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Agricultural development must integrate multiple objectives at the same time, including food security, income, and environmental sustainability. To help achieve these objectives, development practitioners need to prioritize concrete livelihood practices to promote to rural households. But trade-offs between objectives can lead to dilemmas in selecting practices. In addition, heterogeneity among farming households requires targeting different strategies to different types of households. Existing diversity of household resources and activities, however, may also bear solutions. We explored a new, empirical research method that identifies promising options for multi-objective development by focusing on existing cases of strong multi-dimensional household performance. The "Positive Deviance" approach signifies identifying locally viable livelihood practices from diverse households that achieve stronger performance than comparable households in the same area. These practices are promising for other local households in comparable resource contexts. The approach has been used in other domains, such as child nutrition, but has not yet been fully implemented for agricultural development with a focus on the simultaneous achievement of multiple objectives. To test our adapted version of the Positive Deviance approach, we used a quantitative survey of over 500 rural households in South-Eastern Tanzania. We identified 54 households with outstanding relative performance regarding five key development dimensions (food security, income, nutrition, environmental sustainability, and social equity). We found that, compared to other households with similar resource levels, these "positive deviants" performed strongest for food security, but only slightly better for social equity. We then re-visited a diverse sub-sample for qualitative interviews, and identified 14 uncommon, "deviant" practices that plausibly contributed to the households' superior outcomes. We illustrate how these practices can inform specific recommendations of practices for other local households in comparable resource contexts. The study demonstrates how, with the Positive Deviance approach, empirical observations of individual, outstanding households can inform discussions about locally viable agricultural development solutions in diverse household context., Competing Interests: The authors have declared that no competing interests exist.

Farmers in Africa have long adapted to climatic and other risks by diversifying their farming activities. Using a multi-scale approach, we explore the relationship between farming diversity and food security and the diversification potential of African agriculture and its limits on the household and continental scale. On the household scale, we use agricultural surveys from more than 28,000 households located in 18 African countries. In a next step, we use the relationship between rainfall, rainfall variability, and farming diversity to determine the available diversification options for farmers on the continental scale. On the household scale, we show that households with greater farming diversity are more successful in meeting their consumption needs, but only up to a certain level of diversity per ha cropland and more often if food can be purchased from off-farm income or income from farm sales. More diverse farming systems can contribute to household food security; however, the relationship is influenced by other factors, for example, the market orientation of a household, livestock ownership, nonagricultural employment opportunities, and available land resources. On the continental scale, the greatest opportunities for diversification of food crops, cash crops, and livestock are located in areas with 500-1,000 mm annual rainfall and 17%-22% rainfall variability. Forty-three percent of the African cropland lacks these opportunities at present which may hamper the ability of agricultural systems to respond to climate change. While sustainable intensification practices that increase yields have received most attention to date, our study suggests that a shift in the research and policy paradigm toward agricultural diversification options may be necessary., (© 2018 The Authors Global Change Biology Published by John Wiley & Sons Ltd.)

We calculated a simple indicator of food availability using data from 93 sites in 17 countries across contrasted agroecologies in sub-Saharan Africa (>13,000 farm households) and analyzed the drivers of variations in food availability. Crop production was the major source of energy, contributing 60% of food availability. The off-farm income contribution to food availability ranged from 12% for households without enough food available (18% of the total sample) to 27% for the 58% of households with sufficient food available. Using only three explanatory variables (household size, number of livestock, and land area), we were able to predict correctly the agricultural determined status of food availability for 72% of the households, but the relationships were strongly influenced by the degree of market access. Our analyses suggest that targeting poverty through improving market access and off-farm opportunities is a better strategy to increase food security than focusing on agricultural production and closing yield gaps. This calls for multisectoral policy harmonization, incentives, and diversification of employment sources rather than a singular focus on agricultural development. Recognizing and understanding diversity among smallholder farm households in sub-Saharan Africa is key for the design of policies that aim to improve food security.

There is concern that food insecurity will increase in southern Africa due to climate change. We quantified the response of maize yield to projected climate change and to three key management options - planting date, fertilizer use and cultivar choice - using the crop simulation model, agricultural production systems simulator (APSIM), at two contrasting sites in Zimbabwe. Three climate periods up to 2100 were selected to cover both near- and long-term climates. Future climate data under two radiative forcing scenarios were generated from five global circulation models. The temperature is projected to increase significantly in Zimbabwe by 2100 with no significant change in mean annual total rainfall. When planting before mid-December with a high fertilizer rate, the simulated average grain yield for all three maize cultivars declined by 13% for the periods 2010-2039 and 2040-2069 and by 20% for 2070-2099 compared with the baseline climate, under low radiative forcing. Larger declines in yield of up to 32% were predicted for 2070-2099 with high radiative forcing. Despite differences in annual rainfall, similar trends in yield changes were observed for the two sites studied, Hwedza and Makoni. The yield response to delay in planting was nonlinear. Fertilizer increased yield significantly under both baseline and future climates. The response of maize to mineral nitrogen decreased with progressing climate change, implying a decrease in the optimal fertilizer rate in the future. Our results suggest that in the near future, improved crop and soil fertility management will remain important for enhanced maize yield. Towards the end of the 21st century, however, none of the farm management options tested in the study can avoid large yield losses in southern Africa due to climate change. There is a need to transform the current cropping systems of southern Africa to offset the negative impacts of climate change., (© 2015 John Wiley & Sons Ltd.)

To target food security interventions for smallholder households, decision makers need large-scale information, such as maps on poverty, food security and key livelihood activities. Such information is often based on expert knowledge or aggregated data, despite the fact that food security and poverty are driven largely by processes at the household level. At present, it is unclear if and how household level information can contribute to the spatial prediction of such welfare indicators or to what extent local variability is ignored by current mapping efforts. A combination of geo-referenced household level information with spatially continuous information is an underused approach to quantify local and large-scale variation, while it can provide a direct estimate of the variability of welfare indicators at the most relevant scale. We applied a stepwise regression kriging procedure to translate point information to spatially explicit patterns and create country-wide predictions with associated uncertainty estimates for indicators on food availability and related livelihood activities using household survey data from Uganda. With few exceptions, predictions of the indicators were weak, highlighting the difficulty in capturing variability at larger scale. Household explanatory variables identified little additional variation compared to environmental explanatory variables alone. Spatial predictability was strongest for indicators whose distribution was determined by environmental gradients. In contrast, indicators of crops that were more ubiquitously present across agroecological zones showed large local variation, which often overruled large-scale patterns. Our procedure adds to existing approaches that often only show large-scale patterns by revealing that local variation in welfare is large. Interventions that aim to target the poor must recognise that diversity in livelihood activities for income generation within any given area often overrides the variability of livelihood activities between distant regions in the country. [ABSTRACT FROM AUTHOR]

Tropical land use is one of the leading causes of global environmental change. Sustainable agricultural development aims to reduce the negative environmental impacts of tropical land use whilst enhancing the well-being of the smallholder farmers residing in those areas. Interventions with this goal are typically designed by scientists educated in the Western tradition, and often achieve lower than desired uptake by smallholder farmers. We build on work done in farm type classification and studies of factors that influence adaptation, trialling a suite of household survey questions to elucidate the motivational factors that influence a farmer's willingness to adapt to external change. Based on a sample of 1015 households in the rubber growing region of Xishuangbanna, South-west China, we found that farm types based on structural characteristics (e.g. crops, livelihoods) could not be used to accurately predict farmers' motivations to adapt. Amongst all six farm types identified, the full range of motivational typologies was found. We found six motivational types, from most to least likely to adapt, named: Aspirational Innovators, Conscientious, Copy Cats, Incentive-centric, Well Settled, and Change Resistant. These groups roughly corresponded with those identified in literature regarding diffusion of innovations, but such classifications are rarely used in development literature. We predict that only one third of the population would be potentially willing to trial a new intervention, and recommend that those sectors of the population should be identified and preferentially targeted by development programs. Such an approach requires validation that these motivational typologies accurately predict real behaviour – perhaps through a panel survey approach. Dedicated data gathering is required, beyond what is usually carried out for ex-ante farm typologies, but with some refinements of the methodology presented here the process need not be onerous. An improved suite of questions to appraise farmers' motivations might include value orientations, life satisfaction, and responses to various scenarios, all phrased to be locally appropriate, with a scoring system that uses the full range of potential scores and a minimum of follow up and peripheral questions. [ABSTRACT FROM AUTHOR]

Highlights: [•] We model farming systems evolution in the Kenyan highlands. [•] We link socio-economic scenarios with regional and household models. [•] Intensification, diversification and stagnation can occur simultaneously in a region. [•] Socio-political conditions modify the opportunity costs of resource use in farming systems. [•] Multi-scale models and scenarios useful for linking global change and local scale processes. [ABSTRACT FROM AUTHOR]

Abstract: African smallholder farming systems are complex, dynamic systems with many interacting biophysical subcomponents. In these systems the major inputs and outputs are managed by human agency – the farmers. To analyse potential developmental pathways of smallholder farming systems in sub-Saharan Africa (SSA), we recognised the need for a tool that can capture the effects and consequences of decision-making on the use of resources. Here we describe and apply such a new modelling tool, developed within the NUANCES framework (Nutrient Use in ANimal and Cropping systems: Efficiencies and Scales), called NUANCES-FARMSIM (FARM SIMulator), an integrated crop – livestock model developed to analyse African smallholder farm systems. NUANCES-FARMSIM was used to analyse a representative case study farm in the highlands of Western Kenya, a site for which each of the components of FARMSIM has been thoroughly tested. We present the results of a sensitivity analysis which showed the model to be sufficiently robust to identify key management options that explain most of the variability in farm productivity, and the long-term consequences of these options for the case study farm. The analyses showed clearly that the most important decisions are those related to the interactions between the different components of the farm and therefore justify the need of integrating crop and livestock components within one modelling tool. The allocation of limited resources across the farm, and the way organic matter is recycled or redistributed within the farm determines the long-term production capacity of the system. The results of the sensitivity analyses further showed that for the case study farm in Western Kenya a strong focus on improving the reliability of the subsystem level or process descriptions will only result in minor improvement in simulating productivity at farm level. [Copyright &y& Elsevier]

Ongoing and projected changes to rural communities in sub-Saharan Africa (SSA) are unprecedented in scale and pace. This paper investigates to what extent significant changes in livelihoods, poverty and food security performance are already taking place. The study focuses on households in Lushoto district (n = 147), a remote but urban linked area of Tanzania. Within the short time period between 2012 and 2015, 77% of households made changes in farm resources or farm characteristics. Households in the study site can be broadly classified as ‘Rising high value crop’, ‘Rising livestock’, ‘Subsisting mixed’ and ‘Subsisting crops’. Some of the most substantial changes we observed in the three year period of study were most likely not related to any of the agricultural orientated interventions that are being promoted in the region, but are likely endogenous changes. The land expansion seen in the ‘Rising’ households (n = 58) provides a counterpoint to the trend established in the literature of decreasing farm sizes across lower income countries more broadly, and specifically in Africa. The strategy of land expansion is risky, potentially representing a future of winners and losers, ultimately with some land-holders falling further into poverty rather than leveraging their agricultural enterprises to improve their well-being. Our results show that in sites like Lushoto with a good rural to urban connection (increasingly common in SSA), households can be agile and diverse and agency interventions are aiming for a moving target. In order to achieve income and food security outcomes, targeted and rapid monitoring tools will be needed. [ABSTRACT FROM AUTHOR]