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

Abstract

• Information on predictive quality of pesticide risk indicators is scarce • Outputs of 26 indicators and 1 model were compared to pesticide measurements in water • 3 comparison tests were performed for a dataset of 1040 measurements from 3 sites • Predictive quality was low to medium for the indicators and acceptable for the model • The model and indicators with medium predictive quality can be recommended for use 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 b 0.58). Overall, more complex indicators taking into account the soil, the climatic and the G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v 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.

Potential development of Irish agricultural sustainability indicators for current and future policy evaluation needs

“…Strassemeyer et al, 2017). It is difficult to recommend an ideal dataset to fully address pesticide risk due to the wide range and varying efficacy of pesticide risk indicators available (Pierlot et al, 2017). However, integration of pesticide usage with additional farm management data, either by extra detail in specific pesticide surveys, or by collecting further pesticide data (either directly or by linking surveys) on farms involved in wider sustainability assessment, could potentially contribute to an improved understanding of pesticide risk.…”

Current status of agricultural pesticide pollution in Turkey and evaluation of alternative control methods

“…Su dengesini kurabilmek, akış/sediment modellemesini yapabilmek için meteorolojik, topografik, toprak ve arazi yapısı ile ilgili verilerin, pestisitlerin taşınımını modelleyebilmek için de pestisit kullanımına ait verilerin sağlıklı ve yeterli olması gerekmektedir. SWAT dışında, Monte Carlo simülasyonu [109], [110], Stics-Pest [111], WATPPASS-"Watershed Agricultural Techniques and Pesticide Practices ASSessment"- [112], ve MACRO [113]- [115] pestisitlerin taşınımı ve akıbetini modellemek için kullanılmaktadır.…”

Pesticide use in sugar beet cultivation in Germany and assessment of the associated environmental risks using the risk indicator SYNOPS‐GIS

“…Pierlot et al . compared different risk indicators 21 . Indicators focusing only on properties of AI like the Pesticide Load Indicator 22 or the Environmental Impact Quotient 23 showed a lower predictive quality for risk assessment and effects of multiple pesticide exposure.…”