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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha; Santa Teresa. |
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Data corrente: |
14/02/2014 |
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Data da última atualização: |
10/11/2020 |
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Autoria: |
TEIXEIRA, S. M.; THUNG, M. D. T. (Ed.). |
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Título: |
Sócio-economia e tecnologias de produção : o caso das cultivares melhoradas de feijão (Phaseolus vulgaris L.). |
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Ano de publicação: |
1994 |
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Fonte/Imprenta: |
Brasília-DF : EMBRAPA-SPI: EMBRAPA-SPSB, 1994. |
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Páginas: |
186p. |
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Descrição Física: |
il. |
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Idioma: |
Português |
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Conteúdo: |
Sócio-economia, produção e tecnologia de feijão no Brasil; Adoção de cultivares melhoradas de feijão no Estado do Espirito Santo; Adoção de cultivares melhoradas de feijão em Goiás; Adoção de cultivares melhoradas de feijão no Estado de Minas Gerais; Adoção de cultivares melhoradas de feijão no Estado do Rio de Janeiro; Adoção de cultivares melhoradas de feijão no Estado de Santa Catarina; Impacto da adoção de cultivares melhoradas de feijão em estados selecionados do Brasil. Neste estudo analisam-se os sistemas de produção em uso pelos agricultores em cinco Estados importantes produtores de feijão - ES, GO, MG, RJ e SC. Foram entrevistados um total de 710 produtores que utilizaram cultivares de feijão em diferenciados sistemas de cultivo |
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Palavras-Chave: |
Adoção; Aspecto econômico; Aspecto social; Brasil; Cerrado; Cultivar; Cultivar - Melhoramento; Economia agrícola; Economia rural; EMBRAPA; Feijão; Feijao - Aspecto economico; Feijoeiro; Grão; Melhoramento genético; Melhoramento genético vegetal; Produção; Produção agrícola; Produção Tecnológica; Semente; Sociologia rural; Transferência de tecnologia; Variedade. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01924nam a2200409 a 4500
001 1002053
005 2020-11-10
008 1994 bl uuuu 00u1 u #d
100 1 $aTEIXEIRA, S. M.
245 $aSócio-economia e tecnologias de produção$bo caso das cultivares melhoradas de feijão (Phaseolus vulgaris L.).
260 $aBrasília-DF : EMBRAPA-SPI: EMBRAPA-SPSB$c1994
300 $a186p.$cil.
520 $aSócio-economia, produção e tecnologia de feijão no Brasil; Adoção de cultivares melhoradas de feijão no Estado do Espirito Santo; Adoção de cultivares melhoradas de feijão em Goiás; Adoção de cultivares melhoradas de feijão no Estado de Minas Gerais; Adoção de cultivares melhoradas de feijão no Estado do Rio de Janeiro; Adoção de cultivares melhoradas de feijão no Estado de Santa Catarina; Impacto da adoção de cultivares melhoradas de feijão em estados selecionados do Brasil. Neste estudo analisam-se os sistemas de produção em uso pelos agricultores em cinco Estados importantes produtores de feijão - ES, GO, MG, RJ e SC. Foram entrevistados um total de 710 produtores que utilizaram cultivares de feijão em diferenciados sistemas de cultivo
653 $aAdoção
653 $aAspecto econômico
653 $aAspecto social
653 $aBrasil
653 $aCerrado
653 $aCultivar
653 $aCultivar - Melhoramento
653 $aEconomia agrícola
653 $aEconomia rural
653 $aEMBRAPA
653 $aFeijão
653 $aFeijao - Aspecto economico
653 $aFeijoeiro
653 $aGrão
653 $aMelhoramento genético
653 $aMelhoramento genético vegetal
653 $aProdução
653 $aProdução agrícola
653 $aProdução Tecnológica
653 $aSemente
653 $aSociologia rural
653 $aTransferência de tecnologia
653 $aVariedade
700 1 $aTHUNG, M. D. T.
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
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Fechar
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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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