Literature DB >> 24630647

Technical note: Evaluation of an ear-attached movement sensor to record cow feeding behavior and activity.

J P Bikker1, H van Laar2, P Rump3, J Doorenbos4, K van Meurs3, G M Griffioen3, J Dijkstra5.   

Abstract

The ability to monitor dairy cow feeding behavior and activity could improve dairy herd management. A 3-dimensional accelerometer (SensOor; Agis Automatisering BV, Harmelen, the Netherlands) has been developed that can be attached to ear identification tags. Based on the principle that behavior can be identified by ear movements, a proprietary model classifies sensor data as "ruminating," "eating," "resting," or "active." The objective of the study was to evaluate this sensor on accuracy and precision. First, a pilot evaluation of agreement between 2 independent observers, recording behavior from 3 cows for a period of approximately 9h each, was performed. Second, to evaluate the sensor, the behavior of 15 cows was monitored both visually (VIS) and with the sensor (SENS), with approximately 20 h of registration per cow, evenly distributed over a 24-h period, excluding milking. Cows were chosen from groups of animals in different lactation stages and parities. Each minute of SENS and VIS data was classified into 1 of 9 categories (8 behaviors and 1 transition behavior) and summarized into 4 behavioral groups, namely ruminating, eating, resting, or active, which were analyzed by calculating kappa (κ) values. For the pilot evaluation, a high level of agreement between observers was obtained, with κ values of ≥ 0.96 for all behavioral categories, indicating that visual observation provides a good standard. For the second trial, relationships between SENS and VIS were studied by κ values on a minute basis and Pearson correlation and concordance correlation coefficient analysis on behavior expressed as percentage of total registration time. Times spent ruminating, eating, resting, and active were 42.6, 15.9, 31.6, and 9.9% (SENS) respectively, and 42.1, 13.0, 30.0, and 14.9% (VIS), respectively. Overall κ for the comparison of SENS and VIS was substantial (0.78), with κ values of 0.85, 0.77, 0.86, and 0.47 for "ruminating," "eating," "resting," and "active," respectively. Pearson correlation and concordance correlation coefficients between SENS and VIS for "ruminating," "eating," "resting," and "active" were 0.93, 0.88, 0.98, and 0.73, and 0.93, 0.75, 0.97, and 0.35, respectively. In conclusion, the results provide strong evidence that the present ear sensor technology can be used to monitor ruminating and resting behavior of freestall-housed dairy cattle. Our results also suggest that this technology shows promise for monitoring eating behavior, whereas more work is needed to determine its suitability to monitor activity of dairy cattle.
Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  dairy cow behavior; ruminating; sensor technology

Mesh:

Year:  2014        PMID: 24630647     DOI: 10.3168/jds.2013-7560

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  9 in total

Review 1.  Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions.

Authors:  Sarah Morrone; Corrado Dimauro; Filippo Gambella; Maria Grazia Cappai
Journal:  Sensors (Basel)       Date:  2022-06-07       Impact factor: 3.847

2.  TECHNICAL NOTE: Development of a pressure sensor-based system for measuring rumination time in pre-weaned dairy calves.

Authors:  Mehdi Eslamizad; Lisa-Maria Tümmler; Michael Derno; Matthias Hoch; Björn Kuhla
Journal:  J Anim Sci       Date:  2018-11-21       Impact factor: 3.159

3.  Evaluation and application potential of an accelerometer-based collar device for measuring grazing behavior of dairy cows.

Authors:  J Werner; C Umstatter; L Leso; E Kennedy; A Geoghegan; L Shalloo; M Schick; B O'Brien
Journal:  Animal       Date:  2019-02-08       Impact factor: 3.240

4.  A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement.

Authors:  Gamaliel Simanungkalit; Jamie Barwick; Frances Cowley; Robin Dobos; Roger Hegarty
Journal:  Animals (Basel)       Date:  2021-04-17       Impact factor: 2.752

5.  A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle.

Authors:  Anna H Stygar; Yaneth Gómez; Greta V Berteselli; Emanuela Dalla Costa; Elisabetta Canali; Jarkko K Niemi; Pol Llonch; Matti Pastell
Journal:  Front Vet Sci       Date:  2021-03-29

6.  Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches.

Authors:  Catherine McVey; Fushing Hsieh; Diego Manriquez; Pablo Pinedo; Kristina Horback
Journal:  Sensors (Basel)       Date:  2021-12-21       Impact factor: 3.576

7.  Prediction of Cow Calving in Extensive Livestock Using a New Neck-Mounted Sensorized Wearable Device: A Pilot Study.

Authors:  Carlos González-Sánchez; Guillermo Sánchez-Brizuela; Ana Cisnal; Juan-Carlos Fraile; Javier Pérez-Turiel; Eusebio de la Fuente-López
Journal:  Sensors (Basel)       Date:  2021-12-02       Impact factor: 3.576

8.  Validation of NEDAP Monitoring Technology for Measurements of Feeding, Rumination, Lying, and Standing Behaviors, and Comparison with Visual Observation and Video Recording in Buffaloes.

Authors:  Ray Adil Quddus; Nisar Ahmad; Anjum Khalique; Jalees Ahmed Bhatti
Journal:  Animals (Basel)       Date:  2022-02-25       Impact factor: 2.752

9.  Assessing the Accuracy of Leg Mounted Sensors for Recording Dairy Cow Behavioural Activity at Pasture, in Cubicle Housing and a Straw Yard.

Authors:  Gemma Charlton; Carrie Gauld; Fabio Veronesi; Steven Mark Rutter; Emma Bleach
Journal:  Animals (Basel)       Date:  2022-03-03       Impact factor: 2.752

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.