RESUMEN
BACKGROUND: Thanks to the changes in aquatic risk assessment within the marketing authorization (MA) process in France, the contamination of surface water through the subsurface drainage network is better accounted for. The measure adopted by risk regulations is to prohibit any use of selected pesticides on drained plots. Herbicide solutions on subsurface-drained plots are becoming scarce due to a limited number of innovations combined with the re-approvals process. Autumn weed management then becomes a major issue for winter cropping systems on drained plots. Unlike runoff prevention, few risk management measures are available to prevent the risks associated with drained plots. RESULTS: We analyzed data from La Jaillière, an ARVALIS experimental site (nine plots, 1993 to 2017), representative of scenario D5 from the EU FOCUS Group, for four herbicides (isoproturon, aclonifen, diflufenican, flufenacet). Our study demonstrates the relevance of the time application management measure by showing the decreasing trend in the transfer of pesticides in drained plots. In addition, it validates, still on the La Jaillière site, the hypothesis of a management measure based on an indicator of soil profile saturation before drainage flow (soil wetness index, SWI). CONCLUSIONS: A conservative measure consisting of restricting pesticide applications during autumn, when the SWI is <85% of saturation, reduces the risk by a factor of 4-12 for quantification above the predicted no-effect concentration and values of maximum or flow weight average concentrations by 70- and 27-fold, ratio of exported pesticide by 20-fold, and total flux by 32. This measure based on SWI threshold appears to be more efficient than those using other restriction factors. SWI can be easily calculated by considering the local weather data and soil properties for any drained field. © 2023 Society of Chemical Industry.
Asunto(s)
Herbicidas , Plaguicidas , Contaminantes Químicos del Agua , Plaguicidas/análisis , Grano Comestible , Suelo/química , Herbicidas/química , Agua , Contaminantes Químicos del Agua/análisis , AgriculturaRESUMEN
Stakeholders involved in actions to reduce the use and the impacts on the environment or human health of pesticides need operational tools to assess crop protection strategies in regard to these impacts. I-Phy3 brings together all improvements introduced since the first version of the indicator to better meet user's needs and requirements of integrating processes. I-Phy3 was deeply modified to ensure its predictive quality. I-Phy 3 is structured in three levels of aggregation in form of hierarchical fuzzy decision trees designed with the CONTRA method. At the 1st level, five basic subindicators assess the risk of contamination (RC) for the different transfer pathways involved in surface water, ground water and atmosphere contamination: leaching, runoff, drainage, drift, volatilization. At the 2nd level, RC subindicators are aggregated with a toxicity variable (human or aquatic) in a risk indicator. At the 3rd level, the global indicator I-Phy3 results from the aggregation of three risk indicators for groundwater, surface waters and air. I-Phy3 yielded better validation results than its previous versions. This effort to assess the predictive quality of the indicator should be pursued and completed by a feasibility and utility test by end-users. A subindicator on risk of soil contamination is a gap which remains to fill.
RESUMEN
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.