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| 3. |  | SOUZA, M. B. A. de.; MANTOVANI, E. C.; SILVA, J. G. F. da.; VICENTE, M. R.; VIEIRA, G. H. S. Análise técnica da cafeicultura irrigada por pivô central no Norte do Espírito Santo e Extremo Sul da Bahia. Engenharia na Agricultura, viçosa, v. 19, n. 5, p. 450-458, 2011. Nota Técnica.| Biblioteca(s): Biblioteca Rui Tendinha. |
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 | Acesso ao texto completo restrito à biblioteca da Biblioteca Rui Tendinha. Para informações adicionais entre em contato com biblioteca@incaper.es.gov.br. |
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
19/06/2019 |
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Data da última atualização: |
25/08/2022 |
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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: |
FERRÃO, L. F. V.; FERRÃO, R. G.; FERRÃO, M. A. G.; FONSECA, A. F. A. da.; GARCIA, A. A. F. |
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Afiliação: |
Luís Felipe Ventorim Ferrão., Escola Superior de Agricultura Luiz de Queiroz (ESALQ).; Romário Gava Ferrão, Incaper; Maria Amélia Gava Ferrão, Incaper/Embrapa Café; Aymbiré Francisco Almeida da Fonseca, Incaper/Embrapa Café; Antonio Augusto Franco Garcia., Universidade de São Paulo (USP). |
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Título: |
A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Tree Genetics & Genomes, Germany, v. 13, n. 95, p. 13, 2017. |
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Páginas: |
13 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract Genomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-bysequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodnessof-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10?17% higher) and lower prediction errors than traditional GBLUP. The results may be used as basis for additional studies into the genus Coffea and can be expanded for similar perennial crops. MenosAbstract Genomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-bysequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodnessof-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10?17% higher) and lower prediction errors ... Mostrar Tudo |
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Palavras-Chave: |
Genomic selection. |
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Thesaurus NAL: |
GBLUP; Gbs; Genomic selection; Genotyping by sequencing; MET; Multi environment trials; Perennial crops. |
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Categoria do assunto: |
-- |
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Marc: |
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001 1021430
005 2022-08-25
008 2017 bl uuuu u00u1 u #d
100 1 $aFERRÃO, L. F. V.
245 $aA mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora.$h[electronic resource]
260 $c2017
300 $a13 p.
520 $aAbstract Genomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-bysequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodnessof-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10?17% higher) and lower prediction errors than traditional GBLUP. The results may be used as basis for additional studies into the genus Coffea and can be expanded for similar perennial crops.
650 $aGBLUP
650 $aGbs
650 $aGenomic selection
650 $aGenotyping by sequencing
650 $aMET
650 $aMulti environment trials
650 $aPerennial crops
653 $aGenomic selection
700 1 $aFERRÃO, R. G.
700 1 $aFERRÃO, M. A. G.
700 1 $aFONSECA, A. F. A. da.
700 1 $aGARCIA, A. A. F.
773 $tTree Genetics & Genomes, Germany$gv. 13, n. 95, p. 13, 2017.
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