Whereas quality is an increasingly important aspect of peach fruit [Prunus persica (L.) Batsch] production at this time, it is still not adequately addressed in crop models. Our objective was to develop a model to assess an essential trait of peach fruit quality (the refractometric index at harvest) to include it in existing crop models and to address the issue of quality in programs dealing with the improvement of crop management. The model predicts the fruit refractometric index, an indicator of sugar content (the most decisive parameter in consumer satisfaction) commonly used by the fruit industry. The model was simple enough so that it could be easily linked to carbon-based crop models. It was calibrated and tested using several independent data sets representing many growing conditions. To account for the effect of uncertainty in input and model parameters, the output of the model was qualified by a prediction interval. Results indicated that the model accurately predicted refractometric indices under 12% (relative root mean squared error values of 0.09 and 0.12 for two data sets), which corresponds to the fruit industry's range of interest. Prediction intervals revealed that the uncertainty in model parameters has moderate effects, whereas the uncertainty of the model input has important effects