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a Unité d'Agronomie, INRA, BP 27, 31326 Castanet Tolosan Cedex, France
b Unité de Biométrie et d'Intelligence Artificielle, INRA, BP 27, 31326 Castanet Tolosan Cedex, France
c Laboratoire d'Agronomie, INRA INA P-G, 78850 Thiverval-Grignon, France
* Corresponding author (wallach{at}toulouse.inra.fr)
Received for publication February 22, 2000. The adjustment of the parameters in mechanistic crop models to field data, using an automatic procedure, is essential to ensure efficient and objective use of measured data. However, it is in general numerically impossible, and in any case undoubtedly unwise, to adjust all the model parameters to the measured data. There is currently no widely accepted solution to this problem. This paper proposes a new approach to parameter adjustment, and applies it to a model of corn growth and development. One begins by defining a criterion of model goodness-of-fit, which should be adapted to the goal of the modeling exercise, and a corresponding criterion of model prediction error. 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 according to how much each improves the goodness-of-fit of the model. In the second step, the number of parameters actually adjusted is chosen to minimize the prediction error criterion. This approach has the advantage of explicitly using prediction quality as a criterion. As a by-product, it leads to adjusting relatively few parameters (in our example, 3 out of the 26 potentially adjustable parameters), which considerably reduces the numerical problems. The procedure is quite straightforward to apply, although it does require substantial computing time.
Abbreviations: himax, model parameter, maximum harvest index • LAI, leaf area index • MSEP, mean squared error of prediction • p2logi, model parameter that appears in the logistic equation for leaf area index • r2hi, model parameter that describes the effect of water stress on harvest index
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