Calibrating AquaCrop model using genetic algorithm with multi‐objective functions applying different weight factors

Abstract

AbstractFast and efficient calibration is essential for the effective application of crop models. However, many formulas, parameters, and nonlinear responses in crop models make calibration difficult and time consuming. Using an intelligent optimization algorithm to calibrate the model has advantages in global search ability, optimization speed, and automatic calibration compared to the manual trial and error method, although performance may depend strongly on the objective function used. This study evaluated the use of an improved genetic algorithm, namely elite genetic algorithm (EGA), for calibration of a water‐driven crop model (AquaCrop) using three different objective functions separately, which comprise observed variables from harvest and in‐season data and differ in calculating the weight factors of these variables. Observations of maize (Zea mays L.) and wheat (Triticum aestivum L.) under different irrigation treatments were used for model calibration and validation. The results showed satisfactory calibration performances for the EGA applying the three objective functions, that is, the coefficient of determination and index of agreement were all >0.97 for canopy cover (CC) and biomass of both maize and wheat, and also showed good agreement between simulated and observed soil water storage. The three objective functions differed in calibration speed and performance, since they differ in error source and calculation, moreover, they performed similar or better than manual calibration. The validation results showed that the AquaCrop model calibrated by the EGA can predict CC, biomass, yield, and soil water storage of maize and wheat. In general, calibration of the AquaCrop model using EGA greatly improves the model application efficiency for irrigation management.