This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observationsaimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model.