Microbial quality of agricultural water in Central Florida

<div><p>The microbial quality of water that comes into the edible portion of produce is believed to directly relate to the safety of produce, and metrics describing indicator organisms are commonly used to ensure safety. The US FDA Produce Safety Rule (PSR) sets very specific microbiological water quality metrics for agricultural water that contacts the harvestable portion of produce. Validation of these metrics for agricultural water is essential for produce safety. Water samples (500 mL) from six agricultural ponds were collected during the 2012/2013 and 2013/2014 growing seasons (46 and 44 samples respectively, 540 from all ponds). Microbial indicator populations (total coliforms, generic <i>Escherichia coli</i>, and enterococci) were enumerated, environmental variables (temperature, pH, conductivity, redox potential, and turbidity) measured, and pathogen presence evaluated by PCR. <i>Salmonella</i> isolates were serotyped and analyzed by pulsed-field gel electrophoresis. Following rain events, coliforms increased up to 4.2 log MPN/100 mL. Populations of coliforms and enterococci ranged from 2 to 8 and 1 to 5 log MPN/100 mL, respectively. Microbial indicators did not correlate with environmental variables, except pH (<i>P</i><0.0001). The <i>invA</i> gene (<i>Salmonella</i>) was detected in 26/540 (4.8%) samples, in all ponds and growing seasons, and 14 serotypes detected. Six STEC genes were detected in samples: <i>hly</i> (83.3%), <i>fliC</i> (51.8%), <i>eaeA</i> (17.4%), <i>rfbE</i> (17.4%), <i>stx-</i>I (32.6%), <i>stx-</i>II (9.4%). While all ponds met the PSR requirements, at least one virulence gene from <i>Salmonella</i> (<i>invA-</i>4.8%) or STEC (<i>stx-</i>I-32.6%, <i>stx-</i>II-9.4%) was detected in each pond. Water quality for tested agricultural ponds, below recommended standards, did not guarantee the absence of pathogens. Investigating the relationships among physicochemical attributes, environmental factors, indicator microorganisms, and pathogen presence allows researchers to have a greater understanding of contamination risks from agricultural surface waters in the field.</p></div

Calculated MWQP values for all ponds for three consecutive growing seasons (MPN/100 mL).

<p>Calculated MWQP values for all ponds for three consecutive growing seasons (MPN/100 mL).</p

Precipitation and microbial indicator correlations for all ponds.

<p>Precipitation and microbial indicator correlations for all ponds.</p

Most Probable Number of Total coliform (♦), Generic <i>E</i>. <i>coli</i> (■), and enterococci (▲) (Log MPN/100 mL) from six agricultural ponds (Pond 1: A, Pond 2: B, Pond 3: C) with daily rainfall (mm/Day) in Central Florida.

<p><i>invA</i> positive sampling days were shown with “*”.</p

The positive percentages of STEC and <i>invA</i> genes (%) for each pond.

<p>The positive percentages of STEC and <i>invA</i> genes (%) for each pond.</p

Pearson product moment correlation coefficients (r) with <i>P</i>-values determined between each of the physical, chemical, and biological water attributes for all ponds combined.

<p>Pearson product moment correlation coefficients (r) with <i>P</i>-values determined between each of the physical, chemical, and biological water attributes for all ponds combined.</p

Most Probable Number of Total coliform (♦), Generic <i>E</i>. <i>coli</i> (■), and enterococci (▲) (Log MPN/100 mL) from six agricultural ponds (Pond 4: D, Pond 5: E, Pond 6: F) with daily rainfall (mm/Day) in Central Florida.

<p><i>invA</i> positive sampling days were shown with “*”.</p

Physical conditions of the ponds when sampling started in 2012.

<p>Physical conditions of the ponds when sampling started in 2012.</p

List of <i>Salmonella</i> isolates and summary of tests applied to each <i>invA</i> positive sample.

<p>List of <i>Salmonella</i> isolates and summary of tests applied to each <i>invA</i> positive sample.</p

Table_1_Factors associated with foodborne pathogens and indicator organisms in agricultural soils.pdf

Soil can be a route for contamination of fresh fruits and vegetables. While growers routinely manage soil nutrient levels, little research exists on the synergistic or antagonistic effects of soil nutrients on foodborne pathogens. Data on foodborne pathogen prevalence in unamended soils is also relatively limited in literature. This study evaluated foodborne pathogen prevalence (Salmonella, Listeria monocytogenes) and concentration of indicator bacteria (total coliforms, generic Escherichia coli) in agricultural soils, and characterized associations between soil properties (e.g., macro- and micro-nutrient levels) and each microbial target. Three Virginia produce farms, representing different regions and soil types, were sampled four times over 1 year (October 2021–November 2022). For each individual farm visit, composite soil samples were collected from 20 sample sites (25 m2) per farm per visit for microbial and nutrient analysis (n = 240). Samples (25 g) were processed for Listeria spp. and Salmonella using a modified FDA BAM method; samples (5 g) were enumerated for generic E. coli and total coliforms (TC) using Petrifilm. Presumptive Listeria spp. and Salmonella isolates were confirmed by PCR using the sigB and invA genes, respectively. Soil nutrients from each sample were tested and evaluated for their association with each microbial target by Bayesian Mixed Models. Salmonella prevalence was 4.2% (10/240), with 90% (9/10) recovered on Farm C. Listeria spp. and L. monocytogenes prevalence were 10% (24/240) and 2.5% (6/240), respectively. When samples were positive for generic E. coli (107/240), the average concentration was 1.53 ± 0.77 log10 CFU/g. Soil pH was positively associated with L. monocytogenes [Odds Ratio (OR) = 5.5] and generic E. coli (OR = 4.9) prevalence. There was no association between Salmonella prevalence and any evaluated factor; however, Salmonella was 11.6 times more likely to be detected on Farm C, compared to other farms. Results show pathogen prevalence was relatively low in unamended soils, and that factors influencing prevalence and concentration varied by microbial target and farm.</p