Elvidge 2000; Sutton and Costanza 2002; Ebener, Murray, Tandon et al. 2005, Doll, Muller, and Morley 2006; Henderson, Storeygard, and Weil 2012; Sutton, Elvidge, and Ghosh 2007; Chen and Nordhaus, 2011; Ghosh, Anderson, and Powell et al. 2009). Night lights are seen as indicators of where people – especially urban people – are concentrated, and more importantly, where economic activity (particularly non‐agricultural) is concentrated.

The second is population. Nightlights seemed to capture industrial and commercial activity reasonably well, but it performed poorly in capturing significant portions of total GDP in countries that have relatively large agricultural sectors.

Both types of methods use national or subnational GDP measures, and distribute the measures to each pixel based on the intensity of the indicator: night lights or population density. In the latter case, the assumption is normally that GDP per capita is uniform across people in the national or subnational unit for which data are available.

Often the distribution happens using separate measures for rural and non‐rural. If the two are distinguished, often nightlights are used with non‐rural GDP distribution while population is used to distribute to rural areas the agricultural GDP. Because this is a mechanistic approach, in whatever way the population data set has an error, there will be a corresponding error in the gridded agricultural GDP. Furthermore, in whatever way the true distribution deviates from the assumption of perfectly even distribution of agricultural GDP per person this will also produce errors.

Nordhaus (2006) uses subnational GDP data to distribute the GDP based on population per pixel. He then does post‐distribution analysis at a pixel level with climate and geographic variables. He does not distinguish between urban and rural populations, even though data for most countries show a gap between rural and urban income. Kummu, Taka, and Guillaume (2018) use a similar methodology, and Yetman, Gaffin, and Xing (2004) use it to generate a future gridded GDP based on a future gridded population. The method to grid GDP by UNEP and the World Bank distinguishes between rural and urban population assuming the latter to have a higher GDP per capita and allocates GDP using an estimated ratio of urban to rural GDP per capita by country (UNISDR 2011).<sup>6</sup> Ghosh, Powell, Elvidge, et al. (2010) use LandScan population grids (e.g. Dobson et al. 2000) to distribute national level agricultural GDP into the rural area, then use night lights to distribute the remaining GDP to urban areas. Rafa, Moyer, and Wang et al. (2017) also use a hybrid method for distributing rural and urban GDP in Uganda. Likewise, Gunasekera et al. (2015) use separate models for the non‐agricultural and agricultural sectors with refinements of the non‐agricultural model in Blanchard et al. (mimeo).

<mark>Turning to the first of two new methodologies for generating gridded agricultural GDP, we note that previous work of spatial allocation models includes GDP models (Ghosh et al. 2010; Nordhaus 2006; UNISDR 2011; and Gunasekera et al. 2015), population models (Tobler et al. 1997) and agricultural models such as MapSPAM (You et al. 2006) and Global Agro‐Ecological Zones (GAEZ) (FAO/IIASA 2012). GAEZ has a total crop production data set for the year 2000 that combines actual yield and production and FAO crop statistics (international) prices for 23 major commodities.</mark>

> 6  Available at Global Risk Data Platform: http://preview.grid.unep.ch.

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