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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio-Norte. Para informações adicionais entre em contato com cpamn.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Meio-Norte. |
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Data corrente: |
20/05/2022 |
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Data da última atualização: |
20/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ALBUQUERQUE, J. R. T. de; LINS, H. A.; SANTOS, M. G. dos; FREITAS, M. A. M. de; OLIVEIRA, F. S. de; SOUZA, A. R. E. de; SILVEIRA, L. M. da; NUNES, G. H. de S.; BARROS JÚNIOR, A. P.; VIEIRA, P. F. de M. J. |
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Afiliação: |
JOSÉ RICARDO TAVARES DE ALBUQUERQUE, UFERSA; HAMURÁBI ANIZIO LINS, UFERSA; MANOEL GALDINO DOS SANTOS, UFERSA; MÁRCIO ALEXANDRE MOREIRA DE FREITAS, UFV; FERNANDO SARMENTO DE OLIVEIRA, UFERSA; ALMIR ROGÉRIO EVANGELISTA DE SOUZA, Instituto Federal de Educação, Ciência e Tecnologia de Alagoas, Piranhas, Alagoas, Brasil.; LINDOMAR MARIA DA SILVEIRA, UFERSA; GLAUBER HENRIQUE DE SOUSA NUNES, UFERSA; AURÉLIO PAES BARROS JÚNIOR, UFERSA; PAULO FERNANDO DE MELO JORGE VIEIRA, CPAMN. |
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Título: |
Adaptability and stability of soybean (Glycine max L.) genotypes in semiarid conditions. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Euphytica, v. 218, n. 61, 2022. |
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Páginas: |
12 p. |
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DOI: |
https://doi.org/10.1007/s10681-022-03012-0 |
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Idioma: |
Inglês |
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Conteúdo: |
Soybean production in Brazil is concentrated in the central and southern regions of the country, although expansion is occurring toward the northeast, where semi-arid conditions are predominant. There is little information on the behavior of soybean cultivars in semiarid climates; therefore, the objective of this study was to evaluate the interaction of soybean genotypes by environment, adaptability, and phenotypic stability under semiarid conditions. |
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Palavras-Chave: |
Interação genótipo x ambiente; REML/BLUP. |
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Thesagro: |
Glycine Max; Produtividade. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01367naa a2200301 a 4500
001 2143324
005 2022-05-20
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s10681-022-03012-0$2DOI
100 1 $aALBUQUERQUE, J. R. T. de
245 $aAdaptability and stability of soybean (Glycine max L.) genotypes in semiarid conditions.$h[electronic resource]
260 $c2022
300 $a12 p.
520 $aSoybean production in Brazil is concentrated in the central and southern regions of the country, although expansion is occurring toward the northeast, where semi-arid conditions are predominant. There is little information on the behavior of soybean cultivars in semiarid climates; therefore, the objective of this study was to evaluate the interaction of soybean genotypes by environment, adaptability, and phenotypic stability under semiarid conditions.
650 $aGlycine Max
650 $aProdutividade
653 $aInteração genótipo x ambiente
653 $aREML/BLUP
700 1 $aLINS, H. A.
700 1 $aSANTOS, M. G. dos
700 1 $aFREITAS, M. A. M. de
700 1 $aOLIVEIRA, F. S. de
700 1 $aSOUZA, A. R. E. de
700 1 $aSILVEIRA, L. M. da
700 1 $aNUNES, G. H. de S.
700 1 $aBARROS JÚNIOR, A. P.
700 1 $aVIEIRA, P. F. de M. J.
773 $tEuphytica$gv. 218, n. 61, 2022.
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Embrapa Meio-Norte (CPAMN) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
04/10/2021 |
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Data da última atualização: |
08/12/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MORAIS JÚNIOR, O. P.; BRESEGHELLO, F.; DUARTE, J. B.; COELHO, A. S. G.; BORBA, T. C. O.; AGUIAR, J. T.; NEVES, P. C. F.; MORAIS, O. P. |
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Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR, UFG; FLAVIO BRESEGHELLO, CNPAF; JOAO BATISTA DUARTE, UFG; ALEXANDRE S. G. COELHO, UFG; TEREZA CRISTINA DE OLIVEIRA BORBA, CNPAF; JORDENE T. AGUIAR; PERICLES DE CARVALHO FERREIRA NEVES, CNPAF; ORLANDO PEIXOTO DE MORAIS, CNPAF. |
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Título: |
Assessing prediction models for different traits in a rice population derived from a Recurrent Selection Program. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Crop Science, v. 58, n. 6, p. 2347-2359, Nov./Dec. 2018. |
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ISSN: |
0011-183X |
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DOI: |
https://doi.org/10.2135/cropsci2018.02.0087 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection (GS) is a promising approach to improve rice (Oryza sativa L.) populations by using genome-wide markers for selection prior to phenotyping to estimate breeding values. In this study, our objectives were to compare certain prediction models with different struc-tures of genetic relationship and statistical approaches for relevant traits in rice and to discuss some implications for integrating GS into a recurrent selection program of irrigated rice. We assessed nine models in terms of predictive potential, using empirical data from S1:3 progenies phenotyped for eight traits with different heritabilities and genotyped with 6174 high-quality single nucleotide polymorphism markers. For all traits, marker-based models outperformed prediction based on pedigree records alone. A similar level of accuracy was observed for many models, although the level of prediction stability and prediction bias varied widely. Random forest was slightly superior for less complex traits, although with high predic-tion bias, whereas the semiparametric RKHS method (reproducing kernel Hilbert spaces) was superior for many traits, showing high stability and low bias. Bayesian variable selec-tion method Bayes Cp showed acceptable accuracy and stability for several traits and thus could be useful for genomic prediction aiming at persisting accuracy for a long-term recurrent selection. |
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Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Seleção Recorrente. |
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Thesaurus NAL: |
Plant breeding; Recurrent selection; Rice. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02349naa a2200313 a 4500
001 2135012
005 2021-12-08
008 2018 bl uuuu u00u1 u #d
022 $a0011-183X
024 7 $ahttps://doi.org/10.2135/cropsci2018.02.0087$2DOI
100 1 $aMORAIS JÚNIOR, O. P.
245 $aAssessing prediction models for different traits in a rice population derived from a Recurrent Selection Program.$h[electronic resource]
260 $c2018
520 $aGenomic selection (GS) is a promising approach to improve rice (Oryza sativa L.) populations by using genome-wide markers for selection prior to phenotyping to estimate breeding values. In this study, our objectives were to compare certain prediction models with different struc-tures of genetic relationship and statistical approaches for relevant traits in rice and to discuss some implications for integrating GS into a recurrent selection program of irrigated rice. We assessed nine models in terms of predictive potential, using empirical data from S1:3 progenies phenotyped for eight traits with different heritabilities and genotyped with 6174 high-quality single nucleotide polymorphism markers. For all traits, marker-based models outperformed prediction based on pedigree records alone. A similar level of accuracy was observed for many models, although the level of prediction stability and prediction bias varied widely. Random forest was slightly superior for less complex traits, although with high predic-tion bias, whereas the semiparametric RKHS method (reproducing kernel Hilbert spaces) was superior for many traits, showing high stability and low bias. Bayesian variable selec-tion method Bayes Cp showed acceptable accuracy and stability for several traits and thus could be useful for genomic prediction aiming at persisting accuracy for a long-term recurrent selection.
650 $aPlant breeding
650 $aRecurrent selection
650 $aRice
650 $aArroz
650 $aMelhoramento Genético Vegetal
650 $aOryza Sativa
650 $aSeleção Recorrente
700 1 $aBRESEGHELLO, F.
700 1 $aDUARTE, J. B.
700 1 $aCOELHO, A. S. G.
700 1 $aBORBA, T. C. O.
700 1 $aAGUIAR, J. T.
700 1 $aNEVES, P. C. F.
700 1 $aMORAIS, O. P.
773 $tCrop Science$gv. 58, n. 6, p. 2347-2359, Nov./Dec. 2018.
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