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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Bibliographic Details
Main Authors: Le Bellec, Fabrice, Vélu, Alice, Fournier, Pascal, Le Squin, Sandrine, Michels, Thierry, Tendero, Agnès, Bockstaller, Christian
Format: article biblioteca
Language:eng
Subjects: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,
Online Access:http://agritrop.cirad.fr/575654/
http://agritrop.cirad.fr/575654/1/575654.pdf
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id dig-cirad-fr-575654
record_format koha
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic 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
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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