{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T07:49:18Z","timestamp":1791272958163,"version":"4.1.0"},"reference-count":17,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2013,3,15]],"date-time":"2013-03-15T00:00:00Z","timestamp":1363305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advances in global positioning system (GPS) technology have dramatically enhanced the ability to track and study distributions of free-ranging livestock. Understanding factors controlling the distribution of free-ranging livestock requires the ability to assess when and where they are foraging. For four years (2008\u20132011), we periodically collected GPS and activity sensor data together with direct observations of collared cattle grazing semiarid rangeland in eastern Colorado. From these data, we developed classification tree models that allowed us to discriminate between grazing and non-grazing activities. We evaluated: (1) which activity sensor measurements from the GPS collars were most valuable in predicting cattle foraging behavior, (2) the accuracy of binary (grazing, non-grazing) activity models vs. models with multiple activity categories (grazing, resting, traveling, mixed), and (3) the accuracy of models that are robust across years vs. models specific to a given year. A binary classification tree correctly removed 86.5% of the non-grazing locations, while correctly retaining 87.8% of the locations where the animal was grazing, for an overall misclassification rate of 12.9%. A classification tree that separated activity into four different categories yielded a greater misclassification rate of 16.0%. Distance travelled in a 5 minute interval and the proportion of the interval with the sensor indicating a head down position were the two most important variables predicting grazing activity. Fitting annual models of cattle foraging activity did not improve model accuracy compared to a single model based on all four years combined. This suggests that increased sample size was more valuable than accounting for interannual variation in foraging behavior associated with variation in forage production. Our models differ from previous assessments in semiarid rangeland of Israel and mesic pastures in  the United States in terms of the value of different activity sensor measurements for identifying grazing activity, suggesting that the use of GPS collars to classify cattle grazing behavior will require calibrations specific to the environment and vegetation being studied.<\/jats:p>","DOI":"10.3390\/s130303711","type":"journal-article","created":{"date-parts":[[2013,3,15]],"date-time":"2013-03-15T12:08:39Z","timestamp":1363349319000},"page":"3711-3723","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["Assessing Herbivore Foraging Behavior with GPS Collars in  a Semiarid Grassland"],"prefix":"10.3390","volume":"13","author":[{"given":"David","family":"Augustine","sequence":"first","affiliation":[{"name":"Rangeland Resources Research Unit, United States Department of Agriculture\u2013Agricultural Research Service, 1701 Centre Avenue, Fort Collins, CO 80525, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Derner","sequence":"additional","affiliation":[{"name":"Rangeland Resources Research Unit, United States Department of Agriculture\u2013Agricultural Research Service, 8408 Hildreth Road, Cheyenne, WY 82009, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2013,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.2111\/08-068.1","article-title":"Dietary selection by domestic grazing ruminants in temperature pastures: Current state of knowledge, methodologies, and future direction","volume":"62","author":"Soder","year":"2009","journal-title":"Rangel. 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Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/3\/3711\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:45:34Z","timestamp":1760219134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/3\/3711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,3,15]]},"references-count":17,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2013,3]]}},"alternative-id":["s130303711"],"URL":"https:\/\/doi.org\/10.3390\/s130303711","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,3,15]]}}}