Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2010-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/11/1/362/ |
| _version_ | 1811305303945248768 |
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| author | Yehuda Yehuda Arieh Brosh Amit Dolev Iris Schoenbaum Eugene D. Ungar Zalmen Henkin |
| author_facet | Yehuda Yehuda Arieh Brosh Amit Dolev Iris Schoenbaum Eugene D. Ungar Zalmen Henkin |
| author_sort | Yehuda Yehuda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:24:08Z |
| format | Article |
| id | doaj.art-fe3b76d863ca41aeae3b2afb9df907bf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:24:08Z |
| publishDate | 2010-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fe3b76d863ca41aeae3b2afb9df907bf2022-12-22T02:54:34ZengMDPI AGSensors1424-82202010-12-0111136238310.3390/s110100362Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and PedometryYehuda YehudaArieh BroshAmit DolevIris SchoenbaumEugene D. UngarZalmen HenkinThe 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.http://www.mdpi.com/1424-8220/11/1/362/calibrationdiscriminant analysispartition analysisgrazing behaviorclassificationGPS collarmotion sensorspedometerstep count |
| spellingShingle | Yehuda Yehuda Arieh Brosh Amit Dolev Iris Schoenbaum Eugene D. Ungar Zalmen Henkin Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry Sensors calibration discriminant analysis partition analysis grazing behavior classification GPS collar motion sensors pedometer step count |
| title | Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry |
| title_full | Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry |
| title_fullStr | Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry |
| title_full_unstemmed | Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry |
| title_short | Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry |
| title_sort | inference of the activity timeline of cattle foraging on a mediterranean woodland using gps and pedometry |
| topic | calibration discriminant analysis partition analysis grazing behavior classification GPS collar motion sensors pedometer step count |
| url | http://www.mdpi.com/1424-8220/11/1/362/ |
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