The simulation of phenological development in dynamic crop model: The Bayesian comparison of different methods

The main objective of our study was to use Bayesian methods to quantify the uncertainties related to phenological development of maize (Zea mays L.) under various climate conditions. For this purpose, five different phenological methods were implemented in the dynamic crop growth model, which was subsequently optimized, using the data acquired at three different locations in Slovenia. The sensitivity analysis of the crop model was performed in order to find the set of most influential physiological parameters. Subsequent Bayesian model comparison was used in order to quantify the impact of phenological method selection on the final maize yield. The results revealed the importance of using an appropriate phenological method in order to correctly estimate the duration of the growing season and yield, when used within dynamic crop model. The limitations of the phenological methods used in this study are discussed. The selection of phenological method itself did not have a significant influence on the yield estimation, except in years with high temperatures and limiting water conditions. This raises the concern that inaccurate simulation of phenological development may increase the uncertainties of impact assessment on crop yield where crop models are fed with future climate projections.

Research highlights▶ Main effects of WOFOST parameters to total output variance under different growing conditions. ▶ Bayesian calibration reduced uncertainty of parameters in dynamic crop model. ▶ Posterior distribution of model parameters are site-specific. ▶ Significant differences can occur between linear and nonlinear phenological methods. ▶ Wang–Engel model most accurately simulates phenological development of maize.