Daily behavioral measures recorded by precision technology devices may indicate bovine respiratory disease status in preweaned dairy calves

M. C. Cantor, J. H.C. Costa

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Precision technology devices can measure and detect relative changes in an animal's behavior to possibly create alerts to intervene and to administer treatments. However, the association of relative changes in daily feeding and activity behaviors in calves with bovine respiratory disease (BRD) status is still largely unexplored. The objective of this case-control study was to determine if daily behavioral patterns of preweaning dairy calves (measured by precision technologies) change before BRD diagnosis. This case-control study enrolled 33 pairs of calves (33 BRD calves matched by age, sex, and birthdate to 33 controls) health scored daily for BRD for the preweaning period (until 50 d on the automated feeder). A pedometer (IceQube, Ice Robotics) was attached to the left rear leg to track activity (lying time, lying bouts, total steps, and acceleration activity index). At 3.0 ± 2.0 d of age, calves were trained to use the automated feeder, which recorded milk and calf starter intake, drinking speed, and feeder visits. Calves were allotted a maximum 10 L/d of milk replacer (Cow's Match, Land O'Lakes Animal Milk Products Co.; 140 g/L) and starter (Special Calf Starter and Grower, Baghdad Feeds), ad libitum. Calves were scored daily for signs of BRD using the Wisconsin scoring system, and their lungs were scored with ultrasonography twice weekly. Outward signs of BRD as defined by the Wisconsin scoring system and an area of consolidated lung ≥3.0 cm2 was identified as BRD (d 0). Relative changes in daily behaviors were calculated using d −5 before BRD diagnosis as the baseline for each calf for each behavior. Linear mixed models were used to investigate the association of BRD status with feeding behaviors, activity, and relative changes in calf behavior over a 5-d period before diagnosis, as well as the BRD status × day interaction. Calves with a BRD bout were diagnosed at an average age of 33.0 ± 9.0 d (mean ± standard deviation) and weighed 56.1 ± 9.7 kg. Over the period, BRD calves had reduced milk and starter intake, greater lying times, and fewer lying bouts, step counts, and activity indices when compared with healthy calves. Furthermore, there was a BRD status × day interaction for relative changes in unrewarded visits, and relative changes in calf starter intake. Specifically, BRD calves had a decline in relative changes in their unrewarded visits on d −4, −2, −1, and 0 compared with healthy calves. This study suggests that there is the potential to use feeding and activity behaviors to identify BRD development in preweaning calves. However, utilization of an animals' daily behavioral patterns in real time is fundamental for developing disease detection algorithms, thus we suggest relative changes in unrewarded visits may be useful for algorithm development when d −5 is used as a baseline. Future research should investigate the potential of feeding behavior and activity levels collectively to indicate BRD status in calves using machine learning techniques.

Original languageEnglish
Pages (from-to)6070-6082
Number of pages13
JournalJournal of Dairy Science
Volume105
Issue number7
DOIs
StatePublished - Jul 2022

Bibliographical note

Funding Information:
The research for this study was funded by a USDA NIFA Hatch Grant Project KY007100 at the University of Kentucky (Lexington, KY). The authors thank Heather Neave (Aarhus University, Aarhus, Denmark) and veterinarian Maria Eduarda Reis (Sao Paulo, Brazil) for their help with this manuscript. The research concept, data collection, statistical analysis and writing were conducted by M. C. Cantor under the supervision and editing of co-author J. H. C. Costa. The authors also thank the dairy staff, and the countless students visiting and also attending the University of Kentucky to assist with data collection for this trial, especially Megan Woodrum Setser, Charlotte Pertuisel, Justine Alary, Clemence Dudoit, Mathilde Campedelli, Giulia Gobbo Rodrigues, Emily Rice, Anna Hawkins, and Gustavo Mazon. The authors have not stated any conflicts of interest.

Publisher Copyright:
© 2022 American Dairy Science Association

Keywords

  • accelerometer
  • automated feeder
  • intervention
  • precision livestock farming

ASJC Scopus subject areas

  • Food Science
  • Animal Science and Zoology
  • Genetics

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