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


Book Description

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.













To Reach the Poor


Book Description

Local farming communities throughout the world face productivity constraints, environmental concerns, and diverse nutritional needs. Developing countries address these challenges in a number of ways. One way is public research that produces genetically modified (GM) crops and recognize biotechnology as a part of the solution. To reach these communities, GM crops, after receiving biosafety agreement, must be approved for evaluation under local conditions. However, gaps between approvals in the developed and developing world grow larger, as the process of advancing GM crops in developing countries becomes increasingly difficult. In several countries, only insect resistant cotton has successfully moved from small, confined experimental trials to larger, open trials and to farms. By far, most GM crop approvals have been for commercial products that perform well under tropical conditions. However, complete information on public GMcrop research in developing countries has not been assessed. “Will policies and research institutions in the developing world stimulate the safe use of publicly fundedGM food crops?” The relatively few GM crops approved from public research, coupled with growing regulatory, biosafety capacity, trade, and political concerns, argue to the contrary. To tackle this issue, we identified and analyzed public research pipelines for GM crops among 16 developing countries and transition economies. Respondents reported 209 genetic transformation events for 46 different crops at the time when the survey was conducted. The pipelines demonstrate scientific progress among publicly funded crop research institutes in participating countries. Information and findings are presented for GM crops nearing final stages of selection. Additional details are provided for the types of genes and traits used, the breadth of genetic resources documented, implications for regulation, and the type of research partnerships employed. Regulations, GM crop approvals, choice of transgene, and policy implications are discussed as they affect this research. Based on these findings, recommendations are presented that would help sustain and increase efficiency of publicly supported research while meeting biosafety requirements. To do so, the study examines results concerning investments and choices made in research, capacity, and policy development for biotechnology. These indicate the risk and potential for GM technologies in developing countries. Policy makers, those funding biotechnology, and other stakeholders can use this information to prioritize investments, consider product advancement, and assess relative magnitude of potential risks, and benefits.