Gis-crop model based decision support system to evaluate corn and soybean prescriptions

Crop growth models are increasingly being used as a tool to evaluate causes of spatial yield variability and to predict economic performance of prescriptions. However, using crop models at this spatial scale is a difficult task because of the demands of managing vast amounts of input and output data required for the analysis. The purpose of this project was to develop a GIS-based decision support system (DSS) to simplify the process of using the CROPGRO-Soybean and CERES-Maize crop growth models to evaluate variable rate prescriptions. A DSS called Crop Models Analyst was developed to utilize the ArcView 3.1 GIS and maintain functionality with the current crop models written in FORTRAN. The DSS allows users to: 1) create maps of any of the 200 variables predicted by the models on each day of simulation; 2) interactively run the model from the DSS to test hypotheses and to make comparisons with measured data; and 3) evaluate prescriptions over multiple years of weather data. The interface was used to compute the optimum soybean population across a field for one case study, and to select between two soybean varieties across a field for a second case study. The DSS proves to be a valuable tool for researchers working with crop growth models. Users will benefit from this DSS because it saves a tremendous amount of time in its ability to rapidly generate maps and set up optimum prescription scenarios. Potential decision modules or new versions of the model can easily, be added in the future.