Literature DB >> 28675875

Predictive quality of 26 pesticide risk indicators and one flow model: A multisite assessment for water contamination.

Frédéric Pierlot1, Jonathan Marks-Perreau2, Benoît Réal2, Nadia Carluer3, Thibaut Constant4, Abdeljalil Lioeddine4, Paul van Dijk5, Jean Villerd6, Olivier Keichinger7, Richard Cherrier8, Christian Bockstaller9.   

Abstract

Stakeholders need operational tools to assess crop protection strategies in regard to environmental impact. The need to assess and report on the impacts of pesticide use on the environment has led to the development of numerous indicators. However, only a few studies have addressed the predictive quality of these indicators. This is mainly due to the limited number of datasets adapted to the comparison of indicator outputs with pesticide measurement. To our knowledge, evaluation of the predictive quality of pesticide indicators in comparison to the quality of water as presented in this article is unprecedented in terms of the number of tested indicators (26 indicators and the MACRO model) and in terms of the size of datasets used (data collected for 4 transfer pathways, 20 active ingredients (a.i.) for a total of 1040 comparison points). Results obtained on a.i. measurements were compared to the indicator outputs, measured by: (i) correlation tests to identify linear relationship, (ii) probability tests comparing measurements with indicator outputs, both classified in 5 classes, and assessing the probability i.e. the percentage of correct estimation and overestimation (iii) by ROC tests estimating the predictive ability against a given threshold. Results showed that the correlation between indicator outputs and the observed transfers are low (r<0.58). Overall, more complex indicators taking into account the soil, the climatic and the environmental aspects yielded comparatively better results. The numerical simulation model MACRO showed much better results than those for indicators. These results will be used to help stakeholders to appropriately select their indicators, and will provide them with advice for possible use and limits in the interpretation of indicator outputs.
Copyright © 2017 Elsevier B.V. All rights reserved.

Keywords:  Groundwater; Pesticide transfer; Predictive quality assessment; Surface water

Year:  2017        PMID: 28675875     DOI: 10.1016/j.scitotenv.2017.06.112

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Modeling the risk of water pollution by pesticides from imbalanced data.

Authors:  Aneta Trajanov; Vladimir Kuzmanovski; Benoit Real; Jonathan Marks Perreau; Sašo Džeroski; Marko Debeljak
Journal:  Environ Sci Pollut Res Int       Date:  2018-04-30       Impact factor: 4.223

  1 in total

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