期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 83, 期 -, 页码 126-142出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2016.05.014
关键词
APSIM; Sugarcane; GLUE; MCMC; Bayesian; Calibration
类别
资金
- Sugar Research Australia
- James Cook University, Master's Thesis project [STU076]
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. (C) 2016 Elsevier Ltd. All rights reserved.
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