Drinking frequency and locomotor activity of cows and calves as measured by triaxial accelerometers and proximity loggers

José-Alfonso Abecia; Ignacio Torre

doi:10.31893/jabb.2025030

Keywords:

Abstract

Understanding cattle drinking behavior is essential for improving animal welfare and management strategies. This study monitored the drinking frequency and locomotor activity of pregnant cows, nursing cows, and their suckling calves via proximity loggers with integrated accelerometers. Three nursing cows, their 4-month-old calves, and 4 late-pregnancy cows were observed for seven consecutive days under farm conditions. The daily number of visits to the water trough did not differ significantly among nursing cows (3.1 ± 0.9), pregnant cows (3.4 ± 0.2), and calves (4.0 ± 0.4). All groups showed a drinking peak at 0900 h, with additional peaks at 1100 h for pregnant cows and at 1700 h for nursing cows and calves. Nursing cows were consistently more active than calves or pregnant cows and tended to have higher circadian MESOR and amplitude values. Calves had a significantly lower circadianity index, suggesting weaker and less stable daily rhythms. In conclusion, while drinking frequency was similar across physiological classes, circadian analyses revealed differences in rhythm robustness, with nursing cows exhibiting the most stable patterns and calves showing evidence of immature circadian regulation.
References
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2. Abecia, J. A., Erden, P., & Canto, F. (2024). Using proximity loggers in studies of sheep behavior: An estimation of the daily frequency of water consumption. Animal Biotelemetry, 12, 19. https://doi.org/10.1186/s40317-024-00376-z
3. Beede, D. K. (1993). Water nutrition and quality for dairy cattle. In Western Large Herd Management Conference (pp. 194–205). Las Vegas, USA.
4. Ben Meir, Y. A., Garcia, F., Cohen-Zinder, M., & Shabtay, A. (2025). Use of proximity loggers to estimate affiliative and agonistic relationships among group-housed Holstein calves. Journal of Applied Animal Welfare Science, 28, 209–221. https://doi.org/10.1080/10888705.2023.2250262
5. Boyland, N. K., Mlynski, D. T., James, R., Brent, L. J. N., & Croft, D. P. (2016). The social network structure of a dynamic group of dairy cows: From individual to group level patterns. Applied Animal Behaviour Science, 174, 1–10. https://doi.org/10.1016/j.applanim.2015.11.016
6. Brooks, E., & Canal, M. M. (2013). Development of circadian rhythms: Role of postnatal light environment. Neuroscience & Biobehavioral Reviews, 37, 551–560. https://doi.org/10.1016/j.neubiorev.2013.02.012
7. Costa, J. H. C., von Keyserlingk, M. A. G., & Weary, D. M. (2016). Invited review: Effects of group housing of dairy calves on behavior, cognition, performance, and health. Journal of Dairy Science, 99, 2453–2467. https://doi.org/10.3168/jds.2015-10144
8. Davidson, J. A., & Beede, D. K. (2009). Exercise training of late-pregnant and nonpregnant dairy cows affects physical fitness and acid-base homeostasis. Journal of Dairy Science, 92, 548–562. https://doi.org/10.3168/jds.2008-1458
9. Duffy, J. F., Zitting, K. M., & Chinoy, E. D. (2015). Aging and circadian rhythms. Sleep Medicine Clinics, 10, 423–434. https://doi.org/10.1016/j.jsmc.2015.08.002
10. Foris, B., Vandresen, B., Sheng, K., Krahn, J., Weary, D. M., & von Keyserlingk, M. A. G. (2023). Automated, longitudinal measures of drinking behavior provide insights into the social hierarchy in dairy cows. JDS Communications, 5, 411–415. https://doi.org/10.3168/jdsc.2023-0487
11. Hedlund, L., & Rolls, J. (1977). Behavior of lactating dairy cows during total confinement. Journal of Dairy Science, 60, 1807–1812. https://doi.org/10.3168/jds.S0022-0302(77)84104-0
12. Islam, M. N., Yoder, J., Nasiri, A., Burns, R. T., & Gan, H. (2023). Analysis of the drinking behavior of beef cattle using computer vision. Animals, 13, 2984. https://doi.org/10.3390/ani13182984
13. Kour, H., Patison, K. P., Corbet, N. J., & Swain, D. L. (2021). Recording cattle maternal behaviour using proximity loggers and tri-axial accelerometers. Applied Animal Behaviour Science, 240, 105349. https://doi.org/10.1016/j.applanim.2021.105349
14. Molcan, L. (2019). Time distributed data analysis by cosinor. bioRxiv. https://doi.org/10.1101/805960
15. Murphy, M. R., Davis, C. L., & McCoy, G. C. (1983). Factors affecting water consumption by Holstein cows in early lactation. Journal of Dairy Science, 66, 35–38. https://doi.org/10.3168/jds.S0022-0302(83)81750-0
16. NRC. (2021). Nutrient requirements of dairy cattle: Eighth revised edition, 2021. National Academies Press. https://doi.org/10.17226/25806
17. Piccione, G., Caola, G., & Refinetti, R. (2005). Temporal relationships of 21 physiological variables in horse and sheep. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 142, 389–396. https://doi.org/10.1016/j.cbpa.2005.07.019
18. Raussi, S., Boissy, A., Delval, E., Pradel, P., Kaihilahti, J., & Veissier, I. (2005). Does repeated regrouping alter the social behaviour of heifers? Applied Animal Behaviour Science, 93, 1–12.
19. Rosenberger, K., Costa, J. H. C., Neave, H. W., von Keyserlingk, M. A. G., & Weary, D. M. (2017). The effect of milk allowance on behavior and weight gains in dairy calves. Journal of Dairy Science, 100, 504–512. https://doi.org/10.3168/jds.2016-11195
20. Sowell, B. F., Branine, M. E., Bowman, J. G., Hubbert, M. E., Sherwood, H. E., & Quimby, W. (1999). Feeding and watering behavior of healthy and morbid steers in a commercial feedlot. Journal of Animal Science, 77, 1105–1112. https://doi.org/10.2527/1999.7751105x
21. Triguero-Ocaña, R., Vicente, J., & Acevedo, P. (2019). Performance of proximity loggers under controlled field conditions: An assessment from a wildlife ecological and epidemiological perspective. Animal Biotelemetry, 7, 24. https://doi.org/10.1186/s40317-019-0186-2
22. Tsai, Y. C., Hsu, J. T., Ding, S. T., Arcega Rustia, D. J., & Lin, T. T. (2020). Assessment of dairy cow heat stress by monitoring drinking behaviour using an embedded imaging system. Biosystems Engineering, 199, 97–108. https://doi.org/10.1016/j.biosystemseng.2020.03.013
23. von Keyserlingk, M. A., Olenick, D., & Weary, D. M. (2008). Acute behavioral effects of regrouping dairy cows. Journal of Dairy Science, 91, 1011–1016. https://doi.org/10.3168/jds.2007-0532
24. Williams, L. R., Bishop-Hurley, G. J., Anderson, A. E., & Swain, D. L. (2017). Application of accelerometers to record drinking behaviour of beef cattle. Animal Production Science, 59, 122–132. https://doi.org/10.1071/AN17052
25. Williams, L. R., Moore, S. T., Bishop-Hurley, G. J., & Swain, D. L. (2020). A sensor-based solution to monitor grazing cattle drinking behaviour and water intake. Computers and Electronics in Agriculture, 168, 105141. https://doi.org/10.1016/j.compag.2019.105141
26. Zoidis, Z., & Hadjigeorgiou, I. (2017). Effects of drinking saline water on food and water intake, blood and urine electrolytes and biochemical and haematological parameters in goats: A preliminary study. Animal Production Science, 58, 1822–1828. https://doi.org/10.1071/AN16539

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How to cite

Abecia, J.-A., & Torre, I. (2025). Drinking frequency and locomotor activity of cows and calves as measured by triaxial accelerometers and proximity loggers. Journal of Animal Behaviour and Biometeorology, 13(4), 2025030. https://doi.org/10.31893/jabb.2025030

[1] Abecia, J. A., Canto, F., Palacios, C., Nieto, J., Plaza, J., García, E. P., & Torre, I. (2025). Locomotor activity and proximity relationships of nursing cows and their calves. Journal of Animal Behaviour and Biometeorology, 13, 2025018.

[2] Abecia, J. A., Erden, P., & Canto, F. (2024). Using proximity loggers in studies of sheep behavior: An estimation of the daily frequency of water consumption. Animal Biotelemetry, 12, 19. https://doi.org/10.1186/s40317-024-00376-z

[3] Beede, D. K. (1993). Water nutrition and quality for dairy cattle. In Western Large Herd Management Conference (pp. 194–205). Las Vegas, USA.

[4] Ben Meir, Y. A., Garcia, F., Cohen-Zinder, M., & Shabtay, A. (2025). Use of proximity loggers to estimate affiliative and agonistic relationships among group-housed Holstein calves. Journal of Applied Animal Welfare Science, 28, 209–221. https://doi.org/10.1080/10888705.2023.2250262

[5] Boyland, N. K., Mlynski, D. T., James, R., Brent, L. J. N., & Croft, D. P. (2016). The social network structure of a dynamic group of dairy cows: From individual to group level patterns. Applied Animal Behaviour Science, 174, 1–10. https://doi.org/10.1016/j.applanim.2015.11.016

[6] Brooks, E., & Canal, M. M. (2013). Development of circadian rhythms: Role of postnatal light environment. Neuroscience & Biobehavioral Reviews, 37, 551–560. https://doi.org/10.1016/j.neubiorev.2013.02.012

[7] Costa, J. H. C., von Keyserlingk, M. A. G., & Weary, D. M. (2016). Invited review: Effects of group housing of dairy calves on behavior, cognition, performance, and health. Journal of Dairy Science, 99, 2453–2467. https://doi.org/10.3168/jds.2015-10144

[8] Davidson, J. A., & Beede, D. K. (2009). Exercise training of late-pregnant and nonpregnant dairy cows affects physical fitness and acid-base homeostasis. Journal of Dairy Science, 92, 548–562. https://doi.org/10.3168/jds.2008-1458

[9] Duffy, J. F., Zitting, K. M., & Chinoy, E. D. (2015). Aging and circadian rhythms. Sleep Medicine Clinics, 10, 423–434. https://doi.org/10.1016/j.jsmc.2015.08.002

[10] Foris, B., Vandresen, B., Sheng, K., Krahn, J., Weary, D. M., & von Keyserlingk, M. A. G. (2023). Automated, longitudinal measures of drinking behavior provide insights into the social hierarchy in dairy cows. JDS Communications, 5, 411–415. https://doi.org/10.3168/jdsc.2023-0487

[11] Hedlund, L., & Rolls, J. (1977). Behavior of lactating dairy cows during total confinement. Journal of Dairy Science, 60, 1807–1812. https://doi.org/10.3168/jds.S0022-0302(77)84104-0

[12] Islam, M. N., Yoder, J., Nasiri, A., Burns, R. T., & Gan, H. (2023). Analysis of the drinking behavior of beef cattle using computer vision. Animals, 13, 2984. https://doi.org/10.3390/ani13182984

[13] Kour, H., Patison, K. P., Corbet, N. J., & Swain, D. L. (2021). Recording cattle maternal behaviour using proximity loggers and tri-axial accelerometers. Applied Animal Behaviour Science, 240, 105349. https://doi.org/10.1016/j.applanim.2021.105349

[14] Molcan, L. (2019). Time distributed data analysis by cosinor. bioRxiv. https://doi.org/10.1101/805960

[15] Murphy, M. R., Davis, C. L., & McCoy, G. C. (1983). Factors affecting water consumption by Holstein cows in early lactation. Journal of Dairy Science, 66, 35–38. https://doi.org/10.3168/jds.S0022-0302(83)81750-0

[16] NRC. (2021). Nutrient requirements of dairy cattle: Eighth revised edition, 2021. National Academies Press. https://doi.org/10.17226/25806

[17] Piccione, G., Caola, G., & Refinetti, R. (2005). Temporal relationships of 21 physiological variables in horse and sheep. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 142, 389–396. https://doi.org/10.1016/j.cbpa.2005.07.019

[18] Raussi, S., Boissy, A., Delval, E., Pradel, P., Kaihilahti, J., & Veissier, I. (2005). Does repeated regrouping alter the social behaviour of heifers? Applied Animal Behaviour Science, 93, 1–12.

[19] Rosenberger, K., Costa, J. H. C., Neave, H. W., von Keyserlingk, M. A. G., & Weary, D. M. (2017). The effect of milk allowance on behavior and weight gains in dairy calves. Journal of Dairy Science, 100, 504–512. https://doi.org/10.3168/jds.2016-11195

[20] Sowell, B. F., Branine, M. E., Bowman, J. G., Hubbert, M. E., Sherwood, H. E., & Quimby, W. (1999). Feeding and watering behavior of healthy and morbid steers in a commercial feedlot. Journal of Animal Science, 77, 1105–1112. https://doi.org/10.2527/1999.7751105x

[21] Triguero-Ocaña, R., Vicente, J., & Acevedo, P. (2019). Performance of proximity loggers under controlled field conditions: An assessment from a wildlife ecological and epidemiological perspective. Animal Biotelemetry, 7, 24. https://doi.org/10.1186/s40317-019-0186-2

[22] Tsai, Y. C., Hsu, J. T., Ding, S. T., Arcega Rustia, D. J., & Lin, T. T. (2020). Assessment of dairy cow heat stress by monitoring drinking behaviour using an embedded imaging system. Biosystems Engineering, 199, 97–108. https://doi.org/10.1016/j.biosystemseng.2020.03.013

[23] von Keyserlingk, M. A., Olenick, D., & Weary, D. M. (2008). Acute behavioral effects of regrouping dairy cows. Journal of Dairy Science, 91, 1011–1016. https://doi.org/10.3168/jds.2007-0532

[24] Williams, L. R., Bishop-Hurley, G. J., Anderson, A. E., & Swain, D. L. (2017). Application of accelerometers to record drinking behaviour of beef cattle. Animal Production Science, 59, 122–132. https://doi.org/10.1071/AN17052

[25] Williams, L. R., Moore, S. T., Bishop-Hurley, G. J., & Swain, D. L. (2020). A sensor-based solution to monitor grazing cattle drinking behaviour and water intake. Computers and Electronics in Agriculture, 168, 105141. https://doi.org/10.1016/j.compag.2019.105141

[26] Zoidis, Z., & Hadjigeorgiou, I. (2017). Effects of drinking saline water on food and water intake, blood and urine electrolytes and biochemical and haematological parameters in goats: A preliminary study. Animal Production Science, 58, 1822–1828. https://doi.org/10.1071/AN16539

Abstract

References

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