Relative Discriminating Powers of GGE and AMMI Models in the Selection of Tropical Soybean (Glycine max L. Merr.) Genotypes

Amira, Julius Olayiwola; Ojo, D. K.; Ariyo, O. J.; Oduwaye, O.A.; Ayo-Vaugha, M.A.

doi:10.3923/ijpbg.2013.139.145

Cited by 28 publications

(41 citation statements)

References 3 publications

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“…The G1 and G4 genotypes were the most unstable and generated average yields higher than those of G23, at 3490.05 and 3683.62 kg/ ha, respectively. Genotype G7 showed the greatest yield and the greatest averages for most of the environments studied, and a higher average yield of 5916.67 kg/ha was obtained in Sinop in season 2011/2012 (Table 3), which is consistent with the results reported by Amira et al (2013), who observed that the most productive genotypes were among the most unstable. Unstable genotypes respond better to environmental oscillations.…”

Section: Resultssupporting

confidence: 82%

“…This result is lower than that observed by Amira et al (2013) (86.6%) and similar to that found by Asfaw et al (2009) (61.50%). When using the first two principal components, four clusters of environments (mega-environments) were formed using the GGE biplot methodology (Figure 3), corroborating the results obtained by Amira et al (2013).…”

Section: Figurasupporting

confidence: 71%

“…Several researchers have utilized this methodology for soybean cultivation (Meotti et al, 2012;Amira et al, 2013;Polizel et al, 2013). The GGE biplot, which is a modification of AMMI, is superior to the method from which it originates as it considers the effects of the genotypes along with the G x E interaction, while AMMI estimates them as additive effects (Yan et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods

Sousa¹,

Hamawaki²,

Nogueira³

et al. 2015

Genet. Mol. Res.

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ABSTRACT. In the final phases of new soybean cultivar development, lines are cultivated in several locations across multiple seasons with the intention of identifying and selecting superior genotypes for quantitative traits. In this context, this study aimed to study the genotype-byenvironment interaction for the trait grain yield (kg/ha), and to evaluate the adaptability and stability of early-cycle soybean genotypes using the additive main effects and multiplicative interaction (AMMI) analysis,

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Section: Resultssupporting

confidence: 82%

Section: Figurasupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods

Sousa¹,

Hamawaki²,

Nogueira³

et al. 2015

Genet. Mol. Res.

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“…Essa representação simultânea de cada genótipo e ambiente, além de fornecer informações sobre a estabilidade e a adaptabilidade fenotípica, permite o zoneamento agronômico e a escolha de locais-chave para o processo de avaliação e seleção de cultivares. Em soja, esse tipo de análise vem sendo bastante utilizado (Yan, 2001;Asfaw et al, 2009;Meotti et al, 2012;Martins et al, 2012;Amira et al, 2013).…”

Section: Introductionunclassified

Análise AMMI da produtividade de grãos em linhagens de soja selecionadas para resistência à ferrugem asiática

Yokomizo¹,

Duarte

Vello

et al. 2013

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi quantificar os efeitos da interação genótipo x ambiente (GxE) sobre a produtividade de grãos em progênies de soja pré-selecionadas para resistência à ferrugem asiática (Phakopsora pachyrhizi). Doze ensaios de avaliação de progênies (linhagens F 6 e F 7 ) foram conduzidos em diferentes ambientes (combinação de locais, anos e tratamentos fungicidas para controle de doenças de final de ciclo, incluindo ou não a ferrugem). A análise "additive main effects and multiplicative interaction" (AMMI) capturou, como padrão da interação GxE, 57% da variação associada aos resíduos de não aditividade, dos quais 44% foram retidos no primeiro componente principal de interação e o restante, no segundo. O primeiro componente associou-se a diferenças entre os anos de avaliação, o que denota imprevisibilidade na predição. O segundo componente, no entanto, associou-se ao manejo diferenciado do cultivo, no que se refere ao controle ou não das doenças. Entre os genótipos de ampla adaptabilidade produtiva, as linhagens USP 02-16.045 e USP 10-10 apresentaram desempenho destacado.Termos para indexação: Glycine max, Phakopsora pachyrhizi, biplot, interação genótipo x ambiente. AMMI analysis of grain yield in soybean lines selected for resistance to Asian rustAbstract -The objective of this work was to quantify the effects of genotype x environment (GxE) interaction on grain yield in soybean progenies pre-selected for resistance to Asian soybean rust (Phakopsora pachyrhizi). Twelve trials evaluating progenies (lines F 6 and F 7 ) were carried out in different environments (combination of locations, years, and fungicide treatments to control late season diseases, including or not rust). Additive main effects and multiplicative interaction (AMMI) analysis captured, as GxE interaction pattern, 57% of the variance associated with the residues of non-additivity, of which 44% were retained in the first principal component of interaction, and the remainder in the second. The first component was associated with the differences between the years of evaluation, which denotes unpredictability. The second component, however, was associated with different crop managements, related to the control or not of the diseases. Among genotypes with wide yield adaptability, the USP 02-16.045 and USP 10-10 lineages stood out.

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“…This factor together with the intrinsic characteristics of genotype x environment interaction (G x E) culminate in phenotype expression. Breeders through plant breeding seek to mitigate the effect of this interaction in the development of adapted and stable cultivars for various regions and climate conditions (Amira et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Adaptability and stability of soybean cultivars for grain yield and seed quality

et al. 2017

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ABSTRACT. This study aimed at verifying the adaptability and stability of soybean cultivars, considering the grain yield and quality of seeds, adopting univariate and multivariate approaches. The experiments were conducted in two crops, three environments, in 2013/2014 and 2014/2015 crop seasons, in the county of Inconfidentes, Lavras, and Patos de Minas, in the Minas Gerais State, Brazil. We evaluated 17 commercial soybean cultivars. For adaptability and stability evaluations, the Graphic and GGE biplot methods were employed. Previously, a selection index was estimated based on the sum of the standardized variables (Z index). The data relative to grain yield, mass of one thousand grain, uniformity test (sieve retention), and germination test were standardized (Z ij ) per cultivar. With the sum of Z ij , we obtained the selection index for the four traits evaluated together. In the Graphic method evaluation, cultivars NA 7200 RR and CD 2737 RR presented the highest values for selection index Z. By the GGE biplot method, we verified that cultivar NA 7200 RR presented greater stability in both univariate evaluations, for grain yield, and for selection index Z.

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Relative Discriminating Powers of GGE and AMMI Models in the Selection of Tropical Soybean (Glycine max L. Merr.) Genotypes

Cited by 28 publications

References 3 publications

Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods

Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods

Análise AMMI da produtividade de grãos em linhagens de soja selecionadas para resistência à ferrugem asiática

Adaptability and stability of soybean cultivars for grain yield and seed quality

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