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dc.contributor.authorSILVA, C. S. D.eng
dc.contributor.authorSILVA, T. E. daeng
dc.contributor.authorSGUIZATTO, A. L. L.eng
dc.contributor.authorMACHADO, A. F.eng
dc.contributor.authorSILVA, A. S.eng
dc.contributor.authorCOSTA, J. H. C.eng
dc.contributor.authorCAMPOS, M. M.eng
dc.contributor.authorPACIULLO, D. S. C.eng
dc.contributor.authorGOMIDE, C. A. de M.eng
dc.contributor.authorMORENZ, M. J. F.eng
dc.date.accessioned2026-03-12T10:49:35Z-
dc.date.available2026-03-12T10:49:35Z-
dc.date.created2026-03-12
dc.date.issued2026
dc.identifier.citationJDS Communications, v. 7, p. 179-184, 2026.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185317-
dc.descriptionAccurate estimation of DMI is essential for optimizing nutrition, efficiency, and economic performance in modern dairy herds. However, most existing equations to estimate DMI are designed for herd-level predictions in purebred Holstein cows. This study evaluated the accuracy and precision of machine learning (ML) algorithms to predict daily individual DMI in Holstein × Gyr crossbred lactating cows using a supervised and integrative approach that combined behavior monitoring data, cow phenotypes, and weather features. Data from 31 cows were individually and consecutively collected over 18 d. Twenty-two cows (71% of the dataset) were used to train 4 linear regression models (multiple linear, ridge, lasso, and elastic net) and 3 ensemble algorithms (random forest, gradient boosting, and extreme gradient boosting) through leave-one-group-out cross-validation, with the number of folds equal to the number of cows (k = 22). The remaining 9 cows were used as an external test set. Among all algorithms, Gradient boosting achieved the best overall performance, yielding moderate precision (R2 = 0.68) and accuracy (root mean squared error = 1.60 kg/d) metrics on test data. Our results indicate that gradient boosting is more suitable for capturing complex nonlinear relationships underlying daily DMI compared with the other models evaluated. Further advancements in ML-based DMI prediction should consider integrating intra- and interindividual variability in feeding behavior and accounting for animal-specific effects.
dc.language.isoeng
dc.rightsopenAccess
dc.titlePredicting individual dry matter intake in Holstein × Gyr cows using behavior-monitoring sensor, phenotypic, and weather data with supervised machine learning.
dc.typeArtigo de periódico
dc.subject.thesagroBovino
dc.subject.thesagroGado Leiteiro
dc.subject.thesagroMatéria Seca
dc.subject.thesagroIngestão
dc.subject.thesagroNutrição Animal
dc.subject.thesagroGado Mestiço
dc.subject.thesagroVaca Leiteira
dc.subject.thesagroGado Holandês
dc.subject.thesagroGado Gir
dc.subject.nalthesaurusDairy cattleeng
dc.subject.nalthesaurusAnimal nutritioneng
dc.subject.nalthesaurusCrossbredseng
dc.subject.nalthesaurusDairy cowseng
dc.subject.nalthesaurusDry matter intakeeng
dc.description.notesShort communication.eng
riaa.ainfo.id1185317
riaa.ainfo.lastupdate2026-03-12
dc.identifier.doihttps://doi.org/10.3168/jdsc.2025-0850
dc.contributor.institutionCAMILA SOUSA DA SILVA, CPAMNeng
dc.contributor.institutionTADEU E. DA SILVA, UNIVERSITY OF VERMONTeng
dc.contributor.institutionANNA L. L. SGUIZATTOeng
dc.contributor.institutionANDREIA FERREIRA MACHADO, CNPGLeng
dc.contributor.institutionABIAS SANTOS SILVA, CPAAeng
dc.contributor.institutionJOÃO H. C. COSTA, UNIVERSITY OF VERMONTeng
dc.contributor.institutionMARIANA MAGALHAES CAMPOS, CNPGLeng
dc.contributor.institutionDOMINGOS SAVIO CAMPOS PACIULLO, CNPGLeng
dc.contributor.institutionCARLOS AUGUSTO DE MIRANDA GOMIDE, CNPGLeng
dc.contributor.institutionMIRTON JOSE FROTA MORENZ, CNPGL.eng
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