Simulation of consumer exposure to deoxynivalenol according to wheat crop management and grain segregation: Case studies and methodological considerations
2005
Bail, Marianne Le | Verger, Philippe | Doré, Thierry | Fourbet, Jean-François | Champeil, Agnès | Ioos, Renaud | Leblanc, Jean-Charles | Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Méthodologies d'Analyse de Risque Alimentaire (MET@RISK) ; Institut National de la Recherche Agronomique (INRA) | Agronomie ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Unité de Mycologie (USC INRAE LSV-mycologie) ; Laboratoire de la santé des végétaux (LSV) ; Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
International audience
Afficher plus [+] Moins [-]anglais. We combined agronomic data and a model simulating exposure based on consumption data to assess the impact of crop management and grain segregation procedures on consumer exposure to deoxynivalenol. We used three scenarios of soil tillage at a regional scale and three scenarios of grain segregation for a supply area. The soil tillage scenarios were applied to a range of mean crop contamination levels, with various coefficients representing the degree of tillage. The grain segregation scenarios were applied to two real datasets of DON content distributions. We found that the increase in consumer exposure in response to increases in "risky" crop management practices such as direct-drilling depends largely on mean contamination and on the value of the tillage coefficient. The results for grain segregation procedures showed that exposure was most strongly affected by contamination distributions as the segregation procedure minimising risk differed for the two datasets.
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