An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
Environmental Modelling & Software
A package of parameter estimation methods and implementation for the STICS crop-soil model
Environmental Modelling & Software
Environmental Modelling & Software
Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling
Environmental Modelling & Software
Environmental Modelling & Software
Modeling and prediction of nonlinear environmental system using Bayesian methods
Computers and Electronics in Agriculture
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This study presents a Bayesian approach for the parameters' identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling.