Mechanistic vegetation models with large parameter sets and high temporal resolution are currently usedin grassland studies. They need a parsimonious screening method to identify the most influential param-eters for the grassland system in specific contexts (weather, soil, management). This is basic to betterunderstand and make use of the outputs from these models. In this study, Morris’ method was applied totest the sensitivity of a variety of outputs of the Pasture Simulation model (PaSim) to its parameters in sixEuropean multi-year grassland sites (one of them run under both extensive and intensive managementregimes). Twenty-eight parameters related to plant physiology and animal digestion were screened andranked for their sensitivity (under two distributional assumptions of parameter values), with the objec-tive of determining their stability across sites and the minimum requirements for parameter calibration.The sensitivity analysis results proved that PaSim response is fairly stable across European sites, withonly a few differences. Key results are that (1) seven influential parameters of vegetation development,aboveground growth and carbon/nitrogen partitioning were globally identified with both uniform andGaussian distributions of parameter values, (2) two additional parameters (associated with leaf and stemfibre content) were also recognized as relevant for animal CH4emissions target output, (3) listing ofkey parameters differed, but not widely, across sites and targeted outputs, and between distributions(ranking was more plastic), (4) first-order sensitivity rank (strength, ) was generally similar to (orhigher than) higher-order sensitivity (spread, ), indicating that parameters showing high interactionwith other parameters or non-linearities are those with also a high direct effect on output. Overall, Mor-ris’ method proved to be an effective and reliable tool to identify key vegetation parameters for the useof PaSim in the European conditions.