A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model. (September 2016)

• Record Type: Journal Article
• Authors: Sexton, J.; Everingham, Y.; Inman-Bamber, G.
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 126
• Page End: 142
• Publication Date: 2016-09
• Subjects: APSIM -- Sugarcane -- GLUE -- MCMC -- Bayesian -- Calibration; 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.2016.05.014
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Process based agricultural systems models allow researchers to investigate the interactions between variety, environment and management. The 'Sugar' module in theA griculturalP roductionsS ystems sIM ulator (APSIM-Sugar) currently includes definitions for 14 sugarcane varieties, most of which are no longer commercially grown. This study evaluated the use of two Bayesian approaches to calibrate sugarcane varieties in APSIM-Sugar: Generalized Likelihood Uncertainty Estimation (GLUE) and Markov Chain Monte Carlo (MCMC). Both GLUE and MCMC calibrations were able to accurately simulate green biomass and sucrose yield in both a theoretical and real world evaluation. In the theoretical evaluation GLUE and MCMC parameter estimates accurately reflected differences between two pre-defined sugarcane varieties. We found that the MCMC approach can be used to calibrate varieties in APSIM-Sugar based on yield data. With appropriate variety definitions, APSIM-Sugar could be used for early risk assessment of adopting new varieties. Highlights: We evaluate two Bayesian methods of calibrating sugarcane varieties in a crop model. Variety parameters can be estimated using limited biomass and sucrose yield data. We were able to calibrate differences between parameters of two pre-defined varieties. MCMC calibration estimates of variety parameter values were physically meaningful. Bayesian calibration can be used to routinely update crop models for new varieties.