Abstract:The water-driven AquaCrop model is used extensively for simulating crop growth and water use. A three-year field experiment (2015-2017) of foxtail millet (Setaria italica) that was grown using plastic film mulching (PM) and no mulching (NM) was conducted in a rain-fed region of China to simulate canopy cover (CC), biomass, soil water content (SWC), yield, evapotranspiration (ETc), and water use efficiency (WUE). The year 2015 was much drier and warmer than the two other years. The model was calibrated using field data from 2016 and validated using the data from 2015 and 2017. Simulations of CC, biomass, and yield achieved favorable performance for both PM and NM in all years, as indicated by the high determination coefficient (R 2 ), model efficiency (EF), small root mean square error (RMSE), normalized root mean square error (NRMSE), and deviations < 10%. Simulations of SWC, ETc, and WUE gave acceptable results for both PM and NM in the normal year (2017). However, low R 2 and EF, and large NRMSE, RMSE, and deviations were observed in the predictions of PM and NM for SWC, ETc, and WUE in the dry year (2015) with a severe drought stress, indicating that the model performed unsatisfactorily under severe drought stress condition that was caused by the adverse weather. In addition, the simulation performance of NM was more favorable than that of PM for most crop growth and water use indexes under no drought stress condition.
Fast 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.
INTRODUCTIONCrop models are important tools for designing and assessing agricultural production systems, and they have been widely
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