A New Approach to Recording Rumination Behavior in Dairy Cows
Abstract
:1. Introduction
2. Materials and Methods
2.1. Animals and Housing
2.2. Sensor Data
2.3. Visual Observations
2.4. Data Processing
2.5. Statistical Analysis
3. Results
3.1. Duration of Rumination Cycles
3.2. Number of Regurgitations
3.3. Classification Performance
4. Discussion
Kind of Sensor System | Statistical Method | Accuracy Value | Reference |
---|---|---|---|
Noseband pressure sensor | Spearman correlation coefficient (SCC) | 0.98–1.00 | Braun et al. [26] |
Noseband pressure sensor | SCC | 0.91–0.96 | Zehner et al. [44] |
Noseband pressure sensor | Concordance correlation coefficient (CCC) | 0.94 | Werner et al. [27] |
Noseband pressure sensor | CCC | 0.96 | Li et al. [45] |
Accelerometer sensor, ear-attached | Pearson correlation coefficient (PCC) | 0.93 | Bikker et al. [28] |
CCC | 0.93 | ||
Accelerometer sensor, collar-attached | SCC CCC | 0.83 0.86 | Leso et al. [29] |
Accelerometer sensor, halter-attached | Precision | 99% | Ding et al. [30] |
Accelerometer sensor, reticulum bolus | F1-score | 0.86 | Hamilton et al. [47] |
Accelerometer sensor, reticulum bolus | PCC | 0.72 | Capuzzello et al. [31] |
Acoustic sensor, collar-attached | PCC | 0.93 | Schirmann et al. [32] |
Acoustic sensor, collar-attached | PCC | 0.65 | Elischer et al. [35] |
Acoustic sensor, halter-attached | F1-score | 0.75 | Chelotti et al. [33] |
Video | Success rate | 92.03% | Chen et al. [36] |
Video and modified YOLOv8-pose model | Accuracy | 94.4% | Li et al. [48] |
Triaxial vision system | Sensitivity | 0.97 | Song et al. [37] |
Accelerometer sensor combined with differential pressure sensor, nose clip | Sensitivity Specificity | 99.1–100% 87.8–95% | Hoffmann et al. [present study] |
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Beauchemin, K.A. Ingestion and mastication of feed by dairy cattle. Vet. Clin. N. Am. Food Anim. Pract. 1991, 7, 439–463. [Google Scholar] [CrossRef]
- Hoy, S. Investigations on factors with influence on automatically measured duration of rumination in cows. Zuechtungskunde 2014, 86, 145–156. [Google Scholar]
- Reece, W.O.; Rowe, E.W. Digestion and absorption. In Functional Anatomy and Physiology of Domestic Animals, 5th ed.; Reece, W.O., Rowe, E.W., Eds.; Wiley-Blackwell: Singapore, 2017; pp. 341–401. [Google Scholar]
- Dirksen, G. Digestive system. In The Clinical Examination of Cattle, 3rd ed.; Dirksen, G., Gründer, H.-D., Stöber, M., Eds.; Paul Parey: Berlin, Germany, 1990; pp. 288–400. [Google Scholar]
- Frandson, R.D.; Wilke, W.L.; Fails, A.D. Physiology of digestion. In Anatomy and Physiology of Farm Animals, 7th ed.; Wiley-Blackwell: Ames, IA, USA, 2009; pp. 361–374. [Google Scholar]
- Sammad, A.; Luo, H.; Qiu, W.; Galindez, J.M.; Wang, Y.; Guo, G.; Huang, X.; Wang, Y. Automated monitoring of seasonal and diurnal variation of rumination behaviour: Insights into thermotolerance management of holstein cows. Biosyst. Eng. 2022, 223, 115–128. [Google Scholar] [CrossRef]
- Cocco, R.; Canozzi, M.E.; Fischer, V. Rumination time as an early predictor of metritis and subclinical ketosis in dairy cows at the beginning of lactation: Systematic review-meta-analysis. Prev. Vet. Med. 2021, 189, 105309. [Google Scholar] [CrossRef]
- Siivonen, J.; Taponen, S.; Hovinen, M.; Pastell, M.; Lensink, J.; Pyörälä, S.; Hänninen, L. Impact of acute clinical mastitis on cow behaviour. Appl. Anim. Behav. Sci. 2011, 132, 101–106. [Google Scholar] [CrossRef]
- Gáspárdy, A.; Efrat, G.; Bajcsy, A.C.; Fekete, S.G. Electronic monitoring of rumination activity as an indicator of health status and production traits in high-yielding dairy cows. Acta Vet. Hung. 2014, 62, 452–462. [Google Scholar] [CrossRef]
- Antanaitis, R.; Juozaitiene, V.; Malasauskiene, D.; Televicius, M.; Urbutis, M.; Rutkaukas, A.; Sertvytyte, G.; Baumgartner, W. Identification of changes in rumination behavior registered with an online sensor system in cows with subclinical mastitis. Vet. Sci. 2022, 9, 454. [Google Scholar] [CrossRef]
- Soriani, N.; Panella, G.; Calamari, L. Rumination time during the summer season and its relationships with metabolic conditions and milk production. J. Dairy Sci. 2013, 96, 5082–5094. [Google Scholar] [CrossRef]
- Abeni, F.; Galli, A. Monitoring cow activity and rumination time for an early detection of heat stress in dairy cow. Int. J. Biometeorol. 2017, 61, 417–425. [Google Scholar] [CrossRef]
- Moretti, R.; Biffani, S.; Chessa, S.; Bozzi, R. Heat stress effects on holstein dairy cows’ rumination. Animal 2017, 11, 2320–2325. [Google Scholar] [CrossRef] [PubMed]
- Müschner-Siemens, T.; Palmini, A.; Heinicke, J.; Hoffmann, G.; Belik, V.; Amon, T. Influence of thermal heat load accumulation on daily rumination time of lactating holstein cows in a zone with temperate climate. J. Therm. Biol. 2024, 122, 103856. [Google Scholar] [CrossRef]
- Reith, S.; Hoy, S. Relationship between daily rumination time and estrus of dairy cows. J. Dairy Sci. 2012, 95, 6416–6420. [Google Scholar] [CrossRef]
- Pahl, C.; Hartung, E.; Mahlkow-Nerge, K.; Haeussermann, A. Feeding characteristics and rumination time of dairy cows around estrus. J. Dairy Sci. 2015, 98, 148–154. [Google Scholar] [CrossRef]
- Okine, E.K.; Mathison, G.W. Effects of feed intake on particle distribution, passage of digesta, and extent of digestion in the gastrointestinal tract of cattle. J. Anim. Sci. 1991, 69, 3435–3445. [Google Scholar] [CrossRef]
- Byskov, M.V.; Nadeau, E.; Johansson, B.E.O.; Norgaard, P. Variations in automatically recorded rumination time as explained by variations in intake of dietary fractions and milk production, and between-cow variation. J. Dairy Sci. 2015, 98, 3926–3937. [Google Scholar] [CrossRef]
- Paudyal, S.; Maunsell, F.; Richeson, J.; Risco, C.; Donovan, A.; Pinedo, P. Peripartal rumination dynamics and health status in cows calving in hot and cool seasons. J. Dairy Sci. 2016, 99, 9057–9068. [Google Scholar] [CrossRef]
- Schirmann, K.; Weary, D.M.; Fleuwieser, W.; Chapinal, N.; Cerri, R.L.A.; von Keyserlingk, M.A.G. Short communication: Rumination and feeding behaviors differ between healthy and sick dairy cows during the transition period. J. Dairy Sci. 2016, 99, 9917–9924. [Google Scholar] [CrossRef]
- Calamari, L.; Soriani, N.; Panella, G.; Petrera, F.; Minuti, A.; Trevisi, E. Rumination time around calving: An early signal to detect cows at greater risk of disease. J. Dairy Sci. 2014, 97, 3635–3647. [Google Scholar] [CrossRef]
- Antanaitis, R.; Juozaitiene, V.; Dzermeikaite, K.; Baceninaite, D.; Sertvytyte, G.; Danyla, E.; Rutkauskas, A.; Viora, L.; Baumgartner, W. Change in rumination behavior parameters around calving in cows with subclinical ketosis diagnosed during 30 days after calving. Animals 2023, 13, 595. [Google Scholar] [CrossRef]
- Paudyal, S. Using rumination time to manage health and reproduction in dairy cattle: A review. Vet. Q. 2021, 41, 292–300. [Google Scholar] [CrossRef]
- Braun, U.; Zurcher, S.; Hassig, M. Evaluation of eating and rumination behaviour in 300 cows of three different breeds using a noseband pressure sensor. BMC Vet. Res. 2015, 11, 231. [Google Scholar] [CrossRef]
- Beauchemin, K.A.; Zelin, S.; Genner, D.; Buchanan-Smith, J.G. An automatic system for quantification of eating and ruminating activities of dairy cattle housed in stalls. J. Dairy Sci. 1989, 72, 2746–2759. [Google Scholar] [CrossRef]
- Braun, U.; Trosch, L.; Nydegger, F.; Hassig, M. Evaluation of eating and rumination behaviour in cows using a noseband pressure sensor. BMC Vet. Res. 2013, 9, 164. [Google Scholar] [CrossRef]
- Werner, J.; Leso, L.; Umstatter, C.; Niederhauser, J.; Kennedy, E.; Geoghegan, A.; Shalloo, L.; Schick, M.; O’Brien, B. Evaluation of the rumiwatchsystem for measuring grazing behaviour of cows. J. Neurosci. Methods 2018, 300, 138–146. [Google Scholar] [CrossRef] [PubMed]
- Bikker, J.P.; van Laar, H.; Rump, P.; Doorenbos, J.; van Meurs, K.; Griffioen, G.M.; Dijkstra, J. Technical note: Evaluation of an ear-attached movement sensor to record cow feeding behavior and activity. J. Dairy Sci. 2014, 97, 2974–2979. [Google Scholar] [CrossRef] [PubMed]
- Leso, L.; Becciolini, V.; Rossi, G.; Camiciottoli, S.; Barbari, M. Validation of a commercial collar-based sensor for monitoring eating and ruminating behaviour of dairy cows. Animals 2021, 11, 2852. [Google Scholar] [CrossRef]
- Ding, L.; Lv, Y.; Jiang, R.; Zhao, W.; Li, Q.; Yang, B.; Yu, L.; Ma, W.; Gao, R.; Yu, Q. Predicting the feed intake of cattle based on jaw movement using a triaxial accelerometer. Agriculture 2022, 12, 899. [Google Scholar] [CrossRef]
- Capuzzello, G.; Viora, L.; Borelli, E.; Jonsson, N.N. Evaluation of an indwelling bolus equipped with a triaxial accelerometer for the characterisation of the diurnal pattern of bovine reticuloruminal contractions. J. Dairy Res. 2023, 90, 9–15. [Google Scholar] [CrossRef]
- Schirmann, K.; von Keyserlingk, M.A.G.; Weary, D.M.; Veira, D.M.; Heuwieser, W. Technical note: Validation of a system for monitoring rumination in dairy cows. J. Dairy Sci. 2009, 92, 6052–6055. [Google Scholar] [CrossRef]
- Chelotti, J.O.; Vanrell, S.R.; Martinez Rau, L.S.; Galli, J.R.; Planisich, A.M.; Utsumi, S.A.; Milone, D.H.; Giovanini, L.L.; Rufiner, H.L. An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle. Comput. Electron. Agric. 2020, 173, 105443. [Google Scholar] [CrossRef]
- Burfeind, O.; Schirmann, K.; von Keyserlingk, M.A.G.; Veira, D.M.; Weary, D.M.; Heuwieser, W. Technical note: Evaluation of a system for monitoring rumination in heifers and calves. J. Dairy Sci. 2011, 94, 426–430. [Google Scholar] [CrossRef]
- Elischer, M.F.; Arceo, M.E.; Karcher, E.L.; Siegford, J.M. Validating the accuracy of activity and rumination monitor data from dairy cows housed in a pasture-based automatic milking system. J. Dairy Sci. 2013, 96, 6412–6422. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.J.; He, D.J.; Fu, Y.X.; Song, H.B. Intelligent monitoring method of cow ruminant behavior based on video analysis technology. Int. J. Agric. Biol. Eng. 2017, 10, 194–202. [Google Scholar] [CrossRef]
- Song, X.; van der Tool, P.P.J.; Koerkamp, P.; Bokkers, E.A.M. Hot topic: Automated assessment of reticulo-ruminal motility in dairy cows using 3-dimensional vision. J. Dairy Sci. 2019, 102, 9076–9081. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.A.; Zhang, Q.; Zhang, L.A.; Li, J.; Li, M.A.; Liu, Y.Q.; Shi, Y.Y. Progress of machine vision technologies in intelligent dairy farming. Appl. Sci. 2023, 13, 7052. [Google Scholar] [CrossRef]
- Strutzke, S.; Fiske, D.; Hoffmann, G.; Ammon, C.; Heuwieser, W.; Amon, T. Technical note: Development of a noninvasive respiration rate sensor for cattle. J. Dairy Sci. 2019, 102, 690–695. [Google Scholar] [CrossRef]
- Kononoff, P.; Lehman, H.A.; Heinrichs, A. Technical note—A comparison of methods used to measure eating and ruminating activity in confined dairy cattle. J. Dairy Sci. 2002, 85, 1801–1803. [Google Scholar] [CrossRef]
- Lindgren, E. Validation of Rumination Measurement Equipment and the Role of Rumination in Dairy Cow Time Budgets. Master’s Thesis in Animal Science, Department of Animal Nutrition and Management, Swedish University of Agriculture Sciences (SLU), Uppsala, Sweden, 2009. [Google Scholar]
- Steeneveld, W.; Vernooij, J.C.M.; Hogeveen, H. Effect of sensor systems for cow management on milk production, somatic cell count, and reproduction. J. Dairy Sci. 2015, 98, 3896–3905. [Google Scholar] [CrossRef]
- Wobschall, A.S. Sensor-Based Analysis of the Eating and Ruminating Behaviour of Cows. Ph.D. Thesis, Humboldt University, Berlin, Germany, 2017. [Google Scholar]
- Zehner, N.; Umstatter, C.; Niederhauser, J.J.; Schick, M. System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows. Comput. Electron. Agric. 2017, 136, 31–41. [Google Scholar] [CrossRef]
- Li, Z.; Cheng, L.; Cullen, B. Validation and use of the rumiwatch noseband sensor for monitoring grazing behaviours of lactating dairy cows. Dairy 2021, 2, 104–111. [Google Scholar] [CrossRef]
- Vanrell, S.R.; Chelotti, J.O.; Galli, J.R.; Utsumi, S.A.; Giovanini, L.L.; Rufiner, H.L.; Milone, D.H. A regularity-based algorithm for identifying grazing and rumination bouts from acoustic signals in grazing cattle. Comput. Electron. Agric. 2018, 151, 392–402. [Google Scholar] [CrossRef]
- Hamilton, A.W.; Davison, C.; Tachtatzis, C.; Andonovic, I.; Michie, C.; Ferguson, H.J.; Somerville, L.; Jonsson, N.N. Identification of the rumination in cattle using support vector machines with motion-sensitive bolus sensors. Sensors 2019, 19, 1165. [Google Scholar] [CrossRef]
- Li, J.; Liu, Y.; Zheng, W.; Chen, X.; Ma, Y.; Guo, L. Monitoring cattle ruminating behavior based on an improved keypoint detection model. Animals 2024, 14, 1791. [Google Scholar] [CrossRef]
- Qiao, Y.; Guo, Y.; Yu, K.; He, D. C3d-convlstm based cow behaviour classification using video data for precision livestock farming. Comput. Electron. Agric. 2022, 193, 106650. [Google Scholar] [CrossRef]
- Borchers, M.R.; Chang, Y.M.; Tsai, I.C.; Wadsworth, B.A.; Bewley, J.M. A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors. J. Dairy Sci. 2016, 99, 7458–7466. [Google Scholar] [CrossRef]
Year | 2018 | 2018 | 2022 | 2022 |
Rumination behavior during observation period | yes | no | yes | no |
Number of cows (n) | 6 | 6 | 13 | 6 |
Method of visual observation | Video | Video | Direct | Direct |
Total duration of observations (h:m:s) | 03:07:10 | 01:00:00 | 06:14:47 | 01:00:00 |
Number of visually observed regurgitations (n) | 201 | 0 | 378 | 0 |
Number of sensor-based regurgitations (n) | 201 | 3 | 227 | 5 |
Number of visually observed regurgitations not recorded by the sensor because of a streaming gap (n) | 0 | 0 | 149 | 0 |
Number of minutes with streaming gaps in the sensor data (n) | 0 | 0 | 0 | 19 |
Visually observed regurgitations not visible in the recorded sensor-based data (n) | 0 | 0 | 2 | 0 |
Year and data basis | 2018 | 2022 (raw data) | 2022 (after correction of sensor gaps) |
Sensitivity (Se, true positive rate) | 100.0 | 60.1 | 99.1 |
Specificity (Sp, true negative rate) | 95.0 | 91.7 | 87.8 |
Positive predicted value (PPV) | 98.5 | 97.8 | 97.8 |
Negative predicted value (NPV) | 100.0 | 26.7 | 94.7 |
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Hoffmann, G.; Strutzke, S.; Fiske, D.; Heinicke, J.; Mylostyvyi, R. A New Approach to Recording Rumination Behavior in Dairy Cows. Sensors 2024, 24, 5521. https://doi.org/10.3390/s24175521
Hoffmann G, Strutzke S, Fiske D, Heinicke J, Mylostyvyi R. A New Approach to Recording Rumination Behavior in Dairy Cows. Sensors. 2024; 24(17):5521. https://doi.org/10.3390/s24175521
Chicago/Turabian StyleHoffmann, Gundula, Saskia Strutzke, Daniel Fiske, Julia Heinicke, and Roman Mylostyvyi. 2024. "A New Approach to Recording Rumination Behavior in Dairy Cows" Sensors 24, no. 17: 5521. https://doi.org/10.3390/s24175521