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Pesticide transfer models in crop and watershed systems: A review

Mottes C., Lesueur Jannoyer M., Le Bail M., Malézieux E.. 2014. Agronomy for Sustainable Development, 34 (1) : p. 229-250.

DOI: 10.1007/s13593-013-0176-3

Pesticides are now occurring worldwide in almost all water resources, thus threatening the health of humans and other life. As a consequence, there is a strong social demand for designing safe cropping systems with less or no hazardous pesticides. Safe cropping systems can be designed now using pesticide transfer models. These models are mathematical tools that allow to predict the flow and concentration of pesticides in a field or a watershed. Here, we review the effects of agricultural practices on runoff, leaching, erosion, and drift from eight watershed models and nine field models. Our main findings are the following: (1) though models claim they account for practices, their effects cannot be represented. We present a method and four practice levels to assess the effects of practices in models, using tillage as an example. (2) The conceptual structure of the model highly influences the predicted distribution and transfer of pesticides. For instance, the pesticide levels remaining on the soil surface after plowing ranges from 0 % of the dose applied for the MIKE SHE-DAISY model to 100 % for GLEAMS, annAGNPS, SoilFug, and PestLCI. Only the Root Zone Water Quality Model (RZWQM) simulates pesticide interception by mulch during pesticide application. (3) Models should better take into account mulching, e.g., plastic, crop residues and associated crops, and other innovative practices. (4) A change in scale is needed for drift in watershed models. Here, topological watershed representations are the most promising way for upscaling the effects of practices. (5) Non-conservative calculations of pesticide interception by watershed mitigation structures (SWAT) should be carefully checked because these calculations underestimate the risk of pollution at the outlet. How models simulate practices will no longer be a secret for model users who apply our methodology and recommendations when selecting a model. We provide recommendations for improving tools to assess practices.

Mots-clés : martinique; france

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