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 | Acesso ao texto completo restrito à biblioteca da Embrapa Soja. Para informações adicionais entre em contato com valeria.cardoso@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
20/08/2010 |
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Data da última atualização: |
25/01/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ZOBIOLE, L. H. S.; OLIVEIRA JUNIOR, R. S. de; KREMER, R. J.; MUNIZ, A. S.; OLIVEIRA JUNIOR, A. de. |
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Afiliação: |
LUIZ HENRIQUE SAES ZOBIOLE, UEM; RUBEM SILVÉRIO DE OLIVEIRA JUNIOR, UEM; ROBERT JOHN KREMER, USDA - Missouri; ANTONIO SARAIVA MUNIZ, UEM; ADILSON DE OLIVEIRA JUNIOR, CNPSo. |
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Título: |
Nutrient accumulation and photosynthesis in glyphosate-resistant soybeans is reduced under glyphosate use. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
Journal of Plant Nutrition, London, v. 33, n. 12, p. 1860-1873, 2010. |
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DOI: |
10.1080/01904167.2010.491890 |
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Idioma: |
Português |
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Conteúdo: |
Global production of glyphosate-resistant (GR) soybean [Glycine max (L.) Merr.] continues to increase annually; however, there are no particular specific fertilizer recommendations for the transgenic varieties used in this system largely because reports of glyphosate effects on mineral nutrition of GR soybeans are lacking. Several metabolites or degradation products of glyphosate have been identified or postulated to cause undesirable effects on GR soybeans. In this work we used increasing glyphosate rates in different application on cv. 'BRS 242 GR' in order to evaluate photosynthetic parameters, macro- and micronutrient uptake and accumulation and shoot and root dry biomass production. Increasing glyphosate rates revealed a significant decrease in photosynthesis, macro and micronutrients accumulation in leaf tissues and also decreases in nutrient uptake. The reduced biomass in GR soybeans represents additive effects from the decreased photosynthetic parameters as well as lower availability of nutrients in tissues of the glyphosate treated plants. |
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Thesagro: |
Erva daninha; Herbicida; Soja. |
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Thesaurus Nal: |
Herbicides; Soybeans; Weeds. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 01807naa a2200253 a 4500
001 1860557
005 2017-01-25
008 2010 bl --- 0-- u #d
024 7 $a10.1080/01904167.2010.491890$2DOI
100 1 $aZOBIOLE, L. H. S.
245 $aNutrient accumulation and photosynthesis in glyphosate-resistant soybeans is reduced under glyphosate use.
260 $c2010
520 $aGlobal production of glyphosate-resistant (GR) soybean [Glycine max (L.) Merr.] continues to increase annually; however, there are no particular specific fertilizer recommendations for the transgenic varieties used in this system largely because reports of glyphosate effects on mineral nutrition of GR soybeans are lacking. Several metabolites or degradation products of glyphosate have been identified or postulated to cause undesirable effects on GR soybeans. In this work we used increasing glyphosate rates in different application on cv. 'BRS 242 GR' in order to evaluate photosynthetic parameters, macro- and micronutrient uptake and accumulation and shoot and root dry biomass production. Increasing glyphosate rates revealed a significant decrease in photosynthesis, macro and micronutrients accumulation in leaf tissues and also decreases in nutrient uptake. The reduced biomass in GR soybeans represents additive effects from the decreased photosynthetic parameters as well as lower availability of nutrients in tissues of the glyphosate treated plants.
650 $aHerbicides
650 $aSoybeans
650 $aWeeds
650 $aErva daninha
650 $aHerbicida
650 $aSoja
700 1 $aOLIVEIRA JUNIOR, R. S. de
700 1 $aKREMER, R. J.
700 1 $aMUNIZ, A. S.
700 1 $aOLIVEIRA JUNIOR, A. de
773 $tJournal of Plant Nutrition, London$gv. 33, n. 12, p. 1860-1873, 2010.
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Registro original: |
Embrapa Soja (CNPSO) |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
05/07/2019 |
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Data da última atualização: |
30/10/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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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. |
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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. |
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Título: |
Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p. |
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DOI: |
10.1371/journal.pone.0215315 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Bayesian-inference; Breeding values; Genomic selection; Inferência Bayesian; Mixed models; Modelo misto; Seed protein; Seleção genômica. |
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Thesagro: |
Soja. |
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Thesaurus NAL: |
Agronomic traits; Prediction; Soybeans. |
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Categoria do assunto: |
G Melhoramento Genético |
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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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