Sow S. auteur aut IRD Senghor Y. auteur aut IRD Sadio K. auteur aut IRD Vezy R. auteur aut IRD Roupsard O. auteur aut IRD Affholder F. auteur aut IRD N'Dienor M. auteur aut IRD Clermont Dauphin Cathy auteur aut IRD Gaglo E. K. auteur aut IRD Ba S. Y. A. auteur aut IRD Tounkara A. auteur aut IRD Balde A. B. auteur aut IRD Agbohessou Y. auteur aut IRD Seghieri Josiane auteur aut IRD Sall S. N. auteur aut IRD Couedel A. auteur aut IRD Leroux L. auteur aut IRD Jourdan C. auteur aut IRD Diaite D. S. auteur aut IRD Falconnier G. N. auteur aut IRD text journalArticle eng text/pdf born digital access 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 nearpotential 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; specialized Crop model Sustainable intensification Calibration Validation Virtual experiment Senegal SENEGAL 076 082 020 306 109206 [15 p.] 2024 0378-4290 https://www.documentation.ird.fr/hor/fdi:010088848 10.1016/j.fcr.2023.109206 0378-4290 [F B010088848] https://www.documentation.ird.fr/hor/fdi:010088848 https://www.documentation.ird.fr/intranet/publi/2024-02/010088848.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2024-02-07 2026-06-12 fdi:010088848 fre