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

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

doi:10.1016/j.foodres.2017.09.029

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

Food Res Int. 2017 Dec:102:355-363. doi: 10.1016/j.foodres.2017.09.029. Epub 2017 Sep 13.

Authors

Hao Pang¹, Rachel McEgan², Abhinav Mishra¹, Shirley A Micallef³, Abani K Pradhan⁴

Affiliations

¹ Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA.
² Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD 20742, USA.
³ Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD 20742, USA; Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA.
⁴ Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA. Electronic address: akp@umd.edu.

PMID: 29195959
DOI: 10.1016/j.foodres.2017.09.029

Abstract

This study sought to investigate the prevalence of Listeria species (including L. monocytogenes) in a mixed produce and dairy farm and to identify specific meteorological factors affecting Listeria spp. presence. Environmental samples were collected monthly from locations within the mixed farm over 14months and were analyzed for Listeria spp. Meteorological factors were evaluated for their association with the presence of Listeria spp. by using logistic regression (LR) and random forest (RF). The developed LR model identified wind speed and precipitation as significant risk factors (P<0.05), indicating higher wind speed at day 2 prior to sampling and higher average precipitation over the previous 25days before sampling increased the probability of isolation of Listeria spp. from the mixed farm. Results from RF revealed that average wind speed at day 2 prior to sampling and average precipitation in the previous 25days before sampling were the most important factors influencing the presence of Listeria spp., which supported the findings from LR. These findings indicate that the occurrence of Listeria spp. was influenced by wind speed and precipitation, suggesting run-off and wind-driven dust might be possible routes of pathogen transmission in mixed farms. The developed LR and RF models, with robust predictive performances as measured by the area under the receiver operating characteristic curves, can be used to predict Listeria spp. contamination risk in a mixed farm under different weather conditions and can help with the evaluation of farm management practices and the development of control strategies aimed at reducing pre-harvest microbial contamination in a mixed farming system.

Keywords: Listeria species; Meteorological factors; Mixed farm.

Publication types

Comparative Study
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Crop Production / methods*
Dairying / methods*
Food Microbiology
Listeria / classification
Listeria / genetics
Listeria / isolation & purification*
Manure / microbiology*
Microbiology
Rain*
Seasons*
Soil Microbiology
Time Factors
Vegetables / growth & development
Vegetables / microbiology*
Water
Wind*

Substances

Manure
Water