It is common among dairy calves to develop disease and metabolic problems in the early lactation period. The development of a method for identifying calves that are more likely to have a poor lactation period will allow for more effective interventions on preventing inefficiencies. The project team is using machine learning technology to analyze prepartum rumination activity based on wearable sensors cows are wearing that will predict the likelihood of postpartum problems.
What is the challenge?
The transition period of the cow from pregnant nonlactating to nonpregnant lactating is an important change in the physiological condition of the cow, accompanied by major changes in the endocrinology and metabolism of the cow. This research will explore the potential long-term effects of transition cow biology on the uterine environment, which is crucial for fertility.
Addressing the problem:
The team will test a novel strategy of trace minerals supplementation to improve the health and performance of dairy cows. Using this research platform, the team will add biological samples and lab analyses in the research to investigate factors that determine the success of the transition period and the consequence for uterine and ruminal physiology.
This project will aim to design strategies of precision management of lactating cows with different physiological and metabolic needs with the ultimate outcome to optimize health, welfare and performance of dairy cows globally.
University of Alberta will provide access to the Bovine Genomics Lab where analysis of rumen will take place (Dr. Leluo Guan), Michigan State where the metabolomics analyses of uterine flushing will be conducted University (Dr. Todd Lydic)
Collaborators and students:
Dr. Michael Steele, Dr. Dan Tulpan, Dr. Stephen Leblanc, Dr. Brian McBride