What We Do
HarvestChoice generates knowledge products to help guide strategic decisions to improve the well-being of the poor in sub-Saharan Africa through more productive and profitable farming. To this end, HarvestChoice has developed and continues to expand upon a spatially explicit, landscape level evaluation framework. HarvestChoice’s evolving list of knowledge products includes maps, datasets, working papers, country briefs, user-oriented tools, and spatial and economic models designed to target the needs of investors, policymakers, and research analysts who are working to improve the food supply of the world's poor.
Our Spatially Explicit Approach
HarvestChoice is cultivating a novel hub of geographically tagged datasets organized into a matrix of 10km x 10km grid cells spanning sub-Saharan African. This data-rich platform allows more fine-grained visualization of the enormous mix of farming, cultural, and socio -economic conditions that exist across Africa. ross Africa. Beyond big data, HarvestChoice also offers interactive tools (for example, Mappr & Tablr) for users to craft their own scenarios by manipulating and overlaying over 650 agricultural indicators of their choosing. Examples of spatially explicit data sets available through HarvestChoice include:
- Characteristics of soil and climate and accessibility of markets and ports
- Incidence and severity of poverty and characteristics of farm households
- Farm production systems and area, yield, and production of major food crops
- Potential crop yields under different technologies, practices, and climate scenarios
- Potential distribution and persistence of major crop and livestock pests and diseases
HarvestChoice Guiding Questions:
- Where are the poor and what is their welfare status?
- On what farming systems do the poor most depend?
- What constraints and risks limit the productivity of those farming systems and the prospects for progress?
- What investments and innovations might best sustainably raise farm productivity?
- What would be the broader impacts of such change?
- Who would gain and who might lose?
About the Underlying Data
HarvestChoice mines the latest data sources from global databases, literature, national household surveys and agriculture censuses; using a variety of techniques, cropping system and economic models, statistical analyses, and geospatial tools, HarvestChoice harmonizes data into a standardized, geo-tagged database, informally dubbed the “CELL5M” (a matrix of 5 arc-minute grid cells). Through the newly released HarvestChoice data API, programmers can access our data and methods to describe, query, and aggregate data to suit their own purposes. HarvestChoice knowledge products, including tools, maps & data, and publications, are openly accessible and free to the public through our user-friendly website: harvestchoice.org
Small Taste of Big Data: Highlights from HarvestChoice
The Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of harvested area, production, and yield within disaggregated, gridded units. Covering 42 crops and growing, SPAM reveals spatial patterns of crop performance at the confluence of geography and farming systems.
Mappr & Tablr
Mappr interacts with HarvestChoice’s core collection of gridded data to better visualize and analyze the spatial distribution and patterns of farming and households in sub-Saharan Africa. HarvestChoice’s core includes over 650 layers of data, including population density, poverty, rainfall, crop harvested areas, and travel time to market. Mappr allows users to home in on any one of 300,000 grid cells or to aggregate cells for a specified watershed, agroecological zone, or market area, allowing users to combine indicators from multiple layers to produce customized maps, charts, and tables. Tablr performs many of the same functions but includes more tailored chart capabilities and also serves as a more intimate interface for more advanced users. Both are freely available on the HarvestChoice website.