|
|
Registro Completo |
Biblioteca(s): |
Embrapa Agrobiologia; Embrapa Agropecuária Oeste; Embrapa Pantanal; Embrapa Roraima; Embrapa Soja; Embrapa Trigo; Embrapa Unidades Centrais. |
Data corrente: |
04/04/1997 |
Data da última atualização: |
25/09/2014 |
Tipo da produção científica: |
Circular Técnica |
Autoria: |
LANTMANN, A. F.; ROESSING, A. C.; SFREDO, G. J.; OLIVEIRA, M. C. N. de. |
Título: |
Adubacao fosfatada e potassica para a sucessao soja-trigo em latossolo roxo distrofico sob semeadura direta. |
Ano de publicação: |
1996 |
Fonte/Imprenta: |
Londrina: Embrapa-Soja, 1996. |
Páginas: |
44p. |
Série: |
(Embrapa-Soja. Circular Tecnica, 15). |
Idioma: |
Português |
Conteúdo: |
Adubação fosfatada para a soja; Adubação potássica para a soja; Adubação fosfatada para o trigo; Adubação potássica para o trigo; Efeitos da ausência de adubação para a soja na sucessão soja-trigo; Efeitos da adubação e da correção da acidez sobre os índices de fertilidade do solo; Acidez do solo; Disponibilidade de potássio; Disponibilidade de fósforo; Rendimento do trigo; Rendimento da soja; Análise econômica dos efeitos da adubação para o sistema soja-trigo; Considerações finais; Recomendações; Tabela de unidades. |
Palavras-Chave: |
Brasil; Direct sowing; Double-cropping; Fertilization; Manuring; Paraná; Semeadura direta; Soybean; Succession; Sucessão; Sucessao de cultura. |
Thesagro: |
Adubação; Adubo; Glycine Max; Pesquisa; Soja; Solo; Trigo. |
Thesaurus Nal: |
Brazil; no-tillage; research; sequential cropping; soil; wheat. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/35349/1/1996-Circular-Tecnica.n.15-Trigo-295x21-05.05.2011-OK.pdf
|
Marc: |
LEADER 01697nam a2200457 a 4500 001 1445885 005 2014-09-25 008 1996 bl uuuu u0uu1 u #d 100 1 $aLANTMANN, A. F. 245 $aAdubacao fosfatada e potassica para a sucessao soja-trigo em latossolo roxo distrofico sob semeadura direta. 260 $aLondrina: Embrapa-Soja$c1996 300 $a44p. 490 $a(Embrapa-Soja. Circular Tecnica, 15). 520 $aAdubação fosfatada para a soja; Adubação potássica para a soja; Adubação fosfatada para o trigo; Adubação potássica para o trigo; Efeitos da ausência de adubação para a soja na sucessão soja-trigo; Efeitos da adubação e da correção da acidez sobre os índices de fertilidade do solo; Acidez do solo; Disponibilidade de potássio; Disponibilidade de fósforo; Rendimento do trigo; Rendimento da soja; Análise econômica dos efeitos da adubação para o sistema soja-trigo; Considerações finais; Recomendações; Tabela de unidades. 650 $aBrazil 650 $ano-tillage 650 $aresearch 650 $asequential cropping 650 $asoil 650 $awheat 650 $aAdubação 650 $aAdubo 650 $aGlycine Max 650 $aPesquisa 650 $aSoja 650 $aSolo 650 $aTrigo 653 $aBrasil 653 $aDirect sowing 653 $aDouble-cropping 653 $aFertilization 653 $aManuring 653 $aParaná 653 $aSemeadura direta 653 $aSoybean 653 $aSuccession 653 $aSucessão 653 $aSucessao de cultura 700 1 $aROESSING, A. C. 700 1 $aSFREDO, G. J. 700 1 $aOLIVEIRA, M. C. N. de
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Soja (CNPSO) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
18/09/2018 |
Data da última atualização: |
18/09/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; MORAIS, O. P.; MAGALHÃES JÚNIOR, A. M. |
Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR, UFG; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; ORLANDO PEIXOTO DE MORAIS, CNPAF; ARIANO MARTINS DE MAGALHAES JUNIOR, CPACT. |
Título: |
Single-step reaction norm models for genomic prediction in multienvironment recurrent selection trials. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Crop Science, v. 58, n. 2, p. 592-607, Mar./Apr. 2018. |
ISSN: |
0011-183X |
DOI: |
10.2135/cropsci2017.06.0366 |
Idioma: |
Inglês |
Conteúdo: |
In recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs. MenosIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bi... Mostrar Tudo |
Palavras-Chave: |
Multienvironment prediction. |
Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Progênie; Seleção Recorrente. |
Thesaurus NAL: |
Genomics; Plant breeding; Recurrent selection; Rice; Variety trials. |
Categoria do assunto: |
G Melhoramento Genético |
Marc: |
LEADER 02811naa a2200337 a 4500 001 2095887 005 2018-09-18 008 2018 bl uuuu u00u1 u #d 022 $a0011-183X 024 7 $a10.2135/cropsci2017.06.0366$2DOI 100 1 $aMORAIS JÚNIOR, O. P. 245 $aSingle-step reaction norm models for genomic prediction in multienvironment recurrent selection trials.$h[electronic resource] 260 $c2018 520 $aIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs. 650 $aGenomics 650 $aPlant breeding 650 $aRecurrent selection 650 $aRice 650 $aVariety trials 650 $aArroz 650 $aMelhoramento Genético Vegetal 650 $aOryza Sativa 650 $aProgênie 650 $aSeleção Recorrente 653 $aMultienvironment prediction 700 1 $aDUARTE, J. B. 700 1 $aBRESEGHELLO, F. 700 1 $aCOELHO, A. S. G. 700 1 $aMORAIS, O. P. 700 1 $aMAGALHÃES JÚNIOR, A. M. 773 $tCrop Science$gv. 58, n. 2, p. 592-607, Mar./Apr. 2018.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Expressão de busca inválida. Verifique!!! |
|
|