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| 161. |  | MATIELLO, J. B.; SATINATO, R.; MIGUEL, A. E.; PAULINO, A. J.; PAULINI, A. E.; STEVANATO, S. G.; BRAGANÇA, J. B.; UBIRATAN, V.; BARROS, A. S. A.; RUSSO, A. F. A moderna cafeicultura de montanha : como plantar e tratar os cafezais em regiões acidentadas. Rio de Janeiro : Fundação Procafé, 1994. 22 p.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 163. | | DE MUNER, L. H.; ZANDONADI, C. U.; VICOSI, D. B.; PAULA, E. de.; GUARÇONI, R. G.; FORNAZIER, M. J. Avaliação do aspecto económico da sustentabilidade de sistemas de cultivo da cafeicultura arábia familiar no Espírito Santo, Brasil. In: SIMPÓSIO INCAPER PESQUISA, 1. , Vitória, ES. Avaliação do aspecto económico da sustentabilidade de sistemas de cultivo da cafeicultura arábia familiar no Espírito Santo, Brasil. Editores, Andre Guarçoni Martins ... [et al]., Vitória, ES : Incaper, 2022. Anais... Vitória, ES : Incaper, p. 51, 2022.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 164. | | SILVA, T. C. R. da.; FERRÃO, L. F. V.; FERRÃO, R. G.; FERRÃO, M. A. G.; CECON, P. R.; TÓFFANO, J. L.; FONSECA, A. F. A. da.; VOLPI, P. S.; VERDIN FILHO, A. C.; MENDONÇA, R. F. de. Avaliação de clones de café conilon no Sul do Estado do Espírito Santo pelo método Tocher. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 5., 2009, Guarapari. O melhoramento e os novos cenários da agricultura: anais. Vitória: SBMP: Incaper, 2009. 1 CD-ROM. (Incaper. Documentos, 11). CBMP 2009. 4p.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 165. | | ROMÃO, W.; CORREIA, R. M.; PEREIRA, L. L.; ARAÚJO, J. B. S.; PADOVAN, M. da P.; PARTELLI, F. L.; FILGUEIRAS, P. R.; LACERDA JR, V. Análise do café conilon cultivado em sistemas agroflorestais por análise sensorial, espectrometria de massas com ionização por electrospray e ressonância ciclotrônica de íons por transformada de fourier e espectroscopia na região do infravermelhoo próximo portátil. In: CONGRESSO CAPIXABA DE PESQUISA AGROPECUÁRIA, 1., Vitória, ES. Anais 2021 : congresso capixaba de pesquisa agropecuária [recurso eletrônico]. Vitória, ES: Incaper, 2021. color. PDF ; 25,4 MB. E-book, no formato PDF. (Incaper, Documentos, 289). Pedro Luís Pereira Teixeira de Carvalho, Carlos Henrique Rodrigues de Oliveira, José Aires Ventura, Marcos Vinicius Winckler Caldeira e Romário Gava Ferrão, editores. p. 297| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 166. | | FORNAZIER, M. J.; VOLPI, P. S.; FERRÃO, M. A. G.; VERDIN FILHO, A. C.; FERRÃO, R. G.; FONSECA, A. F. A. da.; MIGUEL, G. S.; PEREIRA, A. A.; FAZUOLI, L. C. Broca dos ramos, Xylosandrus compactus, em cafés robusta no Espírito Santo. In: CONGRESSO BRASILEIRO DE PESQUISAS CAFEEIRAS, 35., 2009, Araxá. Trabalhos apresentados... Varginha, MG: Fundação PROCAFÉ, 2009.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 171. | | VERDIN FILHO, A. C.; DE MUNER, L. H.; FERRÃO, R. G.; FONSECA, A. F. A. da.; FERRÃO, M. A. G.; FORNAZIER, M. J.; VOLPI, P. S.; SILVA, J. G. F. da.; KROHLING, C. A.; COSTA, H.; MARRE, W. B.; COMÉRIO, M.; PERINNI, J. L.; SOUZA, T. da S. de. Cafeicultura sustentável: boas práticas de produção para o cafeeiro conilon no Espírito Santo. Vitória, ES: Incaper, 2023. Não paginado. (Incaper. Documentos, 304). Folder técnico.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 177. | | PREZOTTI, L. C.; BRAGANÇA, S. M.; GUARÇONI M., A.; LANI, J. A. Calagem e adubação. In: FERRÃO, R. G.; FONSECA, A. F. A. da.; BRAGANÇA, S. M.; FERRÃO, M. A. G.; DE MUNER, L. H. (Ed.). Café Conilon. Vitória: Incaper, 2007. 328-343 p.| Biblioteca(s): Biblioteca Rui Tendinha. |
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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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