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Registro Completo |
Biblioteca(s): |
Embrapa Agropecuária Oeste; Embrapa Arroz e Feijão; Embrapa Cerrados; Embrapa Soja; Embrapa Unidades Centrais. |
Data corrente: |
06/04/2006 |
Data da última atualização: |
26/10/2009 |
Autoria: |
MARTINS, E de S.; REATTO, A.; CARVALHO JÚNIOR, O. A. de; GUIMARÃES, R. F. |
Título: |
Ecologia de paisagem: conceitos e aplicações potenciais no Brasil. |
Ano de publicação: |
2004 |
Fonte/Imprenta: |
Planaltina, DF: Embrapa Cerrados, 2004. |
Páginas: |
35 p. |
Série: |
(Embrapa Cerrados. Documentos, 121). |
Idioma: |
Português |
Conteúdo: |
ABSTRACT: The paper treats of the Landscape Ecology and of their potential applications in Brazil. The theme presents a punctual approach in Brazil, but it can be of great importance in the ecological studies and int the definition of public politics of biological conservation and of the relationship with the cultural landscapes. |
Palavras-Chave: |
Brasil; Caracterização; Characterization; Landscape; Paisagem. |
Thesagro: |
Cerrado; Ecologia; Meio Ambiente. |
Thesaurus Nal: |
Brazil; ecology; environment. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CPAC-2009/26898/1/doc_121.pdf
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Marc: |
LEADER 01116nam a2200301 a 4500 001 1569511 005 2009-10-26 008 2004 bl uuuu u0uu1 u #d 100 1 $aMARTINS, E de S. 245 $aEcologia de paisagem$bconceitos e aplicações potenciais no Brasil. 260 $aPlanaltina, DF: Embrapa Cerrados$c2004 300 $a35 p. 490 $a(Embrapa Cerrados. Documentos, 121). 520 $aABSTRACT: The paper treats of the Landscape Ecology and of their potential applications in Brazil. The theme presents a punctual approach in Brazil, but it can be of great importance in the ecological studies and int the definition of public politics of biological conservation and of the relationship with the cultural landscapes. 650 $aBrazil 650 $aecology 650 $aenvironment 650 $aCerrado 650 $aEcologia 650 $aMeio Ambiente 653 $aBrasil 653 $aCaracterização 653 $aCharacterization 653 $aLandscape 653 $aPaisagem 700 1 $aREATTO, A. 700 1 $aCARVALHO JÚNIOR, O. A. de 700 1 $aGUIMARÃES, R. F.
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Registro original: |
Embrapa Cerrados (CPAC) |
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Registro Completo
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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