Generating Gridded Agricultural Gross Domestic Product for Brazil: A Comparison of Methodologies

Thomas Timothy Chambers, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Brian Blankespoor and Erwin Kalvelagen

No 8985, Policy Research Working Paper Series from The World Bank

Abstract: This 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 national- and/or subnational-level data.

Keywords: Livestock and Animal Husbandry; Crops and Crop Management Systems; Climate Change and Agriculture; Energy and Natural Resources; Forests and Forestry; Forestry; Coastal and Marine Resources; Food Security (search for similar items in EconPapers)
Date: 2019-08-19
New Economics Papers: this item is included in nep-agr
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Citations: View citations in EconPapers (1)

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