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Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry
Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization—the Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
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
Beef Cattle Section, Newe Ya’ar Regional Research Center, Agricultural Research Organization, P.O. Box 1021, Ramat Yishay 30095, Israel
Migal–Galilee Technology Center, P.O. Box 831, Kiryat Shmona 11016, Israel
* Author to whom correspondence should be addressed.
Received: 10 November 2010; in revised form: 13 December 2010 / Accepted: 20 December 2010 / Published: 31 December 2010
Abstract: 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.
Keywords: calibration; discriminant analysis; partition analysis; grazing behavior; classification; GPS collar; motion sensors; pedometer; step count
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Cite This Article
MDPI and ACS Style
Ungar, E.D.; Schoenbaum, I.; Henkin, Z.; Dolev, A.; Yehuda, Y.; Brosh, A. Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry. Sensors 2011, 11, 362-383.
Ungar ED, Schoenbaum I, Henkin Z, Dolev A, Yehuda Y, Brosh A. Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry. Sensors. 2011; 11(1):362-383.
Ungar, Eugene D.; Schoenbaum, Iris; Henkin, Zalmen; Dolev, Amit; Yehuda, Yehuda; Brosh, Arieh. 2011. "Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry." Sensors 11, no. 1: 362-383.