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). https://doi.org/10.3389/fsufs.2020.561517
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). https://doi.org/10.3389/fsufs.2019.00124
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. https://doi.org/10.1128/aem.03088-15
Other project relevant literature:
Broman, K. W., & Woo, K. H. (2018). Data Organization in Spreadsheets. The American Statistician, 72(1), 2-10. https://doi.org/10.1080/00031305.2017.1375989
Wickham, H. (2014). Tidy Data. 2014, 59(10), 23. https://doi.org/10.18637/jss.v059.i10