Sensitivity analysis for mathematical simulation models is helpful in identifying influential parameters for model outputs. Representative sets of APEX (Agricultural Policy/Environmental eXtender) model data from across the U.S. were used for sensitivity analysis to identify influential parameters for APEX outputs of crop grain yields, runoff/water yield, water and wind erosion, nutrient loss, and soil carbon change for a national assessment project: the Conservation Effects Assessment Project (CEAP). The analysis was based on global sensitivity analysis techniques. A test case, randomly selected from the representative sets of APEX model data, was first analyzed using both the variance-based sensitivity analysis technique and the enhanced Morris method. The analysis confirmed the reliability of the enhanced Morris measure in screening subsets of influential and non-influential parameters. Therefore, the enhanced Morris method was used for the national assessment, where the cost of applying variance-based techniques would be excessive. Although sensitivities are dynamic in both temporal and spatial dimensions, the very influential parameters (ranking 1st and 2nd) appear very influential in most cases. Statistical analyses identified that the NRCS curve number index coefficient is very influential for runoff and water-related output variables, such as soil loss by water, N and P losses in runoff. The Hargreaves PET equation exponent, moisture fraction required for seed germination, RUSLE C factor coefficient, and the potential heat units are influential for more than two APEX outputs studied.
