Background
As newer technologies come along, greater opportunities for in-depth knowledge present themselves to the modern farmer. In agriculture and agricultural research, the use of sophisticated technology to help improve crop and animal production is growing rapidly. This paper investigates one such application in which beef cattle are monitored for feed efficiency using a network of Radio Frequency Identification (RFID) detectors, feed and animal scales, and other programmed equipment. 
Objective
The system discussed in this study is intended to assist in the discovery of new knowledge that can be used to improve efficiency in the food supply chain. We discuss how the collection, processing and analysis of the data obtained through an Internet of Things (IoT) network is able to monitor cattle, determine feed efficiency, control the equipment used, how the network and its operation will help to improve beef production while maintaining good animal health, and how this is leading to discoveries in what can be considered an autocatalytic process.
Method
We created a prototype system consisting of a set of smart devices arranged as an Internet of things that was designed to fully automate the feeding process of beef cattle and collect and analyze feed consumption data on a per animal basis. Combined with periodic growth measurements, these data are used to calculate feed efficiency in order to assist in making better decisions of animal husbandry.
Results
The process of building and analyzing data from the prototypical system has highlighted problems encountered and offered solutions to provide a much more efficient monitoring system that can lower the costs of raising production animals.
Conclusion
This study helps to point the way towards improved animal health and improved efficiency in farming operations, which can lead to greater production levels. As food stocks decline or remain steady, more efficient operations such as this may help feed a world whose human population continues to grow.

Keywords:
Internet of Things, Agriculture, Data Analysis, RFID, Residual Feed Intake, Feed Efficiency.