Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water

Abstract

19 20 21 22 23 produce farms. Managing water-associated risks does not lend itself to one-size-fits-all 24 approaches due to the heterogeneous nature of freshwater environments, and because 25 environmental conditions affect the likelihood of pathogen contamination and the relationship 26 between indicator organism levels (e.g., E. coli) and pathogen presence. To improve our ability 27 to develop location-specific risk management practices, a study was conducted in two produce-28 growing regions to (i) characterize the relationship between E. coli levels and pathogen presence 29 in agricultural water, and (ii) identify environmental factors associated with pathogen detection. 30Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 31 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds 32 Ratio [OR]=2.18), and eaeA-stx codetection (OR=6.49) was significantly greater for MS 33 compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also 34 showed that eaeA-stx codetection in AZ (OR=50.2) and NY (OR=18.4), and Salmonella 35 detection in AZ (OR=4.4) were significantly associated with E. coli levels, while Salmonella 36 detection in NY was not. Random forest analysis indicated that interactions between 37 environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of 38 pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of 39 pathogen detection. Our findings suggest that (i) environmental heterogeneity, including 40 interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not 41 be a suitable indicator of the food safety risks. Instead, targeted methods that utilize 42 environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict 43 when there is a high or low risk of surface water being contaminated by pathogens) are needed to 44 assess and mitigate the food safety risks associated with preharvest water use. By identifying 45 environmental factors associated with an increased likelihood of detecting pathogens in 46 agricultural water, this study provides information that (i) can be used to assess when pathogen 47 contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted 48 interventions for individual water sources, providing an alternative to existing one-size-fits-all 49 approaches. 50 51 52 3 3Preharvest surface water use for produce production (e.g., irrigation, fertigation, pesticide 53 application, dust abatement) has repeatedly been identified as a factor associated with an 54

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

“…At each sampling, one 10-L grab sample was collected for detection of each set of microbial targets: (i) Listeria [Listeria spp. (excluding monocytogenes) and L. monocytogenes], (ii) Salmonella and (iii) the stx and eaeA genes (molecular markers for the potential presence of pathogenic E. coli; Hamilton et al, 2010;Melton-Celsa, 2014) as previously described (Weller et al, 2020;30-L total). A separate 1-L grab sample was also collected to characterize E. coli levels.…”

“…However, interpretation of E. coli-based water quality tests is complicated by spatiotemporal variation in the microbial quality of surface water (Goyal et al, 1977;Hipsey et al, 2008;Payment and Locas, 2011;Pandey et al, 2012;Benjamin et al, 2013;Cooley et al, 2014;Weller et al, 2015bWeller et al, , 2020. For example, 71% (15/21), 63% (33/52), and 6% (2/32) of surface water samples collected from the same site in Upstate New York in 2013 (unpublished), 2014 (Weller et al, 2015a), and 2017 (Weller et al, 2020), respectively, were Listeria monocytogenes-positive. E. coli levels at this site also varied by > 2 log 10 MPN/100-mL over the course of the 2017 growing season (Weller et al, 2020).…”

“…For example, 71% (15/21), 63% (33/52), and 6% (2/32) of surface water samples collected from the same site in Upstate New York in 2013 (unpublished), 2014 (Weller et al, 2015a), and 2017 (Weller et al, 2020), respectively, were Listeria monocytogenes-positive. E. coli levels at this site also varied by > 2 log 10 MPN/100-mL over the course of the 2017 growing season (Weller et al, 2020). Thus, to improve growers' ability to identify and address on-farm food safety hazards, targeted approaches that account for this variation are needed.…”

“…Due to the availability of spatial data (e.g., from government databases, Google), analyses that utilize such data can facilitate identification of factors associated with foodborne pathogen detection in agricultural water (Benjamin et al, 2013;Strawn et al, 2013a;Weller et al, 2015aWeller et al, , 2016; these factors can then be used to develop the aforementioned targeted approaches. Past studies have shown that microbial water quality is affected by the ecological context unique to each water source (e.g., upstream landscape) as well as conditions (e.g., weather, physicochemical water quality) at time of water use (e.g., McEgan et al, 2013a;Bradshaw et al, 2016;Weller et al, 2020). It is therefore essential to understand how contamination risks vary in response to weather, physicochemical water quality, and upstream landscape factors as well as interactions between these factors.…”

“…Specifically, these studies determined that whether a water source meets the proposed standard is largely a function of when the water samples were collected and was not associated with the presence of food safety hazards at the time of water use (Havelaar et al, 2017;Truitt et al, 2018;Weller et al, 2020). Since the relationship between E. coli levels and foodborne pathogen presence in agricultural water varied widely within and between past studies (Edberg et al, 2000;Harwood et al, 2005;Wilkes et al, 2009;Payment and Locas, 2011;Benjamin et al, 2013;Pachepsky et al, 2015;Antaki et al, 2016;Weller et al, 2020), concerns have also been raised about the standard's reliance on generic E. coli as an indicator of potential food safety hazards. In fact, a review that compiled the findings of 40 studies with data on E. coli levels and pathogen presence in surface water found a significant association between E. coli levels and pathogen presence in only 18% of the datasets (Pachepsky et al, 2015).…”