Relationship of the Poultry Microbiome to Pathogen Colonization, Farm Management, Poultry Production, and Foodborne Illness Risk Assessment

Despite the continuous progress in food science and technology, the global burden of foodborne illnesses remains substantial, with pathogens in food causing millions of infections each year. Traditional microbiological culture methods are inadequate in detecting the full spectrum of these microorganisms, highlighting the need for more comprehensive detection strategies. This review paper aims to elucidate the relationship between foodborne pathogen colonization and the composition of the poultry microbiome, and how this knowledge can be used for improved food safety. Our review highlights that the relationship between pathogen colonization varies across different sections of the poultry microbiome. Further, our review suggests that the microbiome profile of poultry litter, farm soil, and farm dust may serve as potential indicators of the farm environment's food safety issues. We also agree that the microbiome of processed chicken samples may reveal potential pathogen contamination and food quality issues. In addition, utilizing predictive modeling techniques on the collected microbiome data, we suggest establishing correlations between particular taxonomic groups and the colonization of pathogens, thus providing insights into food safety, and offering a comprehensive overview of the microbial community. In conclusion, this review underscores the potential of microbiome analysis as a powerful tool in food safety, pathogen detection, and risk assessment.

Using farm management practices to predict Campylobacter prevalence in pastured poultry farms

Contamination of poultry products by Campylobacter is often associated with farm management practices and processing plant practices. A longitudinal study was conducted on 11 pastured poultry farms in southeastern United States from 2014 to 2017. In this study, farm practices and processing variables were used as predictors for a random forest (RF) model to predict Campylobacter prevalence in pastured poultry farms and processing environments. Individual RF models were constructed for fecal, soil and whole carcass rinse after processing (WCR-P) samples. The performance of models was evaluated by the area under curve (AUC) from the receiver operating characteristics curve. The AUC values were 0.902, 0.894, and 0.864 for fecal, soil, and WCR-P models, respectively. Relative importance plots were generated to predict the most important variable in each RF model. Animal source of feces was identified as the most important variable in fecal model and the soy content of the brood feed was the most important variable for soil model. For WCR-P model, the average flock age showed the strongest impact on RF model. These RF models can help pastured poultry growers with food safety control strategies to reduce Campylobacter prevalence in pastured poultry farms.

Predicting Salmonella prevalence associated with meteorological factors in pastured poultry farms in southeastern United States

Consumer demand has increased for pastured poultry products as the drive for sustainable farming practices and ethical treatments of livestock have become popular in the press. It is necessary to identify the important meteorological factors associated with the prevalence of Salmonella in the pastured poultry settings since the presence of Salmonella in the environment could lead to contamination of the final product. The objective of this study was to develop a model to describe the relationship between meteorological factors and the presence of Salmonella on the pastured poultry farms. The random forest method was used to develop a model where 83 meteorological factors were included as the predicting variables. The soil model identified humidity as the most important variable associated with Salmonella prevalence, while high wind gust speed and average temperature were identified as important meteorological variables in the feces model. The developed models were robust in predicting the prevalence of Salmonella in pastured poultry farms with the area under receiver operating characteristic (ROC) curve values of 0.884 and 0.872 for the soil model and feces model, respectively. The predictive models developed in this study can provide users with practical and effective tools to make informed decisions with scientific evidence regarding the meteorological parameters that are important to monitor for increased on-farm Salmonella prevalence.

Gastrointestinal microbiomes of broilers and layer hens in alternative production systems

Alternative poultry production systems consisting of free-range or pasture flock raised poultry continues to increase in popularity. Based on the perceived benefits of poultry products generated from these alternative poultry production systems, they have commercial appeal to consumers. Several factors impact the health and well being of birds raised and maintained in these types of production systems. Exposure to foodborne pathogens and potential for colonization in the gastrointestinal tract has to be considered with these types of production systems. The gastrointestinal tract microbial composition and function of birds grown and maintained in alternative poultry operations may differ depending on diets, breed, and age of bird. Dietary variety and foraging behavior are potential influential factors on bird nutrition. The gastrointestinal tract microbiomes of birds raised under alternative poultry production systems are now being characterized with next-generation sequencing to identify individual microbial members and assess the impact of different factors on the diversity of microbial populations. In this review, the gastrointestinal tract microbiota contributions to free-range or pasture-raised broiler and egg layer production systems, subsequent applications, and potential future directions will be discussed.

Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms

Foodborne pathogens such as Listeria spp. contain the ability to survive and multiply in poultry farming environments, which provides a route of contamination for poultry processing environments and final poultry products. An understanding of the effect of meteorological variables on the prevalence of Listeria spp. in the farming environment is lacking. Soil and feces samples were collected from 11 pastured poultry farms from 2014 to 2017. Random forest (RF) and gradient boosting machine (GBM) predictive models were generated to describe and predict Listeria spp. prevalence in feces and soil samples based on meteorological factors at the farming location. This study attempted to demonstrate the use of GBM models in a food safety context and compare their use to RF models. Both feces models performed very well, with area under the curve (AUC) values of 0.905 and 0.855 for the RF and GBM models, respectively. The soil GBM model outperformed the RF model with AUCs of 0.873 and 0.700, respectively. The developed models can be used to predict the prevalence of Listeria spp. in pastured poultry farm environments and should be of great use to poultry farmers, producers, and risk managers.