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Inference of Animal Activity from GPS Collar Data on Free-Ranging Cattle
Year:
2005
Source of publication :
Rangeland Ecology and Management
Authors :
Genizi, Abraham
;
.
Gutman, Mario
;
.
Henkin, Zalmen
;
.
Ungar, Eugene David
;
.
Volume :
58
Co-Authors:
Ungar, E.D., Department of Agronomy and Natural Resources, Institute of Field and Garden Crops, Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
Henkin, Z., MIGAL - Galilee Technology Center, P. O. Box 831, Qiryat Shemona, 11016, Israel
Gutman, M., Department of Agronomy and Natural Resources, Institute of Field and Garden Crops, Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
Dolev, A., MIGAL - Galilee Technology Center, P. O. Box 831, Qiryat Shemona, 11016, Israel
Genizi, A., Department of Statistics and Experiment Design, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Ganskopp, D., USDA Agricultural Research Service, Eastern Oregon Agricultural Research Center, 67826-A Hwy 205, Burns, OR 97720, United States
Facilitators :
From page:
256
To page:
266
(
Total pages:
11
)
Abstract:
Global positioning systems (GPSs) enable continuous and automatic tracking of an animal's position. The value of such spatial-temporal information can be improved if the corresponding activity of the animal is known. We evaluated the potential of Lotek GPS collars to predict activity of beef cattle on extensive rangeland in 2 contrasting foraging environments. Collars were configured to record animal location at intervals of 20 minutes (United States) or 5 minutes (Israel), together with counts from 2 motion sensors. Synchronized field observations of collared cows were conducted in 1999 (United States) and in 2002 and 2003 (Israel). Grazing, traveling (without grazing), and resting activities were recorded as minutes out of 20 for each category (United States), or as a single category (Israel). For the US data, stepwise regression models of grazing, traveling, and resting time accounted for 74%-84% of the variation, on the basis of the motion sensor counts for the left-right axis and the distances between GPS fixes. Regression tree analysis of grazing time yielded a simple model (4 splits) that accounted for 85% of the variation. For the Israeli data, the misclassification rates obtained by discriminant analysis and classification tree analysis of animal activity were 14% and 12%, respectively. In both analyses, almost all grazing observations were correctly classified, but other activities were sometimes misclassified as grazing. Distance alone is a poor indicator of animal activity, but grazing, traveling, and resting activities of free-ranging cattle can be inferred with reasonable accuracy from data provided by Lotek GPS collars.
Note:
Related Files :
cattle
Classification and regression trees
GPS
Grazing time
Motion sensors
Show More
Related Content
More details
DOI :
10.2111/1551-5028(2005)58[256:IOAAFG]2.0.CO;2
Article number:
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
30226
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:52
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Scientific Publication
Inference of Animal Activity from GPS Collar Data on Free-Ranging Cattle
58
Ungar, E.D., Department of Agronomy and Natural Resources, Institute of Field and Garden Crops, Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
Henkin, Z., MIGAL - Galilee Technology Center, P. O. Box 831, Qiryat Shemona, 11016, Israel
Gutman, M., Department of Agronomy and Natural Resources, Institute of Field and Garden Crops, Volcani Center, P. O. Box 6, Bet Dagan 50250, Israel
Dolev, A., MIGAL - Galilee Technology Center, P. O. Box 831, Qiryat Shemona, 11016, Israel
Genizi, A., Department of Statistics and Experiment Design, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Ganskopp, D., USDA Agricultural Research Service, Eastern Oregon Agricultural Research Center, 67826-A Hwy 205, Burns, OR 97720, United States
Inference of Animal Activity from GPS Collar Data on Free-Ranging Cattle
Global positioning systems (GPSs) enable continuous and automatic tracking of an animal's position. The value of such spatial-temporal information can be improved if the corresponding activity of the animal is known. We evaluated the potential of Lotek GPS collars to predict activity of beef cattle on extensive rangeland in 2 contrasting foraging environments. Collars were configured to record animal location at intervals of 20 minutes (United States) or 5 minutes (Israel), together with counts from 2 motion sensors. Synchronized field observations of collared cows were conducted in 1999 (United States) and in 2002 and 2003 (Israel). Grazing, traveling (without grazing), and resting activities were recorded as minutes out of 20 for each category (United States), or as a single category (Israel). For the US data, stepwise regression models of grazing, traveling, and resting time accounted for 74%-84% of the variation, on the basis of the motion sensor counts for the left-right axis and the distances between GPS fixes. Regression tree analysis of grazing time yielded a simple model (4 splits) that accounted for 85% of the variation. For the Israeli data, the misclassification rates obtained by discriminant analysis and classification tree analysis of animal activity were 14% and 12%, respectively. In both analyses, almost all grazing observations were correctly classified, but other activities were sometimes misclassified as grazing. Distance alone is a poor indicator of animal activity, but grazing, traveling, and resting activities of free-ranging cattle can be inferred with reasonable accuracy from data provided by Lotek GPS collars.
Scientific Publication
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