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Title: Modeling deficit irrigation of maize with the DayCent Model

item ZHANG, YAO - Colorado State University
item HANSEN, NEIL - Brigham Young University
item Trout, Thomas
item Nielsen, David
item PAUSTIAN, KEITH - Colorado State University

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/21/2018
Publication Date: 7/2/2018
Citation: Zhang, Y., Hansen, N., Trout, T.J., Nielsen, D.C., Paustian, K. 2018. Modeling deficit irrigation of maize with the DayCent Model. Agronomy Journal. 110:1-11. http://doi:10.2134/agronj2017.10.0585.

Interpretive Summary: Water is increasingly in greater demand by municipalities for human needs and industrial/manufacturing enterprises. These uses are in competition with agricultural needs for irrigation water. Limited irrigation strategies will therefore become more prevalent in irrigated corn production. Cropping systems models are needed to accurately assess the impacts of limited irrigation on corn production. This paper describes the implementation of an improved algorithm for estimating leaf area development in the DayCent model and the subsequent effects on yield estimation. Simulated soil water content, biomass, and grain yields compared well with the field measurements. Using this improved model will assist farmers and water managers in making decisions regarding use of limited irrigation strategies for saving water.

Technical Abstract: The dramatic increase in water demand in municipal and industrial sections results in less water available for crop irrigation in the semi-arid region of U.S. One of the promising solutions to close the gap between water demand and supply is limited irrigation which increases crop yield per drop and saves water. Dynamic models can be used to understand the impacts of limited irrigation in agroecosystems and provide support on decision making. In this study, we presented the parameterization and validation results of the new version of the DayCent agro-ecosystem model using three limited irrigation experiments of maize. Overall, the DayCent model with the improved algorithm in leaf area development provided an accurate estimation of green leaf area index (GLAI) for full and limited irrigation treatments, though the method tended to over-predict the GLAI at late vegetative growth period in the limited irrigation treatments. Simulated soil water content (SWC), biomass, and grain yields compared well with the field measurements. We tested the drought stress coefficient (Ks) method in the FAO Irrigation and Drainage Paper No. 56 as an alternative to the original DayCent method. We found there was a marginal difference between the two Ks methods in the major output variables but the FAO method was preferred due to its simplicity and ease for parameterization. In conclusion, the DayCent model was capable of simulating the responses in agroecosystems like maize under water deficit conditions; and could be used a guide for application of limited irrigation strategies for water saving.