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| Registros recuperados : 1 | |
| 1. |  | FIDALGO, E. C. C.; SCHULER, A. E.; TURETTA, A. P. D.; PEDREIRA, B. da C. C. G.; VEZZANI, F. M.; HISSA, H. R.; FERREIRA, J. N.; MONTEIRO, J. M. G.; GARCIA, J. R.; MENDONÇA, M. M. de; UZEDA, M. C.; IKEMOTO, S. M.; CAMPANHA, M. M.; PRADO, R. B. Serviços ecossistêmicos no contexto da produção e consumo sustentáveis. In: TORRES, L. A.; CAMPOS, S. K. (ed.). Megatendências da Ciência do Solo 2030. Brasília, DF: Embrapa, 2022. E-book. cap. 7.| Biblioteca(s): Embrapa Agrobiologia; Embrapa Amazônia Oriental; Embrapa Milho e Sorgo; Embrapa Solos. |
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| Registros recuperados : 1 | |
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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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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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