Helping farmers to reduce herbicide environmental impacts
While pesticides help to effectively control crop pests, their collateral effects often harm the environment.On the French island of Reunion in the Indian Ocean, over 75% of the pesticides used are herbicidesand they are regularly detected in water. Agri-environmental models and pesticide risk indicators canbe used to predict and to help pesticide users to reduce environmental impacts. However, while thecomplexity of models often limits their use to the field of research, pesticide risk indicators, which areeasier to implement, do not explicitly identify the technical levers that farmers can act upon to limitsuch transfers on their scale of action (the field). The aim of this article is to contribute to developinga decision support tool to guide farmers in implementing relevant practices regarding the reduction ofpesticide transfers. In this article, we propose a methodology based on classification and regression trees.We applied our methodology to a pesticide risk indicator (I-PHY indicator) for identifying the importanceof the variables, their interactions and relative weight in contributing to the score of the indicator. Weapplied our methodology to the assessment of transfer risks linked to the use of 20 herbicides appliedto all soils in Reunion and according to different climate, plot management and product applicationscenarios (4096 scenarios tested). We constructed regression trees which identified, for each herbicideon each soil type, the contribution made by each input variable to the construction of the indicator score.The tree is represented graphically, and this aids exploration and understanding. The 20 herbicides weredivided into 3 groups that differed through the main contributing variable to the indicator score. Thesevariables were all technical levers available to farmers to limit transfer risks. These trees then becomedecision support tools specific to each pesticide user, enabling them to take appropriate decisions witha view to reducing pesticide environmental impacts.
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dig-cirad-fr-575654 |
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Bibliográfico |
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Europa del Oeste |
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Biblioteca del CIRAD Francia |
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eng |
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H02 - Pesticides P02 - Pollution H60 - Mauvaises herbes et désherbage F40 - Écologie végétale protection des plantes herbicide pratique culturale impact sur l'environnement pollution de l'eau protection de l'environnement méthodologie système d'aide à la décision méthode statistique évaluation du risque modélisation environnementale modèle risque classification analyse de régression http://aims.fao.org/aos/agrovoc/c_5978 http://aims.fao.org/aos/agrovoc/c_3566 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_24420 http://aims.fao.org/aos/agrovoc/c_8321 http://aims.fao.org/aos/agrovoc/c_15898 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_37932 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6612 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 http://aims.fao.org/aos/agrovoc/c_6543 http://aims.fao.org/aos/agrovoc/c_3081 H02 - Pesticides P02 - Pollution H60 - Mauvaises herbes et désherbage F40 - Écologie végétale protection des plantes herbicide pratique culturale impact sur l'environnement pollution de l'eau protection de l'environnement méthodologie système d'aide à la décision méthode statistique évaluation du risque modélisation environnementale modèle risque classification analyse de régression http://aims.fao.org/aos/agrovoc/c_5978 http://aims.fao.org/aos/agrovoc/c_3566 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_24420 http://aims.fao.org/aos/agrovoc/c_8321 http://aims.fao.org/aos/agrovoc/c_15898 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_37932 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6612 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 http://aims.fao.org/aos/agrovoc/c_6543 http://aims.fao.org/aos/agrovoc/c_3081 |
spellingShingle |
H02 - Pesticides P02 - Pollution H60 - Mauvaises herbes et désherbage F40 - Écologie végétale protection des plantes herbicide pratique culturale impact sur l'environnement pollution de l'eau protection de l'environnement méthodologie système d'aide à la décision méthode statistique évaluation du risque modélisation environnementale modèle risque classification analyse de régression http://aims.fao.org/aos/agrovoc/c_5978 http://aims.fao.org/aos/agrovoc/c_3566 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_24420 http://aims.fao.org/aos/agrovoc/c_8321 http://aims.fao.org/aos/agrovoc/c_15898 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_37932 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6612 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 http://aims.fao.org/aos/agrovoc/c_6543 http://aims.fao.org/aos/agrovoc/c_3081 H02 - Pesticides P02 - Pollution H60 - Mauvaises herbes et désherbage F40 - Écologie végétale protection des plantes herbicide pratique culturale impact sur l'environnement pollution de l'eau protection de l'environnement méthodologie système d'aide à la décision méthode statistique évaluation du risque modélisation environnementale modèle risque classification analyse de régression http://aims.fao.org/aos/agrovoc/c_5978 http://aims.fao.org/aos/agrovoc/c_3566 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_24420 http://aims.fao.org/aos/agrovoc/c_8321 http://aims.fao.org/aos/agrovoc/c_15898 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_37932 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6612 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 http://aims.fao.org/aos/agrovoc/c_6543 http://aims.fao.org/aos/agrovoc/c_3081 Le Bellec, Fabrice Vélu, Alice Fournier, Pascal Le Squin, Sandrine Michels, Thierry Tendero, Agnès Bockstaller, Christian Helping farmers to reduce herbicide environmental impacts |
description |
While pesticides help to effectively control crop pests, their collateral effects often harm the environment.On the French island of Reunion in the Indian Ocean, over 75% of the pesticides used are herbicidesand they are regularly detected in water. Agri-environmental models and pesticide risk indicators canbe used to predict and to help pesticide users to reduce environmental impacts. However, while thecomplexity of models often limits their use to the field of research, pesticide risk indicators, which areeasier to implement, do not explicitly identify the technical levers that farmers can act upon to limitsuch transfers on their scale of action (the field). The aim of this article is to contribute to developinga decision support tool to guide farmers in implementing relevant practices regarding the reduction ofpesticide transfers. In this article, we propose a methodology based on classification and regression trees.We applied our methodology to a pesticide risk indicator (I-PHY indicator) for identifying the importanceof the variables, their interactions and relative weight in contributing to the score of the indicator. Weapplied our methodology to the assessment of transfer risks linked to the use of 20 herbicides appliedto all soils in Reunion and according to different climate, plot management and product applicationscenarios (4096 scenarios tested). We constructed regression trees which identified, for each herbicideon each soil type, the contribution made by each input variable to the construction of the indicator score.The tree is represented graphically, and this aids exploration and understanding. The 20 herbicides weredivided into 3 groups that differed through the main contributing variable to the indicator score. Thesevariables were all technical levers available to farmers to limit transfer risks. These trees then becomedecision support tools specific to each pesticide user, enabling them to take appropriate decisions witha view to reducing pesticide environmental impacts. |
format |
article |
topic_facet |
H02 - Pesticides P02 - Pollution H60 - Mauvaises herbes et désherbage F40 - Écologie végétale protection des plantes herbicide pratique culturale impact sur l'environnement pollution de l'eau protection de l'environnement méthodologie système d'aide à la décision méthode statistique évaluation du risque modélisation environnementale modèle risque classification analyse de régression http://aims.fao.org/aos/agrovoc/c_5978 http://aims.fao.org/aos/agrovoc/c_3566 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_24420 http://aims.fao.org/aos/agrovoc/c_8321 http://aims.fao.org/aos/agrovoc/c_15898 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_37932 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6612 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 http://aims.fao.org/aos/agrovoc/c_6543 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Le Bellec, Fabrice Vélu, Alice Fournier, Pascal Le Squin, Sandrine Michels, Thierry Tendero, Agnès Bockstaller, Christian |
author_facet |
Le Bellec, Fabrice Vélu, Alice Fournier, Pascal Le Squin, Sandrine Michels, Thierry Tendero, Agnès Bockstaller, Christian |
author_sort |
Le Bellec, Fabrice |
title |
Helping farmers to reduce herbicide environmental impacts |
title_short |
Helping farmers to reduce herbicide environmental impacts |
title_full |
Helping farmers to reduce herbicide environmental impacts |
title_fullStr |
Helping farmers to reduce herbicide environmental impacts |
title_full_unstemmed |
Helping farmers to reduce herbicide environmental impacts |
title_sort |
helping farmers to reduce herbicide environmental impacts |
url |
http://agritrop.cirad.fr/575654/ http://agritrop.cirad.fr/575654/1/575654.pdf |
work_keys_str_mv |
AT lebellecfabrice helpingfarmerstoreduceherbicideenvironmentalimpacts AT velualice helpingfarmerstoreduceherbicideenvironmentalimpacts AT fournierpascal helpingfarmerstoreduceherbicideenvironmentalimpacts AT lesquinsandrine helpingfarmerstoreduceherbicideenvironmentalimpacts AT michelsthierry helpingfarmerstoreduceherbicideenvironmentalimpacts AT tenderoagnes helpingfarmerstoreduceherbicideenvironmentalimpacts AT bockstallerchristian helpingfarmerstoreduceherbicideenvironmentalimpacts |
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1792498802860490752 |
spelling |
dig-cirad-fr-5756542024-01-28T22:36:40Z http://agritrop.cirad.fr/575654/ http://agritrop.cirad.fr/575654/ Helping farmers to reduce herbicide environmental impacts. Le Bellec Fabrice, Vélu Alice, Fournier Pascal, Le Squin Sandrine, Michels Thierry, Tendero Agnès, Bockstaller Christian. 2015. Ecological Indicators, 54 : 207-216.https://doi.org/10.1016/j.ecolind.2015.02.020 <https://doi.org/10.1016/j.ecolind.2015.02.020> Helping farmers to reduce herbicide environmental impacts Le Bellec, Fabrice Vélu, Alice Fournier, Pascal Le Squin, Sandrine Michels, Thierry Tendero, Agnès Bockstaller, Christian eng 2015 Ecological Indicators H02 - Pesticides P02 - Pollution H60 - Mauvaises herbes et désherbage F40 - Écologie végétale protection des plantes herbicide pratique culturale impact sur l'environnement pollution de l'eau protection de l'environnement méthodologie système d'aide à la décision méthode statistique évaluation du risque modélisation environnementale modèle risque classification analyse de régression http://aims.fao.org/aos/agrovoc/c_5978 http://aims.fao.org/aos/agrovoc/c_3566 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_24420 http://aims.fao.org/aos/agrovoc/c_8321 http://aims.fao.org/aos/agrovoc/c_15898 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_37932 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6612 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 La Réunion France http://aims.fao.org/aos/agrovoc/c_6543 http://aims.fao.org/aos/agrovoc/c_3081 While pesticides help to effectively control crop pests, their collateral effects often harm the environment.On the French island of Reunion in the Indian Ocean, over 75% of the pesticides used are herbicidesand they are regularly detected in water. Agri-environmental models and pesticide risk indicators canbe used to predict and to help pesticide users to reduce environmental impacts. However, while thecomplexity of models often limits their use to the field of research, pesticide risk indicators, which areeasier to implement, do not explicitly identify the technical levers that farmers can act upon to limitsuch transfers on their scale of action (the field). The aim of this article is to contribute to developinga decision support tool to guide farmers in implementing relevant practices regarding the reduction ofpesticide transfers. In this article, we propose a methodology based on classification and regression trees.We applied our methodology to a pesticide risk indicator (I-PHY indicator) for identifying the importanceof the variables, their interactions and relative weight in contributing to the score of the indicator. Weapplied our methodology to the assessment of transfer risks linked to the use of 20 herbicides appliedto all soils in Reunion and according to different climate, plot management and product applicationscenarios (4096 scenarios tested). We constructed regression trees which identified, for each herbicideon each soil type, the contribution made by each input variable to the construction of the indicator score.The tree is represented graphically, and this aids exploration and understanding. The 20 herbicides weredivided into 3 groups that differed through the main contributing variable to the indicator score. Thesevariables were all technical levers available to farmers to limit transfer risks. These trees then becomedecision support tools specific to each pesticide user, enabling them to take appropriate decisions witha view to reducing pesticide environmental impacts. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/575654/1/575654.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.ecolind.2015.02.020 10.1016/j.ecolind.2015.02.020 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolind.2015.02.020 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.ecolind.2015.02.020 |