Citation:

McCaffrey, K., E. Paulukonis, S. Raimondo, S. Sinnathamby, S. Purucker, AND L. Oliver. A multi-scale approach for identification of potential pesticide use sites impacting vernal pool critical habitat in California. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 857(1):159274, (2023). https://doi.org/10.1016/j.scitotenv.2022.159274

Impact/Purpose:

The US EPA's Office of Pesticide Programs is responsible for conducting pesticide risk assessments which require estimates of agricultural pesticide applications. Potential pesticide use sites are generated from the USDA's Cropland Data Layer (CDL) which is derived from satellite imagery and as such, has inherent errors. A probabilistic crop modeling approach that corrects inaccuracies in CDLs using the National Land Cover Dataset is presented, combined with a multi-scale sequential sampling analysis to render a field-level crop footprint for use in risk assessments. This approach offers a potential tool for risk assessments to improve field-level accuracy of crops to which pesticides are applied.

Description:

Spatially explicit ecological risk assessment (ERA) requires estimating the overlap between chemical and receptor distribution to evaluate the potential impacts of exposure on nontarget organisms. Pesticide use estimation at field level is prone to error due to inconsistencies between ground-reporting and geospatial data coverage; attempts to rectify these inconsistencies have been limited in approach and rarely scaled to multiple crop types. We built upon a previously developed Bayesian approach to combine multiple crop types for a probabilistic determination of field-crop assignments and to examine co-occurrence of critical vernal pool habitats and bifenthrin application within a 5-county area in California (Madera, Merced, Sacramento, San Joaquin, and Stanislaus counties). We incorporated a multi-scale repeated sampling approach with an area constraint to improve the delineation of field boundaries and better capture variability in crop assignments and rotation schemes. After comparing the accuracy of the spatial probabilistic approach to USDA Census of Agriculture crop acreage data, we found our approach allows more specificity in the combination of crop types represented by the potential application area and improves acreage estimates when compared to traditional deterministic approaches. In addition, our multi-scale sampling scheme improved estimates of bifenthrin acreage variability for co-occurrence analysis and allowed for estimates of crop rotations that were previously uncaptured. Our approach could be leveraged for more realistic, spatially resolved exposure and effects models both in and outside of California.

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