Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies

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dc.creatorThomas, Timothy S.
dc.creatorYou, Liangzhi
dc.creatorWood-Sichra, Ulrike
dc.creatorRu, Yating
dc.creatorBlankespoor, Brian
dc.creatorKalvelagen, Erwin
dc.date2019-12-13
dc.date2024-06-21T09:11:03Z
dc.date2024-06-21T09:11:03Z
dc.date.accessioned2026-06-27T15:05:41Z
dc.descriptionThis paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperform the prediction of agricultural GDP from the traditional method that distributes agricultural GDP using rural population. The paper finds that the best prediction method is spatial disaggregation using a regression approach for all the key crops and contributors to agricultural GDP. However, the issue of degrees of freedom is an important limiting factor, as the approach requires sufficient subnational data. The cross-entropy method with readily available spatially distributed crop, livestock, forest, and fish allocation far outperforms the traditional method, at least in the case of Brazil, and can operate with nationaland/or subnational-level data.
dc.identifierhttps://hdl.handle.net/10568/147075
dc.identifier.urihttp://hdl.handle.net/123456789/94348
dc.languageen
dc.publisherWorld Bank
dc.rightsOpen Access
dc.sourceThomas, Timothy S.; You, Liangzhi; Wood-Sichra, Ulrike; Ru, Yating; Blankespoor, Brian; and Kalvelagen, Erwin. 2019. Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies. Policy Research Working Paper 8985. https://doi.org/10.1596/1813-9450-8985
dc.subjectgross agricultural product
dc.subjectspatial data
dc.subjectregional accounting
dc.subjectspatial distribution
dc.subjectagriculture
dc.subjectgross national product
dc.titleGenerating gridded agricultural gross domestic product for Brazil : A comparison of methodologies
dc.typeWorking Paper

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)