Skip to main content
ARS Home » Research » Publications at this Location » Publication #130505
Title: GPFARM SIMULATION OF SOIL NUTRIENT CYCLING UNDER MAIZE

Author
item Shaffer, Marvin
item YU, MEI - COLORADO STATE UNIVERSITY
item Bartling, Patricia
item HO, NAM - COLORADO STATE UNIVERSITY
item McMaster, Gregory
item Ascough Ii, James
item Ahuja, Lajpat
item Weltz, Mark

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/29/2001
Publication Date: 10/24/2001
Citation: Shaffer, M.J., Yu, M., Bartling, P.N., Ho, N., Mcmaster, G.S., Ascough Ii, J.C., Ahuja, L.R., Weltz, M.A. 2001. Gpfarm simulation of soil nutrient cycling under maize. Agronomy Abstracts. American Society of Agronomy Meetings, October 2001.

Interpretive Summary: Simulating soil nutrient cycling under a range of agricultural management scenarios is essential for agricultural practice, as well as environmental protection. The objective of this study is to test the capability of GPFARM software to simulate corn yield, biomass, and soil nutrient cycling under different agricultural management. The treatments include high/low plant density, full/no fertilization, and medium/no irrigation. Comparisons are presented for model predictions versus field observations for 3 years of data from the South Farm research plots in Fort Collins. The results show that GPFARM gives reasonable predictions for yield and biomass of corn and associated residual NO3-N under different fertilization, irrigation, and plant density treatments. The model was able to estimate soil residual NO3- N levels for both non-fertilized and fertilized cases where crop N uptake early in the growing season played a key role. Corn biomass and yield under both irrigated and non-irrigated conditions were simulated reasonably well using a common model parameter set for the corn variety thus demonstrating the ability of the model stress function to handle dryland and irrigated conditions.

Technical Abstract: Simulating soil nutrient cycling under a range of agricultural management scenarios is essential for agricultural practice, as well as environmental protection. The objective of this study is to test the capability of GPFARM software to simulate corn yield, biomass, and soil nutrient cycling under different agricultural management. The treatments include high/low plant density, full/no fertilization, and medium/no irrigation. Comparisons are presented for model predictions versus field observations for 3 years of data from the South Farm research plots in Fort Collins. The results show that GPFARM gives reasonable predictions for yield and biomass of corn and associated residual NO3-N under different fertilization, irrigation, and plant density treatments. The model was able to estimate soil residual NO3- N levels for both non-fertilized and fertilized cases where crop N uptake early in the growing season played a key role. Corn biomass and yield under both irrigated and non-irrigated conditions were simulated reasonably well using a common model parameter set for the corn variety thus demonstrating the ability of the model stress function to handle dryland and irrigated conditions.