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

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.

[1]  S. Moore,et al.  Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. , 2006, Journal of animal science.

[2]  Peter I. Corke,et al.  Wireless sensor devices for animal tracking and control , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[3]  Pawel Kulakowski,et al.  Wireless Sensor Network Deployment for Monitoring Wildlife Passages , 2010, Sensors.

[4]  Milena Radenkovic,et al.  Wireless mobile ad-hoc sensor networks for very large scale cattle monitoring , 2006 .

[5]  R. Cooke,et al.  Impacts of temperament on Nellore cattle: physiological responses, feedlot performance, and carcass characteristics. , 2015, Journal of animal science.

[6]  T Grandin,et al.  Assessment of stress during handling and transport. , 1997, Journal of animal science.

[7]  B. White,et al.  Remote noninvasive assessment of pain and health status in cattle. , 2013, The Veterinary clinics of North America. Food animal practice.

[8]  L. Keeling,et al.  Omnivores Going Astray: A Review and New Synthesis of Abnormal Behavior in Pigs and Laying Hens , 2016, Front. Vet. Sci..

[9]  Kevin Ashton,et al.  That ‘Internet of Things’ Thing , 1999 .

[10]  Jason Thornton,et al.  High-Density Distributed Sensing for Chemical and Biological Defense , 2009 .

[11]  C. Chang,et al.  AN APPROACH FOR TRACKING WILDLIFE USING WIRELESS SENSOR NETWORKS , 2022 .

[12]  Alan S. Brown.  Iconic DesignWe Asked Six Leading Industrial Designers to tell us Which Products Inspired them. The Breadth of their Choices was Unexpected , 2014 .

[13]  D. Shike.  Beef Cattle Feed Efficiency , 2013 .

[14]  Arnab Raha,et al.  Powering the Internet of Things , 2014, 2014 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).

[15]  T. Meuwissen,et al.  Genetic and statistical properties of residual feed intake. , 1993, Journal of animal science.

[16]  G. Melville,et al.  Response to selection for net feed intake in beef cattle. , 2001 .

[17]  B. White,et al.  Determination of value of bovine respiratory disease control using a remote early disease identification system compared with conventional methods of metaphylaxis and visual observations. , 2015, Journal of animal science.

[18]  S. Troy,et al.  Association of Temperament and Acute Stress Responsiveness with Productivity, Feed Efficiency, and Methane Emissions in Beef Cattle: An Observational Study , 2016, Front. Vet. Sci..

[19]  J. Archer,et al.  Optimum postweaning test for measurement of growth rate, feed intake, and feed efficiency in British breed cattle. , 1997, Journal of animal science.

[20]  Jun Zhang,et al.  Optimizing distribution strategy for perishable foods using RFiD and sensor technologies , 2010 .