Multi‐modeling (MM) approaches allow increasing modeling accuracy through a combination of different modeling structures for the simulation of plant growth and yield. The Decision Support System for Agrotechnology Transfer (DSSAT) 4.7 modeling platform currently includes three different wheat (Triticum aestivum L.) models (CERES, N‐Wheat, and Cropsim). However, the main obstacle for using an MM approach is the calibration procedure. Calibration is time consuming and complex, especially if the user is not familiar with all three models. It results in a subjective calibration optimum and might discriminate models if the user is less trained. To avoid these conflicts, an automated calibration program which optimizes cultivar coefficients based on the root means square error (RMSE) of time‐series data was developed to ensure objective calibration results across three different wheat models and to highlight the potential of MM approaches for decision support in the future. Model calibration was performed on a 4‐yr nitrogen wheat fertilizer trial (0–240 kg ha⁻¹ N) in southwest Germany. The evaluation mean showed satisfying results for the calibration (d‐index = .93) and evaluation dataset (d‐index = .81). By comparing different years, the MM approach improved modeling accuracy in most cases. Especially in the drought season of 2018, the MM approach revealed higher modeling accuracy for yield (d‐index = .61) in contrast to a single simulation of CERES (d‐index = .34) and Cropsim (d‐index = .39). This demonstrated the advantage of an MM approach as different modeling structures could compensate for errors that occur in single modeling approaches.

中文翻译：

---

为DSSAT小麦模型实施自动时间序列校准方法以增强多模型方法

多重建模（MM）方法可通过组合不同的建模结构来模拟植物生长和产量，从而提高建模精度。农业技术转让决策支持系统（DSSAT）4.7建模平台当前包括三种不同的小麦（Triticum aestivumL.）模型（CERES，N-Wheat和Cropsim）。但是，使用MM方法的主要障碍是校准程序。校准既费时又复杂，特别是如果用户不熟悉这三种型号的话。它导致主观校准最佳，并且如果用户受过较少的培训，则可能会区分模型。为避免这些冲突，开发了一种自动校准程序，该程序基于时间序列数据的均方根误差（RMSE）优化了品种系数，以确保在三种不同小麦模型上的客观校准结果，并突出了MM决策方法的潜力将来的支持。模型校准是在4年氮气小麦肥料试验（0–240千克公顷^{-1）下进行的}N）在德国西南部。对于校准（d-index = .93）和评估数据集（d-index = .81），评估平均值显示令人满意的结果。通过比较不同的年份，MM方法在大多数情况下提高了建模精度。特别是在2018年的干旱季节，与CERES（d-指数= 0.34）和Cropsim（d-index = 0.39）的单个模拟相比，MM方法显示出更高的产量建模精度（d-index = 0.61） ）。这证明了MM方法的优势，因为不同的建模结构可以补偿单一建模方法中发生的错误。