Modelling pesticides transfer to surface water at the catchment scale: a multi-criteria analysis

Payraudeau, Sylvain; Grégoire, Caroline

doi:10.1007/s13593-011-0023-3

Cited by 55 publications

(32 citation statements)

References 108 publications

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“…A review was published by Holvoet et al (2007) who recommended a combined use of monitoring data and models, and a multi-criteria comparison by Payraudeau and Gregoire (2011). The present study extends the GR model to water quality.…”

Section: Model Structurementioning

confidence: 84%

Sampling strategies based on sets of behavioural parameters. Application to a simple pesticide fate model

Leviandier

Madier

Payraudeau

2011

Stoch Environ Res Risk Assess

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Sampling strategies are necessary to minimise the cost of data acquisition. We consider the case when several samples must be analysed at the same time, knowing rainfall data, optionally discharge data. We assume also that a rainfall-runoff and pesticide transfer model is available. Three methods are presented based on behavioural sets calculated on prior events, which provide optimal sampling times with respect to different statistical criteria, for a fixed number of samples. A methodology to compare these strategies, adapted to a context of data scarcity is proposed for proper validation. We consider that the combination of a model and a strategy is efficient if it does better than a strategy without a model based on equal volumes. Even with a simple and relatively poor model, it can be concluded that the different strategies are efficient and show similar efficiency in spite of their different levels of conceptual and algorithmic complexity.

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Section: Model Structurementioning

confidence: 84%

Sampling strategies based on sets of behavioural parameters. Application to a simple pesticide fate model

Leviandier

Madier

Payraudeau

2011

Stoch Environ Res Risk Assess

Self Cite

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“…In addition, except in cases where the soil is the target of an application, the amount of drops striking it are also accounted as a form of drift (Payraudeau & Gregoire, 2012). This way, weather and field conditions create specific local and temporal situations, which comprise a set of uncontrollable factors able to impair application efficiency.…”

Section: Introductionmentioning

confidence: 99%

Spraying Quality of Crop Protection Products Using Two Droplet Spectra in Three Periods of the Day

et al. 2017

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Spraying of crop protection products can be affected by weather conditions and spray nozzles, altering deposition patterns and coverage of the target surface. The objective of this research was to analyze the quality of sprayings performed in three periods of the day (9:00 am, 2:00 pm, and 6:00 pm), using two different droplet sizes (fine and coarse) in soybeans. Coverage and deposition were evaluated in the upper, middle, and lower portion of soybean canopy. Spraying the products at 2:00 pm increased leaf coverage in the medium portion but also increased losses to the soil. Spray nozzles with fine droplet sizes provided greater coverage and deposition in the medium and upper portion of the crop for the sprayings performed in the morning. If compared to coarse droplets, fine droplets increased leaf coverage and had no interference with the losses to the soil.

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“…Many models can be used to simulate these different pollution processes (Mottes et al, 2013) but their complexity often limits their use to the field of research and to specific assessment situations (Voltz et al, 2005). Many pesticide environmental risk indicators based on operational models that are easier to implement have been developed for predicting or assessing the environmental risks linked to pesticide use (Bockstaller et al, 2009;Payraudeau and Gregoire, 2012). These pesticide risk indicators can be used to assess these risks, which are often difficult to perceive as a whole (Payraudeau and Gregoire, 2012;Reus et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Many pesticide environmental risk indicators based on operational models that are easier to implement have been developed for predicting or assessing the environmental risks linked to pesticide use (Bockstaller et al, 2009;Payraudeau and Gregoire, 2012). These pesticide risk indicators can be used to assess these risks, which are often difficult to perceive as a whole (Payraudeau and Gregoire, 2012;Reus et al, 2002). However, as these indicators are less accurate than models, Voltz et al (2005) recommended using them as a decision-support tool to prioritize risk situations rather than as a tool to predict pesticide flows.…”

Section: Introductionmentioning

confidence: 99%

Helping farmers to reduce herbicide environmental impacts

Bellec

Velu

Fournier

et al. 2015

Ecological Indicators

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While pesticides help to effectively control crop pests, their collateral effects often harm the environment.On the French island of Reunion in the Indian Ocean, over 75% of the pesticides used are herbicidesand they are regularly detected in water. Agri-environmental models and pesticide risk indicators canbe used to predict and to help pesticide users to reduce environmental impacts. However, while thecomplexity of models often limits their use to the field of research, pesticide risk indicators, which areeasier to implement, do not explicitly identify the technical levers that farmers can act upon to limitsuch transfers on their scale of action (the field). The aim of this article is to contribute to developinga decision support tool to guide farmers in implementing relevant practices regarding the reduction ofpesticide transfers. In this article, we propose a methodology based on classification and regression trees.We applied our methodology to a pesticide risk indicator (I-PHY indicator) for identifying the importanceof the variables, their interactions and relative weight in contributing to the score of the indicator. Weapplied our methodology to the assessment of transfer risks linked to the use of 20 herbicides appliedto all soils in Reunion and according to different climate, plot management and product applicationscenarios (4096 scenarios tested). We constructed regression trees which identified, for each herbicideon each soil type, the contribution made by each input variable to the construction of the indicator score.The tree is represented graphically, and this aids exploration and understanding. The 20 herbicides weredivided into 3 groups that differed through the main contributing variable to the indicator score. Thesevariables were all technical levers available to farmers to limit transfer risks. These trees then becomedecision support tools specific to each pesticide user, enabling them to take appropriate decisions witha view to reducing pesticide environmental impacts. (Résumé d'auteur

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Modelling pesticides transfer to surface water at the catchment scale: a multi-criteria analysis

Abstract: International audienc

Cited by 55 publications

References 108 publications

Sampling strategies based on sets of behavioural parameters. Application to a simple pesticide fate model

Sampling strategies based on sets of behavioural parameters. Application to a simple pesticide fate model

Spraying Quality of Crop Protection Products Using Two Droplet Spectra in Three Periods of the Day

Helping farmers to reduce herbicide environmental impacts

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