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| 1. |  | ANGELINI, M. E.; FONTANA, A.; LANDI, A.; MERMUT, A. R.; MOREIRA, A. L.; LOPATKA, A.; LABAZ, B.; IRINA, B.; VANDENBYGAART, B.; OLGA, B.; PÁLKA, B.; SMRECZAK, B.; CLERICI, C.; PINHEIRO JÚNIOR, C. R.; FERGUSON, C.; OLGA, C.; VAN HUYSSTEEN, C. W.; MONGER, C.; WEI, D.; RODRÍGUEZ, D. M.; LINDBO, D.; NURSYAMSI, D.; CAHYANA, D.; BEAUDETTE, D.; NUREMBERG, E.; LIU, F.; FONTES, F.; OLIVEIRA, F. P. de; ZHANG, G.; OLEG, G.; PEREIRA, G.; SCHULZ, G.; VASQUES, G. M.; IAAICH, H.; MORRÁS, H. J. M.; HUSEIN, H. H.; GUTIÉRREZ DÍAZ, J. S.; IVELIC-SÁEZ, J.; KOBZA, J.; FUENTE, J. C. de la; HE, J.; NIKOLAI, K.; RODRÍGUEZ JIMÉNEZ, L. M.; WANG, L.; TENTI VUEGEN, L.; JIN, L.; MORETTI, L. M.; ANJOS, L. H. C. dos; SANTOS, L. A. C. dos; PFEIFFER, M.; PEREIRA, M. G.; GUEVARA SANTAMARIA, M.; BOLAÑOS-BENAVIDES, M.; DELL'ACQUA, M.; SAKSA, M.; COELHO, M. R.; CAMPOS, M. C. C.; NEVENA, M.; ORDOÑEZ DELGADO, N.; BATKHISHIG, O.; SERRATO ALCAREZ, P. K.; MOUSSSADEK, R.; DART, R. de O.; DALMOLIN, R. S. D.; OROZAKUNOVA, R.; RADIC, S.; TOMA, S.; WILLS, S. A.; ROECKER, S.; VALLE, S.; KIENAST-BROWN, S.; NAKISKO, S.; KUYPER, T. W.; SOLOVEI, V.; IVAN, V.; CHERLINKA, V.; MORA, V. R. de la; LEBED, V.; CARDONA, W. A.; GENG, X.; LI, Y.; ZHANG, Y.; SULAEMAN, Y.; ZALAVSKYI, Y.; YIGINI, Y.; TAKATA, Y.; SUN, Z. Global distribution and characteristics of black soils. In: FAO. Global status of black soils. Rome, 2022. cap. 2, p. 14-69.| Biblioteca(s): Embrapa Solos. |
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Registro Completo
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
03/03/2017 |
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Data da última atualização: |
03/03/2017 |
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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: |
FARIAS, F. J. C.; CARVALHO, L. P. de; SILVA FILHO, J. L. da; TEODORO, P. E. |
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Afiliação: |
FRANCISCO JOSE CORREIA FARIAS, CNPA; LUIZ PAULO DE CARVALHO, CNPA; JOAO LUIS DA SILVA FILHO, CNPA; PAULO EDUARDO TEODORO, UFV. |
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Título: |
Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 15, n. 2, 2016. |
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Idioma: |
Inglês |
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Conteúdo: |
Seed cotton yield is a trait governed by multiple genes that cause changes in the performance of genotypes depending on the cultivation environment. Breeding programs examine the genotype x environment interaction (GE) using precise statistical methods, such as AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction). The AMMI method combines the analysis of variance and principal components, to adjust the main effects (genotypes and environments) and the effects of GE interaction, respectively. The GGE biplot groups the genotype additive effect together with the multiplicative effect of the GE interaction, and submits both of these to the principal components analysis. The aim of this study was to investigate the association between the AMMI and GGE biplot methods and select cotton genotypes that simultaneously showed high productivity of seed cotton and stability in Mato Grosso environments. Trials were conducted with cotton cultivars in eight environments across Mato Grosso State in the 2008/2009 crop season. The experiment used a randomized block design with 16 genotypes and four replicates per genotype x environment combination. Data for seeds cotton productivity were analyzed by AMMI and GGE biplot methods. Both methods were concordant in the discrimination of environments and genotypes for phenotypic stability. The genotypes BRS ARAÇÁ and LD 05 CV had high seed cotton productivity and phenotypic stability, and could be grown in all environments across Mato Grosso State. MenosSeed cotton yield is a trait governed by multiple genes that cause changes in the performance of genotypes depending on the cultivation environment. Breeding programs examine the genotype x environment interaction (GE) using precise statistical methods, such as AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction). The AMMI method combines the analysis of variance and principal components, to adjust the main effects (genotypes and environments) and the effects of GE interaction, respectively. The GGE biplot groups the genotype additive effect together with the multiplicative effect of the GE interaction, and submits both of these to the principal components analysis. The aim of this study was to investigate the association between the AMMI and GGE biplot methods and select cotton genotypes that simultaneously showed high productivity of seed cotton and stability in Mato Grosso environments. Trials were conducted with cotton cultivars in eight environments across Mato Grosso State in the 2008/2009 crop season. The experiment used a randomized block design with 16 genotypes and four replicates per genotype x environment combination. Data for seeds cotton productivity were analyzed by AMMI and GGE biplot methods. Both methods were concordant in the discrimination of environments and genotypes for phenotypic stability. The genotypes BRS ARAÇÁ and LD 05 CV had high seed cotton productivity and pheno... Mostrar Tudo |
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Thesagro: |
Algodão; Genótipo; Gossypium hirsutum. |
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Thesaurus NAL: |
Cotton. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/156949/1/Biplot-analysis-of-phenotypic-stability.pdf
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Marc: |
LEADER 02178naa a2200205 a 4500
001 2065921
005 2017-03-03
008 2016 bl uuuu u00u1 u #d
100 1 $aFARIAS, F. J. C.
245 $aBiplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso.$h[electronic resource]
260 $c2016
520 $aSeed cotton yield is a trait governed by multiple genes that cause changes in the performance of genotypes depending on the cultivation environment. Breeding programs examine the genotype x environment interaction (GE) using precise statistical methods, such as AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction). The AMMI method combines the analysis of variance and principal components, to adjust the main effects (genotypes and environments) and the effects of GE interaction, respectively. The GGE biplot groups the genotype additive effect together with the multiplicative effect of the GE interaction, and submits both of these to the principal components analysis. The aim of this study was to investigate the association between the AMMI and GGE biplot methods and select cotton genotypes that simultaneously showed high productivity of seed cotton and stability in Mato Grosso environments. Trials were conducted with cotton cultivars in eight environments across Mato Grosso State in the 2008/2009 crop season. The experiment used a randomized block design with 16 genotypes and four replicates per genotype x environment combination. Data for seeds cotton productivity were analyzed by AMMI and GGE biplot methods. Both methods were concordant in the discrimination of environments and genotypes for phenotypic stability. The genotypes BRS ARAÇÁ and LD 05 CV had high seed cotton productivity and phenotypic stability, and could be grown in all environments across Mato Grosso State.
650 $aCotton
650 $aAlgodão
650 $aGenótipo
650 $aGossypium hirsutum
700 1 $aCARVALHO, L. P. de
700 1 $aSILVA FILHO, J. L. da
700 1 $aTEODORO, P. E.
773 $tGenetics and Molecular Research$gv. 15, n. 2, 2016.
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Embrapa Algodão (CNPA) |
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