Identifying pesticide mixtures at country-wide scale
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 cock...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Peer Community In
2024-10-01
|
| Series: | Peer Community Journal |
| Subjects: | |
| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.472/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206430029840384 |
|---|---|
| author | Cairo, Milena Monnet, Anne-Christine Robin, Stéphane Porcher, Emmanuelle Fontaine, Colin |
| author_facet | Cairo, Milena Monnet, Anne-Christine Robin, Stéphane Porcher, Emmanuelle Fontaine, Colin |
| author_sort | Cairo, Milena |
| collection | DOAJ |
| description | 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 nature. 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. 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. 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. |
| format | Article |
| id | doaj-art-b9b07b74127d43149f662afa27059aa0 |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-b9b07b74127d43149f662afa27059aa02025-02-07T10:17:17ZengPeer Community InPeer Community Journal2804-38712024-10-01410.24072/pcjournal.47210.24072/pcjournal.472Identifying pesticide mixtures at country-wide scale Cairo, Milena0https://orcid.org/0000-0002-2859-1514Monnet, Anne-Christine1https://orcid.org/0000-0002-9192-5839Robin, Stéphane2Porcher, Emmanuelle3https://orcid.org/0000-0002-9264-8239Fontaine, Colin4https://orcid.org/0000-0001-5367-5675Centre d'Ecologie et des Sciences de la COnservation, 55 rue Buffon, 75005 Paris, FranceCentre d'Ecologie et des Sciences de la COnservation, 55 rue Buffon, 75005 Paris, FranceLaboratoire de Probabilités, Statistique et Modélisation, Campus Jussieu Tour 16-26, 1er étage 4, Place Jussieu 75005 Paris / Bâtiment Sophie Germain 5ème étage Avenue de France, 75013 Paris, FranceCentre d'Ecologie et des Sciences de la COnservation, 55 rue Buffon, 75005 Paris, FranceCentre d'Ecologie et des Sciences de la COnservation, 55 rue Buffon, 75005 Paris, FranceWild 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 nature. 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. 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. 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.https://peercommunityjournal.org/articles/10.24072/pcjournal.472/Active substances, Cluster, mixture model, expectation-maximization algorithm, risk assessment |
| spellingShingle | Cairo, Milena Monnet, Anne-Christine Robin, Stéphane Porcher, Emmanuelle Fontaine, Colin Identifying pesticide mixtures at country-wide scale Peer Community Journal Active substances, Cluster, mixture model, expectation-maximization algorithm, risk assessment |
| title | Identifying pesticide mixtures at country-wide scale
|
| title_full | Identifying pesticide mixtures at country-wide scale
|
| title_fullStr | Identifying pesticide mixtures at country-wide scale
|
| title_full_unstemmed | Identifying pesticide mixtures at country-wide scale
|
| title_short | Identifying pesticide mixtures at country-wide scale
|
| title_sort | identifying pesticide mixtures at country wide scale |
| topic | Active substances, Cluster, mixture model, expectation-maximization algorithm, risk assessment |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.472/ |
| work_keys_str_mv | AT cairomilena identifyingpesticidemixturesatcountrywidescale AT monnetannechristine identifyingpesticidemixturesatcountrywidescale AT robinstephane identifyingpesticidemixturesatcountrywidescale AT porcheremmanuelle identifyingpesticidemixturesatcountrywidescale AT fontainecolin identifyingpesticidemixturesatcountrywidescale |