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

Daniel Weller,Erika Ganda,Erika Mudrak,Martin Wiedmann,Renata Ivanek,Sherry Roof,Natalie Brassill,Channah Rock

doi:10.3389/fmicb.2020.00134

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.

Highlights

Preharvest surface water use for produce production has repeatedly been identified as a factor associated with an increased likelihood of foodborne pathogen contamination of fresh produce [e.g., (Holvoet et al., 2014; Mody et al, 2011; Strawn et al, 2013b; Weller et al, 2015b)]
Our findings suggest that (i) environmental heterogeneity, including interactions between environmental factors, affect microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of the food safety risks associated with preharvest water use
Our findings indicate that approaches that account for temporal variation in environmental conditions, including microbial water quality, are needed to help identify and address food safety risks associated with the preharvest use of surface water for produce production

Summary

Introduction

Preharvest surface water use for produce production (e.g., irrigation, fertigation, pesticide application, dust abatement) has repeatedly been identified as a factor associated with an increased likelihood of foodborne pathogen contamination of fresh produce [e.g., (Holvoet et al., 2014; Mody et al, 2011; Strawn et al, 2013b; Weller et al, 2015b)]. Promising alternative approaches include models that predict risk of pathogen presence at specific times and sampling locations along a waterway by using a variety of different, spatially explicit input data To develop these alternative approaches additional data on factors (e.g., weather, physiochemical water quality) that drive temporal variation in E. coli levels and pathogen presence in different regions and under different conditions is needed. Understanding how temporal variation in environmental factors affects microbial water quality, and how sampling methods affect our ability to detect pathogens in agricultural water is essential to effectively manage the food safety risks associated with preharvest water use.

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