10.7910/DVN/PRFF8VInternational Food Policy Research Institute (IFPRI)Harvard Dataverse2019Agricultural SciencesComputer and Information ScienceEarth and Environmental Sciencesstatisticsplant productionspatial distributionspatial databaseFood securityAgricultural productionGeographical information systemsGuo, ZheZheGuoInternational Food Policy Research Institute (IFPRI)You, LiangzhiInternational Food Policy Research Institute (IFPRI)Koo, JawooInternational Food Policy Research Institute (IFPRI)International Food Policy Research InstituteYou, Liangzhi (International Food Policy Research Institute (IFPRI))You, Liangzhi (International Food Policy Research Institute (IFPRI))Guo, Zhe (International Food Policy Research Institute (IFPRI))Wood-Sichra, Ulrike (International Food Policy Research Institute (IFPRI))Wood-Sichra, Ulrike (International Food Policy Research Institute (IFPRI))Ru, Yating (International Food Policy Research Institute (IFPRI))Jeon, Seong-Min (International Food Policy Research Institute (IFPRI))Spatial Data and Analytics ThemeKoo, Jawoo (International Food Policy Research Institute (IFPRI))CGIAR Platform for Big Data in AgricultureCGIAR Research Program on Policies, Institutions, and MarketsInternational Food Policy Research Institute20192019-10-092019-10-092025-02-04spatial data10.5194/essd-2020-116639144504301166340620143651190144284552166106028143470662156093566178793689154362107149149715166554280113096005184388916204667942169794960text/plainapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zip4.2Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.For more information and data download options, please visit the MapSPAM website at https://mapspam.info. Funding support for MapSPAM was provided by CGIAR Platform for Big Data in Agriculture, CGIAR Research Program on Policies, Institutions, and Markets (PIM), The Bill and Melinda Gates Foundation, NASA Harvest (https://nasaharvest.org), USAID Feed the Future Innovation Lab for Small Scale Irrigation (https://ilssi.tamu.edu), USAID Feed the Future Innovation Lab for Sustainable Intensification (https://www.k-state.edu/siil), and Macro Agriculture Research Institute (http://mari.hzau.edu.cn).Washington, DC, USAUSAID Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (AID-OAA-L-14-00006)USAID Feed the Future Innovation Lab for Small Scale Irrigation (AID-OAA-A-13-0005)Bill and Melinda Gates Foundation (BMGF)OPPGD1450NASA HarvestUnited States Agency for International Development (USAID)