Modelling pesticides leaching in cropping systems: Effect of uncertainties in climate, agricultural practices, soil and pesticide properties. (November 2018)

• Record Type: Journal Article
• Authors: Lammoglia, Sabine-Karen; Brun, François; Quemar, Thibaud; Moeys, Julien; Barriuso, Enrique; Gabrielle, Benoît; Mamy, Laure
• Is Part Of: Environmental modelling & software. Volume 109(2018)
• Page Start: 342
• Page End: 352
• Publication Date: 2018-11
• Subjects: Uncertainty analysis -- Latin hypercube sampling -- Meta-model -- Pesticide leaching -- STICS-MACRO; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.08.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Modelling of pesticide leaching is paramount to managing the environmental risks associated with the chemical protection of crops, but it involves large uncertainties in relation to climate, agricultural practices, soil and pesticide properties. We used Latin Hypercube Sampling to estimate the contribution of these input factors with the STICS-MACRO model in the context of a 400 km 2 catchment in France, and two herbicides applied to maize: bentazone and S-metolachlor. For both herbicides, the most influential input factors on modelling of pesticide leaching were the inter-annual variability of climate, the pesticide adsorption coefficient and the soil boundary hydraulic conductivity, followed by the pesticide degradation half-life and the rainfall spatial variability. This work helps to identify the factors requiring greater accuracy to ensure better pesticide risk assessment and to improve environmental management and decision-making processes by quantifying the probability and reliability of prediction of pesticide concentrations in groundwater with STICS-MACRO. Highlights: Uncertainty analysis on the pesticide outputs of STICS-MACRO model was performed. Climate, agricultural practices, soil and pesticide properties were considered. Influential factors were climate temporal variability, Kf, hydraulic conductivity. Small spatial variation in rainfall led to high variation in pesticide concentrations. The results help to improve environmental management and decision-making processes.