Title page for ETD etd-07192012-115725

Type of Document Master's Thesis
Author Tholen, Andrea
URN etd-07192012-115725
Title The use of animal activity data and milk components as indicators of clinical mastitis
Degree Master of Science
Department Dairy Science
Advisory Committee
Advisor Name Title
Petersson-Wolfe, Christina S. Committee Chair
Akers, Robert Michael Committee Member
Currin, John F. Committee Member
De Vries, Albert Committee Member
  • animal activity
  • milk component
  • mastitis
  • detection
Date of Defense 2012-06-14
Availability unrestricted
A study was conducted to examine the correlation between a novel behavior monitoring system and a validated data logger. We concluded that the behavior monitoring system was valid for tracking daily rest time in dairy cows (R=0.96); however the correlation values for rest bouts and rest duration were relatively low, (R=0.64) (R=0.47), respectively. Daily monitoring of animal activity and milk components can be used to detect mastitis prior to clinical onset. Data from 268 cases with clinical mastitis and respective controls (n=268) from Virginia Tech and the University of Florida dairy herds were examined. Variables collected included daily milk yield, electrical conductivity, milk fat, protein, and lactose percent, as well as activity measurements including daily rest time, daily rest duration, daily rest bouts, and daily steps taken. Variables were collected for case and control cows in the 14 d prior to and after clinical diagnosis, for a total 29 d monitoring period. A milk sample was aseptically collected upon detection of clinical signs as observed by milker’s at both farms. A statistical method (candisc discriminant analysis) was used to combine all measurements and sensitivity and specificity was calculated. Virginia Tech cows on d -1 (sensitivity=95%, specificity=95%), Virginia Tech and University of Florida cows on d -1 (sensitivity=88%, specificity=90). Overall, daily monitoring of animal activity and milk components can detect mastitis prior to onset of clinical signs of disease. This may allow producers to intervene and make proactive management decisions regarding herd health prior to clinical diagnosis.
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