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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
01/12/2016 |
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Data da última atualização: |
01/12/2016 |
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Tipo da produção científica: |
Publicação em Anais de Congresso |
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Autoria: |
FAVARATO, L. F.; DOUSSEAU, S.; BUFFON, S. B.; BARKER, D. L.; SILVA, J. F. da.; OLIVEIRA, F. de T. G. de.; ARANTES, L. de O.; CALATRONI, D. |
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Afiliação: |
Luiz Fernando Favarato, Incaper; Sara Dousseau Arantes, Incaper; Stanley Bravo Buffon, EMBRAPA/Incaper; Dayane Littig Barker, CEUNES-UFES; Jasmini Fonseca da Silva, PIBIC/FAPES/Incaper; Felipe de Tássio Gonçalves de Oliveira, EMBRAPA/Incaper; Lucio de Oliveira Arantes, Incaper; Dominique Calatroni, PIBIC/FAPES/Incaper. |
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Título: |
Avaliação do crescimento inicial de diferentes pesos e mudas do abacaxizeiro cv. 'Vitória'. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA E TECNOLÓGICA (SICT) DO INCAPER, 1., 2016. JORNADA DE INICIAÇÃO CIENTÍFICA, DESENVOLVIMENTO TECNOLÓGICO E INOVAÇÃO DO IFES, 11., 2016. Venda Nova do Imigrante, ES : IFES; Incaper, 2016. |
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Idioma: |
Português |
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Conteúdo: |
Objetivou-se com este trabalho aferir o crescimento inicial de diferentes pesos de mudas do abacaxizeiro cv. ?Vitória?. O delineamento experimental utilizado foi em blocos casualizados no esquema de parcelas subdivididas no tempo. Os tratamentos utilizados foram faixa 1 (pesos entre 301 a 400g) e faixa 2 (pesos entre 401 a 500g) nas parcelas e época de avaliação aos 90, 120 e 150 dias após o plantio nas subparcelas, com quatro repetições. Mudas com mais reservas apresentaram acentuado crescimento vegetativo atingindo maior altura de planta e diâmetro de caule nas épocas avaliadas. A realização de novos estudos possibilitaria verificar se maiores faixas de pesos, ao longo do tempo, tem relação direta com a produtividade e qualidade de frutos provenientes dos plantios de mudas do tipo rebentão. |
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Palavras-Chave: |
Abacaxizeiro; Mudas; Variedade Vitória. |
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Categoria do assunto: |
-- |
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URL: |
http://biblioteca.incaper.es.gov.br/digital/bitstream/item/2507/1/I-SICT-PIBIC-009.pdf
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Marc: |
LEADER 01686nam a2200229 a 4500
001 1013458
005 2016-12-01
008 2016 bl uuuu u01u1 u #d
100 1 $aFAVARATO, L. F.
245 $aAvaliação do crescimento inicial de diferentes pesos e mudas do abacaxizeiro cv. 'Vitória'.$h[electronic resource]
260 $aIn: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA E TECNOLÓGICA (SICT) DO INCAPER, 1., 2016. JORNADA DE INICIAÇÃO CIENTÍFICA, DESENVOLVIMENTO TECNOLÓGICO E INOVAÇÃO DO IFES, 11., 2016. Venda Nova do Imigrante, ES : IFES; Incaper$c2016
520 $aObjetivou-se com este trabalho aferir o crescimento inicial de diferentes pesos de mudas do abacaxizeiro cv. ?Vitória?. O delineamento experimental utilizado foi em blocos casualizados no esquema de parcelas subdivididas no tempo. Os tratamentos utilizados foram faixa 1 (pesos entre 301 a 400g) e faixa 2 (pesos entre 401 a 500g) nas parcelas e época de avaliação aos 90, 120 e 150 dias após o plantio nas subparcelas, com quatro repetições. Mudas com mais reservas apresentaram acentuado crescimento vegetativo atingindo maior altura de planta e diâmetro de caule nas épocas avaliadas. A realização de novos estudos possibilitaria verificar se maiores faixas de pesos, ao longo do tempo, tem relação direta com a produtividade e qualidade de frutos provenientes dos plantios de mudas do tipo rebentão.
653 $aAbacaxizeiro
653 $aMudas
653 $aVariedade Vitória
700 1 $aDOUSSEAU, S.
700 1 $aBUFFON, S. B.
700 1 $aBARKER, D. L.
700 1 $aSILVA, J. F. da.
700 1 $aOLIVEIRA, F. de T. G. de.
700 1 $aARANTES, L. de O.
700 1 $aCALATRONI, D.
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
04/07/2018 |
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Data da última atualização: |
12/04/2024 |
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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.; CARBONETTO, P.; STEPHENS, M.; GARCIA, A. A. F. |
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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. |
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Título: |
Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, june 2018. |
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Idioma: |
Português |
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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 |
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Palavras-Chave: |
Cafe conilon. |
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Thesaurus NAL: |
Coffea canephora; Genomic. |
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Categoria do assunto: |
G Melhoramento Genético |
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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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Biblioteca Rui Tendinha (BRT) |
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