Vegetative filter strips (VFSs) are a well-established best management practice supported by the US Natural Resources Conservation Service (NRCS) used to mitigate non-point source nutrient and pesticide contributions to surface waters. The US EPA has a history of requiring that VFSs be specified on pesticide labels as a practice to reduce potential ecological and human health exposure as a condition for pesticide registrations. The effectiveness of VFSs in reducing off-site losses of pesticides is known to vary considerably, with important determining factors that include soil characteristics, filter strip slope, filter vegetation type, climate/storm intensity, and filter strip design and maintenance. This variability and uncertainty in VFS effectiveness has been one of the reasons why modeling the benefits of VFSs has not yet been adopted as a standard component in aquatic exposure modeling required for pesticide registrations. The Vegetative Filter Strip Model (VFSMOD) is a mechanistic model that has seen extensive use in recent years by academia and industry to quantify the effectiveness of VFSs and better understand the factors that affect the variability of their performance. As part of a refined aquatic exposure risk assessment, VFSMOD was integrated with EPA’s PRZM and VVWM models to account for the benefits of a VFS required by the current pesticide label. The refined exposure modeling approach probabilistically sampled the observed distribution of soil, slope, and weather conditions found near aquatic habitats of 12 different crops and regions. The thousands of simulations conducted in this analysis showed both substantial inter-region and intra-region variability in VFS effectiveness at reducing total pesticide losses and receiving water peak concentrations. This paper will present the methods that enabled the probabilistic simulation approach, patterns observed for VFS effectives, and the implications for receiving water exposure predictions.

Variability in buffer effectiveness based on VFSMOD simulations in a probabilistic exposure assessment

254th National Meeting (2017)

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