Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1187625
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorMONTEIRO, G. O. de A.
dc.contributor.authorDIFANTE, G. dos S.
dc.contributor.authorMONTAGNER, D. B.
dc.contributor.authorEUCLIDES, V. P. B.
dc.contributor.authorCASTRO, M.
dc.contributor.authorRODRIGUES, J. G.
dc.contributor.authorPEREIRA, M. de G.
dc.contributor.authorSANTANA, J. C. S.
dc.contributor.authorITAVO, L. C. V.
dc.contributor.authorNANTES, R. T.
dc.contributor.authorCAMPOS, J. A.
dc.contributor.authorCOSTA, A. B. da
dc.contributor.authorMATSUBARA, E. T.
dc.date.accessioned2026-06-16T17:48:42Z-
dc.date.available2026-06-16T17:48:42Z-
dc.date.created2026-06-16
dc.date.issued2026
dc.identifier.citationScientific Reports, v. 16, article number 5805, 2026.
dc.identifier.issn2045-2322
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1187625-
dc.descriptionUnderstanding forage quality is essential for meeting animal demands and optimizing production. This study aimed to: (i) test the applicability of machine learning models with tabular data such as climate variables, light interception (LI), nitrogen dose (N dose), interval between grazing (GI), and pre- (HPRE) and post-grazing height (HPOST) to predict leaf crude protein (CP) content of tamani grass pastures; (ii) identify which variables contribute most to CP prediction. A set of 90 instances was used with 80% for training and validation and 20% for testing. The hyperparameters were adjusted with grid-search on the training set. We tested Linear Regression (LR), Multilayer Perceptron (MLP), Decision Trees (DT), Random Forest (RF), and XGBoost. The MLP (r=0.75, R2 =44.18%, MAE=1.55), RF (r=0.78, R2 =49.07%, MAE=1.59) and XGBoost (r=0.78, R2 =56.65% MAE=1.45) models presented the best prediction results (p<0.001). The variables most important in predicting CP content were GI, followed by N dose, HPRE and HPOST. XGBoost outperformed other tested models (p<0.001). Tabular data, including N dose, GI, HPRE, HPOST, LI, and climatic variables, is a viable alternative for predicting CP. In conclusion, the results of this study suggest that management practices may have a greater influence on the chemical composition of Tamani grass than environmental conditions, although further research with larger and more diverse datasets is needed to confirm these findings
dc.language.isoeng
dc.rightsopenAccess
dc.titleMachine learning models for crude protein prediction in Tamani grass pastures.
dc.typeArtigo de periódico
dc.subject.thesagroPanicum Maximum
dc.subject.thesagroPastagem
dc.subject.thesagroProteína Bruta
dc.subject.nalthesaurusCarbon sequestration
dc.subject.nalthesaurusCrude protein
dc.subject.nalthesaurusMegathyrsus maximus
dc.subject.nalthesaurusPasture management
riaa.ainfo.id1187625
riaa.ainfo.lastupdate2026-06-16
dc.identifier.doihttps://doi.org/10.1038/s41598-026-36949-6.
dc.contributor.institutionGABRIELA OLIVEIRA DE AQUINO MONTEIRO, UNIVERSIDADE ESTADUAL PAULISTA JÚLIO DE MESQUITA FILHO; GELSON DOS SANTOS DIFANTE, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; DENISE BAPTAGLIN MONTAGNER, CNPGC; VALERIA PACHECO BATISTA EUCLIDES, CNPGC; MARINA CASTRO, INSTITUTO POLITÉCNICO DE BRAGANÇA; JÉSSICA GOMES RODRIGUES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; MARISLAYNE DE GUSMÃO PEREIRA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JULIANA CAROLINE SANTOS SANTANA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; LUIS CARLOS VINHAS ITAVO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; RAFAEL TORRES NANTES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JECELEN ADRIANE CAMPOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; ANDERSON BESSA DA COSTA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDSON TAKASHI MATSUBARA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL.
Aparece nas coleções:Artigo em periódico indexado (CNPGC)

Arquivos associados a este item:
Arquivo TamanhoFormato 
Machine-learning-models-2026.pdf2 MBAdobe PDFVisualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace