Improving rice phenology simulations based on the Bayesian model averaging method

Abstract

Crop simulation models play an increasingly important role in agricultural management. Therefore, improving crop models’ accuracy and reliability has become a key issue. Previous studies have shown that multi-model ensembles (MMEs) perform better than individual models and are capable of assessing model uncertainty. Here, we study the use of the Bayesian model averaging (BMA) method to improve the simulation performance of rice phenology models. The main objective of this paper is to apply BMA to five individual rice phenology models (CERES-Rice, the beta model, ORYZA2000, the rice clock model, and the simulation model for the rice-weather relationship) and to compare its simulation performance with an MME based on the mean value (Mean-MME). Furthermore, we quantify and evaluate the uncertainty of the individual and ensemble models. Finally, we analyze the prediction results of two ensemble models and five individual models under future climate scenarios. The result indicates that, compared with the accuracies of individual models, the accuracies of BMA and Mean-MME are 12.75% and 11.48% higher, respectively. BMA performs the best when the individual models had the best parameters, effectively optimizing the phenology simulation results of the individual models. For individual and ensemble models, the squared bias accounts for 48–88%, which is the largest contributor to overall prediction uncertainty. Under the future climate scenarios, the five models predict a range of 0.40–9.78 days in the phenological period, whereas the two ensemble models predict a smaller range of 0.54–1.63 days, which shows that the MMEs reduce the uncertainty of the individual models. We conclude that BMA is suitable for improving the accuracy and reliability of crop modeling.