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Identifying pesticide mixtures at country-wide scaleuse asterix (*) to get italics
Milena Cairo, Anne-Christine Monnet, Stéphane Robin, Emmanuelle Porcher, Colin FontainePlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p style="text-align: justify;">Wild organisms are likely exposed to complex mixtures of pesticides owing to the large diversity of substances on the market and the broad range agricultural practices. The consequences of such exposure are still poorly understood, first because of potentially strong synergistic effects, making cocktails effects not predictable from the effects of single compounds, but also because little is known about the actual exposure of organisms to pesticide mixtures in natura.&nbsp;</p> <p style="text-align: justify;">We aimed to identify the number and composition of pesticide mixtures potentially occurring in French farmland, using a database of pesticide purchases in postcodes. We developed a statistical method based on a model-based clustering (mixture model) to cluster postcodes according to the identity, purchase probability and quantity of 279 active substances.&nbsp;</p> <p style="text-align: justify;">We found that the 5,642 French postcodes can be clustered into a small number of postcode groups (ca. 20), characterized by a specific pattern of pesticide purchases, i.e. pesticide mixtures. Substances defining mixtures can be sorted into “core” substances highly probable in most postcode groups and “discriminating” substances, which are specific to and highly probable in some postcode groups only, thus playing a key role in the identity of pesticide mixtures. We found 12 core substances: two insecticides (deltamethrin and lambda-cyhalothrin), six herbicides (glyphosate, diflufenican, fluroxypyr, MCPA, 2,4-d, triclopyr) and four fungicides (fludioxonil, tebuconazole, difenoconazole, thiram). The number of discriminating substances per postcode group ranged from 2 to 74. These differences in substance purchases seemed related to differences in crop composition but also potentially to regional effects.&nbsp;</p> <p style="text-align: justify;">Overall, our analyses return (1) sets of molecules that are likely to be part of the same pesticide mixtures, for which synergetic effects should be investigated further and (2) areas within which biodiversity might be exposed to similar mixture composition. This information will hopefully be of interest for future ecotoxicological studies to characterise the actual impacts of pesticide cocktails on biodiversity in the field.</p> should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https:// should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
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Active substances, cluster, mixture model, expectation-maximization algorithm, risk assessment
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Environmental pollution, Environmental risk assessment, Method standardization, Other
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2022-10-14 17:13:06
Pierre Labadie
Clémentine FRITSCH, Patrice Couture