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

Sexton, J., Everingham, Y., and Inman-Bamber, G. (2016) A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model. Environmental Modelling & Software, 83. pp. 126-142.

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Abstract

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.

Item ID: 44178
Item Type: Article (Research - C1)
ISSN: 1873-6726
Keywords: APSIM; sugarcane; GLUE; MCMC; Bayesian; calibration
Funders: Sugar Research Australia (SRA), James Cook University (JCU)
Projects and Grants: SRA Scholarship STU076
Date Deposited: 26 Jul 2016 01:06
FoR Codes: 07 AGRICULTURAL AND VETERINARY SCIENCES > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation @ 50%
01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50%
SEO Codes: 82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8203 Industrial Crops > 820304 Sugar @ 100%
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