Using farm practice variables as predictors of Listeria spp. prevalence in pastured poultry farms

Authors: GOLDEN, CHASE - University Of Georgia; Rothrock, Michael; MISHRA, ABHINAV - University Of Georgia

Submitted to: Frontiers in Sustainable Food Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/28/2019
Publication Date: 3/22/2019
Citation: Golden, C.E., Rothrock Jr, M.J., Misra, A. 2019. Using farm practice variables as predictors of Listeria spp. prevalence in pastured poultry farms. Frontiers in Sustainable Food Systems. 3(15). https://doi.org/10.3389/fsufs.2019.00015
DOI: https://doi.org/10.3389/fsufs.2019.00015

Interpretive Summary: 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.

Technical Abstract: 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.