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Registro Completo |
Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
15/12/2021 |
Data da última atualização: |
30/12/2021 |
Tipo da produção científica: |
Periódico |
Título: |
INCAPER EM REVISTA. |
Complemento do título: |
Indicações geográficas e certificação na agropecuária capixaba. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Vitória: Incaper, v. 11 e 12, jan. a dez. 2021. |
Páginas: |
115p. |
ISSN: |
2179-5304 |
DOI: |
10.54682/ier |
Idioma: |
Português |
Conteúdo: |
Nesta edição do Incaper em Revista aborda o tema Indicações Geográficas e Certificação na Agropecuária Capixaba e traz importantes informações sobre o assunto para o Estado do Espírito Santo em nove artigos e duas entrevistas que foram produzidos por profissionais envolvidos com o processo de registro de Indicação Geográfica (IG) de diferentes produtos. |
Palavras-Chave: |
Café conilon; Certificação na Agropecuária; Espírito Santo (Estado); IG Café Montanhas do Espírito Santo; IG capixaba; IG da pimenta-rosa; IG do Socol; Indicação geográfica; Pimenta-rosa; Socol. |
Thesagro: |
Aroeira; Café; Café Robusta; Certificação de Produto; Coffea Arábica; Coffea Canephora; Legislação; Legislação de Alimento. |
Thesaurus NAL: |
Coffea; Coffea arabica var. arabica. |
Categoria do assunto: |
Q Segurança Alimentar |
URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/123456789/4274/1/INCAPER-EMREVISTA-v.11ev12-2021.pdf
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Marc: |
LEADER 01395nam a2200373 a 4500 001 1023769 005 2021-12-30 008 2021 bl uuuu u00u1 u #d 022 $a2179-5304 024 7 $a10.54682/ier$2DOI 245 $aINCAPER EM REVISTA.$h[electronic resource] 260 $aVitória: Incaper, v. 11 e 12, jan. a dez. 2021.$c2021 300 $a115p. 520 $aNesta edição do Incaper em Revista aborda o tema Indicações Geográficas e Certificação na Agropecuária Capixaba e traz importantes informações sobre o assunto para o Estado do Espírito Santo em nove artigos e duas entrevistas que foram produzidos por profissionais envolvidos com o processo de registro de Indicação Geográfica (IG) de diferentes produtos. 650 $aCoffea 650 $aCoffea arabica var. arabica 650 $aAroeira 650 $aCafé 650 $aCafé Robusta 650 $aCertificação de Produto 650 $aCoffea Arábica 650 $aCoffea Canephora 650 $aLegislação 650 $aLegislação de Alimento 653 $aCafé conilon 653 $aCertificação na Agropecuária 653 $aEspírito Santo (Estado) 653 $aIG Café Montanhas do Espírito Santo 653 $aIG capixaba 653 $aIG da pimenta-rosa 653 $aIG do Socol 653 $aIndicação geográfica 653 $aPimenta-rosa 653 $aSocol
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Registro |
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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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