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A growing body of evidence shows that more intensive dairy systems can be good for both nature and people. Little research considers whether such systems correspond with local priorities and preferences. Using a mixed methods approach, this study examined the effects of three intensification scenarios on milk yield and emission intensities in Kenya and Tanzania. Scenarios included (a) an incremental change to feed management; (b) adaptive change by replacing grasslands with nutrient-rich fodder crops; and (c) multiple change involving concurrent improvements to breeds, feeds and concentrate supplementation. These scenarios were co-constructed with diverse stakeholder groups to ensure these resonate with local preferences and priorities. Modelling these scenarios showed that milk yield could increase by 2-15% with incremental changes to over 200% with multiple changes. Greenhouse gas emission intensities are lowest under the multiple change scenario, reducing by an estimated 44%. While raising yields, incremental change conversely raises emission intensities by 9%. Our results suggest that while future interventions that account for local priorities and preferences can enhance productivity and increase the uptake of practices, far reaching shifts in practices are needed to reduce the climatic footprint of the sector. Since this does not align with local priorities and preferences in many situations, future low-emission development initiatives should place more emphasis on geographic and stakeholder heterogeneity when designing targeting and implementation strategies. This suggests that in low-income countries, bottom-up approaches may be more likely to improve dairy productivity and align with mitigation targets than one-size-fits-all approaches.

A growing body of evidence shows that more intensive dairy systems can be good for both nature and people. Little research considers whether such systems correspond with local priorities and preferences. Using a mixed methods approach, this study examined the effects of three intensification scenarios on milk yield and emission intensities in Kenya and Tanzania. Scenarios included (a) an incremental change to feed management; (b) adaptive change by replacing poor quality grass with nutrient-rich fodder crops; and (c) multiple change involving concurrent improvements to breeds, feeds and concentrate supplementation. These scenarios were co-constructed with diverse stakeholder groups to ensure these resonate with local preferences and priorities. Modelling these scenarios showed that milk yield could increase by 2%-15% with incremental changes to over 200% with multiple changes. Greenhouse gas emission intensities are lowest under the multiple change scenario, reducing by an estimated 44%. While raising yields, incremental change conversely raises emission intensities by 9%. Our results suggest that while future interventions that account for local priorities and preferences can enhance productivity and increase the uptake of practices, far-reaching shifts in practices are needed to reduce the climatic footprint of the dairy sector. Since top-down interventions does not align with local priorities and preferences in many situations, future low-emission development initiatives should place more emphasis on geographic and stakeholder heterogeneity when designing targeting and implementation strategies. This suggests that in low-income countries, bottom-up approaches may be more likely to improve dairy productivity and align with mitigation targets than one-size-fits-all approaches.

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

The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 753 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socio-economic and food security indicators like the Poverty Probability Index, the Household Food Insecurity Access Scale and dietary diversity. These variables are used to quantify more than 40 different aggregate indicators on farm household characteristics, welfare, productivity and economic performance. Between 2015 and the beginning of 2018, the survey instrument has been applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here cover the raw data, the indicator calculation code and the resulting indicator values, and can be used to quantify on- and off-farm pathways to food security, diverse diets and reduced poverty of rural smallholder farm households., The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 753 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socio-economic and food security indicators like the Poverty Probability Index, the Household Food Insecurity Access Scale and dietary diversity. These variables are used to quantify more than 40 different aggregate indicators on farm household characteristics, welfare, productivity and economic performance. Between 2015 and the beginning of 2018, the survey instrument has been applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here cover the raw data, the indicator calculation code and the resulting indicator values, and can be used to quantify on- and off-farm pathways to food security, diverse diets and reduced poverty of rural smallholder farm households.