A CASE OF COMBINED USE OF CROP SIMULATION-MODELS AND GENERAL LINEAR-MODELS

Crop models predict both the evolution of some crop variable (e.g. bio mass, referred to as the intermediate variable) and a final variable o f interest (e.g. final yield). These models have proved to be very pow erful tools, but the quality of their prediction is generally unknown. This article presents a statistical methodology to improve the yield prediction of those crop models. Their error in simulating the value o f a final variable (i.e. the final error) can be estimated through the errors they commit in simulating an intermediate variable (i.e. inter mediate errors). The general linear model theory was used to model the se errors and to elaborate the Best Linear Unbiased Predictor (BLUP). The properties of this predictor permit the estimation of its variance , and thus gives a confidence interval for the new yield prediction, w hich the crop models cannot do. The innovative aspect of this methodol ogy resides in the identification of an exponential covariance functio n to describe the relationships between intermediate errors and of a l inear function for the covariance between intermediate and final error s. This methodology was applied on two sets of data using two differen t crop models, CERES-Wheat and EPIC, respectively. Average 90 and 60% decreases of the Mean Square Error of Prediction (MSEP) were obtained for EPIC and CERES-Wheat, respectively. This method also proved to be more efficient than a simple correction in unbiasing the yield predict ed by the model. (C) 1997 Elsevier Science B.V.