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Registros recuperados : 5 | |
2. | | DAHER, R. F.; PEREIRA, A. V.; MENEZES, B. R. da S.; CASSARO, S.; NOVO, A. A. C.; FURLANI, E. P.; AMARAL JÚNIOR, A. T.; PEREIRA, M. G.; STIDA, W. F.; VIDAL, A. K. F. Canonical correlations among morpho-agronomic and chemical traits in hybrids between elephant grass and millet. Australian Journal of Crop Science, v. 12, n. 2, p. 210-216, 2018. Biblioteca(s): Embrapa Gado de Leite. |
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3. | | MENEZES, B. R. S.; DAHER, R. F.; GRAVINA, G. de A.; PEREIRA, A. V.; SOUSA, L. B.; RODRIGUES, E. V.; SILVA, V. B.; GOTTARDO, R. D.; SCHNEIDER, L. S. A.; NOVO, A. A. C. Estimates of heterosis parameters in elephant grass (Pennisetum purpureum Schumach.) for bioenergy production. Chilean Journal of Agricultural Research, v.75, n.4, p. 395-401, 2015. Biblioteca(s): Embrapa Gado de Leite. |
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4. | | SILVA, V. B.; DAHER, R. F.; ARAÚJO, M. S. B.; SOUZA, Y. P.; CASSARO, S.; MENEZES, B. R. S.; GRAVINA, L. M.; NOVO, A. A. C.; TARDIN, F. D.; AMARAL JÚNIOR, A. T. Prediction of genetic gains by selection indices using mixed models in elephant grass for energy purposes. Genetics and Molecular Research, Ribeirão Preto, v. 16, n. 3, p. 1-8, 2017. Biblioteca(s): Embrapa Milho e Sorgo. |
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5. | | ROCHA, A. dos S.; DAHER, R. F.; GRAVINA, G. de A.; PEREIRA, A. V.; RODRIGUES, E. V.; VIANA, A. P.; SILVA, V. Q. R. da; AMARAL JUNIOR, A. T. do; NOVO, A. A. C.; OLIVEIRA, M. L. F.; OLIVEIRA, E. da S. Comparison of stability methods in elephant-grass genotypes for energy purposes. African Journal of Agricultural Research, v. 10, n. 47, p. 4283-4294, 2015. Biblioteca(s): Embrapa Gado de Leite. |
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Registros recuperados : 5 | |
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Registro Completo
Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
14/12/2017 |
Data da última atualização: |
14/12/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
SILVA, V. B.; DAHER, R. F.; ARAÚJO, M. S. B.; SOUZA, Y. P.; CASSARO, S.; MENEZES, B. R. S.; GRAVINA, L. M.; NOVO, A. A. C.; TARDIN, F. D.; AMARAL JÚNIOR, A. T. |
Afiliação: |
Universidade Estadual do Norte Fluminense Darcy Ribeiro; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Universidade Federal Rural do Rio de Janeiro; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Universidade Estadual do Norte Fluminense Darcy Ribeiro; FLAVIO DESSAUNE TARDIN, CNPMS; Universidade Estadual do Norte Fluminense Darcy Ribeiro. |
Título: |
Prediction of genetic gains by selection indices using mixed models in elephant grass for energy purposes. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Genetics and Molecular Research, Ribeirão Preto, v. 16, n. 3, p. 1-8, 2017. |
DOI: |
10.4238/gmr16039781 |
Idioma: |
Inglês |
Conteúdo: |
Genetically improved cultivars of elephant grass need to be adapted to different ecosystems with a faster growth speed and lower seasonality of biomass production over the year. This study aimed to use selection indices using mixed models (REML/BLUP) for selecting families and progenies within full-sib families of elephant grass (Pennisetum purpureum) for biomass production. One hundred and twenty full-sib progenies were assessed from 2014 to 2015 in a randomized block design with three replications. During this period, the traits dry matter production, the number of tillers, plant height, stem diameter, and neutral detergent fiber were assessed. Families 3 and 1were the best classified, being the most indicated for selection effect. Progenies 40, 45, 46, and 49 got the first positions in the three indices assessed in the first cut. The gain for individual 40 was 161.76% using Mulamba and Mock index. The use of selection indices using mixed models is advantageous in elephant grass since they provide high gains with the selection, which are distributed among all the assessed traits in the most appropriate situation to breeding programs. |
Palavras-Chave: |
Matriz de energia; Modelo misto. |
Thesagro: |
Capim elefante; Energia; Índice de Seleção. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/169052/1/Prediction-genetic.pdf
|
Marc: |
LEADER 02061naa a2200301 a 4500 001 2082611 005 2017-12-14 008 2017 bl uuuu u00u1 u #d 024 7 $a10.4238/gmr16039781$2DOI 100 1 $aSILVA, V. B. 245 $aPrediction of genetic gains by selection indices using mixed models in elephant grass for energy purposes.$h[electronic resource] 260 $c2017 520 $aGenetically improved cultivars of elephant grass need to be adapted to different ecosystems with a faster growth speed and lower seasonality of biomass production over the year. This study aimed to use selection indices using mixed models (REML/BLUP) for selecting families and progenies within full-sib families of elephant grass (Pennisetum purpureum) for biomass production. One hundred and twenty full-sib progenies were assessed from 2014 to 2015 in a randomized block design with three replications. During this period, the traits dry matter production, the number of tillers, plant height, stem diameter, and neutral detergent fiber were assessed. Families 3 and 1were the best classified, being the most indicated for selection effect. Progenies 40, 45, 46, and 49 got the first positions in the three indices assessed in the first cut. The gain for individual 40 was 161.76% using Mulamba and Mock index. The use of selection indices using mixed models is advantageous in elephant grass since they provide high gains with the selection, which are distributed among all the assessed traits in the most appropriate situation to breeding programs. 650 $aCapim elefante 650 $aEnergia 650 $aÍndice de Seleção 653 $aMatriz de energia 653 $aModelo misto 700 1 $aDAHER, R. F. 700 1 $aARAÚJO, M. S. B. 700 1 $aSOUZA, Y. P. 700 1 $aCASSARO, S. 700 1 $aMENEZES, B. R. S. 700 1 $aGRAVINA, L. M. 700 1 $aNOVO, A. A. C. 700 1 $aTARDIN, F. D. 700 1 $aAMARAL JÚNIOR, A. T. 773 $tGenetics and Molecular Research, Ribeirão Preto$gv. 16, n. 3, p. 1-8, 2017.
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Embrapa Milho e Sorgo (CNPMS) |
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