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Uncertainty in wheat phenology simulation induced by cultivar parameterization under climate warming

Liu, Leilei, Wallach, Daniel, Li, Jun, Liu, Bing, Zhang, Linxiang, Tang, Liang, Zhang, Yu, Qiu, Xiaolei, Cao, Weixing, Zhu, Yan
European journal of agronomy 2018 v.94 pp. 46-53
Triticum aestivum, computer software, crop models, crop production, crops, cultivars, flowering, global warming, least squares, model uncertainty, nonlinear models, parameter uncertainty, phenology, prediction, sowing, spring, spring wheat, temperature, winter, winter wheat, China
Rigorous calibration of crop phenology models, providing both best-estimate parameters and estimates of parameter uncertainty, is essential for evaluating how crops will respond to future environmental and management changes. Least squares parameter estimation is a widely used approach to calibration of nonlinear models, and there are many software packages available for implementing this approach. However, these packages are rarely if ever used for complex phenology models because of technical difficulties. The purpose of this research is to overcome these difficulties, in particular the issue of a model which is a discontinuous function of the parameters. The calculations were conducted with the WheatGrow phenology model, but the approach is applicable to other complex phenology models. The approach was used to calibrate WheatGrow phenology for 4 widely used cultivars in the main winter wheat production region of China. The resulting fit to the data was quite good (root mean squared error (RMSE) of 3–4 days for flowering and maturity). The coefficients of variation (CV) of the parameters ranged from 6% to 40%. Furthermore, the model was used to predict the effect of warming on phenology, and the uncertainty in those predictions. The results showed that each degree of warming reduced the time from sowing to flowering by 7–8 days for the spring cultivars and 3–4 days for the winter cultivars. The time form flowering to maturity is hardly affected. In addition, the higher the temperature, the larger the uncertainty in the predictions. Comparison with variability in multi-model ensembles suggests that parameter uncertainty is less than the model uncertainty.