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CONTEXT: Despite recent improvements in living standards, a substantial proportion of farm households in sub-Saharan Africa (SSA) is food insecure, and increasing crop productivity could help address this problem. OBJECTIVE: We estimated the effect of increasing maize yields with mineral fertilizer on household food security and on regional and national maize supply in two East African countries - Uganda and Tanzania. METHODS: We estimated maize yield response to nitrogen (N) fertilization with a machine learning model trained on 15,952 observations of maize responses to fertilizer across SSA. Together with spatial price data, we used this model to quantify the profit-maximizing N fertilizer input for a nationally-representative sample of 4188 agricultural households in the two countries. We computed a food availability indicator for all households. RESULTS AND CONCLUSIONS: The mean profit-maximizing N input was 82 kg/ha in Tanzania, but it was much lower in Uganda (24 kg/ha) mostly because of less favorable prices. The profit-maximizing N input was above the reported N input for 95% of the households in Tanzania and for 43% of the households in Uganda. It was predicted to increase the food availability ratio of food insecure maize growers by 95% in Tanzania, and by 25% in Uganda. The administrative regions where maize supply could increase most were not the same as the regions where the increase in household-level food security was largest. With increased fertilization, food insecure maize growing households (35% in Tanzania and 42% in Uganda) could only contribute about 20% of the overall increase in maize supply, whereas the 20 to 30% food secure households that have a larger area planted with maize could contribute >60%. SIGNIFICANCE: Our study makes two key contributions: i) a substantial increase in national maize supply is more likely to come from already food secure households with relatively large farms, while food insecure households with small farms may nevertheless increase their household-level food security through maize intensification, and ii) high potential areas to increase maize domestic production do not necessarily match with areas where there is immediate scope to improve household-level food security.

Since the early 2000s, digital soil maps have been successfully used for various applications, including precision agriculture, environmental assessments and land use management. Globally, however, there are large disparities in the availability of soil data on which digital soil mapping (DSM) models can be fitted. Several studies attempted to transfer a DSM model fitted from an area with a well-developed soil database to map the soil in areas with low sampling density. This usually is a challenging task because two areas have hardly ever the same soil-forming factors in two different regions of the world. In this study, we aim to determine whether finding homosoils (i.e., locations sharing similar soil-forming factors) can help transferring soil information by means of a DSM model extrapolation. We hypothesize that within areas in the world considered as homosoils, one can leverage on areas with high sampling density and fit a DSM model, which can then be extrapolated geographically to an area with little or no data. We collected publicly available soil data for clay, silt, sand, organic carbon (OC), pH and total nitrogen (N) within our study area in Mali, West Africa and its homosoils. We fitted a regression tree model between the soil properties and environmental covariates of the homosoils, and applied this model to our study area in Mali. Several calibration and validation strategies were explored. We also compared our approach with existing maps made at a global and a continental scale. We concluded that geographic model extrapolation within homosoils was possible, but that model accuracy dramatically improved when local data were included in the calibration dataset. The maps produced from models fitted with data from homosoils were more accurate than existing products for this study area, for three (silt, sand, pH) out of six soil properties. This study would be relevant to areas with very little or no soil data to carry critical soils and environmental risk assessments at a regional level.

Green manure can be a source of nutrients for crops to spare some of the mineral fertilizer required for sustainable intensification of agriculture in smallholder context. We explored with a calibrated model the potential of legume green manure to help spare mineral fertilizer for sustainable intensification of rainfed rice production in Madagascar highlands. This study builds on a two-year rice experiment with rice and green manure as previous crop. The experiment investigated the response of rainfed rice growth to the incorporation into the soil of i) rice residue only (C:N ratio of 44.9) and ii) mucuna + crotalaria green manures (C:N ratio of 18.7). Two levels of fertilizer were investigated: manure only (5 t DM ha−1) (F1) and manure (5 t DM ha−1) in combination with mineral fertilizer (136 kg N ha−1) (F2). The crop model STICS was calibrated against observed soil water and soil mineral N, plant N uptake, aboveground biomass and grain yield to reproduce the impact of residue type and fertilizer input on mineralization of organic N in soil and in crop residue, crop growth and grain yield. The calibrated crop model was then used to explore rice response to mineral N fertilizer, with or without green manure, for the historical climate (1994–2018). Relative Root Mean Square Error (rRMSE) of model simulations fell within the observed relative variation in grain yield due to treatment effect. With green manure, mineral N available for rice increased compared with the situation without green manure, leading to greater agronomic use efficiency of N fertilizer. Green manure helped spare mineral fertilizer to achieve a reference yield set at 75 % of potential yield. This reference yield (3.67 t ha−1 on average across years) was reached with 40 kg N ha−1 with 6 t ha−1 of green manure, and 100 kg N ha−1 without green manure. The analysis of rice yield inter-annual variability indicated that (i) N leaching increased with total seasonal rainfall, which undermined the benefits of green manure on rice productivity in wetter years, and (ii) the increase in rice productivity allowed by the green manure did not fully compensate the rice yield foregone with the cultivation of the green manure. The calibrated model can be a useful tool to explore additional management strategies that will help maximize the benefit of green manure for sustainable intensification of rice-based cropping systems, especially in low-input cropping systems.

Intercropping is a key entry point for sustainable intensification of cropping systems in sub-Saharan Africa where variable rainfall conditions prevail. Crop simulation models can complement field experiments to assess the agronomic and environmental performances of intercropping systems under diverse climatic conditions, including hypothetical future climate. So far, crop models that can handle intercropping, such as STICS, have not often been extensively evaluated for tropical conditions and for species grown by farmers in sub-Saharan Africa. The objective of this study was to evaluate the performance of the calibrated STICS model to simulate sorghum-cowpea intercropping systems in rainfed conditions in West Africa. We used data from field experiments conducted at the N'Tarla Agronomic Station in Mali in 2017 and 2018. Two varieties of sorghum (local and improved) with contrasting photoperiod sensitivities were grown as sole crop and intercropped with cowpea, with additive design. Two sowing dates and two levels of mineral fertilization were also investigated. Model simulations were evaluated with observed data for phenology, leaf area index (LAI), aboveground biomass, grain yield and in-season soil moisture. Large variations in aboveground biomass of sorghum and cowpea was observed in the experiment (i.e. 3.5 – 9.6 t/ha for sorghum and 0.4–2.5 t/ha for cowpea), owing to the treatments (i.e. sole vs intercrop, early vs late sowing, no fertilizer input vs fertilizer input). Such variations were satisfactorily reproduced by the model, with EF of 0.81 in calibration and 0.58 in evaluation (with relative rRMSE of 23 % and 43 %) across crops. Sorghum AGB simulations were more accurate (rRMSE of 21 % and EF of 0.54) than cowpea AGB simulations (rRMSE of 25 % and EF of −0.09). The two main observed features of the intercropping system were well reproduced by the model. Firstly, cowpea and sorghum aboveground biomass decreased with intercropping compared with sole cropping, and the decrease in cowpea biomass was greater than the decrease in sorghum biomass. Secondly, despite a reduction in sorghum and cowpea yield, Land Equivalent Ratio of the intercropping for aboveground biomass was always above one. With regard to grain yield, observed LER was above one only in the non-fertilized treatment. The model failed at reproducing this behavior, probably because of insufficiently accurate calibration of the process leading to grain yield formation: rRMSE for grain yield was 49 % in calibration and 41 % in evaluation. Based on these findings, we discuss avenues to improve model calibration and use the model to explore options for sustainable intensification in land constrained sub-Saharan Africa.

International audience; Rainfed crop production predominates in West Africa. Rice is an important staple food, especially in Senegal. The scope for increase in rice production under irrigated conditions is uncertain. Rainfed rice is therefore a key component for regional food security impelling agronomists to assess climate change impact on rainfed rice yield and to design rainfed rice ideotypes suited to future climate scenarios. The DSSAT CSM-CERES-Rice model was thus calibrated and evaluated on 19 agronomic experiments conducted in 2012, 2013, and 2014, in 6 locations, with 21 cultivars and two fertilization levels (20 and 80 kg N ha−1). Simulations were then carried out with the crop model forced with the downscaled projections of seven climate models, with and without considering the impact of an increase in atmospheric [CO2], using an ensemble of global circulation models and two Representative Concentration Pathways (RCP2.6 and RCP8.5). Simulated rice yield was divided by two over the century with RCP8.5 and stagnated with RCP2.6. Elevated [CO2] significantly increased yields, but this effect could not offset the yield decline due to elevated temperatures. Cultivars with longer vegetative phases and greater temperature tolerance were better adapted to climate change than current cultivars. Using these new cultivars with the recommended fertilization rate (80 kg N ha−1) could offset the yield decline due to climate change. For the first time, we bring together a study based on a process-based crop model handling crop response to elevated [CO2], a large set of field experiments and up-to-date climate projections (i) to provide useful insights into plausible impacts of climate change on rainfed rice in Senegal and (ii) to identify cultivar characteristics relevant for adaptation to future possible climates. Our findings will help set priorities for breeding resilient cultivar in the region.

International audience; Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

International audience; In the mountainous areas of South-East Asia, family farms have shifted from subsistence to input-intensified and market-oriented maize-based farming systems, resulting in a substantial increase in farm income, but also in new environmental threats: deforestation, biodiversity loss, soil erosion, herbicide leaching and soil fertility degradation. In this typical case study of cash-strapped farms, where the balance between socio-economic and environmental dimensions of sustainability is complex, we used participatory methods (serious games and Q-methodology), combined with agronomic field monitoring, to identify relevant farm and field-level criteria for sustainability assessment.Serious games at farm level showed that short-term socio-economic dimensions prevailed over environmental dimensions in farmers' objectives. However, farmers also greatly valued their capacity to transfer a viable farm to the next generation and avoid herbicide use. Serious games at field level showed that some farmers were willing to preserve soil fertility for future generations. The agronomic field monitoring showed that maize yield deviations from potential water-limited yield were primarily due to weed infestation favoured by low sowing density, due to uncontrolled moto-mechanized crop establishment. This technical failure at the beginning of the maize cycle led to herbicide overuse, poor returns on investment for fertilizer, and increased exposure to soil erosion.Combining the perspectives of scientists and farmers led to the following set of locally-relevant criteria: i) at farm level: farm income, diversity of activities, farmer autonomy, farmer health, workload peaks, soil fertility transfer between agroecological zones in the landscape, rice and forage self-sufficiency; ii) at field level: resource use efficiency, soil fertility, erosion and herbicide risks, susceptibility to pests, weeds and climate variability, biodiversity, land productivity, economic performance, labour productivity and work drudgery. Our approach helped to identify key relevant sustainability criteria and could be useful for designing alternatives to current maize-based cropping systems, and contributed to informing priority-setting for institutional development and agricultural policies in the region.

International audience; Developing relevant decision-support tools for policymakers to support large-scale implementation of climate-smart agriculture in the Global South is challenging given the great diversity in biophysical, socio-technical, and organizational conditions. This article describes a pilot exercise inspired by the recommendation domain literature that aimed at mapping, beyond “classical” biophysical and socio-technical variables, the institutional variables (i.e., the existence of policy incentives in national policy documents) that could influence the large-scale implementation of climate-smart agricultural practices. Four practices were considered: cereal-legume intercropping, fodder legume cultivation, farmer managed natural regeneration (FMNR) of Parkia biglobosa, and crop residue mulching. The biophysical and socio-technical variables were classified based on thresholds identified in the literature and mapped with a geographic information system. The policy documents considered were investment plans, adaptation plans for climate change, nationally determined contributions, and Technology Needs Assessments project reports. Sixteen policy documents for four countries were thoroughly reviewed and classified as unfavorable, intermediate, and favorable for the four selected practices, based on a decision tree built for that purpose. Our analysis shows that areas where biophysical, socio-technical, and institutional variables are aligned for the four practices considered are small, particularly for fodder legume cultivation and crop residue mulching. For cereal-legume intercropping, incentives from national policies strongly differ from one country to another while for FMNR of Parkia biglobosa policies are more homogeneously conducive across countries. Nonetheless, it was possible to identify areas where biophysical, socio-technical, and institutional dimensions of the transition toward climate-smart agriculture (CSA) were aligned, for example, cereal-legume intercropping in southern Mali. The delineating of favorable and unfavorable areas allows specific recommendations to be made for policymakers as levers for action differ in favorable, intermediate, and unfavorable zones. Based on the exploration made for the four practices, this study highlights the need for further articulations from local to national scale to implement CSA.

International audience; CONTEXT: Fostering diversity within agricultural systems can substantially contribute to improved food security among smallholder farmers. Agroforestry parklands are diverse agricultural landscapes where trees can provide an array of ecosystem services. Previous studies analyzing the agricultural landscape diversity-food security nexus in agroforestry parklands have only considered tree cover. OBJECTIVE: We propose an original empirical approach that combines the analysis of spatial data on agricultural landscape diversity with agricultural field monitoring and household surveys. These three sources of data were used to scrutinize the direct and indirect contributions of agricultural landscape diversity to food availability and food access. METHODS: Millet yield was used as a proxy for food availability, and household food access was approximated using the Household Food Insecurity Access Scale (HFIAS) indicator. Two contrasted agroforestry parklands of Central Senegal were chosen as case studies. Firstly, we used a Gradient Boosting Machine (GBM) algorithm to disentangle the relative contribution of landscape diversity, biophysical and crop management variables in explaining millet yield variability. Secondly, we investigated the pathways linking agricultural landscape di-versity to HFIAS using a Correlation Network Analysis (CNA). RESULTS AND CONCLUSIONS: The GBM model explained 77% and 84% of millet yield variability for the two parklands, respectively, with landscape diversity variables accounting for 53% and 47% of relative influence. Among the landscape diversity variables, tree species richness and tree density were the most important ones. Millet yield was positively associated with tree density in the Nioro site until a threshold of 5 trees/ha, and with tree species richness in the two sites. The CNA showed that greater tree cover and larger tree patches were moderately correlated with HFIAS. This suggests that tree species with large crown, as it the case for most fruit bearing tree species in the region, are the main species contributing directly to food access. Agricultural land-scape diversity contributed mainly indirectly to household food access through an "agroecological pathway", i.e. by the provision of ecosystem services regulating and supporting crop production. SIGNIFICANCE: Using an integrated landscape approach relying on up-to-date remote sensing data and recent advances in data analysis methods, our study shows that tree species diversity matters as much as the amount of tree cover for the production of food, and it can contribute to improve food security. We bring a more nuanced picture of the contribution of agricultural landscape diversity to food security suggesting that land management policies supporting food security should consider both tree density and tree species diversity to optimize the co -benefits of trees for the different food security dimensions.

International audience; Faba bean (Vicia Faba L.) is the second most widely grown grain legume in Europe after pea (Pisum Sativum L.) and presents several agronomic and environmental advantages when inserted in cropping systems (e.g. decreased dependency on synthetic fertilisers and N provision to the subsequent crop). However, yield variability due to several factors including heat and drought impede wide adoption of faba bean by farmers. Soil-crop models provide quantitative information to evaluate these processes and help to design innovative cropping system including legumes. The STICS model was chosen for its agro-environmental purpose and its genericity allowing crop rotation simulation and robustness for a wide range of pedoclimatic conditions. However, there is so far for STICS no parameterization for faba bean. We calibrated 38 crop related parameters based on literature, direct measurements and sequential estimation using the optimisation tool OptimiSTICS and a dataset of winter faba bean grown in two sites in France with contrasting soil conditions over several growing seasons (2002–2015). Data from 22 experimental plots were used for calibration and the remaining independent 13 plots were used for model evaluation. After calibration, the STICS model reproduced adequately phenology, Leaf Area Index and dynamic growth of above ground biomass, uptake of mineral N, N2 fixation and grain yield, with satisfactory model efficiency (0.56 to 0.81) and low relative bias (−7% to 2%). The model adequately reproduced the large observed variation in faba bean grain yield (0.42–4.70 t ha−1) and total N2 fixed at harvest (62–172 kg N ha−1) in the contrasted years and soil conditions of this study. Simulations indicated that water stress was the overriding factor driving yield and N2 fixation variability. Simulation of temporal crop growth and water stresses during grain onset and grain filling allowed a robust and credible agronomic diagnosis of the causes of this variability for faba bean crops not significantly damaged by pests and diseases. Water supply/ demand ratio averaged over a period of six days preceding beginning of grain filling explained 78% of the observed grain yield variability while water deficit factor for N2 fixation averaged over a period of 20 days following the beginning of grain filling explained 83% of the variability of fixed N2 at harvest. Our work provides a first calibration and evaluation of the STICS model for faba bean. It offers the opportunity to quantify the ecosystem services associated with crop rotations including faba bean and the effect of climate change on the performance of such rotations.

SUMMARYThe large diversity of farms and farming systems in sub-Saharan Africa calls for agricultural improvement options that are adapted to the context in which smallholder farmers operate. The socio-ecological niche concept incorporates the agro-ecological, socio-cultural, economic and institutional dimensions and the multiple levels of this context in order to identify which options fit best. In this paper, we illustrate how farming systems analysis, following the DEED cycle of Describe, Explain, Explore and Design, and embedding co-learning amongst researchers, farmers and other stakeholders, helps to operationalize the socio-ecological niche concept. Examples illustrate how farm typologies, detailed farm characterization and on-farm experimental work, in combination with modelling and participatory approaches inform the matching of options to the context at regional, village, farm and field level. Recommendation domains at these gradually finer levels form the basis for gradually more detailed baskets of options from which farmers and other stakeholders may choose, test and adjust to their specific needs. Tailored options identified through the DEED cycle proof to be more relevant, feasible and performant as compared to blanket recommendations in terms of both researcher and farmer-identified criteria. As part of DEED, on-farm experiments are particularly useful in revealing constraints and risks faced by farmers. We show that targeting options to the niches in which they perform best, helps to reduce this risk. Whereas the conclusions of our work about the potential for improving smallholders’ livelihoods are often sobering, farming systems analysis allows substantiating the limitations of technological options, thus highlighting the need for enabling policies and institutions that may improve the larger-scale context and increase the uptake potential of options.

International audience; Climate change could undermine grain legumes ability to fix atmospheric nitrogen and their contribution to increase cropping systems sustainability. Pea (Pisum sativum L.) and faba bean (Vicia faba L.) are the two most widely grown grain legumes in Europe, yet the potential impact of climate change on their performances has not been quantified. We calibrated and evaluated the STICS soil-crop model for spring pea, winter pea and winter faba bean using experimental data from southwestern France and explored the effect of contrasting climate change scenarios. After calibration, STICS accurately simulated grain yield and amount of N2 fixed for the experimental growing seasons. Assuming no change in crop management, mean and inter-annual variability of grain yield and fixed N2 were assessed for historical (1995−2015), mid-term (2020−2040) and long-term (2060−2080) periods in one location in southwestern France. We considered projections from three climate models and two Representative [CO2] Pathways (RCP 4.5 and RCP 8.5). The climate models spanned a wide range of changes in temperature (+0.3 to +4.1 °C) and rainfall (−15% to +8%) depending on time horizon and RCP. Simulated grain yield increased over the long term in most scenarios (+1 to +25%), and spring pea tended to benefit less than winter pea and winter faba bean. Nevertheless, for the climate scenario with a decrease in rainfall and the strongest increase in temperature, simulated spring pea grain yield decreased by 28% while winter legumes yields were less affected (−14% for pea and no decrease for faba bean). Simulated changes in the amount of N2 fixed followed the grain yield response. Temperature rise caused a shortening in crop cycle duration. Simulated temperature stress significantly increased for spring and winter pea in most climate change scenarios while winter faba bean was rather unaffected due to greater upper temperature thresholds. N2 fixation of spring pea was reduced by above-optimal temperature during its vegetative growth in spring while N2 fixation of winter legumes was enhanced by the increase in temperature during their vegetative growth in winter. Simulated drought stress only increased in the climate scenario predicting a decrease in rainfall. Overall, [CO2] increase would allow offsetting negative effects of temperature and drought on grain yield and N2 fixation, except for climate scenarios involving a decrease in rainfall and the strong increase in temperature. The contrasted simulated response of winter and spring grain legumes to climate change in southwestern France points to the opportunity to tap grain legume diversity and cultivar choice as an adaptation strategy.

Agroforestry is pointed out by the Intergovernmental Panel on Climate Change report as a key option to respond to climate change and land degradation while simultaneously improving global food security ( IPCC, 2019 ). Faidherbia albida parklands are widespread in Sub-Saharan Africa and provide several ecosystem services to populations, notably an increase in crop productivity. While remote sensing has been proven useful for crop yield assessment in smallholder farming system, it has so far ignored the woody component. We propose an original approach combining remote sensing, landscape ecology and statistical modelling to i) improve the accuracy of millet yield prediction in parklands and ii) identify the main drivers of millet yield spatial variation. The parkland of Central Senegal was chosen as a case study. Firstly, we calibrated a remote sensing-based linear model that accounted for vegetation productivity and tree density to predict millet yield. Integrating parkland structure improved the accuracy of yield estimation. The best model based on a combination of Green Difference Vegetation Index and number of trees in the field explained 70% of observed yield variability (relative Root Mean Squared Error (RRMSE) of 28%). The best model based solely on vegetation productivity (no information on parkland structure) explained only 46% of the observed variability (RRMSE = 34%). Secondly we investigated the drivers of the spatial variability in estimated yield using Gradient Boosting Machine algorithm (GBM) and biophysical and management factors derived from geospatial data. The GBM model explained 81% of yield spatial variability. Predominant drivers were soil nutrient availability (i.e. soil total nitrogen and total phosphorous) and woody cover in the surrounding landscape of fields. Our results show that millet yield increases with woody cover in the surrounding landscape of fields up to a woody cover of 35%. These findings have to be strengthened by testing the approach in more diversified and/or denser parklands. Our study illustrates that recent advances in earth observations open up new avenues to improve the monitoring of parkland systems in smallholder context.

International audience; Assessing how livelihoods in rural sub-Saharan Africa might change given future trends in socio-economic and biophysical conditions helps to identify and direct effective efforts towards poverty reduction. Based on existing literature, hypothetical changes in farmer practices and policy interventions were described and used to build five contrasting scenarios towards the year 2027. A simulation framework was developed to assess food self-sufficiency and income per capita now and in the future for a representative village of 99 households in Southern Mali. In the current situation, 26% of the farms ware food self-sufficient and above the 1.9 US$ day(-1) poverty line. This percentage would fall to 13% in the "Business as usual" scenario. In the "Dairy development" scenario, with intensification of livestock production and support to the milk sector, 27% of farms would be food self-sufficient and non-poor. Additional policy interventions targeting family planning and job creation outside agriculture would be needed to improve both household food self-sufficiency and income per capita. In this optimistic scenario, 77% of the farms would be non-poor and food self-sufficient in 2027. Additional programs to promote Integrated Pest Management, small-scale mechanization and mineral fertilizer on traditional cereals could allow a drastic increase in productivity and would lift 94% of the farm population out of poverty. Considering the entire heterogeneous farm population was crucial to accurately assess pathways out of poverty. Our study stresses the need for a strategic and multi-sectoral combination of interventions to improve livelihoods.

International audience; Farm systems were re-designed together with farmers during three years (2013-2015) in Southern Mali with the aim to improve income without compromising food self-sufficiency. A cyclical learning model with three steps was used: Step 1 was the co-design of a set of crop/livestock technical options, Step 2 the on-farm testing and appraisal of these options and Step 3 a participatory ex-ante analysis of re-designed farm systems incorporating the tested options. Two iterations of the cycle were performed, in order to incorporate farmers' point of view and researchers' learning. We worked together with 132 farmers representing four farm types: High Resource Endowed with Large Herd (HRE-LH); High Resource Endowed (HRE); Medium Resource Endowed (MRE) and Low Resource Endowed (LRE) farms. In the first cycle of 2012-2014 farmers re-designed their farms and the reconfigurations were assessed ex mite using the average yields and gross margins obtained in the 2013 on-farm trials. HRE-LH farmers experienced a disappointing decrease in food self-sufficiency and MRE farmers were disappointed by the marginal improvement in gross margin. In a second cycle in 2014-2015, farmer insights gathered during field days and statistical analysis of trial results allowed a better understanding of the variability of option performance and the link with farm context: niches were identified within the farms (soil type/previous crop combinations) where options performed better. The farm systems were re-designed using this niche specific information on yield and gross margin, which solved the concerns voiced by farmers during the first cycle. Without compromising food self-sufficiency, maize/cowpea intercropping in the right niche combined with stall feeding increased HRE-LH and HRE farm gross margin by 20-26% respectively (i.e. 690 and 545 US$ year(-1)) with respect to the current farm system. Replacement of sorghum by soyabean (or cowpea) increased MRE and LRE farm gross margin by 29 and 9% respectively (i.e. 545 and 32 US$ year(-1)). Farmers highlighted the saliency of the niches and the re-designed farm system, and indicated that the extra income could be reinvested in the farm. Our study demonstrates the feasibility and the usefulness of a cyclical and adaptive combination of participatory approaches, on-farm trials and ex-ante analysis to foster learning by farmers and researchers, allowing an agile reorientation of project actions and the generation of innovative farm systems that improve farm income without compromising food self-sufficiency. The re-designed farm systems based on simple, reproducible guidelines such as farm type, previous crop and soil type can be scaled-out by extension workers and guide priority setting in (agricultural) policies and institutional development.

African mixed crop–livestock systems are vulnerable to climate change and need to adapt in order to improve productivity and sustain people’s livelihoods. These smallholder systems are characterized by high greenhouse gas emission rates, but could play a role in their mitigation. Although the impact of climate change is projected to be large, many uncertainties persist, in particular with respect to impacts on livestock and grazing components, whole-farm dynamics and heterogeneous farm populations. We summarize the current understanding on impacts and vulnerability and highlight key knowledge gaps for the separate system components and the mixed farming systems as a whole. Numerous adaptation and mitigation options exist for crop–livestock systems. We provide an overview by distinguishing risk management, diversification and sustainable intensification strategies, and by focusing on the contribution to the three pillars of climate-smart agriculture. Despite the potential solutions, smallholders face major constraints at various scales, including small farm sizes, the lack of response to the proposed measures and the multi-functionality of the livestock herd. Major institutional barriers include poor access to markets and relevant knowledge, land tenure insecurity and the common property status of most grazing resources. These limit the adoption potential and hence the potential impact on resilience and mitigation. In order to effectively inform decision-making, we therefore call for integrated, system-oriented impact assessments and a realistic consideration of the adoption constraints in smallholder systems. Building on agricultural system model development, integrated impact assessments and scenario analyses can inform the co-design and implementation of adaptation and mitigation strategies.F

Key words: longitudinal study, farm typology, food self-sufficiency, income, legumes, ex-ante analysis, participatory research, scenario. Smallholder agriculture in sub-Saharan Africa provides basis of rural livelihoods and food security, yet farmers have to cope with land constraints, variable rainfall and unstable institutional support. This study integrates a diversity of approaches (household typology and understanding of farm trajectories, on-farm trials, participatory ex-ante trade-off analysis) to design innovative farming systems to confront these challenges. We explored farm trajectories during two decades (1994 to 2010) in the Koutiala district in southern Mali, an area experiencing the land constraints that exert pressure in many other parts of sub-Saharan Africa. We classified farms into four types differing in land and labour productivity and food self-sufficiency status. During the past two decades, 17% of the farms stepped up to a farm type with greater productivity, while 70% of the farms remained in the same type, and only 13% of the farms experienced deteriorating farming conditions. Crop yields did not change significantly over time for any farm type and labour productivity decreased. Together with 132 farmers in the Koutiala district, we tested a range of options for sustainable intensification, including intensification of cereal (maize and sorghum) and legume (groundnut, soyabean and cowpea) sole crops and cereal-legume intercropping over three years and cropping seasons (2012-2014) through on-farm trials. Experiments were located across three soil types that farmers identified – namely black, sandy and gravelly soils. Enhanced agronomic performance was achieved when targeting legumes to a given soil type and/or place in the rotation: the biomass production of the cowpea fodder variety was doubled on black soils compared with gravelly soils and the additive maize/cowpea intercropping option after cotton or maize resulted in no maize grain penalty, and 1.38 t ha−1 more cowpea fodder production compared with sole maize. Farm systems were re-designed together with the farmers involved in the trials. A cyclical learning model combining the on-farm testing and participatory ex-ante analysis was used during four years (2012-2015). In the first cycle of 2012-2014, farmers were disappointed by the results of the ex-ante trade-off analysis, i.e marginal improvement in gross margin when replacing sorghum with soybean and food self-sufficiency trade-offs when intercropping maize with cowpea. In a second cycle in 2014-2015 the farm systems were re-designed using the niche-specific (soil type/previous crop combinations) information on yield and gross margin, which solved the concerns voiced by farmers during the first cycle. Farmers highlighted the saliency of the niches and the re-designed farm systems that increased farm gross margin by 9 to 29% (depending on farm type and options considered) without compromising food self-sufficiency. The involvement of farmers in the co-learning cycles allowed establishment of legitimate, credible and salient farm reconfiguration guidelines that could be scaled-out to other communities within the “old cotton basin”. Five medium-term contrasting socio-economic scenarios were built towards the year 2027, including hypothetical trends in policy interventions and change towards agricultural intensification. A simulation framework was built to account for household demographic dynamics and crop/livestock production variability. In the current situation, 45% of the 99 households of the study village were food self-sufficient and above the 1.25 US$ day-1 poverty line. Without change in farmer practices and additional policy intervention, only 16% of the farms would be both food self-sufficient and above the poverty line in 2027. In the case of diversification with legumes combined with intensification of livestock production and support to the milk sector, 27% of farms would be food self-sufficient and above the poverty line. Additional broader policy interventions to favour out-migration would be needed to lift 69% of the farms out of poverty. Other additional subsidies to favour yield gap narrowing of the main crops would lift 92% of the farm population out of poverty. Whilst sustainable intensification of farming clearly has a key role to play in ensuring food self-sufficiency, and is of great interest to local farmers, in the face of increasing population pressure other approaches are required to address rural poverty. These require strategic and multi-sectoral approaches that address employment within and beyond agriculture, in both rural and urban areas.

Options that contribute to sustainable intensification offer an avenue to improve crop yields and farmers’ livelihoods. However, insufficient knowledge on the performance of various options in the context of smallholder farm systems impedes local adaptation and adoption. Therefore, together with farmers in southern Mali we tested a range of options for sustainable intensification including intensification of cereal (maize and sorghum) and legume (groundnut, soyabean and cowpea) sole crops and cereal-legume intercropping during three years on on-farm trials. There was huge variability among fields in crop yields of unamended control plots: maize yielded from 0.20 to 5.24 t ha−1, sorghum from 0 to 3.53 t ha−1, groundnut from 0.10 to 1.16 t ha−1, soyabean from 0 to 2.48 t ha−1 and cowpea from 0 to 1.02 t ha−1. This variability was partly explained by (i) soil type and water holding capacity, (ii) previous crop, its management and the nutrient carry-over and (iii) inter-annual weather variability. Farmers recognized three soil types: gravelly soils, sandy soils and black soils. Yields were very poor on gravelly soils and two to three times greater (depending on the crop) on black soils. Yields were also poor at the end of the typical crop rotation, i.e., after sorghum and millet, and 1.3–1.7 times greater (depending on the crop) after the fertilized crops maize and cotton. We diagnosed a number of cases of technology failure where no improvement in yield was observed with hybrid varieties of maize and sorghum and rhizobial inoculation of soyabean. Regardless of soil type and previous crop, mineral fertilizer improved yields by 34–126% depending on the crop. Targeting options to a given soil type and/or place in the rotation enhanced their agronomic performance: (i) the biomass production of the cowpea fodder variety was doubled on black soils compared with gravelly soils, (ii) the additive maize/cowpea intercropping option after cotton or maize resulted in an average overall LER of 1.47, no maize grain penalty, and 1.38 t ha−1 more cowpea fodder production compared with sole maize. Soil type and position in the rotation, two indicators easy to assess by farmers and extension workers, allowed the identification of specific niches for enhanced agronomic performance of legume sole cropping and/or intercropping.

Dans la zone du Mali-Sud, un des defis pour maintenir la fertilite des sols sous culture continue est d’aborder le partage ou le devenir des biomasses vegetales produites par les cultures, necessaires aux animaux de l’exploitation, d’une part, et au maintien sur le long terme d’un stock organique minimal au niveau de l’horizon de sol travaille, d’autre part. Une etude a ete conduite pour simuler des bilans simplifies en carbone organique stable de sols cultives au niveau de trois terroirs differencies, a partir d’un seuil minimal theorique moyen a maintenir sur la profondeur 0 a 20 cm. Prealablement, nous avons quantifie les differents usages des residus de recolte de dix exploitations par village selon la typologie couramment utilisee. Avec les pratiques dominantes actuelles de restitutions organiques (enfouissement dans le sol des litieres de parcs associees a 25 % de la production totale de feces de bovins et des reliquats de residus de cultures restant a terre en fin de saison seche), tous les types d’exploitation ont presente des bilans organiques fortement deficitaires. Parmi les differents scenarios d’amelioration envisages, celui incluant une augmentation de la biomasse produite par unite de surface, associee dans deux des trois villages a l’arret du brulis des residus et au doublement de l’apport en feces, permettrait d’envisager un equilibre en termes de bilan organique pour la grande majorite des exploitations sur l’ensemble de leurs champs, tout en gardant le niveau de prelevement actuel realise par les bovins lors de la vaine pâture ( Vp). Cette demarche originale, associant pratiques paysannes et bilans simplifies en carbone stable (CS) du sol aux echelles de l’exploitation agricole et du terroir villageois, meriterait d’etre appliquee dans d’autres situations au Mali-Sud et plus generalement dans d’autres contextes de polyculture elevage.

Institutional support for smallholders has been the motor for the expanding cotton production sector in southern Mali since the 1970s. Smallholder farms exhibit diverse resource endowments and little is known on how they benefit from and cope with changes in this institutional support. In this paper we explore farm trajectories during two decades (1994 to 2010) and their link with farm resource endowment and government support. We distinguished a favourable period for cotton production and an unfavourable period during which institutional support collapsed. A panel survey that monitored 30 farms in the Koutiala district in southern Mali over this period was analysed. Based on indicators of resource endowment and using Ascending Hierarchical Classification (AHC), farms were grouped into four types: High Resource Endowed farms with Large Herds (HRE-LH), High Resource Endowed (HRE) farms, Medium Resource Endowed (MRE) farms and Low Resource Endowed (LRE) farms. Average yield, labour productivity and food self-sufficiency status of each type were calculated. Farms remaining in the same type were classified as ‘hanging in’, while farms moving to a type of higher yields, labour productivity and food self-sufficiency status were classified as ‘stepping up’, and farms following the opposite trajectory of deteriorating farming conditions were classified as ‘falling down’. The LRE farms differed from all other farm types due to lower yields, while both LRE and HRE farms differed from the MRE and HRE-LH farm types due to a combination of less labour productivity and less food self-sufficiency. During those two decades, 17% of the farms ‘stepped up’, while 70% of the farms remained ‘hanging in’, and only 13% of the farms ‘fell down’. We found no obvious negative impact of the collapse of government support on farm trajectories. For MRE, HRE and HRE-LH farms, average N and P use intensity increased from 1994 to 2004 and then decreased during the following cotton crisis. On the other hand, organic fertilizer use intensity increased continuously over the entire monitoring period for HRE-LH and MRE farms. Crop yields did not change significantly over time for any farm type and labour productivity decreased. We discuss how technical options specific for different farm types (increase in farm equipment, sale of cereals, incorporation of legumes and intensification of milk production) and broader institutional change (improvement in finance system and infrastructure, tariffs) can enhance ‘step up’ trajectories for farming households and avoid stagnation (‘hanging in’) of the whole agricultural sector.