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The long term goal of this project is therefore to provide dairy farmers the tools to produce milk that is free of MAP bacteria. The specific objectives of the project are to: <br/>1) Validate a milk MAP quantitative real time PCR test on bulk milk samples; <br/>2) Identify the most important risk factors for bulk milk contamination with MAP through statistical analysis and mathematical modeling of collected field data in a longitudinal MAP infection dynamics study on two farms and statistical analysis of risk factors for bulk milk contamination with MAP in a cross-sectional study of 300 US dairy farms; <br/>3) Develop a risk assessment model for MAP concentration in bulk milk, based on the information obtained from objective 2 using @Risk software. <br/>4) Define intervention strategies, based on the risk assessment model developed under objective 3, to ensure that bulk milk from dairy farms known to be infected with MAP has a low risk of MAP contamination.
Non-Technical Summary:<br/>
Paratuberculosis, or Johnes disease, is a chronic, progressive granulomatous enteritis that affects dairy cattle and other species. The causal organism of Johnes disease is Mycobacterium avium subsp. paratuberculosis (MAP). A high proportion of dairy farms in the US has MAP infected cattle. Due to similarities in disease presentation of Johnes in cattle and Crohns disease in humans, there is a concern over the potential role that MAP may play in Crohn's disease. For this reason, reducing viable MAP in raw and pasteurized milk is of important.
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Approach:<br/>
Our approach will have four main components: The first step will be to validate the use of a milk quantitative PCR test and use previously collected bulk milk samples from 300 dairy farms to evaluate MAP presence in bulk milk. Second, we will analyze longitudinal data from two herds with a known MAP infection prevalence and cross-sectional data from three hundred dairy herds with the complete range of MAP infection prevalence. For this project we will focus on the relationship between pre-harvest management practices and MAP contamination of milk. Data will be collected on two longitudinally followed farms: farm A and farm B. One herd (A) currently has an average to low MAP-fecal-shedding prevalence, and one herd (B) currently has a high MAP-fecal-shedding prevalence. Data analysis will be performed on a large data set from 300 US dairy farms. The dataset on these farms includes MAP ELISA titer and extensive questionnaire data. We will perform qPCR analysis of the stored bulk milk samples to obtain MAP copy numbers. This comprehensive data set allows us to evaluate the relationship between management practices and MAP prevalence as estimated by antibody ELISA and the relationship between management practices and MAP bulk milk contamination as measured by qPCR, corrected for MAP prevalence as measured by MAP antibody ELISA. Based on the gathered data, a mathematical model of MAP transmission and MAP contamination of the environment will be developed. Third, we will use the collected data to develop risk assessment models that explain and predict MAP contamination of raw milk. Finally, and fourth, we will be using the collected data and the developed models to design optimal sustainable MAP-free milk programs.