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

A large proportion of rural households, particularly in the dry land areas, representative for more than 10% of the world's land surface and up to 80% in Morocco, depend for their livelihoods on livestock. They exploit livestock's capacity to live in very harsh environments using herd-mobility at multiple scale level. Understanding the multiple contributions of livestock to the household and national economy raises complex research issues and challenges linked with the multitude of goods and services derived from livestock, their interactions with other family activities, and the local and national context. The objective of our research was therefore to analyse the diversity and assess the resilience of livelihood strategies of farming households oriented to livestock using a set of data collected in the dry land areas (oases and mountainous zones) of Morocco and discuss the livelihood outcome indicators. To achieve this, we have realized a cross-sectional analysis of livelihoods and adaptive capacity, to select a set of pertinent indicators. These indicators have been developed using an adapted version of the Rural Household Multi-Indicator Survey (RHoMIS) toolkit for pastoral and agropastoral household systems. Our results highlight the critical importance of livelihood diversification (off-farm diversification, livestock diversification, and crop diversification) in building household resilience and the livelihood outcomes. While livelihood strategies undoubtedly contribute to livelihood outcomes, there is also a critical iterative process, i.e., livelihood outcomes also influence the livelihood strategies at the farming households. The present work proposes an aggregated indicator of livelihood outcomes allowing us to capture the heterogeneity of living conditions of agropastoral systems by considering the main drivers of this system, i.e., mobility, livestock species, and physiological stage composition of the herd. This approach could constitute a valuable con

Smallholder farming in sub-Saharan Africa keeps many rural households trapped in a cycle of poor productivity and low incomes. Two options to reach a decent income include intensification of production and expansion of farm areas per household. In this study, we explore what is a “viable farm size,” i.e., the farm area that is required to attain a “living income,” which sustains a nutritious diet, housing, education and health care. We used survey data from three contrasting sites in the East African highlands—Nyando (Kenya), Rakai (Uganda), and Lushoto (Tanzania) to explore viable farm sizes in six scenarios. Starting from the baseline cropping system, we built scenarios by incrementally including intensified and re-configured cropping systems, income from livestock and off-farm sources. In the most conservative scenario (baseline cropping patterns and yields, minus basic input costs), viable farm areas were 3.6, 2.4, and 2.1 ha, for Nyando, Rakai, and Lushoto, respectively—whereas current median farm areas were just 0.8, 1.8, and 0.8 ha. Given the skewed distribution of current farm areas, only few of the households in the study sites (0, 27, and 4% for Nyando, Rakai, and Lushoto, respectively) were able to attain a living income. Raising baseline yields to 50% of the water-limited yields strongly reduced the land area needed to achieve a viable farm size, and thereby enabled 92% of the households in Rakai and 70% of the households in Lushoto to attain a living income on their existing farm areas. By contrast, intensification of crop production alone was insufficient in Nyando, although including income from livestock enabled the majority of households (73%) to attain a living income with current farm areas. These scenarios show that increasing farm area and/or intensifying production is required for smallholder farmers to attain a living income from farming. Obviously such changes would require considerable capital and labor investment, as well as land reform and

Rollout of development interventions using a one-size-fits-all model can achieve economies of scale but neglects to account for variability in farm and farmer characteristics. A data-driven approach to incorporate farmer diversity in scaling strategies may help to achieve greater development impact. However, interpreting the multiplicity of smallholder characteristics is complex, time-consuming, and the ways in which the insights gained can be implemented is poorly understood. Navigating these tensions, we present a farm typology study carried out in collaboration with a large development organisation (the “scaling partner”) promoting agricultural inputs in Rwanda. This study was conducted late in the scaling pathway, in order to finesse the scaling strategy, rather than to target intervention selection. Drawing on nearly 3000 interviews from 17 districts of the Western, Southern, and Eastern Provinces of Rwanda, the typology differentiates households along two axes: 1. prosperity (a cornerstone of conventional typologies), and 2. adoption of inputs (fertilisers and improved crop varieties). We used an efficient household survey tool, a minimum-variable approach, and concepts from the study of adoption of agricultural innovations. Through an action-research collaboration with the scaling organisation we adapted the methods and the findings to be “actionable. Approximately two-thirds of the study population were using fertilisers and improved seed to some extent. Along each prosperity stratum, however, there were multiple degrees of adoption, demonstrating the value of including adoption information in typology constructions. Ten farm types were identified, where the key differences along the prosperity axis were land area cultivated and livestock owned, and the key differences along the adoption axis were perceptions of input efficacy, access to training, and education level. We also present a simple decision tree model to assign new households to a farm type. The f

Climate change increasingly challenges smallholder farming and our ability to achieve Sustainable Development Goal 2 (Zero Hunger) in sub-Saharan Africa. Agricultural interventions are needed that aim at improving the food insecurity of the most vulnerable rural households. Interventions must fit the local context of a diverse population of rural households, and a key challenge is to identify which kinds of interventions work in which regions and for which households. Micro-level information can account for this diversity, but is an underused source of information for planning of interventions at national and sub-national levels. In this thesis, I explored how micro-level information from cross-country household survey data can be used for effective planning of interventions. A further research aim was to understand within-country patterns of livelihood strategies in relation to food security and vulnerability to climate change of rural households in Uganda. Cross-country household data from the World Bank Living Standard Measurement Survey – Integrated Surveys on Agriculture (LSMS-ISA) were used to 1) aggregate household level information to higher levels (e.g. districts, regions, livelihood zones), 2) spatially interpolate household level information and 3) identify hotspot areas of household vulnerability. I used data that I collected from two sites in Uganda for an in-depth analysis on current coping strategies of households for climate and price variability. Household food security was approximated using a food availability indicator that quantified the contribution of livelihood activities to household food availability. Livelihood strategies of rural households across Uganda varied with household food availability. They changed from subsistence-oriented on-farm activities to market-oriented on-farm and off-farm activities as household food availability increased. Aggregation revealed spatial differences in food availability and livelihood activities. However

Household surveys are one of the most commonly used tools for generating insight into rural communities. Despite their prevalence, few studies comprehensively evaluate the quality of data derived from farm household surveys. We critically evaluated a series of standard reported values and indicators that are captured in multiple farm household surveys, and then quantified their credibility, consistency and, thus, their reliability. Surprisingly, even variables which might be considered ‘easy to estimate’ had instances of non-credible observations. In addition, measurements of maize yields and land owned were found to be less reliable than other stationary variables. This lack of reliability has implications for monitoring food security status, poverty status and the land productivity of households. Despite this rather bleak picture, our analysis also shows that if the same farm households are followed over time, the sample sizes needed to detect substantial changes are in the order of hundreds of surveys, and not in the thousands. Our research highlights the value of targeted and systematised household surveys and the importance of ongoing efforts to improve data quality. Improvements must be based on the foundations of robust survey design, transparency of experimental design and effective training. The quality and usability of such data can be further enhanced by improving coordination between agencies, incorporating mixed modes of data collection and continuing systematic validation programmes.

Rural households in sub-Saharan Africa earn a substantial part of their living from rain-fed smallholder agriculture, which is highly sensitive to climate change. There is a growing number of multi-level assessments on impacts and adaptation options for African smallholder systems under climate change, yet few studies translate impacts at the individual crop level to vulnerability at the household level, at which other livelihood activities need to be considered. Further, these assessments often use representative household types rather than considering the diversity of households for the identification of larger-scale patterns at sub-national and national levels. We developed a framework that combines crop suitability maps with a household food availability analysis to quantify household vulnerability to climate-related impacts on crop production and effects of adaptation options. The framework was tested for Uganda, identifying four hotspots of household vulnerability across the country. Hotspots were visually identified as areas with a relatively high concentration of vulnerable households, experiencing a decline in household crop suitability. About 30% of the households in the hotspots in (central) southwest were vulnerable to a combination of 3 °C temperature increase and 10% rainfall decline through declining suitability for several key crops (including highland banana, cassava, maize and sorghum). In contrast only 10% of the households in West Nile and central northern Uganda were negatively affected, and these were mainly affected by declining suitability of common beans. Households that depended on common beans and lived at lower elevations in West Nile and central north were vulnerable to a 2 to 3 °C temperature increase, while households located at higher elevations (above 1100–2000 m.a.s.l. depending on the crop) benefited from such an increase. Options for adaptation to increasing temperatures were most beneficial in northern Uganda, while drought-relat

Our understanding of food security in sub-Saharan Africa (SSA) has been hampered by limitations in the temporal and spatial representativeness of data. Food balance sheets provide scalable estimates of per capita food availability, but fail to represent food access, stability and their causal linkages. In contrast, rural household surveys represent detailed conditions for one or multiple points in time, but are influenced by survey timing and are often limited in geographical coverage. This study draws on a large sample of rural land-holding households in SSA (n = 6,353) to identify household level food access deficiencies and to understand the associations with rural livelihoods and food sourcing behavior throughout the year. Food access deficiencies were identified using food security of access and diet diversity indicators. Dietary diversity and channel of access (farm or purchased) were enumerated for the “flush” and “lean” periods and food security of access was enumerated for the lean period only - making the results of this study independent of survey timing. As many as 39% of households were classified as severely food insecure (in terms of food access) and as many as 49% of households were likely to be deficient in micronutrients in the lean period. Vulnerability to food insecurity and micronutrient deficiencies differed by household composition, agricultural livelihood characteristics and agro-ecological zone. Dairy, fruit and vitamin A-rich produce were predominantly accessed through the farm channel. Households with a livestock component to their farm had a lower prevalence of severe food insecurity and higher diet diversity scores. These findings have implications for the development of nutrition-sensitive and nutrition-specific interventions. Interventions need to be tailored to agro-ecological zone, household composition, scale of operation and production mix. Increasing income will not necessarily result in improved diet diversity or healthy dietary