Résumé
SARRA-Py is an open-source, Python-based adaptation of the long-standing SARRA crop model family–specifically building upon SARRA-H to enable spatially explicit agroclimatic simulations in tropical and data-limited environments. By leveraging Python's geospatial libraries (e.g., Xarray), SARRA-Py extends SARRA-H's proven crop physiology routines to large-scale, raster-based analyses, streamlines ingestion of diverse climate inputs with minimal preprocessing, and eases model customization via a modular code structure. Users interact with SARRA-Py primarily through Jupyter notebooks that provide guided workflows for data preparation, parameter configuration, and visualization of results. This design closes the gap between point-based crop models and broader geospatial frameworks, offering a practical tool for agricultural risk management, climate adaptation studies, and yield forecasting. Consequently, SARRA-Py fosters reproducible, scenario-based analyses and informs decision-making in vulnerable regions where water deficits, sparse ground observations, and climate variability threatens food security.