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Method for Classifying Behavior of Livestock on Fenced Temperate Rangeland in Northern China

1
The United Graduate School of Agricultural Sciences, Tottori University, 4-101 Koyama-Minami, Tottori 680-8553, Japan
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Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan
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International Platform for Dryland Research and Education, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan
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Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 73000, China
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Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao 028300, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5334; https://doi.org/10.3390/s19235334
Received: 27 August 2019 / Revised: 5 November 2019 / Accepted: 29 November 2019 / Published: 3 December 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Different livestock behaviors have distinct effects on grassland degradation. However, because direct observation of livestock behavior is time- and labor-intensive, an automated methodology to classify livestock behavior according to animal position and posture is necessary. We applied the Random Forest algorithm to predict livestock behaviors in the Horqin Sand Land by using Global Positioning System (GPS) and tri-axis accelerometer data and then confirmed the results through field observations. The overall accuracy of GPS models was 85% to 90% when the time interval was greater than 300–800 s, which was approximated to the tri-axis model (96%) and GPS-tri models (96%). In the GPS model, the linear backward or forward distance were the most important determinants of behavior classification, and nongrazing was less than 30% when livestock travelled more than 30–50 m over a 5-min interval. For the tri-axis accelerometer model, the anteroposterior acceleration (–3 m/s2) of neck movement was the most accurate determinant of livestock behavior classification. Using instantaneous acceleration of livestock body movement more precisely classified livestock behaviors than did GPS location-based distance metrics. When a tri-axis model is unavailable, GPS models will yield sufficiently reliable classification accuracy when an appropriate time interval is defined. View Full-Text
Keywords: livestock; behavior classification; GPS; accelerometer; Random Forest; Kappa coefficient; dryland livestock; behavior classification; GPS; accelerometer; Random Forest; Kappa coefficient; dryland
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Gou, X.; Tsunekawa, A.; Peng, F.; Zhao, X.; Li, Y.; Lian, J. Method for Classifying Behavior of Livestock on Fenced Temperate Rangeland in Northern China. Sensors 2019, 19, 5334.

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