Agri-STAMP: A spatial model representing the use and fate of pesticides over the long term
2025
Tran, Annelise | Dufleit, Victor | Bonnal, Vincent | Degenne, Pascal | Lavarenne, Jeremy | Lecat, Lucie | Cattan, Philippe | 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) | Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | European Commission;EC;UE;http://dx.doi.org/10.13039/501100000780 | Conseil Regional de la Guadeloupe;Guadeloupe Regional Council;GLP;http://dx.doi.org/10.13039/501100013698
Source Agritrop Cirad (https://agritrop.cirad.fr/612168/) * Autres projets (id;sigle;titre): ;GESSICa;(FRA) Projet Facteurs de risque associés aux Cancers en Guadeloupe: Environnement, contexte Socioéconomique//
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Mostrar más [+] Menos [-]Inglés. The widespread use of pesticides in agriculture has long been associated with serious health risks, including cancer, diabetes, neurological disorders, and reproductive issues. However, pinpointing the specific pesticides responsible for these health effects remains a challenge due to limited datasets and the delayed onset of diseases. Traditional epidemiological studies often rely on case-control studies methods, with in recent approaches land use as a proxy for pesticide exposure. In this study, we introduce Agri-STAMP, a spatial process-based model designed to estimate pesticide contamination over time by accounting for agricultural practices, pesticide application techniques, and environmental factors. We applied Agri-STAMP to Guadeloupe, French West Indies, where the pesticide chlordecone has had significant health impacts. The model integrates diverse data sources, including soil types, rainfall patterns, land use, and pesticide application records. Simulations were run from 1972 to 2014 on 65 pesticide active substances used in banana, sugar cane and market gardening crop systems in Guadeloupe. Results indicated strong correlations between predicted and observed pesticide concentrations (Kendall's τ comprised between 0.46 and 0.6), suggesting the model's potential for accurate predictions. Moreover, the quantities of pesticide active substances simulated by the Agri-STAMP model were significantly correlated with the actual quantities sold according to the national database on crop pesticide sales between 2009 and 2014 (Pearson R = 0.95, CI = 0.92–0.97). The proposed modeling approach, which is adaptable to different agricultural contexts, offers valuable insights into historical and current pesticide contamination, which can be used to support epidemiological studies on cancer incidence and guide public health policies. The Agri-STAMP tool also provides a basis for discussing and guiding land management decisions to mitigate health risks, making it a promising asset for future research and policy-making.
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Información bibliográfica
Este registro bibliográfico ha sido proporcionado por Institut national de la recherche agronomique