Enabling smallholder farmers to sustainably improve their food, energy and water nexus while achieving environmental and economic benefits

Renewable and Sustainable Energy Reviews 120 (2020) 109645

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Enabling smallholder farmers to sustainably improve their food, energy and water nexus while achieving environmental and economic benefits Mahesh K. Gathala a, *, Alison M. Laing b, T.P. Tiwari a, J. Timsina a, Md. S. Islam a, A. K. Chowdhury c, C. Chattopadhyay c, A.K. Singh d, B.P. Bhatt e, R. Shrestha f, N.C.D. Barma g, D. S. Rana h, Tamara M. Jackson i, B. Gerard j a

International Maize and Wheat Improvement Center, Dhaka, Bangladesh CSIRO Agriculture and Food, Brisbane, Australia Uttar Banga Krishi Vishwavidyalaya (UBKV), Pundibari, Coochbehar, West Bengal, India d Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India e ICAR-RCER, Patna, Bihar, India f Nepal Agricultural Research Council, Kathmandu, Nepal g Bangladesh Wheat and Maize Research Institute, Dinajpur, Bangladesh h International Rice Research Institute, New Delhi, India i Graham Centre for Agricultural Innovation, Charles Sturt University, Wagga Wagga, Australia j International Maize and Wheat Improvement Center, El Batan, Texcoco, Mexico City, Mexico b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Cropping systems South Asia Energy efficiency Water efficiency Conservation agriculture-based sustainable intensification Meta-analysis

Traditional cropping practices in the Eastern Gangetic Plains, South Asia, are resource intensive, requiring large inputs of water, energy and human labor. They are also inefficient, with relatively low productivity for the inputs used although the climate, soil and water resources of the region indicate that greater productivity is achievable. In on-farm experiments conducted across three countries (Bangladesh, India, Nepal) we compared the performance of traditional and improved management practices to understand which better facilitated the production of food-grain crops while reducing energy and water demands, thus improving the sustainability of cropping system energy requirements. Benefits of improved over traditional management practices included increases of up to 10% in crop grain yields; up to 19% in water productivity; up to 26% in energy productivity; and reductions of up to 50% in labor. These metrics combined to reduce the cost of production under improved management by up to 22% and to increase gross margins by up to 100% (although in most instances gross margins increased by 12–32%). CO2equivalent emissions reduced by 10%–17% compared to traditional practices. The principles behind the improved management practices, which we demonstrate improve the food-energywater nexus while concurrently promoting more sustainable use of energy resources, are applicable across smallholder farming systems throughout South Asia and in many emerging-economy countries. These im­ provements to traditional management practices combined with our approach of supporting farmers through the implementation of new methods has widespread applications and the potential to assist many countries tran­ sitioning to low-energy, sustainable food production.

Abbreviations: ACIAR, Australian Centre for International Agricultural Research; AUD, Australian dollar; ANOVA, Analysis of variance; BAU, Bihar Agricultural University; CASI, Conservation agricultural-based sustainable intensification; CO2, Carbon dioxide; CSIRO, Commonwealth Scientific and Industrial Research Or­ ganization; CT, Conventional tillage; CU, Curtain University; DSR, Direct seeded rice; DoA, Nepal Department of Agriculture-Nepal; DoA, WB Department of Agriculture-West Bengal; DFAT, Department of Foreign Affairs and Trade; EGP, Eastern Gangetic Plains; Em, Energy manual; Ef, Energy fertilizer; Ei, Energy input; EP, Energy productivity; Ha, hectare; ICAR-RCER, Indian Council of Agricultural Research-Research Complex for Eastern Region; iDE, International Development En­ terprises; IFPRI, International Food Policy Research Institute; IWMI, International Water Management Institute; Kg, Kilogram; Mg, Mega kilogram; NARC, Nepal Agricultural Research Council; PTR, Puddled transplanted rice; RL, Rice-lentil; RM, Rice-maize; RR, Rice-Rice; RW, Rice-wheat; RWJ, Rice-wheat-jute; RWMb, Ricewheat-mungbean; SREY, System rice equivalent yield; SRFSI, Sustainable and Resilient Farming Systems Intensification; SDIP, Sustainable Development Investment Portfolio; TEU, Total energy use; UBKV, Uttar Banga Krishi Vishwavidyalaya; UPTR, Unpuddled transplanted rice; UQ, University of Queensland; ZT, Zero tillage. * Corresponding author. E-mail address: [email protected] (M.K. Gathala). https://doi.org/10.1016/j.rser.2019.109645 Received 29 June 2019; Received in revised form 12 November 2019; Accepted 30 November 2019 Available online 19 December 2019 1364-0321/© 2019 Elsevier Ltd. All rights reserved.

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conventionally tilled (CT) production systems: diesel-powered tractors are used for land preparation and diesel or electricity is required to pump water for irrigation in this predominantly groundwater-dependent region [9–11]. High energy use results in high emissions of greenhouse gases [11–13]. Removal from the field or burning of residues from the previous crop prior to sowing the next reduces soil moisture and contributes to soil nutrient losses and air pollution [14–16]. Alternative production practices incorporating the three principles of conservation agriculture (minimal soil disturbance; crop rotations; maintenance of ground cover [12,17]) have been shown to reduce crop water, energy and labor requirements while maintaining or increasing cropping system productivity in small studies across parts of South Asia [e.g. 9, 13, 18]. Conservation agriculture-based sustainable intensifica­ tion (CASI) encourages, as far as practicable for risk averse smallholder farmers, the adoption of conservation agriculture principles for crop management. Additionally, CASI encourages farmers to improve crop irrigation and nutrient management efficiencies, while providing op­ portunities for micro-entrepreneurs and emerging small businesses to deliver agro-chemicals and mechanization-based agricultural services in their local communities. CASI based cropping practices include mech­ anized crop establishment: rice is either direct seeded (DSR) without

1. Introduction As demand for food increases globally to support a growing popu­ lation, there is also pressure to produce food more efficiently, using less water and energy and within the current area of agricultural land [1,2]. These food production pressures are acutely felt across South Asia, including the Eastern Gangetic Plains (EGP) which includes the eastern Indian states of Bihar and West Bengal, northwest Bangladesh, and the lowland eastern Terai region of Nepal (Fig. 1). The EGP has a population of over 400 million, the majority of whom are poor and malnourished [3], although the region has climatic, hydrologic and edaphic conditions well suited to crop production and has the potential to be a major area of food production [4–6]. The challenge of how to sustainably increase food production with efficient, sustainable use of water, energy and labor inputs is widespread across South Asia and other emerging-economy regions include Southeast Asia sub-Saharan Africa and much of South America. Cropping systems in the EGP are traditionally rice-based, with two crops produced each year: a rainfed rice crop is grown in the wet monsoon season (June to October), followed by an irrigated crop in the dry season (November to April). Rice, wheat, maize and lentil are the most common dry season crops [7,8]. Energy demands are high under

Fig. 1. An approximation of South Asia (B) showing the approximate boundaries of India (light grey), Nepal (pink) and Bangladesh (aqua blue) with the EGP (light purple shaded area); and the EGP (A) showing experimental sites in different districts of Nepal (blue circles), Bihar (blue triangles), West Bengal (red triangles) and Bangladesh (red circles). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 2

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tillage or transplanted without puddling (UPTR), while other crops are directly sown with zero tillage (ZT). Mechanized crop establishment alone has been shown to reduce water, energy and labor requirements, production costs and greenhouse gas emissions on both research stations and in farmers’ fields across the EGP [10,19–22]. Prior to this research, however, an integrated assessment of the effect of all CASI practices throughout a range of districts and cropping systems across the EGP in terms of productivity, water and energy efficiency, and labor usage had not been conducted. The objectives of this study were 1) to examine the performance of CASI-managed and traditional CT-managed cropping systems; to assess the effectiveness of CASI 2) under different cropping systems and 3) across different regions of the EGP; and 4) to describe the enabling social and policy frameworks which supported the uptake of CASI. We con­ ducted participatory trials on 436 farmers’ fields across eight districts of the EGP over three years, from 2015 to 2017, and in six cropping sys­ tems: rice-wheat (RW), rice-maize (RM), rice-lentil (RL), rice-rice (RR), rice-wheat-mungbean (RWMb) and rice-wheat-jute (RWJ). We analysed field trial data to assess CASI and CT practices across cropping systems and districts in terms of productivity, water and energy usage and effi­ ciency, labor requirements, economics and CO2-equivalent emissions.

specialized recommendations of local agronomic institutions. Across the farms in any one district management practices for the experimental treatments were consistent. Sinha et al. [23] reported that topsoil (0–15 cm depth) soil organic carbon levels across the research sites before the commencement of research were, relative to levels throughout the EGP, low (0.44–0.49%) in Bihar, moderate (0.62–0.99%) in Bangladesh and high (107–1.13%) in Nepal and West Bengal.

2. Materials and methods

In each cropping system and for each district we examined the per­ formance of the four experimental treatments in terms of ten metrics: system rice equivalent yield (t.ha 1); irrigation water applied (ha-cm); total in-crop water (ha-cm, the combined irrigation water and rainfall received during crop growing season); water productivity (kg grain m 3, a measure of the efficiency of converting total in-crop water to grain; total energy (MJ MJ ha 1, the total energy used in grain production), energy productivity (kg grain MJ 1, quantifying the efficiency of con­ verting total in-crop energy to grain), labor (person days ha 1); CO2equivalent emissions produced from diesel and agro-chemicals used during crop production (Mg ha 1); cost of production (AUD ha 1); and gross margin (AUD ha 1). Details of the calculations to derive these metrics, and the input data underpinning them, are described in Islam et al. [9] for the production and water use metrics. In order to calculate the total energy used to produce each crop we recorded all inputs (e.g. fertilizers, crop residues, seeds, irrigation water, pesticides, fuel, labor) for each treatment. These management operation and input data were transformed into energy equivalents using conversion coefficients ob­ tained from the published literature (Supplementary Table 1). We calculated the total energy used (TEU; MJ ha 1) from the following equation: � � (1) TEU ¼ Em þ Ef þ Ei

2.2. Experimental treatments In all cropping systems a baseline treatment, T1, with conventional tillage (CT) in both monsoon (rice) and dry season (rice, wheat, maize, lentil) crops was compared to three CASI based treatments. T2 was a partial-CASI treatment where CT puddled transplanted rice (PTR) was followed by a zero tillage (ZT) dry season crop. In T3 direct seeded rice (DSR) was followed by a ZT dry season crop, and in T4 unpuddled transplanted rice (UPTR) was followed by a ZT crop. T3 and T4 represent full CASI practice. Complete treatment details are given in Islam et al. [9]; the treatments are summarised in Table 1. 2.3. Data collection and analysis

2.1. On-farm trials On-farm trials were conducted on 40 to 50 farms in each of eight districts of the EGP, over three years from 2015 to 2017 inclusive. The latter two years’ field trial data, from the winter (November to April) 2015/16 dry season to the summer (June to October) 2017 monsoon season were used in analyses. First-year data were excluded from ana­ lyses as this year was used to provide training in new techniques and ensure methodological consistency across sites. Field trials were con­ ducted on land which had previously been under traditionally-managed rice-based cropping systems. The field trials are described in detail in Islam et al. [9]. Briefly, the eight project districts were Rajshahi and Rangpur in north-western Bangladesh; Malda and Coochbehar in West Bengal and Madhubani and Purnea in Bihar, India; and Sunsari and Dhanusha in the eastern plain region (Terai) of Nepal (Fig. 1). RW system trials were conducted in all eight districts, RM trials in six districts (Coochbehar, Malda, Pur­ nea, Rajshahi, Rangpur and Sunsari), and RL trials in four districts (Coochbehar, Dhanusha, Malda and Sunsari). A small number of addi­ tional trials were conducted according to farmers’ interest: rice-rice (RR) in Rajshahi, rice-wheat-mungbean (RWMb) in Malda and Rajshahi, and rice-wheat-jute (RWJ) in Coochbehar and Rangpur. The study sites fell into two general ecologies defined by rainfall, land type and cropping system. Rice-wheat was generally widespread, while rice-lentil systems were traditionally more prevalent in the drier regions of Bihar and Nepal which have more limited irrigation resources, and rice-rice cropping systems were more common in the higher rainfall regions of Bangladesh and West Bengal, which also had greater irrigation facilities. Rice-maize is not a traditional cropping system in the EGP; it has emerged across the region as a potentially more profitable system. In all, trials were conducted in 436 farmers’ fields, with 213 farmers participating in the RW system trials, 129 in RM, 44 in RL, 24 in RWJ, 23 in RWMb and 3 farmers in the RR trials. On-farm trials produce more variable results than on-station experiments; the large number of rep­ licates (farmers) in our field experiments, in RW, RM and RL systems particularly, enabled us to examine experimental treatments under realworld cropping system conditions while maintaining analytical rigor. Results from the RW, RM and RL trials were statistically strongly robust; those from the RR, RWMb and RWJ trials were indicative only. Weather, soil and hydrological characteristics for the trial sites are summarised in Islam et al. [9]. Crop, water and fertilizer management varied among districts according to farmers’ expert knowledge and the

where Em is the manual energy from labor, Ef is the energy from fuel and Ei is the energy from all crop inputs (i.e. seed, fertilizer, chemicals and crop residues); each energy term is multiplied by an energy-equivalent factor shown in Supplementary Table 1. Energy productivity (EP; kg MJ 1) was calculated by dividing the grain yield by the total energy required to produce it, according to the following equation: EP ¼

Grain TEU

(2)

where Grain is system equivalent grain yield and TEU is defined in equation (1). We calculated carbon dioxide-equivalent (CO2-eq) emissions from the fuel, fertilizer and agrochemicals used for each operation in each experimental treatment. These CO2-eq emissions include fuel burned during cropping system cycle production (e.g. land preparation, crop establishment, drawing irrigation water from surface and sub-surface sources, harvesting and post-harvesting processing etc.) and in the production and transport of fertilizers and agrochemicals and do not include estimates of methane or nitrous oxide emissions as these were 3

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Table 1 Crop establishment details for each experimental treatment. Treatment

Designation

Wet season crop

Dry season crop

T1

CT PTR-CTW/ CTM/CTL

Rice: soil is tilled up to five times, and compressed to form a hard layer. Rice is manually transplanted; standing water is used to control weeds.

T2

CT PTR-ZTW/ ZTM/ZTL ZT DSR-ZTW/ ZTM/ZTL

Rice: as for T1.

Wheat: soil is tilled once, then seed and fertilizer are manually broadcast; soil is tilled up to three more times to incorporate both into the soil and control weeds. Maize: soil tilled up to five times to control weeds and incorporate fertilizer. Seed manually sown. Lentil: 1) seed and fertilizer (if used) broadcast together immediately after rice harvest, followed by one tillage. 2) seed broadcast into standing rice crop shortly before harvest, rice stubble used as a mulch to facilitate lentil germination. Wheat/maize/lentil: herbicide applied before crop sowing, rice stubble retained. Seed mechanically sown without prior tillage. Wheat/maize/lentil: as for T2.

T3 T4

U PTR-ZTW/ ZTM/ZTL

Rice: herbicide applied before crop sowing, dry season crop stubble retained. Seed mechanically sown without prior tillage, additional herbicide applied before crop emergence. Rice: herbicide applied before mechanical transplanting into an untilled field, additional herbicide applied before crop emergence.

not measured during the field experiments. CO2-eq emissions (Mg ha 1) were calculated using the following equation: CO2

eq ¼ ½ðTEU; fuel x CO2

eqÞ þ ðTEU; fertilizers x CO2

þ ðTEU; agrochemicals x CO2

eq�

eqÞ

Wheat/maize/lentil: as for T2.

and CO2-equivalent emissions) examined under the CASI treatments and X(CT) is the mean of the same ten metrics in the CT treatment. As all data were from on-farm trials with multiple replications the standard de­ viations were calculated based on the number of observations. Effect sizes were weighted based on the number of replicates using equation (5).

(3)

where the TEU terms are the total energy used in each category multi­ plied by the relevant CO2-equivalent conversion factor (from Supple­ mentary Table 1). Labor use was recorded for each operation in cropping system and treatment, and converted to an 8-h person day per hectare of land. The cost of production and, gross margins (net income) for each cropping system and treatment were calculated by assigning an economic value to each input, management operation and output (i.e. crop grain and sto­ ver). The values of inputs, management operations and outputs (grain and stover) were obtained from farmers and local markets and are shown in Supplementary Table 2. Total cost of production was the sum of all inputs while gross margins were calculated by subtracting the total variable production cost from the gross return. All costs and gross margins were converted into Australian dollars (AUD). The AUD was chosen to provide a standardized currency across which to compare economic data from three countries and currencies, and because the project funder was an Australian agency. The data were unevenly distributed, with unequal replications across districts and cropping systems, so we used a mixed analysis of variance (ANOVA) model in SAS statistical software v9.4 [24] to compare experimental results. Year, replication (farmer), treatment, district, node and their two- and three-way interactions were considered factors in the analyses. Treatments, cropping systems and districts were fixed, while other effects and their interactions were random within the ana­ lyses. All three-way interactions were insignificant; they were removed from subsequent analyses to increase the power of significance of the ANOVA. A meta-analysis was undertaken using data from 436 on-farm trials to compare the performance of full CASI practice to the CT baseline across cropping systems and districts. We used MetaWin 2.0 software [25] to firstly calculate the size of an effect as the natural log of the ratio of the mean of the ten metrics examined under the CASI treatments (T3 and T4) to the mean of the same metrics under the CT treatment (T1) using equation (4). � � XðCASIÞ Effect size ¼ ln R (4) XðCTÞ

Weight ¼

ðnCASI x nCT Þ ðnCASI þ nCT Þ

(5)

where n(CASI) and n(CT) are the number of replications for the CASI treatments and the CT treatment, respectively. Extreme outliers, more than three standard deviations from the weighted-mean effect size within each category, were removed a priori. Secondly, effect sizes were combined by using a mixed-effect model to calculate the cumulative effect size with 95% confidence intervals [26]. The mixed-effect model is a random-effect meta-analytical model for categorical data [25], and assumes random variation within a group and fixed variation between groups. The cumulative effect was signifi­ cant if the confidence intervals did not overlap with zero. Results were back-transformed and reported as the percentage change under CASI relative to the CT baseline. Differences were considered significant when p values were less than 0.05. 3. Results and discussion 3.1. Experimental treatments 3.1.1. Full and partial CASI compared to CT Over all ten metrics assessed and across all cropping systems and districts, T1 (CT) consistently performed worst while T3 and T4 (full CASI options) performed best (Table 2a and 2b). This was due to higher productivity and lower inputs in the CASI treatments than in the CT treatment. The partial CASI treatment, T2, performed better than T1 but not as well as the full CASI options. In terms of productivity, represented by system rice-equivalent yield (SREY), T1 yielded 11.25 Mg ha 1; this was significantly (p ¼ 0.05) less than the SREY obtained for T3 and T4 (11.70 and 11.73 Mg ha 1), and less than the SREY for T2 (11.62 Mg ha 1; Table 2A). These results are also shown in the meta-analysis where, compared to the T1 baseline, SREY was 2.6% higher in T2, 4.0% higher in T3, and 4.2% higher in T4 (Fig. 2A). In general, rice production was comparable under CT and CASI treatments while the productivity of dry season crops increased under CASI [9]. Yield increase in dry season crop yields was due to 1) improved crop establishment under mechanized sowing compared to broadcast seed (particularly for wheat and lentil), and improved soil structure which led to 2) reduced waterlogging of seeds and seedlings, improving germination rates and root development; 3) improved soil

where X(CASI) is the mean of the ten metrics (system rice equivalent yield, irrigation water, total in-crop water, water productivity, total energy, energy productivity, labor, cost of production, gross margins 4

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Table 2A The performance of treatments, cropping systems and districts in the EGP across multiple parameters; system rice equivalent yield (SREY), irrigation water, total water, water productivity and total energy. Options

System rice equivalent yield (Mg ha

1

)

Irrigation water (hacm)

Total water (ha-cm)

Treatmentsy (value in parentheses shows the total number of data points and total number of farmers) T1 (751,362) 11.25bz 35.11 148.34 T2 (719, 353) 11.62ab 29.75 141.46 T3 (622, 313) 11.70a 29.14 143.40 T4 (778, 360) 11.73a 30.12 142.31 Systemsz (value in parentheses shows the total number of data points and total number of farmers) RW (1442, 213) 8.93d 17.49b 112.69d RM (847, 129) 12.33b 21.94bc 121.26c RL (276, 44) 12.24b 9.62e 81.89e RWMb (168, 23) 11.63c 19.75cd 136.56b RWJ (103, 24) 13.14a 24.61b 211.99a RR (24, 3) 11.19bc 92.75a 198.88a District (value in parentheses shows the total number of data points and total number of farmers) Coochbehar (475, 55) 11.02b 25.89c 173.22b Dhanusha (361, 41) 11.26b 29.14bc 147.65bc Madhubani (344, 52) 10.48b 27.62c 84.15e Malda (512, 46) 12.04ab 36.92b 161.29b Purnea (338, 57) 11.07b 49.78a 127.19cd Rajshahi (348, 44) 10.80b 28.57bc 113.13d Rangpur (268, 34) 13.69a 22.46c 125.09cd Sunsari (224, 34) 12.24ab 27.83bc 219.30a Analysis of variance (probability) Year (Y) – 0.259 0.240 Node 0.002 0.003 0.002 Farmer <0.001 <0.001 <0.001 T*S 0.194 0.016 0.038 Y*T 0.133 – – D*T 0.018 0.012 0.253 Treatment (T) 0.037 0.089 0.348 System (S) <.0001 <0.001 <0.001 District (D) 0.002 <0.001 <0.001

Water productivity (kg grain m 3)

Total energy (MJ ha

0.84b 0.91a 0.92a 0.91a

33757az 32292b 30038c 30467c

0.88c 1.29a 1.07b 0.81cd 0.74d 0.57e

28070c 34025b 20449d 35568a 32986b 38731a

0.57c 0.61c 1.48a 0.57c 1.00abc 0.98bc 1.23ab 0.73bc

28145c 29009c 33007ab 32553ab 34851a 32549ab 33359a 29635bc

0.240 0.001 <0.001 – 0.356 0.044 0.018 <0.001 <0.001

0.248 0.002 <0.001 0.060 0.148 0.003 0.003 <0.001 <0.001

1

)

y ¼ treatments are described in text and Table 1. z ¼ within a column means followed by the same letter are not significantly different (p ¼ 0.05) using Tukey’s HST test; the different systems represent to RW ¼ ricewheat, RM ¼ rice-maize, RL ¼ rice-lentil, RWMb ¼ rice-wheat-mungbean, RWJ ¼ rice-wheat-jute, RR ¼ rice-rice.

moisture arising from better water retention over the whole crop growing season; 4) improved water use efficiency due to greater soil-root contacts; 5) improved root development with the removal of the hard-pan layer established in CT rice; 6) improvements in soil temperature; and 7) increased soil microbial activity due to a more favorable root-zone micro-environment. In all crops fertilizer use effi­ ciency was greater under CASI than under CT practice. These results have been previously observed in smaller studies in the EGP and South Asia [13,27–30]. Irrigation water use was greatest in T1 (35.11 ha-cm); irrigation used in all other treatments was comparable and lower: 29.75 ha-cm in T2, 29.14 ha-cm in T3, and 30.12 ha-cm in T4 (Table 2A). Similarly, the total water (irrigation and rainfall) used in crop production was highest in T1 (148.34 ha-cm) and lower and comparable in all other treatments: 141.46 ha-cm in T2, 143.40 ha-cm in T3 and 142.31 ha-cm in T4 (Table 2A). Water productivity was significantly (p ¼ 0.05) lower in T1 (0.84 kg grain m 3) than in T2, T3 or T3 (0.91, 0.92, 0.91 kg grain m 3; Table 2A). Results from the meta-analysis of these metrics show that reductions in irrigation water use from the T1 baseline ranged from 16.3% to 37.5% from T2 to T4 (Fig. 3A), while total in-crop water use decreased, relative to T1, by between 3.0% (T4) and 7.3% (T2; Fig. 3B). Water productivity increased over the baseline by 5.3% in T2, 7.2% in T3 and 8.3% in T4 (Fig. 3C). Water savings were achieved under CASI in irrigated winter crops but not in (rain-fed) monsoon rice crops. In full or partial CASI treatments, less water was required for the first irrigation than in the CT treatment because pre-sowing tillage in the CT treatment resulted in a looser topsoil layer over which irrigation water flowed more slowly than the topsoil of CASI treatments: thus more irrigation water was required under CT to adequately wet an equivalent area [13, 28]. As well, retained crop residues in the CASI treatments reduced

evaporation and retained soil moisture: thus less irrigation water was required through the crop growing season [13,21]. Both the partial and full CASI treatments used CASI practices in the dry winter season when most irrigation was applied (the partial CASI treatment used CT practice in the monsoon rice crop); thus water saving effects of CASI are demonstrated in T2 as well as T3 and T4. Total energy use was significantly (p ¼ 0.05) higher in T1 (33,757 MJ MJ ha 1) than in T2 (32,939 MJ ha 1), which itself was significantly higher than T3 or T4 (30,038 or 30,467 MJ ha 1; Table 2A). Reflecting these data, energy productivity, the amount of grain produced per unit of energy, was significantly lower in T1 (0.28 kg grain kg grain MJ 1) than in T2 (0.31 kg grain MJ 1), which was significantly lower than T3 and T4 (both 0.33 kg grain MJ 1; Table 2B). In the meta-analysis, total energy use relative to T1 decreased by 6.1% in T2, 10.5% in T3, and 11.0% in T4 (Fig. 2B). Energy productivity increased over the T1 baseline by 10.3% in T2, 7.9% in T3, and 17.5% in T4 (Fig. 2D). Under CT practice, tillage and (in the case of rice) manual crop establishment required considerably more energy than that required for mechanized crop establishment under CASI practice. As well, the energy to pump irrigation water was higher under CT than under CASI due to higher water requirements in CT [11]. The use of agro-chemicals (her­ bicides and pesticides especially) was higher under CASI than under CT, however this component of total energy used was relatively small. CT practice (T1) required significantly (p ¼ 0.05) more labor (152.66 person days ha 1) than partial CASI (T2; 129.61 person days ha 1); this in turn was significantly more labor than was required by either of the full CASI treatments (T3 93.92 person days ha 1; T4 104.28 person days ha 1; Table 2B). Meta-analysis showed that the required labor was reduced from the T1 baseline by 14.1% in T2, by 41.6% in T3, and by 35.2% in T4 (Fig. 3D). The most labor-intensive operations in the 5

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Table 2B The performance of treatments, cropping systems and districts in the EGP across multiple parameters; energy productivity, labor, CO2-equivalent emission, cost of production and gross margin. Options

Energy productivity (kg grain MJ 1)

Labor (Person days ha

1

CO2-equivalent emission (Mg ha 1)

)

Treatmentsy (value in parentheses shows the total number of data points and total number of farmers) T1 (751,362) 0.28cz 152.66a 1.75a T2 (719, 353) 0.31b 129.61b 1.61b T3 (622, 313) 0.33a 93.92c 1.53c T4 (778, 360) 0.33a 104.28c 1.55c Systems (value in parentheses shows the total number of data points and total number of farmers) RW (1442, 213) 0.29c 102.88d 1.41e RM (847, 129) 0.41a 111.94c 1.72c RL (276, 44) 0.32b 91.75e 0.96f RWMb (168, 23) 0.25d 128.33b 1.77b RWJ (103, 24) 0.30c 156.69a 1.62d RR (24, 3) 0.30bcd 129.10b 2.17a District (value in parentheses shows the total number of data points and total number of farmers) Coochbehar (475, 55) 0.36a 108.27d 1.48c Dhanusha (361, 41) 0.31abc 109.02d 1.49c Madhubani (344, 52) 0.27c 128.14bc 1.69ab Malda (512, 46) 0.29bc 111.87cd 1.75a Purnea (338, 57) 0.27c 136.88ab 1.79a Rajshahi (348, 44) 0.27c 117.86cd 1.69ab Rangpur (268, 34) 0.36ab 150.17a 1.48c Sunsari (224, 34) 0.36a 98.72d 1.50bc Analysis of variance (probability) Year (Y) 0.254 0.274 0.241 Node 0.001 0.002 0.002 Farmer <0.001 <0.001 <0.001 T*S 0.072 0.006 0.044 Y*T 0.159 0.127 0.227 D*T 0.004 0.001 0.002 Treatment (T) <0.001 <0.001 <0.001 System (S) <0.001 <0.001 <0.001 District (D) <0.001 <0.001 <0.001

Cost of production (AUD ha 1)

Gross margin (AUD ha 1)

1819a 1647b 1499d 1541c

1920c 2205b 2396a 2362a

1354d 1561c 1131e 1878a 1819b 2018a

1539d 2762a 2145b 1846c 2851a 2181bc

1545bc 1422cd 1593b 1821a 1541bc 1907a 1813a 1371d

2322ab 1889b 2343ab 2243ab 2284ab 1860b 2649a 2175ab

0.241 0.001 <0.001 0.023 0.246 0.001 <0.001 <0.001 <0.001

0.242 0.002 <0.001 0.168 – 0.003 <0.001 <0.001 0.002

y ¼ treatments are described in text and Table 1. z ¼ within a column means followed by the same letter are not significantly different (p ¼ 0.05) using Tukey’s HST test; the different systems represent to RW ¼ ricewheat, RM ¼ rice-maize, RL ¼ rice-lentil, RWMb ¼ rice-wheat-mungbean, RWJ ¼ rice-wheat-jute, RR ¼ rice-rice.

production of any crop are the pre-sowing tillage operations and, for CT rice, transplanting [9]. In T1 the soil was tilled before both crops while in T2 it was tilled before the rice crop. In both T1 and T2 rice was transplanted. In T3 rice was directly sown into the soil, using very little labor to operate the planting machine only, while in T4 rice was pre­ pared in a nursery before being transplanted into unpuddled, untilled soil: this process required more labour than mechanized direct seeding, but significantly less than traditional transplanting [37]. The cost of production was highest in T1 (1819 AUD ha 1), followed by T2 (1647 AUD ha 1), T4 (1541 AUD ha 1) and T3 (1499 AUD ha 1; Table 2B). Differences between all treatments were significant (p ¼ 0.05). Gross margins were lowest in T1 (1920 AUD ha 1), significantly (p ¼ 0.05) higher in T2 (2205 AUD ha 1), and significantly higher again in T3 and T4 (2396 and 2362 AUD ha 1; Table 2B). In the meta-analysis, relative to the T1 baseline production costs decrease by 9.7% in T2, by 17.9% in T3 and by 15.8% in T4 (Fig. 3E). Gross margins were higher than T1 in all CASI treatments: by 14.7% in T2, by 25.3% in T3 and by 24.6% in T4 (Fig. 3F). These economic data directly reflect the lower productivity and higher water, energy and labor costs of CT compared to partial and full CASI. CO2-equivalent emissions were highest in T1 (1.75 Mg ha 1), significantly (p ¼ 0.05) lower in T2 (1.61 Mg ha 1), and significantly lower again in T3 and T4 (1.53 and 1.55 Mg ha 1; Table 2B). Metaanalysis showed reductions in CO2-equivalent emissions from a T1 baseline of 7.4% in T2, 9.8% in T3 and 12.2% in T4 (Fig. 2C). The lower CO2-equivalent emissions under full and partial CASI reflect the lower energy usage in these systems compared to CT [11]. Agro-chemical use is higher under CASI than under CT, but the contribution of chemicals to the CO2-equivalent emission budget is small. After three years of

experimental treatments, Sinha et al. [23] observed a trend of increasing soil organic carbon under full CASI compared to CT; they suggest that it may take five or more years after a change in tillage practice for a clear change in soil organic carbon to become evident. Sinha et al. [23] expect that, as a result of the improved management practices, sequestration of carbon in soil will increase under full CASI compared to CT. This is likely to contribute to lower overall CO2-equivalent emissions under full CASI management. 3.2. Cropping systems 3.2.1. Relative performance of six cropping systems All four experimental treatments were examined together using ANOVA to compare the performance of the cropping systems across the ten assessment metrics. In terms of productivity and incorporating all four treatments, the triple cropping system RWJ had the highest SREY (13.14 Mg ha 1), however both RM (12.33 Mg ha 1) and RL (12.24 Mg ha 1) were higher than the RWMb triple cropping system (Table 2A). Maize, jute and lentil are all high-value crops. The RW system was significantly (p ¼ 0.05) the least productive, with a SREY of 8.93 Mg ha 1. Intensifying the RW system to RWJ or RWMb significantly increased system productivity. Irrigation water use was significantly (p ¼ 0.05) lowest in the RL system (9.62 ha-cm) and significantly (p ¼ 0.05) highest in the RR system (92.75 ha-cm; Table 2A). The RW, RM and RWMb systems used similar amounts of irrigation (17.49, 21.94, 19.75 ha-cm), while the RWJ system had slightly higher irrigation (24.61 ha-cm). Patterns of total water use followed similar trends, with total water use lowest in the RL system (81.89 ha-cm) and highest in the RWJ (211.99 ha-cm) and RR 6

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Fig. 2. Meta-analysis of on-farm trials showing the percentage change for partial (T2) and full (T3 and T4) CASI treatments over the CT (T1) baseline in terms of four key metrics: A system rice equivalent yield (SREY); B total energy; C CO2-equivalent emissions; D energy productivity, across all cropping systems and districts in the EGP. * shows the significance level at p ¼ 0.05 over CT. Error bars show 95% confidence intervals; if they do not overlap treatment results are significantly different from each other.

(198.88 ha-cm) systems (Table 2A). Water productivity was signifi­ cantly (p ¼ 0.05) highest in the RM system (1.29 kg grain MJ 1) fol­ lowed by the RL system (1.07 kg grain MJ 1) while the RR system was significantly the least water productive system (0.57 kg grain MJ 1; Table 2A). Rice is a water-intensive crop and the irrigated dry season rice crop required significantly more water than any other crop. While the RR system results were from a small sample they are consistent with other published data [31]. Jute is also a water-intensive crop, requiring water both for growing and for post-harvest processing, however it was grown as a partially-irrigated crop and harvested around the onset of the monsoon and so water productivity was higher for the RWJ system than for the RR system. Maize is a water-efficient crop, with high productivity for the amount of input water received, and lentil was primarily grown as a low-input crop, with no irrigation water applied. Wheat is less water-efficient than maize and so the water productivity of the RW and the RWMb systems were significantly lower than that of the RM (or RL) system. The total energy used in each cropping system was significantly (p ¼ 0.05) highest in the RR and RWMb systems (38,731 and 35,568 MJ ha 1), followed by the RM and RWJ systems (34,025 and 32,986 MJ ha 1), and the RW system (28,070 MJ ha 1) while the RL system used significantly (p ¼ 0.05) the least total energy (20,449 MJ ha 1; Table 2A). In terms of energy productivity, the RM system was signifi­ cantly the most productive (0.41 kg grain MJ 1) while the RWMb sys­ tem (0.25 kg grain MJ 1) was the least productive (Table 2B). The RW, RL, RWJ and RR systems had similar energy productivities, ranging between 0.29 kg grain MJ 1 (RW) to 0.32 kg grain MJ 1 (RL). There was a considerable range in the energy inputs from the high-energy RR system, through the medium-intensity RW and RWJ systems to the lowenergy RL system: the relative SREY of each system (high in RL and RWJ, moderate in RR and low in RW) resulted in comparable energy

productivity values. Labor was significantly (p ¼ 0.05) highest in the RWJ system (156.69 person days ha 1), high in the RR and RWMb systems (129.10 and 128.33 person days ha 1), and significantly lowest in the RL system (91.75 person days ha 1; Table 2B). Jute required considerable postharvest processing: currently this is performed manually, however there is scope for mechanization which would significantly reduce labor requirements (and also energy usage and production costs), while increasing gross margins [11,19,31]. Labor was also high in rice crops: due to transplanting (where performed) and irrigation in the dry season. The RWMb system had high total labour requirements as all three component crops required reasonable amounts of labor. Lentil was established by broadcasting the seed into rice shortly before harvesting the rice crop; additionally it was not irrigated or fertilized and so the lentil crop and the RL system had very low labor requirements. The cost of production was significantly (p ¼ 0.05) highest in the RWMb and RR systems (1878 and 2018 AUD ha 1), followed (with significance at all levels) by RWJ (1819 AUD ha 1), RM (1561 AUD ha 1), RW (1354 AUD ha 1) and the RL system (1131 AUD ha 1; Table 2B). In contrast, gross margins were significantly (p ¼ 0.05) highest in the RWJ and RM systems (2851 and 2762 AUD ha 1), fol­ lowed by RR, RL and RWMb (2181 2145 and 1846 AUD ha 1; Table 2B). The RW system had significantly (p ¼ 0.05) the lowest gross margin (1539 AUD ha 1). Gross margins incorporate system productivity and water, energy and labor usage: the RM system was highly productive and efficient in terms of water, energy and labor use across the EGP; this result has been observed in smaller studies elsewhere [10,11,19,21]. The RWJ system was also highly productive and, while water intensive in the jute crop, used more rainwater than irrigation water in generating a marketable product. The RW system was of limited profitability in the EGP: the optimal growing season for wheat is short and there was a high 7

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Fig. 3. Meta-analysis of on-farm trials showing the percentage change for partial (T2) and full (T3 and T4) CASI treatments over the CT (T1) baseline in terms of six key metrics: A irrigation water; B total water; C water productivity; D labor; E production cost; F gross margin, across all cropping systems and districts in the EGP. * shows the significance level at p ¼ 0.05 over CT. Error bars show 95% confidence intervals; if they do not overlap treatment results are significantly different from each other.

risk of terminal heat stress if the crop was planted late, e.g. if monsoon rains were late and thus the wet season rice was delayed [29]. However with intensification, e.g. to RWJ or RWMb systems depending on local climate and soil conditions, the RW system had the potential to become much more profitable. Jute is a highly productive, high value crop which grows well in West Bengal and Bangladesh, where rainfall, required to grow and process the crop, is plentiful. Mungbean has high potential in the drier areas of Bihar and Nepal; it is more risky in West Bengal and Bangladesh as the crop is susceptible to high rainfall and humidity. Intensifying the RW system into locally specialized triple-cropping sys­ tems under CASI practices is a research area worthy of continued investigation. There was significant (p ¼ 0.05) difference in CO2-equivalent emis­ sions from each cropping system; emissions were highest in the RR system (2.17 Mg ha 1) and lowest in the RL system (0.96 Mg ha 1; Table 2B). The CO2-equivalent emissions largely followed trends in energy usage among cropping systems, which were high in RR due to

high irrigation pumping requirements of water-intensive dry season rice, and low in the RL system due to the low-energy lentil crop with minimal irrigation or fertilizer inputs. 3.2.2. Cropping systems under CASI compared to CT Meta-analyses of cropping systems compared full CASI practice (i.e. the mean of T3 and T4 data) to the CT (T1) baseline; comparisons were made across all districts. As shown in 3.1.1, results from the partial CASI practice (T2) fell between the T1 and T3/T4 treatment results. These results suggest that there is potential to apply CASI practices successfully in smallholder cropping systems in other emerging-economy countries. SREY under CASI significantly increased compared to the CT base­ line for all cropping systems. SREY was highest for the RR (10.4% in­ crease) and RWMb (7.8%) systems, followed in order by the RL (5.5%), RW (4.4%), RWJ (2.8%) and RM (2.7%) cropping systems (Fig. 4A). Benefits under CASI were relatively low for the averaged RM system results for two main reasons: farmers experienced some challenges in 8

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establishing maize under mechanized planting; and productivity was particularly low in Purnea [9], where farmers were successfully pro­ ducing high yields of CT maize and were less interested in exploring new management practices. Overall, further research and investment into the fine-tuning of locally appropriate crop management and machinery will contribute to further increases in productivity under CASI relative to CT, and will also enable farmers to better understand the likely risks and rewards of CASI. There is potential to improve the productivity and profitability of other cropping systems, e.g. the RR and RWMb systems, through CASI practices which are more efficient in terms of water, en­ ergy and labour than CT [9,11,23]. However, the RR system is resource (water, energy, labor) intensive so while there are potential production benefits there are also tradeoffs, particularly in terms of energy and water usage and CO2-equivalent emissions. Use of irrigation water under CASI compared to CT increased in the RL system by 9.9% and decreased in all other cropping systems (Fig. 5A). The decrease in water use in the RWMb system under CASI, of 4.9% was not significant, while in the RR, RW and RM systems significant irriga­ tion usage declines (of 15.3%, 17.1%, and 25.0%, respectively) were observed. Greatest irrigation water savings (of 52.5%) were observed in the RWJ system. In terms of total (irrigation and rainfall) in-crop water use, there was no significant difference in total water usage between CASI and CT practice in the RL or RWMb systems (Fig. 5B). Total water usage was significantly lower in all other systems; by 2.4%–3.5% in the RM and RW systems, by 7.6% in the RR system and by 11.1% in the RWJ system. All cropping systems were significantly more water-productive under CASI than under CT (Fig. 5C). The change in water productivity was greatest under RR (19.2%), while the remaining systems had similar changes in water productivity, ranging from 7% (RM) to 10% (RL). These gains in water productivity under CASI practices reflect the fact that CASI practices use less water and are more water efficient than CT

practice [9]. Water productivity gains in the RR system under CASI compared to CT occurred because CT dry-season rice is a very water intensive crop [9,19] and CASI practices considerably reduce the amount of water required to produce an equivalent or greater yield. Compared to CT, all cropping systems used significantly less energy under CASI (Fig. 4B). Greatest savings were observed in RR (14.0%) and smallest savings in RWJ (7.1%); remaining cropping systems achieved savings between 9.6% (RM) and 11.9% (RW). All cropping systems were significantly more energy efficient under CASI than under CT, with greatest gains in energy productivity from the RR (26.6%), RWMb (20.7%) and RL (20.0%) systems and smaller gains in the RW (18.8%), RWJ (16.1%) and RM (15.2%) systems (Fig. 4D). Regardless of cropping system, energy savings under CASI are the result of lower tillage, irri­ gation water, fuel and labor requirements [11,21]. The RR system under CT is the most energy intensive system due to energy requirements for land preparation, manual transplanting and high irrigation in the dry-season crop [9]. CASI greatly reduces these energy requirements. In contrast, other cropping systems are less energy intensive under CT and thus potential for energy savings under CASI are lower. Labor usage decreased in all cropping systems under CASI compared to the CT baseline (Fig. 5D). All savings were significant, with RW, RM, RL and RR cropping systems saving between 26.2% and 40.2%. Greatest savings were in the RWMb system (54.5%), while smallest savings were in the RWJ system (26.7%). Labor requirements under CASI were lower than under CT because mechanization replaced much of the manual effort required for land preparation and crop establishment [10,23]. Cost of production was significantly lower in all cropping systems under CASI than under CT (Fig. 5E). Greatest savings were in the RR system (22.0%) and smallest in the RWJ system (8.0%). Gross margins were all significantly higher under CASI than under CT (Fig. 5F), and there was large variability in the benefits achieved. Greatest changes

Fig. 4. Meta-analysis of on-farm trials showing the percentage change in cropping systems with full CASI options (T3 and T4) from the CT (T1) baseline in terms of four key metrics: A system rice equivalent yield (SREY); B total energy; C CO2-equivalent emissions; D energy productivity, across all districts in the EGP. * ¼ shows the significance level at p ¼ 0.05 over CT. Error bars show 95% confidence intervals; if they do not overlap treatment results are significantly different from each other. 9

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Fig. 5. Meta-analysis of on-farm trials showing the percentage change in cropping systems with full CASI options (T3 and T4) from the CT (T1) baseline in terms of six key metrics: A irrigation water; B total water; C water productivity; D labor; E production cost; F gross margin, across all districts in the EGP. * shows the significance level at p ¼ 0.05 over CT. Error bars show 95% confidence intervals; if they do not overlap treatment results are significantly different from each other.

from CT were observed in the RR (101.2%) and RWMb (54.3%) systems, while there were relatively smaller benefits over CT in the RW (31.6%), RM (18.5%), RL (17.2%) and RWJ (12.6%) systems. Under CASI prac­ tice gross margins increased due to savings in inputs (labor, fuel and water) and slightly increased yields, across all cropping systems. These results were more marked in the RR system where benefits in CASI over CT were greatest. CO2-equivalent emissions were significantly reduced under CASI for all cropping systems (Fig. 4C). Emissions from the majority of cropping systems were reduced by 10.1%–12.9%, while those from the RR system were reduced by 16.9%. Savings in CO2-equivalent emissions under CASI compared to CT reflect reductions in energy and water usage across all cropping systems [11–13,18].

ANOVA to compare the performance of the districts against the ten assessment metrics. While district-level results were varied, with com­ plex factors underpinning the data, overall CASI performed better than CT across all assessment metrics, indicating the potential of these improved management practices for smallholder farmers in many different climatic, edaphic and cultural circumstances globally. The West Bengal and Bangladesh districts of Coochbehar, Malda, Rajshahi and Rangpur had higher rainfall and better (less acidic, higher organic carbon) soils than the Bihar and Nepal districts of Madhubani, Purnea, Dhanusha and Sunsari. In Bangladesh there was a strong gov­ ernment supported irrigation network while in West Bengal irrigation water was readily available, and farmers received government subsidies on electricity used to pump water. In contrast, in Nepal and Madhubani there had historically been little focus on irrigation infrastructure (including the supply of electricity and aquifers); current facilities, where they existed, were poor. Purnea had reasonably good irrigation facilities compared to Madhubani and the Nepal districts. As well, farmers in Purnea had a less punitive land tenancy system than in

3.3. Districts 3.3.1. Relative performance of eight districts All four experimental treatments were examined together using 10

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Madhubani: increased and more efficient crop production increased profits for farmers, whereas in Madhubani increased crop yields increased farmers’ land rental payments. Farmers in Rangpur and Pur­ nea, especially, were commercially focused, preferring to produce high yielding cash crops (e.g. maize) in the dry season. Farmers from West Bengal, Bangladesh and Purnea expected to achieve food security each year and could afford to explore options to intensify and diversify their cash crop options, while farmers in Madhubani and Nepal were more focused on achieving food security than on producing crops for sale. In general, SREY was high in districts (e.g. Rangpur, 13.69 Mg ha 1) where climate and soil conditions were favorable and farmers preferred high yielding cropping systems, such as RM, rather than lower yielding systems such as RW or RL (Table 2A). SREY was low in districts where farmers either had little incentive to increase their crop yields due to punitive land-tenure arrangements (e.g. Madhubani, 10.48 Mg ha 1) or where farmers preferred to grow lower-yielding systems such as RW (e. g. Rajshahi; 10.80 Mg ha 1). Irrigation water use depended on average rainfall and access to irrigation: usage was highest in Purnea (49.78 ha-cm) where rainfall was low but infrastructure made irrigation readily accessible to farmers, and lowest in Madhubani (27.62 ha-cm) where irrigation infrastructure was poor and water pricing was considerably higher than in other districts [9,32,33], and in Coochbehar and Rangpur (25.89 and 22.46 ha-cm) where rainfall was high (Table 2A). Conversely, water productivity was highest in Madhubani (1.48 kg grain m 3) as farmers achieved relatively good yields on very little water, and lowest in Coochbehar, Dhanusha and Malda (0.57, 0.61 and 0.57 kg grain m 3; Table 2A). Water was plentiful in Coochbehar and Malda so farmers were relatively inefficient with it, while in Dhanusha crop productivity was reasonably low while water use relatively high. Energy usage was highest in districts where there was a history of producing high value dry season crops for commercial markets with high inputs (e.g. Purnea 34,851 MJ ha 1 and Rangpur 33,359 MJ ha 1; Table 2A). Energy use was lowest in districts where irrigation demands were low (e.g. Coochbehar, 28,145 MJ ha 1) or where risk averse farmer produced relatively low yields from low-input systems for home con­ sumption only (e.g. Dhanusha 29,009 MJ ha 1 and Sunsari 29,635 MJ ha 1). Energy productivity was highest in districts where farmers either used very efficiently the little energy they consumed (e.g. Coochbehar and Sunsari, 0.36 MJ ha 1 in both districts; Table 2B) or they achieved very high yields and thus efficiently converted the energy used into agronomic produce (e.g. Rangpur, also 0.36 MJ ha 1). Energy produc­ tivity was lowest both in districts where farmers were prepared to expend considerably energy to achieve higher cash-crop yields (e.g. Purnea and Rajshahi, both 0.27 MJ ha 1) or where farmers had low cropping system productivity and average energy use (e.g. Madhubani, also 0.27 MJ ha 1). Labor was highest where farmers invested more in crop production to generate more saleable product (e.g. Rangpur, 150.17 person days ha 1 and Purnea, 136.88 person days ha 1), and lowest either where farmers could not afford to invest heavily in on-farm labor (e.g. Dha­ nusa, 109.02 person days ha 1 and Sunsari, 98.72 person days ha 1) or where mechanization was well supported within rural communities (e.g. Coochbehar, 108.27 person days ha 1; Table 2B). Production costs were high in districts with high inputs (e.g. those in West Bengal, Bangladesh and Purnea) and lower in districts where farmers were more risk averse and used fewer inputs (e.g. those in Nepal and Madhubani; Table 2B). Gross margins were highest where yields were high and farmers more commercially focused (e.g. Rangpur, 2649 AUD ha 1) and lowest in districts where productivity was lower (e.g. Dhanusha, 1889 AUD ha 1) or where high production costs were com­ bined with lower cropping system productivity (e.g. Rajshahi, 1860 AUD ha 1; Table 2B). CO2-equivalent emissions were high in districts where (pumped) irrigation water and agro-chemical usage were high (e.g. Malda, 1.75 Mg ha 1 and Purnea, 1.79 Mg ha 1; Table 2B) and low in the districts

where irrigation usage was low (e.g. Coochbehar, Dhanusha and Rangpur where emissions ranged between 1.48 and 1.49 Mg ha 1). 3.3.2. Districts under CASI compared to CT Meta-analyses at the district level compared full CASI practice (mean of T3 and T4 data) to the CT (T1) baseline; comparisons were made across all cropping systems. With some relatively small exceptions, full CASI performed as well as or better than CT across all districts, sug­ gesting that there is potential to apply CASI practices successfully in other smallholder cropping systems in emerging-economy countries globally. In terms of productivity, all study districts except Purnea achieved higher SREY under CASI than under CT (Fig. 6A). SREY in Purnea was 1.7% less under CASI than under CT; the difference is largely because Purnea farmers preferred traditional practices, particularly in maize cultivation, and were less interested in exploring new land preparation and crop establishment practices. SREY gains in all other districts under CASI were due to improved fertilizer use efficiency, better crop estab­ lishment under most crops (but not necessarily maize) and better rootsoil interactions. As well, CASI practices insulated crops against cli­ matic shocks and increased their resilience to heat stress, storms and waterlogging [34]. Water use was significantly more efficient under CASI than under CT for all districts except Dhanusha and Maldda (where there was no change from CT; Fig. 7A and B): the improvement in water productivity is largely due to improved yields under CASI and thus more efficient use of a similar water input (Fig. 7C). Greatest improvements were observed at Madhubani (improvement of 19.3%) where crop yields under CASI were relatively greatly improved. Similarly, but to a lesser extent, in other districts water productivity increased by between 4.9% (Sunsari) and 8.8% (Rangpur) due to increasing productivity and SREY under CASI. In all districts less water is required under CASI for the first irri­ gation compared to CT, and water is conserved through the growing season due to the mulching effect of retained residues. Irrigation water usage was reduced under CASI at all districts except Dhanusha (no change from CT) or Malda (4.3% increase; Fig. 7A). Greatest irrigation savings were observed in Coochbehar (46.8%), with the next-highest irrigation savings in Rangpur (25.0%). Total (irrigation and rainfall) water used was significantly lower under CASI than CT in all districts except Dhanusha (increase of 3.2%) and Malda (no change from CT), although in Sunsari the saving in total water used was very small (1.1%; Fig. 7B). Greatest savings in total water applied were observed in Coochbehar, Madhubani and Rangpur (savings around 8%). Total energy usage was reduced in all districts under CASI compared to CT, suggesting that CASI provides options to reduce energy usage under a range of edaphic, climatic and social conditions (Fig. 6B). En­ ergy savings were least at Sunsari (7.5%) and greatest at Madhubani (13.2%). In terms of the efficient use of energy, all districts had signif­ icantly higher energy productivity under CASI than under CT (Fig. 6D). Energy productivity reflects energy usage and crop productivity: all districts decreased the former while increasing the latter under CASI. Coochbehar, Dhanusha, Purnea, Rangpur and Sunsari all had increases in energy productivity of around 15%, Malda and Rajshahi had increases of around 21%, while Madhubani (25.7%) had the greatest increase in energy productivity. In terms of labor, all districts used less labor under CASI than under CT (Fig. 7D). Savings were least in Madhubani (22.1%) and greatest in Malda (52.0%) and then Rajshahi (43.7%), while at the remaining five districts labor savings of around 33% were observed. In Madhubani the majority of trials were in RW systems while in other districts there was a greater variety of cropping systems, with more opportunities for labor saving under CASI than in the RW system. Production costs were reduced under CASI at all districts, compared to CT (Fig. 7E). Savings were smallest at Coochbehar (12.3%), with savings at all other districts varying between 15% (Madhubani and Rangpur) and 19%–20.0% (Purnea and Rajshahi). All districts achieved 11

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Fig. 6. Meta-analysis of on-farm trials showing the percentage change in districts with full CASI options (T3 and T4) from the CT (T1) baseline in terms of four key metrics: A system rice equivalent yield (SREY); B total energy; C CO2-equivalent emissions; D energy productivity, across all cropping systems in the EGP. * shows the significance level at p ¼ 0.05 over CT. Error bars show 95% confidence intervals; if they do not overlap treatment overlap results are significantly different from each other.

Mg CO2-eq.Mg grain 1 for the RM system and around 0.20 Mg CO2-eq. Mg grain 1 for the RW system. The majority of data at the higher end of the X-axes represent CT (T1; treatment data not displayed) and have relatively low SREY and gross margins for higher CO2-equivalent emissions. Weaker relationships observed in the RL system are likely to be due to greater variability in lentil yields than in wheat or maize yields: this is due to less reliable in-crop water (lentil crops are rainfed and at risk from waterlogging from poorly timed rainfall), reduced in­ puts, and higher incidences of pests or disease [9].

higher gross margins under CASI than under CT (Fig. 7F): benefits were greatest at Rajshahi (51.5%) and least at Purnea (12.6%) where farmers were comfortable with traditional CT practices under which they already achieved relatively high yields and thus were less inclined to explore alternative management practices. CO2-equivalent emissions were significantly reduced at all districts under CASI compared to CT; again indicating a potential benefit of CASI for many smallholder farmers in other emerging-economy countries (Fig. 6C). Savings in emissions of around 10%–15% were observed at all districts except Dhanusha and Madhubani where greater savings of 16.7% and 20.4% were achieved. In Dhanusha and Madhubani there is less variety in cropping systems (RW is favored); these data reflect emission savings from primarily one cropping system and thus may not be representative of wider results.

3.5. Lessons for effective policy support This demonstration of the benefits of CASI relative to CT over a range of cropping systems and districts within the EGP was achieved through bottom-up farmer- and researcher-driven experiments which were most successful when the social and policy environment enabled innovation and supported (limited) risk taking and the exploration of new crop management practices. Our findings relating to the facilitation of an enabling environment to support testing of CASI practises are readily applicable in other emerging-economy countries to enable sustainable food production with reduced energy usage. We found the multi-stakeholder approach piloted in West Bengal and subsequently extended into Purnea (Bihar) and Rangpur (Bangladesh) to be most effective (Table 3). The project team engaged with existing but under-utilized in-village institutional frameworks of farmer groups, which took responsibility for crop establishment and other agricultural machinery, promoted CASI practices locally and were hubs for CASI training activities. We built trust with local partners and communities and developed positive relationships with village and community level leaders. CASI experiments established under the aegis of the farmer groups were demonstrated within villages and districts, exposing the

3.4. Relationships between system rice equivalent yield, gross margins and CO2-equivalent emissions Comparing both system rice equivalent yield (SREY) and system gross margins to system CO2-equivalent emissions across all treatments and districts showed that both SREY and gross margins were negatively correlated to CO2-equivalent emissions for RW, RM and RL cropping systems (Fig. 8). We observed stronger, more significant relationships in the RM and RW systems and weaker but still significant relationships in the RL system. These results, which indicate that high SREY and high gross margins are possible with reduced CO2-equivalent emissions across a range of agronomic environments, may be valid for smallholder farmers in other emerging-economy countries globally. Most of the data at lower values on the X-axes represent full or partial CASI options (T2 to T4; treatment data not displayed), with high SREY and high gross margins achieved for CO2-equivalent emissions lower than around 0.16 12

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Fig. 7. Meta-analysis of on-farm trials showing the percentage change in districts with full CASI options (T3 and T4) from the CT (T1) baseline in terms of six key metrics: A irrigation water; B total water; C water productivity; D labor; E production cost; F gross margin, across all cropping systems in the EGP. * shows the significance level at p ¼ 0.05 over CT. Error bars show 95% confidence intervals; if they do not overlap treatment overlap results are significantly different from each other.

new practices to more farmers. Many different stakeholders were connected through the farmer groups to facilitate awareness of, uptake and outscaling of CASI. As well as communities of farmers from new villages and districts, stakeholder groups included a) governments and policymakers; b) development and extension agencies who promoted CASI and provided training and technical backstopping; c) researchers and scientists who contributed to the dissemination of and training in CASI practices; d) financial in­ stitutions who supported farmer group investments through microloans; and e) agro-industries who provided seeds, fertilizers and other agro-chemicals to the farmer groups. In West Bengal, as the benefits of CASI were established in the original project villages the original farmer groups incorporated as companies and themselves supported the establishment of new farmer groups designed to promote and support CASI in new villages and districts. A strength of farmer groups was their ability to link with external agencies (certification bodies, finance institutions, agro-chemical and

fertilizer agencies, machinery manufacturers) to provide information, improve commercial viability, ensure access to machinery and identify and exploit market opportunities. These groups also linked farming communities with public extension officers and with higher level publicsector stakeholders. Importantly, this approach was flexible and readily adaptable to different social or cultural contexts and with different stakeholders according to local community requirements. Recognition of the value of CASI and provision of widespread sup­ port within institutional frameworks was facilitated by community leaders and governments at village, district and state/regional scales. Ongoing linkages between the bottom-up, community focused ap­ proaches to testing CASI on farms and top-down support from policy­ makers, governments and community leaders would further enhance the awareness and uptake of CASI practices across the EGP, and would facilitate the uptake of CASI practices in other emerging-economy countries. Agricultural mechanization is a key component of CASI: increasing 13

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Fig. 8. System rice equivalent yield (SREY) and gross margin (GM) against system CO2-equivalent emissions for different cropping systems in the EGP (data include all treatments and districts): (A) rice-wheat based systems; (B) rice-maize systems; (C) rice-lentil systems. Values in parentheses show the total number (n) of data points.

mechanization reduces energy, water and CO2-equivalent emissions from cropping systems and also underpins sustainable intensification of food production. Agricultural mechanization is a policy priority in many countries, including the three where our research was conducted. Gov­ ernments can assist farmers to prioritise more sustainable machinery options by providing capital subsidies targeted towards machinery that promotes resource conservation, such as the direct seeding and unpud­ dled transplanting machines used in CASI practices. no or very low

import duties and other domestic taxes on agricultural equipment and spare parts [35]. Consequently, agriculture is mechanized to a high degree in Bangladesh (80% of land preparation is mechanized compared to 45% in India [36]) due to the relatively competitive machine rental markets even for very small landholdings and where farmers have low levels of capital. In order to further widen the reach of CASI, ongoing support from governments is required to make better linkages between institutions 14

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Table 3 Engagement by the project team with different stakeholder groups to implement CASI in project villages, across districts, and at the state/national level. Example drawn from experiences in Coochbehar and Malda (West Bengal), Purnea (Bihar), and Rangpur (Bangladesh). Stakeholder groups Local communities of farmers

Immediate community ➢ Farmer groups organized for micro-entrepreneurship ➢ Farmer groups facilitate integration of all stakeholders ➢ Training & implementation of CASI in project trials ➢ Trust developed

Governance

➢ Village & community leaders engaged ➢ Trust developed ➢ (local researchers engaged on project activities)

Researchers & scientists Development & extension agencies

➢ Local awareness raising ➢ Capacity building and training for local agencies

Finance

➢ Small scale microfinance opportunities run through farmer groups

Agro-industries

➢ (supplied by project)

Wider community ➢ CASI experiments demonstrated within and outside villages ➢ Capacity building & knowledge awareness through farmer field days, exposure visits ➢ CASI trials extended ➢ Farmer groups support integration of stakeholders ➢ District level policymakers & governors engaged and supportive of CASI ➢ Demonstrate CASI to local universities & research agencies ➢ Capacity building and knowledge awareness ➢ More widespread CASI awareness raising ➢ Build capacity for technical backstopping ➢ Microfinance operations extended within villages ➢ More microfinance opportunities supported ➢ Supply chains for agro-chemicals, fer­ tilizers and machinery developed

and facilitate capacity building. The benefits to legislators of increasing the adoption of CASI-based practices are many: CASI practices enable farmers to sustainably intensify their production systems, producing more crop product while conserving energy and water resources and reducing the demand for agricultural labor. As well, CASI practices contribute to reductions in agricultural greenhouse gas emissions [12, 13,18], contributing to national efforts to address climate change. Government policies which support the purchase, maintenance and use of CASI machinery will be crucial to its uptake in the EGP, in South Asia and in many other emerging-economy countries.

State level ➢ Original farmer groups incorporated as companies ➢ Farmer groups in new villages/districts established under the aegis of farm group companies ➢ CASI practices tested in new villages/ districts ➢ National/state policymakers & governors engaged and supportive of CASI ➢ CASI demonstrated more widely to universities & research agencies ➢ CASI practices taught in academic curricula ➢ Awareness raising in new regions ➢ Support implementation of CASI in new regions ➢ Larger-scale finance operations available within communities & farmer groups ➢ Supply chains extended ➢ New machinery dealerships established

cost-efficient and sustainable for farmers. Intensifying double cropping systems becomes more feasible and attractive under CASI as the reten­ tion of crop residues after harvest is desired; combined with mechanized crop establishment the time between crops in rotation is reduced, as is the risk of terminal heat stress in drought-intolerant crops such as wheat. Implementation and uptake of CASI was strongest when it was part of a community-based implementation framework which connected with external stakeholders including researchers and extension agents, financial institutions and agro-industries, and which engaged with, and provided evidence of success to, policymakers and governments. With community and governance support, CASI is a feasible and realistic option for smallholder farmers in many emerging-economy countries to increase their productivity while reducing water, energy and labour requirements, and CO2-equivalent emissions.

4. Conclusions Our results, from research conducted on over 400 farmers’ fields across three countries and in six cropping systems, demonstrated that CASI practices increase the productivity and profitability of rice-based cropping systems in the EGP while reducing water, energy and labor requirements and CO2-equivalent emissions. Benefits were achieved under partial (dry season only) CASI and increased under full CASI. The benefits of CASI practises are not limited to the EGP region and CASI is likely to be an effective means for smallholder farmers in many countries to sustainably improve their local food-energy-water nexus while reducing CO2-equivalent emissions. Results were nuanced across the cropping systems and study dis­ tricts. Compared to the CT baseline, CASI performed significantly better in terms of grain productivity for all cropping systems and all districts except Purnea, significantly better in terms of water productivity for all cropping systems and all districts except Dhanusha, and significantly better in terms of energy productivity and labor for all cropping systems and all districts. Consequently, across all cropping systems and districts, compared to CT CASI reduced production costs and increased gross margins, while also reducing CO2-equivalent emissions. Our results demonstrate potential benefits of intensifying the RW system into RWMb or RWJ systems; these trials (and those of RR) were conducted on fewer farms and results would be strengthened with larger-scale trials. Further research into intensification of the RW system could identify options to improve wheat-based cropping systems which are appropriate to local climate and soil conditions and which are also

Author contributions M K Gathala conceived and led the experiments and contributed to data analysis and paper writing A M Laing contributed to the experimental design and data analysis and led the paper writing S Islam led data collation and analysis; J Timsina contributed in the paper writing T P Tiwari, A Chowdhury, C Chattopadhyay, A K Singh, B P Bhatt, R Shrestha and N C D Barma facilitated the implementation of field trials and data collection D S Rana supported the metadata analyses. T M Jackson contributed to analysis and policy engagement discussion B G�erard oversaw the design and smooth operation of the project Acknowledgements Research was conducted as part of the Sustainable and Resilient Farming Systems Intensification in the Eastern Gangetic Plains (SRFSI) project (CSE/2011/077) funded by the Australian Centre for Interna­ tional Agricultural Research (ACIAR) and the Australian Government’s Department of Foreign Affairs and Trade (DFAT) under the auspices of the Sustainable Development Portfolio (SDIP). The contents and 15

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opinions expressed herein are those of the authors and do not necessarily reflect the views of ACIAR or DFAT. We are deeply thankful to our smallholder farmer research partners in the EGP for conducting on-farm trials. We thank our national and international collaborating institutions: BARI, DAE and RDRS in Bangladesh; ICAR-RCER, UBKV, BAU, DoA-WB, JEEViKA and Sakhi in India, and NARC and DoA-Nepal in Nepal; international partners IWMI, IFPRI, IRRI, and iDE; and the Australian research partners CSIRO, UQ and CU, who assisted us to successfully complete this research. We thank Dr Eric Huttner (ACIAR) for comments he made on an earlier version of the MS.

[14] Singh Y, Sidhu HS. Management of cereal crop residues for sustainable rice-wheat production system in the Indo-Gangetic Plains of India. Proc Indian Nat Sci Acad 2014;80:95–114. [15] Yadvinder-Singh, Bijay-Singh, Timsina J. Crop residue management for nutrient cycling and improving soil productivity in rice-based cropping systems in the tropics. Adv Agron 2005;85:269–407. [16] Groot AE, Bolt JS, Jat HS, Kumar M, Agarwal T, Block V. Business models of SMEs as a mechanism for scaling climate smart technologies: the case of Punjab, India. J Clean Prod 2019;210:1109–19. [17] Kassam A, Friedrich T, Derpsch R. Global spread of conservation agriculture. Int J Environ Stud 2019;76:29–51. [18] Padre TA, Rai M, Kumar V, Gathala M, Sharma PC, et al. Quantifying changes to the global warming potential of rice-wheat systems with the adoption of conservation agriculture in north-western India. Agric Ecosyst Environ 2016;219: 125–37. [19] Alam MM, Ladha JK, Faisal MW, Sharma S, Saha A, Noor S, Rahman MA. Improvement of cereal-based cropping systems following the principles of conservation agriculture under changing agricultural scenarios in Bangladesh. Field Crop Res 2015;175:1–15. [20] Chakraborty D, Ladha JK, Rana DS, Jat ML, Gathala MK, Yadav S, Rao AN, Ramesha MS, Raman A. A global analysis of alternative tillage and crop establishment practices for economically and environmentally efficient rice production. Sci Rep 2017;7(1):9342. [21] Gathala MK, Timsina J, Islam MS, Krupnik TJ, Bose TR, Islam N, Rahman MM, Hossain MI, Harun-Rashid, Ghosh AK, Hasan MMK, Khayer MA, Islam MZ, Tiwari TP, McDonald AJ. Productivity, profitability, and energetics: a multicriteria and multi-location assessment of farmers’ tillage and crop establishment options in intensively cultivated environments of South Asia. Field Crop Res 2016; 186:32–46. [22] Haque ME, Bell RW, Islam MA, Rahman MA. Minimum tillage unpuddled transplanting: an alternative crop establishment strategy for rice in conservation agriculture cropping systems. Field Crop Res 2016;185:31–9. [23] Sinha AK, Ghosh A, Dhar T, Bhattacharya PM, Mitra B, Rakesh S, Paneru P, Srestha SR, Manandhar S, Beura K, Dutta S, Pradha AK, Rao KK, Hossain A, Siddquie N, Molla MSH, Chaki AK, Gathala MK, Islam Md S, Dalal RC, Gaydon DS, Laing AM, Menzies NW. Trends in key soil parameters under conservation agriculture-based sustainable intensification farming practices in the Eastern Ganga Alluvial Plains. Soil Res 2019. https://doi.org/10.1071/SR19162. [24] Littell RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger O. SAS for mixed models. second ed. Cary, NC: SAS Institute; 2006. p. 814. [25] Rosenberg MS, Adams DC, Gurevitch. J. Metawin: statistical software for metaanalysis version 2.0. Sunderland, MA: Sinauer Associates; 2000. [26] Adams DC, Gurevitch J, Rosenberg MS. Resampling tests for meta-analysis of ecological data. Ecology 1997;78:1277–83. [27] Gathala MK, Ladha JK, Kumar V, Saharawat YS, Kumar V, Sharma PK. Effect of tillage and crop establishment methods on physical properties of a mediumtextured soil under a seven-year rice wheat rotation. Soil Sci Soc Am J 2011;75: 1851–62. [28] Gathala MK, Kumar V, Sharma PC, Saharawat YS, Jat HS, Singh M, Kumar A, Jat ML, Humphreys E, Sharma DK, Sharma S, Ladha JK. Optimizing intensive cereal-based cropping systems addressing current and future drivers of agricultural change in the north-western Indo-Gangetic Plains of India. Agric Ecosyst Environ 2013;177:85–97. [29] Jat ML, Gathala MK, Saharawat YS, Ladha JK, Singh Y. Conservation agriculture in intensive rice-wheat rotation of western indo-gangetic plains: effect on crop physiology, yield, water productivity and economic profitability. Int J Environ Sci Nat Res 2019;18:1–15. [30] Patra S, Julich S, Feger K-H, Jat ML, Jat H, Sharma PC, Schwarzel K. Soil hydraulic response to conservation agriculture under irrigated intensive cereal-based cropping systems in a semiarid climate. Soil Tillage Res 2019;192:151–63. [31] Bell RW, Haque Md E, Jahiruddin M, Rahman Md M, Begum M, Miah MMA, Islam Md A, Hossen Md A, Salahin N, Zahan T, Hossain MM, Alam Md K, Mahmud MNH. Conservation agriculture for rice-based intensive cropping by smallholders in the eastern Gangetic Plain. Agriculture 2019;9:2–17. [32] IFPRI. Mechanization in the eastern gangetic plains (EGP): a review of policies and programmes with a focus on Indian EGP. 2016. [33] IWMI. Assessment of water resources and demand for irrigation in nine districts within the Eastern Gangetic Plains. Sri Lanka: International Water Management Institute Colombo; 2015. [34] Aryal JP, Sapkota TB, Stirling CM, Jat ML, Jat HS, Rai M, Mittal S, Sutaliya JM. Conservation agriculture-based wheat production better copes with extreme climate events than conventional tillage-based systems: a case of untimely excess rainfall in Haryana, India. Agric Ecosyst Environ 2019;233:325–35. [35] Joshi P, Khan T, Kishore A. Policies for sustainable intensification of agriculture in eastern gangetic plains (EGP). 2017 [New Delhi]. [36] Biggs S, Justice S. Rural and agricultural mechanization: a history of the spread of small engines in selected Asian countries. 2015. Retrieved from, http://ebrary.ifpri .org/cdm/ref/collection/p15738coll2/id/129214. [37] Gathala MK, Laing A, Tiwari TP, Timsina J, et al. Improving smallholder farmers’ gross margins and labor-use efficiency across a range of cropping systems in the Eastern Gangetic Plains. Submitted to World Development; 2019.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.rser.2019.109645. References [1] FAO. Save and grow: a policymaker’s guide to the sustainable intensification of smallholder crop production. Rome: United Nations Food and Agriculture Organization; 2011. p. 102. [2] FAO. The state of the world’s land and water resources for food and agriculture (SOLAW) – managing systems at risk. Rome, Italy: Food and Agriculture Organization of the United Nations; 2011. [3] Ericksen P, Thornton P, Notenbaert A, Cramer L, Jones P, Herrero M. Mapping hotspots of climate change and food insecurity in the global tropics. CCAFS report no. 5. CGIAR research program on climate change, agriculture and food security (CCAFS). Copenhagen, Denmark. Available online at: www.ccafs.cgiar.org; 2011. [4] Singh VK, Yadvinder-Singh, Dwivedi BS, Singh Majumdar K, Jat ML, Mishra RP, Rani M. Soil physical properties, yield trends and economics after five years of conservation agriculture-based rice-maize system in north-western India. Soil Tillage Res 2016;155:133–48. [5] Dobermann A, Nelson R. Opportunities and solutions for sustainable food production: background research paper submitted to the high level panel on the post-2015 development agenda. United Nations: Sustainable Development Solutions Network; 2013. p. 36. [6] Darbas T, Sulaiman R, Mittal N, Devkota K, Brown PR. Institutional analysis for agricultural innovation: synthesis. CSIRO Australia, Internal Discussion Document for SRFSI project; 2015. p. 27. [7] Tiwari TP, Gathala MK, Maharjan S. Informing policies for removing barriers to scaling conservation agriculture based sustainable intensification in the Eastern Gangetic Plains. Canberra, Australia: Report submitted to ACIAR; 2017. CIMMYT, Dhaka, Bangladesh. [8] Timsina J, Wolf J, Guilpart N, van Bussel L, Grassini P, van Wart J, Hossain A, Rashid H, Islam S, van Ittersum M. Can Bangladesh produce enough cereals to meet future demand? Agric Syst 2018;163:36–44. [9] Islam S, Gathala MK, Tiwari TP, Timsina J, Laing AM, Maharjan S, Chowdhury AK, Bhattacharya PM, Dhar T, Mitra B, Kumar S, Srivastwa PK, Dutta KS, Shrestha R, Manandhar S, Shrestha SR, Paneru P, Siddquie N, Hossain A, Islam R, Ghosh AK, Rahman MA, Kumar U, Rao KK, G� erard B. Conservation agriculture for sustainable intensification: an opportunity to increase crop, system yields and water productivity for smallholders of the Eastern Gangetic Plain. Field Crop Res 2019; 238:1–17. [10] Gathala MK, Timsina J, Islam MS, Rashid MH, Ghosh AK, Krupnik TJ, Tiwari TP, McDonald A. Conservation agriculture-based tillage and crop establishment options can maintain farmers’ yield and increase profits in South Asia’s rice-maize systems: evidence from Bangladesh. Field Crop Res 2015;172:85–98. [11] Gathala MK, Laing AM, Tiwari TP, Timsina J, Islam S, Bhattacharya PM, Dhar T, Ghosh A, Sinha AK, Hossain S, Hossain I, Molla S, Rashid M, Kumar S, Kumar R, Chaudhary B, Jha SK, Ghimire P, Bastola B, Chaubey RK, Gerard B. Energyefficient, sustainable crop production practices benefit smallholder farmers and the environment of the Eastern Gangetic Plains, South Asia. Submit J Clean Product 2019. [12] Ladha JK, Rao AN, Raman A, Padre AT, Dobermann A, Gathala MK, Kumar V, Sharawat Y, Sharma S, Piepho HP, Alam MM, Liak R, Rajendran R, Reddy CK, Parsad R, Sharma PC, Singh SS, Saha A, Noor S. Agronomic improvements can make future cereal systems in South Asia far more productive and result in a lower environmental footprint. Glob Chang Biol 2015 ISSN: 1354-1013. 01–21. [13] Kumar V, Jat HS, Sharma PC, Balwinder-Singh, Gathala MK, Malik RK, Kamboj BR, Yadav AK, Ladha JK, Raman A, Sharma DK, McDonald A. Can productivity and profitability be enhanced in intensively managed cereal systems while reducing the environmental footprint of production? Assessing sustainable intensification options in the breadbasket of India. Agric Ecosyst Environ 2018;252:132–47. https://doi.org/10.1016/j.agee.2017.10.006.

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