Calibration of the phenology sub-model of APSIM-Oryza: Going beyond goodness of fit. (August 2015)

• Record Type: Journal Article
• Authors: Nissanka, Sarath P.; Karunaratne, Asha S.; Perera, Ruchika; Weerakoon, W.M.W.; Thorburn, Peter J.; Wallach, Daniel
• Is Part Of: Environmental modelling & software. Volume 70(2015:Aug.)
• Page Start: 128
• Page End: 137
• Publication Date: 2015-08
• Subjects: Phenology -- Rice -- Calibration -- Uncertainty -- Photoperiod sensitivity -- Prediction -- Least squares; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.04.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Parameterizing the phenology of new crop varieties is a major challenge in crop modeling. Here we consider calibration of the phenology sub-model of the widely used crop model APSIM-Oryza, using commonly available varietal data. We show that the dynamic phenology sub-model can be well approximated by a static model, with three equations. It is then straightforward to estimate the parameters using any standard statistical software package. The approach is applied to four rice varieties from Sri Lanka. The software provides not only the best-fit parameters, but also uncertainty information about those parameters. This is essential for understanding how well the model will predict out of sample. Here the photoperiod sensitivity coefficient has large uncertainty, and so predictions for day lengths outside the data set are very unreliable. The uncertainty information is also used to show that in our case, doing more field trials would have very little effect on uncertainty. Highlights: Estimating the parameters related to phenology is essential for modeling new varieties. We show how the dynamic phenology sub model of APSIM-Oryza can be well approximated by simple static equations. It is then easy to apply standard statistical methods to obtain both best-fit parameters and parameter uncertainty. The uncertainty shows that extrapolation to other day lengths is unreliable, and that more field trials are unnecessary.