Evaluating the U.S. Food Safety Modernization Act Produce Safety Rule Standard for Microbial Quality of Agricultural Water for Growing Produce

Abstract

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.

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

“…For example, turbidity was ranked as an important factor in 7 of the 15 random forest analyses performed here, and was associated detecting pathogens in samples collected from both AZ and NY waterways. Multiple studies (Christensen et al, 2000; Francy et al, 2013; Havelaar et al, 2017; Horman et al, 2004; Nagels et al, 2002; Rao et al, 2015; Topalcengiz et al, 2017), including the study reported here and the Ecuadorian study discussed above (Rao et al, 2015), found a positive association between E. coli levels and temperature, or between E. coli levels and turbidity. Francy et al (Francy et al, 2013) surveyed recreational water quality at 22 Ohio beaches along inland lakes and found that turbidity was one of the best predictors of E. coli levels.…”

“…Similarly, Francy et al (Francy et al, 2013) found that the strength and direction of correlation between water temperature and E. coli levels in 22 Ohio lakes were different for different water sources in the same region. Overall, the findings from this and other studies suggest that turbidity but not temperature may be useful as a supplemental indicator of the microbial quality of surface water (Havelaar et al, 2017; Stocker et al, 2016). Our data do however suggest that temperature is strongly associated with microbial water quality, and inclusion of temperature as a factor in more sophisticated algorithms (e.g., AI tools, machine learning-based predictive risk maps) for predicting when and where water is likely to be contaminated by foodborne pathogens may be useful.…”

“…For example, interpretation of E. coli test results is complicated by temporal variation in microbial water quality (Goyal et al, 1977; Hipsey et al, 2008; Pandey et al, 2012; Payment and Locas). Since the samples used to determine if a water source meets the proposed standard can be collected up to 4 years before the water source is used for produce production, meeting the standard also may be a poor approximation of water quality at time of use (Havelaar et al, 2017; Truitt et al, 2018). Acceptance of E. coli -based water standards is further complicated by conflicting data on the relationship between E. coli levels and pathogen presence in the literature (Benjamin et al, 2013; Economou et al, 2013; Harwood et al, 2005; McEgan et al, 2013a; Pachepsky et al, 2015; Wilkes et al, 2009).…”

Meeting Report: Key Outcomes from a Collaborative Summit on Agricultural Water Standards for Fresh Produce

“…Havelaar et al. () also characterized water sources in Florida in relation to the Subpart E microbial standards and found that E. coli and turbidity were adequate predictors of Salmonella in 150 mL samples for the six water sources sampled. However, the authors note that further mechanistic modeling, including climate and other environmental factors that may affect contamination, is necessary because the statistical criteria in Subpart E by themselves do not allow growers to identify and react to contamination in a timely manner.…”

“…McEgan, Mootian, Goodridge, Schaffner, and Danyluk (2013) characterized water quality for sources in Florida and found a lack of correlation between Salmonella levels and microbial indicators, as well as a lack of correlation between Salmonella levels and physicochemical indicators. Havelaar et al (2017) also characterized water sources in Florida in relation to the Subpart E microbial standards and found that E. coli and turbidity were adequate predictors of Salmonella in 150 mL samples for the six water sources sampled. However, the authors note that further mechanistic modeling, including climate and other environmental factors that may affect contamination, is necessary because the statistical criteria in Subpart E by themselves do not allow growers to identify and react to contamination in a timely manner.…”