Gamcaf: A Geospatial Agricultural Management and Crop Assessment Framework for Regional Food Security.

The Geospatial Agricultural Management and Crop Assessment Framework (GAMCAF) was developed to assess crop production potential and associated biophysical constraints in a geospatial context. The tool was initially developed to assess crop production capacity within the U.S. Eastern Seaboard Region (ESR) in the context of regional food security. GAMCAF utilizes a scripting language (PYTHON) to integrate geospatial databases, explanatory crop models (SPUDSIM, MAIZSIM), a weather generator (CLIGEN), and a soil hydraulic properties estimator (ROSETTA) within an ArcGIS framework. Databases include fine-scale geospatial inputs (up to 30-m resolution) from SSURGO, historical and spatially referenced climatic data from NOAA, land-use classification and current production areas based on the Common Land Unit (USDA-FSA), National Land Cover Database (USDOI-USGS), and Cropland Data Layer (USDA-NASS). Based on user specified scenarios, GAMCAF automatically assembles location-specific input files for the crop models, executes the corresponding model runs, and compiles and aggregates the results to county levels throughout each state in the region. The result is an automated tool which conducts thousands of model runs to estimate spatially referenced crop yield and water and nitrogen use for each given intersection of climate, land-use, dominant soil type, and management practice. Recent results related to establishing ESR production capacity using explanatory crop models for potato (SPUDSIM) and corn (MAIZSIM) will be discussed including sensitivity to climate change, land-use, and irrigation practice.