Classifying Livestock Grazing Behavior with the Use of a Low Cost GPS and Accelerometer

Abstract

The ability to remotely track livestock through the use of GPS technology has tremendous potential to study livestock use patterns on the landscape. The use of high frequency accelerometers may give researchers and managers the ability to accurately partition GPS points into differing behaviors, giving further insight into livestock grazing selection, pasture use, and changes in forage preference through time. The objectives of this study were to 1) develop a classification algorithm to discriminate between graze and non-graze behaviors using a combination of metrics derived from a high frequency accelerometer motion sensor and a GPS data logger and 2) assess the accuracy of the classification algorithm using model error rates and expectant livestock behavior patterns