Statistical and graphical methods for evaluation of DSSAT crop model

This paper discussed statistical and graphical evaluation methods for DSSAT model using field experiments of maize, soybean, potato and winter wheat. The results indicated that R2 is not a good statistic for model evaluation because it tests the goodness of fit of a linear regression y = + x + where random error, , was assumed to follow normality, independence and equal variance. Model evaluation aims at testing residual error d = y -x（y measured data, x simulated data），but not estimating regression coefficients, , , RMSE, E, EF and d are all good difference measures, they do not need follow three assumptions, and they have clear physical meaning. Large sample size increase reliability of statistics. Graphical evaluation is a necessary method for model evaluation. Time series and residual error graphs are two basic graphical methods for model evaluation if there are measured data. Simulation graphs can also be used to display relationships among outputs or against time, to analyze residual errors or mistakes even if no measured data. EasyGrapher program is a useful tool for statistical and graphical evaluations of DSSAT models output.