Abstract: Crop models used as parts of integrated assessments often need to be run on regional, national and global scales. Calibration is an important step in the application procedure, yet on scales like this the process needs to be simplified in order to meet data requirements and computational limits. The question arises if a model calibrated in such a “simple” fashion still performs adequately at field scale, and if parameters not calibrated in the process can nevertheless be used with some confidence in later stages of the assessment. To answer this question, we applied the crop model EPIC to the simulation of sugarcane in Sao Paulo, Brazil. We once calibrated the model using Bayesian calibration to data on yield, aboveground biomass, and root weight measured in four years on two field trials in Sao Paulo. For the second calibration we used a simplified approach and calibrated the model only to FAOSTAT yield data for the whole of Brazil. Both calibrated models were applied to the simulation of stalk yield, aboveground biomass and root weight on a third field trial, and to the simulation of mean yields in Sao Paulo. The results showed that both models were able to adequately depict yields on both scales, but that the model calibrated to only national yield data was not able to accurately simulate root biomass, and to a lesser degree aboveground biomass. We conclude that a simplified calibration performs adequately on both scales, but that non-calibrated parameters may only be used with caution.