GPFARM MODELING OF CORN YIELD AND RESIDUAL SOIL NITRATE-N

Authors: Shaffer, Marvin; Bartling, Patricia; McMaster, Gregory

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/4/2003
Publication Date: 5/1/2004
Citation: Shaffer, M.J., Bartling, P.N., Mcmaster, G.S. 2004. Gpfarm modeling of corn yield and residual soil nitrate-n. Computers and Electronics in Agriculture. May 2004. Volume 43, Issue 2, Pages 87-107.

Interpretive Summary: The Great Plains Framework for Agricultural Resource Management (GPFARM) decision support system (DSS) was developed to assist farmers and ranchers with key strategic management decisions, but requires additional testing before general adoption by the agricultural community. The purpose of this research was to evaluate GPFARM simulation of continuous corn (Zea mays L.) yields and soil residual nitrates (NO3-N) under a range of irrigated and fertilized conditions, and planting densities. The GPFARM model was tested using a 3-year field data set and was shown to give predicted values that do not trend higher or lower than expected. The overall confidence ranges for GPFARM simulated results were ±1,420 and ±55.6 kg/ha for the corn yields and soil residual NO3-N, respectively. Agricultural producers and action agencies should feel confident in using the model for corn yield and related soil NO3-N estimates in strategic management planning and environmental assessment studies.

Technical Abstract: U.S. agriculture is facing low commodity prices to farmers, foreign competition, environmental concerns, and weather fluctuations such as droughts. Producers need to be able to quickly reevaluate their position in the marketplace and select management systems that make sense for each individual farm or ranch. The Great Plains Framework for Agricultural Resource Management (GPFARM) decision support system (DSS) was developed to assist farmers and ranchers with key strategic management decisions, but requires additional testing before general adoption by the Agricultural community. The purpose of this research was to evaluate GPFARM simulation of continuous corn (Zea mays L.) yields and soil residual nitrates under irrigated and partially irrigated conditions, fertilized and non fertilized applications, and high and low planting densities. Validation results for a 3-year field data set indicated the model could simulate corn yields and soil residual NO3-N without bias at the P < 0.05 level with R2 values for predicted versus observed corn yields and soil residual NO3-N of 0.830 and 0.383, respectively. Average extended modeling error (EME), a measure of modeling error outside the range of errors in the validation measurements was 221 and 24.7 kg/ha for corn grain yields and soil residual NO3-N, respectively. The overall 95% confidence bands for GPFARM simulated results ranged from ±1,420 and ±55.6 kg/ha for the corn yields and soil residual NO3-N, respectively. Agricultural producers and action agencies should feel confident in using the model for corn yield and related soil NO3-N estimates in strategic management planning and environmental assessment studies requiring this range of accuracy.