Today, managing individual variability in dairy farming systems is a potentially innovative way to face the challenges of efficiency and robustness in a changing and uncertain environment. Precision farming has primarily developed through the automation of data acquisition but interpretative tools to capitalize on this raw material are lacking. The challenge is to design tools as translators of individual time series data on animal performance into phenotypic information providing quantification on variability and further useful benchmarks for decision support. In this study, we present a model of body weight trajectory with explicit representation of perturbations, and a fitting procedure on data to infer a theoretical unperturbed weight trajectory and extract perturbation features. This model-based interpretation of data provides estimates of the timing and intensity of perturbations affecting cows. The model is based on generic models describing growth and liveweight changes over repeated reproductive cycles throughout lifespan, associated with a model detecting individual perturbations. Model parameters provide quantification of unperturbed and perturbed patterns of liveweight trajectories with an estimate of the timing and intensity of perturbations affecting cows. The model was implemented in R and fitted to individual liveweight time series data recorded in commercial French Holstein herds. We performed a statistical analysis on the estimated model parameters to provide an overview of the variability between cows related to liveweight trajectories and detected perturbations. The present communication describes the model and its fitting procedure, presents results of the fitting procedure and puts into perspective the use of this method and more generally of model-based approaches as management tools in the context of precision farming.