Most agronomic crop models use a reservoir tipping-bucket approach to model the water budget in the soil. Soil available water capacity (AWC) is the main soil property considered in this approach. Because AWC is difficult to measure, uncertainty in AWC may be high. We developed a method using a specific kriging technique to determine the effects of uncertainty in AWC on crop model predictions. The AqYield crop model was used as an example to assess the effects of uncertainty in AWC on two agronomic output variables (grain yield and drainage). The factors considered were the climatic region, crop type and soil depth. We assessed the results using the coefficient of variation (CV) and sets of critical values for which CV exceeded 5, 10 and 15%. The experiment provided insight into the criticality of AWC uncertainty over a wide range of agropedoclimatic situations according to crop, model and output of interest. The method revealed the greater effect of AWC uncertainty on both outputs for the spring crop than for the winter crop and identified cases where AWC uncertainty was critical. There was a stronger effect of AWC uncertainty on yield for shallow soil and climatic water deficit conditions. For each situation, the AWC uncertainty levels were determined above or below which the impact becomes significant on a given output because the sensitivity was very dependent on climate-crop-soil combinations. It was also observed that uncertainty in AWC had little effect in AqYield for a wide range of situations. The method developed uses a small number of model simulations to produce accurate results to better understand the impact of this major soil input data according to the target model and specific objectives. It could help to determine the level of accuracy needed in AWC measurement, depending on the objectives. Highlights The method quantifies the effects of AWC uncertainty on crop models with a tipping-bucket approach. This method can assess the impact of uncertainty with only a few runs using a kriging approach. The method identifies critical situations for a wide range of agropedoclimatic conditions. Critical region graphs give critical thresholds for accuracy needed in AWC.