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.

中文翻译：

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使用具有不同权重因子的具有多目标函数的遗传算法校准AquaCrop模型

快速有效的校准对于作物模型的有效应用至关重要。但是，作物模型中的许多公式，参数和非线性响应使得校准困难且耗时。尽管性能可能很大程度上取决于所使用的目标函数，但与手动试验和错误方法相比，使用智能优化算法来校准模型在全局搜索能力，优化速度和自动校准方面具有优势。这项研究评估了使用改进的遗传算法（即精英遗传算法（EGA））分别使用三个不同的目标函数来校准水驱动作物模型（AquaCrop）的功能，这些目标函数包括来自收获和季节数据的观测变量，以及在计算这些变量的权重因子方面有所不同。玉米的观察玉米（Zea mays L.）和小麦（Triticum aestivumL.）在不同灌溉处理下用于模型校准和验证。结果表明，应用这三个目标函数对EGA的校准性能令人满意，即玉米和小麦的冠层覆盖量和生物量的测定系数和一致性指数均> 0.97，并且两者之间也显示出良好的一致性。模拟和观察土壤水储量。这三个目标函数的校准速度和性能有所不同，因为它们的误差来源和计算方法有所不同，而且，它们执行的性能与手动校准相似或更好。验证结果表明，通过EGA校准的AquaCrop模型可以预测玉米和小麦的CC，生物量，产量和土壤水储量。一般来说，