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Registro Completo |
Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
05/12/2016 |
Data da última atualização: |
05/12/2016 |
Tipo da produção científica: |
Publicação em Anais de Congresso |
Autoria: |
VOLPI, P. S.; FONSECA, A. F. A. da.; COMÉRIO, M.; KAULZ, M.; FERRÃO, R. G.; FERRÃO, M. A. G.; COSTA, E. B.; NUNES, W. |
Afiliação: |
Paulo Sérgio Volpi, Incaper; Aymbiré Francisco Almeida da Fonseca, Incaper/Embrapa Café; Marcone Comério, Incaper; Marciano Kaulz, Incaper; Romário Gava Ferrão, Incaper; Maria Amélia Gava Ferrão, Incaper/Embrapa Café; UFES; CBP&D-Café/INCAPER. |
Título: |
Avaliação de diferentes tipos de condução inicial no cultivo de coffea canephora. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE PESQUISAS CAFEEIRAS, 42., 2016, Serra Negra, SP. Produzir mais café, com economia, só com boa tecnologia: resumos. Minas Gerais : Fundação Procafé, 2016 |
Idioma: |
Português |
Palavras-Chave: |
Café Conilon; Coffea canephora; Programada de ciclo – PPC. |
Categoria do assunto: |
-- |
URL: |
http://biblioteca.incaper.es.gov.br/digital/bitstream/item/2528/1/BRT-DiferentestiposconducaoConilon-verdin.pdf
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Marc: |
LEADER 00798nam a2200217 a 4500 001 1013531 005 2016-12-05 008 2016 bl uuuu u01u1 u #d 100 1 $aVOLPI, P. S. 245 $aAvaliação de diferentes tipos de condução inicial no cultivo de coffea canephora.$h[electronic resource] 260 $aIn: CONGRESSO BRASILEIRO DE PESQUISAS CAFEEIRAS, 42., 2016, Serra Negra, SP. Produzir mais café, com economia, só com boa tecnologia: resumos. Minas Gerais : Fundação Procafé$c2016 653 $aCafé Conilon 653 $aCoffea canephora 653 $aProgramada de ciclo – PPC 700 1 $aFONSECA, A. F. A. da. 700 1 $aCOMÉRIO, M. 700 1 $aKAULZ, M. 700 1 $aFERRÃO, R. G. 700 1 $aFERRÃO, M. A. G. 700 1 $aCOSTA, E. B. 700 1 $aNUNES, W.
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Registro Completo |
Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
04/07/2018 |
Data da última atualização: |
12/04/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
FERRÃO, L. F. V.; FERRÃO, R. G.; FERRÃO, M. A. G.; FONSECA, A. F. A. da.; CARBONETTO, P.; STEPHENS, M.; GARCIA, A. A. F. |
Afiliação: |
Luis Felipe Ventorim Ferrão, ESALQ; Romário Gava Ferrão, Incaper; Maria Amélia Gava Ferrão, Incaper/Embrapa Café; Aymbiré Francisco Almeida da Fonseca, Incaper/Embrapa Café; Peter Carbonetto, Research Computing Center, University of Chicago; Matthew Stephens, Research Computing Center, University of Chicago; Antonio Augusto Franco Garcia, ESALQ. |
Título: |
Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Heredity, june 2018. |
Idioma: |
Português |
Conteúdo: |
Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee. MenosGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our... Mostrar Tudo |
Palavras-Chave: |
Cafe conilon. |
Thesaurus NAL: |
Coffea canephora; Genomic. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/item/4674/1/s41437-018-0105-y.pdf
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Marc: |
LEADER 02393naa a2200229 a 4500 001 1020469 005 2024-04-12 008 2018 bl uuuu u00u1 u #d 100 1 $aFERRÃO, L. F. V. 245 $aAccurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.$h[electronic resource] 260 $c2018 520 $aGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee. 650 $aCoffea canephora 650 $aGenomic 653 $aCafe conilon 700 1 $aFERRÃO, R. G. 700 1 $aFERRÃO, M. A. G. 700 1 $aFONSECA, A. F. A. da. 700 1 $aCARBONETTO, P. 700 1 $aSTEPHENS, M. 700 1 $aGARCIA, A. A. F. 773 $tHeredity, june 2018.
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