Quantifying the normal behavior patterns of cattle can provide researchers and animal health-care providers with baseline activity information that is useful for determining the effects of various environmental and procedural stimuli on behavior. In addition, the development of a baseline for behavior patterns of cattle may allow for the evaluation of disease effects on various activities (eg, feeding, drinking, lying, and standing behaviors). Commercial production settings as well as research settings for production, health, and welfare research routinely house cattle in large groups, which make it difficult to monitor behavior of individual cattle.
Current subjective scoring systems have limited ability for use in behavioral analyses,1–3 and more technologically advanced and objective methods have been suggested for their potential use in identifying signs of clinical illness or abnormal behavior in cattle. Investigators have reported4 that infrared thermography may be capable of detecting cattle with respiratory tract disease, whereas others have reporteda the use of temperature monitoring devices that were implanted in steers for the early detection of diseases (such as respiratory tract disease) known to cause elevations in body temperature. In another study5 in dairy cattle, investigators found that information recorded by pedometers could be used to predict ovulation and therefore improve fertilization rates. There have been other studies6–8 conducted to investigate the use of behaviors such as feeding and drinking as indicators of animal health, and they have revealed a distinct relationship between those behaviors and unfavorable outcomes with respect to animal health. Cattle behavior (activity), such as standing, lying, feeding, and drinking, may be useful for identifying animals at risk for disease. However, investigators must first be able to define and monitor normal behavior patterns for individual cattle to be able to successfully delineate behavior associated with disease.
Accelerometers are small and noninvasive devices that are used to objectively monitor animal behavior, and their use is not likely to have an influence on natural behavior patterns. Accelerometers use continuous and individual-animal sampling methods to generate data sets of unique behaviors that would be difficult to acquire through the use of other monitoring schemes. Quantification of cattle activity by use of accelerometers has been reported9 to be extremely accurate for describing normal lying and standing behaviors (99.2% and 98%, respectively) that can prove to be difficult to assess with conventional methods. Investigators in previous studies have implemented accelerometer-based activity monitoring systems in several animal species, including cattle,10 dogs,11 cats,12 and horses,13 and demonstrated the usefulness of these systems as a behavioral monitoring tool. Accelerometer-type automatic activity monitoring systems have been validated and used to describe behaviors of dairy cattle14 and have been used to describe behavior in dairy calves.15
Because behavior has been used to analyze the health and well-being of cattle, there is a need for an objective analysis of natural, undisturbed behavior. By more effectively defining typical behavior patterns of cattle, instances of conditions (eg, infectious diseases) in cattle that require an intervention can be more precisely and efficiently identified. The purpose of the study reported here was to describe the effects that impact typical behavior patterns in cattle, which specifically includes percentage of time spent lying throughout the day, day-to-day variation, and calf-to-calf variation. Our hypotheses were that cattle have both circadian rhythms and daily patterns for behavior and individual calves have varied amounts of activity.
Reid ED, Dahl GE. Peripheral and core body temperatures sensing using radio-frequency implants in steers challenged with lipopolysaccharide (abstr). J Anim Sci 2005;83(suppl 1):352.
GP1 programmable accelerometer, Sensr Co, Elkader, Iowa.
Manhattan Municipal Airport, Manhattan, Kan.
Insightful Miner, Insightful Corp, Seattle, Wash.
SAS, version 9.1, SAS Institute Inc, Cary, NC.
Duff GC, Galyean ML. Board-invited review: recent advances in management of highly stressed, newly received feedlot cattle. J Anim Sci 2007; 85:823–840.
Weary DM, Huzzey JM, von Keyserlingk MAG. Using behavior to predict and identify ill health in animals. J Anim Sci 2009; 87:770–777.
Frost AR, Schofield CP, Beaulah SA, et al. A review of livestock monitoring and the need for integrated systems. Comput Electron Agric 1997; 17:139–159.
Schaefer AL, Cook NJ, Church JS, et al. The use of infrared thermography as an early indicator of bovine respiratory disease complex in calves. Res Vet Sci 2007; 83:376–384.
Roelofs JB, van Eerdenburg FJ, Soede NM, et al. Pedometer readings for estrous detection and as predictor for time of ovulation in dairy cattle. Theriogenology 2005; 64:1690–1703.
Sowell BF, Branine ME, Bowman JGP, et al. Feeding and watering behavior of healthy and morbid steers in a commercial feedlot. J Anim Sci 1999; 77:1105–1112.
Sowell BF, Bowman JGP, Branine ME, et al. Radio frequency technology to measure feeding behavior and health of feedlot steers. Appl Anim Behav Sci 1998; 59:277–284.
Buhman MJ, Perino LJ, Galyean ML, et al. Association between changes in eating and drinking behaviors and respiratory tract disease in newly arrived calves at a feedlot. Am J Vet Res 2000; 61:1163–1168.
Robert B, White BJ, Renter DG, et al. Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle. Comput Electron Agric 2009; 67:80–84.
White BJ, Coetzee JF, Renter DG, et al. Evaluation of two-dimensional accelerometers to monitor behavior of beef calves after castration. Am J Vet Res 2008; 69:1005–1012.
Hansen BD, Lascelles BD, Keene BW, et al. Evaluation of an accelerometer for at-home monitoring of spontaneous activity in dogs. Am J Vet Res 2007; 68:468–475.
Lascelles BD, Hansen BD, Thomson A, et al. Evaluation of a digitally intergrated accelerometer-based activity monitor for the measurement of activity in cats. Vet Anaesth Analg 2008; 35:173–183.
Keegan KG, Yonezawa Y, Pai PF, et al. Evaluation of a sensor-based system of motion analysis for detection and quantification of forelimb and hind limb lameness in horses. Am J Vet Res 2004; 65:665–670.
Endres MI, Barberg AE. Behavior of dairy cows in an alternative bedded-pack housing system. J Dairy Sci 2007; 90:4192–4200.
Trenel P, Jensen MB, Decker EL, et al. Technical note: quantifying and characterizing behavior in dairy calves using the IceTag automatic recording device. J Dairy Sci 2009; 92:3397–3401.
Federation of Animal Science Societies. Guide for the care and use of agricultural animals in research and teaching. 3rd ed. Champaign, Ill: Federation of Animal Science Societies, 2010.
Mitlohner FM, Morrow-Tesch JL, Wilson SC, et al. Behavioral sampling techniques for feedlot cattle. J Anim Sci 2001; 79:1189–1193.
Olson BE, Wallander RT. Influence of winter weather and shelter on activity patterns of beef cows. Can J Anim Sci 2002; 82:491–501.
Arnold GW. Comparison of the time budgets and circadian patterns of maintenance activities in sheep, cattle and horses grouped together. Appl Anim Behav Sci 1984; 13:19–30.
DeVries TJ, von Keyserlingk MAG. Time of feed delivery affects the feeding and lying patterns of dairy cows. J Dairy Sci 2005; 88:625–631.
Hornbuckle WE. General physical examination of the cat and dog. In: Morgan RV, ed. Handbook of small animal practice. 2nd ed. New York: Churchill Livingstone, 1992; 3.
Schrader L. Consistency of individual behavioural characteristics of dairy cows in their home pen. Appl Anim Behav Sci 2002; 77:255–266.