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The agricultural productivity and sustainability in Eastern Gangetic Plain Zones of India are threatened because of the inefficiency of current production practices, shortage of resources, and socioeconomic constraints. We hypothesized the potential impact of intensified cereal systems with mung bean as a third crop within the annual cropping cycle. We assessed economic, social, and environmental indicators for intensified and current cropping system management practiced by different farm types in the region using the FarmDESIGN model. Building on a farm typology constructed for the region in our past research, we used five types of farmers: part-time (PT), well-endowed (WE), small-scale (SS) crop and livestock mix, medium-scale (MS), and resource-poor farmers (RP) in this study. The performance indicators of the 229 original cropping systems cultivated within the 43 farms varied strongly in the eight performance indicators. This variability of cropping systems performance within the farm types resulted in the absence of significant differences between the types. Compared to the original cropping systems, the intensified cropping systems with mung bean not only performed high in dietary energy (DE) production and organic matter (OM) inputs into the soil but also had high application rates of biocides and minimized losses of nitrogen (N). The intervention systems were low in labor requirement and scored at an intermediate level for crop gross margin, water use, and greenhouse gas (GHG) emissions. The ranges of areas of maize- and rice-based systems that could be replaced by intensified systems were largest for the WE and RP farm types. This was reflected in large ranges of change in the performance indicators, but no significant differences in response were found between the farm types. The intensification of maize- and rice-based systems with the proposed intervention cropping systems involving mung bean would result in increased profitability, higher DE yield, and lower requirements for labor and water as the proportion of the farms being converted increases. However, the use of biocides would increase, while the intervention cropping systems would have no significant effect on OM input, GHG emissions, and soil N losses. [ABSTRACT FROM AUTHOR]

Summary: Farmers around the world are increasingly vulnerable: climate variability is identified as the primary stressor, but unfavorable biophysical circumstances and disturbances in the socioeconomic domain (labor dynamics and price volatility) also affect farm management and production. To deal with these disturbances, adaptations are recognized as essential. Antifragility acknowledges that adaptations and volatility are inherent characteristics of complex systems and abandons the idea of returning to the pre-disturbance system state. Instead, antifragility recognizes that disturbances can trigger reorganization, enabling selection and removal of weaker system features and allowing the system to evolve toward a better state. In this study, we assessed the vulnerability of different types of smallholder farms in Bihar, India, and explored the scope for more antifragile farming systems that can 'bounce back better' after disturbances. Accumulation of stocks, creation of optionality (i.e., having multiple options for innovation) and strengthening of farmer autonomy were identified as criteria for antifragility. We had focus group discussions with in total 92 farmers and found that most expressed themselves to be vulnerable: they experienced challenges but had limited adaptive capacity to change their situation. They mostly made short-term decisions to cope with or mitigate urgent challenges but did not engage in strategic planning driven by longer-term objectives. Instead, they waited for governmental support to improve their livelihoods. Despite being confronted with similar challenges, four positive deviant farmers showed to be more antifragile: their diverse farming systems were abundant in stocks and optionality, and the farmers were distinguished in terms of their autonomy, competence, and connectedness to peers, the community, and markets. To support antifragility among regular farmers, adaptations at policy level may be required, for example, by shifting from a top-down toward a bottom-up adaptation and innovation regime where initiative and cooperation are encouraged. With a more autonomous orientation, farmers' intrinsic motivation is expected to increase, enabling transitions at the farm level. In this way, connected systems can be developed which are socioeconomically and biophysically adaptive. When practices, knowledge, and skills are continuously developed, an antifragile system with ample stocks and optionality may evolve over time. [ABSTRACT FROM AUTHOR]

The effect of the spatial resolution of digital terrain models (DTMs) on topography and soil erosion modelling is well documented for low resolutions. Nowadays, the availability of high spatial resolution DTMs from unmanned aerial vehicles (UAVs) opens new horizons for detailed assessment of soil erosion with hydrological models, but the effects of DTM resolution on model outputs at this scale have not been systematically tested. This study combines plot-scale soil erosion measurements, UAV-derived DTMs, and spatially explicit soil erosion modelling to select an appropriate spatial resolution based on allowable loss of information. During 39 precipitation events, sediment and soil samples were collected on five bounded and unbounded plots and four land covers (forest, fallow, maize, and eroded bare land). Additional soil samples were collected across a 220 ha watershed to generate soil maps. Precipitation was collected by two rain gauges and vegetation was mapped. A total of two UAV campaigns over the watershed resulted in a 0.60 m spatial-resolution DTM used for resampling to 1, 2, 4, 8, and 15 m and a multispectral orthomosaic to generate a land cover map. The OpenLISEM model was calibrated at plot level at 1 m resolution and then extended to the watershed level at the different DTM resolutions. Resampling the 1 m DTM to lower resolutions resulted in an overall reduction in slope. This reduction was driven by migration of pixels from higher to lower slope values; its magnitude was proportional to resolution. At the watershed outlet, 1 and 2 m resolution models exhibited the largest hydrograph and sedigraph peaks, total runoff, and soil loss; they proportionally decreased with resolution. Sedigraphs were more sensitive than hydrographs to spatial resolution, particularly at the highest resolutions. The highest-resolution models exhibited a wider range of predicted soil loss due to their larger number of pixels and steeper slopes. The proposed evaluation method was shown to be appropriate and transferable for soil erosion modelling studies, indicating that 4 m resolution (<5 % loss of slope information) was sufficient for describing soil erosion variability at the study site. [ABSTRACT FROM AUTHOR]

In Mexico, a political shift to fit into neoliberal directives since the 80s has brought several consequences to rural households and their trajectories. This study focused on the relation between drivers of change and household trajectory. The study was carried out in Santa Catarina Tayata, in the state of Oaxaca, Mexico. 44 household heads were interviewed to recall their production system and economic activities at significant moments. The results were summarized as variables describing the socio-economic and agronomic situation of households over three decades: 1988-1997, 1998-2007, and 2008-2017. Household types were identified for each decade. Three household types were distinguishable in the first two decades, and five in the last decade. Drivers of change such as international markets, land tenure, government support, and migration were connected to household trajectories. Results showed that household type diversity increased, while half of the households changed types at one point according to different strategies. Changes in land tenure in the study area in the early 2000s were attributable to the PROCEDE national program, which acted to lift restrictions on land tenure, facilitating the buying and selling of land. The implementation of PROCEDE resulted in fewer households relying on borrowed or rented land while allowing others to expand their land and form a new household type. A migration process enabled some households to invest in land or animal production as a "step up" strategy. The majority of households that received agriculture-related subsidies tended to engage solely in agricultural activities. Half of the households that did not benefit from subsidies engaged in off-farm to diversify their income, suggesting that the type of governmental support can have an impact on farming activities. Household types that had more animal production or larger areas did not tend to engage in off-farm activities because of high demands for labor in their production systems. This type of study can be used to monitor policy impact and households' strategic responses, to arrive at better articulation of policy objectives and policy impact while considering household type diversity. • Household types increased over time, as consequence of changes in land tenure and migration. • The proportion of household beneficiated from government support rose from 35 to 65%. • Households on and off-farm activities can be influenced by the type of governmental support. • Households with larger areas or with more livestock did not tend to engage in off-farm activities. [ABSTRACT FROM AUTHOR]

The effect of spatial resolution of digital terrain models (DTM) on topography and soil erosion modelling is well documented for low resolutions. Nowadays, the availability of high spatial resolution DTM from unmanned aerial vehicles (UAV) opens new horizons for detailed assessment of soil erosion with hydrological models, but effects of DTM resolution on model outputs at this scale have not been systematically tested. This study combines plot scale soil erosion measurements, UAV-derived DTM, and spatially explicit soil erosion modelling to select an appropriate spatial resolution based on allowable loss of information. During 39 precipitation events, sediment and soil samples were collected on five bounded and unbounded plots and four land covers (forest, fallow, maize, and eroded bare land). Additional soil samples were collected across a 220 ha watershed to generate soil maps. Precipitation was collected by two rain gauges and vegetation was mapped. Two UAV campaigns over the watershed resulted in a 0.60 m spatial resolution DTM used for resampling to 1, 2, 4, 8, and 15 m; and a multispectral orthomosaic to generate a land cover map. The OpenLISEM model was calibrated at plot level at 1 m resolution and then extended to the watershed level at the different DTM resolutions. Resampling the 1 m DTM to lower resolutions resulted in an overall reduction of slope. This reduction was driven by migration of pixels from higher to lower slope values; its magnitude was proportional to resolution. At the watershed outlet, 1 and 2 m resolution models exhibited the largest hydrograph and sedigraph peaks, total runoff and soil loss; they proportionally decreased with resolution. Sedigraphs were more sensitive than hydrographs to spatial resolution, particularly at the highest resolutions. The highest resolution models exhibited a wider range of predicted soil loss due to their larger number of pixels and steeper slopes. The proposed evaluation method showed to be appropriate and transferable for soil erosion modelling studies, indicating that 4 m resolution (< 5 % loss of slope information) was sufficient for describing soil erosion variability at the study site. [ABSTRACT FROM AUTHOR]

Agro‐food systems are undergoing rapid innovation in the world and the system's continuum is promoted at different scales with one of the main outcomes to improve nutrition of consumers. Consumer knowledge through educational outreach is important to food and nutrition security and consumer demands guide breeding efforts. Maize is an important part of food systems. It is a staple food and together with rice and wheat, they provide 60% of the world's caloric intake. In addition to being a major contributor to global food and nutrition security, maize forms an important part of the culinary culture in many areas of Africa, the Americas, and Asia. Maize genetics are being exploited to improve human nutrition with the ultimate outcome of improving overall health. By impacting the health of maize consumers, market opportunities will be opened for maize producers with unique genotypes. Although maize is a great source of macronutrients, it is also a source of many micronutrients and phytochemicals purported to confer health benefits. The process of biofortification through traditional plant breeding has increased the protein, provitamin A carotenoid, and zinc contents of maize. The objective of this paper is to review the innovations developed and promoted to improve the nutritional profiles of maize and outcomes of the maize agro‐food system. [ABSTRACT FROM AUTHOR]

SUMMARY: Understanding farm diversity is essential to delineate recommendation domains for new technologies, but diversity is a subjective concept, and can be described differently depending on the way it is perceived. Historically, new technologies have been targeted primarily based on agro-ecological conditions, largely ignoring socioeconomic conditions. Based on 273 farm households' surveys in Ethiopia, we compare two approaches for the delineation of farm type recommendation domains for crop and livestock technologies: one based on expert knowledge and one based on statistical methods. The expert-based typology used a simple discriminant key for stakeholders in the field to define four farm types based on Tropical Livestock Unit, total cultivated surface and the ratio of these two indicators. This simple key took only a few minutes to make inferences about the potential of adoption of crop and livestock technologies. The PCA-HC analysis included a greater number of variables describing the farm (land use, household size, cattle, fertilizer, off-farm work, hiring labour, production). This analysis emphasized the multi-dimensional potential of such a statistical approach and, in principle, its usefulness to grasp the full complexity of farming systems to identify their needs in crop and livestock technologies. A sub-sampling approach was used to test the impact of data selection on the diversity represented in the statistical approach. Our results show that diversity structure is significantly impacted according to the choice of a sub-sample of 15 of the 20 variables available. This paper shows the complementarity of the two approaches and demonstrates the influence of data selection within large baseline data sets on the total diversity represented in the clusters identified. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected all sectors and human activities around the World. In this article we present a first attempt to understand the immediate impact of COVID-19 and the sanitary measures taken by governments on farming systems in Central America and Mexico (CAM). Through a review of information generated in these initial months of the COVID-19 pandemic (webinars, blogs, electronic publications, media) and 44 interviews with key informants across the region, we have identified the main impacts felt by different types of farming systems in the region. From corporate agricultural production systems, to small and medium scale entrepreneurs and smallholder subsistence farm households, all types of farming systems were impacted, more or less severely, by the different measures implemented by governments such as reduced mobility, closure of public and private venues and restrictions in borders. Larger corporate farming systems with vertical market integration and high level of control or coordination within the supply chain, and smallholder or subsistence farming systems with important focus on production for self-consumption and little external input use, were both relatively less impacted and showed greater adaptive capacity than the medium and small entrepreneurial farming systems dependent on agriculture as their primary income and with less control over the upstream and downstream parts of their supply chain. All types of farming systems implemented a series of mechanisms to cope with the COVID-19 pandemic including the development of alternative value chains, food and agricultural products delivery systems and the exponential use of digital means to communicate and maintain the viability of the different agricultural systems. Collective action and organization of farmers also proved to be an important coping mechanism that allowed some farmers to acquire inputs and deliver outputs in the context of restricted mobility, price volatility, and general uncertainty. Some features of the CAM region played an important role in mediating the impact of COVID-19 and associated sanitary measures. We identify as particularly relevant the nature of agricultural exports, the current structure of the agricultural sector, the diversified livelihood strategies of rural households, and the importance of mobility for rural livelihoods. The results presented focus only in the immediate effect of COVID-19 pandemic and the mechanisms implemented by farmers in the first months. Whether these impacts and response mechanisms will result in a transformation of the farming systems towards greater resilience and sustainability is still an open question. [Display omitted] • COVID-19 pandemic has had differentiated impacts for distinct types of farming systems. • Small and medium scale entrepreneurial systems were the farming systems most affected in the CAM region. • Large scale and smallholder farming systems showed greater resilience to COVID-19 pandemic. • Great diversity of mechanisms was implemented by farmers and other actors in the agri-food system. • Special features of the agri-food system in the CAM region mediated the effect of COVID-19. [ABSTRACT FROM AUTHOR]

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. • A method to select locally relevant sustainability criteria. • Field monitoring complements farmers' perception of sustainability to select criteria. • Beyond standard economic criteria, farmers also valued social and environmental sustainability criteria. • Farmers' capacity to insure good crop establishment drives cropping system sustainability. [ABSTRACT FROM AUTHOR]

Maize is an important staple food that has cultural, economic, environmental, and nutritional impacts in the world. In addition to being a cornerstone crop for human nutrition, maize is in high demand due to global investments in animal feed and ethanol for biofuel. The inputs needed and highly valued outputs of the maize agro-food system can be aligned with the Sustainable Development Goals (SDGs), with the aim of supporting the production of nutritious maize for healthy humans. The purpose of this paper is to use the SDGs as a framework to illustrate to readers from multiple disciplines how the example of the maize agro-food system can be important in ensuring global food and nutrition security. • Maize is a staple crop with cultural, historical, and contemporary significance. • The maize agro-food system requires substantial agricultural inputs. • Maize production is essential for global food security. • Maize contributes to food and nutrition security in populations worldwide. • Agro-food systems should align with the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Integration of farms into markets with adoption of maize as a cash crop can significantly increase income of farms of the developing world. However, in some cases, the income generated may still be very low and maize production may also have strong negative environmental and social impacts. Maize production in northern Laos is taken as a case to study how far can farms' performance be improved with improved crop management of maize with the following changes at field level: good timing and optimal soil preparation and sowing, allowing optimal crop establishment and low weed infestation. We compared different farm types' performance on locally relevant criteria and indicators embodying the three pillars of sustainability (environmental, economic and social). An integrated assessment approach was combined with direct measurement of indicators in farmers' fields to assess eleven criteria of local farm sustainability. A bio-economic farm model was used for scenario assessment in which changes in crop management and the economic environment of farms were compared to present situation. The farm model was based on mathematical programming maximizing income under constraints related to i) household composition, initial cash and rice stocks and land type, and ii) seasonal balances of cash, labour and food. The crop management scenarios were built based on a diagnosis of the causes of variations in the agronomic and environmental performances of cropping systems, carried out in farmers' fields. Our study showed that moderate changes in crop management on maize would improve substantially farm performance on 4 to 6 criteria out of the 11 assessed, depending on farm types. The improved crop management of maize had a high economic attractiveness for every farm type simulated (low, medium and high resource endowed farms) even at simulated production costs more than doubling current costs of farmers' practices. However, while an improvement of the systems performance was attained in terms of agricultural productivity, income generation, work and ease of work, herbicide leaching, improved soil quality and nitrogen balance, trade-offs were identified with other indicators such as erosion control and cash outflow needed at the beginning of the cropping season. Using farm modelling for multicriteria assessment of current and improved maize cropping systems for contrasted farm types helped capture main opportunities and constraints on local farm sustainability, and assess the trade-offs that new options at field level may generate at farm level. [Display omitted] • We applied a novel method to explore effects of changes in cropping systems on farm sustainability. • Farm modelling was combined with direct measurement to jointly feed a multicriteria assessment of maize-based farms. • The farm model provided insights on the feasibility of changes in cropping systems. • Optimal soil preparation and sowing for maize fields appeared key to improve farms' sustainability in a study case in Laos. • In Laos, moderate changes in crop management would improve farm income while reducing herbicide leaching risks. [ABSTRACT FROM AUTHOR]

Climate change scenarios suggest largely detrimental impacts on agricultural production from a deterioration of renewable natural resources. Over the last 15 years, a new field of research has focused on the interactions between climate and conflict risk, particularly as it relates to competition over natural resources and livelihoods. Within this field, there has been less attention to the potential for resource competition to be managed in ways that yield greater cooperation, local adaptation capacity, social-ecological resilience, and conflict mitigation or prevention. The challenge of increasing social-ecological resilience in small-scale agriculture is particularly acute in the socioeconomically and agroecologically marginalized Western Highlands of Guatemala. Not only is climate change a threat to agriculture in this region, but adaptation strategies are challenged by the context of a society torn apart by decades of violent conflict. Indeed, the largely indigenous population in the Western Highlands has suffered widespread discrimination for centuries. The armed conflict has left a legacy of a deeply divided society, with communities often suspicious of outsider interventions and in many cases with neighbors pitted against each other. We use the example of the Buena Milpa agricultural development project to demonstrate how grassroots approaches to collective action, conflict prevention, and social-ecological resilience, linking local stakeholder dynamics to the broader institutional and governance context, can bear fruit amidst postconflict development challenges. Examples of microwatershed management and conservation of local maize varieties illustrate opportunities to foster community-level climate adaptation strategies within small-scale farming systems even in deeply divided societies. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important staple foods as it provides 55% of the carbohydrates and 20% of the food calories and protein consumed worldwide. Demand for wheat is projected to continue to grow over the coming decades, particularly in the developing world, to feed an increasing population. More than 22% of global area under wheat is located in South Asia which is home to about 25% of the world's population. The Intergovernmental Panel on Climate Change has projected that in the 21st century South Asia is going to be hit hard by climate change. Changes in mean annual temperature will exceed 2°C above the late-20th-century baseline and there can be declines in the absolute amount of precipitation during December to February, when wheat is grown in the region. Temperature, precipitation, and enhanced CO2 level in the atmosphere, the three climate change drivers can affect wheat cropping both directly at plant level and indirectly through changes in properties and processes in the soil, shifts in nutrient cycling, insect pest occurrence, and plant diseases. Studies pertaining to the effects of climate change on soil processes and properties are now becoming available and it is becoming increasingly clear that climate change will impact soil organic matter dynamics, including soil organisms and the multiple soil properties that are tied to organic matter, soil water, and soil erosion. Warmer conditions will stimulate soil N availability through higher rates of mineralization so that fertilizer management in wheat is also going to be governed by emerging climate change scenarios. Similarly, higher temperatures and altered precipitation regimes will determine the net irrigation water requirements of wheat. Several simulation models have projected reduced wheat yields in the emerging climate change scenarios, but occurrence of an extreme heat event around senescence can lead to crop models to underestimate the effects of heat on senescence by as much as 50% for late sowing dates for 2°C rise in mean temperature. So as to project productivity of wheat in South Asia in the emerging climate change scenarios with increased certainty, integrated holistic modeling studies will be needed which also take into account effect of extreme heat events as well as the contribution of altered soil processes and properties. [ABSTRACT FROM AUTHOR]

Several resource use efficient technologies and practices have been developed and deployed to address the challenges related to natural resource degradation and climatic risks management in rice-wheat (RW) rotation of Indo-Gangetic Plains (IGP). However, the practices applied in isolation may not be effective as much as in combination due to changing input responses under varied weather abnormalities. Therefore, a multi-location farmer’s participatory strategic research was conducted to evaluate the effects of layering key technologies, practices and services in varied combinations and compared with business as usual (farmer’s practice) for productivity (crop, water and energy), profitability and global warming potential (GWP) in a RW system. Altogether, six scenarios were compared that includes; Farmer’s practice (FP); Improved FP (IFP) with low intensity of adaptive measures; IFP with high intensity of adaptive measures (IFP-AM); Climate smart agriculture (CSA) with low intensity of adaptive measures (CSA-L); CSA with medium intensity of adaptive measures (CSA-M); CSA with high intensity of adaptive measures (CSA-H). Results revealed that climate smart agricultural practice with high intensity of adaptive measures (CSA-H) recorded 7–9 and 19–26% higher system productivity and profitability, respectively compared to farmers’ practice in all the three years. CSAPs (mean of CSA-L, CSA-M and CSA-H) improved the system productivity and profitability by 6 and 19% (3 yrs’ mean) whereas, IFPs (mean of IFP and IFP-AM) by 2 and 5%, respectively compared to farmer’s practice (11.79 t ha −1 and USD 1833 ha −1 ). CSA with high (CSA-H) and medium (CSA-M) intensity of adaptive measures saved 17–30% of irrigation water and improved irrigation and total water productivity (WP I and WP I+R ) by 29–54 and 21–38%, respectively compared to FP in the study years. Across the years, CSA-H improved the energy-use-efficiency (EUE) and energy productivity (EP) by 43–61 and 44–56% respectively, compared to farmers’ practice. On 3 years mean basis, CSA-H lowered global warming potential (GWP) and greenhouse gas intensity by 40 and 44% respectively, compared to FP (7653 kg CO 2  eq ha −1  yr −1 and 0.64 kg kg −1  CO 2  eq ha −1  yr −1 ). On 3 years mean basis, our study revealed that CSA with high intensity of adaptive measures (CSA-H) increased 8% in system productivity, 23% in profitability, 31% in total water productivity and 53% in energy productivity with 24% less water while reducing the GWP by 40%. The improvement in yield, income as well as use efficiency of water and energy and reduction in GHGs was increasing with layering of portfolio of practices on farmers’ practice. This study helps in prioritizing the technological practices from the portfolio of CSAPs for maximizing crop productivity, profitability and input use efficiency while improving the adaptive capacity and reducing the environmental footprints. [ABSTRACT FROM AUTHOR]

Analyses of yield gaps i.e. the difference between observed and attainable crop yields in a given location, have raised expectations of significant potential progress in crop productivity in sub-Saharan African countries. However, an important question remains unanswered: Are those biophysically-determined attainable yields possible given the socio-economic context of farming systems in sub-Saharan Africa? In this study, we explored the potential increase in efficiency of crop production given the diversity of farming systems and livelihood strategies for the case study of smallholder farmers in Malawi. We implemented a non-parametric frontier efficiency method, Data Envelopment Analysis (DEA), which allows for the assessment of technical efficiency with respect to a production frontier. The frontier efficiency is based on best-performing farms in terms of input minimization and output maximization. Based on survey data of 102 households, we first built a typology of farming systems and distinguished two types, i.e. “maize-based smallholders under land pressure” (type 1) and “diversified crop-livestock producers” (type 2). By comparing results from farming system type-specific frontiers with those from an enveloping meta-frontier, we showed that the efficiency yield gap was overestimated by 13% in the case of the meta-frontier approach. Moreover, based on observed farming system-specific livelihood strategies, we defined different directions for reaching the efficiency frontier. For type 1 farming system, we assumed efficiency increase through reduction of both labor and inputs. For type 2 farming system, as income was mainly derived from agricultural activities, we assumed that efficiency increase could be achieved through increase in outputs, i.e. total calorie production from all cultivated crops. We quantified efficiency scores and identified their determinants to provide more specific recommendations on the levers for action to increase efficiency of crop production. Common determinants for both farming system types were adult equivalents in the household and specific efficiency determinants were percentage of cultivated land and average walking time to fields for the type 1 farming system, and farmer’s age and percentage of cultivated land allocated to groundnuts for the type 2 farming system. It is clear that maize-based smallholder farmers under land pressure have little room for improvement of crop yields, and assessing potential gains through more efficient input use is more appropriate than increasing crop yields per se. In this context, a more rational strategy for improving livelihoods is to stimulate labor markets for off-farm income, rather than pursuing increased crop production by closing the yield gap. [ABSTRACT FROM AUTHOR]

Alternative agricultural systems, such as organic farming (OF), are promising options to sustain both agriculture productivity and environmental health. However, the adoption of OF by farmers is occurring more slowly than is advocated. A key factor limiting farmers is an inability to predict socio-economic consequences of converting to OF. To overcome this, we developed a novel method of integrated assessment of agricultural systems (IAAS) and applied it to scenarios of development of OF in the Camargue region, South of France. In collaboration with the local stakeholders, we characterized the agricultural systems at different spatial scales and defined scenario related to the future of agriculture and to OF. We then used agent-based modeling with farmers and bio-economic modeling with local stakeholders for scenario assessment. We examined the effects on the development of OF systems of key factors such as the ongoing reform in the European Common Agricultural Policy and the effects of regulations for decreased use of pesticides. The policy reform implied trends towards a diversification of crops and greater possibility for conversion to OF. Development of OF at the regional level led to improved environmental performance, but caused a decrease in profitability of the rice supply chains. In light of the observed trade-off between rice production and OF development, objectives and options towards more sustainable agricultural systems were discussed with farmers and local stakeholders. Stakeholders' assessment of the framework provided insights on the positive and specific aspects of the IAAS methodology requiring improvement. The complementarities of agent-based and bio-economic modeling provide stakeholders with a better-informed assessment of diverse scenarios, for the development of more sustainable agricultural systems. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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%. 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. [Display omitted] • Food security analysis with current and profit-maximizing N input in East Africa • Food secure households are main contributors of national increase in grain supply • Food insecure households also benefit for their food security • Variations in maize and fertilizer price impact strongly opportunities to reduce food insecurity [ABSTRACT FROM AUTHOR]

Improving farming systems in resource-poor contexts is often difficult as farmers face multiple challenges to implement the innovations developed by researchers. Viable solutions may however be present within local communities by positive deviant farmers, i.e. farmers that outperform positively compared to others. This study develops a positive deviance informed methodology to support redesign of farming systems, with the aim to improve farm productive, economic and environmental performances. We tested the methodology in Bihar, India, using survey data from 43 farms and the indicators of operating profit, soil organic matter balance, water use and dietary energy production. Positive deviant farms and practices were first identified and then recombined into a redesigned farm in consultation with farmers. The FarmDESIGN model was used to calculate current farm performance and to explore potential alternative farm configurations in the redesign. We found that outstanding performance on all indicators could only be reached by integrating high livestock density with an optimal combination of crop practices, which confirms the key role of interactions among components in mixed crop-livestock systems to improve all dimensions of farm sustainability. The redesigns outperformed all real farms on the indicators assessed. Farmers confirmed the viability of the redesigns in focus group discussions and their suggestions can serve as useful input for a next cycle of farm redesign. Since all suggestions are locally practiced and have proven to be accessible, affordable and recognizable, we conclude that our methodology based on positive deviant farms and practices yields promising results with a large potential to boost agricultural development for resource-scarce smallholder farmers. • We integrate the positive deviance approach with redesign of farming systems. • Redesign allowed doubling indicator performance relative to the population mean. • Indicators only improved when combining high livestock density and crop practices. • Redesign is based on best practices present within community and therefore viable. • Farmers' feedback was obtained in focus groups to inform a new cycle of redesign. [ABSTRACT FROM AUTHOR]