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Cited by 14Animal Behavior and Welfare StudiesSmart Agriculture and AIAnimal health and immunology

The Use of an Activity Monitoring System for the Early Detection of Health Disorders in Young Bulls

Open access
Belaid, Mohammed Anouar, Rodriguez-Prado, Maria, Chevaux, Eric, Calsamiglia, Sergio·2019·Animals
DOI 10.3390/ani9110924PMC PMC6912257
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Abstract

Bulls (n = 770, average age = 127 days, SD = 53 days of age) were fitted with an activity monitoring device for three months to study if behavior could be used for early detection of diseases. The device measured the number of steps, lying time, lying bouts, and frequency and time of attendance at the feed bunk. All healthy bulls (n = 699) throughout the trial were used to describe the normal behavior. A match-pair test was used to assign healthy bulls for the comparison vs. sick bulls. The model was developed with 70% of the data, and the remaining 30% was used for the validation. Healthy bulls did 2422 ± 128 steps/day, had 28 ± 1 lying bouts/day, spent 889 ± 12 min/day lying, and attended the feed bunk 8 ± 0.2 times/d for a total of 95 ± 8 min/day. From the total of bulls enrolled in the study, 71 (9.2%) were diagnosed sick. Their activities changed at least 10 days before the clinical signs of disease. Bulls at risk of becoming sick were predicted 9 days before clinical signs with a sensitivity and specificity of 79% and 81%, respectively. The validation of the model resulted in a sensitivity, specificity, and accuracy of 92%, 42%, and 82 %, respectively, and a 50% false positive and 12.5% false negative rates. Results suggest that activity-monitoring systems may be useful in the early identification of sick bulls. However, the high false positive rate may require further refinement.

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  • Incorporating behavioral indicators, such as time at the feeding bunk, along with periods of lying down and walking into a predictive model enhances the detection capabilities of farm personnel when systematic assessments are not available.

    “Incorporating behavioral indicators, such as time at the feeding bunk, along with periods of lying down and walking into a predictive model [247], enhances the detection capabilities of farm personnel when systematic assessments are not available [87].”

    From Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview (2024) · cites this paper as [247]

    The [247] citation is attached to the behavioral indicators used in the predictive model, not to the later claim about farm personnel. Results §3.3 says the model included feed-bunk attendance, lying bouts, and lying time. Discussion §4.3 says step counts did not provide additional information, so the cited paper does not support walking as an indicator in the final model. (LLM, conf=0.88)

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Research works printed but not linked: 23

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Authors
Belaid, M. A., Rodriguez-Prado, M., Chevaux, E., & Calsamiglia, S.
Affiliations
  • Universitat Autònoma de Barcelona (ES)
  • Animal Nutrition and Welfare Service, Department of Animal and Food Sciences, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
  • Lallemand (France) (FR)
Corresponding author
Calsamiglia, Sergio
ORCID
Venue
Animals
Publisher
MDPI AG
Published
2019-11-05
Volume
9
Issue
11
Pages
924
ISSN
2076-2615 (ISSN-L)
DOI
10.3390/ani9110924
PMC
PMC6912257
OpenAlex
W2983620073
Cited by
14
References
2
Citations by year
2025: 1 · 2024: 5 · 2023: 1 · 2022: 1 · 2021: 4 · 2020: 1 · 2017: 1
FWCI
1.98
Type
journal article
Language
en
Open access
Licence
Locations
  • Animals (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Lallemand SAS (Contract, No specific number)
Topics
  • Health Sciences › Veterinary › Small Animals › Animal Behavior and Welfare Studies
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Health Sciences › Veterinary › Small Animals › Animal health and immunology
Keywords
activity monitoring, early disease detection, young bulls, cattle behavior, lying behavior, step count, disease prediction model, sensitivity and specificity, false positive rate, animal welfare

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