Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

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.

Keywords: E. coli; Listeria; Salmonella; agricultural water; irrigation; produce safety.

FIGURE 1

Results of random forest analyses that identified factors associated with E. coli levels, and the likelihood of codetecting eaeA and stx, and detecting Salmonella, L. monocytogenes, and Listeria spp. (including L. monocytogenes) in GS. The y-axis shows the factors ranked from most to least important. The x-axis shows NVI; a higher NVI (relative to all factors in the plot) equates to a stronger association between outcome and factor. NVI ≤ 0 indicates no association. Five, overlapping time frames (0–1, 0–2, 0–3, 0–4, or 0–5 days BSC) were used to calculate the values of the weather factors with the exception of rainfall; rainfall was calculated on a daily basis (0–1, 1–2, 2–3, 3–4, 4–5 days BSC). Separate forests were then developed for each outcome (e.g., E. coli levels in AZ, likelihood of Salmonella isolation in NY) and time frame in each state. The results for the forest with the highest Kappa score for each outcome are reported here. Thus, the time frame for the forest reported here differs for each combination of outcome and state. BSC, before sample collection.

FIGURE 2

Bootstrapping was used to simulate water sampling to create microbial water quality profiles (MWQP) composed of 20 samples (N = 10,000 MWQPs per waterway). The graphs show the geometric mean and statistical threshold value (STV) for all MWQPs for each waterway. The blue line represents the proposed FSMA standard cut-offs (geometric mean < 126 CFU/100 mL and STV < 410 CFU/100 mL; Food and Drug Administration, 2015). Note that the y-axis varies between plots.