Parameter estimation for crop models: A new approach and application to a corn model

The adjustment of the parameters in mechanistic crop models to field data, using an automatic procedure, is essential to ensure efficient and objectiv e use of measured data. However, it is in general numerically impossible, a nd in any case undoubtedly unwise, to adjust all the model parameters to th e measured data. There is currently no widely accepted solution to this pro blem. This paper proposes a new approach to parameter adjustment, and appli es it to a model of corn growth and development. One begins by defining a c riterion of model goodness-of-fit, which should be adapted to the goal of t he modeling exercise, and a corresponding criterion of model prediction err or. For the latter we propose a cross validation version of the goodness-of -fit criterion. In Step 1 of the algorithm, one orders the parameters accor ding to how much each improves the goodness-of-fit of the model. In the sec ond step, the number of parameters actually adjusted is chosen to minimize the prediction error criterion. This approach has the advantage of explicit ly using prediction quality as a criterion. As a by-product, it leads to ad justing relatively few parameters tin our example, 3 out of the 26 potentia lly adjustable parameters), which considerably reduces the numerical proble ms. The procedure is quite straightforward to apply, although it does requi re substantial computing, time.