1. Crop Science, 13 (6), 630. \n\n\n\nhttps://doi.org/10.2135/cropsci1973.0011183X001300060013x \n\n\n\nAICC., 2018. Krishi DIary. Hariharbhawan, Lalitpur: Ministry of \nAgriculture and Livestock Development, Government of Nepal. \nRetrieved from https://aitc.gov.np/public/uploads/Pdffile/Krishi \n\n\n\nDiary 2075 Final-31546.pdf \n\n\n\nArancon, N.Q.N.N.Q., Edwards, C.A.C.C.A., Lee, S., Byrne, R., Atik, A., Y\u0131lmaz, \nB., Preece, J.E., 2004. Influence of vermicomposts on plant growth. Soil \nNutrients, 28(3), 1\u201325. \n\n\n\nhttps://doi.org/doi:10.1201/9781420039719.ch20 \n\n\n\nAyoola, O., Makinde, E., 2009. Maize Growth, Yield and Soil Nutrient \nChanges With N-Enriched Organic Fertilizers. African Journal of Food \n\n\n\nAgriculture Nutrition and Development, 9 (1), 580\u2013592. Retrieved from \nhttps://www.ajol.info/index.php/ajfand/article/viewFile/19214/366\n44  \n\n\n\nBaker, R., Elad, Y., Chet, I., 1984. The Controlled Experiment in the \n\n\n\nScientific Method with Special Emphasis on Biological Control. The \nAmerican Phytopathological Society, 74 (9), 1019\u20131021. Retrieved \nfrom \n\n\n\nhttps://www.apsnet.org/publications/phytopathology/backissues/D\nocuments/1984Articles/Phyto74n09_1019.pdf \n\n\n\nChang, Y.C., Chang, Y.C., Baker, R., Kleifeld, O., Chet, I., 1986. Increased \n\n\n\nGrowth of Plants in the Presence of the Biological Control Agent \nTrichoderma harzianum. Plant Disease, 70 (2), 145\u2013148. \nhttps://doi.org/10.1094/PD-70-145 \n\n\n\nEdwards, C.A., Burrows, I., 1988. potential of earthworm composts as plant \n\n\n\ngrowth media. Earthworms in Waste and Environmental Management \n/ Edited by Clive A. Edwards and Edward F. Neuhauser. Retrieved from \nhttp://agris.fao.org/agris-\n\n\n\nsearch/search.do?recordID=US201302696106 \n\n\n\nFDA., 2016. Cucumbers PDF. Retrieved from \nhttps://www.wifss.ucdavis.edu/wp-\n\n\n\ncontent/uploads/2016/10/Cucumbers_PDF.pdf \n\n\n\nGravel, V., Antoun, H., Tweddell, R.J., 2007. Growth stimulation and fruit \nyield improvement of greenhouse tomato plants by inoculation with \nPseudomonas putida or Trichoderma atroviride: Possible role of indole \n\n\n\nacetic acid (IAA). Soil Biology and Biochemistry, 39 (8), 1968\u20131977. \nhttps://doi.org/10.1016/j.soilbio.2007.02.015 \n\n\n\nHanada, T., 1991. Effect of Mulching in Temperate Regions in Japan. Ayabe. \n\n\n\nRetrieved from \nhttp://www.fftc.agnet.org/htmlarea_file/library/20110801145616/e\nb332.pdf \n\n\n\nHarman, G.E., 2006. Overview of Mechanisms and Uses of Trichoderma \n\n\n\nspp. Phytopathology, 96 (2), 190\u2013194. \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 4(2) (2020) 81-85 \n\n\n\nCite the Article: Abishkar Khatiwada, Pragya Adhikari (2020). Effect Of Various Organic Fertilizers On Seedling Health And Vigour Of Different Varieties Of Cucumber In \nRautahat Condition. Malaysian Journal of Sustainable Agriculture, 4(2): 81-85. \n\n\n\nhttps://doi.org/10.1094/PHYTO-96-0190 \n\n\n\nHarman, G.E., Petzoldt, R., Comis, A., Chen, J., 2004. Interactions Between \n\n\n\nTrichoderma harzianum Strain T22 and Maize Inbred Line Mo17 and \nEffects of These Interactions on Diseases Caused by Pythium ultimum \n\n\n\nand Colletotrichum graminicola. Phytopathology, 94 (2), 147\u2013153. \nhttps://doi.org/10.1094/PHYTO.2004.94.2.147 \n\n\n\nKhan, R., Saxena, S.K., 1997. Integrated management of root knot \nnematode Meloidogyne javanica infecting tomato using organic \n\n\n\nmaterials and Paecilomyces lilacininus. Bioresource Technology, 61, \n247\u2013250. \n\n\n\nKrishna, J.B., Raj, B.M., Hari, G.K., Khanal, M., Bahadur, G.S., Dhakal, R., \nSthapit, B., 2017. Released and Promising Crop Varieties for Mountain \n\n\n\nAgriculture in Nepal (1959 2016)). Retrieved from \nhttp://agrobiodiversityplatform.org/cropbiodiversity/files/2017/03/\n\n\n\nVariety-Catalogue-Ebook.pdf \n\n\n\nLorito, M., Farkas, V., Rebuffat, S., Bodo, B., Kubicek, C.P., 1996. Cell wall \nsynthesis is a major target of mycoparasitic antagonism by \nTrichoderma harzianum. Journal of Bacteriology, 178 (21), 6382\u20136385. \n\n\n\nhttps://doi.org/10.1128/jb.178.21.6382-6385.1996 \n\n\n\nMalepszy, S., Niemirowicz-Szczytt, K., 1991. Sex determination in \ncucumber (Cucumis sativus) as a model system for molecular biology. \nPlant Science, 80 (1\u20132), 39\u201347. https://doi.org/10.1016/0168-\n\n\n\n9452(91)90271-9 \n\n\n\nOdoemena, C.S., 1988. Breaking of seed coat dormancy in a medicinal plant \nTetrapleura tetraptera (Schum &amp; Thonn). The Journal of \n\n\n\nAgricultural Science, 111 (2), 393\u2013394. \nhttps://doi.org/10.1017/S0021859600083349 \n\n\n\nOsunlaja, S.O., 1989. Effect of organic soil amendments on the incidence of \nbrown spot disease in maize caused by Physoderma maydis. Journal of \n\n\n\nBasic Microbiology, 29, 501\u2013505. \n\n\n\nOusley, M.A., Lynch, J.M., Whipps, J.M., 1994. Potential of Trichoderma spp. \n\n\n\nas consistent plant growth stimulators. Biology and Fertility of Soils, 17 \n(2), 85\u201390. https://doi.org/10.1007/BF00337738 \n\n\n\nPapavizas, G.C., 1985. Trichoderma and Gliocladium: Biology, Ecology, and \n\n\n\nPotential for Biocontrol. Annual Review of Phytopathology, 23(1), 23\u2013\n54. https://doi.org/10.1146/annurev.py.23.090185.000323\n\n\n\nPishgar-Komleh, S.H., Omid, M., Heidari, M.D., 2013. On the study of energy \nuse and GHG (greenhouse gas) emissions in greenhouse cucumber \n\n\n\nproduction in Yazd province. Energy, 59, 63\u201371. \nhttps://doi.org/10.1016/j.energy.2013.07.037 \n\n\n\nRaviv, M., Chen, Y., Inbar, Y., 1986. Peat and peat substitutes as growth \nmedia for container-grown plants. In the Role of Organic Matter in \n\n\n\nModern Agriculture, pp. 257\u2013287. Dordrecht: Springer Netherlands. \nhttps://doi.org/10.1007/978-94-009-4426-8_11  \n\n\n\nRawat, M., 2013. Evaluation of f1 hybrids of cucumber (Cucumis sativus L.) \n\n\n\nfor early fruit yield in polyhouse under tarai condition of Uttarakhand. \nRetrieved from \nhttps://krishikosh.egranth.ac.in/handle/1/5810081906 \n\n\n\nVinale, F., Sivasithamparam, K., Ghisalberti, E.L., Marra, R., Woo, S.L., \n\n\n\nLorito, M., 2008. Trichoderma\u2013plant\u2013pathogen interactions. Soil \nBiology and Biochemistry, 40 (1), 1\u201310. \nhttps://doi.org/10.1016/j.soilbio.2007.07.002 \n\n\n\nWilson, D.P., Carlile, W.R., 1989. Plant Growth in Potting Media Containing \nWorm-Worked Duck Waste. Acta Horticulturae, (238), 205\u2013220. \nhttps://doi.org/10.17660/ActaHortic.1989.238.24 \n\n\n\nYedidia, I., Benhamou, N., Chet, I., 1999. Induction of defense responses in \ncucumber plants (Cucumis sativus L.) by the Biocontrol agent \nTrichoderma harzianum. Applied and Environmental Microbiology, 65 \n(3), 1061\u20131070.\n\n\n\n\n\n"
"\n\n Malaysian Journal of Sustainable Agriculture (MJSA)1(2) (2017) 02-05\n\n\n\nWATER RESOURCES MANAGEMENT IN LIBYA: CHALLENGES AND FUTURE PROSPECTS\nJauda R. Jouda Hamad1,2, Marlia M. Hanafiah1*, Wan Zuhairi W. Yaakob1\n\n\n\n1School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, \n43600 Bangi, Selangor, Malaysia\n2Marine Science Department, Faculty of Natural Resource and Environmental Science, Omar Al-Mukhtar University, Libya\n*Corresponding Author E-mail: mhmarlia@ukm.edu.my; Tel.: 03-89215865; fax: +603-89253357\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article history: \n\n\n\nReceived 12 September 2017 \nAccepted 19 October 2017 \nAvailable online 30 October 2017\n\n\n\nKeywords: \n\n\n\nWater shortage; Water \navailability; Sustainable water \nresources management; Human \nhealth; Libya\n\n\n\nABSTRACT\n\n\n\nWater shortage or scarcity is becoming a major concern for many nations across the world. The situation is worsened \nby rapid urbanization and population growth in developing countries, thus increase competition for water used for \nirrigated agriculture. Various efforts have been made by the authorities in the developing countries to provide \nsufficient water and improve the quality of water resources. Yet, there are still many developing countries facing \nshortages of water for domestic and agricultural purposes, especially during the dry months of the year. Libya is one \nof the Northern African countries that have been experiencing water shortages especially in urban areas. This paper \naims to identify the current situation and constraints of water resources management in Libya. The latter part is \ndevoted to the solutions and recommendations at individual, community, state and government levels that can help \nsolving the water problems in Libya. A number of previous studies on the water resources management and \nchallenges perceived by both developed and developing countries were critically reviewed. It was found that water \nscarcity in developing countries is expected to be worsen as their population are expected to increase gradually year \nby year and it can be summarized from the reviewed previous studies that lack of government planning, industrial \nand human wastes along with government intervention and mismanaging water resources are some of the critical \nconstraints towards achieving sustainable management in most of the countries including Libya. Potential solutions \nsuch as improving supply demand and good quality management of water resources must be taken into consideration. \nIn addition, active participation from the local residents by enhancing awareness amongst them would be one of the \nsupportive strategies to minimize the constraints. Sustainable economic and environmental management together \nwith efficient use of water is required to conserve our clean water supply.\n\n\n\nCite this article as: Jauda R. Jouda Hamad, Marlia M. Hanafiah, Wan Zuhairi W. Yaakob  (2017).  Water Resources Management \nIn Libya: Challenges And Future Prospects.   Malaysian Journal of Sustainable Agriculture,  1(2):02-05.\n\n\n\n1. INTRODUCTION\n\n\n\nWater is vital for sustaining all existence life forms and access to clean and \nsafe drinking water is an important for human need [1]. Developing cities \nare increasingly facing critical water plight in terms of imbalance between \nsupply and demand [2]. Water issues of the world are neither identical, nor \nfixed or consistent over time. They often fluctuate very significantly from \none location to another, even within a single nation, from one season to \nanother, and also from one year to another. Solutions to water problems \ndepend not only on water availability, but also on several other factors \namong processes through water supply management [3]. In addition, the \nwater is in movement, or consistent as it is in lakes, it fixedly contains \ninapposite materials, some due to natural causes but others due to human \nactivities. All these, plus natural differences in water availability, make its \nrationalistic planning and management a very complex and difficult task \nunder the best of circumstances [4]. The current state of water institutional, \ninfrastructure and water management policies in Libya permit the \nrecognitions and evaluation of a range of options for improving water use \ncapacity in agriculture and the potential role of water pricing in \naccomplishing sustainability of water sources [5]. The condition of water \nsupply has turned into more problematic with quickly increasing population \nand minimum rainfall. Consequently, soon after the discovery of fresh \ngroundwater in the deserts of southern Libya, the local authority has made \nmassive efforts to address its water shortfall problems, fundamentally \nthrough the enforcement of The Great Manmade River Project to sustain its \neconomy, however, it does not solve the water scarcity in Libya [6].\n\n\n\n2. WATER RESOURCES AND AVAILABILITY\n\n\n\nWater resource and availability play a vital role in both the environment \nand human life. Eight countries have reviewed in terms of water resources \nin each country accordingly on both climate change and geographical \nlocation. The data presented in Table 1 shows the difference between each \ncountry with aim to compare the average of water availability in these \ncountries.\n\n\n\nTable 1: Water resources and availability in eight selected countries.  \n\n\n\nContents List available at RAZI Publishing \nMalaysian Journal of Sustainable Agriculture (MJSA)\n\n\n\nISSN: 2521-2931 (Print)\nISSN: 2521-294X (Online)\n\n\n\nJournal Homepage:  : http://www.razipublishing.com/journals/malaysian- journal- \nofsustainable-agriculture-mjsa/\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.02.05\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited\n\n\n\n\nhttps://doi.org/10.26480/mjsa.02.2017.02.05\n\n\n\n\n\n\n3  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 02-05  \n\n\n\n3. CURRENT AND FUTURE WATER CONSUMPTION IN LIBYA\n\n\n\nEstimation was done in 2008 for future consumption in Libya including \nagricultural, domestic and industrial uses during 2006 to 2020. The future \nevaluation of water utilization for all potential purposes specified the total \nwater consumption rising from 6,293.89 million m3 in 2006 to 12,473.20 \nmillion m3 in 2020 with an average of compound annual rate of 4.97% \n[15]. In 2020, it is expected that the increase would be 98% of the water \nconsumption in 2006. Table 3 shows some remarkable variation for water \ndemands in different usage and purposes in Libya due to unsuitable \nclimatic and soil conditions.  \n\n\n\nTable 3: Water demands by different uses in Libya, 2006-2020 forecasts. \n\n\n\nA study on water resources in Nigeria and found groundwater resources \nfrom boreholes and hand dug wells have become the greater important \nsources of public and private water in towns and rural areas and also \nmentioned that expeditious growth in population, civilization, \nindustrialization and competitiveness for economic development [7]. Water \nresource has become impotent for depletion and degradation and there is \nan imbalance between demand and availability of water resources. In \ncontrast, carried out a study in India and reported that India\u2019s annual \nprobable core of groundwater renew from rainfall is around 342.43 km3 \n[8]. In consequences, the population of India is predicted to be 1,333 \nmillion and 1,581 million in high growth of 2025 and 2050, respectively. A \nresearch found that Sudan is affected by lack of awareness and legislation \nof the country as well as unequal distribution among the cities, and the \nvariance between water supply and demand is growing with time due to \nthe rapid population growth and aridity [9].    \n\n\n\nIn comparison, a research investigated the current situation of water \nresource and its management in Ireland [10]. They observed that Ireland is \nthe richest country in the world in terms of water availability. The great \nmajority of drinking water in Ireland is extracted from surface water \n(81.9%) and limited fraction coming from groundwater 10.3% and springs \n7.8%. A researcher also stated in their research that local authorities are in \ncharge of providing water to most of the households and businesses in the \ncountry [10]. A study by identified that the groundwater resource in \nFinland is 250 m3 per day while the annual rainfall is ranging from -5% and \n+10% depends on the catchment area [11]. An attempt was made in China \nto evaluate the main water resources and considered large with rating sixth \nin the world per capita representing only 25% of the world average [12]. \nFurthermore, the allocation of water resources is spatially and seasonally \nexplicit. Northern and southern part of China holds only 18% of the total \nwater in spite of having 65% of the total arable land. By contrast, the south \nreceives water from summer rainfalls, which is often wasted through \nflooding according to the findings found that North of China faces severe \nwater scarcity.  In a study, they have estimated the water recourse in \nAustralia and delineated that the country\u2019s water resources are extremely \nchanging and reflecting with climate change seasonally [13]. Therefore, \nAustralia\u2019s water resources are good in quantities and qualities and out of \nthe water stress ranked in the world.\n\n\n\nIn comparison, the main water sources in Libya come from four sources \nwhich are groundwater supplies almost 95% of Libya\u2019s needs; surface \nwater only with 2% comprising rainwater and dam constructions; \ndesalinated from sea water provides 2% and wastewater recycling 1% \n(Figure 1).  A research divided the major sources of groundwater in Libya \ninto five water basins namely Jabal al-Akhdar, Kufra as -Sarir, Jefara plain \nregion, Nafusah Al-Hamada and Murzek [5].  Groundwater in the country \ncan be grouped into renewable resources, mainly found in superficial \naquifers, and the non-renewable resources (fossil water) come up against \nin deep aquifers. Groundwater reservoir characteristics are presented in \nTable 2. They also identified the situation of water shortage in Libya and \nthey considered it worse due to several reasons such as growing \npopulation, its predominant arid geography and desertification under a \nchanging climate. An increasing dependence on oil earnings has seen \nagriculture reduce to only 2% by 2007, and facing remarkable challenges \nwith domestic food supplies that unable to meet demand and cultivation \nlimited by a lack of arable land and water supply [14]. The current \nrestricted water resources will get even more and more limited. This more-\nrequired water later on might come at a high budgetary, conservation and \nenvironmental costs.  \n\n\n\nFigure 1: Percentages of available water in Libya  \n\n\n\nTable 2: Groundwater reservoirs characteristics \n\n\n\nSource: Lawgali (2008) \n\n\n\n4. WATER POLLUTION\n\n\n\nWater pollution is a paramount concern existing in both developed and \ndeveloping nation\u2019s drinking water.  It has been suggested that it is the \nleading worldwide cause of death and disease accounts for the death of \nmore than 14,000 people daily [16]. Physico-chemical and bacteriological \nanalyses conducted in Ilorin, Nigeria investigated the quality of drinking \nwater and found that total hardness ranged between 51 - 175.5 mg/l while \nconductivity was between 65 \u2013 318\u00b5s. Ca and Mg+2 varied between 33.7 - \n102.3 mg/l and 3.5 - 57.1 mg/l respectively. E. coli and coliforms were \nfound high counts and concluded that the main sources of pollution were \nspecified to be the direct runoff from the industries and refuse dumps \nwithin the Asa Dam industrial estate [17]. Another study carried out on \nwater pollution in Aligarh city, India examined several trace elements (Ni, \nZn, Fe, Pb, Cd, Co, Cu and Mn), reported that excessive ratio of some \nelements in the drinking water and poor health of residents are existing \ncause of spreading of diseases in Aligarh and the water considered \npolluted [18]. In comparison, groundwater is of a good quality in the north-\nsouth aquifer, this is because the aquifer lies close to the sources of natural \nrecharge from the Sirir Basin Aquifer. In the area of north Alwahat near the \noil settlement where groundwater over-abstraction has been most severe \nsaline intrusion has occurred. In the Jakera area, north of Alwahat, the \nwater quality is too poor even to grow salt-tolerant crops such as dates. A \nstudy conducted in Al Wahat area (Libya) mentioned that the quality of \nwater drawn from wells is deteriorating with time. 34 water samples \ncollected from domestic and agricultural water wells were analyzed to \nidentify the water type and ions concentration and found that significant \nincrease of dissolved salts, especially nitrates [19]. Therefore, irrigation \nwells revealed that suffering from nitrate contamination caused an \nincrease of the chance of nitrate pollution.\n\n\n\nElsewhere, evaluated the quality of drinking water in Finland [20]. Data \nwere collected from different size waterworks in the whole country \nincluded wells, water cooperatives, and small, medium-sized, large \nwaterworks and consumer\u2019s tap. A study concluded that drinking water in \nFinland matched with high quality and out of any substances [20].  \nSimilarly, a study conducted in Sudan stated that resigning salinity of \ndrinking water was observed together with human incompatibility and \nincrease in livestock mortalities also in high concentrations and a toxic \nhealth endangerment were assessed for lead and barium [21]. A study \ninvestigated water quality in Ireland and their results showed that \nconcentration of arsenic in groundwater is below 7.5 \u03bcg [22]. They \nindicated that several areas with elevated arsenic concentrations in \ngroundwater were treated using technologies.  In China, evaluated the \nquality of drinking water and they found out that heavy metal releases of \nMn, Ni, Cu, Pb, Cr and as found in high ratio in both groundwater and \nresidential wells [23]. Investigation of water quality has been reported in \nAustralia to examine the concentration of eight heavy metals in surface and \ngroundwater and found that metal concentrations (except Cu, Mn, Ni, and \nZn) in both surface and groundwater were above the drinking water \nquality guidelines [24].  \n\n\n\nCite this article as: Jauda R. Jouda Hamad, Marlia M. Hanafiah, Wan Zuhairi W. Yaakob  (2017).  Water Resources Management \nIn Libya: Challenges And Future Prospects.   Malaysian Journal of Sustainable Agriculture,  1(2):02-05.\n\n\n\n\n\n\n\n\n4  Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 02-05  \n\n\n\n7. CONCLUSION\n\n\n\nWater shortage or scarcity is becoming a major concern for many nations \nacross the world and Libya without exception. The situation is worsened by \nrapid urbanization and population growth in developing countries.  This \nstudy was aimed at identifying the current situation of water resources \nmanagement and the constraints currently facing by Libya. As conclusion, it \ncan be said that water shortage in Libya is due to some natural causes and \nhuman activities. Climate change is the main reason of water scarcity due to \nits geographical location. Lack of financial support from high authorities is \nseen as another reason leading to water shortage and scarcity. Currently, \nLibya is considered poor in managing its water supply management and \nresources because of the instability of political condition in the country. \nThese issues have made the situation of water management resources \nunsustainable.  Perhaps, one of the critical problems that hinder \nsustainable water management development in Libya is the lack of \nrenewable water resources. Therefore, it is suggested that installing \ndesalination according to Libya's geographical location which is in the \nMediterranean Sea could help to elevate water shortage issue. There is \nindeed an urgent need for Libya to sustain its water resource management \nand this could be possible if the above-mentioned solutions be taken into \nserious consideration by the Libyan government.    \n\n\n\nACKNOWLEDGEMENT \n\n\n\nMarlia Mohd Hanafiah was financed by the UKM research grants \n(FRGS/2/2013/STWN01/UKM03/1) and (TD-2014-012). Special thanks \nalso extended to the financial support provided by the Ministry of Higher \nEducation in Libya.  \n\n\n\nREFERENCES \n\n\n\n[1] Iyer, V., Choudhury, N., Azhar, G.S., and Somvanshi, B. 2014. Drinking \nWater Quality Surveillance in a Vulnerable Urban Ward of Ahmedabad. \nHealth, 6, 1165-.12-25.\n\n\n\n[2] Raj, K. 2013. Sustainable Urban Habitats and Urban Water Supply: \nAccounting for Unaccounted for Water in Bangalore City, India. Current \nUrban Studies, 1, 156-165.\n\n\n\n[3] Biswas, A. K. 2004. Integrated water resources management: a \nreassessment: a water forum contribution. Water international, 29, \n248-256.\n\n\n\n[4] Biswas, A. K. 2008. Integrated water resources management: Is it \nworking? International Journal of Water Resources Development, 24, 5-22.\n\n\n\n[5] Abdudayem, A., and Scott, A. H. 2014. Water infrastructure in Libya and \nthe water situation in agriculture in the Jefara region of Libya. African \nJournal of Economic and Sustainable Development, 3, 33-64.\n\n\n\n[6] Wheida, E., and Verhoeven, R. 2007. An alternative solution of the water \nshortage problem in Libya. Water Resources Management, 21, 961-982.\n\n\n\n[7] Nwankwoala, H. O. 2014. Problems and options of integrated water \nresources management in Nigeria: administrative constraints and policy \nstrategies. International Letters of Natural Sciences, 9, 12-25.\n\n\n\n[8] Bhattacharyya, A., Reddy, J. S., Ghosh, M., and Naika, R. H. 2015. Water \nResources in India: Its Demand, Degradation and Management. \nInternational Journal of Scientific and Research Publications, 5 (12), 1-11.\n\n\n\n[9] Omer, A. M. 2010. Water resources management and sustainable \ndevelopment in Sudan. International Journal of Water Resources and \nEnvironmental Engineering, 2, 190-207.\n\n\n\n[10] Zhao, Y., and Crosbie, D. 2012. Water pricing in Ireland: a techno-\neconomic and political assessment. International Journal of Environmental \nStudies, 69, 427-442.\n\n\n\n[11] Arola, T. 2015. Groundwater as an energy resource in Finland. \nAcademic Dissertation. Department of Geosciences and Geography, \nUniversity of Helsinki, Finland.\n\n\n\n[12] Piao, S., Ciais, P., Huang, Y., Shen, Z., Peng, S., Li, J., Zhou, L., Liu, H., Ma, \nY., and Ding, Y. 2010. The impacts of climate change on water resources and \nagriculture in China. Nature, 467, 43-51.\n\n\n\n[13] Chartres, C., and Williams, J. 2006. Can Australia overcome its \nwater scarcity problems?Journal of Developments in Sustainable \nAgriculture, 1, 17-24.\n\n\n\n[14] Wheida, E., and Verhoeven, R. 2004. Desalination as a water supply \n\n\n\n5. CONSTRAINTS OF WATER SUPPLY MANAGEMENT\n\n\n\nIn Nigeria, approximately 60-70% of the population is currently without \neither water or wastewater services, leakage rate is around 50%, illegal \nconnections are rising and urban community does not have a proper \nsewerage system [26]. Water supply sector in India is facing challenges to \nensure supply for potable water to half of people who are currently living \nwithout access to sustainable safe drinking water. Therefore, major \nconstraints facing by many developing countries include increasing \nscarcity of water, low pricing, high subsidy, high cost of recovery, high \nnon-revenue water due to poor maintenance [2]. The quality of drinking \nwater is affected by many factors including the source of water supply, \nwater treatment techniques (sedimentation, filtration, disinfection) and \ndistribution mode. In addition, funding from the high authority in charge \nof water supply is provided and high level of awareness on sustainability \nof water [20]. In Sudan, several problems related to water supply include \nmonetary issue, imbalance between water resource and demand due to \npoor techniques and climate variability as well as lack of awareness \namong residents and local community [9].    \n\n\n\nWhile extensive survey in Finland for drinking water distributed by the \nwaterworks has usually in high quality in terms of hygiene and sanitation \naspects. A conclusion drawn indicated that Ireland\u2019s public and private \nsectors are participating in water supply management [27]. They also \nstated that training and awareness programs are conducted among the \nsociety to educate them for using their water duly.  In China, observed that \nthe key to success in water management depends on how field regulators \napproach polluting problems [28].   \n\n\n\nHowever, the lack of technical support and training was seriously affected \nthe water supply management. Therefore, as a technical constraint in \nLibya, the study carried out that one of the existing problems in water \nsupply was the deterioration of desalination station in each coastal city \nacross the county, cost barrier, lack of regular maintenance, poor technical \nand the low awareness of its integration [14]. Libya faced many problems \nmainly related to managerial and technical failures [14]. Moreover, \nanother study in Libya, found that government of Libya was not funded \nthe water supply sectors equally to support the water needs [19]. \nFurthermore, water distribution system in Libya is ancient and facing \nbarriers of leakages and also abstraction equipment as well as the size of \nthe network pipeline and reported that only limited repairs are being \ndone to critical infrastructure in each city of Libya.  Since the Libyan\u2019s \nrevolution in 2011, many negative impacts happened in the country which \ncause troubles to the people lead to the damage of public utilities such as \nwater pipes system and electricity power generation. Therefore, the \nscarcity of freshwater resources proportionally increases the intensity of \npotential for political conflicts over water within the Libyan cities. \n\n\n\n6. THE WAY FORWARD TOWARDS SUSTAINABLE WATER RESOURCE \nMANAGEMENT\n\n\n\nWater is becoming a scarce and valuable resource as population and \nconsumption rise. Water consumption is increasing gradually day by day \nand. Human factors which influence the availability of water, including  \npopulation, dams and daily usage of water by both individuals and \ngovernment levels contribute to water crisis. Evaluation of these factors, \nas well as technology and action to support healthy water supplies, is \nbecome crucial, particularly in arid regions like Libya. However, in order \nto help the country of Libya to be sustained in using their water resource \nmanagement, the following solutions are highlighted:  \n\n\n\nInnovative policies and new technologies that reduce wastewater are \nhelping countries across the Middle East and North Africa to deal with \nchronic water shortages.\n\n\n\nEducation to improve lifestyle and reduce water consumption is \nneeded. People need to be motivated to change their behaviour in \nconsuming water.\n\n\n\nIrrigation and agriculture practices need to be improved. Improving \nthis issue will help in filling the gaps between the water supply and \ndemand since large percentage of the world\u2019s freshwater is used for \nagriculture.\n\n\n\nInternational framework and cooperation need to be established. \nAlthough this is very hard to be achieved in many countries but \nmaking international accords will be beneficial in dealing water \nscarcity issues.\n\n\n\nRecycling wastewater is required. This can also be another solution to \nhelp with the water shortage. This is currently one of the ways to save \nthe consumption of water and avoid scarcity where some countries \nlike Singapore are trying to recycle to cut water imports and become \nmore self-sufficient.\n\n\n\n1.\n\n\n\n2.\n\n\n\n3.\n\n\n\n4.\n\n\n\n5.\n\n\n\nCite this article as: Jauda R. Jouda Hamad, Marlia M. Hanafiah, Wan Zuhairi W. Yaakob  (2017).  Water Resources Management \nIn Libya: Challenges And Future Prospects.   Malaysian Journal of Sustainable Agriculture,  1(2):02-05.\n\n\n\n\n\n\n\n\n5 Malaysian Journal of Sustainable Agriculture (MJSA) 1(2) (2017) 02-05  \n\n\n\ntechnique in Libya. Desalination, 165, 8997.\n\n\n\n[15] Lawgali, F. F. 2008. Forecasting water demand for agricultural, \nindustrial and domestic use in Libya. International Review of Business \nResearch Papers, 4, 231-248.\n\n\n\n[16] Aboyeji, O. O. 2013. Freshwater pollution in some Nigerian local \ncommunities, causes, consequences and probable solutions. Academic \nJournal of Interdisciplinary Studies, 2, (13) 111- 117.\n\n\n\n[17] Adekunle, A. S., and Eniola, I. 2008. Impact of industrial effluents on \nquality of segment of Asa River within an industrial estate in Ilorin, \nNigeria. New York Science Journal, 1, 17-21.\n\n\n\n[18] Taqveem, Ali, K. 2011. Trace elements in the drinking water and their \npossible health effects in Aligarh City, India. Journal of Water Resource and \nProtection, 3 (7), 522-530.\n\n\n\n[19] Alamin, S., Fewkes, A., and Goodhew, S. 2010. Investigating the \nsustainability of water management in Alwahat, Libya. WIT Transactions \non Ecology and the Environment, 129, 607-617.\n\n\n\n[20] M\u00e4kinen, R. 2008. Drinking water quality and network materials in \nFinland: summary report. Publications of Finnish Institute of Drinking \nWater, 5.\n\n\n\n[21] Pragst, F., Stieglitz, K., Runge, H., Runow, K.-D., Quig, D., Osborne, R.,\n\n\n\n Runge, C., and Ariki, J. 2017. High concentrations of lead and barium in hair \nof the rural population caused by water pollution in the Thar Jath oilfields \nin South Sudan. Forensic Science International, 274, 99-106.\n\n\n\n[22] Mcgrory, E. R., Brown, C., Bargary, N., Williams, N. H., Mannix, A., \nZhang, C., Henry, T., Daly, E., Nicholas, S., and Petrunic, B. M. 2017. Arsenic \ncontamination of drinking water in Ireland: A spatial analysis of occurrence \nand potential risk. Science of the Total Environment, 579, 1863-1875.\n\n\n\n[23] Sun, Y., Liu, N., Shang, J., and Zhang, J. 2017. Sustainable utilization of \nwater resources in China: A system dynamics model. Journal of Cleaner \nProduction, 142, 613-625.\n\n\n\n[24] Saha, N., Rahman, M. S., Ahmed, M. B., Zhou, J. L., Ngo, H. H., and Guo, W. \n2017. Industrial metal pollution in water and probabilistic assessment of \nhuman health risk. Journal of Environmental Management, 185, 70-78.\n\n\n\n[25] Hall, D. 2006. Water and electricity in Nigeria. PSIRU Reports.\n\n\n\n[26] Kelly-Quinn, M., Blacklocke, S., Bruen, M., Earle, R., O\u2019neill, E., \nO\u2019sullivan, J., and Purcell, P. 2014. Dublin Ireland: a city addressing \nchallenging water supply, management, and governance issues. Ecology \nand Society, 19, (4),10-13.\n\n\n\n[27] Yu, X., Geng, Y., Heck, P., and Xue, B. 2015. A review of China\u2019s rural \nwater management. Sustainability, 7, 5773-5792.\n\n\n\nCite this article as: Jauda R. Jouda Hamad, Marlia M. Hanafiah, Wan Zuhairi W. Yaakob  (2017).  Water Resources Management \nIn Libya: Challenges And Future Prospects.   Malaysian Journal of Sustainable Agriculture,  1(2):02-05.\n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 08-12 \n\n\n\nCite The Article: Intsar H. H. Al-Hilfy, S. A. Wahid, H. M. K. Al-Abodi, S. A. A. Al-Salmani, Md. Reaz Mahamud, Prof. Dr. Md. Bellal Hossain (2019). Grain Yield And Quality Of \nWheat As Affected By Cultivars And Seeding Rates. Malaysian Journal of Sustainable Agriculture, 3(1): 08-12. \n\n\n\n\n\n\n\nISSN: 2521-2931 (Print) \nISSN: 2521-294X (Online) \nCODEN : MJSAEJ \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 15 November 2018  \nAccepted 17 December 2018 \nAvailable online 2 January 2019 \n\n\n\nABSTRACT\n\n\n\nAn experiment was conducted at Research Station, State Board for Seeds Testing and Certification, Baghdad, Iraq \nduring 2015-2016 and 2016-2017 seasons to determine the effect of six seeding rates (80, 100, 120, 140, 160, and \n180 kg ha-1) on yield and quality of three wheat cultivars (Bohooth 22, Bohooth 158, and Rasheed). The experiment \nwas laid out in Randomized Complete Blocks Design (RCBD) with split plots arrangement placing seeding rates in \nmain plots and cultivars in sub-plots with three replicates. Seeding rate 140 kg ha-1 gave highest grain yield (4.95 \nand 4.99 t ha-1) for both seasons, respectively. Seeding rate 80 kg ha-1 gave highest protein content (13.95% and \n13.68%) and gluten (34.46% and 32.95%) for both seasons, respectively. Seeding rate of 140 kg ha-1 gave the \nhighest protein yield (618.60 kg ha-1) during the first season, while seeding rate of 120 kg ha-1 gave the highest yield \nin this trait (621.02 kg ha-1) during the second season. Rasheed cultivar plants produced highest grain yield (4.77 \nand 4.89 t ha-1), whereas Bohooth 158 plants recorded highest protein content (13.13% and 13.28%) and gluten \n(34.85% and 33.21%) for both seasons, respectively. So, it's recommended to cultivate the three studied wheat \ncultivars at seeding rate 140 kg ha-1 to obtain highest grain yield, whereas seeding rate 80 kg ha-1 is the best to get \nthe highest protein and gluten content. \n\n\n\nKEYWORDS \n\n\n\nWheat, seeding rates, grain yield, protein, gluten.\n\n\n\n1. INTRODUCTION \n\n\n\n Wheat (Triticum aestivum L.) is one of the most important cereal crops \nused as a food all over the world. It ranks first in the world's cereal \nproduction and is a staple food for approximately one third of the world's \npopulation [1]. The grain contains about 60-80% carbohydrates, 8-15% \nprotein, 1.5-2% fat, 1.5-2% inorganic ions, and vitamins (B complex and \nE) in small amounts [2]. Wheat grain quality is a complex trait resulting \nfrom the interaction between several protein components. It has been \nobserved that the grains quality is a function of grain composition \npredominantly in proteins, which in turn depends on many factors [3]. \nWheat is the main strategic crop in Iraq, and the date of its cultivation \nextends to several thousand years since the emergence of the first \ncommunities in the world. The harvested area during 2014 winter season \nwas estimated 2109455 ha, whereas the average yield per hectare for the \nsame season was estimated 2396.4 kg ha-1 on the basis of the total \nharvested area [4]. Despite this, Iraq has faced a large and expanding \ndeficit in the production of this crop, especially during the past three \ndecades, and in order to overcome the deficit, several million tons of wheat \ngrains were imported every year, which was a burden on the country \nbudget. The reasons behind this deficit might be due to poor crop \nmanagement, including seeding rates and the use of old cultivars. For \nachieving high yield and quality of wheat, it is necessary to use all the \ncultural practices completely and on time and adapt them to cultivars [5]. \nSo, the aim of this study was to determine the effect of different seeding \nrates on the grain yield and quality of three bread wheat cultivars. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nA field experiment was carried out at Abu-Ghuraib Research Station, State \n\n\n\nBoard for Seeds Testing and Certification, Ministry of Agriculture, \n\n\n\nBaghdad, Iraq during the winter season of the years 2015/2016 and \n\n\n\n2016/2017 to determine the effect of six seeding rates on yield and quality \n\n\n\nof three bread wheat cultivars. The experiment was laid out according to \n\n\n\nrandomized complete blocks design having split plot arrangements with \n\n\n\nthree replicates. Seeding rates (80, 100, 120, 140, 160, and 180 kg ha-1) \n\n\n\nwere allocated in the main plots and the three cultivars (Bohooth 22, \n\n\n\nBohooth, 158 and Rasheed) in the sub-plots. The net of sub plot area was \n\n\n\n6 m2 (ten rows, three meters long, 20 cm apart, and 5 cm depth). Seed bed \n\n\n\nwas prepared by ploughing the field three times with cultivator followed \n\n\n\nby planking. Wheat grains were sown manually on 25th November in both \n\n\n\nseasons with single row hand drilling. Before sowing, soil was analyzed for \n\n\n\nits physico-chemical properties which were clay loam in texture with 70.0 \n\n\n\nand 88.3 ppm available nitrogen, 5.38 and 4.95 ppm available phosphorus, \n\n\n\n372.0 and 428.0 ppm available potassium, 3.6 and 2.4 dSm-1 Ec and 7.0 and \n\n\n\n7.2 pH for both seasons, respectively. The nitrogen fertilizer (200 kg ha-1) \n\n\n\nin the form of urea (46%) was applied as per treatment in four splits, one \n\n\n\nat the time of sowing, second at growth stage ZGS:13, third at ZGS:32, and \n\n\n\nforth at ZGS:40 according to Zadoks scale, while phosphorus (100 kg ha-1) \n\n\n\nwas added at the time of planting in a form of  tri super phosphate (P2O5 \n\n\n\n46%) [6,7]. All plots received uniform cultural practices. The recorded \n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA)\nDOI : http://doi.org/10.26480/mjsa.01.2019.08.12\n\n\n\n   REVIEW ARTICLE \n\n\n\nGRAIN YIELD AND QUALITY OF WHEAT AS AFFECTED BY CULTIVARS AND \n\n\n\nSEEDING RATES \nIntsar H.H, Al-Hilfy1*, S.A. Wahid2*, H.M.K. Al-Abodi3, S. A. A. Al-Salmani4, Md. Reaz Mahamud5, Md. Bellal Hossain6 \n\n\n\n1Department of Field Crops Science, College of Agriculture, University of Baghdad.  \n2Mesopotamia State Company for Seed Production, Ministry of Agriculture, Baghdad, Iraq,  \n3Office of Agricultural Researches, Ministry of Agriculture Baghdad, Iraq  \n4Department of Field Crops, College of Agricultural Sciences University of Anbar. \n5Department of Nutrition and Food Engineering, Daffodil International University \n6Department of Nutrition and Food Engineering, Daffodil International University \n*Corresponding Author Email: dr.intsar_hadi@yahoo.com,safa_20003@yahoo.com,reaz.nfe@daffodilvarsity.edu.bd,headnfe@daffodilvarsity.edu.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited \n\n\n\n\nmailto:dr.intsar_hadi@yahoo.com\n\n\nmailto:safa_20003@yahoo.com\n\n\nmailto:reaz.nfe@daffodilvarsity.edu.bd\n\n\nmailto:headnfe@daffodilvarsity.edu.bd\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 08-12 \n\n\n\nCite The Article: Intsar H. H. Al-Hilfy, S. A. Wahid, H. M. K. Al-Abodi, S. A. A. Al-Salmani, Md. Reaz Mahamud, Prof. Dr. Md. Bellal Hossain (2019). Grain Yield And Quality Of \nWheat As Affected By Cultivars And Seeding Rates. Malaysian Journal of Sustainable Agriculture, 3(1): 08-12. \n\n\n\ndata were analyzed statistically by using statistical software package \n\n\n\nGenstat version (12). The least significant differences (L.S.D) at the level of \n\n\n\n0.05 probability was employed to compare the differences among the \n\n\n\ntreatment means [8]. The studied traits are: \n\n\n\n\u2022 Grain yield (t ha-1): At maturity, one square meter was harvested \n\n\n\nmanually from each plot. Spikes were threshed manually and \n\n\n\ngrains were separated and weighed. Then the grain yield was \n\n\n\nconverted into tons per hectare. The moisture equation below was \n\n\n\nused to measure the moisture of harvested grains. The weight was \n\n\n\nthen converted to one ton [9]: \n\n\n\n100- M1 \nY(M2) =  \u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640\u0640 \u00d7 Y(M1) \n\n\n\n 100 \u2013 M2 \n\n\n\nY (M2) = grain weight at the level to be measured (14%). \nY (M1) = grain weight at actual moisture level (moisture level \nat harvest). \nM1 = the actual percentage of moisture. \nM2 = required percentage of moisture. \n\n\n\n\u2022 Protein content in grains (%): Total nitrogen content in grains was \n\n\n\nestimated using Kernelyzer-M apparatus from Brabender \n\n\n\n(Germany) which belongs to the Agricultural Researches Office - \n\n\n\nMinistry of Agriculture. \n\n\n\n\u2022 Gluten content in grains (%): gluten is estimated using the same \n\n\n\ndevice used to estimate the protein content in grains. \n\n\n\n\u2022 Protein yield (kg ha-1): estimated according to the following \n\n\n\nequation: \n\n\n\nProtein yield (kg ha -1) = grain yield (kg ha -1) \u00d7 percentage of protein \n\n\n\nin grains \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\n3.1 Grain yield (t ha-1) \n\n\n\nData in Table 1 show that the effect of seeding rates, cultivars, and their \ninteraction on this trait was significant for both seasons. Grain yield \nincreased with increasing seeding rates from 80 to 140 kg ha-1 which \nrecorded highest grain yield (4.95 and 4.99 t ha-1), then followed with a \ndecrease till the seeding rate 180 kg ha-1 which gave the lowest yield (3.84 \nand 3.97 t ha-1) for both seasons, respectively. The highest grain yield \nrecorded with plants sown at seeding rate 140 kg ha-1 might be due the \nsuperiority of the plants sown at this seeding rate in some growth and \nyield traits compared to other seeding rates (unpublished data). \n\n\n\nIt is clear from Table 1 that the cultivar Rasheed gave the highest grain \nyield (4.77 and 4.89 t ha-1), then followed by Bohooth 22 (4.45 and 4.65 t \nha-1), whereas the lowest values were obtained from cultivar Bohooth 158 \nplants (4.07 and 4.23 t ha-1) for both seasons, respectively. Cultivar \nRasheed achieved an increase in this trait 7.19% and 17.33% during the \nfirst season, and 5.25% and 15.74% during the second season in \ncomparison with Bohooth 22 and Bohooth 158 cultivars, respectively. The \nsuperiority of Rasheed cultivar could be due to its superiority in a number \nof growth and yield traits compared to two other cultivars (unpublished \ndata). \n\n\n\nTable 1: Effect of seeding rates, cultivars, and their interaction on the grain yield (t ha-1) during the seasons 2015-2016 and 2016-2017 \n\n\n\nSeeding rates\n(kg ha-1) \n\n\n\nFirst season 2015-2016 \n\n\n\nMeans \n\n\n\nSecond season 2016-2017 \n\n\n\nMeans \nCultivars Cultivars \n\n\n\nBohooth 22 Bohooth 158 Rasheed Bohooth  22  Bohooth 158 Rasheed \n\n\n\n80 4.47 3.68 4.78 4.31 4.64 3.73 4.82 4.40 \n\n\n\n100 4.51 3.82 4.86 4.39 4.75 4.13 4.93 4.60 \n\n\n\n201  4.69 4.33 5.13 4.72 4.86 4.55 5.15 4.86 \n\n\n\n140 4.84 4.79 5.22 4.95 4.93 4.83 5.22 4.99 \n\n\n\n160 4.47 4.19 4.47 4.38 4.68 4.64 4.77 4.70 \n\n\n\n180 3.75 3.59 4.17 3.84 4.02 3.46 4.45 3.97 \n\n\n\nL.S.D 5% 0.38 0.26 0.39 0.26 \n\n\n\nMeans 4.45 4.07 4.77 4.65 4.23 4.89 \n\n\n\nL.S.D 5% 0.15 0.16 \n\n\n\nRegarding the effect of interaction between seeding rates and cultivars, its \nobvious from Table 1 that the response of cultivars to increased seeding \nrates was different, where the grain yield for all cultivars increased with \nincreasing seeding rates till 140 kg ha-1, then a reduction in this trait \noccurred with increasing seeding rates till 180 kg ha-1 during both seasons. \nRasheed cultivar recorded highest grain yield (5.22 and 5.22 t ha-1) when \nplants sown at seeding rate 140 kg ha-1, while the lowest values in this trait \nwere obtained from Bohooth 158 plants when sown at seeding rate 180 \nkg ha-1 (3.59 and 3.46 t ha-1) for both seasons, respectively.   \n\n\n\n3.2 Protein content (%) \n\n\n\nData in Table 2 show that the effect of seeding rates, cultivars and their \ninteraction was significant on this trait for both seasons. There was a \ndecrease in the means of this trait with increasing seeding rates. Seeding \nrate of 80 kg ha-1 gave the highest protein content (13.95% and 13.68%), \nwhereas lowest means for the same trait were recorded with seeding rate \n180 kg ha-1 (11.76% and 11.62%) for both seasons, respectively. \n\n\n\nThis decrease in protein content with increasing seeding rates might be \n\n\n\ndue to the high competition between plants in the unit area for nutrients \n(the most important of which is nitrogen), as well as light, causing a \ndecrease in the protein content due to the lack of necessary assimilates to \nform the protein compared to the low seeding rates, and consequently an \nincrease in the percentage of this trait. The result of this study was in \nagreement with those obtained and found that the increase in seeding \nrates caused a decrease in protein content [10,11].  \n\n\n\nThe cultivars significantly differed in this trait. Bohooth 158 recorded the \nhighest percentage of protein (13.13% and 13.28%), while Rasheed \ncultivar recorded the lowest percentage (12.41% and 12.20%) for both \nstudy seasons, respectively. \n\n\n\nIt is noted that Rasheed cultivar, which achieved the highest percentage of \ngrain yield in this study, is the same that gave the lowest percentage of this \ntrait, whereas the highest percentage of the same trait was recorded with \nBohooth 158 cultivar, which gave the lowest grain yield. The reason for \nthe difference between cultivars might be due to the variation in their \ncompetition on various growth factors at different seeding rates, (nitrogen \nis considered the most important factor, because it is the basic component \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 08-12 \n\n\n\nCite The Article: Intsar H. H. Al-Hilfy, S. A. Wahid, H. M. K. Al-Abodi, S. A. A. Al-Salmani, Md. Reaz Mahamud, Prof. Dr. Md. Bellal Hossain (2019). Grain Yield And Quality Of \nWheat As Affected By Cultivars And Seeding Rates. Malaysian Journal of Sustainable Agriculture, 3(1): 08-12. \n\n\n\nof protein) [12].  \n\n\n\nTable 2: Effect of seeding rates, cultivars, and their interaction on the protein content (%) during the seasons 2015-2016 and 2016-2017 \n\n\n\nSeeding \n\n\n\nrates   (kg ha-\n\n\n\n1) \n\n\n\nFirst season 2015-2016 \n\n\n\nMeans \n\n\n\nSecond season 2016-2017 \n\n\n\nMeans Cultivars Cultivars \n\n\n\nBohooth 22 \nBohooth \n\n\n\n158 \nRasheed Bohooth  22  \n\n\n\nBohooth \n158 \n\n\n\nRasheed \n\n\n\n80 13.91 14.41 13.51 13.95 13.49 14.32 13.22 13.68 \n\n\n\n100 13.51 13.81 13.11 13.48 13.15 14.25 13.09 13.50 \n\n\n\n120 13.01 13.31 12.61 12.98 12.65 13.45 12.32 12.81 \n\n\n\n140 12.61 12.91 12.01 12.51 12.25 12.92 12.06 12.41 \n\n\n\n160 12.31 12.41 11.71 12.15 11.96 12.60 11.49 12.01 \n\n\n\n180 11.85 11.91 11.51 11.76 11.69 12.16 11.02 11.62 \n\n\n\nL.S.D 5% 0.19 0.1032 0.17 0.12 \n\n\n\nMeans 12.87 13.13 12.41 12.53 13.28 12.20 \n\n\n\nL.S.D 5% 0.08 0.06 \n\n\n\nAs for interaction, the cultivar Bohooth 158 achieved highest percentage \nof protein (14.41% and 14.32%) when sown at seeding rate of 80 kg ha-1, \nwhereas lowest percentage was recorded with Rasheed cultivar plants \nsown at seeding rate of 180 kg ha-1 (11.51% and 11.02%) for both seasons, \nrespectively. \n\n\n\n3.3 Wet gluten percentage )%( \n\n\n\nThe data in Table 3 show a significant effect of seeding rates, cultivars and \ntheir interaction on the percentage of wet gluten for both seasons. The \npercentage of this trait decreased with increasing seeding rates during \nboth seasons. Seeding rate 80 kg ha-1, recorded the highest percentage of \nwet gluten (34.46% and 32.95%), while seeding rate 180 kg ha-1 gave the \nlowest percentage of the same trait (31.07% and 29.67%) for both \nseasons, respectively. The reason for the decrease in this trait with \nincreasing seeding rates might be due to the increased competition among  \n\n\n\nplants at high seeding rates for nutrients and light, which caused a \ndecrease in the percentage of nitrogen in the green parts of the plants at \ndifferent growth stages during both seasons, resulting in a decrease in the \npercentage of wet protein, and consequently a decrease in gluten \npercentage which is considered the largest component of protein. \n\n\n\nRegarding cultivars, Bohooth 158 was superior in this trait and recorded \nthe highest percentage of wet gluten (34.85% and 33.21%), whereas \nRasheed cultivar plants recorded the lowest percentage of the same trait \n(31.27% and 29.72%) for both seasons, respectively. In regard to the effect \nof interaction between seeding rates and cultivars on this trait, the cultivar \nBohooth 158 gave the highest percentage (36.43% and 35.08%) with \nseeding rate 80 kg ha-1, while the lowest percentage was with Rasheed \nplants when sown at seeding rate 180 kg ha-1 (29.44% and 28.00%) for \nboth seasons, respectively. \n\n\n\nTable 3: Effect of seeding rates, cultivars, and their interaction on the wet gluten percentage (%) during the seasons 2015-2016 and 2016-2017 \n\n\n\nSeeding \nrates (kg ha-\n\n\n\n1) \n\n\n\nFirst season 2015-2016 \n\n\n\nMeans \n\n\n\nSecond season 2016-2017 \n\n\n\nMeans Cultivars Cultivars \n\n\n\nBohooth 22 \nBohooth \n\n\n\n158 \nRasheed Bohooth  22  \n\n\n\nBohooth \n158 \n\n\n\nRasheed \n\n\n\n80 34.23 36.43 32.73 34.46 32.89 35.08 30.89 32.95 \n\n\n\n100 33.93 36.03 32.34 34.10 32.79 34.60 30.60 32.67 \n\n\n\n120 33.33 35.23 31.84 33.47 31.46 33.46 30.12 31.68 \n\n\n\n140 32.44 34.63 31.04 32.70 30.51 32.51 30.00 31.00 \n\n\n\n160 31.84 34.03 30.24 32.04 30.03 32.16 28.69 30.29 \n\n\n\n180 31.04 32.73 29.44 31.07 29.55 31.46 28.00 29.67 \n\n\n\nL.S.D 5% 0.21 0.12 0.40 0.24 \n\n\n\nMeans 32.80 34.85 31.27 31.20 33.21 29.72 \n\n\n\nL.S.D 5% 0.09 0.17 \n\n\n\n3.4 Protein yield (kg ha-1) \n\n\n\nTable 4 reveals that the effect of seeding rates, cultivars, and their \n\n\n\ninteraction on this trait was significant for both seasons. The means of this \ntrait decreased with increasing seeding rate from 80 to 100 kg ha-1 and \nthen increased till the seeding rate 140 kg ha-1, which recorded the highest \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 08-12 \n\n\n\nCite The Article: Intsar H. H. Al-Hilfy, S. A. Wahid, H. M. K. Al-Abodi, S. A. A. Al-Salmani, Md. Reaz Mahamud, Prof. Dr. Md. Bellal Hossain (2019). Grain Yield And Quality Of \nWheat As Affected By Cultivars And Seeding Rates. Malaysian Journal of Sustainable Agriculture, 3(1): 08-12. \n\n\n\nprotein content in the grains (618.60 kg ha-1), and then a decrease in the \nmeans of this trait occurred with increasing seeding rates till 180 kg ha-1, \nwhich recorded the lowest value (450.55 kg ha-1) in the first season. In the \nsecond season, the means of the same trait increased with increasing \nseeding rates from 80 to 120 kg ha-1, which recorded the highest mean \n(621.02 kg ha-1), then a decrease occurred with increasing seeding rate to \n180 kg ha-1, which gave the lowest protein yield in the grains (459.98 kg \nha-1). \n\n\n\nIn terms of cultivars, Rasheed recorded the highest protein yield in grains \n(593.45 and 597.43 kg ha-1), followed by Bohooth 22, which gave a protein \nyield of 574.10 and 583.19 kg ha-1, while Bohooth 158 recorded the lowest \n\n\n\nmeans in this trait (533.48 and 560.86 kg ha-1) for both seasons, \nrespectively.  \nRegarding the interaction, the response of the cultivars to the different \nseeding rates varied during both seasons. The highest yield (647.12 kg ha-\n\n\n\n1) was recorded with planting Rasheed at seeding rate 120 kg ha-1 during \nthe first season, while the same cultivar gave the highest mean (644.60 kg \nha-1) at seeding rate 100 kg ha-1 during the second season. The lowest \nmeans of this trait were obtained from Bohooth 158 cultivar plants at \nseeding rate 180 kg ha-1 (427.87 and 420.25 kg ha-1) for both seasons, \nrespectively. \n\n\n\nTable 4: Effect of seeding rates, cultivars, and their interaction on the protein yield (kg ha-1) during the seasons 2015-2016 and 2016-2017 \n\n\n\nSeeding \nrates (kg ha-\n\n\n\n1) \n\n\n\nFirst season 2015-2016 \n\n\n\nMeans \n\n\n\nSecond season 2016-2017 \n\n\n\nMeans Cultivars Cultivars \n\n\n\nBohooth 22 \nBohooth \n\n\n\n158 \nRasheed Bohooth  22  \n\n\n\nBohooth \n158 \n\n\n\nRasheed \n\n\n\n80 621.46 530.44 645.60 599.17 625.58 534.20 637.26 599.01 \n\n\n\n100 608.79 527.64 637.08 591.17 625.24 588.68 644.60 619.51 \n\n\n\n120 610.59 576.45 647.12 611.39 615.46 612.58 635.00 621.02 \n\n\n\n140 610.35 618.34 627.10 618.60 603.58 624.49 629.63 619.23 \n\n\n\n160 549.81 520.15 523.63 531.19 559.67 584.94 548.00 564.20 \n\n\n\n180 443.63 427.87 480.15 450.55 469.62 420.25 490.06 459.98 \n\n\n\nL.S.D 5% 48.55 32.89 51.19 37.41 \n\n\n\nMeans 574.10 533.48 593.45 583.19 560.86 597.43 \n\n\n\nL.S.D 5% 19.34 19.22 \n\n\n\n4. RECOMMENDATIONS \n\n\n\nIt is recommended to plant wheat at a seeding rate 140 kg ha-1 to obtain \nhighest yield of grains, while the seeding rate 80 kg ha-1 is the best to get \nhighest percentage of protein and gluten in the grains. \n\n\n\nREFERENCES \n\n\n\n[1] Nizamani, G.S., Tunio, S., Buriro, U.A., Keerio, M.I. 2014. Influence of \n\n\n\ndifferent seed rates on yield contributing traits in wheat varieties. Journal\n\n\n\nof Plant Sciences, 2(5), 232-236. \n\n\n\n Shahzad, M.A., Din, W.U., Sahi, S.T., Khan, M.M., Ehsanullah, Ahmad, M.  \n\n\n\n2007. Effect of sowing dates and seed treatment on grain yield and quality \n\n\n\nof wheat. Pakistan Journal of Agricultural Sciences, 44(4), 581-583. \n\n\n\n[3] Farooq, O., Ali, M., Naeem, M., Sattar, A., Ijaz, M., Sher, A., Iqbal, M.M. \n\n\n\n2015. Impact of sowing time and planting method on the quality traits of \n\n\n\nwheat. Journal of Global Innovations in Agricultural and Social Sciences, \n\n\n\n3(1), 8-11. \n\n\n\n[4] FAO. 2014. Food and Agriculture Organization statistical data. \n\n\n\nAvailable at http://www.fao.org/faostat/en/#data/QC \n\n\n\n[5] Zecevic, V., Boskovic, J., Knezevic, D., Micanovic, D. 2014. Effect of \n\n\n\nseeding rate on grain quality of winter wheat. Chilean Journal of\n\n\n\nAgricultural Research, 74(1), 23-28. \n\n\n\n[6] Zadoks, J.C., Change, T.T., Knozak, C.F. 1974. Adecimal code for growth \n\n\n\nstages of cereals. Weed Res., 14, 415-421. \n\n\n\n[7] Jadoaa, Abbas, K. 1995. Wheat Facts and Guidelines. Publications of the \n\n\n\nMinistry of Agriculture. Extension and Agricultural Cooperation office. P. \n\n\n\n487. \n\n\n\n[8] Steel, R.G., Torrie, J.H. 1980. Principles and Procedures of Statistics: A \n\n\n\nbiometrical Approach (2nd edn). McGraw Hill Book Co. USA. P. 481. \n\n\n\n[9] AOAC. 1980. Official Methods of Analysis 13thed. The Association of \n\n\n\nOfficial Analytical Chemists. Washington DC, U.S.A. \n\n\n\n[10] Gooding, M.J., Pinyosinwat, A., Ellis, R.H. 2002. Responses of wheat \n\n\n\ngrain yield and quality to seed rate. The Journal of Agricultural Science, \n\n\n\n138, 317-331. \n\n\n\n[11] Daaboush, T.A., Bader, A.A.Y., Al-Absi, W. 2014. Response of some \n\n\n\nlocal Yemeni wheat cultivars to seeding rates and nitrogen fertilization. \n\n\n\nYemeni Journal of Agriculture and Veterinary Sciences, 1 (2), 73-87. \n\n\n\n[12] Abdulkerim, J., Tana, T., Eticha, F. 2015. Response of bread wheat \n\n\n\n(Triticum aestivum L.) varieties to seeding rates at Kulumsa, South Eastern \n\n\n\nEthiopia. Asian Journal of Plant Sciences, 14(2), 50-58 \n\n\n\n\nhttp://www.fao.org/faostat/en/#data/QC\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 3(1) (2019) 08-12 \n\n\n\nCite The Article: Intsar H. H. Al-Hilfy, S. A. Wahid, H. M. K. Al-Abodi, S. A. A. Al-Salmani, Md. Reaz Mahamud, Prof. Dr. Md. Bellal Hossain (2019). Grain Yield And Quality Of \nWheat As Affected By Cultivars And Seeding Rates. Malaysian Journal of Sustainable Agriculture, 3(1): 08-12. \n\n\n\nAUTHOR DETAILS \n\n\n\nMd. Reaz Mahamud  \nAsst. Technical Officer \nDepartment of Nutrition and Food Engineering Daffodil \nInternational University \nreaz.nfe@daffodilvarsity.edu.bd \n\n\n\nProf. Dr. Md. Bellal Hossain \nHead \nDepartment of Nutrition and Food Engineering Daffodil \nInternational University \nheadnfe@daffodilvarsity.edu.bd \n\n\n\n\nmailto:reaz.nfe@daffodilvarsity.edu.bd\n\n\nmailto:headnfe@daffodilvarsity.edu.bd\n\n\n\n"
"\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 117-123 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjsustainagri.com \n\n\n\nDOI: \n\n\n\n10.26480/mysj.02.2022.117.123 \n\n\n\nCite The Article: Olusegun Adebayo Ojumoola, Ayokanmi Samson Owa, Oluwatobi Samuel Akin -Boaz, Ridwan Adetomiwa Adeagbo (2022). \nSurvival and Morphometrics of The Black Soldier Fly, Hermetia Illucens (Diptera: Stratiomyidae) Reared on Common Market Food Wastes \n\n\n\nin Nigeria. Journal of Sustainable Agricultures, 6(2): 117-123. \n\n\n\nISSN: 2521-2931 (Print ) \nISSN: 2521-293X (Online) \nCODEN: MJSAEJ \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Sustainable Agriculture \n\n\n\n(MJSA) \nDOI: http://doi.org/10.26480/mjsa.02.2022.117.123\n\n\n\nSURVIVAL AND MORPHOMETRICS OF THE BLACK SOLDIER FLY, Hermetia illucens \n(DIPTERA: STRATIOMYIDAE) REARED ON COMMON MARKET FOOD WASTES IN \nNIGERIA \n\n\n\nOlusegun Adebayo Ojumoolaa*, Ayokanmi Samson Owaa, Oluwatobi Samuel Akin-Boaza, Ridwan Adetomiwa Adeagbob \n\n\n\na Department of Crop Protection, University of Ilorin, Ilorin, Kwara, Nigeria \nb Department of Microbiology, University of Ibadan, Ibadan, Oyo, Nigeria \n*Corresponding Author Email: ojumoola.oa@unilorin.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 22 June 2022 \nAccepted 28 July 2022 \nAvailable online 04 August 2022\n\n\n\nPurpose: This study investigated the suitability of nine common market food wastes in Nigeria for rearing \nHermetia illucens. Methods: Substrate suitability was determined through periodic assessment for survival, \nand measurement of body length, width and weight of H. illucens on each substrate in the laboratory. Results: \nSurvival of H. illucens larvae and pre-pupae on maize flour, cowpea flour, over-ripe banana peels, amaranth \nleaves, watermelon peels, and bread was comparable to the control substrate (chicken feed). In contrast, \nsurvival of larvae to pre-pupae on cabbage and pineapple flesh was significantly lower than on the control. \nGenerally, larvae and adults reared on chicken feed had significantly higher body size and weight compared \nto those on pineapple flesh or pineapple peels. Conclusion: Due to their inherently high moisture, low protein \nand low carbohydrate contents, pineapple flesh and pineapple peels are the least suitable substrates for H. \nillucens survival and growth in the study. \n\n\n\nKEYWORDS \n\n\n\nMunicipal organic solid wastes, waste valorization, larval composting, larval biomass \n\n\n\n1. INTRODUCTION \n\n\n\nThe world population is fast increasing and has been projected to be more \nthan 9 billion by the year 2050 (United Nations, 2015). This projected \npopulation growth necessitates a 70 percent increase in food production \nto meet imminent increases in food demand and consumption (van Huis \net al., 2013). As global food production and consumption increase due to \nrising populations and urbanization, it is expected that the amount of food \nwastes generated worldwide would also increase (Singh and Kumari, \n2019; Kim et al., 2021). Food wastes refer to all components of food that \nare discarded during the production, distribution, sales, processing and \nconsumption of food (Kim et al., 2021; Liu et al., 2016). Food wastes may \ntherefore include kitchen, restaurant or market wastes generated from \nfoods like cereals, pulses, vegetables, fruits, livestock meat, animal offals, \nand other plant and animal based foods (Kim et al., 2021, Kiran et al., \n2014). Already, up to 1.3 billion tonnes of food from households and \ncommunities is wasted annually (FAO, 2011) with wastes from fruits, \nvegetables and cereals including rice accounting for about 7%, 20% and \n30%, respectively of food wastes produced globally (Kiran et al., 2014). \nFurthermore, food wastes form between 25 \u2013 45 percent of the more than \ntwo billion tons of municipal solid wastes generated globally (Nichols and \nSmith, 2019; Eggleston et al., 2019; Br\u00e1s et al., 2020). Regrettably, only \nabout 16% of all municipal solid wastes produced in the world are \nrecycled annually (Kim et al., 2021, Br\u00e1s et al., 2020; Eggleston et al., \n2019). A large proportion of municipal organic solid wastes (MOSW) are \ndumped in landfills, and inappropriately on roadsides or in water \nchannels. Annually, decomposing MOSW in landfills contribute to the \nincrease in global emissions of methane and other greenhouse gases that \nare implicated in global warming and climate change (S\u00e1nchez et al., 2015; \nSamayoa et al., 2016; Couth and Trois, 2009). Thus in addition to \n\n\n\npotentially contributing to food insecurity, global increase in food wastes \ncan have deleterious effects on human health and environment (Nichols \nand Smith, 2019), especially in the poorer regions of the world. The \nforegoing thus highlights the importance of efficient and sustainable waste \nmanagement strategies. \n\n\n\nThe treatment or composting of MOSW with larvae of the ubiquitous black \nsoldier fly, Hermetia illucens (Diptera: Stratiomyidae) has in recent times \ngained popularity for its cost-effectiveness, and sustainability (Lalander et \nal., 2019; Singh and Kumari, 2019; Liu et al., 2019; Kim et al., 2021). The \nBlack Soldier Fly (BSF) has five distinct life stages viz egg, larva, pre-pupa \nlarva, pupa, and adult (Da Silva and Hesselberg, 2019). Female flies lay \nabout 400 to 800 eggs in dry sheltered cavities close to decomposing \norganic materials or waste streams (Dortmans et al., 2017). Eggs hatch \ninto neonate larvae within an average of four days, after which they feed \nvoraciously on available organic wastes, and develop through five larval \ninstars between 14 \u2013 16 days under optimal conditions (Dortmans et al., \n2017). At maturity, the final larva which is about 25 mm long and 5 mm \nwide becomes a pre-pupa that is dark brown to charcoal grey in color. The \npre-pupa empties its digestive tract and does not feed. Instead, it replaces \nits mouthpart with a hook-like structure with which it drags itself out of \nthe food substrate to a dry and safe pupation site (Dortmans et al., 2017; \nDa Silva and Hesselberg, 2019). Pupation in BSF takes an average of eight \ndays with a range of between two to three weeks, depending on prevailing \nconditions. At the end of pupation, the adult flies emerge from the pupal \ncase, and repeats the mating and egg-laying cycle for 5 to 8 days after \nwhich they die (Da Silva and Hesselberg, 2019). Adult BSF do not feed on \nanything except water. Instead they rely on fat stored during the larval \nstage for their nutrition. In addition, the flies do not harm humans, crops \nor animals, and do not vector any pathogens (Diener et al., 2011).  \n\n\n\n\nmailto:ojumoola.oa@unilorin.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 117-123 \n\n\n\nCite The Article: Olusegun Adebayo Ojumoola, Ayokanmi Samson Owa, Oluwatobi Samuel Akin -Boaz, Ridwan Adetomiwa Adeagbo (2022). \nSurvival and Morphometrics of The Black Soldier Fly, Hermetia Illucens (Diptera: Stratiomyidae) Reared on Common Market Food Wastes \n\n\n\nin Nigeria. Journal of Sustainable Agricultures, 6(2): 117-123. \n\n\n\nThe Black Solider Fly Larvae (BSFL) is able to feed and grow on a wide \narray of waste streams including food wastes, livestock manure and \nhuman excreta (Diener et al., 2011; Zhou et al., 2013; Lalander et al., 2013; \nOonincx et al., 2015). The BSFL is favoured above other fly larvae for waste \nvalorization because of its excellent ability to convert organic wastes into \nquality larva-meal with 30-40 % protein and 28-35 % oil contents, and \norganic compost (Zhou et al., 2013; Cickova et al., 2015). In view of rising \nprices of livestock feed ingredients, especially fish meal and oils, BSFL \nprovide a sustainable alternative source of cheap and high-quality animal-\nprotein (Spring, 2013; Lalander et al, 2019). In addition, fats obtained from \ndefatted BSFL can also serve as excellent alternatives to edible oil crops \nfor the production of high-quality biodiesel (Schiavone et al., 2017; Ewald \net al., 2020). Despite its potentials to grow on a variety of waste streams, \nstudies have shown that biomass quality, nutritional composition and \ndevelopment duration of BSFL is affected by the types and composition of \nrearing substrates amongst other factors (Spranghers et al., 2016; \nCammack and Tomberlin, 2017). For example BSFL reared on a protein or \nfibre rich-substrate developed at a much slower rate than those reared on \na balanced diet of processed cereal leftovers (Tschirner and Simon, 2015). \nAlso, BSFL survival on digested sludge, restaurant wastes, fruits-vegetable \nmix, and poultry feed was 39%, 87%, 90%, and 93%, respectively.  \n\n\n\nIn Nigeria, the actual inclusion of BSFL as a protein source component in \nlivestock and aquaculture feed is just gaining prominence amongst \nfarmers and feed millers (Omoloye et al., unpublished). Nevertheless, \nknowledge of its nutritional benefits in livestock and fish feed is fast \nincreasing. In a recent baseline study conducted in Nigeria, about 77% of \nfish farmers and 94% of poultry farmers were willing to include BSFL as a \nfeed component (Omoloye et al., unpublished). On the other hand, there is \npaucity of information on the application of BSFL-composting as a waste \nmanagement strategy in Nigeria. To enhance the adoption of BSFL as an \nalternative source of protein in animal feed and a cost-effective waste \nvalorization technology in Nigeria, increased low-cost production of BSFL \nmust be achieved under artificial rearing conditions. This can be done by \nidentifying cheap, ubiquitous and easily sourced MOSW in Nigeria with \nexcellent potentials for rearing high-quality BSFL. However, studies on the \nsuitability of common organic waste streams in Nigeria for BSFL rearing \nare scanty. Since the composition and quality of food wastes differ from \ncountry to country (Kim et al., 2021, Kiran et al., 2014), and even between \nrural and urban regions due to differences in consumption patterns \n(Boateng et al., 2016), we investigated the suitability of common food \nwastes generated in urban markets in Nigeria. Specifically, we studied the \nsurvival and morphometrics of Black Soldier Fly, Hermetia illucens L. \nreared on fruit-, vegetable-, and grain- wastes commonly generated in \nNigerian food markets.  \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1   H. Illucens \n\n\n\nAbout three (3) days old H. illucens eggs were obtained from a local BSF \nfarmer in Ibadan, Nigeria. The eggs were taken to the laboratory in the \nDepartment of Crop Protection, University of Ilorin, Ilorin, Nigeria were \nthey were maintained at ambient conditions (29.4\u00b10.9oC, 74.0\u00b13.8 % R.H \nand 12-hour photoperiod) until hatching. Eggs hatched into neonate BSFL \na day after receipt and were immediately transferred into chicken feed \n(Hybrid Feeds Super Starter for <2 weeks old broilers with ~ 70 \u2013 80 % \nmoisture content) on which they were reared for seven days until the start \nof experiments.  \n\n\n\n2.2   Food Waste Substrates \n\n\n\nIn this study, BSFL were reared on one of the following food waste \nsubstrates namely \u2013 cabbage leaves, infested cowpea flour, infested maize \nflour, overripe banana peels, pineapple peels, pineapple flesh, stale bread, \nwatermelon peels and amaranth leaves (Figure. 1). Each food waste \nsubstrate was obtained from local food markets in Ilorin, Nigeria where \nthey had been discarded as wastes. Immediately after collection, \nsubstrates were taken to the laboratory and separately pulverized with a \npestle in a small wooden mortar. Where necessary, an appropriate amount \nof distilled water was added to the substrate to enhance optimal larval \ngrowth. Information on description and specific processing of each \nsubstrate is outlined in Table 1. In addition to the nine substrates, chicken \nfeed (Hybrid Feeds super starter for broilers between 0- 2 weeks old) was \nincluded as a control substrate. For every 20 g of chicken feed, 30 mL of \ndistilled water was added to assure optimal moisture content for larval \ngrowth. \n\n\n\n2.3   Experimental set-up \n\n\n\nTen 7-day larvae were introduced into plastic containers (250 mL volume \nand 9.5 cm base diameter) containing 20 g of a substrate type that was \nfreshly prepared. Each plastic container and its contents were then \ncovered with a plastic lid that was perforated for ventilation. Experiments \nwere laid out in a completely randomized design with five replicates per \nsubstrate type (Fig. 1). On the 19th day after egg hatch (12 days after set-\nup), all pre-pupa were transferred from each substrate into dry plastic \ncontainer (16 cm x 11.5 cm x 4.5 cm) where they pupated and emerged \ninto adult flies. Experiments were set-up in the laboratory at the \nDepartment of Crop Protection, University of Ilorin, Ilorin, Kwara State, \nNigeria (8\u00b0 30' N 4\u00b0 40.8' E) under ambient environmental conditions \n(29.4\u00b10.9oC, 74.0\u00b13.8 % R.H and 12-hour photoperiod).  \n\n\n\nFigure 1: Food waste substrates and other experimental materials (A) Minced cabbage leaves (B) Chicken feed (C) Infested cowpea grains in a wooden \nmortar with pestle (D) Weevil damaged maize grains (E) Overripe banana peels (F) Pineapple with flesh and peels (G) Minced bread crumbs (H) Minced \n\n\n\nwatermelon peels with rind (I) Mashed amaranth leaves being weighed on a digital (S Mettler) precision balance (J) cross section of the different \nsubstrates in plastic containers \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 117-123 \n\n\n\nCite The Article: Olusegun Adebayo Ojumoola, Ayokanmi Samson Owa, Oluwatobi Samuel Akin -Boaz, Ridwan Adetomiwa Adeagbo (2022). \nSurvival and Morphometrics of The Black Soldier Fly, Hermetia Illucens (Diptera: Stratiomyidae) Reared on Common Market Food Wastes \n\n\n\nin Nigeria. Journal of Sustainable Agricultures, 6(2): 117-123. \n\n\n\nTable 1: Type, description and specific processing of market waste substrates used for rearing Black Soldier Fly, Hermetia illucens L. \n\n\n\nMarket waste substrate Description of substrate Specific substrate processing \n\n\n\nCabbage leaves \nWrinkled light-green leaves peeled off from \n\n\n\nthe entire vegetable \nLeaves were minced and mashed. For every 20 g of the mashed \n\n\n\nsubstrate, 10 mL of distilled water was added. \n\n\n\nInfested cowpea flour Mould infested cowpea grains \nGrains were crushed into flour. For every 20 g of flour, 30 mL of \n\n\n\ndistilled water was added. \n\n\n\nInfested maize flour \nMaize grains decimated by maize weevils and \n\n\n\nriddled with holes \nGrains were crushed into flour. For every 20 g of flour, 30 mL of \n\n\n\ndistilled water was added. \n\n\n\nOverripe banana peels Blackened banana peels \nPeels were minced and mashed into pulp. For every 20 g of the pulpy \n\n\n\nsubstrate, 10 mL of distilled water was added. \n\n\n\nPineapple peels Golden yellow peels of ripe pineapple \nPeels were minced and mashed into pulp. No water was added to the \n\n\n\npulpy substrate. \n\n\n\nPineapple flesh Yellow flesh of ripe pineapple \nFlesh was mashed into pulp. No water was added to the pulpy \n\n\n\nsubstrate. \n\n\n\nStale bread \nThe inner soft crumb of a week old bread loaf \n\n\n\nmade from strong flour (hard wheat flour) \nCrumbs were minced manually. For every 20 g of crumbs, 50 mL of \n\n\n\ndistilled water was added. \n\n\n\nWatermelon peels \nGreen watermelon peels with white rinds but \n\n\n\nwithout red flesh \nBoth peels and rinds were minced and mashed. No water was added \n\n\n\nto the pulpy substrate \n\n\n\nWilted amaranth leaves Green leaves of wilted amaranthus vegetables \nLeaves were minced and mashed. For every 20 g of the mashed \n\n\n\nsubstrate, 5 mL of distilled water was added. \n\n\n\n2.4   Data Collection \n\n\n\nAt the beginning of the experiment, five larvae were sampled from each \nsubstrate treatment and weighed on a digital sensitive balance (S. Mettler; \n0.01g precision). Data was thereafter collected every other day \u2013 from the \nsecond day to the twelfth day after set-up \u2013 on the percentage survival of \nlarvae per treatment. In addition, larval body length and width were \nassessed on the fourth and eighth day after set-up. On the other hand, body \nlength and width of pre-pupae was measured on the twelfth day after set-\nup. Furthermore, on the fourth and eighth day after set-up, five larvae \nwere randomly sampled and weighed. Similarly, five pre-pupae were \nsampled and weighed on the twelfth day after-setup. After the emergence \nof adult, morphometric data was collected on fly body length (measured \n\n\n\nfrom frontoclypeal area of the head to abdomen tip); head capsule width \n(measured as the distance between the lateral sides of the head); thorax \nwidth; and abdominal width. Morphometrics was done using a carbon \nfibre composite digital caliper (0.1 mm precision).  \n\n\n\n2.5   Data Analysis \n\n\n\nData on survival and morphometrics was submitted to a one-way Analysis \nof Variance (ANOVA) test to identify statistically significant differences \nbetween substrates. Where significant differences were found, ANOVA \nwas followed with the Tukey\u2019s Honestly Significant Difference (HSD) post \nhoc test at 5% level of significance. Data analyses and graphical \nillustrations were done in R version 4.1.2 (R Core Team, 2021).  \n\n\n\n3. RESULTS \n\n\n\nTable 2: Survival of Black Soldier Fly Larvae, Hermetia Illucens Reared on Different Substrates \n\n\n\nRearing Substrate \n*Survival (%)\n\n\n\n2 DAS 4 DAS 6 DAS 8 DAS 10 DAS 12 DAS \n\n\n\nCabbage leaves 62.0\u00b14.90 c 58.0\u00b13.74 c 58.0\u00b15.82 c 58.0\u00b15.82 a 58.0\u00b15.82 ab 58.0\u00b15.82 ab \n\n\n\nChicken feed 100.0\u00b10.00 a 96.0\u00b12.45 a 96.0\u00b12.45 a 86.0\u00b15.09 a 86.0\u00b15.09 a 86.0\u00b15.09 a \n\n\n\nInfested cowpea \nflour \n\n\n\n90.0\u00b14.46 ab 82.0\u00b16.62 ab 82.0\u00b16.62 ab 78.0\u00b15.82 a 78.0\u00b15.82 a 78.0\u00b15.82 a \n\n\n\nInfested maize flour 92.0\u00b15.82 a 92.0\u00b15.82 a 92.0\u00b15.82 a 88.0\u00b17.34 a 86.0\u00b16.77 a 86.0\u00b16.77 a \n\n\n\nOverripe banana \npeels \n\n\n\n100.0\u00b10.00 a 98.00\u00b12.00 a 94.0\u00b13.99 a 78.0\u00b110.18 a 78.0\u00b110.18 a 78.0\u00b110.18 a \n\n\n\nPineapple flesh 68.0\u00b14.89 bc 66.0\u00b13.99 bc 62.0\u00b13.74 bc 62.0\u00b13.74 a 46.0\u00b15.09 b 46.0\u00b15.10 b \n\n\n\nPineapple peels 84.0\u00b15.09 abc 84.0\u00b15.09 ab 84.0\u00b15.09 ab 82.0\u00b14.89 a 80.0\u00b13.16 a 80.0\u00b13.16 a \n\n\n\nStale Bread 82.0\u00b15.82 abc 78.0\u00b14.89 abc 76.0\u00b13.99 abc 76.0\u00b13.99 a 72.0\u00b16.62 ab 72.0\u00b16.62 ab \n\n\n\nWatermelon peels 94.0\u00b13.99 a 92.0\u00b13.74 a 92.0\u00b13.74 a 78.0\u00b18.59  a 74.0\u00b16.77 ab 72.0\u00b16.62 ab \n\n\n\nWilted amaranth \nleaves \n\n\n\n84.0\u00b16.77 abc 78.0\u00b15.82 abc 78.0\u00b15.82 abc 78.0\u00b15.82 a 78.0\u00b15.82 a 78.0\u00b15.82 a \n\n\n\nF9, 40 = 7.10 F9, 40 = 7.81 F9, 40 = 7.32 F9, 40 = 2.19 F9, 40 = 3.89 F9, 40 = 3.92 \n\n\n\np<0.0001 p<0.0001 p<0.0001 p=0.045 p=0.001 p=0.001 \n\n\n\nAt two days after setup, 100% survival of BSFL was observed in chicken \nfeed and overripe banana peels (Table 2). Considerably high survival of \nlarvae also occurred on watermelon peels (94.0\u00b13.99%), infested maize \nflour (92.0\u00b15.82%) and infested cowpea flour (90.0\u00b14.46%). The \nforegoing values were significantly higher (F9, 40 = 7.10, p<0.0001) from \nthat recorded on cabbage leaves (62.0\u00b14.90%). Conversely, at two days \nafter setup, no significant difference (p>0.05) was found in the number of \nlarvae that survived on cabbage leaves and on amaranth leaves \n(84.0\u00b16.77%), or pineapple peels (84.0\u00b15.09%), or stale bread \n(82.0\u00b15.82%), or pineapple flesh (68.0\u00b14.89%). A steady decline in \npercentage survival of larvae was observed from the fourth to the 12th day \nafter setup when the larvae pre-pupated (Table 2). Within this period, \nBSFL survival decreased from 96.0\u00b12.45% to 86.0\u00b15.09% in chicken feed; \n\n\n\n92.0\u00b15.82% to 86.0\u00b16.77% in infested maize flour; 98.00\u00b12.00% to \n78.0\u00b110.18% in overripe banana peels; and from 92.0\u00b13.74% to \n72.0\u00b16.62% in watermelon peels. In contrast, the lowest percentage \nsurvival of larvae occurred on pineapple flesh, with values of 66.0\u00b13.99% \nand 46.0\u00b15.10% on the fourth and 12th day after setup. Similarly, larvae \nreared on cabbage leaves had moderate survival with fairly constant \npercentage values from the fourth (58.0\u00b13.74%) to 12th (58.0\u00b15.82%) day \nafter setup. Generally, percentage survival of BSFL on chicken feed, \ninfested maize flour, overripe banana, or watermelon peels were \nstatistically the same but significantly higher than on pineapple flesh or \ncabbage leaves at the 10th (F9, 40 = 3.89, p=0.001) and 12th (F9, 40 = 3.92, \np=0.001) day after setup (Table 2). \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 117-123 \n\n\n\nCite The Article: Olusegun Adebayo Ojumoola, Ayokanmi Samson Owa, Oluwatobi Samuel Akin -Boaz, Ridwan Adetomiwa Adeagbo (2022). \nSurvival and Morphometrics of The Black Soldier Fly, Hermetia Illucens (Diptera: Stratiomyidae) Reared on Common Market Food Wastes \n\n\n\nin Nigeria. Journal of Sustainable Agricultures, 6(2): 117-123. \n\n\n\nTable 3: Body length and width of Black Soldier Fly, Hermetia illucens larva and pre-pupa reared on different substrates \n\n\n\nRearing Substrate \n\n\n\nLarva Pre-pupa Larva \n\n\n\n4 DAS* 8 DAS** 12 DAS*** \n\n\n\nLength (mm) Width (mm) Length (mm) Width (mm) Length (mm) Width (mm) \n\n\n\nCabbage leaves 11.89\u00b10.34 abc 2.48\u00b10.07 bcd 14.97\u00b10.35 abc 3.24\u00b10.08 ab 12.3\u00b10.48 cd 2.46\u00b10.20 e \n\n\n\nChicken feed 13.11\u00b10.44 a 3.08\u00b10.13 a 16.62\u00b10.39 a 3.57\u00b10.32 a 19.43\u00b10.42 a 4.02\u00b10.12 a \n\n\n\nInfested cowpea flour 9.90\u00b10.43 de 2.53\u00b10.14 bcd 13.12\u00b10.40 cd 2.70\u00b10.14 bcd 14.03\u00b10.42 bc 3.00\u00b10.08 cde \n\n\n\nInfested maize flour 12.29\u00b10.34 ab 2.73\u00b10.09 abc 15.91\u00b10.50 a 3.01\u00b10.12 abc 16.17\u00b10.82 b 2.92\u00b10.15 de \n\n\n\nOverripe banana peels 12.59\u00b10.35 a 2.79\u00b10.10 ab 14.73\u00b10.45 abc 2.92\u00b10.11 abc 19.32\u00b10.33 a 3.74\u00b10.11 ab \n\n\n\nPineapple flesh 7.23\u00b10.31 f 1.89\u00b10.08 e 11.82\u00b10.67 d 2.02\u00b10.13 d 9.91\u00b10.57 e 1.64\u00b10.19 f \n\n\n\nPineapple peels 8.50\u00b10.41 ef 2.11\u00b10.11 de 11.82\u00b10.30 d 2.50\u00b10.16 cd 11.19\u00b10.33 de 2.60\u00b10.25 de \n\n\n\nStale Bread 10.31\u00b10.44 cd 2.32\u00b10.06 cde 15.74\u00b10.34 ab 3.20\u00b10.08 ab 15.64\u00b10.57 b 3.14\u00b10.08 bcd \n\n\n\nWatermelon peels 10.56\u00b10.38 bcd 2.58\u00b10.05 bc 13.85\u00b10.46 bc 2.92\u00b10.12 abc 18.39\u00b10.39 a 3.60\u00b10.11 abc \n\n\n\nWilted amaranth leaves 10.56\u00b10.38 bcd 2.41\u00b10.10 bcd 13.24\u00b10.51 cd 2.46\u00b10.09 cd 11.65\u00b10.25 de 2.54\u00b10.10 de \n\n\n\nF9, 240 = 23.39 F9, 240 = 12.51 F9, 240 = 14.47 F9, 240 = 8.85 F9, 240 = 52.59 F9, 240 = 25.07 \n\n\n\np<0.0001 p<0.0001 p<0.0001 p<0.0001 p<0.0001 p<0.0001 \n\n\n\nValues are mean \u00b1 standard error  \n\n\n\nValues in a column followed by the same letter(s) are not significantly different (Tukey HSD test, \u03b1 = 0.05) \n\n\n\nDAS: Days After Setup \n\n\n\n*4 DAS: Larvae were 11 days old \n\n\n\n**8 DAS:  Larvae were 15 days old \n\n\n\n***12 DAS:  Larvae were 19 days old and at the pre-pupa stage \n\n\n\nOn the fourth day after setup, larvae reared on chicken feed, overripe \nbanana peels, and infested maize flour had the longest body length \n(13.11\u00b10.44 mm, 12.59\u00b10.35 mm and 12.29\u00b10.34 mm) and body width \n(3.08\u00b10.13 mm, 2.79\u00b10.10 mm, and 2.73\u00b10.09 mm), respectively (Table \n3). In contrast, the shortest body lengths (8.50\u00b10.41 mm and 7.23\u00b10.31 \nmm) and body width (2.11\u00b10.11 mm and 1.89\u00b10.08 mm) were recorded \non larvae reared on pineapple peels and pineapple flesh substrates. \nSignificant differences were found in the length (F9, 240 = 23.39, p<0.0001) \nand width (F9, 240 = 12.51, p<0.0001) of larvae reared on pineapple peels \nor pineapple flesh and those of larvae reared on chicken feed, overripe \nbanana peels, cabbage leaves, and watermelon peels at four days after \nsetup (Table 3). Similarly, on the eighth day after setup, body length and \nwidth of larvae reared on chicken feed (16.62\u00b10.39 mm and 3.57\u00b10.32 \nmm), infested maize flour (15.91\u00b10.50 mm and 3.01\u00b10.12 mm), stale \nbread (15.74\u00b10.34 mm and 3.20\u00b10.08 mm), cabbage leaves (14.97\u00b10.35 \nmm and 3.24\u00b10.08 mm), and overripe banana peels (14.73\u00b10.45 mm and \n2.92\u00b10.11 mm) were significantly longer (F9, 240 = 14.47, p<0.0001) and \nwider (F9, 240 = 8.85, p<0.0001) than those of larvae reared on pineapple \n\n\n\nflesh (11.82\u00b10.67 mm and 2.02\u00b10.13 mm) and pineapple peels \n(11.82\u00b10.30 mm and 2.50\u00b10.16 mm) (Table 3). Nevertheless, the highest \npercentage change in larval body length from the fourth to the eight day \nafter setup was occurred in pineapple flesh (63.48%), stale bread \n(52.67%), pineapple peel (39.06%), infested cowpea flour (32.53%), and \nwatermelon peels (31.16%). Similarly, larvae reared on infested cowpea \nflour (43.01%), stale bread (37.93%) and cabbage leaves (30.65%) had \nthe highest change in body width between the fourth and eight day after \nsetup (Table 3). While body length and width of larvae generally increased \nfrom the fourth to the eight day after setup irrespective of rearing \nsubstrate, body length and width of pre-pupae larvae did not increase in \nall substrates at the 12th day after setup (Table 3). At this period only pre-\npupa larvae reared on chicken feed (19.43\u00b10.42 mm), overripe banana \npeels (19.32\u00b10.33 mm), and watermelon peels (18.39\u00b10.39 mm) had \nincreased body length, being significantly longer (F9, 240 = 52.59, p<0.0001) \nthan pre-pupa larvae reared on pineapple flesh (9.91\u00b10.57 mm) and \npineapple peels (11.19\u00b10.33 mm).  \n\n\n\nTable 4: Weight of Black Soldier Fly Larvae, Hermetia Illucens Reared on Different Substrates \n\n\n\nRearing Substrate \n\n\n\nMean weight of five larvae (g) \n\n\n\nLarva Pre-pupa larva \n\n\n\n0 DAS 4 DAS 8 DAS 12 DAS \n\n\n\nCabbage leaves 0.09\u00b10.01 a 0.52\u00b10.01 c 1.00\u00b10.06 abc 0.93\u00b10.1 bc \n\n\n\nChicken feed 0.10\u00b10.00 a 0.95\u00b10.03 a 1.32\u00b10.12 a 1.01\u00b10.05 b \n\n\n\nInfested cowpea flour 0.10\u00b10.01 a 0.78\u00b10.05 ab 0.97\u00b10.09 abc 0.60\u00b10.10 cd \n\n\n\nInfested maize flour 0.13\u00b10.03 a 0.77\u00b10.07 ab 1.38\u00b10.18 a 1.10\u00b10.07 ab \n\n\n\nOverripe banana peels 0.11\u00b10.01 a 0.75\u00b10.08 ab 1.27\u00b10.12 ab 0.92\u00b10.04 bc \n\n\n\nPineapple flesh 0.09\u00b10.00 a 0.43\u00b10.01 cd 0.50\u00b10.17 c 0.50\u00b10.13 d \n\n\n\nPineapple peels 0.10\u00b10.01 a 0.28\u00b10.04 d 0.56\u00b10.09 c 0.54\u00b10.06 d \n\n\n\nStale Bread 0.10\u00b10.00 a 0.80\u00b10.02 ab 1.24\u00b10.05 ab 1.42\u00b10.06 a \n\n\n\nWatermelon peels 0.10\u00b10.01 a 0.62\u00b10.06 bc 0.77\u00b10.07 bc 0.53\u00b10.02 d \n\n\n\nWilted amaranth leaves 0.10\u00b10.01 a 0.53\u00b10.04 c 0.67\u00b10.07 c 0.53\u00b10.04 d \n\n\n\nF9, 40 = 1.32 F9, 40 = 19.71 F9, 40 = 8.84 F9, 40 = 18.34 \n\n\n\np = 0.259 p<0.0001 p<0.0001 p<0.0001 \n\n\n\nValues are mean \u00b1 standard error  \n\n\n\nValues in a column followed by the same letter(s) are not significantly different (Tukey HSD test, \u03b1 = 0.05) \n\n\n\nDAS: Days After Setup \n\n\n\n*4 DAS: Larvae were 11 days old \n\n\n\n**8 DAS:  Larvae were 15 days old \n\n\n\n***12 DAS:  Larvae were 19 days old and at the pre-pupa stage \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 117-123 \n\n\n\nCite The Article: Olusegun Adebayo Ojumoola, Ayokanmi Samson Owa, Oluwatobi Samuel Akin -Boaz, Ridwan Adetomiwa Adeagbo (2022). \nSurvival and Morphometrics of The Black Soldier Fly, Hermetia Illucens (Diptera: Stratiomyidae) Reared on Common Market Food Wastes \n\n\n\nin Nigeria. Journal of Sustainable Agricultures, 6(2): 117-123. \n\n\n\nIn all substrates assessed, larvae generally increased in weight from setup \nday to the eight day after setup (Table 4). On the fourth day after setup, \nlarvae reared on chicken feed had the highest weight (0.95\u00b10.03 g). \nNevertheless, this mean weight value was not significantly different from \nthose recorded for larvae reared on stale bread (0.80\u00b10.02 g), infested \ncowpea flour (0.78\u00b10.05 g), infested maize flour (0.77\u00b10.07 g), and \noverripe banana peels (0.75\u00b10.08 g). Conversely, larvae reared on \npineapple peels (0.28\u00b10.04 g) and pineapple flesh (0.43\u00b10.01 g) had the \nlowest weight at four days after setup, and were significantly lighter (F9, 40 \n= 19.71, p<0.0001) compared to larvae reared on chicken feed, stale bread, \ninfested cowpea flour, infested maize flour, and overripe banana peels \n(Table 4). Similarly, on the eighth day after setup, significantly higher (F9,\n\n\n\n40 = 8.84, p<0.0001) weight values were recorded in larvae reared on \ninfested maize flour (1.38\u00b10.18 g), chicken feed (1.32\u00b10.12 g), overripe \nbanana peels (1.27\u00b10.12 g), and stale bread (1.24\u00b10.05 g).  \n\n\n\nFurthermore, significant differences were found in the weight of pre-pupa \nlarval reared on the different substrates at 12 days after setup (Table 4). \nThe highest larval weight value occurred on stale bread (1.42\u00b10.06 g), \ninfested maize flour (1.10\u00b10.07 g), and chicken feed (1.01\u00b10.05 g). These \nvalues were significantly higher (F9, 40 = 18.34, p<0.0001) than those of \nlarvae on pineapple peels (0.54\u00b10.06 g), pineapple flesh (0.50\u00b10.13 g), \nwilted amaranth leaves (0.53\u00b10.04 g), and watermelon peels (0.53\u00b10.02 \ng). With the exception of pre-pupa larvae on stale bread, which gained \nadditional weight and those on pineapple flesh which experienced no \nweight change, larvae on all other substrates decreased in body weight at \n12 days after setup (Table 4). The observed decrease in larval weight at \nthis period caused percentage weight losses that ranged from 7.0% in \nlarvae reared on cabbage leaves to 38.14% on infested cowpea flour \n(Table 4). \n\n\n\nFigure 2: Morphometrics (A) body length (B) head capsule width (C) \nthorax width (D) abdomen width of adult Black Soldier Fly, Hermetia \n\n\n\nillucens that emerged from larvae reared on ten different rearing \nsubstrates. \n\n\n\nThe body length, head capsule width, thorax width and abdomen width of \nemerged adults varied amongst the ten substrates (Figure 2A-D). Adults \nfrom larvae reared on chicken feed (14.30\u00b10.37 mm), infested maize flour \n(14.01\u00b10.30 mm), infested cowpea flour (13.90\u00b10.23 mm), and wilted \namaranth leaves (13.82\u00b10.24 mm) had significantly longer (F9, 174 = 9.78, \np<0.0001) bodies than adults on cabbage leaves (12.19\u00b10.49 mm), \npineapple flesh (11.84\u00b10.28 mm), and pineapple peels (11.12\u00b10.26 mm) \n(Figure 2A). Similarly, head capsule widths of adults from larvae reared on \ninfested maize flour (2.73\u00b10.13 mm), chicken feed (2.68\u00b10.09 mm), and \nstale bread (2.50\u00b10.10 mm) were only significantly wider (F9, 174 = 5.17, \np<0.0001) than those of adults on pineapple flesh (2.06\u00b10.08 mm) and \npineapple peels (1.98\u00b10.09 mm) (Figure. 2B). In contrast, no significant \ndifference (F9, 174 = 2.46, p = 0.012) was found in the thorax widths of flies \nfrom larvae maintained on the different substrates (Figure 2C). Abdominal \nwidth of the flies (Figure 2D) were, however, significantly wider (F9, 174 = \n4.31, p<0.0001) when larvae were reared on watermelon peels (2.87\u00b10.30 \nmm), infested cowpea flour (2.60\u00b10.07 mm), stale bread (2.60\u00b10.08 mm), \nand chicken feed (2.50\u00b10.09 mm) than when maintained on cabbage \nleaves (2.27\u00b10.09), pineapple flesh (2.17\u00b10.10 mm) and pineapple peels \n(2.06\u00b10.07 mm). Generally, no significant difference (p>0.05) was found \nin the body length, head capsule width, thorax and abdomen widths of \nBSFL reared on pineapple peels and cabbage leaves. \n\n\n\n4. DISCUSSION\n\n\n\nFood quality significantly affects survival, size, and adult fertility of \nartificially reared H. illucens (Nguyen et al., 2013; Gobbi et al., 2013). \nConsequently, this study investigated the effect of common market food \nwastes in Nigeria including cabbage leaves, infested cowpea flour, infested \nmaize flour, overripe banana peels, pineapple flesh, pineapple peel, stale \nbread, watermelon peels and amaranth leaves on larval survival, larval \nweight and on larval and adult body size. Larvae and adult H. illucens \nreared on chicken feed consistently had the highest survival, body size \nincrease and weight gain. Chicken feed, such as the type used in the \npresent study, is known to have high protein and carbohydrate contents \nof 18.5% and 61.81%, respectively (Ofori et al., 2019). Consequently, it is \nused as a high quality control or reference substrate against which the \nperformance of other rearing substrates is checked (Spranghers et al., \n2016). Like the control substrate, infested maize flour supported the \nsurvival of more than 85% of BSFL to the pre-pupae stage and was \ntherefore very suitable for BSFL rearing. This is not unexpected since \nmaize is a primary component of chicken feed, and is reported to contain \nup to 77.5% carbohydrate content (Ape et al., 2016). In contrast, less than \n50% of larvae reared on pineapple flesh survived to the pre-pupa stage. \nThis observation may be due to the moisture content of the pineapple flesh \nused in the present study. Pineapple flesh, the edible part of the pineapple \nfruit, is reported to have a high moisture content of about 87% (Bala and \nBashar, 2017). Excessive moisture (above 80%) is known to result in \ndecreased substrate degradation, and poor BSFL growth (Diener et al., \n2011; Dortmans et al., 2017). Survival of BSFL on pineapple flesh may, \ntherefore be enhanced by dewatering the pulp before use as rearing \nsubstrate. Similarly, less than 60 percent of larvae survived to the pre-\npupa stage on cabbage leaves. This observation may be attributed to the \nphysical state of cabbage leaves used in the present study. Despite being \npulverized, the resultant particles were observed to be larger compared to \nthose obtained from amaranth leaves or other waste substrates used in \nthe study. Digestion of organic waste substrates by BSFL can be enhanced \nby subjecting the substrates to various particle reduction processes \nincluding crushing, milling, and grinding (Pastor et al., 2015). These \nprocesses help increase substrates\u2019 surface area and the biodegradation \nactivities of symbiotic gut bacteria in BSFL thus improving larval growth \n(Jeon et al., 2011; Dortmans et al., 2017). The use of pestle and mortar for \npulverization of substrates was adopted in this study as cheap grinding \nequipment that local farmers in Nigeria can easily access. Nevertheless, it \nmay be necessary to use a mechanical blender for some substrates like \ncabbage leaves so as to enhance the production of smaller and easier to \ndigest particles for BSFL rearing. Stale bread used in the present study was \nmade from hard wheat flour (strong flour) which generally has crude \nprotein and carbohydrate of about 13% and 63.0%, respectively (Mongi et \nal., 2011). Despite having similar high protein and carbohydrate contents \nas chicken feed and maize flours, survival of BSFL on stale bread was not \nas high as the two aforementioned substrates. Sodium chloride is an \nimportant ingredient that is often added in relatively small amounts when \nmaking bread. Several authors (Cho et al., 2020, Kwon and Kim, 2016) \nhave however shown that sodium chloride concentrations in rearing \nsubstrates can significantly inhibit the survival, growth and development \nof BSFL. The presence of sodium chloride in the stale bread may, to some \nextent, be responsible for the sub-optimal survival of BSFL on the \nsubstrate in the present study.  \n\n\n\nThe ability of H. illucens to utilize different biowaste streams for growth \nand development (Cammack and Tomberlin, 2017) is affirmed in the \npresent study by the general increase in BSFL length, width and weight on \nall evaluated food waste substrates. Nevertheless, significant differences \nwere observed in the size and weight of H. illucens reared on the different \nsubstrates types. These differences may be attributed to variations in \nsubstrates\u2019 nutritional composition, which have been reported by several \nauthors. For instance, protein and carbohydrate contents were \nrespectively reported to be 18.5% and 61.81% in chick feed (Ofori et al., \n2019); 8.75% and 77.46% in maize flour (Ape et al., 2016); 12.54% and \n63.25% in bread (Mongi et al., 2011); and 19.71% and 57.17% in cowpea \nflour (Ilesanmi and Gungula, 2016). Similarly, protein and carbohydrate \ncontents were reported as 10.44% and 43.40% in banana peels (Feumba \net al., 2016); 12.42% and 32.16% in watermelon peels (Feumba et al., \n2016); 12.86% and 9.06% in amaranthus leaves (Akinnibosun and Adeola, \n2015). Protein and carbohydrate content was respectively 3.0% and \n82.57% in pineapple flesh or pulp (Bala and Bashar, 2017); 5.11% and \n55.52% in pineapple peels (Feumba et al., 2016); and 1.94% and 4.52% in \ncabbage leaves (Ogbede et al., 2015). Generally, H. illucens larvae and \nadults reared on cabbage leaves, pineapple flesh, and pineapple peels \nsubstrates with relatively low protein content performed poorly with \nregards to increase in body length, width and or weight. The foregoing \nobservation highlights the importance of protein as a key nutrient for BSFL \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 117-123 \n\n\n\nCite The Article: Olusegun Adebayo Ojumoola, Ayokanmi Samson Owa, Oluwatobi Samuel Akin -Boaz, Ridwan Adetomiwa Adeagbo (2022). \nSurvival and Morphometrics of The Black Soldier Fly, Hermetia Illucens (Diptera: Stratiomyidae) Reared on Common Market Food Wastes \n\n\n\nin Nigeria. Journal of Sustainable Agricultures, 6(2): 117-123. \n\n\n\ndevelopment. According to Oonincx et al. (2015), survival and \ndevelopment of BSFL was faster on protein-rich food waste diets. \nSimilarly, Cammack and Tomberlin (2017) also reported significantly \nfaster BSFL development on artificial diets with equal proportion of \nprotein and carbohydrate. On the other hand, despite maintaining a \nconstant carbohydrate content level, protracted development of the \nMediterranean fruit fly, Ceratitis capitata larvae occurred when rearing \ndiet was low in protein (Nash and Chapman, 2014). The secondary place \nof carbohydrate in H. illucens is further highlighted in the present study by \nthe significantly lower body length, body width, and body weight of larvae \nreared on pineapple peels and pineapple flesh both of which are \nconsiderably high in carbohydrate but very low in protein (Feumba et al., \n2016; Bala and Bashar, 2017). The use of published secondary data on the \nnutritional composition of substrates may pose some limitation to the \ninferences made about substrate suitability in the present study. Future \nstudies should therefore include a proximate analysis component to \nascertain the exact nutritional composition of each substrate (Spranghers \net al., 2016). In addition, a mix of these common market food wastes in \nNigeria and their suitability for optimal survival, growth and development \nof H. illucens should be investigated in future research studies. \n\n\n\n5. CONCLUSIONS \n\n\n\nThe potential of the black soldier fly larvae to valorize a wide range of \norganic waste streams offers an efficient and environmentally friendly \napproach for municipal organic waste management. It also provides a \nsustainable method for the production of high quality alternative protein \nfor livestock feed and organic compost for crop production. In this study \nthe effect of common market food waste substrates in Nigeria on the \nsurvival, and morphometrics of black soldier fly larvae and adults was \ninvestigated. Survival and body measurements were generally higher in H. \nillucens reared on the control chicken feed, infested maize flour, stale \nbread substrates, cowpea flour, and banana peels. In contrast, H. illucens \nsurvival and body morphometrics was lowest on pineapple flesh, \npineapple peels, and cabbage leaves. Information provided in this study \nwill enhance selection of suitable market food wastes for low-cost \nproduction of black soldier fly larvae in Nigeria. \n\n\n\nREFERENCES \n\n\n\nAkinnibosun, F. I., Adeola, M. O., 2015. Quality Assessment and Proximate \nAnalysis of Amaranthus hybridus, Celosia argentea and Talinum \ntriangulare obtained from open Markets in Benin City, Nigeria. Journal \nof Applied Science and Environmental Management, 19 (4), pp. 727\u2013\n734. \n\n\n\nApe, D. I., Nwogu, N. A., Uwakwe, E. S., Ikedinobi, C. S., 2016. Comparative \nproximate analysis of maize and sorghum bought from Ogbete main \nmarket of Enugu state, Nigeria. Greener Journal of Agricultural Sciences, \n6(9), pp. 272\u2013275. \n\n\n\nBala, M. I., Bashar, J. B., 2017. Analysis of nutritive contents of some \nNigerian fruit. Bayero Journal of Pure and Applied Sciences, 10(2), pp. \n204\u2013208. \n\n\n\nBoateng, S., Amoako, P., Appiah, D. O., Poku, A. A., Garsonu, E. K., 2016. \nComparative Analysis of Households Solid Waste Management in Rural \nand Urban Ghana. Journal of Environmental and Public Health, Article \nID 5780258, pp. 10. Available from: \nhttp://dx.doi.org/10.1155/2016/5780258 \n\n\n\nBr\u00e1s, I., Silva, E., de Lemos, L. T., 2020. Feasibility of using municipal solid \nwastes rejected fractions as fuel in a biomass power plant. \nEnvironmental Protection Engineering, 46, pp. 53\u201362. \n\n\n\nCammack, J. A., Tomberlin, J. K., 2017. The impact of diet protein and \ncarbohydrate on select life-history traits of the black soldier fly \nHermetia illucens (L.) (Diptera: Stratiomyidae). Insects, 8, pp. 56. \nAvailable from: https://doi.org/10.3390/insects8020056.  \n\n\n\nCho, S., Kim, C. H., Kim, M. J., Chung, H., 2020. Effects of microplastics and \nsalinity on food waste processing by black soldier fly (Hermetia illucens) \nlarvae. Journal of Ecology and Environment, 44, pp. 1\u20139. \n\n\n\nCickova, H., Newton, G. L., Lacy, R. C., Kozanek, M., 2015. The use of fly \nlarvae for organic waste treatment. Waste Management, 35, pp. 68\u201380. \n\n\n\nCouth, R., Trois, C., 2009. Comparison of waste management activities \nacross Africa with respect to carbon emissions. Paper presented at the \nTwelfth International Waste Management and Landfill Symposium, S. \nMargherita di Pula, Cagliari, Italy, 5\u20139 October. \n\n\n\nDa Silva, G. D. P., Hesselberg, T., 2019. A Review of the Use of Black Soldier \nFly Larvae, Hermetia illucens (Diptera: Stratiomyidae), to Compost \nOrganic Waste in Tropical Regions. Neotropical Entomology, 49, pp. \n151\u2013162.  \n\n\n\nDiener, S., Solano, N. M. S., Gutie\u00b4rrez, F. R., Zurbru\u00a8gg, C., Tockner, K., 2011. \nBiological Treatment of Municipal Organic Waste using Black Soldier \nFly Larvae. Waste Biomass Valorization, 2, pp. 357\u2013363.  \n\n\n\nDortmans, B. M. A., Diener, S., Verstappen, B. M., Zurbr\u00fcgg, C., 2017. Black \nSoldier Fly Biowaste Processing - A Step-by-Step Guide. Eawag: Swiss \nFederal Institute of Aquatic Science and Technology, D\u00fcbendorf, \nSwitzerland, pp. 87. \n\n\n\nEggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.), 2019.  \nRefinement to the 2006 IPCC Guidelines for National Greenhouse Gas \nInventories; Institute for Global Environmental Strategies: Hayama, \nJapan, Volume 5. \n\n\n\nEwald, N., Vidakovic, A., Langeland, M., Kiessling, A., Samples, S., Lalander, \nC., 2020. Fatty acid composition of black soldier fly larvae (Hermetia \nillucens)\u2014Possibilities and limitations for modification through diet. \nWaste Management, 102, pp. 40\u201347. \n\n\n\nFAO, 2011. Global food losses and food waste \u2013 Extent, causes and \nprevention. Available from: \nhttp://www.fao.org/docrep/014/mb060e/mb060e.pdf \n\n\n\nEl-Dakar, M. A., Ramzy, R. R., Plath, M., Ji, H., 2021. Evaluating the impact of \nbird manure vs. mammal manure on Hermetia illucens larvae. Journal of \nCleaner Production, 278, 123570. Available from: \nhttps://doi.org/10.1016/j.jclepro.2020.123570 \n\n\n\nFeumba, D. R., Ashwani, R. P., Ragu, S. M., 2016. Chemical composition of \nsome selected fruit peels. European Journal of Food Science and \nTechnology, 4(4), pp. 12\u201321. \n\n\n\nGobbi, P., Martinez-Sanchez, A., Rojo, S., 2013. The effects of larval diet on \nadult life-history traits of the black soldier fly, Hermetia illucens \n(Diptera: Stratiomyidae). European Journal of Entomology, 110, pp. \n461.  \n\n\n\nIlesanmi, J. O. Y., Gungula, D. T., 2016. Proximate Composition of Cowpea, \nVigna unguiculata (L.) Walp grains preserved with mixtures of neem, \nAzadirachta indica A. Juss and moringa, Moringa oleifera seed oils. \nAfrican Journal of Food Science and Technology, 7(5), pp. 118\u2013124. \nAvailable from: http:/dx.doi.org/10.14303/ajfst.2016.083 \n\n\n\nJeon, H., Park, S., Choi, J., Jeong, G., Lee, S.-B., Choi, Y., Lee, S.-J., 2011. The \nintestinal bacterial community in the food waste-reducing larvae of \nHermetia illucens. Current Microbiology, 62, pp. 1390\u20131399. \n\n\n\nKim, C.-H., Ryu, J., Lee, J., Ko, K., Lee, J.-Y., Park, K.Y., Chung, H., 2021. Use of \nBlack Soldier Fly Larvae for Food Waste Treatment and Energy \nProduction in Asian Countries: A \n\n\n\nReview. Processes, 9, pp. 161. Available from: \nhttps://doi.org/10.3390/pr9010161 \n\n\n\nKiran, E. U., Trzcinski, A. P., Ng, W. J., Liu, Y., 2014. Bioconversion of food \nwaste to energy: A review. Fuel, 134, pp. 389\u2013399. \n\n\n\nKwon, J. H., Kim, J. Y., 2016. Treatment efficiency of food waste by the black \nsoldier fly (Hermetia illucens) depending on salinity and moisture \ncontents. Journal of Korea Society of Waste Management, 33, pp. 590\u2013\n597. \n\n\n\nLalander, C., Diener, S., Zurbr\u00fcgg, C., Vinner\u00e5s, B., 2019. Effects of feedstock \non larval development and process efficiency in waste treatment with \nblack soldier fly (Hermetia illucens). Journal of Cleaner Production, 208, \npp. 211\u2013219. \n\n\n\nLalander, C., Diener, S., Magri, M. E., Zurbr\u00fcgg, C., Lindstrom, A., Vinner\u00e5s, \nB., 2013. Faecal sludge management with the larvae of the black soldier \nfly (Hermetia illucens) from a hygiene aspect. Science of the Total \nEnvironment, 458e460 (0), pp. 312\u2013318. \n\n\n\nLiu, C., Hotta, Y., Santo, A., Hengesbaugh, M., Watabe, A., Totoki, Y., Allen, \nD., Bengtsson, M., 2016. Food waste in Japan: Trends, current practices \nand key challenges. Journal of Cleaner Production, 133, pp. 557\u2013564. \n\n\n\nLiu, C., Wang, C., Yao, H., 2019. Comprehensive resource utilization of \nwaste using the black soldier fly (Hermetia illucens (L.) (Diptera: \n\n\n\n\n\n\n\n\nMalaysian Journal of Sustainable Agriculture (MJSA) 6(2) (2022) 117-123 \n\n\n\nCite The Article: Olusegun Adebayo Ojumoola, Ayokanmi Samson Owa, Oluwatobi Samuel Akin -Boaz, Ridwan Adetomiwa Adeagbo (2022). \nSurvival and Morphometrics of The Black Soldier Fly, Hermetia Illucens (Diptera: Stratiomyidae) Reared on Common Market Food Wastes \n\n\n\nin Nigeria. Journal of Sustainable Agricultures, 6(2): 117-123. \n\n\n\nStratiomyidae)). Animals, 9, pp. 349. Available from: \nhttps://doi.org/10.3390/ani9060349 \n\n\n\nMongi, R. J., Ndabikunze, B. K., Chove, B. E., Mamiro, P., Ruhembe, C. C., \nNtwenya, J. G., 2011. Proximate composition, bread characteristics and \nsensory evaluation of cocoyam-wheat composite breads. African \nJournal of Food, Agriculture, Nutrition and Development, 11(7), pp. \n5586\u20135599 \n\n\n\nNash, W. J., Chapman, T., 2014. Effect of dietary components on larval life \nhistory characteristics in the medfly (Ceratitis capitata: Diptera, \nTephritidae). PLoS ONE, 9, e86029. \n\n\n\nNguyen, T. T. X., Tomberlin, J. K., Vanlaerhoven, S., 2013. Influence of \nresources on Hermetia illucens (Diptera: Stratiomyidae) larval \ndevelopment. Journal Medical Entomology, 50(4), 898e906. \n\n\n\nNichols, W., Smith, N., 2019. Waste Generation and Recycling Indices 2019: \nOverview and Findings. Available from: \nhttps://www.circularonline,co,uk/wpontent/uploads/2019/07/Veris\nk_Maplecroft_Waste_Generation_Index_Overview_2019.pdf \n\n\n\nOfori, H. I, Amoah, F. I, Arah, I., Krampah, E. K., 2019. Proximate analysis \nand metabolizable energy of poultry feeds. Journal of Engineering and \nApplied Sciences, 14(5), pp. 1026 -1032. \n\n\n\nOgbede, S. C., Saidu, A. N., Kabiru, A. Y., Busari, M. B., 2015. Nutrient and \nanti-nutrient compositions of Brassica oleracae Var. Capitata L. 5(3), pp. \n19-25. \n\n\n\nOmoloye, A. A., Peters, M., Thomas, K. A., Obayelu, O. A., Ojumoola, O. A., \nAkere, A., Ajiboye, A. and Roskam, C. (Unpublished). Black Soldier Fly \n(BSF) - Baseline Report for the Insects4Feed Impact Cluster, Ibadan \nNigeria. New Generation Nutrition: Netherlands Enterprise Agency, pp. \n62. \n\n\n\nOonincx, D. G. A. B., Van Huis, A., Van Loon, J. J. A., 2015. Nutrient utilisation \nby black soldier flies fed with chicken, pig, or cow manure. Journal of \nInsects as Food and Feed, 1, pp. 131\u2013139.  \n\n\n\nPastor, B., Velasquez, Y., Gobbi, P., Rojo, S., 2015. Conversion of organic \nwastes into fly larval biomass: Bottlenecks and challenges. Journal of \nInsects as Food and Feed, 1, pp. 179\u2013193. \n\n\n\nR Core Team, 2021. R: A Language and environment for statistical \ncomputing. R Foundation for Statistical Computing, Vienna, Austria. \nAvailable from: https:/www.R-project.org/ \n\n\n\nSamayoa, A. C., Chen, W-T., Hwang, S-Y., 2016. Survival and Development \nof Hermetia illucens (Diptera: Stratiomyidae): A Biodegradation Agent \nof Organic Waste. Journal of Economic Entomology, pp. 1\u20136. Available \nfrom: https://doi.org/10.1093/jee/tow201 \n\n\n\nS\u00e1nchez, A., Artola, A., Font, X., Gea, T., Barrena, R., Gabriel, D., S\u00e1nchez-\nMonedero, M. A., Roig, A, Cayuela, M. L., Mondini, C. et al., 2015. \nGreenhouse gas emissions from organic waste composting. \nEnvironmental Chemistry Letters, 13, pp. 223\u2013238, Available from: \nhttps://doi.org/10.1007/s10311-015-0507-5. \n\n\n\nSchiavone, A.; Cullere, M.; De Marco, M.; Meneguz, M.; Biasato, I.; Bergagna, \nS.; Dezzutto, D.; Gai, F.; Dabbou, S.; Gasco, L.; et al., 2016. Partial or total \nreplacement of soybean oil by black soldier fly larvae (Hermetia illucens \nL.) fat in broiler diets: Effect on growth performances, feed-choice, \nblood traits, carcass characteristics and meat quality. Italian Journal \nAnimal Science, 16, pp. 93\u2013100. https://doi.org/10.1186/s40104-017-\n0181-5 \n\n\n\nSingh, A., Kumari, K., 2019. An inclusive approach for organic waste \ntreatment and valorisation using black soldier fly larvae: A review. \nJournal of Environmental Management, 251, pp. 109569. \n\n\n\nSpranghers, T., Ottoboni, M., Klootwijk, C., Ovyn, A., Deboosere, S., De \nMeulenaer, B., Michiels, J, Eeckhouts, M., De Clercq, P. and De Smeti, S., \n2016. Nutritional composition of black soldier fly (Hermetia illucens) \npre-pupae reared on different organic waste substrates. Journal of the \nScience of Food and Agriculture, 97(5), pp. 2594\u20132600. \nhttps://doi.org/10.1002/jsfa.8081. \n\n\n\nSpring, P., 2013. The challenge of cost effective poultry and animal \nnutrition: optimizing existing and applying novel concepts. Lohmann \nInformation, 48 (1), pp. 38\u201346. \n\n\n\nTschirner, M., Simon, A., 2015. Influence of different growing substrates \nand processing on the nutrient composition of black soldier fly larvae \ndestined for animal feed. J Insects Food Feed 1:249 \u2013259. Available \nfrom: https://doi.org/10.3920/JIFF2014.0008. \n\n\n\nUnited Nations, 2015. World Population Prospects: Key Findings and \nAdvance Tables. Department of Economic and Social Affairs, New York, \npp. 59. \n\n\n\nvan Huis, A., Van Itterbeeck, J., Klunder, H., Mertens, E., Halloran, A., Muir, \nG., Vantomme, P., 2013. Edible insects: future prospects for food and \nfeed security. FAO Forestry Paper 171. Rome: FAO (Food and \nAgriculture Organization of the United Nations), Available from: \nwww.fao.org/docrep/018/i3253e/i3253e.pdf  \n\n\n\nZhou, F., Tomberlin, J. K., Zheng, L., Yu, Z., Zhang, J., 2013. Developmental \nand waste reduction plasticity of three black soldier fly strains (Diptera: \nStratiomyidae) raised on different livestock manures. Journal of \nMedical Entomology, 50, pp. 1224\u20131230. \nhttps://doi.org/10.1603/ME13021 \n\n\n\n\n\n\n\n\n\n"