Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water
Author
Weller, DanielBrassill, Natalie
Rock, Channah
Ivanek, Renata
Mudrak, Erika
Roof, Sherry
Ganda, Erika
Wiedmann, Martin
Affiliation
Univ Arizona, Dept Soil Water & Environm SciIssue Date
2020-02-06
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FRONTIERS MEDIA SACitation
Weller D, Brassill N, Rock C, Ivanek R, Mudrak E, Roof S, Ganda E and Wiedmann M (2020) Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water. Front. Microbiol. 11:134. doi: 10.3389/fmicb.2020.00134Journal
FRONTIERS IN MICROBIOLOGYRights
Copyright © 2020 Weller, Brassill, Rock, Ivanek, Mudrak, Roof, Ganda and Wiedmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between Escherichia coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR] = 2.18), and eaeA-stx codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR = 50.2) and NY (OR = 18.4), and Salmonella detection in AZ (OR = 4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.Note
Open access journalISSN
1664-302XPubMed ID
32117154Version
Final published versionae974a485f413a2113503eed53cd6c53
10.3389/fmicb.2020.00134
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Except where otherwise noted, this item's license is described as Copyright © 2020 Weller, Brassill, Rock, Ivanek, Mudrak, Roof, Ganda and Wiedmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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