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United States Department of Agriculture

Agricultural Research Service

Research Project: MECHANISTIC PROCESS-LEVEL CROP SIMULATION MODELS FOR ASSESSMENT OF AGRICULTURAL SYSTEMS

Location: Crop Systems & Global Change

Title: Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units

Authors
item Resop, Jonathan
item Fleisher, David
item Wang, Qingguo -
item Timlin, Dennis
item Reddy, Vangimalla

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 6, 2012
Publication Date: November 1, 2012
Citation: Resop, J.P., Fleisher, D.H., Wang, Q., Timlin, D.J., Reddy, V. 2012. Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units. Computers and Electronics in Agriculture. 89(1):51-61.

Interpretive Summary: Potential crop production capacity for a region (how much can be grown and where) is a complex system that is affected by large-scale phenomena such as global climate change and land use change due to urban development and soil erosion. Production capacity is important to quantify since it is directly related to the food consumption demands of the population. As populations grow and fuel costs increase, there will be a greater demand for locally produced food. While computational crop models have been developed for simulating production at specific sites, more research is needed in the area of simulating production over entire regions. In this paper we have outlined a methodology for organizing publically available data (weather, soil, crop management, and land use) and linking the data to a crop model to simulate production over a region of interest. This methodology allows for researchers to observe trends in crop yield and resource requirements over a large area in an automated manner. Increased knowledge of regional food systems can assist food growers, policy planners, and scientists in further studying potential production capacity under different future scenarios.

Technical Abstract: Crop models are computational tools used for predicting crop yield and natural resource requirements and are frequently used to evaluate different climate or management scenarios at a specific site. However, problems involving land use or climate change would benefit from conducting crop simulations over a broader spatial area using high-resolution, spatially-distributed data. A geospatial interface was developed using the scripting language Python to utilize the explanatory crop model SPUDSIM with the geographic information system (GIS) software ArcGIS. The interface was used to simulate crop production in response to spatially-variable biophysical constraints at multiple field-scale locations and then aggregate to a county level throughout a region. SPUDSIM is a process-based model developed by USDA-ARS for simulating potato production. Spatial data layers included daily climate data generated from the model CLIGEN based on historical data from National Oceanic and Atmospheric Administration (NOAA) weather stations, SSURGO soil data from the Natural Resources Conservation Service (NRCS), management data from the USDA, and land use data from multiple sources. Modeling units (MUs) were defined as the intersection of these layers to create homogeneous field-scale areas. SPUDSIM was used to simulate crop production for each unique combination of climate, soil, and management. Only MUs with land use classifications available for crop production were simulated. The output variables (crop yield, water use, and nitrogen use) from the MUs were mapped to show the spatial distribution within each county and aggregated to the county-level for the region of interest. An example was provided for potato production in Maine and illustrates how potential crop yield varies spatially over the state. The geospatial crop model interface was designed to be flexible and easy to apply to other regions and applications such as analyzing crop productivity under different scenarios.

Last Modified: 9/3/2014
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