Agricultural production statistics are fundamental parameters for agriculture
policy research. Information on acreage and yields of important crops is
critical for understanding trends within what is the most important economic
sector of many developing countries. Sub-national data — i.e. data
organized by administrative units such as regions or districts —
enable the analysis of patterns within countries that may highlight important
policy issues, such as the need to allocate resources to underproductive areas.
However, collecting sub-national data is difficult for developing countries with
limited resources. Even with great effort, and often only on broad regional
scales, enormous data gaps exist and are unlikely to be filled. As a result,
information is often only available at national or very broad sub-national
levels (such as provinces). Such geographically coarse data are unable to
reflect important variations within countries and are insufficient for the
spatial analysis of production patterns and trends. To fill these spatial data
gaps we developed a model to disaggregate production data from coarser to finer
spatial units. Using a cross-entropy approach, our spatial allocation model
attempts to make plausible allocations of crop production from large reporting
units such as a country or state, into smaller spatial units organized as cells
of a regularly-spaced grid. In addition to more detailed information, the
organization of production information in geographic grids allows for greater
analytical possibilities through geographic information systems. The allocation
model works on the basis of available evidence of mapped indicators of
agricultural production, which include farming systems, land cover, crop
biophysical suitability surfaces, commodity prices and local market access. This
article describes the generation of crop distribution maps for Sub-Saharan
Africa for the year 2000 using the spatial allocation model and discusses the
importance of such maps for development analysis and planning.
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