Dynamic modelling of ecological systems is an essential method to understand, predict and control thedynamics of semi-natural ecosystems, which involves complex processes. The main objective of this PhDthesis is to develop a simulation model of the medium- and long-term dynamics of the herbaceous vegetationin permanent grasslands, taking into account both biodiversity and productivity. Grasslandecosystems are often hot spots of biodiversity, which contributes to the temporal stability of their services.On an agricultural perspective, this important biodiversity contributes to the forage quality, andbesides, it induces a higher ability of the vegetation cover to resist to different climatic scenarios (globalwarming, heat and drought waves).However, this key aspect of biodiversity is only poorly included in grassland models : often absent ofmodelling or included in a very simple form. Building on those considerations, this PhD work exposes thewriting of a process-based succession model, described by a system of Ordinary Differential Equationsthat simulates the aboveground vegetation dynamics of a temperate grassland. This model implementedthe main ecological factors involved in growth and competition processes of herbaceous species, and couldbe adjust to any level of diversity, by varying the number and the identity of species in the initial plantcommunity. This formalism of mechanistic models allows us to analyse relationships that link diversity,productivity and stability, in response to different climatic conditions and agricultural management.In mathematical grassland models, plant communities may be represented by a various number of statevariables, describing biomass compartments of some dominant species or plant functional types. The sizeof the initial species pool could have consequences on the outcome of the simulated ecosystem dynamicsin terms of grassland productivity, diversity, and stability. This choice could also influence the modelsensitivity to forcing parameters. To address these issues, we developed a method, based on sensitivityanalysis tools, to compare behaviour of alternative versions of the model that only differ by the identityand number of state variables describing the green biomass, here plant species. This method shows aninnovative aspect, by performing this model sensitivity analysis by using multivariate regression trees. Weassessed and compared the sensitivity of each instance of the model to key forcing parameters for climate,soil fertility, and defoliation disturbances. We established that the sensitivity to forcing parameters ofcommunity structure and species evenness differed markedly among alternative models, according tothe diversity level. We show a progressive shift from high importance of soil fertility (fertilisation level,mineralization rate) to high importance of defoliation (mowing frequency, grazing intensity) as the sizeof the species pool increased.These results highlight the need to take into account the role of species diversity to explain the behaviourof grassland models. Besides, to properly take into account those interactions in the grassland cover, theconsidered species pool size considered in the model needs to be high enough. Finally, we compare modelsimulations of the aboveground vegetation to measures from two experimental sites, the mowing grasslandof Oensingen, and the grazing grassland of Laqueuille. Results of these comparison are promising andhighlight the relevance of the choice and the representation of the different ecological processes includedin this mechanistic model.Thus, this PhD work offers a model, perfectly fitting with current needs on grassland modelling, whichcontribute to a better understanding of the herbaceous vegetation dynamics and interactions betweenproductivity, diversity and stability.