Calibration of Hybrid-Maize Model for Simulation of Soil Moisture and Yield in Production Corn Fields

Abstract

Model calibration is essential for acceptable model performance and applications. The Hybrid-Maize model, developed at the University of Nebraska-Lincoln, is a process-based crop simulation model that simulates maize growth as a function of crop and field management and environmental conditions. In this study, we calibrated and validated the Hybrid-Maize model using soil moisture and yield data from eight commercial production fields in two years. We used a new method for the calibration and multi-parameter optimization (MPO) based on kriging with modified criteria for selecting the parameter combinations. The soil moisture-related parameter combination (SM-PC3) improved simulations of soil water dynamics, but improvement in model performance is still required. The grain yield-related parameter combination significantly improved the yield simulation. We concluded that the calibrated model is good enough for irrigation water management at the field scale. Future studies should focus on improving the model performance in simulating total soil water (TSW) dynamics at different soil depths by including more soil water processes in a more dynamic manner.