Abstract #355
Section: Animal Behavior and Well-Being
Session: Animal Behavior & Well-Being II
Format: Oral
Day/Time: Tuesday 3:15 PM–3:30 PM
Location: 321
Session: Animal Behavior & Well-Being II
Format: Oral
Day/Time: Tuesday 3:15 PM–3:30 PM
Location: 321
# 355
Detection of lame cattle using behavioral and physiological changes as measured by precision dairy monitoring technologies.
B. W. Jones*1, L. M. Mayo1, I. C. Tsai1, A. E. Stone1, Y. M. Chang2, J. M. Bewley1, 1University of Kentucky, Lexington, KY, 2Royal Veterinary College, London, UK.
Key Words: precision dairy monitoring technologies, lameness, accelerometer
Detection of lame cattle using behavioral and physiological changes as measured by precision dairy monitoring technologies.
B. W. Jones*1, L. M. Mayo1, I. C. Tsai1, A. E. Stone1, Y. M. Chang2, J. M. Bewley1, 1University of Kentucky, Lexington, KY, 2Royal Veterinary College, London, UK.
The objective of this study was to detect lame cows using precision dairy monitoring technologies (PDMT). The study was conducted at the University of Kentucky Coldstream Dairy with Holstein dairy cows (n = 134) from June 08, 2014 to July 09, 2015. Table 1 displays the PDMT used. For each cow, general symmetry, tracking, spine curvature, head bobbing, speed, and abduction and adduction were each scored visually on a 1 (sound cow) to 5 (severely lame cow) scale weekly. Final gait score was calculated as a weighted average of all gait aspects as determined via an expert opinion survey: general symmetry was 24%, tracking was 20%, spine curvature was 19%, head bobbing was 15%, speed was 12%, and abduction and adduction was 9% of final weighted gait score. A linear mixed model was used in SAS (Version 9.3 SAS Institute, Inc., Cary, NC) to analyze models for prediction of weekly weighted gait score by using all the PDMT variables. Agreement between weighted gait scores and predicted gait scores were assessed using a concordance correlation coefficient (CCC) to evaluate accuracy. Forty-two percent of predicted gait scores were within 0.25 points and 73% of predicted gait scores were within 0.50 points of the actual weighted gait score. The CCC was deemed moderate at 0.70. These results suggest that the PDMT only moderately detected lame cows.
Table 1. List of Precision dairy monitoring technology (PDMT) and variables measured
| PDMT (company name and country) | ||
| IceQube (IceRobotics Ltd., Edinburgh, Scotland) | Total motion (units/d) | |
| AfiAct Pedometer Plus (Afimilk, Kibbutz Afikim, Israel) | Rest time (hours/d) | |
| Track a Cow (ENGS Systems Innovative Dairy Solutions, Israel) | Steps (number/d) | |
| Lying bouts (number/d) | ||
| Time at the feedbunk (hours/d) | ||
| Feedbunk visits (bouts/d) | ||
| Smartbow (Smartbow GmbH, Jutogasse, Austria) | Rumination (hours/d) | |
| Activity (hours/d) | ||
| Afimilk MPC Analyzer (Afimilk, Kibbutz Afikim, Israel) | Yield (kg/d) | |
| Fat (%/d) | ||
| Protein (%/d) | ||
| Lactose (%/d) | ||
| AfiWeigh (Afimilk, Kibbutz Afikim, Israel) | Body weight (kg/d) |
Key Words: precision dairy monitoring technologies, lameness, accelerometer
