Refining Location-Specific Crop Data to Help Boost Productivity

Agriculture is inherently a location-specific activity, with significant geographical variation in production within and across regions, countries, and agro-ecological zones. To effectively evaluate the potential productivity, food security, growth, and environmental impacts of agricultural production, it is critical to have reliable information on the spatial patterns of crop performance. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments. The increased availability of geo-referenced data and more accessible geographic information systems (GIS) now support the management and analysis of spatial data and provide better opportunities for researchers to help meet these needs.

Large gaps still exist in our knowledge of the current geographic distribution and spatial patterns of crop performance in Sub-Saharan Africa (SSA). This is primarily due to underfunding of national statistical and survey agencies and limited geographic disaggregation of existing data. Such geographically imprecise data are unable to reflect important variations within countries and regions and are insufficient for the spatial analysis of production patterns and trends.

To fill these gaps, the International Food Policy Research Institute (IFPRI) and HarvestChoice has developed and applied a spatial production allocation model (SPAM) for disaggregating crop production data from coarser to finer spatial units. The model uses a cross-entropy approach to make pixel-scale assessments of the spatial distribution of crop production within geopolitical units, such as countries, sub-national provinces, and districts. The model takes into account relevant data, including production statistics, irrigation and rainfed systems, prices, land-use data, satellite imagery, biophysical crop “suitability” assessments, population density, and distance to urban centers.