Early Detection of Bovine Respiratory Disease in Dairy Calves - Exploring visual clinical observation, automated electronic activity data, and biomarker analysis for detection of early signs of respiratory disease in dairy calves
PhD defence by Henrik Hjul Møller
Assessment Committee
Associate professor Sanni Hansen, Department of Veterinary Clinical Sciences, University of Copenhagen (Chairperson)
Professor Peter T. Thomsen, Department of Animal and Veterinary Sciences, Aarhus University
Professor David Barrett, University of Bristol
Supervisors
Associate Professor Nynne Capion
Professor Liza Rosenbaum Nielsen
Associate Professor Mogens Agerbo Krogh
Associate Professor Mette Bisgaard Petersen
Department
Department of Veterinary Clinical Sciences
Graduate Programme
Veterinary and Animal Health Sciences
Place
The defence is conducted as a hybrid defence.
To attend the defence in person:
Department of Veterinary Clinical Sciences, Room: Auditoriet, Højbakkegaard Allé, 2630 Taastrup
To attend the defence online:
Please follow the link to attend the defence online:
https://ucph-ku.zoom.us/j/62899399350?pwd=apAf8qoJy852kZjibFsnNWEH1SXojq.1
Email address to gain access to the thesis: henrikmoeller92@gmail.com.
You will either receive a copy of the thesis or be informed where you can read a physical copy.
Recipients of copies of the thesis are not allowed to share or distribute it due to copyright compliance.
Short description of the thesis
Bovine respiratory disease is one of the most common diseases in young dairy calves, affecting animal welfare and contributing to antimicrobial use. This PhD thesis investigated whether the disease can be detected earlier, before obvious clinical signs appear. Three approaches were studied: visual assessment of the calf, automated activity monitoring using electronic ear tags, and inflammatory biomarkers measured in blood. Calves developing respiratory disease became less active before the disease was detected clinically, while careful visual assessment could identify other indicators of disease. The investigated blood biomarkers were less useful at this early stage. The findings suggest that combining automated monitoring with skilled clinical assessment may improve early disease detection.