Implementation of an automatic time‐series calibration method for the DSSAT wheat models to enhance multi‐model approaches

Abstract

AbstractMulti‐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.