Assessing the spatial distribution of crop production using a generalized cross-entropy approach
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...
| Autores principales: | , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2004
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/155753 |
| _version_ | 1855536082626543616 |
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| author | You, Liangzhi Wood, Stanley |
| author_browse | Wood, Stanley You, Liangzhi |
| author_facet | You, Liangzhi Wood, Stanley |
| author_sort | You, Liangzhi |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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. |
| format | Artículo preliminar |
| id | CGSpace155753 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2004 |
| publishDateRange | 2004 |
| publishDateSort | 2004 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1557532025-11-06T07:18:10Z Assessing the spatial distribution of crop production using a generalized cross-entropy approach You, Liangzhi Wood, Stanley entropy remote sensing spatial distribution crop establishment production data statistical data 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. 2004 2024-10-24T12:42:31Z 2024-10-24T12:42:31Z Working Paper https://hdl.handle.net/10568/155753 en Open Access application/pdf International Food Policy Research Institute You, Liangzhi; Wood, Stanley. 2004. Assessing the spatial distribution of crop production using a generalized cross-entropy approach. EPTD Discussion Paper 126. https://hdl.handle.net/10568/155753 |
| spellingShingle | entropy remote sensing spatial distribution crop establishment production data statistical data You, Liangzhi Wood, Stanley Assessing the spatial distribution of crop production using a generalized cross-entropy approach |
| title | Assessing the spatial distribution of crop production using a generalized cross-entropy approach |
| title_full | Assessing the spatial distribution of crop production using a generalized cross-entropy approach |
| title_fullStr | Assessing the spatial distribution of crop production using a generalized cross-entropy approach |
| title_full_unstemmed | Assessing the spatial distribution of crop production using a generalized cross-entropy approach |
| title_short | Assessing the spatial distribution of crop production using a generalized cross-entropy approach |
| title_sort | assessing the spatial distribution of crop production using a generalized cross entropy approach |
| topic | entropy remote sensing spatial distribution crop establishment production data statistical data |
| url | https://hdl.handle.net/10568/155753 |
| work_keys_str_mv | AT youliangzhi assessingthespatialdistributionofcropproductionusingageneralizedcrossentropyapproach AT woodstanley assessingthespatialdistributionofcropproductionusingageneralizedcrossentropyapproach |