Nonsampling errors such as nonresponse represent an important source of bias in the production of official statistics, as it is the case of the Bavarian Agricultural Report. This inconvenience can be reduced substantially in presence of reliable auxiliary variables that can serve to adjust the estimation weights. The calibration approach represents an important adjustment instrument within a larger class of weighting methods that employ auxiliary variables. The present article analyses the robustness of the calibration approach in close comparison with poststratification. From the empirical findings, it is proven that the calibration approach delivers more reliable estimators in terms of accuracy.