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

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

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Pathogen contamination of agricultural water has been identified as a probable cause of recalls and outbreaks. However, variability in pathogen presence and concentration complicates the reliable identification of agricultural water at elevated risk of pathogen presence. In this study, we collected data on the presence of Salmonella and genetic markers for enterohemorrhagic E. coli (EHEC; PCR-based detection of stx and eaeA) in southwestern US canal water, which is used as agricultural water for produce. We developed and assessed the accuracy of models to predict the likelihood of pathogen contamination of southwestern US canal water. Based on 169 samples from 60 surface water canals (each sampled 1–3 times), 36% (60/169) and 21% (36/169) of samples were positive for Salmonella presence and EHEC markers, respectively. Water quality parameters (e.g., generic E. coli level, turbidity), surrounding land-use (e.g., natural cover, cropland cover), weather conditions (e.g., temperature), and sampling site characteristics (e.g., canal type) data were collected as predictor variables. Separate conditional forest models were trained for Salmonella isolation and EHEC marker detection, and cross-validated to assess predictive performance. For Salmonella, turbidity, day of year, generic E. coli level, and % natural cover in a 500–1,000 ft (~150–300 m) buffer around the sampling site were the top 4 predictors identified by the conditional forest model. For EHEC markers, generic E. coli level, day of year, % natural cover in a 250–500 ft (~75–150 m) buffer, and % natural cover in a 500–1,000 ft (~150–300 m) buffer were the top 4 predictors. Predictive performance measures (e.g., area under the curve [AUC]) indicated predictive modeling shows potential as an alternative method for assessing the likelihood of pathogen presence in agricultural water. Secondary conditional forest models with generic E. coli level excluded as a predictor showed < 0.01 difference in AUC as compared to the AUC values for the original models (i.e., with generic E. coli level included as a predictor) for both Salmonella (AUC = 0.84) and EHEC markers (AUC = 0.92). Our data suggests models that do not require the inclusion of microbiological data (e.g., indicator organism) show promise for real-time prediction of pathogen contamination of agricultural water (e.g., in surface water canals).

Contaminated coring tools may transfer bacteria to iceberg lettuce. The efficiency of coring tool design modifications in reducing bacterial transfer to lettuce heads was evaluated under simulated field operations. The standard coring tool consists of a stainless steel cylindrical tube welded to a tab that is inserted into a plastic handle. Design modifications included removal of the welded portion, incorporation of a shorter front straight bottom edge, or an angled bottom edge toward the front. In the first study, coring tools of four different designs were inoculated by dipping in a tryptic soy broth (TSB) suspension that contained 8.85 Log CFU/mL of Escherichia coli K-12 and then were used to core 100 lettuce heads, consecutively. Use of the standard tool resulted in 91% ± 9% positive lettuce heads. Removing the welded surface from the standard tool resulted in the highest reduction of E. coli transfer (44% ± 11.9% positive lettuce heads, P < 0.05), whereas incorporation of a short front straight edge with no welding resulted in 65.6% ± 5.6% of the cored lettuce heads being positive for E. coli. Removal of the welded surface resulted in a 40% decrease in E. coli contamination among the last 20 cored lettuce heads (81 to 100), which indicates that coring tool design modifications resulted in reduced cross-contamination. In the second study, the transfer of Salmonella to coring tools after their immersion in rinsing solutions was evaluated using imaging. The tools were dip inoculated for 2 min in water, water with lettuce extract, or TSB containing 7 Log CFU/mL bioluminescent Salmonella Newport; they were then imaged to observe spatial distribution of bacteria. There was greater retention and spatial distribution of Salmonella on the surface of tools immersed in water containing lettuce extract than in TSB and water. The results of the second study indicate that rinsing solutions that contain lettuce particulate and organic load could facilitate cross-contamination of Salmonella Newport to tool surfaces.