Assessing the Spatial Distribution of Crop Production Using a Cross-entropy Method

EPTD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised. i ACKNOWLEDGMENTS We thank Ulrike Wood-Sichra for programming assistance and Kate Sebastian, Jordan Chamberlin for data and mapping assistance. We are thankful to Philip Pardey, Gunther Fischer, seminar participants at 2004 Millennium Ecosystem Assessment Conference at Egypt for helpful discussions and comments on preliminary results. Any remaining errors are solely our responsibility. ii 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 shortcut 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.