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3. | | VERARDO, L. L.; SILVA, F. F.; VARONA, L.; RESENDE, M. D. V. de; BASTIAANSEN, J. W. M.; LOPES, P. S.; GUIMARÃES, S. E. F. Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs. Journal of Applied Genetics, v. 56, n. 1, p. 123-132, Feb. 2015. Biblioteca(s): Embrapa Florestas. |
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4. | | VENTURA, H. T.; SILVA, F. F e; VARONA, L.; FIGUEIREDO, E. A. P. de; COSTA, E. V.; SILVA, L. P. da; VENTURA. R.; LOPES, P. S. Comparing multi-trait Poisson and Gaussian Bayesian models for genetic evaluation of litter traits in pigs. Livestock Science, v. 176, p. 47-53, 2015. Biblioteca(s): Embrapa Suínos e Aves. |
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5. | | BRITO, L. C.; CASELLAS, J.; VARONA, L; LOPES, P. S.; VENTURA, H. T.; PEIXOTO, M. G. C. D.; LÁZARO, S. F.; SILVA, F. F. Genetic evaluation of age at first calving for Guzerá beef cattle using linear, threshold, and survival Bayesian models. Journal of Animal Science, v. 96, n. 7, p. 2517-2524, 2018. Biblioteca(s): Embrapa Gado de Leite. |
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6. | | ARBEX, W. A.; SILVA, F. F. e; SILVA, M. V. G. B.; BORGES, C. C. H.; OLIVEIRA, F. C. de; VARONA, L.; VERNEQUE, R. da S. Decision Support in Attribute Selection with Machine Learning Approach. In: CONFERENCIA IBÉRICA DE SISTEMAS Y TECNOLOGÍAS DE INFORMACION, 9., 2014, Barcelona. Actas... Barcelona: Aisti; Salle, 2014. CISTI 2014 Biblioteca(s): Embrapa Gado de Leite. |
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8. | | SILVA, F. F. e; ZAMBRANO, M. F. B.; VARONA, L.; GLÓRIA, L. S.; LOPES, P. S.; SILVA, M. V. G. B.; ARBEX, W. A.; LÁZARO, S. F.; RESENDE, M. D. V. de; GUIMARÃES, S. E. F. Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves. Scientia Agricola, v. 74, n. 1, 2017. 7 P. Biblioteca(s): Embrapa Florestas; Embrapa Gado de Leite. |
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Registro Completo
Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
20/08/2020 |
Data da última atualização: |
04/11/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
OLIVEIRA, I. C. M.; GUILHEN, J. H. S.; RIBEIRO, P. C. de O.; GEZAN, S. A.; SCHAFFERT, R. E.; SIMEONE, M. L. F.; DAMASCENO, C. M. B.; CARNEIRO, J. E. de S.; CARNEIRO, P. C. S.; PARRELLA, R. A. da C.; PASTINA, M. M. |
Afiliação: |
Isadora Cristina Martins Oliveira; José Henrique Soler Guilhen; Pedro César de Oliveira Ribeiro, Universidade Federal de Viçosa; Salvador Alejandro Gezan, VSN International; ROBERT EUGENE SCHAFFERT, CNPMS; MARIA LUCIA FERREIRA SIMEONE, CNPMS; CYNTHIA MARIA BORGES DAMASCENO, CNPMS; José Eustáquio de Souza Carneiro, Universidade Federal de Viçosa; Pedro Crescêncio Souza Carneiro, Universidade Federal de Viçosa; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; MARIA MARTA PASTINA, CNPMS. |
Título: |
Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Field Crops Research, v. 257, 107929, 2020. |
Idioma: |
Inglês |
Conteúdo: |
Biomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomassyield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but alsohighly affected by the environment. The main goal of this study was to investigate the genotype-by-environmentinteraction (G × E) and the stability of sorghum hybrids evaluated for FBY across different locations and years,using factor analytic (FA) mixed models and environmental covariates. Pairwise genetic correlations betweenenvironments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiledthat solely three factors explained more than 79% of the genetic variance, and that more than 60% of theenvironments were clustered in thefirst factor. Moderate correlations were found between some environmentalcovariates and the loadings of FA models for environments, suggesting the possible factors to explain the high G× E between environments clustered in a given factor. For example: precipitation, minimum temperature andspeed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation andaltitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybridsmore responsive to a set of environments, as well as hybrids specifically adapted to a given environment. Finally,FA models can be successfully used to identify the main environmental factors affecting G × E, such as minimumtemperature, precipitation, solar radiation, crop growth cycle and altitude. MenosBiomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomassyield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but alsohighly affected by the environment. The main goal of this study was to investigate the genotype-by-environmentinteraction (G × E) and the stability of sorghum hybrids evaluated for FBY across different locations and years,using factor analytic (FA) mixed models and environmental covariates. Pairwise genetic correlations betweenenvironments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiledthat solely three factors explained more than 79% of the genetic variance, and that more than 60% of theenvironments were clustered in thefirst factor. Moderate correlations were found between some environmentalcovariates and the loadings of FA models for environments, suggesting the possible factors to explain the high G× E between environments clustered in a given factor. For example: precipitation, minimum temperature andspeed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation andaltitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybridsmore responsive to a set of environments, as well as hybrids specifically adapted to a given environment. Finally,FA models can be successfully used to identify the main environment... Mostrar Tudo |
Thesagro: |
Bioenergia; Melhoramento Genético Vegetal; Sorghum Bicolor. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/215435/1/Genotype-environment.pdf
|
Marc: |
LEADER 02518naa a2200277 a 4500 001 2124452 005 2020-11-04 008 2020 bl uuuu u00u1 u #d 100 1 $aOLIVEIRA, I. C. M. 245 $aGenotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates.$h[electronic resource] 260 $c2020 520 $aBiomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomassyield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but alsohighly affected by the environment. The main goal of this study was to investigate the genotype-by-environmentinteraction (G × E) and the stability of sorghum hybrids evaluated for FBY across different locations and years,using factor analytic (FA) mixed models and environmental covariates. Pairwise genetic correlations betweenenvironments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiledthat solely three factors explained more than 79% of the genetic variance, and that more than 60% of theenvironments were clustered in thefirst factor. Moderate correlations were found between some environmentalcovariates and the loadings of FA models for environments, suggesting the possible factors to explain the high G× E between environments clustered in a given factor. For example: precipitation, minimum temperature andspeed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation andaltitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybridsmore responsive to a set of environments, as well as hybrids specifically adapted to a given environment. Finally,FA models can be successfully used to identify the main environmental factors affecting G × E, such as minimumtemperature, precipitation, solar radiation, crop growth cycle and altitude. 650 $aBioenergia 650 $aMelhoramento Genético Vegetal 650 $aSorghum Bicolor 700 1 $aGUILHEN, J. H. S. 700 1 $aRIBEIRO, P. C. de O. 700 1 $aGEZAN, S. A. 700 1 $aSCHAFFERT, R. E. 700 1 $aSIMEONE, M. L. F. 700 1 $aDAMASCENO, C. M. B. 700 1 $aCARNEIRO, J. E. de S. 700 1 $aCARNEIRO, P. C. S. 700 1 $aPARRELLA, R. A. da C. 700 1 $aPASTINA, M. M. 773 $tField Crops Research$gv. 257, 107929, 2020.
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Embrapa Milho e Sorgo (CNPMS) |
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