|DEJONGE, KENDALL - COE, OMAHA, NE
|KALEITA, AMY - IOWA STATE, AMES, IA
|BATCHELOR, WILLIAM - MISSISSIPPI STATE, MS
|PAZ, JOEL - UNIV OF GA, GRIFFIN, GA
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/29/2008
Publication Date: 6/1/2009
Citation: Thorp, K.R., DeJonge, K.C., Kaleita, A.L., Batchelor, W.D., Paz, J.O. 2009. Methodology for the use of DSSAT Models for Precision Agriculture Decision Support. Computers and Electronics in Agriculture. 64(2):276-285.
Interpretive Summary: Precision agriculture is based on the idea that many different aspects of agricultural cropping systems, including soil properties, soil moisture, crop growth and development, crop stress levels, crop yield, crop residues, and pest infestations, are variable in space and time. If this variability can be appropriately measured, analyzed, and interpreted, then that information can be used to facilitate and improve the farm management decisions that ultimately govern the productivity and environmental impacts of agricultural systems. This research describes a decision support software that was developed for analysis of precision agriculture datasets through implementation of the Decision Support System for Agrotechnology Transfer (DSSAT) family of crop growth models. The software has been tested for agricultural systems in the midwestern United States and in the south of Germany. The software gives farmers and agricultural companies involved in precision agriculture a tool for analysis and interpretation of their measured datasets. Pursuit of precision agriculture endeavors through development of such software has the potential to make our agricultural practices more productive, profitable, sustainable, and environmentally responsible. This research will benefit NRCS and consultants.
Technical Abstract: A prototype decision support system (DSS) called Apollo was developed to assist researchers in using the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth models to analyze precision farming datasets. Because the DSSAT models are written to simulate crop growth and development within a homogenous unit of land, the Apollo DSS has specialized functions to manage running the DSSAT models to simulate and analyze spatially variable land and management. The DSS has modules that allow the user to build model input files for spatial simulations across predefined management zones, calibrate the models to simulate historic spatial yield variability, validate the models for seasons not used for calibration, and estimate the crop response and environmental impacts of nitrogen, plant population, cultivar, and irrigation prescriptions. This paper details the functionality of Apollo, and presents the results of an example application.