Prediction and parameter uncertainty for winter wheat phenology models depend on model and parameterization method differences

Abstract

Crop phenological development models are fundamental tools that can be used for scheduling agricultural practices and predicting crop yields. In previous research, different crop phenology models were compared, and different parameterization methods for crop model calibration were evaluated, which revealed that both model structures and parameterization methods were important for crop modeling. However, few studies have considered the combination of both factors and compared these influences simultaneously. Therefore, information regarding the extent of variation in model accuracy and uncertainty depending on model structure and parameterization method is lacking. In this study, we developed three winter wheat phenology models with different structures, i.e., the Agricultural Production System Simulator model for wheat (APSIM-wheat), Wang and Engel model, and sigmoid and exponential function based model, to predict the heading date as a case study. We calibrated these models using three different parameterization methods (augmented Lagrange multiplier method, Nelder-Mead method, and Bayesian optimization with Gaussian process) to investigate their effects on model accuracy and uncertainty. Six-fold cross validation of nine combinations of model calibration (3 models × 3 parameterizations) and their validation revealed that accuracies ranged mostly from 2 to 7 days in the root mean square error (RMSE). The coefficient of variation of RMSE varied widely in among model structures and parameterization methods (∼0.01–0.6). Furthermore, the coefficient of variation of model parameters also varied substantially depending both on model structure and parameterization method. Especially for the model with more parameters, we found that the prediction and parameter stability varied depending on parameterization methods. These findings suggest that both prediction and parameter uncertainty varied with model structure and parameterization method and emphasize the importance of which models and parameterization methods modelers use for robust crop phenology model.