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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]

Produce-related outbreaks have been traced back to the preharvest environment. A longitiidinal study was conducted on five farms in New York State to characterize the prevalence, persistence, and diversity of food-borne pathogens in fresh produce fields and to determine landscape and meteorological factors that predict their presence. Produce fields were sampled four times per year for 2 years. A total of 588 samples were analyzed for Listeria monocytogenes, Salmonella, and Shiga toxin-producing Escherichia coli (STEC). The prevalence measures of L. monocytogenes, Salmonella, and STEC were 15.0,4.6, and 2.7%, respectively. L. monocytogenes and Salmonella were detected more frequently in water samples, while STEC was detected with equal frequency across all sample types (soil, water, feces, and drag swabs). L. monocytogenes sigB gene allelic types 57, 58, and 61 and Salmonella enterica serovar Cerro were repeatedly isolated from water samples. Soil available water storage (AWS), temperature, and proximity to three land cover classes (water, roads and urban development, and pasture/hay grass) influenced the likelthood of detecting L. monocytogenes. Drainage class, AWS, and precipitation were identified as important factors in Salmonella detection. This information was used in a geographic information system framework to hypothesize locations of environmental reservoirs where the prevalence of food-borne pathogens may be elevated. The map indicated that not all croplands are equally likely to contain environmental reservoirs of L. monocytogenes. These findings advance recommendations to minimize the risk of pre-harvest contamination by enhancing models of the environmental constraints on the survival and persistence of food-borne pathogens in fields. [ABSTRACT FROM AUTHOR]

Background Food safety is a priority for all stakeholders associated with the food supply and consumption. Outbreaks and recalls caused by microbial pathogens are widely attributed to contaminated foods, and lead to considerable public health and economic burdens. Scope and approarch This review presented major current trends in food safety research, regulation and strategic development. Specifically we discussed about: (i) worldwide application of genomic data in foodborne pathogen surveillance and outbreak investigation; (ii) Geographic Information Systems (GIS) in food safety to prevent and predict pathogen contamination; (iii) modeling tools adapted from landscape ecology, network analysis and niche modeling to prevent foodborne diseases; and (iv) meta-analysis of food safety data to facilitate decision making. Key findings and conclusions While some of these technologies are receiving increasing recognition around the world (e.g., WGS), others are still at their infancy in regards to their application in food safety (e.g., meta-analysis). The establishment of relevant, valid and multi-dimensional databases is the key to effective application of many new trends in food safety described in this review. [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]