Evaluation of meteorological factors associated with pre-harvest contamination risk of generic Escherichia coli in a mixed produce and dairy farm

Bacteroidales as a fecal contamination indicator in fresh produce industry: A baseline measurement

Foodborne outbreaks caused by fecal contamination of fresh produce represent a serious concern to public health and the economy. As the consumption of fresh produce increases, public health officials and organizations have pushed for improvements in food safety procedures and environmental assessments to reduce the risk of contamination. Visual inspections and the establishment of "buffer zones" between animal feeding operations and producing fields are the current best practices for environmental assessments. However, a generalized distance guideline and visual inspections may not be enough to account for all environmental risk variables. Here, we report a baseline measurement surveying the background Bacteroidales concentration, as a quantitative fecal contamination indicator, in California's Salinas Valley. We collected a total of 1632 samples from two romaine lettuce commercial fields at the time of harvesting through two seasons in a year. The quantification of Bacteroidales concentration was performed using qPCR, revealing a notably low concentration (0-2.00 copies/cm2) in the commercial fields. To further enhance the applicability of our findings, we developed a user-friendly method for real-world fecal contamination risk assessment that seamlessly integrates with industry practices. Through the generation of heatmaps that visually illustrate varying risk levels across fields, this approach can identify site-specific risks and offer fresh produce stakeholders a more comprehensive understanding of their land. We anticipate this work can encourage the use of Bacteroidales in the fresh produce industry to monitor fecal contamination and prevent future foodborne outbreaks.

Identification of Contamination Sources and Assessment of Risk Factors Associated with the Occurrence of Escherichia coli O157:H7 on Small-scale Cow-calf Operations in Oklahoma and Louisiana

Escherichia coli O157:H7 is a human pathogen that exists as part of the commensal microflora of cattle and is shed in animal feces. Little is known about the effect of management practices on its occurrence and transmission on small-scale cow-calf operations. Identification of risk factors associated with farm practices could help implement effective measures to control E. coli O157:H7. This study quantified the risk of E. coli O157:H7 occurrence associated with cow-calf farm practices using risk modeling. Management practices of small-scale cow-calf operations in OK and LA were assessed through survey-based research. Fecal, water, sediments and water-trough-swab samples were collected to determine the incidence of E. coli O157:H7, and potential on-farm contamination sources and risk factors identified. Association between the occurrence of pathogen and farm practices was determined using two risk assessment models (I and II). Model I determined the association of E. coli O157:H7 occurrence with water source, water container, feed, cattle breed, and herd density, while Model II determined its association with farm cleanliness. For both models, logistic regression was followed using a two-step approach, univariable and multivariable analysis. In OK and LA, E. coli O157:H7 was present in 5.8% and 8.8% fecal, 4.4% and 9.4% water, 10.3% and 9.6% sediments, and 1.5% and 10.6% water-trough-swab samples, respectively. In Model I, univariable analysis identified water container and feed, whereas multivariable analysis identified feed as a significant risk factor. In Model II, the univariable analysis found cleanliness of cattle-contact areas, such as, alleyways, water-trough, chute and equipment, to be a significant risk factor. In multivariable analysis, only the cleanliness of water-trough was identified to be a significant risk factor. Results from the study could aid in the development of on-farm best management practices for the reduction of E. coli O157:H7.

Predictive models for the growth of Salmonella spp., Listeria spp., and Escherichia coli in lettuce harvested on Taiwanese farms

This study aimed at developing predictive models for Salmonella, Listeria, and E. coli in lettuce iceberg (Lactuca sativa) locally grown in Taiwan. The models were developed under constant temperature levels (5, 10, 15, 20, and 25°C) and validated under dynamic temperature conditions (18°C for 4 h, 7°C for 48 h, 23°C for 4 h). The result showed that (1) all strains were unable to grow at 5°C except for standard strain of Listeria obtained from the BCRC and (2) the growth rate of locally isolated strains of Salmonella and Listeria was higher than the standard one at certain temperature levels and lower than the growth rates of E. coli. The findings in this study enhance our understanding about the growth variability between Salmonella, Listeria, and E. coli strains on vegetables locally grown in Taiwan and may be used to improve the management of proper storage temperature in the lettuce supply chain in this country. Considering the temperature recommendation for refrigerated food in Taiwan, the findings in this study therefore recommend that fresh vegetables (e.g., lettuce) should be stored at 5°C or lower to prevent the rapid growth of these microorganisms. Finally, the developed models can be used in the assessment of the microbiological risk of Salmonella, Listeria, and E. coli contamination in lettuce locally grown in Taiwan. PRACTICAL APPLICATION: This study developed predictive models describing the growth of Salmonella, Listeria, and E. coli in lettuce locally grown in Taiwan. The models developed in this study can be used in quantitative microbial risk assessment.

Prevalence of Shiga Toxin-Producing Escherichia coli and Salmonella in Native Pecan Orchards as Influenced by Waiting Periods between Grazing and Harvest

ABSTRACT

Animals (grazing, working, or intrusion) in produce production areas may present a potential contamination source of foodborne pathogens on produce. Cattle grazing on native pecan production orchards, a common practice in the southern United States, provides an opportunity to study the impact of grazing practice and waiting periods on contamination rates of foodborne pathogens of tree nuts. Therefore, the objective of this study was to determine the prevalence of Salmonella and Shiga toxin-producing Escherichia coli (STEC) in native pecan production orchards as influenced by waiting periods between grazing cattle and pecan harvest. Soil (10 g), cattle feces (10 g), and in-shell pecans (25 g) were sampled from five cattle-grazed orchards in areas with cattle removed 2 or 4 months before harvest and not removed. Five nongrazing orchards were sampled at harvest for comparison. Detection and isolation of the pathogens were performed by enrichment, selective isolation, and multiplex PCR. Statistical analyses were performed using contingency tables with Pearson's chi-square test. The prevalence of STEC (36%) and Salmonella (29%) in cattle-grazed orchards was significantly higher than in nongrazed orchards (13 and 7%, respectively). STEC prevalence in cattle-grazed orchards was higher (38%) in areas with cattle at harvest than in fenced areas where cattle were removed 2 (29%) and 4 (27%) months before harvest. Salmonella prevalence was similar in areas without fencing (31%) and areas with cattle removed at 2 (22%) and 4 (30%) months before harvest. However, there were no significant differences (P > 0.05) in contamination rates between waiting periods for either pathogen, suggesting a limited impact of waiting periods on reducing the risk of contamination.

HIGHLIGHTS

Exploration of the driving factors and distribution of fecal coliform in rivers under a traditional agro-pastoral economy in Kyrgyzstan, Central Asia

Fecal coliform (FC) in river water is one of the threats to human health. To explore the pollution status of FC in rivers of Kyrgyzstan, a mountainous country with traditional agro-pastoral economy, 184 water samples from the rivers of Kyrgyzstan in low and high river flow period were analyzed. Spatial autocorrelation and classical statistical methods were used to analyze the spatiotemporal distribution and driving factors of FC. The results showed that the surface water quality of Kyrgyz rivers was good, and the concentration range of FC was 0-23 MPN/100 mL. Temporally, the maximum FC concentration was 4 MPN/100 mL in low river flow period, while in the period of high river flow, the highest value reached to 23 MPN/100 mL. Spatially, the concentration of FC in high altitude areas was low, while that in the lowland areas was relatively high, which indicated that animal husbandry in high altitude areas contributed little to FC in rivers, and urban domestic sewage and agricultural activities in lowlands were the main pollution sources of FC in rivers. There was no correlation between FC and hardness, electrical conductivity (EC), pH and total organic carbon (TOC) in river water of Kyrgyzstan, and the distribution of FC in high river flow period was mainly driven by population and human modification of terrestrial systems. The results can provide a basis for the prevention and control of surface water FC pollution and related diseases in Kyrgyzstan.

Overview of the Potential Impacts of Climate Change on the Microbial Safety of the Dairy Industry

Climate change is expected to affect many different sectors across the food supply chain. The current review paper presents an overview of the effects of climate change on the microbial safety of the dairy supply chain and suggest potential mitigation strategies to limit the impact. Raw milk, the common raw material of dairy products, is vulnerable to climate change, influenced by changes in average temperature and amount of precipitation. This would induce changes in the microbial profile and heat stress in lactating cows, increasing susceptibility to microbial infection and higher levels of microbial contamination. Moreover, climate change affects the entire dairy supply chain and necessitates adaptation of all the current food safety management programs. In particular, the review of current prerequisite programs might be needed as well as revisiting the current microbial specifications of the receiving dairy products and the introduction of new pretreatments with stringent processing regimes. The effects on microbial changes during distribution and consumer handling also would need to be quantified through the use of predictive models. The development of Quantitative Microbial Risk Assessment (QMRA) models, considering the whole farm-to-fork chain to evaluate risk mitigation strategies, will be a key step to prioritize actions towards a climate change-resilient dairy industry.

A Predictive Model for Survival of Escherichia coli O157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure

This study aimed at developing a predictive model that captures the influences of a variety of agricultural and environmental variables and is able to predict the concentrations of enteric bacteria in soil amended with untreated Biological Soil Amendments of Animal Origin (BSAAO) under dynamic conditions. We developed and validated a Random Forest model using data from a longitudinal field study conducted in mid-Atlantic United States investigating the survival of Escherichia coli O157:H7 and generic E. coli in soils amended with untreated dairy manure, horse manure, or poultry litter. Amendment type, days of rain since the previous sampling day, and soil moisture content were identified as the most influential agricultural and environmental variables impacting concentrations of viable E. coli O157:H7 and generic E. coli recovered from amended soils. Our model results also indicated that E. coli O157:H7 and generic E. coli declined at similar rates in amended soils under dynamic field conditions.The Random Forest model accurately predicted changes in viable E. coli concentrations over time under different agricultural and environmental conditions. Our model also accurately characterized the variability of E. coli concentration in amended soil over time by providing upper and lower prediction bound estimates. Cross-validation results indicated that our model can be potentially generalized to other geographic regions and incorporated into a risk assessment for evaluating the risks associated with application of untreated BSAAO. Our model can be validated for other regions and predictive performance also can be enhanced when data sets from additional geographic regions become available.