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Biophysical simulation models can help to satisfactorily estimate the crop performance for grain production, their stability across years and their impact on components of hydrological balance in diverse areas once those models have been calibrated and validated with field data. The Inner Argentinian Pampas (IAP) region is very susceptible to both frequent flooding and random droughts due to flat landscape, sub-humid to semi-arid climate, coarse- textured soils and shallow water tables. The Soil Water Balance (SWB) model appears to be suitable for the IAP region since it includes most of the necessary requirements for the particular conditions of such an environment. The objectives of this study were: i) to identify the most sensitive crop parameters of the SWB model for a satisfactory estimation of aerial biomass, grain yield and crop evapotranspiration in the IAP region; ii) to parameterise and calibrate the SWB model for simulation of aerial biomass, grain yield and crop evapotranspiration of wheat (Triticum aestivum L.), soybean [ Glycine max L. (Merr.)] and maize (Zea mays L.); iii) to validate the SWB model using an independent dataset over a wide area in the IAP region. We used data from 9 field experiments for calibration and from 116 field experiments for validation. The most critical parameters, as indicated by a sensitivity analysis, were biomass-transpiration coefficient (K b), radiation use efficiency (e), and extinction coefficient (k), suggesting that they should be locally obtained before promoting the use of the SWB model in a given region. The SWB model, after calibration, was able to accurately estimate crop aerial biomass (d = 0.97–0.99, GSD = 9.5–23.9% and RMSE = 786–2,438 kg ha−1), grain yield (d = 0.95–0.96, GSD = 4.5–10.7% and RMSE = 357–637 kg ha−1), crop evapotranspiration (d = 0.97–0.99; GSD = 8.1–16.1%, RMSE = 17–51 mm) and soil water content (d = 0.87–0.96, GSD = 11.2–15.5% and RMSE = 9.6–13.2 mm). The robustness to estimate water balance, aerial biomass and grain yield fluctuations for wheat, soybean, and maize across different soil textures and rainfall variability reflects the potential of SWB model as a valuable tool to face the main challenges of the agricultural systems in our region. [ABSTRACT FROM AUTHOR]