o:1614
The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle
en
This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30-42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.
Behavioral-Changes; Feeding-Behavior; Foot Disorders; Scoring System; Lying Behavior; Cows; Health; Impact; Associations; Management
1552099
10.3390/ani13071180
2023-05-10T14:06:23.908Z
44
yes
46
Lena
Lemmens
University of Veterinary Medicine Vienna
Katharina
Schodl
University of Natural Resources and Life Sciences Vienna
person
0000-0002-9592-4040
Birgit
Fuerst-Waltl
University of Natural Resources and Life Sciences Vienna
person
0000-0002-4336-5830
Hermann
Schwarzenbacher
ZuchtData EDV-Dienstleistungen GmbH
person
Christa
Egger-Danner
ZuchtData EDV-Dienstleistungen GmbH
person
Kristina
Linke
ZuchtData EDV-Dienstleistungen GmbH
person
Marlene
Suntinger
ZuchtData EDV-Dienstleistungen GmbH
person
Mary
Phelan
MSD Animal Health
person
Martin
Mayerhofer
ZuchtData EDV-Dienstleistungen GmbH
person
Franz
Steininger
ZuchtData EDV-Dienstleistungen GmbH
person
0000-0003-3400-3501
Franz
Papst
Graz University of Technology / Austria and Complexity Science Hub Vienna
person
0000-0001-8559-5928
Lorenz
Maurer
University of Natural Resources and Life Sciences Vienna
person
Johann
Kofler
University of Veterinary Medicine Vienna
person
0000-0001-9664-3446
application/pdf
1097793
https://phaidra.vetmeduni.ac.at/o:1614
no
yes
16
70
1552253
Animals
22
13
7
MDPI
2023