Hostname: page-component-6bf8c574d5-8gtf8 Total loading time: 0 Render date: 2025-02-16T20:25:36.321Z Has data issue: false hasContentIssue false
  • English
  • Français

Rumination and activity levels as predictors of calving for dairy cows

Published online by Cambridge University Press:  10 December 2014

C. E. F. Clark ,
N. A. Lyons ,
L. Millapan ,
S. Talukder ,
G. M. Cronin ,
K. L. Kerrisk  and
S. C. Garcia
C. E. F. Clark*
Affiliation:
Dairy Science Group, University of Sydney, Camden 2570, NSW, Australia
N. A. Lyons
Affiliation:
Dairy and Intensive Livestock Industries, NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Menangle NSW 2568, Australia
L. Millapan
Affiliation:
Department of Animal Production, Faculty of Agronomy, University of Buenos Aires, Buenos Aires 1417, Argentina
S. Talukder
Affiliation:
Dairy Science Group, University of Sydney, Camden 2570, NSW, Australia
G. M. Cronin
Affiliation:
Dairy Science Group, University of Sydney, Camden 2570, NSW, Australia
K. L. Kerrisk
Affiliation:
Dairy Science Group, University of Sydney, Camden 2570, NSW, Australia
S. C. Garcia
Affiliation:
Dairy Science Group, University of Sydney, Camden 2570, NSW, Australia
*
†E-mail: [email protected]
Get access

Abstract

The Australian dairy herd size has doubled over the last 20 years substantially increasing the time that farmers require for individual animal attention to monitor and intervene with events such as calving. Technology will help focus this limited labour resource on individual cows that require assistance. The objective of this experiment was to first determine the profiles of rumination duration and level of activity as determined by sensors between, and within, days around calving and second to use these data to predict the day of calving for pasture-based dairy cows. After 2 weeks from the expected calving date, 27 cows were fitted with SCR HR LD Tags, located in 40×90 m2 paddock and offered ad libitum oaten hay and 2 kg grain-based concentrate/cow per day until calving. Hourly activity and rumination data for each cow, as determined by the SCR tags, were fitted with linear mixed models and all parameters were estimated using restricted maximum likelihood. Rumination duration decreased by 33% over the day prior and the day of calving, with the decline in rumination duration starting the day prepartum. Activity levels were maintained prepartum but increased in the days postpartum. The day of calving was recorded and used to determine the gold standard positive (the day before calving) and negative (all other) dates. A threshold rumination level of 0.9 (decline in rumination duration of 10%) gave the optimal combination of 70% sensitivity and 70% specificity. This experiment shows the potential to use rumination duration to predict the day of calving and the opportunity to use sensor data to monitor animal health.

Keywords

calvingpredictionsensorruminationpasture
Type
Research Article
Information
animal , Volume 9 , Issue 4 , April 2015 , pp. 691 - 695
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aoki, M, Kimura, K and Suzuki, O 2005. Predicting time of parturition from changing vaginal temperature measured by data-logging apparatus in beef cows with twin foetuses. Animal Reproduction Science 86, 1–12.Google Scholar
Barrier, AC, Ruelle, E, Haskell, MJ and Dwyer, CM 2012. Effect of a difficult calving on the vigour of the calf, the onset of maternal behaviour, and some behavioural indicators of pain in the dam. Preventative Veterinary Medicine 103, 248–256.Google Scholar
Burfeind, O, Suthar, VS, Voigtsberger, R, Bonk, S and Heuwieser, W 2011. Validity of prepartum changes in vaginal and rectal temperature to predict calving in dairy cows. Journal of Dairy Science 94, 5053–5061.CrossRefGoogle ScholarPubMed
Cangar, O, Leroy, T, Guarino, M, Vranken, E, Fallon, R, Lenehan, J, Meed, J and Berckmans, D 2008. Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis. Computers and Electronics in Agriculture 64, 53–60.Google Scholar
Dairy Australia 2012. Australian Dairy Industry in focus 2012. Victoria, Australia.Google Scholar
Dechow, CD and Goodling, RC 2008. Mortality, culling by sixty days in milk, and production profiles in high- and low-survival Pennsylvania herds. Journal of Dairy Science 91, 4630–4639.Google Scholar
Dematawewa, CMB and Berger, PJ 1997. Effect of dystocia on yield, fertility and cow losses and an economic evaluation of dystocia scores for Holsteins. Journal of Dairy Science 80, 754–761.Google Scholar
Gregorini, P, Gunter, SA, Beck, PA, Soder, KJ and Tamminga, S 2008. Review: the interaction of diurnal grazing pattern, ruminal metabolism, nutrient supply and management in cattle. The Professional Animal Scientist 24, 308–318.Google Scholar
Gregorini, P, DelaRue, B, McLeod, K, Clark, CEF, Glassey, CB and Jago, J 2012. Rumination behavior of grazing dairy cows in response to restricted time at pasture. Livestock Science 146, 95–98.Google Scholar
Hudson, SJ and Mullord, MM 1977. Investigations of maternal bonding in dairy cattle. Applied Animal Ethology 3, 271–276.Google Scholar
Lidfors, LM, Moran, D, Jung, J, Jensen, P and Castren, H 1994. Behaviour at calving and choice of calving place in cattle kept in different environments. Applied Animal Behaviour Science 42, 11–28.Google Scholar
Mee, JF 2008. Prevalence and risk factors for dystocia in dairy cattle: a review. Veterinary Journal 176, 93–101.Google Scholar
Miedema, H 2009. Investigating the use of behavioural, accelerometer, heart rate measurements to predict calving in dairy cows. PhD Thesis, University of Edinburgh.Google Scholar
Miedema, HM, Cockram, MS, Dwyer, CM and Macrae, AI 2011. Behavioural predictors of the start of normal and dystocic calving in dairy cows and heifers. Applied Animal Behaviour Science 132, 14–19.CrossRefGoogle Scholar
Norton, HW 1956. Gestation period for Holstein-Friesian cows. Journal of Dairy Science 39, 1619–1621.CrossRefGoogle Scholar
Owens, JL, Edey, TN, Bindon, BM and Piper, LR 1985. Parturient behaviour and calf survival in a herd selected for twinning. Applied Animal Behaviour Science 13, 321–333.Google Scholar
Raussi, S 2003. Human–cattle interactions in group housing. Applied Animal Behaviour Science 80, 245–262.CrossRefGoogle Scholar
Rutten, CJ, Velthuis, AGJ, Steeneveld, W and Hogeveen, H 2013. Invited review: sensors to support health management on dairy farms. Journal of Dairy Science 96, 1928–1952.Google Scholar
Schirmann, K, von Keyserlingk, MA, Weary, DM, Veira, DM and Heuwieser, W 2009. Technical note: validation of a system for monitoring rumination in dairy cows. Journal of Dairy Science 92, 6052–6055.Google Scholar
Schirmann, K, Chapinal, N, Weary, DM, Heuwieser, W and von Keyserlingk, MA 2012. Rumination and its relationship to feeding and lying behaviour in Holstein dairy cows. Journal of Dairy Science 95, 3212–3217.Google Scholar
Schirmann, K, Chapinal, N, Weary, DM, Vickers, L and von Keyserlingk, MA 2013. Short communication: rumination and feeding behaviour before and after calving in dairy cows. Journal of Dairy Science 96, 7088–7092.Google Scholar
Taweel, HZ, Tas, BM, Dijkstra, J and Tamminga, S 2004. Intake regulation and grazing behavior of dairy cows under continuous stocking. Journal of Dairy Science 87, 3417–3427.CrossRefGoogle ScholarPubMed
Thomson, BC, Cruickshank, GJ, Poppi, DP and Sykes, AR 1985. Diurnal patterns of rumen fill in grazing sheep. Proceedings of the New Zealand Society of Animal Production 45, 117–120.Google Scholar