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Developing and evaluating threshold-based algorithms to detect drinking behavior in dairy cows using reticulorumen

J A Vázquez-Diosdado1, G G Miguel-Pacheco1, Bobbie Plant1

  • 1School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, United Kingdom.

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Summary
This summary is machine-generated.

A reticuloruminal temperature bolus and cow-day specific thresholding accurately detect cow drinking events. Factors like days in milk and ambient temperature significantly influence drinking behavior, enabling automated health monitoring.

Keywords:
algorithmdairy cattledrinking behaviorprecision livestockreticuloruminal temperature

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Area of Science:

  • Animal Science
  • Dairy Cattle Physiology
  • Behavioral Monitoring

Background:

  • Accurate monitoring of drinking behavior in dairy cattle is crucial for assessing health and welfare.
  • Traditional methods for observing drinking events are labor-intensive and may lack continuous data.
  • Reticuloruminal temperature changes offer a potential physiological indicator of drinking events.

Purpose of the Study:

  • To evaluate the efficacy of a reticuloruminal temperature bolus and thresholding algorithms for detecting cow drinking events.
  • To identify factors influencing drinking behavior patterns in dairy cows.
  • To establish a foundation for automated monitoring of dairy cattle welfare.

Main Methods:

  • 16 cows were fitted with reticuloruminal boluses to record temperature continuously over 4 days.
  • Drinking events were validated using synchronized video recordings and temperature data.
  • Two thresholding algorithms (general-fixed and cow-day specific) were compared, with the latter (μ-n10σ) showing superior performance.

Main Results:

  • The cow-day specific thresholding algorithm (n=10) demonstrated higher accuracy in detecting drinking events compared to a general-fixed threshold.
  • Analysis of data from 54 cows over nearly a year revealed that days in milk, parity, milk production, and ambient temperature significantly affect drinking behavior.
  • Temperature drop characteristics (number, amplitude, recovery time) were influenced by cow-specific factors and environmental conditions.

Conclusions:

  • Reticuloruminal temperature monitoring combined with cow-day specific thresholding provides a reliable method for automated detection of drinking events.
  • Understanding the influence of physiological and environmental factors on drinking behavior is key for interpreting these events.
  • This technology offers a promising platform for continuous, non-invasive monitoring of dairy cattle health and welfare.