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Differences in parameter estimates derived from various methods for the ORYZA(v3) Model

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文献出处
Journal of Integrative Agriculture  2022年02期
论文摘要

Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods(SCE-UA, GA and PEST) and two Bayesian-based methods(GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA(v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method(MCMCPmax) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.

论文目录
关闭目录
1. Introduction
2. Materials and methods
  2.1. Experiments and data
  2.2. ORYZA (v3) Model and parameters
  2.3. The frequentist method
  2.4. The Bayesian-based method
  2.5. Influential factors in the frequentist method for parameter estimation
  2.6. Estimation of the phenology parameters
3. Results
  3.1. Influential factors for the frequentist methods on parameter estimates
  3.2. Comparisons of parameter estimates derived from different methods
  3.3.Goodness of model fit in calibration
  3.4. Goodness of model fit in validation
4. Discussion
  4.1. Differences among parameter estimates derived from the different methods
  4.2. Effects of the differences among parameter estimates
  4.3. Suggestions for method selection in parameter estimation
5. Conclusion
Declaration of competing interest
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