Modeling Deficit Irrigation of Maize with the DayCent Model

A promising solution to help close the gap between water demand and supply in semiarid regions is deficit irrigation strategies that use less water but aim to increase crop yield per unit of water applied. Dynamic models can be used to assess the impacts of deficit irrigation and to support decision-making. In this study, we evaluated the capability of a new version of the DayCent agroecosystem model to model crop growth, yield, and water use in response to water-deficit conditions resulting from different deficit irrigation strategies employed in three field experiments with maize (Zea mays L.) in eastern Colorado. Overall, the DayCent model with improved algorithms for plant canopy development provided accurate estimates of green leaf area index (GLAI) for full and deficit irrigation treatments, though the method tended to overpredict the GLAI during the late vegetative growth period in the deficit irrigation treatments. Simulated soil water content (SWC), biomass, and grain yields compared well with the field measurements. We tested the drought stress coefficient (K-s) method in the FAO Irrigation and Drainage Paper No. 56 as an alternative to the original DayCent method for estimating drought stress. There were only marginal differences between the two K-s methods in predicting the major output variables in DayCent, but the FAO method is simpler and easier to parameterize. Overall, the DayCent model can simulate the responses in agroecosystems with maize under water deficit conditions and could be used as a guide for applying deficit irrigation strategies for water savings.