Context: Agroforestry systems provide critical benefits for food security and climate change mitigation. Yet, they are complex and heteregoneous sytems hard to optimize. The use of process-based crop models provides an opportunity to understand better the interactions between soil, crop, tree and climate and explore the impact of agroforestry on crop growth, for contrasting crop management. Objective: The objectives of this study were to i) calibrate the soil-crop STICS model for pearl millet (Pennisetum glaucum) in order to simulate millet potential growth and impact of water and nitrogen limitations on millet growth in open fields and ii) explore the impacts of the parkland tree Faidherbia albida on millet performance for contrasting N fertilizer inputs. Methods: We gathered a comprehensive database of 28 agronomically contrasting situations, ranging from near-potential growth to drought- and N-stress, either on-station or in a farmer's home- or bush-fields. Parameters governing relevant plant and soil processes for grain yield were calibrated in a stepwise procedure. The calibrated model was used to explore the impact on millet growth of the widely reported benefits of Faidherbia albida, namely a minimum reduction in radiation thanks to the peculiar reverse phenology of this tree, improvement of soil water content at the beginning of the growing season and of organic nitrogen in the topsoil. Results: Model simulations with the calibration dataset were reasonably accurate for aboveground biomass and grain yield. Normalized Root Mean Square Errors (nRMSE) for these variables were 29% and 26%, respectively; model efficiency (EF) was 0.58 for both. The nRMSE ranged from 33% to 53% for Soil Water Content (SWC), plant N uptake, grain number, and leaf-area index (LAI). Model accuracy was lower with the evaluation dataset. In the virtual experiment, millet yield decreased with incoming solar radiation, but only at levels of shading (e.g. below 80% of the radiation obtain