Growing Beef Newsletter
July 2025, Volume 16, Issue 1
Improving beef cattle health through technology and genetic improvement
Audrey Tarochione, Animal Breeding and Genetics graduate student, Iowa State University
Post-weaning bovine respiratory disease complex (BRD) accounts for 75 percent of morbidity and over half of all mortality in feedlots, costing the U.S. feedlot industry as much as $1 billion annually in production losses. This is an economically relevant trait that merits discussion and has potential for improvement. However, as a health trait, BRD resistance is lowly heritable. The industry also does not have a standardized trait definition, and phenotype collection is mostly subjective and inconsistent across the industry. Combined, these factors create barriers to genetic improvement in feedlot health. There is opportunity to adopt technology that could improve the feasibility of selecting against BRD susceptibility. Several tools that are currently available to producers are listed in Table 1.
While bunk systems are designed to measure intake and feed efficiency on individual animals, they can also identify sick animals by detecting changes in feeding behavior. Most other technologies are used to monitor animal health. Monitor systems collect data either continuously or at regular intervals, then data is transmitted to a server. The information is interpreted by algorithms designed by the manufacturer, and health issues are identified by detecting abnormal animal behaviors or conditions. Health status is then made available in user-friendly interfaces, where producers can receive alerts and easily identify animals that may need treatment.
The products described above have been validated on beef cattle in feedlot settings and are backed by published research. Table 2 presents differences in cattle health due to detection method. This study compared traditional methods of pen riders and visual observation to SenseHub ear tags. These results indicate that technology is able to catch BRD sooner and is more sensitive than visual observation. Table 3 lists several studies that found early detection is possible with various technologies. Some BRD cases were detected a week or more before visual observation diagnosed the disease. Early detection is the name of the game in BRD control. Advancements in technology provide an opportunity for early detection.
Benefits of technology for monitoring cattle health are twofold. As prey animals, cattle are adept at concealing illness from even the most experienced caretakers. With enhanced ability to detect health issues early, feedlots can see more positive outcomes in BRD control. Improved calf health during the receiving period equates to better performance on feed and higher cattle value at finishing. Precision livestock tools could also generate phenotype collection. This is necessary for the development of genetic evaluations for BRD traits, which would allow producers to select for improved cattle health and make permanent genetic progress. These technologies are easy to implement, low maintenance, and proven effective, making them a strategic addition to feedlot operations from the standpoint of both cattle health and genetic improvement in BRD resistance.
References
Belaid, M. A., M. Rodriguez-Prado, E. Chevaux, and S. Calsamiglia. 2019. The use of an activity monitoring system for the early detection of health disorders in young bulls. Animals. 9:924. doi:10.3390/ani9110924.
Jackson, K. S., G. E. Carstens, L. O. Tedeschi, and W. E. Pinchak. 2016. Changes in feeding behavior patterns and dry matter intake before clinical symptoms associated with bovine respiratory disease in growing bulls. J. Anim. Sci. 94:1644–1652. doi:10.2527/jas.2015-9993.
Mang, A. V. F. 2015. Advances in detection and diagnosis of bovine respiratory disease in feedlot cattle [Master’s thesis]. University of Calgary, Calgary, Canada. Available from: https://ucalgary.scholaris.ca/handle/11023/2553
Marchesini, G., D. Mottaran, B. Contiero, E. Schiavon, S. Segato, E. Garbin, S. Tenti, and I. Andrighetto. 2018. Use of rumination and activity data as health status and performance indicators in beef cattle during the early fattening period. Vet. J. 231:41–47. doi:10.1016/j.tvjl.2017.11.013.
Pillen, J. L., P. J. Pinedo, S. E. Ives, T. L. Covey, H. K. Naikare, and J. T. Richeson. 2016. Alteration of activity variables relative to clinical diagnosis of bovine respiratory disease in newly received feed lot cattle. Bov. Pract. 50. doi:10.21423/bovine-vol50no1p1-8.
Quimby, W. F., B. F. Sowell, J. G. P. Bowman, M. E. Branine, M. E. Hubbert, and H. W. Sherwood. 2001. Application of feeding behaviour to predict morbidity of newly received calves in a commercial feedlot. Can. J. Anim. Sci. 81:315–320. doi:10.4141/A00-098.
Schupbach, B. S., M. S. Davis, T. D. Jennings, A. L. Dixon, D. G. Renter, and J. S. Nickell. 2025. Comparison of a novel bovine respiratory disease prediction technology and an automated animal disease detection technology to traditional methods in a U.S. feedlot. Transl. Anim. Sci. doi:10.1093/tas/txaf067.
Wolfger, B., K. S. Schwartzkopf-Genswein, H. W. Barkema, E. A. Pajor, M. Levy, and K. Orsel. 2015. Feeding behavior as an early predictor of bovine respiratory disease in North American feedlot systems. J. Anim. Sci. 93:377–385. doi:10.2527/jas.2013-8030.
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