A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model. (September 2016) - Sexton, J.; Everingham, Y.; Inman-Bamber, G.
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.