Surveillance of Climate-smart Agriculture for Nutrition (SCAN): Innovations for monitoring climate, agriculture and nutrition at scale

Climate change will affect the ability to deliver not only the quantity but also the type and quality of food necessary for nutritious diets. Global and regional 'climate-smart agriculture' initiatives offer an opportunity to mitigate climate impacts and improve nutrition outcomes at scale. The Surveillance of Climate-smart Agriculture for Nutrition (SCAN) project develops new way to acquire, integrate and analyze data to determine what is climate-smart and nutrition-sensitive

Co-designed solutions for collecting food & nutrition security data with mobile devices: What worked (and did not) from a NGO, government and research partnership

The extent and implications of under nutrition in Sub-Saharan Africa have catalyzed political action and programs on the ground. For example, the United Nations has declared a decade of action (2016-2026) and the Sustainable Development Goals have set ambitious targets to end hunger and undernutrition by 2030. Countries and development partners are moving too, individually and in coordinated actions through programs such as Scaling Up Nutrition (SUN)

The importance of market signals in crop varietal development: Lessons from Komboka rice variety

Growing high-yielding varieties is crucial for successful crop production and maximizing farmers’ net returns. One such example is IR05N221, locally referred to as Komboka rice variety, which was released in Kenya in 2013. On the one hand, Komboka can bridge the gap in rice imports since yields of existing rice varieties do not meet the increasing rice consumption levels of the Kenyan population. On the other hand, it has taken about seven years for Komboka to be appreciated by farmers, necessitating the need to understand farmer preferences when it comes to adopting a new improved variety. We used a mixed-method study approach by combining quantitative and qualitative data collected regionally and locally in both rainfed and irrigated ecologies. When compared to most of the other rice varieties under evaluation, Komboka was high-yielding, early-maturing, and had moderate tolerance to diseases in both rainfed and irrigated ecologies. However, farmers at the regional level ranked Komboka either at the same or lower rank in terms of sensory attributes. At the local level, farmers predominantly grew older and more aromatic Basmati 370 rice variety for sale, as it fetched them more money, with preferences for both men and women rice farmers being the same. Despite Komboka being a high-yielding variety, Mwea rice farmers’ perceptions and preferences for this improved variety were low. While Komboka was equally aromatic, the lack of a ready market dissuaded these farmers from widely preferring the new Komboka variety. We provide prerequisite information that can support the commercialization and promotion of the Komboka variety. We also show that widespread favourable perception of new varieties hinges on matching preferences between breeders’ efforts for improved rice productivity with farmers’ needs for market competitiveness in these new varieties

The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries

The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 758 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 such as the Probability of Poverty Index, the Household Food Insecurity Access Scale, and household dietary diversity. These variables are used to quantify more than 40 different indicators on farm and household characteristics, welfare, productivity, and economic performance. Between 2015 and the beginning of 2018, the survey instrument was applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here include the raw survey response data, the indicator calculation code, and the resulting indicator values. These data can be used to quantify on- and off-farm pathways to food security, diverse diets, and changes in poverty for rural smallholder farm households

Variability of On-Farm Food Plant Diversity and Its Contribution to Food Security: A Case Study of Smallholder Farming Households in Western Kenya

Traditional mixed agroforestry farms are regarded as sustainable agroecological systems contributing to agrobiodiversity conservation and household food and nutrition security in sub-Saharan Africa. However, in Kenya little is known on the level of agrobiodiversity of these mixed farms and its contribution to food and nutrition security. A case study was conducted to assess food plant and livestock diversity and to identify the biophysical and socioeconomic factors influencing food plant diversity in 30 smallholder farms in Western Kenya. The survey identified six livestock species and 59 food plant species. Higher food plant species richness was found on farms managed by wealthier households and older household heads. However, households with high on-farm food plant richness and diversity were not more food secure than households managing species-poor farms. The nonsignificant relationship between food security and agrobiodiversity during the time of this case study may have resulted from the fact that the surveyed 30 families sourced significant proportions of their food from markets and did not fully depend on their farms for food, particularly for spices and condiments, fruits, and animal source foods. Therefore, we suggest a diversification of farms through livestock and fruit tree farming for improving dietary diversity and incomes of the surveyed households

How Can Pairing Quantitative With Qualitative Data Collection Methods Better Elicit Rice Varietal Selection? Evidence From Burundi

Participatory Varietal Selection (PVS) is the selection by stakeholders of varieties in advanced testing stages by plant breeding programs. With Burundi as a case example, this study incorporated qualitative Focus Group Discussions (FGDs) into the quantitative PVS structure so as to elicit deeper insights into rice trait preferences and illuminate broader issues affecting rice farmers. During two consecutive years, this study surveyed 174 participants across six stakeholder groups (administrators, farmers, custom millers, researchers, seed producers, and traders) in three locations. There were statistically significant associations in rice trait preferences across locations, participating stakeholders, and genders, highlighting preference alignment. Moreover, multiple traits were desired simultaneously, beyond productivity-related traits, and sometimes contradicting researchers’ preferences, especially in rainfed systems. By moving beyond quantitative PVS preference scores as being the only way of gathering trait preference data, this study has shown how the incorporation of qualitative FGDs into the PVS structure can elicit deeper insights on trait preferences and illuminate broader issues affecting rice farmers, which when solved can accelerate the momentum in widespread adoption of new rice varieties

Influence of Seasonal On-Farm Diversity on Dietary Diversity: A Case Study of Smallholder Farming Households in Western Kenya

This study explored the associations between dietary patterns and farm diversity as well as socioeconomic variables during two seasons in rural Western Kenya. As a mean of two surveys, the average dietary diversity scores (DDS) of households and women were low, implying low household economic access to food and low women’s dietary quality. The Food Consumption Score (FCS) showed that acceptable levels of food consumption were realized over seven consecutive days in the 2014 survey by the majority of households (83%) and women (90%). While there was no strong association between the food scores and seven farm diversity indicators, both food scores were significantly associated with the household’s wealth status, ethnicity of both the household head and the spouse, and the education level of the spouse. For holistic household food and nutrition security approaches, we suggest a shift from a focus on farm production factors to incorporating easily overlooked socioeconomic factors such as household decision-making power and ethnicity

The Rural Household Multiple Indicator Survey (RHoMIS) data of 13,310 farm households in 21 countries

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) data of 13,310 farm households in 21 countries

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. (2019-10-31