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On October 29, 2021 at 2:17:55 PM UTC, Gravatar Heiko Figgemeier:
  • Updated description of MapSPAM from

    "Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling, which mainly include the update of base year, the extension of crop list, and the expansion of subnational administrative-unit coverage. Specifically, it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data but also expands the estimates of crop area, yield, and production from 20 to 42 major crops under four farming systems across a global 5 arcmin grid. All the SPAM maps are freely available at the MapSPAM website (, last access: 11 December 2020), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedback from users but is also a platform providing archived global agricultural-production maps for better targeting the Sustainable Development Goals. In particular, SPAM2010 can be downloaded via an open-data repository (DOI:; IFPRI, 2019)." <br> *Yu, Qiangyi; You, Liangzhi; Wood-Sichra, Ulrike; Ru, Yating; Joglekar, Alison K. B.; Fritz, Steffen et al. (2020): A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps. In: Earth Syst. Sci. Data 12 (4), S. 3545–3572. DOI: 10.5194/essd-12-3545-2020.*
    We introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa 2010. The Spatial Production Allocation Model is an effective way to map detailed patterns of crop production using much less specific input data. A variety of information sources are used to generate plausible, disaggregated estimates of crop distribution, which are useful for understanding production and land use patterns. Identifying where trends take place is important for understanding why they take place. Moving the data from coarser units such as countries and subnational provinces, to finer units such as grid cells at 10×10 km resolution, reveals spatial patterns of crop performance, creating a global gridscape at the confluence between geography and agricultural production systems.