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Although flooding introduces microbiological, chemical, and physical hazards onto croplands, few data are available on the spatial extent, patterns, and development of contamination over time postflooding. To address this paucity of information, we conducted a spatially explicit study of Escherichia coli and Salmonella contamination prevalence and genetic diversity in produce fields after the catastrophic flooding that occurred in New England during 2011. Although no significant differences were detected between the two participating farms, both random forest and logistic regression revealed changes in the spatial pattern of E. coli contamination in drag swab samples over time. Analyses also indicated that E. coli detection was associated with changes in farm management to remediate the land after flooding. In particular, E. coli was widespread in drag swab samples at 21 days postflooding, but the spatial pattern changed by 238 days postflooding such that E. coli was then most prevalent in close proximity to surface water features. The combined results of several population genetics analyses indicated that over time postflooding E. coli populations on the farms (i) changed in composition and (ii) declined overall. Salmonella was primarily detected in surface water features, but some Salmonella strains were isolated from soil and drag swab samples at 21 and 44 days postflooding. Although postflood contamination and land management responses should always be evaluated in the context of each unique farm landscape, our results provide quantitative data on the general patterns of contamination after flooding and support the practice of establishing buffer zones between flood-contaminated cropland and harvestable crops in produce fields. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n=1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. [ABSTRACT FROM AUTHOR]