| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Biblioteca Rui Tendinha. |
|
Data corrente: |
28/04/2020 |
|
Data da última atualização: |
21/10/2020 |
|
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
|
Autoria: |
BRIOSCHI, P. F.; COSTA, A. F. da.; GALEANO, E. A. V.; BARBARA, W. P.de F.; ZANÚNCIO JUNIOR, J. S.; FORNAZIER, M. J.; PAULINI JÚNIOR, I. J.; ROSSI, D. A. |
|
Afiliação: |
Priscila Filete Brioschi; Andrea Ferreira da Costa, Incaper; Edileuza Aparecida Vital Galeano, Incaper; Woelpher Pierângelo de Freitas Barbara; José Salazar Zanuncio Junior, Incaper; Mauricio José Fornazier, Incaper; Inarei José Paulini Júnior; Drieli Aparecida Rossi. |
|
Título: |
Análise de custos do café arábica (Coffea arabica) em Venda Nova do Imigrante, ES. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
In: COSTA, A. F. da. (Ed.). Custos na agricultura da Região Serrana do Espírito Santo. São Carlos: Pedro & João Editores, cap. 3, p.33-41, 2020. |
|
Idioma: |
Português |
|
Conteúdo: |
A produção e o consumo de café aumentaram consideravelmente nos últimos 50 anos. Na atualidade, mais de 70 países produzem café, sendo que o
Brasil é o maior produtor (FAO, 2015). |
|
Palavras-Chave: |
Café arábica; Consumo; Custo; Espírito Santo (Estado); Produção; Região Serrana. |
|
Categoria do assunto: |
-- |
|
URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/123456789/4098/1/cafe-arabica-cap3.pdf
|
|
Marc: |
LEADER 01090naa a2200277 a 4500
001 1022207
005 2020-10-21
008 2020 bl uuuu u00u1 u #d
100 1 $aBRIOSCHI, P. F.
245 $aAnálise de custos do café arábica (Coffea arabica) em Venda Nova do Imigrante, ES.$h[electronic resource]
260 $c2020
520 $aA produção e o consumo de café aumentaram consideravelmente nos últimos 50 anos. Na atualidade, mais de 70 países produzem café, sendo que o Brasil é o maior produtor (FAO, 2015).
653 $aCafé arábica
653 $aConsumo
653 $aCusto
653 $aEspírito Santo (Estado)
653 $aProdução
653 $aRegião Serrana
700 1 $aCOSTA, A. F. da.
700 1 $aGALEANO, E. A. V.
700 1 $aBARBARA, W. P.de F.
700 1 $aZANÚNCIO JUNIOR, J. S.
700 1 $aFORNAZIER, M. J.
700 1 $aPAULINI JÚNIOR, I. J.
700 1 $aROSSI, D. A.
773 $tIn: COSTA, A. F. da. (Ed.). Custos na agricultura da Região Serrana do Espírito Santo. São Carlos: Pedro & João Editores, cap. 3, p.33-41, 2020.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Biblioteca Rui Tendinha (BRT) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
|
|
|
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
|
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Biblioteca Rui Tendinha (BRT) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
