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Registro Completo |
Biblioteca(s): |
Embrapa Agrobiologia. |
Data corrente: |
28/10/2019 |
Data da última atualização: |
28/10/2019 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
PACHECO, R. S.; ALVES, B. J. R.; BODDEY, R. M.; FERREIRA, E. P. de B.; STRALIOTTO, R.; ARAUJO, A. P. |
Título: |
Measurement of biological N2 fixation in field-grown common bean cultivars in Brazilian Cerrado soil using 15N natural abundance method. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2019., 2019 |
Volume: |
v.2 |
Páginas: |
p. 492. |
Idioma: |
Português |
Notas: |
WCSS 2018. |
Conteúdo: |
The aim of this study was to estimate the BNF contribution for plant N nutrition by NA method in eight common bean cultivars with different agronomical traits at field conditions. |
Palavras-Chave: |
Rhizobia inoculation. |
Thesagro: |
Cerrado; Feijão; Fixação de Nitrogênio; Phaseolus Vulgaris. |
Categoria do assunto: |
S Ciências Biológicas |
Marc: |
LEADER 01060nam a2200265 a 4500 001 2113524 005 2019-10-28 008 2019 bl uuuu u0uu1 u #d 100 1 $aPACHECO, R. S. 245 $aMeasurement of biological N2 fixation in field-grown common bean cultivars in Brazilian Cerrado soil using 15N natural abundance method. 260 $aIn: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2019.$c2019 300 $ap. 492. v.2 490 $vv.2 500 $aWCSS 2018. 520 $aThe aim of this study was to estimate the BNF contribution for plant N nutrition by NA method in eight common bean cultivars with different agronomical traits at field conditions. 650 $aCerrado 650 $aFeijão 650 $aFixação de Nitrogênio 650 $aPhaseolus Vulgaris 653 $aRhizobia inoculation 700 1 $aALVES, B. J. R. 700 1 $aBODDEY, R. M. 700 1 $aFERREIRA, E. P. de B. 700 1 $aSTRALIOTTO, R. 700 1 $aARAUJO, A. P.
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Registro original: |
Embrapa Agrobiologia (CNPAB) |
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Registro Completo
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
25/07/2018 |
Data da última atualização: |
25/07/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 1 |
Autoria: |
SILVA, F. F.; JEREZ, E. A. Z.; RESENDE, M. D. V. de; VIANA, J. M. S.; AZEVEDO, C. F.; LOPES, P. S.; NASCIMENTO, M.; LIMA, R. O. de; GUIMARÃES, S. E. F. |
Afiliação: |
Fabyano Fonseca Silva, UFV; Elcer Albenis Zamora Jerez, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; José Marcelo Soriano Viana, UFV; Camila Ferreira Azevedo, UFV; Paulo Sávio Lopes, UFV; Moysés Nascimento, UFV; Rodrigo Oliveira de Lima, UFV; Simone Eliza Facioni Guimarães, UFV. |
Título: |
Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Journal of Applied Animal Research, v. 46, n. 1, p. 873-878, 2018. |
DOI: |
10.1080/09712119.2017.1415903 |
Idioma: |
Inglês |
Conteúdo: |
We combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ?LALDA?) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression ? BRR; Bayes A ? BA; Bayes B ? BB; Bayes C ? BC and Bayesian LASSO ? BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets. |
Palavras-Chave: |
x. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/180314/1/2018-M.Deon-JAAQR-Bayesian.pdf
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Marc: |
LEADER 02093naa a2200241 a 4500 001 2093541 005 2018-07-25 008 2018 bl uuuu u00u1 u #d 024 7 $a10.1080/09712119.2017.1415903$2DOI 100 1 $aSILVA, F. F. 245 $aBayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding.$h[electronic resource] 260 $c2018 520 $aWe combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ?LALDA?) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression ? BRR; Bayes A ? BA; Bayes B ? BB; Bayes C ? BC and Bayesian LASSO ? BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets. 653 $ax 700 1 $aJEREZ, E. A. Z. 700 1 $aRESENDE, M. D. V. de 700 1 $aVIANA, J. M. S. 700 1 $aAZEVEDO, C. F. 700 1 $aLOPES, P. S. 700 1 $aNASCIMENTO, M. 700 1 $aLIMA, R. O. de 700 1 $aGUIMARÃES, S. E. F. 773 $tJournal of Applied Animal Research$gv. 46, n. 1, p. 873-878, 2018.
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