2023-05-04T13:11:58.897283

2023-05-04T13:11:58.897285

Global

This data will be updated as new/improved data becomes available

Citation: Goldman, E., M.J. Weisse, N. Harris, and M. Schneider. 2020. “Estimating the Role of Seven Commodities in Agriculture-Linked Deforestation: Oil Palm, Soy, Cattle, Wood Fiber, Cocoa, Coffee, and Rubber.” Technical Note. Washington, DC: World Resources Institute. Available online at: wri.org/publication/estimating-the-role-of-sevencommodities-in-agriculture-linked-deforestation.

Agriculture-Linked Deforestation

WRI

Creative Commons Attribution 4.0 International License (CC-BY)

This data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions.

Provides annual agriculture-linked deforestation estimates for oil palm, soy, cattle, cocoa and coffee for the years 2001-2015 by administrative boundary

This analysis is limited by various data and attribution issues and methodological assumptions, including the following: Commodity data sets have limited coverage and quality. Only oil palm has recent, detailed maps of extent at a global level. The analysis also uses detailed data on South American soy. Outside of these regions and commodities, the analysis relies on global 10-kilometer resolution data on crop and pasture extent. These data are from 2010 (2000 for pasture), so the amount of forest replaced by a specific commodity is assumed to be proportional to its area during that year and may be misrepresented if significant expansion or contraction of that commodity has occurred since then. While Goldman et al. (2020) presents results using detailed pasture data for Brazil, this data set includes pasture results for the coarse method only. The data cannot capture complex land-use change transitions. The analysis does not consider other possible land uses between the deforestation event and the establishment of the commodity. The analysis also does not consider any forms of indirect land-use change (e.g., the target commodity displacing other activities that may, in turn, expand into forested areas). The data measure tree cover loss rather than deforestation directly. All tree cover loss in an area later used for one of the target commodities is assumed to be deforestation because forest replaced with a crop or pasture represents a permanent land-use change. Historical data from Indonesia and Malaysia were used to filter out older oil palm plantations from the analysis to avoid counting old, unproductive oil palm trees being felled as tree cover loss. The data may miss some forms of tree cover loss. The Hansen et al. (2013) tree cover loss data may not detect all changes related to commodity production. Much of the production of cocoa and coffee occurs on very small farms (less than one hectare) that may not be captured by the tree cover loss data. The analysis may also underestimate the conversion of dry forest and woody savanna areas, which are not well represented in the tree cover loss data. For the detailed soy analysis, we define tree cover as any woody vegetation with a minimum of 10 percent canopy cover (analyses for other commodities use 30 percent) to minimize underestimations in South American biomes such as the Cerrado and the Chaco. Further discussion about the methods, assumptions, and limitations of this analysis is available in Goldman et al. (2020).

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