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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
27/01/2021 |
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Data da última atualização: |
27/01/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ALVES, R. S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; ROCHA, J. R. A. S. C.; NUNES, A. C. P.; CARNEIRO, A. P. S.; SANTOS, G. A. dos. |
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Afiliação: |
RODRIGO SILVA ALVES, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; CAMILA FERREIRA AZEVEDO, UFV; FABYANO FONSECA E SILVA, UFV; JOÃO ROMERO DO AMARAL SANTOS DE CARVALHO ROCHA, UFV; ANDREI CAÍQUE PIRES NUNES, UFV; ANTÔNIO POLICARPO SOUZA CARNEIRO, UFV; GLEISON AUGUSTO DOS SANTOS, UFV. |
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Título: |
Optimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Tree Genetics & Genomes, v. 16, n. 2, p. 1-8, 2020. |
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DOI: |
https://doi.org/10.1007/s11295-020-01431-5 |
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Idioma: |
Inglês |
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Conteúdo: |
Reaction norms fitted through random regression models are a powerful tool to identify and quantify the genotype × environment (G × E) interaction and they represent a promising alternative in forest tree breeding for analysis of multi-environment trials. Thus, the objective of this study was to compare random regression models with the compound symmetry model in Eucalyptus breeding for analysis of multi-environment trials. To this end, a data set with 215 Eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The random regression models provided a better fit for both traits. Results showed that there was genotypic variability among Eucalyptus clones and that the reaction norms over the environmental gradients identified the G × E interaction. The compound symmetry model and the random regression models are highly correlated in terms of genotype ranking for both traits. The main advantage of random regression models over the compound symmetry model is the ability to predict genotypic performance in environments where a genotype has not been evaluated. Thus, our results suggest that reaction norms fitted through random regression models can be successfully used in forest tree breeding for analysis of multi-environment trials. |
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Thesagro: |
Árvore Florestal; Interação Genética; Seleção Genótipa. |
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Thesaurus Nal: |
Forest trees; Genotype-environment interaction; Plant selection guides. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02299naa a2200289 a 4500
001 2129599
005 2021-01-27
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11295-020-01431-5$2DOI
100 1 $aALVES, R. S.
245 $aOptimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients.$h[electronic resource]
260 $c2020
520 $aReaction norms fitted through random regression models are a powerful tool to identify and quantify the genotype × environment (G × E) interaction and they represent a promising alternative in forest tree breeding for analysis of multi-environment trials. Thus, the objective of this study was to compare random regression models with the compound symmetry model in Eucalyptus breeding for analysis of multi-environment trials. To this end, a data set with 215 Eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The random regression models provided a better fit for both traits. Results showed that there was genotypic variability among Eucalyptus clones and that the reaction norms over the environmental gradients identified the G × E interaction. The compound symmetry model and the random regression models are highly correlated in terms of genotype ranking for both traits. The main advantage of random regression models over the compound symmetry model is the ability to predict genotypic performance in environments where a genotype has not been evaluated. Thus, our results suggest that reaction norms fitted through random regression models can be successfully used in forest tree breeding for analysis of multi-environment trials.
650 $aForest trees
650 $aGenotype-environment interaction
650 $aPlant selection guides
650 $aÁrvore Florestal
650 $aInteração Genética
650 $aSeleção Genótipa
700 1 $aRESENDE, M. D. V. de
700 1 $aAZEVEDO, C. F.
700 1 $aSILVA, F. F. e
700 1 $aROCHA, J. R. A. S. C.
700 1 $aNUNES, A. C. P.
700 1 $aCARNEIRO, A. P. S.
700 1 $aSANTOS, G. A. dos
773 $tTree Genetics & Genomes$gv. 16, n. 2, p. 1-8, 2020.
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Embrapa Café (CNPCa) |
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| 1. |  | LIEBIG, M. A.; FRANZLUEBBERS, A. J.; ALVAREZ, C.; CHIESA, T. D.; LEWCZUK, N.; PIÑEIRO, G.; POSSE, G.; YAHDJIAN, L.; GRACE, P.; CABRAL, O. M. R.; MARTIN NETO, L.; RODRIGUES, R. de A. R.; AMIRO, B.; ANGERS, D.; HAO, X.; OELBERMANN, M.; TENUTA, M.; MUNKHOLM, L. J.; REGINA, K.; CELLIER, P.; EHRHARDT, F.; RICHARD, G.; DECHOW, R.; AGUS, F.; WIDIARTA, N.; SPINK, J.; BERTI, A.; GRIGNANI, C.; MAZZONCINI, M.; ORSINI, R.; ROGGERO, P. P.; SEDDAIU, G.; TEI, F.; VENTRELLA, D.; VITALI, G.; KISHIMOTO-MO, A.; SHIRATO, Y.; SUDO, S.; SHIN, J.; SCHIPPER, L.; SAVÉ, R.; LEIFELD, J.; SPADAVECCHIA, L.; YELURIPATI, J.; DEL GROSSO, S.; RICE, C.; SAWCHIK, J. MAGGnet: an international network to foster mitigation of agricultural greenhouse gases. Carbon Management, v. 7, N. 3-4, P. 243-248, 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
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