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

[en] 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

Disciplines :

Computer science
Agriculture & agronomy

Author, co-author :

Dumont, Benjamin ; Université de Liège - ULiège > Sciences et technologie de l'environnement > Mécanique et construction

Leemans, Vincent ; Université de Liège - ULiège > Sciences et technologie de l'environnement > Mécanique et construction

Mansouri, Majdi ; Université de Liège - ULiège > Sciences et technologie de l'environnement > Mécanique et construction

Bodson, Bernard ; Université de Liège - ULiège > Sciences agronomiques > Phytotechnie des régions tempérées

Destain, Jean-Pierre ; Université de Liège - ULiège > Sciences agronomiques > Phytotechnie des régions tempérées

Destain, Marie-France ; Université de Liège - ULiège > Sciences et technologie de l'environnement > Mécanique et construction

Language :

English

Title :

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

Publication date :

February 2014

Journal title :

Environmental Modelling and Software

ISSN :

1364-8152

Publisher :

Elsevier Science

Volume :

Pages :

121-135

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://www.sciencedirect.com/science/article/pii/S136481521300265X#

Name of the research project :

Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azotés

Funders :

SPW DG03-DGARNE - Service Public de Wallonie. Direction Générale Opérationnelle Agriculture, Ressources naturelles et Environnement

Available on ORBi :

since 21 December 2013

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