Project Number: 6040-32000-012-006-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2023
End Date: Aug 30, 2028
Research conducted as part of this increase will be coordinated with the Cooperator as part of the project “Reduction of Foodborne Pathogens and Antimicrobial Resistance in Poultry Production Environments”. The projects have the common goal of identifying environmental variables that control the prevalence, persistence, and diversity of Salmonella in pre-harvest poultry production environments. Salmonella is a leading cause of foodborne illness in the U.S. and around the world. Understanding environmental drivers for Salmonella within the pre-harvest production environment will help minimize pathogen loads entering the post-harvest processing facilities, and contribute to a safer food supply for consumers and sustainable production for the poultry industry. Objective 4: Determine pre-harvest environmental and management factors that drive the persistence of zoonotic bacterial pathogens within commercial-scale poultry production houses. Sub-Objective 4.A: Assess the effect of pre-harvest environmental conditions and management practices on the identification, prevalence, and characterization of pathogens during live production Sub-Objective 4.B: Develop analytical models to predict the environmental drivers of pathogen prevalence and persistence within live poultry production systems to improve stakeholder pre-harvest data utilization and implementation.
Sub-Objective 4.A Approach: Levels of pathogens associated with poultry will be determined based on environmental conditions (e.g. dust, moisture, temperature, etc.) during commercial-scale poultry production. These isolated pathogens will then be further identified and characterized to evaluate microbial factors that influence their persistence with live production systems. Optimal environmental factors will be identified with the purpose of minimizing the presence and transmission of pathogens within commercial-scale poultry production houses. Sub-Objective 4.B Approach: Microbiological, physiochemical, and management data from live poultry production systems studies will be utilized as the variable data into appropriate multivariate predictive or machine learning/deep learning-based algorithms. These models will be used to predict the variables that are the most influential drivers of zoonotic bacterial prevalence, persistence, and diversity within pre-harvest poultry live production. These models will be used to begin to develop decision support tools that stakeholders will be able to utilize to indirectly predict pathogen prevalence, persistence, and diversity based on environmental variable measurements to improve poultry food safety.