Big Data Meets the Food Supply: A Network of Cattle Monitoring Systems

  • Michael A. Chilton
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


The beef cattle industry generates $78.2 billion of revenues from nearly 100 million head each year in the U.S. alone. Cattle feed efficiency is a measure of animal growth. Animals with better efficiency may grow at the same rate as animals with lower efficiency, but will eat less to do so. This paper introduces a network of sensors in a cattle production operation designed to measure and report feed efficiency to the farmer. The sensors provide data that is used to monitor and control feed rations to the animals and help the farmer make informed decisions regarding animal grouping, control and genetic line building to improve beef stock quality over time. While cattle feed control and monitoring is itself not a new concept, the system described here adds some automated components to enhance and better control the operation that have not yet been done.


Big data Internet of Things Network sensors Cattle monitoring 


  1. 1.
    Edson, B.: Creating the internet of your things, Microsoft Corporation (2015).
  2. 2.
    Ashton, K.: That ‘Internet of Things’ Thing. RFID J. (2009).
  3. 3.
    Noronha, A., Moriarty, R., O’Connell, K., Villa, N.: Attaining IoT value: how to move from connecting things to capturing insights, gain an edge by taking analytics to the edge. Cisco Systems (2014).
  4. 4.
    West, B.W., Flikkema, P.G., Sisk, T., Koch, G.W.: Wireless sensor networks for dense spatio-temporal monitoring of the environment: a case for integrated circuit, system, and network design. In: Proceedings of the 2001 IEEE Circuits and Systems Society Workshop on Wireless Communications and Networking, Notre Dame (2001)Google Scholar
  5. 5.
    Norige, A., Thornton, J., Schiefelbein, C., Rudzinski, C.: High density distributed sensing for chemical and biological defense. Linc. Lab. J. 18(1), 25–40 (2009)Google Scholar
  6. 6.
    Brown, A.S.: Powering the Internet of Things. Mech. Eng. 136(3), 19–20 (2014)Google Scholar
  7. 7.
    Shi, J., Zhang, J., Qu, X.: Optimization distribution strategy for perishable foods using RFID and sensor technologies. J. Bus. Ind. Mark. 25(8), 596–606 (2010)CrossRefGoogle Scholar
  8. 8.
    Klinkenberg, B.: This is the chip the NFL uses to track its players on the field, BuzzFeed News, 26 August 2015.
  9. 9.
    Sikka, P., Corke, P., Overs, L.: Wireless sensor devices for animal tracking and control. In: Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks, Tampa (2004)Google Scholar
  10. 10.
    Garcia-Sanchez, A.-J., Garcia-Sanchez, F., Losilla, F., Kulakowski, P., Garcia-Haro, J., Rodriguez, A., Lopez-Bao, J.-V., Palomares, F.: Wireless sensor network deployment for monitoring wildlife passages. Sensors 10, 7236–7261 (2010)CrossRefGoogle Scholar
  11. 11.
    Lalooses, F., Susanto, H., Chang, C.H.: An approach for tracking wildlife using wireless sensor networks. In: Proceedings of the International Workshop on Wireless Sensor Networks (NOTERE 2007). IEEE, Marrakesh (2007)Google Scholar
  12. 12.
    Radenkovic, M., Wietrzyk, B.: Wireless mobile ad-hoc sensor networks for very large scale cattle monitoring. In: Proceedings of the 6th Annual Workshop on Applications and Services in Wireless Networks (ASWN 2006), Berlin (2006)Google Scholar
  13. 13.
    Fishell, D.: Cow health monitoring co. targets $10B U.S. market. Mainebiz, Portland (2013).
  14. 14.
    Theurer, M.E., Amrine, E.A., White, B.J.: Remote noninvasive assessment of pain and health status in cattle. Vet. Clin. Food Anim. Pract. 29(1), 59–74 (2013)CrossRefGoogle Scholar
  15. 15.
    Shike, D.: Beef cattle feed efficiency, Iowa State University Digital Repository (2013).
  16. 16.
    White, B.J., Amrine, D.E., Goehl, D.R.: Determination of value of bovine respiratory disease control using a remote early disease identification system compared with conventional methods of metaphylaxis and visual observations. J. Anim. Sci. 93(8), 4115–4122 (2015)CrossRefGoogle Scholar
  17. 17.
    Francisco, C.L., Resende, F.D., Benatti, J.M.B., Castilhos, A.M., Cooke, R.F., Jorge, A.M.: Impacts of temperament on Nellore cattle: physiological response, feedlot performance and carcass characteristics. J. Anim. Sci. 93, 5419–5429 (2015)CrossRefGoogle Scholar
  18. 18.
    Llonch, P., Somarriba, M., Duthie, C.-A., Haskell, M.J., Rooke, J.A., Troy, S., Roeher, R., Turner, S.P.: Association of temperament and acute stress responsiveness with productivity, feed efficiency, and methane emissions in beef cattle: an observational study. Front. Vet. Sci. 3, 1–9 (2016). Article 43CrossRefGoogle Scholar
  19. 19.
    Grandin, T.: Assessment of stress during handling and transport. J. Anim. Sci. 75, 249–257 (1997)CrossRefGoogle Scholar
  20. 20.
    Kennedy, B.W., van der Werf, J.H., Meuwissen, T.H.: Genetic and statistical properties of residual feed intake. J. Anim. Sci. 71, 3239–3250 (1993)CrossRefGoogle Scholar
  21. 21.
    Archer, J.A., Arthur, P.F., Herd, R.M., Parnell, P.F., Pitchford, W.S.: Optimum post-weaning test for measurement of growth rate, feed intake and feed efficiency in British breed cattle. J. Anim. Sci. 75, 2014–2032 (1997)CrossRefGoogle Scholar
  22. 22.
    Nkrumah, J.D., Okine, E.K., Mathison, G.W., Schmid, K., Li, C., Basarab, J.A., Price, M.A., Wang, Z., Moore, S.S.: Relationships of feedlot efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84(1), 145–153 (2006)CrossRefGoogle Scholar
  23. 23.
    Arthur, P.F., Archer, J.A., Herd, R.M., Melville, G.J.: Response to selection for net feed intake in beef cattle. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics, vol. 14, pp. 135–138 (2001)Google Scholar
  24. 24.
    U.S. Department of Agriculture (USDA), National Agricultural Statistics Service: Overview of the United States cattle industry (2016). Accessed 24 June 2016

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Kansas State UniversityManhattanUSA

Personalised recommendations