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Registro Completo |
Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
07/04/2017 |
Data da última atualização: |
25/09/2017 |
Tipo da produção científica: |
Documentos |
Autoria: |
GALEANO, E. A. V.; VINAGRE, D.; OLIVEIRA, N. A. de.; BORGES, V. A. J.; CHIPOLESCH, J. M. A. |
Afiliação: |
Edileuza Aparecida Vital Galeano, Incaper; Danieltom Vinagre, Estagiário do Incaper; Niceleia Araujo de Oliveira, Incaper; Vanessa Alves Justino Borges, Incaper; João Marcos Augusto Chipolesch, Incaper. |
Título: |
Síntese da produção agropecuária do Espírito Santo 2014/2015. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Vitória, ES : Incaper, 2017. |
Páginas: |
232 p. |
Série: |
(Incaper. Documentos 247). |
ISSN: |
1519-0528 |
Idioma: |
Português |
Conteúdo: |
A Síntese da Produção Agropecuária no Espírito Santo 2014/2015 apresenta uma exposição geral e concisa dos dados da produção agropecuária capixaba referente aos anos de 2014 e 2015. O documento também traz figuras em forma de mapas da distribuição espacial da produção nos municípios. No que se refere ao acompanhamento dos preços, são apresentadas gráficos com os preços dos produtos levantados pelo Incaper nos anos 2014 e 2015 conforme metodologia descrita em Galeano et al 2016b). Esta segunda edição apresenta também os dados da produção agropecuária de todos os municípios capixabas. |
Palavras-Chave: |
Agricultura; Espírito Santo (Estado); Incaper; Pecuária; Silvicultura. |
Categoria do assunto: |
-- |
URL: |
http://biblioteca.incaper.es.gov.br/digital/bitstream/item/2699/1/BRT-sintese-2014-2015-final.pdf
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Marc: |
LEADER 01314nam a2200253 a 4500 001 1014728 005 2017-09-25 008 2017 bl uuuu 00u1 u #d 022 $a1519-0528 100 1 $aGALEANO, E. A. V. 245 $aSíntese da produção agropecuária do Espírito Santo 2014/2015.$h[electronic resource] 260 $aVitória, ES : Incaper$c2017 300 $a232 p. 490 $a(Incaper. Documentos 247). 520 $aA Síntese da Produção Agropecuária no Espírito Santo 2014/2015 apresenta uma exposição geral e concisa dos dados da produção agropecuária capixaba referente aos anos de 2014 e 2015. O documento também traz figuras em forma de mapas da distribuição espacial da produção nos municípios. No que se refere ao acompanhamento dos preços, são apresentadas gráficos com os preços dos produtos levantados pelo Incaper nos anos 2014 e 2015 conforme metodologia descrita em Galeano et al 2016b). Esta segunda edição apresenta também os dados da produção agropecuária de todos os municípios capixabas. 653 $aAgricultura 653 $aEspírito Santo (Estado) 653 $aIncaper 653 $aPecuária 653 $aSilvicultura 700 1 $aVINAGRE, D. 700 1 $aOLIVEIRA, N. A. de. 700 1 $aBORGES, V. A. J. 700 1 $aCHIPOLESCH, J. M. A.
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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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