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Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice

Wallach, Daniel, Nissanka, Sarath P., Karunaratne, Asha S., Weerakoon, W.M.W., Thorburn, Peter J., Boote, Kenneth J., Jones, James W.
European journal of agronomy 2017 v.88 pp. 53-62
crop models, rice, phenology, least squares, global warming, parameter uncertainty, model uncertainty, temperature, case studies, prediction, Sri Lanka
We consider predictions of the impact of climate warming on rice development times in Sri Lanka. The major emphasis is on the uncertainty of the predictions, and in particular on the estimation of mean squared error of prediction. Three contributions to mean squared error are considered. The first is parameter uncertainty that results from model calibration. To take proper account of the complex data structure, generalized least squares is used to estimate the parameters and the variance-covariance matrix of the parameter estimators. The second contribution is model structure uncertainty, which we estimate using two different models. An ANOVA analysis is used to separate the contributions of parameter and model uncertainty to mean squared error. The third contribution is model error, which is estimated using hindcasts. Mean squared error of prediction of time from emergence to maturity, for baseline +2°C, is estimated as 108days2, with model error contributing 86days2, followed by model structure uncertainty which contributes 15days2 and parameter uncertainty which contributes 7days2. We also show how prediction uncertainty is reduced if prediction concerns development time averaged over years, or the difference in development time between baseline and warmer temperatures.