A TECHNIQUE TO DEVELOP AND VALIDATE SIMULATION-MODELS

The development and validation of simulation models requires access to extensive and detailed datasets on the subject to be modelled. The av ailability of such data is often limited. Consequently, this paper exp lores the use of resampling techniques in the estimation of parameters for and the assessment of accuracy of simulation models. A descriptio n of jackknife and cross-validation techniques is presented, as well a s an application of these techniques to the fitting of a crop simulati on model to a limited dataset. The example concerns the application of a simulation model for the growth of maize (Zea mays) in northern Aus tralia. Jackknife techniques were applied to the estimation of the pot ential kernel number and the potential kernel growth rate of a specifi ed maize hybrid, and the prediction accuracy of the estimation of grai n yield was assessed by cross-validation. The jackknife estimates were found to differ from the estimates obtained from a single fit either to all the data or to subsets of half the data sampled from the datase ts. In addition, a more reliable estimate of the prediction variance w as found from the cross-validation step compared to that found in the more traditional validation method of using one half of a dataset inde pendent of the estimation half. The use of the jackknife and cross-val idation techniques permitted a limited dataset to be used in both the parameter estimation and validation processes of model development.