LAE, Université de Lorraine, INRA, 54500, Vandoeuvre, France; Chambre Régionale d'Agriculture Grand Est, Pôle Recherche Développement et Innovations, France.
Arvalis - Institut du Végétal, France.
Sci Total Environ. 2017 Dec 15;605-606:655-665. doi: 10.1016/j.scitotenv.2017.06.112. Epub 2017 Jul 1.
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.
利益相关者需要操作工具来评估作物保护策略对环境的影响。需要评估和报告农药使用对环境的影响，这导致了许多指标的发展。然而，只有少数研究涉及这些指标的预测质量。这主要是由于适应于将指标输出与农药测量进行比较的数据集数量有限。据我们所知，就测试的指标数量（26 个指标和 MACRO 模型）和使用的数据集大小（为 4 种转移途径、20 种活性成分（a.i.）收集的数据，总共有 1040 个比较点）而言，与本文提出的水质相比，评估农药指标的预测质量是前所未有的。在 a.i.测量中获得的结果与通过以下方法测量的指标输出进行了比较：（i）相关性测试以识别线性关系，（ii）概率测试比较测量值与指标输出值，两者均分为 5 类，并评估概率，即正确估计和高估的百分比，（iii）通过 ROC 测试估计针对给定阈值的预测能力。结果表明，指标输出与观察到的转移之间的相关性较低（r<0.58）。总体而言，考虑土壤、气候和环境方面的更复杂指标产生了相对较好的结果。数值模拟模型 MACRO 显示出比指标更好的结果。这些结果将用于帮助利益相关者适当选择他们的指标，并为他们提供关于指标输出的可能使用和解释限制的建议。
