{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T03:38:51Z","timestamp":1790221131175,"version":"4.1.0"},"reference-count":54,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior (CAPES, Finance code 001)","award":["409620\/2022-0"],"award-info":[{"award-number":["409620\/2022-0"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior (CAPES, Finance code 001)","award":["409400\/2021-1"],"award-info":[{"award-number":["409400\/2021-1"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","award":["409620\/2022-0"],"award-info":[{"award-number":["409620\/2022-0"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","award":["409400\/2021-1"],"award-info":[{"award-number":["409400\/2021-1"]}]},{"name":"Instituto Federal Goiano and CEAGRE (Edital 19\/21)","award":["409620\/2022-0"],"award-info":[{"award-number":["409620\/2022-0"]}]},{"name":"Instituto Federal Goiano and CEAGRE (Edital 19\/21)","award":["409400\/2021-1"],"award-info":[{"award-number":["409400\/2021-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings\u20148 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature\u2013humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.<\/jats:p>","DOI":"10.3390\/ani14111567","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T05:14:02Z","timestamp":1716786842000},"page":"1567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1112-6699","authenticated-orcid":false,"given":"Thaisa Campos","family":"Marques","sequence":"first","affiliation":[{"name":"Departamento de Zootecnia, Instituto Federal Goiano, Rio Verde 75901-970, Brazil"},{"name":"Department of Population Health and Reproduction, University of California, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Let\u00edcia Ribeiro","family":"Marques","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Instituto Federal Goiano, Rio Verde 75901-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2368-943X","authenticated-orcid":false,"given":"Patrick Bezerra","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Instituto Federal Goiano, Rio Verde 75901-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8377-6469","authenticated-orcid":false,"given":"Fabio Soares","family":"de Lima","sequence":"additional","affiliation":[{"name":"Department of Population Health and Reproduction, University of California, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9486-7128","authenticated-orcid":false,"given":"Tiago","family":"do Prado Paim","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Instituto Federal Goiano, Rio Verde 75901-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen Martins","family":"Le\u00e3o","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Instituto Federal Goiano, Rio Verde 75901-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"ref_1","first-page":"14","article-title":"Relationship between body condition score and pregnancy rates following artificial insemination and subsequent natural mating in beef cows on commercial farms in New Zealand","volume":"80","author":"Weik","year":"2020","journal-title":"N. 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