Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 303.12 KB
Add to list
  1. Search results
  2. Calf welfare monitored using physiolo...

Calf welfare monitored using physiologically based Precision Livestock Farming technology during different sampling techniques of the respiratory tract

Alberto Peña Fernandez (UGent) , Florian Debruyne (UGent) , Glenn Van Steenkiste (UGent) , Jade Bokma (UGent) , Stan Jourquin (UGent) , Thomas Lowie (UGent) , Justine Clinquart (UGent) , Bart Pardon (UGent) and Daniel Berckmans
(2023) 2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings.
Author
Organization
Abstract
To understand how much feed energy is lost in the lack of animal welfare in livestock processes, we explore possibilities for monitoring animal welfare in an objective way by measuring physiological variables. Worldwide the majority of calves suffer from pneumonia. Sampling techniques have been developed to detect such pneumonia. The concern is to know whether this technique impacts animal welfare. The objective of this paper is to describe how we detect the effect of three out of five sampling techniques of the respiratory tract on calf welfare. First experiments were conducted on three male Holstein-Friesian calves under thermal controlled experimental conditions. Following stressors were applied: deep nasopharyngeal swabbing (DNS), non-endoscopic bronchoalveolar lavage (nBAL), transtracheal wash (TTW), blood sampling and animal fixation. Each calf was wearing a sensor measuring heart rate (BPM, 3 Hz) and activity (x-, y-, z-accelerations; 26 Hz). A data-based mechanistic model adapts to each individual calve and next, the real-time model adapts to individual variations during possible stressful sampling techniques. The model decomposes the measured total heart rate into different components, namely heart rate components required for: the basal metabolism, the physical activity, the thermal component and finally the mental component. The data-based mechanistic model for the dynamic response of the mental component during a sampling technique exhibits an R2 = (95 ± 4) % and Young Identification Criterion YIC = (-7.4 ± 3.4). These models are first order for the DNS, second order model split in a feedback configuration for the TTW and no conclusive structure for the nBAL techniques. The individual model parameters for each calf vary from b0 = 0.10 ± 0.03 bpm-1 to b0 = 11.9 ± 0.6 bpm-1, confirming individually different responses of each calf as expected.
Keywords
Physiological data, stress monitoring, respiratory sampling techniques, PLF

Downloads

  • Download
    (...).docx
    • full text (Accepted manuscript)
    • |
    • UGent only (changes to open access on 2025-05-25)
    • |
    • ZIP archive
    • |
    • 303.12 KB

Citation

Please use this url to cite or link to this publication:

MLA
Peña Fernandez, Alberto, et al. “Calf Welfare Monitored Using Physiologically Based Precision Livestock Farming Technology during Different Sampling Techniques of the Respiratory Tract.” 2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings, 2023.
APA
Peña Fernandez, A., Debruyne, F., Van Steenkiste, G., Bokma, J., Jourquin, S., Lowie, T., … Berckmans, D. (2023). Calf welfare monitored using physiologically based Precision Livestock Farming technology during different sampling techniques of the respiratory tract. 2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings. Presented at the United States Precision Livestock Farming Conference 2023 (USPLF23), Knoxville, Tennessee, US.
Chicago author-date
Peña Fernandez, Alberto, Florian Debruyne, Glenn Van Steenkiste, Jade Bokma, Stan Jourquin, Thomas Lowie, Justine Clinquart, Bart Pardon, and Daniel Berckmans. 2023. “Calf Welfare Monitored Using Physiologically Based Precision Livestock Farming Technology during Different Sampling Techniques of the Respiratory Tract.” In 2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings.
Chicago author-date (all authors)
Peña Fernandez, Alberto, Florian Debruyne, Glenn Van Steenkiste, Jade Bokma, Stan Jourquin, Thomas Lowie, Justine Clinquart, Bart Pardon, and Daniel Berckmans. 2023. “Calf Welfare Monitored Using Physiologically Based Precision Livestock Farming Technology during Different Sampling Techniques of the Respiratory Tract.” In 2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings.
Vancouver
1.
Peña Fernandez A, Debruyne F, Van Steenkiste G, Bokma J, Jourquin S, Lowie T, et al. Calf welfare monitored using physiologically based Precision Livestock Farming technology during different sampling techniques of the respiratory tract. In: 2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings. 2023.
IEEE
[1]
A. Peña Fernandez et al., “Calf welfare monitored using physiologically based Precision Livestock Farming technology during different sampling techniques of the respiratory tract,” in 2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings, Knoxville, Tennessee, US, 2023.
@inproceedings{01H2AMB69800ER4QWEZM4J3Y0K,
  abstract     = {{To understand how much feed energy is lost in the lack of animal welfare in livestock processes, we explore possibilities for monitoring animal welfare in an objective way by measuring physiological variables. Worldwide the majority of calves suffer from pneumonia. Sampling techniques have been developed to detect such pneumonia. The concern is to know whether this technique impacts animal welfare. The objective of this paper is to describe how we detect the effect of three out of five sampling techniques of the respiratory tract on calf welfare. First experiments were conducted on three male Holstein-Friesian calves under thermal controlled experimental conditions. Following stressors were applied: deep nasopharyngeal swabbing (DNS), non-endoscopic bronchoalveolar lavage (nBAL), transtracheal wash (TTW), blood sampling and animal fixation. Each calf was wearing a sensor measuring heart rate (BPM, 3 Hz) and activity (x-, y-, z-accelerations; 26 Hz).   A data-based mechanistic model adapts to each individual calve and next, the real-time model adapts to individual variations during possible stressful sampling techniques. The model decomposes the measured total heart rate into different components, namely heart rate components required for: the basal metabolism, the physical activity, the thermal component and finally the mental component. The data-based mechanistic model for the dynamic response of the mental component during a sampling technique exhibits an R2 = (95 ± 4) % and Young Identification Criterion YIC = (-7.4 ± 3.4). These models are first order for the DNS, second order model split in a feedback configuration for the TTW and no conclusive structure for the nBAL techniques. The individual model parameters for each calf vary from b0 = 0.10 ± 0.03 bpm-1 to b0 = 11.9 ± 0.6 bpm-1, confirming individually different responses of each calf as expected.}},
  author       = {{Peña Fernandez, Alberto and Debruyne, Florian and Van Steenkiste, Glenn and Bokma, Jade and Jourquin, Stan and Lowie, Thomas and Clinquart, Justine and Pardon, Bart and Berckmans, Daniel}},
  booktitle    = {{2nd United States Precision Livestock Farming Conference (USPLF 2023), Proceedings}},
  keywords     = {{Physiological data,stress monitoring,respiratory sampling techniques,PLF}},
  language     = {{eng}},
  location     = {{Knoxville, Tennessee, US}},
  pages        = {{15}},
  title        = {{Calf welfare monitored using physiologically based Precision Livestock Farming technology during different sampling techniques of the respiratory tract}},
  year         = {{2023}},
}