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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
23/03/2023 |
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Data da última atualização: |
11/07/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERRAO, M. A. G.; FONSECA, A. F. A. da; VOLPI, P. S.; SOUZA, L. C. de; COMÉRIO, M.; VERDIN FILHO, A. C.; RIVA-SOUZA, E. M.; MUNOZ, P. R.; FERRÃO, R. G.; FERRÃO, L. F. V. |
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Afiliação: |
MARIA AMELIA GAVA FERRAO, CNPCa; AYMBIRE F. A. DA FONSECA, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; PAULO S. VOLPI, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; LUCIMARA C. DE SOUZA, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; MARCONE COMÉRIO, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; ABRAÃO C. VERDIN FILHO, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; ELAINE M. RIVA-SOUZA, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; PATRICIO R. MUNOZ, UNIVERSITY OF FLORIDA; ROMÁRIO G. FERRÃO, MULTIVIX GROUP; LUÍS FELIPE V. FERRÃO, UNIVERSITY OF FLORIDA. |
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Título: |
Genomic-assisted breeding for climate-smart coffee. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
The Plant Genome, e20321, 2023. |
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Páginas: |
19 p. |
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DOI: |
https://doi.org/10.1002/tpg2.20321 |
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Idioma: |
Inglês |
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Conteúdo: |
Coffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from diferente geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes. |
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Thesaurus Nal: |
Climate; Coffea canephora var. laurentii; Disease resistance; Plant breeding. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1152628/1/Genomic8208assisted-breeding-for-climate8208smart-coffee.pdf
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Marc: |
LEADER 02317naa a2200301 a 4500
001 2152628
005 2023-07-11
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/tpg2.20321$2DOI
100 1 $aFERRAO, M. A. G.
245 $aGenomic-assisted breeding for climate-smart coffee.$h[electronic resource]
260 $c2023
300 $a19 p.
520 $aCoffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from diferente geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.
650 $aClimate
650 $aCoffea canephora var. laurentii
650 $aDisease resistance
650 $aPlant breeding
700 1 $aFONSECA, A. F. A. da
700 1 $aVOLPI, P. S.
700 1 $aSOUZA, L. C. de
700 1 $aCOMÉRIO, M.
700 1 $aVERDIN FILHO, A. C.
700 1 $aRIVA-SOUZA, E. M.
700 1 $aMUNOZ, P. R.
700 1 $aFERRÃO, R. G.
700 1 $aFERRÃO, L. F. V.
773 $tThe Plant Genome, e20321, 2023.
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Registro original: |
Embrapa Café (CNPCa) |
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Registro Completo
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
03/05/2005 |
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Data da última atualização: |
03/08/2018 |
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Autoria: |
DUARTE, J. B.; VENCOVSKY, R. |
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Afiliação: |
João Batista Duarte, Universidade Federal de Goiás - UFG/Escola de Agronomia e Engenharia de Alimentos; Roland Vencovsky, Universidade de São Paulo - Usp/Escola Superior de Agricultura “Luiz de Queiroz” - Esalq/Departamento de Genética. |
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Título: |
Spatial statistical analysis and selection of genotypes in plant breeding. |
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Ano de publicação: |
2005 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 40, n. 2, p. 107-114, fev. 2005 |
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Idioma: |
Inglês |
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Notas: |
Título em português: Seleção de genótipos e análise estatística espacial no melhoramento de plantas. |
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Conteúdo: |
The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained. |
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Palavras-Chave: |
augmented design; autocorrelação; correlated data; dados correlacionados; delineamento aumentado; geoestatística; information recovery; mixed model; modelo misto; recuperação de informação. |
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Thesaurus NAL: |
autocorrelation; geostatistics. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/107908/1/Spatial.pdf
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Marc: |
LEADER 02107naa a2200289 a 4500
001 1113848
005 2018-08-03
008 2005 bl uuuu u00u1 u #d
100 1 $aDUARTE, J. B.
245 $aSpatial statistical analysis and selection of genotypes in plant breeding.
260 $c2005
500 $aTítulo em português: Seleção de genótipos e análise estatística espacial no melhoramento de plantas.
520 $aThe objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.
650 $aautocorrelation
650 $ageostatistics
653 $aaugmented design
653 $aautocorrelação
653 $acorrelated data
653 $adados correlacionados
653 $adelineamento aumentado
653 $ageoestatística
653 $ainformation recovery
653 $amixed model
653 $amodelo misto
653 $arecuperação de informação
700 1 $aVENCOVSKY, R.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 40, n. 2, p. 107-114, fev. 2005
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Embrapa Unidades Centrais (AI-SEDE) |
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