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Several produce-borne outbreaks have been associated with the use of contaminated water during preharvest applications. Salmonella has been implicated in a number of these outbreaks. The purpose of this study was to evaluate the microbial quality of agricultural surface water used in preharvest production on the Eastern Shore of Virginia in accordance with the Food Safety Modernization Act's Produce Safety Rule water standards. The study also examined the prevalence, concentration, and diversity of Salmonella in those water sources. Water samples (1 L) from 20 agricultural ponds were collected during the 2015 and 2016 growing seasons ( n = 400). Total aerobic bacteria, total coliforms, and Escherichia coli were enumerated for each sample. Population levels of each microorganism were calculated per 100-mL sample and log transformed, when necessary. Samples (250 mL) were also enriched for Salmonella. Presumptive Salmonella isolates were confirmed by PCR ( invA gene) and were serotyped. In 2016, the concentration of Salmonella in each sample was also estimated by most probable number (MPN). Indicator bacteria and environmental and meteorological factors were analyzed for their association with the detection of a Salmonella-positive water sample by using logistic regression analysis. Seventeen of the 20 ponds met the Food Safety Modernization Act's Produce Safety Rule standards for production agricultural water. Three ponds did not meet the standards because the statistical threshold value exceeded the limit. Salmonella was detected in 19% of water samples in each year (38 of 200 in 2015 and 38 of 200 in 2016). Of the 118 Salmonella isolates serotyped, 14 serotypes were identified with the most prevalent being Salmonella Newport. E. coli concentration, farm, and total aerobic bacteria concentration were significantly associated with the likelihood of detecting a Salmonella-positive sample The average concentration of Salmonella in all samples was 4.44 MPN/100 mL, with the limit of detection being 3.00 MPN/100 mL. The highest concentration of Salmonella was 93.0 MPN/100 mL. These data will assist in a better understanding of the risks that production water poses to produce contamination events. [ABSTRACT FROM AUTHOR]

The U.S. Food and Drug Administration (FDA) has defined standards for the microbial quality of agricultural surface water used for irrigation. According to the FDA produce safety rule (PSR), a microbial water quality profile requires analysis of a minimum of 20 samples for Escherichia coli over 2 to 4 years. The geometric mean (GM) level of E. coli should not exceed 126 CFU/100 mL, and the statistical threshold value (STV) should not exceed 410 CFU/100 mL. The water quality profile should be updated by analysis of a minimum of five samples per year. We used an extensive set of data on levels of E. coli and other fecal indicator organisms, the presence or absence of Salmonella, and physicochemical parameters in six agricultural irrigation ponds in West Central Florida to evaluate the empirical and theoretical basis of this PSR. We found highly variable log-transformed E. coli levels, with standard deviations exceeding those assumed in the PSR by up to threefold. Lognormal distributions provided an acceptable fit to the data in most cases but may underestimate extreme levels. Replacing censored data with the detection limit of the microbial tests underestimated the true variability, leading to biased estimates of GM and STV. Maximum likelihood estimation using truncated lognormal distributions is recommended. Twenty samples are not sufficient to characterize the bacteriological quality of irrigation ponds, and a rolling data set of five samples per year used to update GM and STV values results in highly uncertain results and delays in detecting a shift in water quality. In these ponds, E. coli was an adequate predictor of the presence of Salmonella in 150-mL samples, and turbidity was a second significant variable. The variability in levels of E. coli in agricultural water was higher than that anticipated when the PSR was finalized, and more detailed information based on mechanistic modeling is necessary to develop targeted risk management strategies. [ABSTRACT FROM AUTHOR]