Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1182698
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorFERNANDES FILHO, C. C.
dc.contributor.authorBARRIOS, S. C. L.
dc.contributor.authorSANTOS, M. F.eng
dc.contributor.authorNUNES, J. A. R.eng
dc.contributor.authorVALLE, C. B. doeng
dc.contributor.authorJANK, L.eng
dc.contributor.authorRIOS, E. F.eng
dc.date.accessioned2025-12-11T17:48:37Z-
dc.date.available2025-12-11T17:48:37Z-
dc.date.created2025-12-11
dc.date.issued2025
dc.identifier.citationG3: Genes, Genomes, Genetics, v. 15, n. 3, jkae306, 2025.
dc.identifier.issn2160-1836
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1182698-
dc.descriptionGenotype selection for dry matter yield (DMY) in perennial forage species is based on repeated measurements over time, referred to as longitudinal data. These datasets capture temporal trends and variability, which are critical for identifying genotypes with desirable performance across seasons. In this study, we have presented a random regression model (RRM) approach for selecting genotypes based on longitudinal DMY data generated from 10 breeding trials and three perennial species, alfalfa (Medicago sativa L.), guineagrass (Megathyrsus maximus), and brachiaria (Urochloa spp.). We also proposed the estimation of adaptability based on the area under the curve and stability based on the curve coefficient of variation. Our results showed that RRM always approximated the (co)variance structure into an autoregressive pattern. Furthermore, RRM can offer useful information about longitudinal data in forage breeding trials, where the breeder can select genotypes based on their seasonality by interpreting reaction norms. Therefore, we recommend using RRM for longitudinal traits in breeding trials for perennial species.
dc.language.isoeng
dc.rightsopenAccess
dc.titleAssessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data.
dc.typeArtigo de periódico
dc.subject.thesagroMatéria Seca
dc.subject.thesagroMedicago Sativaeng
dc.subject.nalthesaurusForage grasses
dc.subject.nalthesaurusGenotype
dc.subject.nalthesaurusMegathyrsus maximus
dc.subject.nalthesaurusRegression analysis
dc.subject.nalthesaurusUrochloa
dc.format.extent2p. 1-15
riaa.ainfo.id1182698
riaa.ainfo.lastupdate2025-12-11
dc.identifier.doihttps://doi.org/10.1093/g3journal/jkae306
dc.contributor.institutionCLAUDIO CARLOS FERNANDES FILHO, CENTRO DE TECNOLOGIA DA CANA-DE-AÇÚCAR
dc.contributor.institutionSANZIO CARVALHO LIMA BARRIOS, CNPGCeng
dc.contributor.institutionMATEUS FIGUEIREDO SANTOS, CNPGCeng
dc.contributor.institutionJOSE AIRTON RODRIGUES NUNES, UNIVERSIDADE FEDERAL DE LAVRASeng
dc.contributor.institutionCACILDA BORGES DO VALLE, CNPGCeng
dc.contributor.institutionLIANA JANK, CNPGCeng
dc.contributor.institutionESTEBAN FERNANDO RIOS, UNIVERSIDADE DA FLORIDA.eng
Aparece nas coleções:Artigo em periódico indexado (CNPGC)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
Assessing-genotype-adaptability-2025.pdf1,54 MBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace