کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6962405 | 1452267 | 2016 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model
ترجمه فارسی عنوان
ارزیابی تئوری و واقعی دنیای دو تکنیک بیزی برای کالیبراسیون پارامترهای مختلف در یک مدل محصول نیشکر
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 83, September 2016, Pages 126-142
Journal: Environmental Modelling & Software - Volume 83, September 2016, Pages 126-142
نویسندگان
J. Sexton, Y. Everingham, G. Inman-Bamber,