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Remotely-sensed and field-collected hydrological, landscape and weather data can predict the quality of surface water used for produce production


There is a clear need for the development of improved, science-based tools to help reduce pre- harvest introduction of microbial produce safety risks through surface water use. The purpose of this project is (i) to identify and prioritize spatial and temporal risk factors for microbial contamination of surface water, and (ii) to develop geospatial models that predict surface water microbial quality, which will be assessed by quantifying generic E. coli and testing for key pathogens (e.g., Salmonella). Spatial and temporal variation in water quality will be assessed by repeatedly testing multiple water sources over two years. Publicly available remotely sensed data (e.g., predominant upstream land-use) will be used to identify factors that are associated with elevated E. coli levels, and an increased risk of pathogen detection. Data collection will be performed in two produce growing regions (AZ and NY) to assess the robustness of our models and their translatability to other regions. These data and models will allow growers to identify times and locations where surface water sources are more likely to be microbially contaminated. This will enable growers to better time water use, testing, and treatment to minimize produce safety risks associated with microbially contaminated surface water.

Wiedmann, Martin; Rock, Channah
Cornell University
University of Arizona
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