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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
22/04/2020 |
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Data da última atualização: |
22/04/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LEMOS, R. do C.; ABREU, A. de F. B.; RAMALHO, M. A. P. |
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Afiliação: |
ROXANE DO CARMO LEMOS, UFLA; ANGELA DE FATIMA BARBOSA ABREU, CNPAF; MAGNO ANTONIO PATTO RAMALHO, UFLA. |
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Título: |
Procedures for identification of superior progenies in successive generations of evaluation in common bean. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Scientia Agricola, v. 77, n. 1, e20180105, 2020. |
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ISSN: |
1678-992X |
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DOI: |
10.1590/1678-992X-2018-0105 |
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Idioma: |
Inglês |
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Conteúdo: |
When breeding the common bean in Brazil, the best progenies are chosen, normally, from solely the generation under analysis at the conclusion of the evaluation, without considering what occurred in the past. However, a number of recently published studies show that if an evaluation were to consider all relevant generations, the gain from selection could be higher, especially when an index that involves information from the population that gave rise to the progenies is used. Thus, the aim of this study was to compare three selection procedures in the evaluation of successive generations and to discuss the implications of the progeny × environment interaction in terms of success of selection. Cycle XV progenies from a bean recurrent selection program were used. The traits evaluated were grain yield, plant architecture and grain type. Analysis of variance was carried out and the variance components and heritabilities were estimated. The same analyses were made using mixed models. A selection index weighted by the effect of populations and progenies within populations (WSI) was also obtained. We estimated the correlations between the classification of the progenies using the three procedures and the coincidence of the best progenies evaluated in S0:4 with the progenies in the previous generations. We found that the classification of the progenies by the BLUP's and WSI did not expressively differ from that obtained when using only the mean, even when a number of generations were considered in the selection. None of the procedures used effectively mitigated the effect of the progeny × environment interaction. MenosWhen breeding the common bean in Brazil, the best progenies are chosen, normally, from solely the generation under analysis at the conclusion of the evaluation, without considering what occurred in the past. However, a number of recently published studies show that if an evaluation were to consider all relevant generations, the gain from selection could be higher, especially when an index that involves information from the population that gave rise to the progenies is used. Thus, the aim of this study was to compare three selection procedures in the evaluation of successive generations and to discuss the implications of the progeny × environment interaction in terms of success of selection. Cycle XV progenies from a bean recurrent selection program were used. The traits evaluated were grain yield, plant architecture and grain type. Analysis of variance was carried out and the variance components and heritabilities were estimated. The same analyses were made using mixed models. A selection index weighted by the effect of populations and progenies within populations (WSI) was also obtained. We estimated the correlations between the classification of the progenies using the three procedures and the coincidence of the best progenies evaluated in S0:4 with the progenies in the previous generations. We found that the classification of the progenies by the BLUP's and WSI did not expressively differ from that obtained when using only the mean, even when a number of generations were ... Mostrar Tudo |
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Palavras-Chave: |
Mixed model approach. |
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Thesagro: |
Feijão; Índice de Seleção; Melhoramento Genético Vegetal; Phaseolus Vulgaris; Seleção Recorrente. |
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Thesaurus Nal: |
Beans; Plant breeding; Recurrent selection; Selection index. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/212431/1/CNPAF-2020-sa.pdf
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Marc: |
LEADER 02518naa a2200289 a 4500
001 2121767
005 2020-04-22
008 2020 bl uuuu u00u1 u #d
022 $a1678-992X
024 7 $a10.1590/1678-992X-2018-0105$2DOI
100 1 $aLEMOS, R. do C.
245 $aProcedures for identification of superior progenies in successive generations of evaluation in common bean.$h[electronic resource]
260 $c2020
520 $aWhen breeding the common bean in Brazil, the best progenies are chosen, normally, from solely the generation under analysis at the conclusion of the evaluation, without considering what occurred in the past. However, a number of recently published studies show that if an evaluation were to consider all relevant generations, the gain from selection could be higher, especially when an index that involves information from the population that gave rise to the progenies is used. Thus, the aim of this study was to compare three selection procedures in the evaluation of successive generations and to discuss the implications of the progeny × environment interaction in terms of success of selection. Cycle XV progenies from a bean recurrent selection program were used. The traits evaluated were grain yield, plant architecture and grain type. Analysis of variance was carried out and the variance components and heritabilities were estimated. The same analyses were made using mixed models. A selection index weighted by the effect of populations and progenies within populations (WSI) was also obtained. We estimated the correlations between the classification of the progenies using the three procedures and the coincidence of the best progenies evaluated in S0:4 with the progenies in the previous generations. We found that the classification of the progenies by the BLUP's and WSI did not expressively differ from that obtained when using only the mean, even when a number of generations were considered in the selection. None of the procedures used effectively mitigated the effect of the progeny × environment interaction.
650 $aBeans
650 $aPlant breeding
650 $aRecurrent selection
650 $aSelection index
650 $aFeijão
650 $aÍndice de Seleção
650 $aMelhoramento Genético Vegetal
650 $aPhaseolus Vulgaris
650 $aSeleção Recorrente
653 $aMixed model approach
700 1 $aABREU, A. de F. B.
700 1 $aRAMALHO, M. A. P.
773 $tScientia Agricola$gv. 77, n. 1, e20180105, 2020.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Pecuária Sudeste. |
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Data corrente: |
13/06/2019 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Documentos |
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Autoria: |
SANTOS, P. M.; TELLES, M. A.; FRAGALLE, C. V. P.; BERNARDI, A. C. de C.; MAIO, A. M. D. de; NOVO, A. L. M.; VIGNA, B. B. Z.; CORRÊA, C. G.; DE MORI, C.; SOUZA, F. H. D. de; OMOTE, H. de S. G.; PEZZOPANE, J. R. M.; SUSSAI, J. P.; PALHARES, J. C. P.; CASTRO, L. M. de; VINHOLIS, M. de M. B.; CAVALLARI, M. M.; GUSMAO, M. R.; GODOY, R.; NOGUEIRA, S. F.; ALVES, T. C. |
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Afiliação: |
PATRICIA MENEZES SANTOS, CPPSE; MILENA AMBROSIO TELLES, CPPSE; CRISTIANE VIEIRA PERES FRAGALLE, CPPSE; ALBERTO CARLOS DE CAMPOS BERNARDI, CPPSE; ANA MARIA DANTAS DE MAIO, CPPSE; ANDRE LUIZ MONTEIRO NOVO, CPPSE; BIANCA BACCILI ZANOTTO VIGNA, CPPSE; Caroline Galharte Corrêa, CNPq; CLAUDIA DE MORI, CPPSE; FRANCISCO HUMBERTO DUBBERN DE SOUZA, CPPSE; HELIO DE SENA GOUVEA OMOTE, CPPSE; JOSE RICARDO MACEDO PEZZOPANE, CPPSE; JULIANA PRISCILA SUSSAI, CPPSE; JULIO CESAR PASCALE PALHARES, CPPSE; LIVIA MENDES DE CASTRO, CPPSE; MARCELA DE MELLO BRANDAO VINHOLIS, CPPSE; MARCELO MATTOS CAVALLARI, CPPSE; MARCOS RAFAEL GUSMAO, CPPSE; RODOLFO GODOY, CPPSE; SANDRA FURLAN NOGUEIRA, CNPMA; TERESA CRISTINA ALVES, CPPSE. |
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Título: |
Encontro de inovação em pastagens: relatório final. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
São Carlos, SP: Embrapa Pecuária Sudeste, 2019. |
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Páginas: |
30 p. |
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Série: |
(Embrapa Pecuária Sudeste. Documentos, 133). |
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ISSN: |
1980-6841 |
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Idioma: |
Português |
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Conteúdo: |
A agricultura brasileira sofreu várias transformações nos últimos anos. O estudo do desenvolvimento agrário brasileiro e da produtividade total dos fatores aponta para o surgimento de uma agricultura intensiva no uso de capital e de tecnologia (ALVES; SOUZA; GOMES, 2013; NAVARRO, 2016). Recentemente, a 'revolução digital' tem transformado a sociedade e vários setores da economia. Nesse contexto, a chamada ?agricultura 4.0? integra processos e informações por meio de recursos tecnológicos, com o objetivo de aumentar a produtividade e a eficiência dos sistemas de produção. |
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Palavras-Chave: |
Pecuária 4-0. |
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Thesagro: |
Pastagem; Produção Animal. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/198545/1/EncontroInovacaoPastagens.pdf
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Marc: |
LEADER 01769nam a2200421 a 4500
001 2109854
005 2020-01-08
008 2019 bl uuuu u0uu1 u #d
022 $a1980-6841
100 1 $aSANTOS, P. M.
245 $aEncontro de inovação em pastagens$brelatório final.$h[electronic resource]
260 $aSão Carlos, SP: Embrapa Pecuária Sudeste$c2019
300 $a30 p.
490 $a(Embrapa Pecuária Sudeste. Documentos, 133).
520 $aA agricultura brasileira sofreu várias transformações nos últimos anos. O estudo do desenvolvimento agrário brasileiro e da produtividade total dos fatores aponta para o surgimento de uma agricultura intensiva no uso de capital e de tecnologia (ALVES; SOUZA; GOMES, 2013; NAVARRO, 2016). Recentemente, a 'revolução digital' tem transformado a sociedade e vários setores da economia. Nesse contexto, a chamada ?agricultura 4.0? integra processos e informações por meio de recursos tecnológicos, com o objetivo de aumentar a produtividade e a eficiência dos sistemas de produção.
650 $aPastagem
650 $aProdução Animal
653 $aPecuária 4-0
700 1 $aTELLES, M. A.
700 1 $aFRAGALLE, C. V. P.
700 1 $aBERNARDI, A. C. de C.
700 1 $aMAIO, A. M. D. de
700 1 $aNOVO, A. L. M.
700 1 $aVIGNA, B. B. Z.
700 1 $aCORRÊA, C. G.
700 1 $aDE MORI, C.
700 1 $aSOUZA, F. H. D. de
700 1 $aOMOTE, H. de S. G.
700 1 $aPEZZOPANE, J. R. M.
700 1 $aSUSSAI, J. P.
700 1 $aPALHARES, J. C. P.
700 1 $aCASTRO, L. M. de
700 1 $aVINHOLIS, M. de M. B.
700 1 $aCAVALLARI, M. M.
700 1 $aGUSMAO, M. R.
700 1 $aGODOY, R.
700 1 $aNOGUEIRA, S. F.
700 1 $aALVES, T. C.
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Embrapa Pecuária Sudeste (CPPSE) |
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