{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T15:41:27Z","timestamp":1788795687327,"version":"build-2803163510"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AFRI Sustainable Agricultural Systems Coordinated Agricultural Project (SAS-CAP)","award":["2021-69012-35952"],"award-info":[{"award-number":["2021-69012-35952"]}]},{"name":"AFRI Sustainable Agricultural Systems Coordinated Agricultural Project (SAS-CAP)","award":["A1638"],"award-info":[{"award-number":["A1638"]}]},{"name":"USDA National Institute of Food and Agriculture","award":["2021-69012-35952"],"award-info":[{"award-number":["2021-69012-35952"]}]},{"name":"USDA National Institute of Food and Agriculture","award":["A1638"],"award-info":[{"award-number":["A1638"]}]},{"name":"Utah Agricultural Experiment Station","award":["2021-69012-35952"],"award-info":[{"award-number":["2021-69012-35952"]}]},{"name":"Utah Agricultural Experiment Station","award":["A1638"],"award-info":[{"award-number":["A1638"]}]},{"name":"Norwegian Institute of Bioeconomy Research (NIBIO)","award":["2021-69012-35952"],"award-info":[{"award-number":["2021-69012-35952"]}]},{"name":"Norwegian Institute of Bioeconomy Research (NIBIO)","award":["A1638"],"award-info":[{"award-number":["A1638"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>This study classified cows\u2019 foraging behaviors using machine learning (ML) models evaluated through random test split (RTS) and cross-validation (CV) data partition methods. Models included Perceptron, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest (RF), and XGBoost (XGB). These models classified activity states (active vs. static), foraging behaviors (grazing (GR), resting (RE), walking (W), ruminating (RU)), posture states (standing up (SU) vs. lying down (LD)), and posture combinations with rumination and resting behaviors (RU_SU, RU_LD, RE_SU, and RE_LD). XGB achieved the highest accuracy for state classification (74.5% RTS, 74.2% CV) and foraging behavior (69.4% CV). RF outperformed XGB in other classifications, including GR, RE, and RU (62.9% CV vs. 56.4% RTS), posture (83.9% CV vs. 79.4% RTS), and behaviors-by-posture (58.8% CV vs. 56.4% RTS). Key predictors varied: speed and Actindex were crucial for GR and W when increasing and for RE and RU when decreasing. X low values were linked to RE_SU and RU_SU, while X and Z influenced RE_LD more. RTS showed higher accuracy in activity states classification while CV in foraging behaviors and by posture classification. These results emphasize CV in RF\u2019s reliability in managing complex behavioral patterns and the importance of continuous recording devices and movement data to monitor cattle behavior accurately.<\/jats:p>","DOI":"10.3390\/ani15070913","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:17:14Z","timestamp":1742962634000},"page":"913","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5645-4923","authenticated-orcid":false,"given":"Bashiri Iddy","family":"Muzzo","sequence":"first","affiliation":[{"name":"Department of Wildland Resources, Quinney College of Natural Resources, Utah State University, Logan, UT 4322-5230, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2028-5504","authenticated-orcid":false,"given":"Kelvyn","family":"Bladen","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Utah State University, 3900 Old Main Hill, Logan, UT 84322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andres","family":"Perea","sequence":"additional","affiliation":[{"name":"Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9323-0208","authenticated-orcid":false,"given":"Shelemia","family":"Nyamuryekung\u2019e","sequence":"additional","affiliation":[{"name":"Division of Food Production and Society, Norwegian Institute of Bioeconomy Research (NIBIO), PB 115, N-1431 \u00c5s, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8868-8468","authenticated-orcid":false,"given":"Juan J.","family":"Villalba","sequence":"additional","affiliation":[{"name":"Department of Wildland Resources, Quinney College of Natural Resources, Utah State University, Logan, UT 4322-5230, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1126\/science.add1347","article-title":"The History and Challenge of Grassy Biomes","volume":"377","author":"Staver","year":"2022","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Giridhar, K., Samireddypalle, A., Giridhar, K., and Samireddypalle, A. 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