Parameter identification of the STICS crop model, using an accelerated formal MCMC approach
An MH-MCMC algorithm is for the first time coupled with the STICS soil-crop model. The DREAM algorithm appears suited to identify parameters of the STICS model. The proposed CV-likelihood function offers similar results to the WLS error model. An appropriate representation of measurement errors decreases simulation quality. A suited error model allows to converge on physically based parameters values.
stics crop model dream parameter estimation bayes