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GIS tools and approaches to food safety at the pre-harvest level

Improve GIS tools and approaches to reduce produce microbial food safety hazard introduction from agricultural water, environmental and animal sources at the pre-harvest level.

The goal is to develop models that allow for the prediction of times and locations with an increased risk of microbial hazards on-farm in different US growing regions. Models will be built using a combination of pathogen and indicator data, remotely sensed weather and adjacent land-use buffer data, and on-site physiochemical data. Outputs from model predictions will assist with region-specific guidance for industry on reducing microbial hazards on-farm from water and soil; as well as, educational programming for extension educators (targeted efforts).


Examples of previous work that illustrates applications of GIS tools and prediction models:

  • Weller, D. L., Love, T. M. T., Belias, A., & Wiedmann, M. (2020). Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production [Original Research]. Frontiers in Sustainable Food Systems, 4(151).

  • Weller, D., Belias, A., Green, H., Roof, S., & Wiedmann, M. (2020). Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production [Original Research]. Frontiers in Sustainable Food Systems, 3(124).

  • Weller, D., Shiwakoti, S., Bergholz, P., Grohn, Y., Wiedmann, M., & Strawn, L. K. (2016). Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields. Applied and Environmental Microbiology, 82(3), 797-807.

Other project relevant literature: