Search

IntroductionThe combined populations of China and India were 2.78 billion in 2020, representing 36% of the world population (7.75 billion). Wheat is the second most important staple grain in both China and India. In 2019, the aggregate wheat consumption in China was 96.4 million ton and in India it was 82.5 million ton, together it was more than 35% of the world's wheat that year. In China, in 2050, the projected population will be 1294–1515 million, and in India, it is projected to be 14.89–1793 million, under the low and high-fertility rate assumptions. A question arises as to, what will be aggregate demand for wheat in China and India in 2030 and 2050?MethodsApplying the Vector Error Correction model estimation process in the time series econometric estimation setting, this study projected the per capita and annual aggregate wheat consumptions of China and India during 2019-2050. In the process, this study relies on agricultural data sourced from the Food and Agriculture Organization of the United States (FAO) database (FAOSTAT), as well as the World Bank's World Development Indicators (WDI) data catalog. The presence of unit root in the data series are tested by applying the augmented Dickey-Fuller test; Philips-Perron unit root test; Kwiatkowski-Phillips-Schmidt-Shin test, and Zivot-Andrews Unit Root test allowing for a single break in intercept and/or trend. The test statistics suggest that a natural log transformation and with the first difference of the variables provides stationarity of the data series for both China and India. The Zivot-Andrews Unit Root test, however, suggested that there is a structural break in urban population share and GDP per capita. To tackle the issue, we have included a year dummy and two multiplicative dummies in our model. Furthermore, the Johansen cointegration test suggests that at least one variable in both data series were cointegrated. These tests enable us to apply Vector Error Correction (VEC) model estimation procedure. In estimation the model, the appropriate number of lags of the variables is confirmed by applying the “varsoc” command in Stata 17 software interface. The estimated yearly per capita wheat consumption in 2030 and 2050 from the VEC model, are multiplied by the projected population in 2030 and 2050 to calculate the projected aggregate wheat demand in China and India in 2030 and 2050. After projecting the yearly per capita wheat consumption (KG), we multiply with the projected population to get the expected consumption demand.ResultsThis study found that the yearly per capita wheat consumption of China will increase from 65.8 kg in 2019 to 76 kg in 2030, and 95 kg in 2050. In India, the yearly per capita wheat consumption will increase to 74 kg in 2030 and 94 kg in 2050 from 60.4 kg in 2019. Considering the projected population growth rates under low-fertility assumptions, aggregate wheat consumption of China will increase by more than 13% in 2030 and by 28% in 2050. Under the high-fertility rate assumption, however the aggregate wheat consumption of China will increase by 18% in 2030 and nearly 50% in 2050. In the case of India, under both low and high-fertility rate assumptions, aggregate wheat demand in India will increase by 32-38% in 2030 and by 70-104% in 2050 compared to 2019 level of consumption.DiscussionsOur results underline the importance of wheat in both countries, which are the world's top wheat producers and consumers, and suggest the importance of research and development investments to maintain sufficient national wheat grain production levels to meet China and India's domestic demand. This is critical both to ensure the food security of this large segment of the world populace, which also includes 23% of the total population of the world who live on less than US $1.90/day, as well as to avoid potential grain market destabilization and price hikes that arise in the event of large import demands.

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.

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.

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.

The Western Highlands of Guatemala are dominated by small-scale farming in indigenous communities. Mountain conditions and overpopulation have led to expansion of the agricultural frontier with con...

Due to complexity of smallholder farms, many times technologies with great potential fail to achieve the desired impact in leveraging productivity and profitability of the farming community. In the Indo-Gangetic Plains there is an urgent need to understand the diversity of farm households, identifying the main drivers deciding their system thus, classifying them into homogenous groups. In the present study, the diversity of smallholder farms was assessed using crop, livestock and income related characteristics and associated farm mechanization. Using principal component analysis and cluster analysis for 252 farm households, 4 farm types were identified i.e. Type 1. Small Farm households with cereal-based cropping system and subsistence livestock (39%), Type 2. Small Farm households with diversified cropping system dominated by cereal and fodder crops with only cattle herd (9%), Type 3. Marginal Farm household with diversified cropping system dominated by cash crop and herd comprising of only cattle (39%), Type 4. Marginal Farm household with diversified cropping system dominated by cereal crops and herd dominated by small ruminants (12%). Based on the constraints identified for different components of farming systems, low-cost interventions were planned for each farm type. These interventions have resulted in 84.8–103.2 per cent increase in the income of the farm HH under study suggesting usefulness of typology-based intervention planning in increasing income of small farm holders.

Changes in social and environmental conditions in the Western Highlands of Guatemala undermine food security and job opportunities. We describe how targeted assistance can build upon traditional agricultural systems to increase adaptive capacity, improve nutrition, provide jobs and thereby reduce pressures to migrate.

Food security is a major challenge in Guatemala, one of the poorest countries in the world. Food insecurity is concentrated in the Western Highlands of Guatemala (WHG) where indigenous communities have been the main victims of social, political and economic marginalization. In this study we characterize the diversity of farming households in the WHG, identify the main sources of food for different types of farm households and assess their food security status through a simple, yet robust, potential food availability indicator. Based on a large and rich dataset of nearly 5000 farm households, our results show the diversity of farming systems in the region, dominated by maize and coffee production, as well as the large differences in their potential food availability. In our model, 52% of farm households in the WHG did not have the means to attain sufficient energy from their agricultural activities. In general, diversified maize-based, coffee-based and specialized coffee farm households had larger proportions of potentially food secure households with 60%, 83% and 74% food secure households, respectively. This contrasted with farm households specialized in maize production and resource-constrained households where there were a greater proportion of households were food insecure. The analytical framework presented here, combining a typology of farm households and their livelihoods with the analysis of their food security status, provides a useful approach for better targeting development interventions towards combating hunger, poverty and malnutrition.

In Mexico, maize forms of production range from industrial scale with hybrid maize, to smallholder farmers who mainly grow local varieties, contributing significantly to the national provision for human consumption. The objective of this study was to describe the local value chains of native maize in a peri-urban community in the east of the Valley of Mexico, exploring its contribution to the livelihoods of the families living within 50 km of Mexico City. Through a mixed quantitative (320 surveys) and qualitative (53 interviews) analysis, we found that more than 60% (250 tons) of the annual production of native maize in San Juan is locally consumed as tortillas: firstly, manually transformed by the women of the village for auto-consumption and local sale, and secondly, mixed by tortillerias with hybrid maize from other regions. More than 400 families in this locality (approximately one third) are directly involved in the production-transformation-consumption chain of native maize. Of the maize producing families, half consume their own product. There are 70 women involved in transformation that thereby obtain 75% more value for the native maize grain. More than a quarter of the tortillas consumed by the population originate in this local production and transformation. Based on this study, in the national current context of food supply and agricultural development, the development of these local markets that are economically equitable is a viable economic model that limits the number of intermediaries and favors diversity of the raw materials, products and actors involved in transformation and distribution.

We present an assessment of the extent, diversity, and nutritional contribution of the milpa through a quantitative analysis of data from a survey conducted in 989 small scale farm households in the Western Highlands of Guatemala (WHG). The milpa is a traditional agricultural system in which maize is intercropped with other species, such as common beans, faba beans, squashes or potatoes. Our study shows that more than two-thirds of the 1,205 plots recorded were under the milpa system, with a great diversity of crop combinations. As shown with the 357 plots for which specific yields were available, milpa systems present higher total productivity than monocropped maize, expressed as total energy yield of the harvested crops in the respective system, and were also better at providing the recommended daily allowances of fourteen essential nutrients, based on a Potential Nutrient Adequacy (PNA) indicator. Maize-bean-potato, maize-potato, and maize-bean-faba intercrops had the highest PNAs, and monocropped maize, the lowest. These results support the implementation of milpa systems tailored to different agro-ecologies in order to improve nutrition in the WHG and a variety of similar regions.

Context The COVID-19 pandemic has been affecting health and economies across the world, although the nature of direct and indirect effects on Asian agrifood systems and food security has not yet been well understood. Objectives This paper assesses the initial responses of major farming and food systems to COVID-19 in 25 Asian countries, and considers the implications for resilience, food and nutrition security and recovery policies by the governments. Methods A conceptual systems model was specified including key pathways linking the direct and indirect effects of COVID-19 to the resilience and performance of the four principal Asian farming and food systems, viz, lowland rice based; irrigated wheat based; hill mixed; and dryland mixed systems. Based on this framework, a systematic survey of 2504 key informants (4% policy makers, 6% researchers or University staff, 6% extension workers, 65% farmers, and 19% others) in 20 Asian countries was conducted and the results assessed and analysed. Results and conclusion The principal Asian farming and food systems were moderately resilient to COVID-19, reinforced by government policies in many countries that prioritized food availability and affordability. Rural livelihoods and food security were affected primarily because of disruptions to local labour markets (especially for off-farm work), farm produce markets (notably for perishable foods) and input supply chains (i.e., seeds and fertilisers). The overall effects on system performance were most severe in the irrigated wheat based system and least severe in the hill mixed system, associated in the latter case with greater resilience and diversification and less dependence on external inputs and long market chains. Farming and food systems' resilience and sustainability are critical considerations for recovery policies and programmes, especially in relation to economic performance that initially recovered more slowly than productivity, natural resources status and social capital. Overall, the resilience of Asian farming and food systems was strong because of inherent systems characteristics reinforced by public policies that prioritized staple food production and distribution as well as complementary welfare programmes. With the substantial risks to plant- and animal-sourced food supplies from future zoonoses and the institutional vulnerabilities revealed by COVID-19, efforts to improve resilience should be central to recovery programmes. Significance This study was the first Asia-wide systems assessment of the effects of COVID-19 on agriculture and food systems, differentiating the effects of the pandemic across the four principal regional farming and food systems in the region.

Land use change results from top-down drivers, such as policies, trade, and migration. Land use change may also result from community-based responses. In Mexico, rural communities govern most of the country's forests. This study aimed to assess how socio-economic and biophysical factors affected the landscape trajectories of rural communities in southern Mexico. It also aimed at evaluating the role of communities in landscape change. Land use change of 63 rural communities was analyzed for the years 1987 and 2017. Four land uses were distinguished: forest, shrubland, agriculture, and bare soil. Five groups of communities were identified according to their socio-economic and biophysical factors. Two groups located in areas with high slopes and elevated marginalization index values showed deforestation patterns. Two other groups, consisting of more than half of the municipalities assessed, showed reforestation trends. The final group did not reveal major changes in land use. Two municipalities with reforestation trends were selected for an in-depth analysis of how community-based responses impacted natural resource management and conservation. Through local assemblies, the population voted for regulations that increased the forest area and reduced the bare soil. There was no evidence that these regulations affected croplands. These results show how a combination of socio-economic and biophysical factors can affect landscape change, but it also shows the often overlooked role of communities as a relevant bottom-up driver of change.

Around 30% of global food is produced by smallholder farmers, yet they constitute the most food-insecure group. In Mexico, food self-sufficiency is declining. Rural policies in the country have stimulated the production of cash crops to the detriment of the traditional intercropping system, themilpa. Such a decline may have negative consequences for the food security of subsistence farmers. This study aimed to assess changes in nutritional self-sufficiency over the last 30 years and the role ofmilpasystems in food security for two communities in the highlands of Oaxaca, Mexico. The study used satellite images, censuses, and field data to estimate food production. Three cropping systems, monoculture of maize, monoculture of common bean, and themilpawere compared in terms of nutrients and vitamins produced. Furthermore, a household typology was developed for each community to contrast nutritional self-sufficiency levels between the different household types. Results showed that themilpaproduced more volume of food per area compared to the other systems. Themilpaalso produced all the nutrients and vitamins (except for B12) required to feed at least 2 persons ha-1. Monocultures of maize lacked vitamins A, B9, B12, and C, and the common bean lacked vitamins A, B12, and C. While farmers recognized the importance of themilpa, they preferred monocultures due to the reduced labor demands of this system. Households that obtained most of their income from off-farm activities had the lowest nutritional self-sufficiency. Enhancing nutritional self-sufficiency through crop diversification has the potential to not only improve the nutrition of subsistence farmers, but also to enhance ecosystem service provision, promote biodiversity conservation and restoration, and improve resilience to climate change.

CONTEXT The COVID-19 pandemic has affected all sectors and human activities around the World. OBJECTIVE 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). METHODS 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. RESULTS AND CONCLUSIONS 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. SIGNIFICANCE 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.

Highlights • Stakeholders’ engagement in the assessment process helps build capacity. • Stakeholders help define local sustainability goals, opportunities, and indicators. • Local champions, coordination, and sustained support are key to success. • Timely monitoring and continual improvement are part of the iterative approach. • Trust, engagement, communication, and capacity are required for improvement., A systematic process for assessing progress toward landscape sustainability goals is developed and tested. Application of the approach builds capacity and promotes continual improvements in management practices, thus enabling timely action to address changing conditions while progressing toward locally defined goals. We consider how the approach applies to agricultural landscapes, that is farm ecosystem interactions with the environment and human well-being. We present lessons learned from applying the assessment approach in two contrasting situations: large, high-input, commercial agriculture in northwestern Mexico and small, low-input family farms in the Western Highlands of Guatemala. Applying the approach reveals five attributes required for success and the means to achieve those conditions. (1) Having a capable local champion for the project is critical. (2) Implementation of the approach must be in concert with local people and organizations as well as with regional and national policies and programs. (3) Identification and engagement of key stakeholders is essential. (4) Application of the approach is not meant to be a one-time effort but rather an ongoing and systematic process. (5) Engagement and buy-in from stakeholders including multiple agency levels is essential for allocation of necessary resources and logistic support in the continuing implementation of the approach.

In the western highlands of Guatemala, the indigenous population is one of the most marginalized communities. The food security of subsistence and infrasubsistence smallholders within this population still relies on domestic agricultural production as the principal livelihood activity and the main source of food. Smallholder production systems in the region are complex, with multiple interacting subsystems functioning at different integration levels and with different temporal dynamics. Previous results, based on a data set of nearly 5,000 farm households using a robust food availability indicator (that quantifies agricultural production, consumption, transformation, and commercialization of produce), have shown large differences in food security between farmers in the western highlands of Guatemala. Another important finding was that more than half of the farm households do not have the means to produce enough energy for home consumption from their agricultural activities. Identifying the constraints, patterns, and underlying processes driving food security could give rise to a set of easy-to-measure variables that quickly and reliably assess household food security status; such a tool would be helpful for decision and policy makers trying to focus on actions more likely to succeed in improving food security in the region. In this study, we developed a predictive model of food security, through the application of machine learning techniques, to identify the most important factors, and their interactions, which account for variability in food security. The resulting “artificial neural network” model performed well, explaining nearly 85% of the variance in food security status. By applying different sensitivity analysis methods, we were able to detect highly non-linear interactions between the different factors driving food security. Land availability per person is detected as the main constraint for attaining food security. The median value of land availability per person in the region is 0.085 ha, explaining why 52% of the farm household population does not produce enough food energy from agriculture. Many other interactions were found between crop land allocation, diversity, yields, and food security, which can help to target interventions to improve the food security status in the region.

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.

Methods to construct farm household typologies may differ in their approach but they share a common feature: they rely mostly on structural farm data. Methods to build functional typologies are far less abundant. Households or communities are complex social-ecological systems that often exhibit patterns in their response to disturbances (e.g., droughts, floods, price shocks, policy change). We propose building functional typologies – classifiable ´behaviours´ of rural actors – using archetype analysis to categorise such responses and identify salient behaviours. We illustrate this approach by examining the response of rural households to recurrent droughts in northern Patagonia, Argentina, as revealed by 23 in-depth interviews about perceptions and strategies concerning droughts. Using the algorithm from Mørup and Hansen (2012), and the corrected Akaike decision rule, we identified three archetypes (A–C) that represented three distinct aggregated responses of households to droughts. Archetype A presented variable degrees of resistance to droughts (production losses < 40%), avoidance and diversification, whereas archetype B exhibited tolerance (higher losses) and some degree of transformability (off-farm income). Archetype C farms exhibited high levels of vulnerability, if some degree of tolerance to high losses, but no real adaptive strategy to speak of. Such pattern could not have been identified through the more commonly used typology building methods such as using multivariate and clustering techniques. By allowing to combine qualitative and quantitative information, and to deal with a relatively small number of observations, archetype analysis appears as highly suitable to delineate functional farm household typologies. Fil: Tittonell, Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Solano Hernández, Ainhoa. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: López Ridaura, Santiago. Centro Internacional de Mejoramiento de Maíz y Trigo; México Fil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina

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.

Agriculture in rural Bihar needs to maintain its productivity while enhancing its biophysical sustainability. To sustainably intensify the predominant rice-wheat systems, alternative cropping patterns with short duration legumes, planted in the usually fallow summer season, were developed. The addition of the legume ensures near-permanent soil cover, breaks the cereal-cereal cycle, and aims to improve soil quality while yielding between 0.6 and 1.2 Mg ha-1 of protein-rich dry matter. On-station experiments previously demonstrated the agronomical feasibility of the alternative cropping patterns while this study addresses the implications of such field-level changes at farm-level for different types of smallholders. We used the model FarmDESIGN to 1) assess current farm performance, 2) explore options to rearrange cropping patterns and 3) assess the impact of cropping patterns with mung bean. We found diverse farm performances, indicating a heterogenous farming community. Re-arranging current cropping patterns gave all farms possibilities to save water, increase soil organic matter content and decrease nitrogen losses but showed trade-offs with operating profit. Higher resource endowed farms had most potential to favourably rearrange the farm and improve multiple performance indicators. Two out of the five farms assessed did not benefit from including the alternative cropping patterns. We conclude that the impact of innovations greatly depends on farm type and current farm features and performance, described by a farm typology.

Research and development agencies, as well as policy makers and agri-food enterprises, need reliable data to support informed decisions that can improve the sustainability of agricultural landscapes. We present a review of agricultural sustainability assessment frameworks (ASAF) that identifies the features most relevant to monitoring progress towards sustainability goals for agricultural landscapes. This qualitative review considers a variety of approaches for defining goals and for selecting stakeholders, spatial and temporal boundaries, indicators, and analytical approaches. We focused on assessment frameworks that (i) include environmental, social, and economic implications of agriculture; (ii) are applicable to multiple, non-specified farm system types; (iii) are described in an English language, peer-reviewed publication; (iv) have been developed for use at a farm system to regional spatial scale; (v) engage stakeholders; (vi) provide case studies; and (vii) could be used in a variety of contexts across the globe. Based on the review, we provide recommendations for further development and use of assessment frameworks to better address the needs of agricultural research, extension, and development organizations. We recommend an agro-ecosystem approach to help stakeholders identify appropriate indicators for their situation. Assessment methods need to be flexible enough for adaptation to a spectrum of agricultural landscapes and changing environmental conditions, and remain relevant as farmers and other stakeholders acquire new information, resources, and different management techniques. We find that to address information gaps across different scales from farm to region will require creativity and some reliance on local knowledge systems to support adaptive management. Assessment results should communicate relationships among ecosystem services and socio-economic activities affected by agricultural landscapes. Visualization tools can facilitate understanding of trade-offs and synergies among sustainability goals as reflected by individual indicators.

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.

The increasingly complex challenges facing agricultural systems require problem-solving processes and systems analysis (SA) tools that engage multiple actors across disciplines. In this article, we employ the theory of affordances to unravel what tools may furnish users, and how those affordances contribute to a tool's usefulness in co-design and co-innovation processes. Affordance is defined as a function provided by an object through an interaction with a user. We first present a conceptual framework to assess the affordances of SA tools. This framework is then applied in a literature review of three SA tools used in agricultural systems research (fuzzy cognitive mapping, bio-economic whole-farm models, and role play and serious games). Through this exercise, we extend the SA tool design and implementation dialogue by illuminating (i) links between lower-level affordances, tool design, and heuristic functioning, and (ii) the central role of use setting and facilitation in mobilizing higher-level, productive affordances. Based on our findings, we make five propositions for how SA tool design and implementation in participatory problem-solving settings can be improved.

Among the most significant impacts of climate change is the potential increase of food insecurity. The predicted impact of temperature rise due to climate change on the crop production and productivity can be mediated through different crop management adaptations such as shifted sowing dates. We investigated the effects of sowing dates on yield stability of wheat across agro-ecosystems and years using multi-environment trials. The objectives of the study were as follows: (i) to evaluate the genotype × environment × management (G × E × M) for wheat genotypes, (ii) to predict yield performance and identify high stable wheat genotypes in different management practices, and (iii) to make genotype-specific management and high performing genotype recommendations within and across agro-ecological regions. A diverse set of twenty-one genotypes was evaluated over three years (2012–2014) under ten levels of crop management practices (ten different dates of sowing: D01-D10) across three agro-ecological regions (BR, MP and PB) of India in replicated trials. Data were analyzed with SASG × E and RG × E programs using SAS and R programming languages, respectively. Results revealed that the impact of shifted sowing dates on yield stability was unevenly spread across management practices. Across locations, the genotype ‘CSW 18’ (G03), ‘DPW 621-50’ (G05), ‘BAZ’ (G01) were the best performer and high stable in early, normal and later sowing dates, respectively. Across and within an individual location(s), the pattern of predicted yield suggests that the low performing genotypes during early sowing dates tend to became high performer during late sowing dates. Similarly, high predictive yield and high stable genotypes from early planting tend to have variable predicted yield with low stability during normal and late sowing dates for across and within an individual location(s). Low predictive yield and low stable genotypes had disease resistant genes and, thus, can be served as parent for future breeding, where trait value low is desired.

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 (WPI and WPI+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 CO2 eq ha−1 yr−1 and 0.64 kg kg−1 CO2 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.

Farmer profit can be increased and air quality improved

Conservation Agriculture (CA) is promoted by research and development (R&amp, D) agencies to sustainably intensify agricultural systems with the goals of improving food security and livelihoods and adapting food systems to global climate change. Despite the many benefits of CA, there are few farmers around the world that have simultaneously implemented all facets of the strategy. In part, this reflects the challenges in applying, adapting, and understanding this complex and multi-dimensional agricultural innovation in the context of diverse farming systems. In this paper, we applied an integrated framework that combines bioeconomic simulation, risk analysis, adoption theory, and impact assessment to investigate how various combinations of CA components (no-tillage, soil cover, crop diversification, plus growing a new crop or variety) performed over a 10-year period in representative farms in a central Mexican case study. We found significant differences in profit, net value, downside risk, and risk-aversion cost between double-component scenarios (and improved CA to a lesser extent) and all other scenarios, which suggested that disaggregating CA into smaller component packages could increase farmer adoption in risky contexts. Our findings provided valuable insights on CA feasibility and could help establish policy and reporting metrics. The study highlighted the need for employing a range of research tools to understand the relative value of agricultural innovations and to identify and reduce trade-offs and uncertainty in farming systems.

The western highlands of Guatemala lie within the area where maize was first domesticated, and maize remains central to farmers' livelihood security. Over 50% of the population in the region are in poverty, and over 48% suffer from chronic malnutrition. Development efforts have focused on improved land management, crop diversification, and improved access to markets, especially for high-value vegetable crops such as snow peas. As a result of successful initiatives worldwide, more attention is being directed at the extent to which farmers can benefit from market opportunities for indigenous crops by receiving a price premium for providing the environmental service of conserving agricultural biodiversity. Such an approach bridges the gap between poverty alleviation and in situ conservation. We explored this potential development pathway through both qualitative and quantitative research. Focus groups were conducted in 5 communities in the maize-growing highlands of Guatemala, followed by a survey of 989 farm households in 59 locations. Our results show that most farmers in the western highlands of Guatemala are severely maize deficient; on average, farm households produce enough maize for only 6.9 months of consumption a year and are forced to purchase maize to meet basic consumption needs. The results are in sharp contrast to research conducted in highland communities in neighboring Mexico, where many farmers are able to sell their maize in relatively lucrative specialty maize markets. In the context of renewed interest in reducing poverty in Central America, our research suggests that rather than focus on market development for local maize varieties, development efforts should target other types of interventions.

Despite considerable development investment, food insecurity remains prevalent throughout East and West Africa. The concept of ‘sustainable intensification’ of agricultural production has been promoted as a means to meet growing food needs in these regions. However, inadequate attention has been given to assessing whether benefits from intensification would be realized by farm households considering highly diverse resource endowments, household and farm characteristics, and agroecological contexts. In this study, we apply a simple energy-based index of food availability to 1800 households from research sites in 7 countries in East and West Africa to assess the food availability status of each of these households and to quantify the contribution of different on- and off-farm activities to food availability. We estimate the effects of two production intensification strategies on food availability: increased cereal crop production from crop-based options, and increased production of key livestock products from livestock-based options. These two options are contrasted with a third strategy: increased off-farm income for each household from broader socioeconomic-based options. Using sensitivity analysis, each strategy is tested against baseline values via incremental production increases. Baseline results exhibit considerable diversity within and across sites in household food availability status and livelihood strategies. Interventions represented in the crop and livestock options may primarily benefit food-adequate and marginally food-inadequate households, and have little impact on the most food-inadequate households. The analysis questions what production intensification can realistically achieve for East and West African smallholders, and how intensification strategies must be augmented with transformational strategies to reach the poorest households.

SUMMARYUnderstanding 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.

The authors regret that in Table 2 of our article, two values are incorrect. The second value in column 4 (Type 3), 12 TLU ha−1 is replaced by ‘2 heads’ and the second value in column 6 (Type 5) 28.2 TLU ha−1 is replaced by ‘–’. The corrected table is reproduced directly below. The authors would like to apologise for any inconvenience caused. DOI of original article: https://doi.org/10.1016/j.agsy.2020.102942

Climate change is likely to lead to increased water scarcity in the coming decades and to changes in patterns of precipitation. The result will be more short-term crop failures and long-term production declines. Improved soil management is key to climate change adaptation and mitigation efforts. There is growing interest in the promotion of climate smart agricultural practices. Many of these are the same practices that were promoted in the 1980s and 1990s under the guise of soil and water conservation. Farmer non-adoption of soil conservation technologies was rife and suggests that different approaches are needed today. Much can be learnt from these past endeavors to ensure that current efforts are better designed and implemented. We use the example of Central America to highlight some of these lessons and suggest alternative ways forward. Technology per se is not the limiting factor; many suitable technologies and practices are extant. What is required is a more nuanced approach to soil conservation efforts. There is a need to focus less on capturing soil once it has been eroded, via the use of cross-slope soil conservation practices, and more on improving soil quality of the soil that remains through improved soil cover. It is also critical to understand farming systems as a whole i.e. the full range of interlinked activities and the multiplicity of goals that farm households pursue. Furthermore, it is important to engage farmers as active players in conservation efforts rather than passive adopters of technologies, and to adopt a board value chain approach and engage a plethora of value chain actors (researchers, extension agents, equipment manufacturers, input suppliers, farmers, traders, and processors) in an agricultural innovation system.

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.

An approach for rapid appraisal of agricultural landscapes was developed and applied to the Yaqui Valley, Mexico, in order to assess progress toward sustainability. Indicators were prioritized with input from stakeholders, and then data were collected to gauge progress toward targets for those metrics. This study identifies and addresses some of the practical challenges and limitations that arise when assessments must rely on readily accessible information. The sources and quality of information to determine baseline and target values and to support future monitoring are reviewed for indicators of soil quality, productivity, biodiversity, vulnerability, poverty, transparency, and economic implications of crop diversity. Appraisal results suggest land management practices that conserve and increase the efficiency of water and nutrient use contribute to achieving goals endorsed by stakeholders. And in this arid, irrigated region, risks for soil compaction and salinization must be monitored and minimized. The approach illustrates how common gaps in reliable and scale-appropriate data can be addressed by focusing on stakeholder priorities and best available information. The approach can be applied in other regions and landscapes to identify and test strategies designed to move toward increasing agricultural sustainability.

Climate model projections coupled with process-based crop models are advocated for assessing impacts of climate change on crop yields and for informing crop-level adaptations. However, most reported studies are vague on the choice of the global circulation models (GCMs) for climate projections, and on the corresponding uncertainty with this type of model simulations. Here we investigated whether climate-crop modelling can be used for identifying crop management-level adaptation options. We focused our analyses on a case study for maize in southern Africa using the APSIM crop growth model and projections from 17 individual climate models for the period 2017–2060 for the contrasting representative concentration pathways 2.6 and 8.5. Intensification of nitrogen fertiliser use (from 30 to 90 kg N ha−1) was simulated as an example of a crop management-level adaptation to climate change. Uncertainties in crop yield predictions were about 30 to 60%, i.e. larger than expected crop responses to most management-level interventions or adaptations. Variation in simulated yields was caused by inter-seasonal rainfall variability and uncertainty with climate models. Some GCMs resulted in significantly different maize yield predictions, without any clear pattern across sites. Given these high uncertainties, we argue that crop modellers should be cautious when informing future crop management adaptation strategies based on climate-crop model ensembles. A better use of crop models is the simulation of crop responses to current weather variability aiming at the identification of crop management practices for coping with climate variability. Promising practices can then be evaluated with farmers on their feasibility over a range of plausible future biophysical and socio-economic farming conditions.

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.

In this paper, we present a framework and a methodology to identify the long-term strategies of small-scale farmers dealing with uncertainties. To do so, we link the Activity System framework to the concept of perceived vulnerability and analyse the small-scale farmers' trajectories. Based on a sample of 34 small-scale coffee producers in Guatemala and through the analysis of case studies and classification methods, we identify some trends in long-term decisionmaking. These trends are mainly built on responsiveness, autonomy, vulnerability shifts and collective means of action. We finally discuss these findings in the light of previous studies carried out in different contexts.

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.

International audience; European farmers are facing challenges that call for important transformations on their agricultural production systems, including an increasing number of regulations aimed at reducing environmental impacts from farming practices. Climate change is also expected to affect agricultural production in most European regions, and in Southern Europe this effect is expected to negatively impact yields. In this study, we present the application of an innovative participatory approach to assess the potential of innovative agricultural systems to reconcile environmental sustainability with economic viability while contributing to local and global food security. Our approach consisted of combining (1) the participation of local stakeholders in the design of narrative scenarios, and (2) an integrated assessment of scenarios through the calculation of indicators at different scale with a bio-economic model. We tested our approach with a case study situated in the Camargue region of Southern France. Rice is currently the main crop in this region, but farmers there face adverse economic conditions linked to the recent reform of European Common Agriculture Policy. After identifying the main drivers of change, local stakeholders developed narrative scenarios and described how farmers would adapt within the context of those changes. These elements were then translated into model inputs. At the regional level, the four scenarios led to variations in farmland acreage (28,000–33,000 ha), as well as the proportion of rice crops (19–75%) and areas cultivated under organic farming standards (8–43%). The four scenarios also led to different values for indicators of agricultural economic welfare, food production, and environmental impacts. Trade-offs between these indicators and the associated objectives assigned to agriculture were identified and discussed with the stakeholders. We end with a discussion of the limitations and advantages of our approach to the participatory development and assessment of locally developed narrative scenarios.

Small-scale coffee producers are particularly exposed to a very fluctuant market. The present work aims at characterising the adaptation of small-scale coffee and honey producers to the 1999–2003 coffee crisis in the Guatemalan Highlands from a systemic and agronomical perspective. Working with a homogeneous sample of 34 families we identify up to 27 different flexibility mechanisms. A qualitative analysis of families' life histories was combined with classification methods. Besides confirming the importance of the availability resources and some well known adaptive responses, such as workload intensification, expenses reduction or the importance of social networks, the classification of households reveals a sequence in the implementation of flexibility mechanisms. The focus on the combination of coffee and honey productions also suggests changing the traditional approaches to agricultural processes toward the adoption of a more systemic perspective. The possible impacts of these findings on technical extension and construction of public policies are then discussed.

International audience

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.

This paper focuses on sustainability evaluation and, more specifically, it describes and analyses the Indicator-based Framework for Evaluation of Natural Resource Management Systems (MESMIS, its Spanish acronym), ten years after its development. This framework fulfilled a pioneering role by proposing an integrated interdisciplinary approach to assess sustainability of peasant NRMS. Results of a thorough analysis of 28 case studies are presented, to seek improvement to the methods. The MESMIS approach is acceptable to a wide range of systems and to stakeholders; it has greatly assisted in sustainability evaluation and has increased understanding of the complexity of such systems, making it a significant tool for sustainability evaluation. The degree to which MESMIS and evaluation in general was shown to be an effective tool in reaching more sustainable systems depended mainly on the type of participation applied; additional guidance and information on participation is essential. Improvement possibilities are further directed to exploration and incorporation into the framework of 'new' tools that have proved valuable for the monitoring and integration of indicators (i.e.simulation models, linear programming and trade-off analysis), tools capable of assessing effects of management on indicators in the long term and thereby increasing the understanding of system attributes

This paper describes the use and role of trade-off analysis as a decisionmaking tool in the implementation of new technologies and sustainability evaluation. As an example, soils in the Purhepecha region in Mexico have been eroding for decades and crop residue retention has been suggested as an effective method to reduce soil loss; but it is not widely implemented. The effect of crop residue retention on systems' sustainability indicators have been investigated using trade-off analysis. The system used by most farmers (35% crop residue retention with free-grazing) reduced soil loss substantially, led to self-sufficiency in forage and maximized net income.

Part III; International audience; Is the regional conversion to Organic Farming (OF) possible? What could be the consequences at different scales in terms of social and economic development as well as nature conservation? Taking the heterogeneity of farms and farming systems in the region into account, are there farmers more prone to conversion and others that face greater obstacles? The objective of this paper is to shed light on some of these question by presenting the results of a scenario assessment carried out with regard to the extension of OF in the Camargue region in southern France. The application of different modelling approaches with great potential for the multi-scale and multi-criteria evaluation of the extension of OF is presented: bio-economic models, agent-based models and land-use/cover change models. According to our results, the most probable conversion in the near future in the Camargue would take place in fields with low salt pressure that belong to livestock breeders and diversified cereal producers. However, the regional conversion to OF is plausible since the region could maintain its economic productivity while decreasing potential harmful impacts on the environment. Finally, the possible conversion trajectories suggest that certain farmers (specialised in rice production) might need greater assistance to ensure such conversion to OF since their economic performance would be hampered during that period. The application of these three approaches to explore the same scenario in one region revealed their complementarity for tackling the complex issue of regional conversion to OF from different angles.

Evaluating the impacts of the development of alternative agricultural systems, such as organic or low-input cropping systems, in the context of an agricultural region requires the use of specific tools and methodologies. They should allow a prospective (using scenarios), multi-scale (taking into account the field, farm and regional level), integrated (notably multicriteria) and participatory assessment, abbreviated PIAAS (for Participatory Integrated Assessment of Agricultural System). In this paper, we compare the possible contribution to PIAAS of three modeling approaches i.e. Bio-Economic Modeling (BEM), Agent-Based Modeling (ABM) and statistical Land-Use/Land Cover Change (LUCC) models. After a presentation of each approach, we analyze their advantages and drawbacks, and identify their possible complementarities for PIAAS. Statistical LUCC modeling is a suitable approach for multi-scale analysis of past changes and can be used to start discussion about the futures with stakeholders. BEM and ABM approaches have complementary features for scenarios assessment at different scales. While ABM has been widely used for participatory assessment, BEM has been rarely used satisfactorily in a participatory manner. On the basis of these results, we propose to combine these three approaches in a framework targeted to PIAAS.