IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Research article
erd

Biofunctool® Approach Assessing Soil Quality underConservation Agriculture and Conventional Tillage for Rainfed Lowland Rice Systems in Cambodia
VENG SAR*
Provincial Department of Agriculture, Forestry and Fisheries, Pailin province, Cambodia
Conservation Agriculture Service Center, Department of Agricultural Land Resources Management,
General Directorate of Agriculture, Cambodia
Faculty of Agriculture, Kasetsart University, Bangkok, Thailand
Email: sarveng@hotmail.com

KANNIKA SAJJAPHAN
Department of Soil Science and Center for Advanced Studies in Agriculture and Food,
Kasetsart University, Bangkok, Thailand

WUTTHIDA RATTANAPICHAI
Department of Soil Science and Center for Advanced Studies in Agriculture and Food,
Kasetsart University, Bangkok, Thailand

ALEXIS THOUMAZEAUCIRAD, Systèmes de pérennes, University of Montpellier, Montpellier, France

VANG SENG
Department of Agricultural Land Resources Management, General Directorate of Agriculture,
Phnom Penh, Cambodia

FLORENT TIVETCIRAD, AIDA, University of Montpellier, Montpellier, France
Received 30 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract Rice productivity is often limited by soil fertility depletion, water availability andaccess. Conservation Agriculture (CA) cropping systems have been designed and tested withthe main objectives of restoring soil fertility, increasing productivity and profitability. This
study assessed changes in soil health under rain-fed lowland rice under (i) conventional tillage(CT), (ii) CA (CA7: 7 years under CA) and (iii) green manure management for one year
(CGM1) and for two years CGM2). Biofunctool®, a multi-functional soil assessment approachbased on a set of seven soil indicators, was used to evaluate changes in three main soil functions(C transformation, nutrient cycling, and soil structure). In addition, soil chemical analyses wereconducted in the 0-5, 5-10, 10-20 and 20-40 cm soil layers to assess changes in nutrient
contents. Our results emphasized positive impacts of CA on C transformation, soil structure andnutrient contents. Soil organic carbon and total N were significantly higher (p < 0.05) under
CA7 in the 0-5 cm layer with up to +7.5 g C kg-1 and +0.74 g N kg-1, respectively. Higher
values of labile C and soil respiration (p < 0.05) were observed under CA in the 0-5 and 5-10cm layers. More stable soil aggregates and improved VESS values (p < 0.05) were also
observed under CA. CA and CGM had 2 to 3 times more available phosphorus than CT in the
0-5 cm layer, and higher values were observed under CA from a depth of 0-20 cm. Higher Ca,
Mg and K contents were recorded under CA and CGM in the 0 to 40-cm soil layer. A SOC
stabilization trend was observed in soils under CA (0-5 and 5-10 cm layers) while a SOCⒸ ISERD

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mineralization trend was observed under CT and CGM. These results emphasize the positive
impacts of CA on maintaining and/or enhancing soil health and in contributing to SOC
accumulation. A diachronic analysis is now needed to assess the long-term on-farm impacts of
CA on soil health and crop performances.
Keywords soil organic carbon dynamic, sustainable intensification, climate change adaptation

INTRODUCTION
Cambodia produced over eight million tons of paddy for a total cultivated area of ~ 3.3 million ha(MAFF, 2018). Rice exports jumped from 100,000 tons in 2010 to ~ 540,000 tons in 2016. Based on
topographic position, rice agroecosystems range from upland rice, rainfed lowland or upper sandy
terraces, flooded rice in the plains, receding rice, and deep-water rice. Soil fertility across the rice
agroecosystems ranges from medium to low (Biswas et al., 2017). Yields are often limited by low
levels of soil nutrients, fluctuating water levels in the paddy field and related impacts on the form andavailability of nutrients in the soil (Pheav et al., 2005). Despite the increase in rice yields over the lasttwo decades, rice yield and profitability of farming systems around the Tonle Sap lake are still low. In
addition, increasing use of chemical fertilizers and pesticides are recorded both in flooded and irrigatedconditions raising concerns about environmental and health issues, and food safety. The low level ofdiversification, the increasing use of pesticides (Flor et al., 2018) and the combination of practices that
deplete soil fertility (i.e., continuous ploughing, use of rotary tillers, low inputs of organic compounds,
burning or removal of crop residue to feed cattle) call for the design and assessment of alternative ricebased cropping systems. There is an urgent need to promote diversified rice cropping systems tomaintain and improve soil quality, increase farmers’ incomes while simultaneously contributing tosafer food production. Several studies have demonstrated the positive impacts of diversified
conservation agriculture (CA) cropping systems which promote the accumulation of soil organiccarbon (SOC) and improve soil fertility (Boddey et al., 2010). Conservation agriculture (CA) is basedon three technical principles with (i) minimum soil disturbance (i.e., no tillage), (ii) a permanent soil
cover, and (iii) diversified cropping systems (FAO, 2014). Diversified CA cropping systems with highbiomass-C inputs (Séguy et al., 2006) insure a continuous supply of fresh organic compounds, thereby
improving soil aggregation (Tivet et al., 2013), increasing soil biodiversity (Lienhard et al., 2013), andSOC content (de Moraes Sá et al., 2015) while enhancing production and ecosystem services(Pittelkow et al., 2015). Multifunctional soil assessments are needed to better understand therelationships between cropping system management and soil health. Thoumazeau et al. (2019 a, b)
proposed an integrative, multifunctional approach, named Biofunctool®, that makes it possible toassess three main soil functions (i) carbon transformation, (ii) nutrient cycling, and (iii) soil structure,with a core set of in-field and low-tech indicators. Three indicators were used to assess the changes of
the carbon transformation including the labile soil organic C fraction (permanganate oxidizable carbon:
POXC) (Weil et al., 2003), the basal soil respiration (SituResp®) (Thoumazeau et al., 2017), and thesoil biological activity using the bait lamina test (van Gestel et al., 2003). Then, three indicators wereused for the soil structure maintenance function by assessing soil aggregate water stability (AggSoil) at
0-5 and 5-10 cm depths (Herrick et al., 2001), water infiltration (Beerkan) (Thoumazeau et al., 2019b),
and visual evaluation of soil structure (VESS) at 0-30 cm depth (Guimarães et al., 2011). Finally, thenutrient cycling function was assessed by quantifying available N, P, Ca, Mg and K.
OBJECTIVE
We hypothesised that rice-based CA cropping systems have direct and positive effects on soil health,increase the main soil functions through C transformation, soil structure and nutrient contents. TheⒸ ISERD

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overall objective of the study was to conduct an integrative and quantified assessment of therelationships between contrasted rice cropping systems (i.e., conventional plough-based tillage (CT),CA and green manure management) and soil health on the flood plains of Lake Tonle Sap using the
Biofunctool® approach.
METHODOLOGY
Study site:
In 2011, an on-farm experimental design was implemented in the hydromorphic plains in Kropeur Kert
village, Banan district, Battambang province (latitude 13°00′32.37″ N, longitude 103°04′27.31″E, 18 m
elevation, no slope). The soil in the 0-20 cm layer comprised 511 g kg-1 clay, 339 g kg-1 silt, 150 g kg-1
sand and 5.19 pH (H2O). The soil is classified as a Vertisol by the FAO and as clayey soil according to
the USDA soil classification. Mean annual precipitation was 1,306 mm and the mean temperature was
27.5 °C.
Cropping systems:
Soil functions and soil physical-chemical characteristics were assessed for four main cropping systems(CA7, CGM1, CGM2 and CT). It should be noted that the on-farm assessment was based on an
unequal number of fields under the same management, with two fields under CA management for 7
years (CA7), two fields under green manure management for one year (CGM1), three fields undergreen manure management for two years (CGM2), and seven fields under conventional plough-based
management (CT). CA was based on no-tillage with a cover crop following wet season rice. Two main
species were used, Stylosanthes guianensis (cv. Ubon Nina) and Centrosema pascuorum (cv.
Cavalcade). Green manure management comprised ploughing the cover crop biomass into the soil a
few weeks before rice was sown. Conventional tillage (CT) included ploughing (6-disc plough) andharrowing, and the residues of the previous crop were incorporated into the soil by ploughing or rotary
tiller.
Soil quality assessment:
According to the integrative view of the soil quality, the indicators used to assess changes in soil
quality should be the result of soil biota-physical-chemical property interactions (Thoumazeau et al.,2019). The Biofunctool® approach was chosen to provide this integrative view and to describe theimpacts of contrasted practices on soil quality. This approach is based on three main soil functions (i)soil carbon transformation, (ii) nutrient cycling and (iii) soil structure.
Soil sampling and soil physical-chemical analysis:
Soil samples were collected on January 25th, 2018 in four soil layers (0-5, 5-10, 10-20, and 20-40 cm)
with three replicates per field. The Biofunctool® approach was applied to the 0-5 and 5-10 cm layers.
In addition, the same two soil layers (0-5 and 5-10 cm) were sampled per subplot to assess water-stable
aggregates. Total C and N concentrations were analysed using a dry combustion method with anelemental CHN analyser (Wright et al., 2008), available P (Bray II method) (Bray and Kurtz, 1945),
and available K, Ca, Mg (AAS method) (Pyle et al., 1995). Available N was quantified on samples
sieved at 2-mm using the Kjeldahl method (Craft et al., 1991).
Data analysis:
Statistical analysis was performed using R software (Dessau and Pipper, 2008). Each Biofunctool®
indicator was first studied separately using a linear-mixed effects model (lme4 package, (Bates et al.,2015). Treatment was defined as the fixed factor and replicates (plots and inner-replicates) as random
factors. After checking the normality of the model residuals and the homoscedasticity of residual
variance, ANOVAs were run using the car package (Fox and Weisberg, 2013). This was followed bypost-hoc mean comparisons, using the Shapiro-Wilk test with Bonferroni adjustment (Hothorn et al.,Ⓒ ISERD

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2008). After studying each indicator separately, the indicators were subjected to Principal Component
Analysis (PCA) (FactoMineR package) (Lê et al., 2008).
RESULTS
Soil Chemical Analysis
Under CA7, higher SOC and N contents (p < 0.05) were recorded in the 0-5 cm soil layer, representing
an increase of up to +7.5 g C kg-1 and +0.74 g N kg-1, respectively (Table 2). Higher N contents wereobserved in the 0-5 and 5-10 cm soil layers under CA7 than under CT and significant differences
between treatments were observed in all soil layers. In addition, higher stratification of SOC and N
contents were observed under CA7 and CGM2 compared with CGM1 and CT in the top soil layer (010 cm). SOC and N contents were more uniformly distributed under CT mainly due to the successive
effect of ploughing which mixed the topsoil layers.
Table 1 Total soil organic carbon and nitrogen contents
Fields
CA7
CGM2
CGM1
CT

0-5
17.75 b11.52 a10.22 a10.75 aTotal OC (g kg-1)
5-10
10-20
9.97 ns
7.19 ab9.90 ns
7.35 b
9.56 ns
5.57 a9.22 ns
5.74 a20-40
4.86 ns
5.05 ns
4.11 ns
4.51 ns

0-5
1.76 b
1.15 a1.13 a1.02 aTotal N (g kg-1)
5-10
10-20
1.09 b0.81 ab1.07 ab
0.86 b
1.05 ab0.78 ab0.91 a0.66 a20-40
0.63 ab
0.67 b
0.66 b0.53 abNote: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9),
CGM1: 1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21).
Different letters indicate significant differences according to Tukey’s test.

Soil Quality: C Transformation, Soil Structure and Nutrient Cycling
Carbon transformation:
Higher values of POXC and SituResp were observed in the 0-5 cm soil layer (p < 0.05) under CA7
than under CGM2, CGM1 and CT, representing higher labile-C inputs and soil biological activity ofthe mesofauna and microflora. No significant difference was observed for the bait lamina, however
higher substrate degradation was observed under CA7.
Table 2 Soil carbon transformation
Fields
CA7
CGM2
CGM1
CT

POXC
(mg C. kg-1)
0-5
5-10
877.3 b404.0 ns443.7 a324.7 ns429.7 a320.2 ns428.0 a290.2 ns

SituResp®
(Absorbance difference)
0-5
5-10
0.71 b0.25 b0.33 ab0.12 ab0.29 a0.13 ab0.26 a0.11 aLamina baits(Substrate degraded)
0-5
5-10
0.53 ns
0.36 ns
0.38 ns0.29 ns0.46 ns
0.40 ns
0.40 ns
0.33 ns

Note: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9),
CGM1: 1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21).
Different letters indicate significant differences according to Tukey’s test. POXC: permanganate oxidizable carbon (POXC);
Situ Resp: Soil respiration

Soil structure stability:
A significant difference was observed in the 5-10 cm soil layer with higher values under CA7
emphasizing the higher soil aggregate stability (Table 4). A low water infiltration rate was observed inⒸ ISERD

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all fields with no significant differences between fields. CA7 exhibited the better visual soil structurewith a lower score.
Table 3 Soil structure stability
Aggregate (score)
0-5
5-10
5.20 ns
5.36 ns
5.59 ns
4.89 ns
5.69 ns
4.81 ns5.41 ns4.61 ns

Fields
CA7
CGM2
CGM1
CT

Beerkan(ml.mn-1)
4.22 ns
3.78 ns
4.17 ns
3.34 ns

0-5
2.00 a2.78 c2.10 a2.29 b

VESS (score)
5-10
2.00 a2.78 a3.00 b
3.75 c10-20
2.50 a2.83 a3.25 b
3.50 bNote: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9),
CGM1: 1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21).
Different letters indicate significant differences according to Tukey’s test.

Nutrient contents:
Under CA7, higher concentrations of available nitrogen were recorded in the 0-5 cm soil layer (p <0.05) and in the other soil layers except in the 20-40 cm layer (Table 5). Two to three times higher
available phosphorus was measured in the 0-5 cm layer under CA7 and CGM1 than under CT andhigher values were observed under CA7 in the three top soil layers. Higher Ca, Mg and K contents
were recorded under CA7 and CGM2 in all soil layers (Table 5).
Table 4 Available phosphorus and nitrogen
Fields
CA7
CGM2
CGM1
CT

0-5
12.34 c5.75 a9.51 b
4.51 aP (mg.kg-1)
5-10
10-20
6.52 b
2.50 b
4.03 a1.85 ab5.66 b
1.73 ab3.60 a1.45 a20-40
1.53 ns
1.51 ns
1.50 ns
1.25 ns

0-51.28 b
0.78 a0.64 a0.81 aN (g.kg-1)
5-10
10-20
0.74 ns
0.55 b0.70 ns
0.58 b
0.59 ns0.26 a0.59 ns
0.36 a20-40
0.28 a0.44 b
0.23 a0.32 aNote: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9), CGM1:
1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21). Different
letters indicate significant differences according to Tukey’s test.

Table 5 Available potassium, calcium and magnesium
K (mg.kg-1)
Ca (mg.kg-1)
Mg (mg.kg-1)
0-5
5-10
10-20 20-40
0-5
5-10
10-20 20-40
0-5
5-10
10-20
20-40
27.27
11.70
10.77
926.6
840.7
812.8
747.5
106.2
98.7
CA7
9.97 b118.8 c103.3 b
c
ns
ns
c
b
b
c
b
ab
22.93
9.47
10.37
10.93
804.0
794.6
809.0
714.4
105.6
106.9
132.9
124.3
CGM2
bc
ns
b
ns
bc
b
b
bc
bc
b
c
b
17.33
8.60
6.46
8.94
648.1
632.3
524.9
548.9
78.5
75.4
77.1
CGM1
79.8 ab
bc
ns
a
ns
ab
a
a
a
a
a
a
15.63
10.24
8.60
10.41
687.1
692.5
609.0
630.8
79.9
84.6
84.3
93.1
CT
a
ns
ab
ns
a
a
a
ab
a
a
a
a
Note: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9), CGM1:
1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21). Different
letters indicate significant differences according to Tukey’s test.
FieldsSOC Stabilization vs. SOC Mineralization
Due to the unbalanced design, the trends and hypothesis presented hereafter need to be confirmed in a
medium to long-term study. We nevertheless present our results here as the relationship between
POXC and SituResp is a promising decision-making tool to assess SOC dynamics. A SOC stabilizationⒸ ISERD

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trend was only observed under CA7 (0-5 and 5-10 cm soil layers) (Fig. 1 a and b). For CT and CGM, atrend of SOC mineralization was observed with negative values of the residues of the linear
relationship between POXC and SituResp. The SOC mineralization trend under CT could result fromsuccessive ploughing and lower inputs of biomass compared with CA. In addition, disruption of soil
aggregates due to ploughing exposed SOC to microbial communities and to mineralization processes(Chen et al., 2009).
(a)

(b)

Note: CA7: 7 years CA (experiment), CGM2: 2 years of cover crops as green manure incorporating into the soil by tillage,
CGM1: 1 year of cover crops as green manure incorporated into the soil by tillage, CT: Conventional tillage as the control.
Regression was made between values of POXC and SituResp® (see Appendix 6). Residual mean values below zero represent
a trend of mineralizable soil organic C, above zero values reflect a trend of short-term SOC stabilization. The vertical lines
represent the standard error per treatment.

Fig. 1 Mean values of regression residuals per treatmentin the 0-5 cm (a) and 5-10 cm (b) soil layers
Principal Component Analysis
A principal component analysis (PCA) was conducted. Three main clusters were identified (1) CT and
CGM1, (2) CGM2 and (3) CA7 (Fig. 2). The first axis separates CA7 from CGM1 and CT, while thesecond axis separates CA7 from CGM2. It should be noted that the main indicators of the
Biofunctool® such as POXC, SituResp and lamina baits are correlated with long-term CA
management (CA7). Other Biofunctool indicators of soil structure stability (i.e., VESS and soil
aggregates) contributed less to both axes. As observed previously, CT and CGM1 are characterised bylower TOC, TN, Ca, Mg contents and SituResp compared with CA7 and CGM2.

Note: CA7: 7 years CA (experiment), CGM2: 2 years of cover crops as green manure, CGM1: 1 year of cover crops as green
manure, CT: Conventional tillage

Fig. 2 Principal component analysis of the impacts of cropping systems on soil functions andsoil chemical properties in the 0-10 cm soil layerⒸ ISERD

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DISCUSSION
This study assessed changes in soil quality under conventional plough-based and conservation
agriculture management, using a multi-functional approach integrating a set of seven soil quality
indicators related to three main soil functions (C transformation, nutrient cycling, and soil structure)(Thoumazeau et al., 2019). Soil physical-chemical analyses were also performed. Including all theindicators in a multivariate analysis made it possible to understand how soil functions in a clayey
Vertisol in the flood plains of Tonle Sap lake are affected by contrasted agricultural practices under atropical climate.
Impacts of Contrasted Cropping Systems on Soil Chemical Properties
The main soil indicators were positively affected by long-term CA practices (CA7) with an increase insoil organic C and N contents, an increase in labile-C (POXC), an improvement of soil aggregation thatcan protect SOC from microbial oxidation compared with under CT. However, a SOC accumulation
trend under CA cropping systems was only detected in the surface soil layer confirming the results ofthe study conducted by Hok et al. (2015). Soil organic C and N contents were stratified with depth
under CA compared with CT as a consequence of crop residues being left on the surface of the soil,
thereby regulating fluctuations in soil temperature and moisture, and limiting the SOC decomposition
rate (Franzluebbers, 2008).
In accordance with the results of Hok et al. (2015) and Pheap, Lefevre et al. (2019), under CA,
SOC and TN contents were higher in the topsoil layer. Zhang and He (2004) reported a gradualincrease in SOC contents in the 0-15 cm layer in the first 30 years of rice cropping in South-East Asia.
The highest accumulation of SOC and TN were associated with the higher biomass input and lessdisturbance (CA7) compared with other practices (CGM1, CGM2 and CT).
Impact of Contrasted Cropping Systems on Three Soil Functions
Carbon transformation:
Part of the carbon transformation function, POXC, defined as a “soil fraction that is sensitive tocropping system management” (Pheap et al., 2019) was indeed affected by the different practices withhigher carbon contents measured in the 0-5 cm layer under CA7. Hok et al. (2018) reported an increase
in POXC in the 0-10 cm layer under CA systems. In a similar experiment in Brazil, Sá et al. (2015)
observed twice higher POXC under the no-till system down to a depth of 20 cm compared with underplough-based management. The increase in the top soil layer could be attributed to the difference indistribution of crop and cover crop residues between CA7 and other practices (Chatterjee and Lal,2009) and due to a smaller amount of crop residues returned to the soil under CT than under CA7.
Concerning soil respiration, our results are in accordance with those of Pheap, Lefèvre et al.
(2019), who reported that SituResp® values tended to be, or were significantly higher under CA, due
to increased soil microbial activity linked to improved microorganism habitats and an increase in soil
labile carbon. Used in combination, POXC and SituResp® play complementary roles by providing aframework for evaluating the relative dynamics of soil organic C stabilisation and mineralisation
processes in agroecosystems (Hurisso et al., 2016). As observed by Pheap, Lefevre et al. (2019), CT
tends to mineralise fresh organic carbon, while CA7 tends to stabilise C inputs.
Soil structure:
The absence of tillage combined with higher biomass inputs resulted in significant changes in soil
physical properties, specifically improved soil aggregation (Lal and research, 1997). Soil physicalproperties improved rapidly under CA compared with under tillage-based systems. More stable
aggregates in the top soil layers and better soil porosity are generally observed under CA (Busari et al.,Ⓒ ISERD

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2015). As observed in our study, VESS was less sensitive to changes in the soil structure comparedwith water aggregate stability. This result is also in agreement with the results of other studies
including those by Castioni et al. (2018) on a Brazilian Oxisol (55% clay) and by Thoumazeau et al.
(2019b) for different land uses in Thailand.
Nutrient cycling and contents:
Ca, Mg and K contents were higher in the 0-10 cm sol layer under CA7 and CGM2 than under otherpractices It is widely reported in the literature that CA with long-term use of cover crops recycle
significant amounts of nutrients through the diversity of roots systems and aboveground biomass
deposition. Continuous decomposition of plant residues enriches the main nutrient contents of the soil.
Busari et al. (2015) reported that exchangeable Ca, Mg, and K were significantly higher in the surface
soil under CA than under CT. Lower P, K, Ca and Mg contents were recorded in CT soils possibly due
to the inversion of the top soil due to ploughing, which shifts less fertile subsoil to the surface with, in
addition, possible leaching (Ali et al., 2006).
CONCLUSION
A multifunctional soil assessment was conducted using the Biofunctool® approach to compare theimpacts of contrasted rice cropping systems on soil health. Long-term CA cropping systems (CA7) andearly green manure management for two consecutive years (CGM2) had positive impacts on the mainsoil functions compared with short-term green manure management (CGM1) and CT. These results
showed that POXC, SituResp®, and VESS are sensitive in-field indicators of the impacts of annual
rice cropping systems on soil health. By contrast, soil aggregate stability, beerkan, and lamina baits areless sensitive to changes. The relationship between POXC and SituResp® revealed a SOC
accumulation trend under long-term CA management and is thus a promising in-field and low-cost
indicator to assess the dynamics of soil organic C (stabilisation vs. mineralisation). The comparison
conducted here, with an unbalanced number of fields among the different cropping systems, only
highlights potential trends in changes in soil fertility. Additional studies are thus needed along with adiachronic analysis to assess the long-term on-farm impacts of CA on soil health, crop performancesand on the provision of ecosystem services.
ACKNOWLEDGEMENTS
This research was made possible through the support from the Department of Agricultural Land
Resources Management (DALRM) from the General Directorate of Agriculture (GDA) that conducted
field activities through the involvement of the team of the Conservation Agriculture Service Center(CASC) of Battambang province. We express our sincere gratitude to the Southeast Asian Regional
Center for Graduate Study and Research in Agriculture (SEARCA), for funding the research activities.
The field activities were made possible through the project Women in Agriculture Network Cambodia(WAgN, Royal University of Agriculture, University of Battambang and Pennsylvania State
University) through support by the United States Agency for International Development Feed the
Future Innovation Labs for Collaborative Research on Sustainable Intensification (Cooperative
Agreement No. AID-OAA-L-14-00006, Kansas State University) and the Conservation Agriculture
Network in South-East Asia (CANSEA). We also thank the Land Development Department (LDD) and
Kasetsart University laboratory for handling and providing assistance for the soil analysis. We arethankful to Mr. Sambo Pheap, lecturer at the Faculty of Agronomy of the Royal University ofAgriculture and Mr. Rotha Chork, Mr. Hang Socheat, Mr. Pengly Koun undergraduate students fromRUA and the lab team for their assistance in collecting soil samples and conduct soil analysis.

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