Identifying and modeling meteorological risk factors associated with pre-harvest contamination of Listeria species in a mixed produce and dairy farm

Pang, Hao; McEgan, Rachel; Mishra, Abhinav; Micallef, Shirley A.; Pradhan, Abani K.

doi:10.1016/j.foodres.2017.09.029

Cited by 38 publications

(29 citation statements)

References 40 publications

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“…Further studies have highlighted the influence of environmental conditions on the spread of bacteria, especially in mixed farms, where there is a risk of not only crop contamination but also of contamination of grass destined for fermentation to be converted to silage. Pang et al (2017) showed that seasonality was not the most important factor for the dissemination of listeria species in soils, but abundant precipitation and high wind speed acted as vectors for pathogen transmission via run-off and windborne dust. A further study found that precipitation and the occurrence of alternating freezing and thawing temperatures before soil sample collection were predictors for the presence of genus Listeria (Ivanek et al, 2009).…”

Section: Birds Wild Animals and Other Environmental Factorsmentioning

confidence: 99%

Listeria monocytogenes dissemination in farming and primary production: Sources, shedding and control measures

et al. 2021

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Listeria monocytogenes (L. monocytogenes) is a well-known pathogenic bacterium that causes the disease listeriosis in both humans and animals. Human transmission mainly occurs via ingestion of contaminated foods and specifically affects pregnant women, newborns, elderly individuals and immunosuppressed individuals. Several outbreaks have been historically associated with the consumption of fresh raw milk and unpasteurized cheese, highlighting the role of good farm hygiene measures to reduce the probability of milk contamination. L monocytogenes is ubiquitous in the environment, and therefore, this bacterium is commonly found in silage, haylage, grazing pastures, crop fields, farmyards and even water. Faeces of wild animals, including gulls and rooks, have also been described as important vectors of the pathogen for farm animal contamination, as well as animal bedding, soils or feed bunk tanks, especially when animals are housed during indoor months. Milking lines, including filters, collectors, bulk tanks and other utensils in the room, have been described as important sites of bacterial detection. The ability of L. monocytogenes to produce biofilms and to survive in humid environments makes elimination difficult and increases its persistence in equipment and on floors, leading to high risk of milk contamination at harvest in farms. This review explores in depth the different sources of L. monocytogenes contamination described in production farms, with a special focus on ruminants, identifying the transmission vectors and analysing the applicable control measures at each stage.

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Section: Birds Wild Animals and Other Environmental Factorsmentioning

confidence: 99%

Listeria monocytogenes dissemination in farming and primary production: Sources, shedding and control measures

et al. 2021

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“…Climate change is expected to influence the microbial profile of dairy products through the direct impact of climate variables and seasons on the microbial ecology of raw milk (Figure 2) [5,9]. These climate variables may include changes in average temperature, relative humidity, average precipitation and sunlight exposure, among others, which are also associated with different seasons [24,85]. Changes in climate variables can lead to the rise of pathogenic or spoilage microorganisms in raw milk.…”

Section: Effects Of Climate and Seasons On The Microbial Ecology Of Raw Milkmentioning

confidence: 99%

“…Several approaches on how climate change effects can be incorporated as a variable in a QMRA can be found in research involved in the production of safe green leafy vegetables [24,[123][124][125]. From these research studies, three methods on how climate change effects can be inputted in a QMRA model were shown.…”

Section: Developing Qmra Models Integrating Climate Change Effectsmentioning

confidence: 99%

“…Considering that definition, the effects of climate change on the microbial changes of dairy products presented in this review encompass both the safety related to foodborne pathogens and spoilage microorganisms. Climate change effects, such as relative humidity, daily temperatures and precipitation, were shown to influence the survival of pathogens in food crops, particularly green leafy vegetables, and in the adjacent environment [13,[20][21][22][23][24][25]. On the other hand, wind speed and ultraviolet radiation were both found to decrease the level of Enterobacteriaceae on fresh produce [10,25].…”

Section: Introductionmentioning

confidence: 99%

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

Feliciano

Boué

Membré

2020

Foods

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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.

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“…Machine learning models have been shown to have use in the food safety industry, with several studies being published on the performance of random forest models (Barco et al, 2012;Gu et al, 2015;Pang et al, 2017) and classification and regression tree models (Mokhtari et al, 2006;Ivanek et al, 2009;Strawn et al, 2013) in a food safety context. Benefits to using RF models is that they are robust to outliers and skewed data, provide variable importance rankings, and compute an unbiased out-of-bag error estimate (Rodriguez-Galiano et al, 2012).…”

Section: Random Forest Model Performancementioning

confidence: 99%

Using Farm Practice Variables as Predictors of Listeria spp. Prevalence in Pastured Poultry Farms

Golden

Rothrock

Mishra

2019

Front. Sustain. Food Syst.

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Predictive models offer food scientists, farmers, and processors tools to help identify variables that lead to an increase in the food safety risk of a product. Foodborne pathogens, such as Listeria spp., pose a major problem for the pastured poultry industry. Currently, there is a lack of understanding of what farm practices lead to higher prevalence of Listeria spp. This study constructed random forest (RF) models to predict the prevalence of Listeria spp. in pastured poultry farming environments and the final broiler product based on major farm practices and variables. Feces, soil, and whole carcass rinse samples were collected from 11 farms in the southeastern United States and evaluated for Listeria spp. presence. The preharvest sample RF model identified the time of year and age of the broiler flock at time of sampling as factors of increased probability of Listeria spp. presence in feces and soil samples. The final product RF model identified brood feed and the presence of chlorine in processing rinse water as the two most important variables associated with an increased likelihood of Listeria spp. presence. Both the preharvest RF model and final sample RF model performed well on a held-out test set, with area under the receiver operating characteristic curve values of 0.876 and 0.887, respectively. The presented models showed the usefulness of RF models in a food safety context. Both RF models will help pastured poultry farmers and processors guide control strategies to manage Listeria contamination in pastured poultry farms and products.

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Identifying and modeling meteorological risk factors associated with pre-harvest contamination of Listeria species in a mixed produce and dairy farm

Cited by 38 publications

References 40 publications

Listeria monocytogenes dissemination in farming and primary production: Sources, shedding and control measures

Listeria monocytogenes dissemination in farming and primary production: Sources, shedding and control measures

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

Using Farm Practice Variables as Predictors of Listeria spp. Prevalence in Pastured Poultry Farms

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