SARRA-Py: A Python-based geospatial simulation framework for agroclimatic modeling
Lavarenne, Jeremy | Mbengue, Asse | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Agence nationale de l'aviation civile et de la météorologie (ANACIM)
Source Agritrop Cirad (https://agritrop.cirad.fr/612815/)
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Показать больше [+] Меньше [-]Английский. 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.
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Эту запись предоставил Institut national de la recherche agronomique