| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Clima Temperado. |
|
Data corrente: |
19/12/2022 |
|
Data da última atualização: |
20/12/2022 |
|
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
|
Autoria: |
RASEIRA, M. do C. B.; FRANZON, R. C.; NAKASU, B. H. |
|
Afiliação: |
MARIA DO CARMO BASSOLS RASEIRA, CPACT; RODRIGO CEZAR FRANZON, CPACT; BONIFACIO HIDEYUKI NAKASU, CPACT. |
|
Título: |
Cultivares. |
|
Ano de publicação: |
2022 |
|
Fonte/Imprenta: |
In: RASEIRA, M. do C. B.; PEREIRA, J. F. M.; CARVALHO, F. L. C. (ed.). Cultivo do pessegueiro. Pelotas: Embrapa Clima Temperado, 2022. (Embrapa Clima Temperado. Sistemas de Produção, 4). ODS. |
|
Páginas: |
p. 40-78. |
|
Idioma: |
Português |
|
Conteúdo: |
Um dos aspectos de fundamental importância na implantação de um pomar é a escolha da cultivar a ser plantada, a qual depende essencialmente, das condições edafoclimáticas da área e da finalidade e características do mercado ao qual se destina a produção. Nesse capítulo são descritas, de forma resumida, as principais cultivares plantadas no Brasil. |
|
Thesagro: |
Fruta de Clima Temperado; Nectarina; Pêssego; Variedade. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1149945/1/22475.pdf
|
|
Marc: |
LEADER 01025naa a2200205 a 4500
001 2149945
005 2022-12-20
008 2022 bl uuuu u00u1 u #d
100 1 $aRASEIRA, M. do C. B.
245 $aCultivares.$h[electronic resource]
260 $c2022
300 $ap. 40-78.
520 $aUm dos aspectos de fundamental importância na implantação de um pomar é a escolha da cultivar a ser plantada, a qual depende essencialmente, das condições edafoclimáticas da área e da finalidade e características do mercado ao qual se destina a produção. Nesse capítulo são descritas, de forma resumida, as principais cultivares plantadas no Brasil.
650 $aFruta de Clima Temperado
650 $aNectarina
650 $aPêssego
650 $aVariedade
700 1 $aFRANZON, R. C.
700 1 $aNAKASU, B. H.
773 $tIn: RASEIRA, M. do C. B.; PEREIRA, J. F. M.; CARVALHO, F. L. C. (ed.). Cultivo do pessegueiro. Pelotas: Embrapa Clima Temperado, 2022. (Embrapa Clima Temperado. Sistemas de Produção, 4). ODS.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Clima Temperado (CPACT) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
17/11/2011 |
|
Data da última atualização: |
11/10/2017 |
|
Tipo da produção científica: |
Nota Técnica/Nota Científica |
|
Autoria: |
SILVA, F. F.; VARONA, L.; RESENDE, M. D. V. de; BUENO FILHO, J. S. S.; ROSA, G. J. M.; VIANA, J. M. S. |
|
Afiliação: |
Fabyano Fonseca Silva, UFV; Luis Varona, Universidad de Zaragoza; MARCOS DEON VILELA DE RESENDE, CNPF; Júlio Sílvio S. Bueno Filho, UFLA; Guilherme J. M. Rosa, University of Wisconsin; José Marcelo Soriano Viana, UFV. |
|
Título: |
A note on accuracy of Bayesian LASSO regression in GWS. |
|
Ano de publicação: |
2011 |
|
Fonte/Imprenta: |
Livestock Science, v. 142, p. 310-314, 2011. |
|
DOI: |
10.1016/j.livsci.2011.09.010 |
|
Idioma: |
Inglês |
|
Notas: |
Short communication. |
|
Conteúdo: |
Several genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, implying that the random ? method is more attractive, since it is much faster to use just one Gibbs sampler run, while the grid must to use one run for each fixed ? value. MenosSeveral genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, impl... Mostrar Tudo |
|
Palavras-Chave: |
Genome wide selection; Penalized regression; SNP markers. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 02366naa a2200241 a 4500
001 1906248
005 2017-10-11
008 2011 bl uuuu u00u1 u #d
024 7 $a10.1016/j.livsci.2011.09.010$2DOI
100 1 $aSILVA, F. F.
245 $aA note on accuracy of Bayesian LASSO regression in GWS.$h[electronic resource]
260 $c2011
500 $aShort communication.
520 $aSeveral genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, implying that the random ? method is more attractive, since it is much faster to use just one Gibbs sampler run, while the grid must to use one run for each fixed ? value.
653 $aGenome wide selection
653 $aPenalized regression
653 $aSNP markers
700 1 $aVARONA, L.
700 1 $aRESENDE, M. D. V. de
700 1 $aBUENO FILHO, J. S. S.
700 1 $aROSA, G. J. M.
700 1 $aVIANA, J. M. S.
773 $tLivestock Science$gv. 142, p. 310-314, 2011.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
