Name: Global Gridded Agricultural Gross Domestic Product (AgGDP)
Creator: World Bank Group
Published: 2022-06-20

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The prior for the crop component in the gridded AgGDP is generated by multiplying the quantity of production from the global SPAM 2010 version 1 dataset (Available at www.mapSPAM.info) (You et al. 2018) with producer prices at the country level from FAOSTAT (FAO 2016) for each crop and then summed together. However, it should be noted that due to limitations in available data, we have some components in output values (crop, livestock, and fishery) whereas others in value added (forestry and hunting). This may result in discrepancies and inconsistencies. See paper for more details, limitations and fit-for-purpose.&nbsp;&nbsp;References:FAO: FAOSTAT Database, http://faostat.fao.org/site/291/default.aspx, 2016.You, L., Wood-Sichra, U., Fritz, S., Guo, Z., See, L., &amp; Koo, J. (2018). Spatial production allocation model (SPAM) 2010 Version 1.0.&nbsp;IFPRI, Washington DC. URl: http://MapSPAM. info.

Global gridded crop production value as a prior in the model (2010 US$ per 5 arc-minute grid) 

We calculate the prior for the component of livestock production in gridded AgGDP based on the distribution maps of the above five primary species from the Gridded Livestock of the World (Robinson et al. 2014, Gilbert et al. 2018} and FAOSTAT’s value of production of livestock products (including meat, milk, eggs, honey and wool) (FAOSTAT 2020). However, it should be noted that due to limitations in available data, we have some components in output values (crop, livestock, and fishery) whereas others in value added (forestry and hunting). This may result in discrepancies and inconsistencies. See paper for more details, limitations and fit-for-purpose.References&nbsp;FAO: FAOSTAT Database, http://www.fao.org/faostat, 2020&nbsp;Gilbert, M., Nicolas, G., Cinardi, G., Van Boeckel, T. P., Vanwambeke, S. O., Wint, G. R., &amp; Robinson, T. P. (2018). Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010.&nbsp;Scientific data,&nbsp;5(1), 1-11.&nbsp;Robinson, T. P., Wint, G. W., Conchedda, G., Van Boeckel, T. P., Ercoli, V., Palamara, E., ... &amp; Gilbert, M. (2014). Mapping the global distribution of livestock.&nbsp;PloS one,&nbsp;9(5), e96084.&nbsp;

Global gridded livestock production value as a prior in the model (2010 US$ per 5 arc-minute grid) 

We estimate both freshwater inland fisheries and marine production values using the FISHSTAT (FAO 2009) data with a classification based on the fish production categories. The inland fishery production value is the result of disaggregating corresponding country level statistics in proportion to areas of inland water bodies in the 5-arcminute pixel. This is a simplified assumption and may cause overestimation in places where there are inland waterbodies, but not much fishery activities going on.&nbsp;The value of marine fisheries production is determined by its proximity to fish landing ports and a composite indicator that equally weighs the number of vessel visits and the total holding capacity of the fishing vessels. We use the port database from the World Port Index (NGIA 2019) and the number of port visits with a vessel hold of fishing vessels from Hosch et al. (2019) to create a composite variable as the prior based on the sum (for each port) of the number of visits (each event in the database) and total vessel hold at the port. The geographic coverage of the ports is calculated for each port using the minimum port distance provided in Hosch et al. (2019). Any distances greater than 150 km were considered as 150 km in this analysis. However, it should be noted that due to limitations in available data, we have some components in output values (crop, livestock, and fishery) whereas others in value added (forestry and hunting). This may result in discrepancies and inconsistencies. See paper for more details, limitations and fit-for-purpose. The data are in floating point to preserve the maximum value and does not imply double precision in the value.&nbsp;References:FAO: Fishery and aquaculture statistics 2009, https://www.fao.org/publications/card/en/c/1030779b-3733-5f5d-b3e4-0e779b94e498/, 2009&nbsp;Hosch, G., Soule, B., Schofield, M., Thomas, T., Kilgour, C., &amp; Huntington, T. (2019). Any port in a storm: vessel activity and the risk of IUU-Caught Fish Passing through the world’s most important fishing ports.&nbsp;Journal of Ocean and Coastal Economics,&nbsp;6(1), 1.&nbsp;National Geospatial-Intelligence Agency: World Port Index, 2019&nbsp;

Global gridded fish production value as a prior in the model (2010 US$ per 5 arc-minute grid) 

The value of wood products prior per pixel is calculated based on forest loss from year 2010 to year 2011 excluding loss due to fire, with an assumption that the forests were mainly cut down for timber production. The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover map (Friedl et al. 2010) for year 2011 is overlaid on top of that for year 2010 to detect the area that has changed from forest to non-forest. However, it should be noted that due to limitations in available data, we have some components in output values (crop, livestock, and fishery) whereas others in value added (forestry and hunting). This may result in discrepancies and inconsistencies. See paper for more details, limitations and fit-for-purpose.&nbsp;Reference:﻿Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., &amp; Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets.&nbsp;Remote sensing of Environment,&nbsp;114(1), 168-182.&nbsp;

Global gridded forest production value (wood products) as a prior in the model (2010 US$ per 5 arc-minute grid) 

Through a data fusion method based on cross-entropy optimization, these local estimates of agricultural GDP are in a global gridded dataset at approximately 10 x 10 kilometers (2010 US$ per 5 arc-minute grid). It should be noted that due to limitations in available data, we have some components in output values (crop, livestock, and fishery) whereas others in value added (forestry and hunting). This may result in discrepancies and inconsistencies. See paper for more details, limitations and fit-for-purpose.&nbsp;

Global gridded agricultural GDP (AgGDP) (2010 US$ per 5 arc-minute grid)

World

The global high resolution gridded Agricultural GDP (corresponding to "agriculture, forestry, and fishing, value added" in World Development Indicators, henceforth AgGDP, dataset at approximately 10x10 km is the result of a data fusion method based on cross-entropy optimization. We disaggregate national and subnational administrative statistics of Agricultural GDP (circa 2010) into the global gridded dataset at using satellite-derived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The data resources include the gridded global estimates at approximately 10x10 km

Global Gridded Agricultural Gross Domestic Product (AgGDP)

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