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Registro Completo |
Biblioteca(s): |
Embrapa Amapá; Embrapa Amazônia Oriental; Embrapa Florestas; Embrapa Meio-Norte; Embrapa Roraima; Embrapa Semiárido; Embrapa Unidades Centrais. |
Data corrente: |
08/06/2010 |
Data da última atualização: |
27/09/2011 |
Tipo da produção científica: |
Autoria/Organização/Edição de Livros |
Autoria: |
FERREIRA, M. do S.; OLIVEIRA, L. C. de; SABOGAL, C.; MATTOS, M. M. de. |
Afiliação: |
MARIA DO SOCORRO GONCALVES FERREIRA, CPATU; LIA CUNHA DE OLIVEIRA, UFRA; CESAR SABOGAL, CIFOR; MARLI MARIA DE MATTOS, IDESP. |
Título: |
Manejo de florestas secundárias: aproveite e maneje bem sua capoeira, ela pode render mais benefícios para você, seus filhos e netos! |
Ano de publicação: |
2010 |
Fonte/Imprenta: |
Belém, PA: Embrapa Amazônia Oriental, 2010. |
Páginas: |
68 p. |
ISBN: |
978-85-87690-86-9 |
Idioma: |
Português |
Notas: |
Cartilha. |
Conteúdo: |
Características da capoeira; Manejo da capoeira; Passos para o manejo da capoeira; Técnicas de manejo da capoeira; Manutenção e proteção das áreas em manejo; Colheita de produtos; Importância das florestas secundárias para a manutenção dos animais; Nomes das espécies. |
Thesagro: |
Capoeira; Floresta; Madeira; Manejo; Vegetação Secundária. |
Categoria do assunto: |
-- K Ciência Florestal e Produtos de Origem Vegetal |
Marc: |
LEADER 00983nam a2200241 a 4500 001 1901427 005 2011-09-27 008 2010 bl uuuu 00u1 u #d 020 $a978-85-87690-86-9 100 1 $aFERREIRA, M. do S. 245 $aManejo de florestas secundárias$baproveite e maneje bem sua capoeira, ela pode render mais benefícios para você, seus filhos e netos! 260 $aBelém, PA: Embrapa Amazônia Oriental$c2010 300 $a68 p. 500 $aCartilha. 520 $aCaracterísticas da capoeira; Manejo da capoeira; Passos para o manejo da capoeira; Técnicas de manejo da capoeira; Manutenção e proteção das áreas em manejo; Colheita de produtos; Importância das florestas secundárias para a manutenção dos animais; Nomes das espécies. 650 $aCapoeira 650 $aFloresta 650 $aMadeira 650 $aManejo 650 $aVegetação Secundária 700 1 $aOLIVEIRA, L. C. de 700 1 $aSABOGAL, C. 700 1 $aMATTOS, M. M. de
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Embrapa Florestas (CNPF) |
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Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
05/07/2019 |
Data da última atualização: |
30/10/2019 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A. |
Afiliação: |
Leonardo Volpato, Universidade Federal de Viçosa; Rodrigo Silva Alves, Universidade Federal de Viçosa; Paulo Eduardo Teodoro, Universidade Federal de Mato Grosso do Sul; MARCOS DEON VILELA DE RESENDE, CNPF; Moysés Nascimento, Universidade Federal de Viçosa; Ana Carolina Campana Nascimento, Universidade Federal de Viçosa; Willian Hytalo Ludke, Universidade Federal de Viçosa; Felipe Lopes da Silva, Universidade Federal de Viçosa; Aluízio Borém, Universidade Federal de Viçosa. |
Título: |
Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p. |
DOI: |
10.1371/journal.pone.0215315 |
Idioma: |
Inglês |
Conteúdo: |
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny. MenosAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided... Mostrar Tudo |
Palavras-Chave: |
Bayesian-inference; Breeding values; Genomic selection; Inferência Bayesian; Mixed models; Modelo misto; Seed protein; Seleção genômica. |
Thesagro: |
Soja. |
Thesaurus NAL: |
Agronomic traits; Prediction; Soybeans. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199239/1/2019-M.Deon-PO-Multi-trait.pdf
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Marc: |
LEADER 02789naa a2200373 a 4500 001 2110400 005 2019-10-30 008 2019 bl uuuu u00u1 u #d 024 7 $a10.1371/journal.pone.0215315$2DOI 100 1 $aVOLPATO, L. 245 $aMulti-trait multi-environment models in the genetic selection of segregating soybean progeny.$h[electronic resource] 260 $c2019 520 $aAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny. 650 $aAgronomic traits 650 $aPrediction 650 $aSoybeans 650 $aSoja 653 $aBayesian-inference 653 $aBreeding values 653 $aGenomic selection 653 $aInferência Bayesian 653 $aMixed models 653 $aModelo misto 653 $aSeed protein 653 $aSeleção genômica 700 1 $aALVES, R. S. 700 1 $aTEODORO, P. E. 700 1 $aRESENDE, M. D. V. de 700 1 $aNASCIMENTO, M. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aLUDKE, W. H. 700 1 $aSILVA, F. L. da 700 1 $aBORÉM, A. 773 $tPLoS ONE$gv. 14, n. 4, e0215315, Apr. 2019. 22 p.
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