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

作者：Sexton J^*; Everingham Y; Inman Bamber G

来源：Environmental Modelling & Software, 2016, 83: 126-142.

DOI：10.1016/j.envsoft.2016.05.014

• APSIM
• Sugarcane
• GLUE
• MCMC
• Bayesian
• Calibration

Process based agricultural systems models allow researchers to investigate the interactions between variety, environment and management. The 'Sugar' module in the Agricultural Productions Systems sIMulator (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.

• 出版日期2016-9