Using a variety of inputs, 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 subnational provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global gridscape 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.