Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

B. Dumont, V. Leemans, M. Mansouri, B. Bodson, J. P. Destain, M. F. Destain

Research output: Contribution to journalArticle

30 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)121-135
Number of pages15
JournalEnvironmental Modelling and Software
Publication statusPublished - 1 Feb 2014



  • Bayes
  • Crop model
  • Parameter estimation

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modelling

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