While rain and irrigation events have been associated with an increased prevalence of foodborne pathogens in produce production environments, quantitative data are needed to determine the effects of ...various spatial and temporal factors on the risk of produce contamination following these events. This study was performed to quantify these effects and to determine the impact of rain and irrigation events on the detection frequency and diversity of Listeria species (including L. monocytogenes) and L. monocytogenes in produce fields. Two spinach fields, with high and low predicted risks of L. monocytogenes isolation, were sampled 24, 48, 72, and 144 to 192 h following irrigation and rain events. Predicted risk was a function of the field's proximity to water and roads. Factors were evaluated for their association with Listeria species and L. monocytogenes isolation by using generalized linear mixed models (GLMMs). In total, 1,492 (1,092 soil, 334 leaf, 14 fecal, and 52 water) samples were collected. According to the GLMM, the likelihood of Listeria species and L. monocytogenes isolation from soil samples was highest during the 24 h immediately following an event (odds ratios ORs of 7.7 and 25, respectively). Additionally, Listeria species and L. monocytogenes isolates associated with irrigation events showed significantly lower sigB allele type diversity than did isolates associated with precipitation events (P = <0.001), suggesting that irrigation water may be a point source of L. monocytogenes contamination. Small changes in management practices (e.g., not irrigating fields before harvest) may therefore reduce the risk of L. monocytogenes contamination of fresh produce.
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 Escherichia coli, and enterococci) were enumerated, environmental variables (temperature, pH, conductivity, redox potential, and turbidity) measured, and pathogen presence evaluated by PCR. Salmonella 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 (P<0.0001). The invA gene (Salmonella) 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: hly (83.3%), fliC (51.8%), eaeA (17.4%), rfbE (17.4%), stx-I (32.6%), stx-II (9.4%). While all ponds met the PSR requirements, at least one virulence gene from Salmonella (invA-4.8%) or STEC (stx-I-32.6%, stx-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.
Environmental (i.e., meteorological and landscape) factors and management practices can affect the prevalence of foodborne pathogens in produce production environments. This study was conducted to ...determine the prevalence of Listeria monocytogenes, Listeria species (including L. monocytogenes), Salmonella, and Shiga toxin-producing Escherichia coli (STEC) in produce production environments and to identify environmental factors and management practices associated with their isolation. Ten produce farms in New York State were sampled during a 6-week period in 2010, and 124 georeferenced samples (80 terrestrial, 33 water, and 11 fecal) were collected. L. monocytogenes, Listeria spp., Salmonella, and STEC were detected in 16, 44, 4, and 5% of terrestrial samples, 30, 58, 12, and 3% of water samples, and 45, 45, 27, and 9% of fecal samples, respectively. Environmental factors and management practices were evaluated for their association with terrestrial samples positive for L. monocytogenes or other Listeria species by univariate logistic regression; analysis was not conducted for Salmonella or STEC because the number of samples positive for these pathogens was low. Although univariate analysis identified associations between isolation of L. monocytogenes or Listeria spp. from terrestrial samples and various water-related factors (e.g., proximity to wetlands and precipitation), multivariate analysis revealed that only irrigation within 3 days of sample collection was significantly associated with isolation of L. monocytogenes (odds ratio = 39) and Listeria spp. (odds ratio = 5) from terrestrial samples. These findings suggest that intervention at the irrigation level may reduce the risk of produce contamination.
may be present in produce-associated environments (e.g., fields, packing houses); thus, understanding its growth and survival on intact, whole produce is of critical importance. The goal of this ...study was to identify and characterize published data on the growth and/or survival of
on intact fruit and vegetable surfaces. Relevant studies were identified by searching seven electronic databases: AGRICOLA, CAB Abstracts, Center for Produce Safety funded research project final reports, FST Abstracts, Google Scholar, PubMed, and Web of Science. Searches were conducted using the following terms:
produce, growth, and survival. Search terms were also modified and "exploded" to find all related subheadings. Included studies had to be prospective, describe methodology (e.g., inoculation method), outline experimental parameters, and provide quantitative growth and/or survival data. Studies were not included if methods were unclear or inappropriate, or if produce was cut, processed, or otherwise treated. Of 3,459 identified citations, 88 were reviewed in full and 29 studies met the inclusion criteria. Included studies represented 21 commodities, with the majority of studies focusing on melons, leafy greens, berries, or sprouts. Synthesis of the reviewed studies suggests
growth and survival on intact produce surfaces differ substantially by commodity. Parameters such as temperature and produce surface characteristics had a considerable effect on
growth and survival dynamics. This review provides an inventory of the current data on
growth and/or survival on intact produce surfaces. Identification of which intact produce commodities support
growth and/or survival at various conditions observed along the supply chain will assist the industry in managing
contamination risk.
•Salmonella and generic E. coli prevalence were 0.418% and 11.3%, respectively.•Mean E. coli concentrations in positive soil samples was 1.56 log CFU/g.•Generic E. coli was detected in Florida soils ...throughout the growing season.•Limiting contact between soil and crops should continue to be emphasized.
Limited data exist on the environmental factors that impact pathogen prevalence in the soil. The prevalence of foodborne pathogens, Salmonella and Listeria monocytogenes, and the prevalence and concentration of generic E. coli in Florida’s agricultural soils were evaluated to understand the potential risk of microbial contamination at the preharvest level. For all organisms but L. monocytogenes, a longitudinal field study was performed in three geographically distributed agricultural areas across Florida. At each location, 20 unique 5 by 5 m field sampling sites were selected, and soil was collected and evaluated for Salmonella presence (25 g) and E. coli and coliform concentrations (5 g). Complementary data collected from October 2021 to April 2022 included: weather; adjacent land use; soil properties, including macro- and micro-nutrients; and field management practices. The overall Salmonella and generic E. coli prevalence was 0.418% (1/239) and 11.3% (27/239), respectively; with mean E. coli concentrations in positive samples of 1.56 log CFU/g. Farm A had the highest prevalence of generic E. coli, 22.8% (18/79); followed by Farm B, 10% (8/80); and Farm C 1.25% (1/80). A significant relationship (p < 0.05) was observed between generic E. coli and coliforms, and farm and sampling trip. Variation in the prevalence of generic E. coli and changes in coliform concentrations between farms suggest environmental factors (e.g. soil properties) at the three farms were different. While Salmonella was only detected once, generic E. coli was detected in Florida soils throughout the duration of the growing season meaning activities that limit contact between soil and horticultural crops should continue to be emphasized. Samples collected during an independent sampling trip were evaluated for L. monocytogenes, which was not detected. The influence of local environmental factors on the prevalence of indicator organisms in the soil presents a unique challenge when evaluating the applicability of more global models to predict pathogen prevalence in preharvest produce environments.
The heterogeneity of produce production environments complicates the development of universal strategies for managing preharvest produce safety risks. Understanding pathogen ecology in different ...produce-growing regions is important for developing targeted mitigation strategies. This study aimed to identify environmental and spatiotemporal factors associated with isolating Salmonella and
from environmental samples collected from 10 Virginia produce farms. Soil (
= 400), drag swab (
= 400), and irrigation water (
= 120) samples were tested for Salmonella and
, and results were confirmed by PCR. Salmonella serovar and
species were identified by the Kauffmann-White-Le Minor scheme and partial
sequencing, respectively. Conditional forest analysis and Bayesian mixed models were used to characterize associations between environmental factors and the likelihood of isolating Salmonella, Listeria monocytogenes (LM), and other targets (e.g.,
spp. and Salmonella enterica serovar Newport). Surrogate trees were used to visualize hierarchical associations identified by the forest analyses. Salmonella and LM prevalence was 5.3% (49/920) and 2.3% (21/920), respectively. The likelihood of isolating Salmonella was highest in water samples collected from the Eastern Shore of Virginia with a dew point of >9.4°C. The likelihood of isolating LM was highest in water samples collected in winter from sites where <36% of the land use within 122 m was forest wetland cover. Conditional forest results were consistent with the mixed models, which also found that the likelihood of detecting Salmonella and LM differed between sample type, region, and season. These findings identified factors that increased the likelihood of isolating Salmonella- and LM-positive samples in produce production environments and support preharvest mitigation strategies on a regional scale.
This study sought to examine different growing regions across the state of Virginia and to determine how factors associated with pathogen prevalence may differ between regions. Spatial and temporal data were modeled to identify factors associated with an increased pathogen likelihood in various on-farm sources. The findings of the study show that prevalence of Salmonella and L. monocytogenes is low overall in the produce preharvest environment but does vary by space (e.g., region in Virginia) and time (e.g., season), and the likelihood of pathogen-positive samples is influenced by different spatial and temporal factors. Therefore, the results support regional or scale-dependent food safety standards and guidance documents for controlling hazards to minimize risk. This study also suggests that water source assessments are important tools for developing monitoring programs and mitigation measures, as spatiotemporal factors differ on a regional scale.
•Salmonella reductions were evaluated in agriculture water treated with sanitizers.•Sanitizer type and contact time were strongly associated with Salmonella reductions.•Outbreak strains were more ...treatment-resistant than environmental strains.•Varying volumes of sanitizers were needed to reach target ppm in the water sources.•Effective treatment requires accounting for water quality to ensure valid dosing
No Environmental Protection Agency (EPA) chemical treatments for preharvest agricultural water are currently labeled to reduce human health pathogens. The goal of this study was to examine the efficacy of peracetic acid- (PAA) and chlorine (Cl)-based sanitizers against Salmonella in Virginia irrigation water. Water samples (100 mL) were collected at three time points during the growing season (May, July, September) and inoculated with either the 7-strain EPA/FDA-prescribed cocktail or a 5-strain Salmonella produce-borne outbreak cocktail. Experiments were conducted in triplicate for 288 unique combinations of time point, residual sanitizer concentration (low: PAA, 6 ppm; Cl, 2-4 ppm or high: PAA, 10 ppm; Cl, 10-12 ppm), water type (pond, river), water temperature (12°C, 32°C), and contact time (1, 5, 10 min). Salmonella were enumerated after each treatment combination and reductions were calculated. A log-linear model was used to characterize how treatment combinations influenced Salmonella reductions. Salmonella reductions by PAA and Cl ranged from 0.0 ± 0.1 to 5.6 ± 1.3 log10 CFU/100 mL and 2.1 ± 0.2 to 7.1 ± 0.2 log10 CFU/100 mL, respectively. Physicochemical parameters significantly varied by untreated water type; however, Salmonella reductions did not (p = 0.14), likely due to adjusting the sanitizer amounts needed to achieve the target residual concentrations regardless of source water quality. Significant differences (p < 0.05) in Salmonella reductions were observed for treatment combinations, with sanitizer (Cl > PAA) and contact time (10 > 5 > 1 min) having the greatest effects. The log-linear model also revealed that outbreak strains were more treatment-resistant. Results demonstrate that certain treatment combinations with PAA- and Cl-based sanitizers were effective at reducing Salmonella populations in preharvest agricultural water. Awareness and monitoring of water quality parameters are essential for ensuring adequate dosing for the effective treatment of preharvest agricultural water.
•Regardless of volume, the rain increased fecal indicator bacteria (FIB) levels in ponds.•In individual ponds, more variance in FIB levels was due to nonspatial factors.•For multiple ponds, more ...variance in FIB levels was due to spatial factors.•Spatial factors driving FIB levels were pond dependent.•FIB levels are spatially independent for sites 56–87 m apart within individual ponds.
Surface water environments are inherently heterogenous, and little is known about variation in microbial water quality between locations. This study sought to understand how microbial water quality differs within and between Virginia ponds. Grab samples were collected twice per week from 30 sampling sites across nine Virginia ponds (n = 600). Samples (100 mL) were enumerated for total coliform (TC) and Escherichia coli (EC) levels, and physicochemical, weather, and environmental data were collected. Bayesian models of coregionalization were used to quantify the variance in TC and EC levels attributable to spatial (e.g., site, pond) versus nonspatial (e.g., date, pH) sources. Mixed-effects Bayesian regressions and conditional inference trees were used to characterize relationships between data and TC or EC levels. Analyses were performed separately for each pond with ≥3 sampling sites (5 intrapond) while one interpond model was developed using data from all sampling sites and all ponds. More variance in TC levels were attributable to spatial opposed to nonspatial sources for the interpond model (variance ratio VR = 1.55) while intrapond models were pond dependent (VR: 0.65–18.89). For EC levels, more variance was attributable to spatial sources in the interpond model (VR = 1.62), compared to all intrapond models (VR < 1.0) suggesting that more variance is attributable to nonspatial factors within individual ponds and spatial factors when multiple ponds are considered. Within each pond, TC and EC levels were spatially independent for sites 56–87 m apart, indicating that different sites within the same pond represent different water quality for risk management. Rainfall was positively and pH negatively associated with TC and EC levels in both inter- and intrapond models. For all other factors, the direction and strength of associations varied. Factors driving microbial dynamics in ponds appear to be pond-specific and differ depending on the spatial scale considered.
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