Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1182698
Título: Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data.
Autoria: FERNANDES FILHO, C. C.
BARRIOS, S. C. L.
SANTOS, M. F.
NUNES, J. A. R.
VALLE, C. B. do
JANK, L.
RIOS, E. F.
Afiliação: CLAUDIO CARLOS FERNANDES FILHO, CENTRO DE TECNOLOGIA DA CANA-DE-AÇÚCAR
SANZIO CARVALHO LIMA BARRIOS, CNPGC
MATEUS FIGUEIREDO SANTOS, CNPGC
JOSE AIRTON RODRIGUES NUNES, UNIVERSIDADE FEDERAL DE LAVRAS
CACILDA BORGES DO VALLE, CNPGC
LIANA JANK, CNPGC
ESTEBAN FERNANDO RIOS, UNIVERSIDADE DA FLORIDA.
Ano de publicação: 2025
Referência: G3: Genes, Genomes, Genetics, v. 15, n. 3, jkae306, 2025.
Páginas: p. 1-15
Conteúdo: Genotype 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.
Thesagro: Matéria Seca
Medicago Sativa
NAL Thesaurus: Forage grasses
Genotype
Megathyrsus maximus
Regression analysis
Urochloa
ISSN: 2160-1836
Digital Object Identifier: https://doi.org/10.1093/g3journal/jkae306
Tipo do material: Artigo de periódico
Acesso: openAccess
Aparece nas coleções:Artigo em periódico indexado (CNPGC)

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