AMMI and GGE Biplot for genotype <i>×</i> environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

Neisse, Anderson Cristiano; Kirch, Jhessica Letícia; Hongyu, Kuang

doi:10.2478/bile-2018-0008

Cited by 64 publications

(63 citation statements)

References 25 publications

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“…Especially, E3 had the shortest vector as compared to the rest environments and recorded above average mean grain yield, therefore, it is the most ideal environment for the tested bread wheat genotypes. Similar results were reported by Golkari et al (2016) and Neisse et al (2018).…”

Section: Stability Analysis By Ammi Modelssupporting

confidence: 92%

AMMI and GGE biplot analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes at moisture deficit environment of Wollo, Ethiopia

Mekonnen¹,

Mekbib²,

Gashaw³

2019

J. Agric. Sci. Pract.

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Twenty-two bread wheat varieties were tested at six locations of eastern Amhara region. The experiment was laid-out using Randomized Complete Block (RCB) design and replicated three times with the objective of estimating the magnitude of Genotype by Environment (GE) interactions for grain yield and stability of bread wheat genotypes. Individual environment and combined analysis of variance were carried out using Genstat software 18th edition and Least Significant Difference (LSD) was employed to separate means. Combined ANOVA for grain yield showed significant difference among genotypes, environments, and GE interactions. Genotypes G1, G9 and G16 out-smarted in grain yield, providing mean grain yield of 3.60, 3.56, and 3.55 tha -1 , respectively. The stability was measured by AMMI and GGE biplot. AMMI-1 select most adapted genotypes such as G9 and G16 for E3 and G17 for E1. G12, G6, G3, G19, G7 and G11 genotypes were suitable for all environments. AMMI-2 biplot showed E1, E5 and E6 contributed large interaction effects while E2, E3 and E4 contributed small interaction effects. GGE biplot identify G9, G16 and G19 for E1, E3, E6 and E5 and G17, G21, G14 and G15 for E1 and E4. G6, G12 and G1 genotypes were good for all environments. Thus, due attention should be given while selecting bread wheat genotypes for the target environments.

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Section: Stability Analysis By Ammi Modelssupporting

confidence: 92%

AMMI and GGE biplot analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes at moisture deficit environment of Wollo, Ethiopia

Mekonnen¹,

Mekbib²,

Gashaw³

2019

J. Agric. Sci. Pract.

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“…The use of principal component analysis in biplots will minimize overlapping of variations so that the group determination can be more objective (Mattjik and Sumertajaya 2011; Leite and Oliveira 2015). Therefore, this analysis can facilitate the determination of characters with the same direction variance to the main characters (Kose et al 2018), especially when using the orthogonal polygonal grouping concept of the outlier object (Leite and Oliveira 2015; Neisse et al 2018). Based on this analysis,these five characters which have the same direction with the productivity can be as the best secondary character candidates in the selection The result of biplot analysis also showed that the maximum temperature of the leaves was the only character with a variance direction in contrast to the productivity group (Figure 1).…”

Section: Resultsmentioning

confidence: 99%

Multivariate analysis to determine secondary characters in selecting adaptive hybrid corn lines under drought stress

et al. 2020

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Abstract. Fadhli N, Farid M, Rafiuddin, Efendi R, Azrai M, Anshori MF. 2020. Multivariate analysis to determine secondary characters in selecting adaptive hybrid corn lines under drought stress. Biodiversitas 21: 3617-3624. The development of adaptive hybrid corn varieties under drought stress needs an effective selection. Multivariate analysis has been reported can increase the effectiveness of selection in plants by attaching the secondary characters in the selection. Therefore, this concept also can be applied to develop adaptive corn varieties under drought stress. The objectives of this study are to determine the main secondary characters and select the best hybrid lines adaptive to drought stress. The experiment was arranged by a nested design, where replications nested under two environmental conditions, namely normal and drought stress. The main factor was genotypes consisted of 30 genotypes and was repeated three times. Moreover, the observations of this research consisted of 20 variables. The result of this research showed that the weight of harvested cob was an effective secondary character as a selection criterion along with productivity in selecting adaptive maize genotypes under drought stress. The number of green leaves was the character outside of the yield component could be as an alternative secondary character besides the weight of harvested cob. The selection results based on the weight of the harvested cob and productivity resulted in 12 hybrid corn lines considered adaptive to drought stress.

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“…In order to explain the phenotypic variations, it is highly dependent on the effect of the environments. Explaining this diversity is upwards sophisticated by the fact that all genotypes don't react in the same way as change in circles and the two environments do not have exactly the same conditions (Neisse et al, 2018). If the performance of genotypes changes in different location, then the interaction of the genotype location is an important factor in plant breeding strategies.…”

Section: Introductionmentioning

confidence: 99%

Proficiency of Biplot Methods (Ammi and Gge) in the Appraisal of Triticale Genotypes in Multiple Environments

Kendal¹

2019

Appl. Ecol. Env. Res.

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The AMMI (additive main effect and multiplicative interaction) and GGE (genotype, genotype x environment) biplot analyses were used to evaluate and identify stability and yield of Triticale genotypes at three different locations throughout two years (2014-15-2015-16). The AMMI analysis of variance showed significant genotype, environment and GE interaction and indicated 1.31, 98.40 and 0.28% of total variation, respectively. The GGE bi-plot analysis indicated 78.19% of the total variation (PC1 (priciple component) 50.01%, and PC2 26.08%). This study has been useful to discriminate genotypes with superior and stable yield evaluated by the AMMI analysis and yield stability index incorporating the AMMI stability value and yield capacity in a single non-parametric index. The AMMI analysis indicated that G4, G8 (candidate) and G6 were found to be quite promising genotypes. In the GGE biplot analysis genotypes were investigated in two mega-environments, and the first megaenvironment covered E3, E5 and E6, and the second mega-environment covered E1, E2 and E4. The genotypes G6, G8, G9 and G11 were the wining genotypes in ME (mega-environment) I, G3, G4 and G12 and in ME II. The GGE and AMMI biplot approaches let us to describe the best genotypes, and G8 to be stable and high yielding for both ME, G6 only for ME I, G4 only for ME II and can be recommended to release as a cultivar.

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AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

Cited by 64 publications

References 25 publications

AMMI and GGE biplot analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes at moisture deficit environment of Wollo, Ethiopia

AMMI and GGE biplot analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes at moisture deficit environment of Wollo, Ethiopia

Multivariate analysis to determine secondary characters in selecting adaptive hybrid corn lines under drought stress

Proficiency of Biplot Methods (Ammi and Gge) in the Appraisal of Triticale Genotypes in Multiple Environments

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