Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry

Sensors

Eugene D. Ungar³  Iris Schoenbaum¹  Zalmen Henkin²  Amit Dolev⁴  Yehuda Yehuda⁴ 

[1] The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, Faculty of Agriculture, Food and Environment, Hebrew University of Jerusalem, Rehovot 76100, Israel; E-Mail:;Beef Cattle Section, Newe Ya’ar Regional Research Center, Agricultural Research Organization, P.O. Box 1021, Ramat Yishay 30095, Israel; E-Mail:;Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization—the Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel;Migal–Galilee Technology Center, P.O. Box 831, Kiryat Shmona 11016, Israel; E-Mail:

关键词: calibration;    discriminant analysis;    partition analysis;    grazing behavior;    classification;    GPS collar;    motion sensors;    pedometer;    step count;   

DOI  :  10.3390/s110100362

来源: mdpi

【 摘 要 】

The advent of the Global Positioning System (GPS) has transformed our ability to track livestock on rangelands. However, GPS data use would be greatly enhanced if we could also infer the activity timeline of an animal. We tested how well animal activity could be inferred from data provided by Lotek GPS collars, alone or in conjunction with IceRobotics IceTag pedometers. The collars provide motion and head position data, as well as location. The pedometers count steps, measure activity levels, and differentiate between standing and lying positions. We gathered synchronized data at 5-min resolution, from GPS collars, pedometers, and human observers, for free-grazing cattle (n = 9) at the Hatal Research Station in northern Israel. Equations for inferring activity during 5-min intervals (n = 1,475), classified as Graze, Rest (or Lie and Stand separately), and Travel were derived by discriminant and partition (classification tree) analysis of data from each device separately and from both together. When activity was classified as Graze, Rest and Travel, the lowest overall misclassification rate (10%) was obtained when data from both devices together were subjected to partition analysis; separate misclassification rates were 8, 12, and 3% for Graze, Rest and Travel, respectively. When Rest was subdivided into Lie and Stand, the lowest overall misclassification rate (10%) was again obtained when data from both devices together were subjected to partition analysis; misclassification rates were 6, 1, 26, and 17% for Graze, Lie, Stand, and Travel, respectively. The primary problem was confusion between Rest (or Stand) and Graze. Overall, the combination of Lotek GPS collars with IceRobotics IceTag pedometers was found superior to either device alone in inferring animal activity.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.