SARRA-Py: A Python-based geospatial simulation framework for agroclimatic modeling

Lavarenne Jeremy, Mbengue Asse. 2025. SARRA-Py: A Python-based geospatial simulation framework for agroclimatic modeling. SoftwareX, 30:102145, 6 p.

https://doi.org/10.1016/j.softx.2025.102145

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Url - autres données associées : https://github.com/SARRA-cropmodels/SARRA-Py / Url - jeu de données - Entrepôt autre : https://zenodo.org/records/14641803

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.

Mots-clés Agrovoc : modèle de simulation, changement climatique, zone agroclimatique, rendement des cultures, gestion du risque, culture pluviale

Mots-clés géographiques Agrovoc : Sénégal

Mots-clés libres : Agroclimatology, Crop modelling, Rainfed cereal crops, Data-scarce environments, Water-balance

Classification Agris : U30 - Méthodes de recherche

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