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Abstract
While agricultural production statistics are reported on a geopolitical – often national -
basis we often need to know the status of production or productivity within specific sub-regions,
watersheds, or agro-ecological zones. Such re-aggregations are typically made using expert
judgments or simple area-weighting rules. We describe a new, entropy-based approach to
making spatially disaggregated assessments of the distribution of crop production. Using this
approach tabular crop production statistics are blended judiciously with an array of other
secondary data to assess the production of specific crops within individual ‘pixels’ – typically 25
to 100 square kilometers in size. The information utilized includes crop production statistics,
farming system characteristics, satellite-derived land cover data, biophysical crop suitability
assessments, and population density. An application is presented in which Brazilian state level
production statistics are used to generate pixel level crop production data for eight crops. To
validate the spatial allocation we aggregated the pixel estimates to obtain synthetic estimates of
municipio level production in Brazil, and compared those estimates with actual municipio
statistics. The approach produced extremely promising results. We then examined the robustness
of these results compared to short-cut approaches to spatializing crop production statistics and
showed that, while computationally intensive, the cross-entropy method does provide more
reliable estimates of crop production patterns.