Title (eng)

The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle

Author

Lena Lemmens   University of Veterinary Medicine Vienna

Johann Kofler   University of Veterinary Medicine Vienna

Franz Papst   Graz University of Technology / Austria and Complexity Science Hub Vienna

Lorenz Maurer   University of Natural Resources and Life Sciences Vienna

Franz Steininger   ZuchtData EDV-Dienstleistungen GmbH

Martin Mayerhofer   ZuchtData EDV-Dienstleistungen GmbH

Mary Phelan   MSD Animal Health

Marlene Suntinger   ZuchtData EDV-Dienstleistungen GmbH

Kristina Linke   ZuchtData EDV-Dienstleistungen GmbH

Christa Egger-Danner   ZuchtData EDV-Dienstleistungen GmbH

Hermann Schwarzenbacher   ZuchtData EDV-Dienstleistungen GmbH

Birgit Fuerst-Waltl   University of Natural Resources and Life Sciences Vienna

Katharina Schodl   University of Natural Resources and Life Sciences Vienna

Publishing

MDPI

Description (eng)

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.

Object languages

English

Date

2023

Rights

This work is licensed under a
CC BY 4.0 - Creative Commons Attribution 4.0 International License.

CC BY 4.0 International

http://creativecommons.org/licenses/by/4.0/

Classification

Behavioral-Changes; Feeding-Behavior; Foot Disorders; Scoring System; Lying Behavior; Cows; Health; Impact; Associations; Management

Member of the Collection(s) (1)