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Overview of animal sensing technologies

Overview of animal sensing technologies

*Bill Mahanna, Ph.D., Dipl ACAN, is a collaborative faculty member at Iowa State University and a board-certified nutritionist for DuPont Pioneer based in Johnston, Iowa. To expedite answers to questions concerning this article or to submit ideas for future articles, please direct inquiries to Feedstuffs, Bottom Line of Nutrition, 5810 W. 78th St., Suite 200, Bloomington, Minn. 55439, or email [email protected]

IT is very apparent that technology "marches on," but from personal experience, it is easy to feel out of step.

At the urging of both my employer and my children, I recently entered the world of Twitter and now have the illusion that I am once again on the cutting edge of technology. Among the first organizations I decided to "follow" on Twitter (besides @Feedstuffs) was the Pennsylvania State University Extension Dairy Team (@PSUDairy).

An example of the power of technology is that after clicking on a link in a PSU Dairy Team Twitter posting, I found out about its "Dairy Cents" smart phone app. This app allows producers to track and benchmark milk prices, feed costs and income over feed costs. After downloading the app and watching two YouTube instructional videos, I now have a very productive tool on my smart phone.

Discovering all this new technology caused me to think back to a conversation I had at the World Dairy Expo last year with a company introducing another technology that was new to me, which was for monitoring rumination in a commercial dairy setting. The topic of animal sensors, especially rumination monitoring, is the focus of this month's column.


History of sensor technology

Animal sensing technology started with individual cow recognition. More recent applications have the goal of monitoring specific health or nutritional parameters to allow quick intervention and to reduce the labor requirements necessary to manage large herds.

A recent review of animal sensing technologies (Rutten et al., 2013) looked at studies involving pedometers (estrous activity), milk conductivity (mastitis) and rumen pH and rumination (metabolic conditions) from the perspective of: (1) sensor technology, (2) data interpretation, (3) integration of the data with other information and (4) resultant decision-making.

The authors made a solid case that technology for "technology's sake" is not of value unless there is a statistically tested relationship with a physiological, behavioral or health issue upon which clear action steps can be implemented.



Producers and nutritionists spend considerable time looking at individual cows for signs of rumination. Most ascribe to the thumb rule that at least 50% of cows that are inactive or lying down should be chewing their cuds as a proxy indicator of a well-functioning rumen.

In studies conducted by graduate students, increased rumination was linked to improved rumen health due to increased saliva buffering action.

Rumination is influenced by dietary composition and physical properties. Studies show that rumination is associated with the time cows spent lying down, but rumination also takes place during activities such as standing and walking.

Some of the earliest work with detecting rumination patterns was published by Canadian researchers (Beauchemin et al., 1990) and involved imbedding transducers in a halter to monitor jaw movements. An algorithm was used to process the digitized signal into discrete chews. Chewing during eating was distinguished from chewing during rumination based on duration, the number of chews per minute and the duration of the preceding and succeeding pauses.

Complex spectral analysis techniques were used to examine the cyclical pattern of rumination behavior. Rumination patterns generally displayed circadian (24-hour) periodicity, but it was evident that individual cows do vary in their patterns.

Technology has advanced since the cumbersome equipment used to monitor rumination in early research studies. The company I visited at the World Dairy Expo was SCR, based in Netanya, Israel. SCR has had versions of its current HR Tag on the market since 2007. The latest offering attaches to the upper part of the cow's neck with a strap and contains a motion sensor, microprocessor, memory chip and a specially tuned microphone that detects the cow's rumination time (RT), chewing rhythm and time between feed boluses.

Acoustical differences between regurgitating feed boluses and mastication can be separated from sounds related to eating. This technology package allows for rumination, heat detection and cow identification functionality (SCR, 2013).

Producer testimonials exist for this technology, but recently published studies also support the merits of tracking and benchmarking individual cow rumination patterns (Table and Figure).

Research from Canada (Schirmann et al., 2009) confirmed that the electronic HR Tag system was highly correlated (R-square = 0.87) with human observations of rumination measured in 51 two-hour observations of 27 Holstein cows.

A similar study (Burfeind et al., 2011) with calves and heifers indicated that the automated system was also an accurate tool for monitoring the rumination of heifers that were nine months of age or older.

Researchers from Italy (Soriani et al., 2012) investigated the use of the HR Tag to monitor RT during the transition period and its relationship to milk yield, health status and incidence of metabolic problems.

They found that cows with reduced RTs before calving exhibited reduced RTs after calving and experienced greater frequency of disease. Further, cows with decreased RTs during the first few days after calving experienced more subclinical disease and metabolic disorders. As expected, the co-efficient of variation for RT between days for individual cows was greatest around calving.

Milk yield recorded during the first 40 days of lactation was numerically 2.4 kg higher for cows displaying the highest RTs. A significant correlation existed between milk yield and the average RT of the previous three days. A greater incidence of clinical diseases was observed among the shortest-RT cows despite there being no differences in the commonly measured rectal temperatures.

Soriani et al. concluded that automatic measurement of RT was useful to obtain information on the health status of cows in the critical transition period.

Recent work by German researchers (Reith and Hoy, 2012) showed that, on average, RT was statistically reduced on the day of estrus, although it was characterized by high variability among cows. The authors, while generally positive about the technology, did express the need for further research into the accuracy, sensitivity and specificity of RT threshold values and if other parameters such as activity should be used in conjunction with RT values.


The Bottom Line

Despite the belief that "walking the pens" and "watching the cows" are the gold standard for determining general cow health and comfort, there is no doubt that new technologies are on the horizon that can have a significant effect on reducing labor on large herds and integrating multiple sources of cow behavioral data.

Benchmarking individual cow rumination and activity data could be an invaluable tool in the early identification of animals that are "thinking about getting sick" for greater scrutiny and possible intervention.



Bar, D., and R. Solomom. 2010. Rumination collars: What can they tell us. Proceedings of The First North American Conference on Precision Dairy Management. Toronto, Ont.

Beauchemin, K.A., R.G. Kachanoski, G.B. Schaalje and J.G. Buchanan-Smith. 1990. Characterizing rumination patterns of dairy cows using spectral analysis. J. Animal Sci. 68:3163-3170.

Burfeind, O., K. Schirmann, M.A.G. von Keyserlingk, D.M. Veira, D.M. Weary and W. Heuwieser. 2010. Technical note: Evaluation of a system for monitoring rumination in heifers and calves. J. Dairy Sci. 94:426-430.

Reith, S., and S. Hoy. 2012. Relationship between daily rumination time and estrus of dairy cows. J. Dairy Sci. 95:6416-6420.

Rutten, C.J., A.G.J. Velthuis, W. Steeneveld and H. Hogeveen. 2012. Invited review: Sensors to support health management on dairy farms. J. Dairy Sci. 96:1-25.

Schirmann, K., M.A.G. von Keyserlingk, D.M. Weary, D.M. Viera and W. Heuwieser. 2009. Technical note: Validation of a system for monitoring rumination in dairy cows. J. Dairy Sci. 92:6052-6055.

Soriani, N., E. Trevisi and L. Calamari. 2012. Relationship between rumination time, metabolic conditions and health status in dairy cows during the transition period. J. Animal Sci. 90:4544-4554.


Changes in RT due to various production and environmental events*












Number of cows






Variation (minutes) from milking cow average RT (std. error)**

-255 (10.4)

-75 (6.2)

-39 (8.8)

-20 (3.4)

-63 (12.9)

*Data from 75 Holstein cows followed from drying off until 150 days in lactation in a commercial Israeli dairy herd.

**Milking cows averaged 482 minutes per day. All events, P < 0.0001.

Adapted from Bar and Solomon (2012).

Overview of animal sensing technologies


Volume:85 Issue:14

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