GGE biplots to analyze soybean multi-environment yield trial data in north Western Ethiopia

Atnaf, Mulugeta; Kidane, Sisay; Abadi, Seltene; Fisha, Zelalem

doi:10.5897/jpbcs13.0403

Cited by 31 publications

(18 citation statements)

References 20 publications

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“…The results of this study imply that environment and GEI are important in controling the expression of yield trait (Gedif et al, 2014). Similar findings in the same crop were also obtained in various studies (Asfaw et al, 2009;Gurmu et al, 2009;Bueno et al, 2013;Atnaf et al, 2013;Jandong et al, 2011;Bhartiya et al, 2017).…”

Section: Discussionsupporting

confidence: 89%

“…GEI is a common phenomenon in a multi-environment yield trials (Hagos and Abbay, 2013), and can be defined as the failure of genotypes to achieve consistent performance (stability) across different environments (Baker, 1988). It has been reported that the GEI may reduces the correlation between phenotype and genotype as well as complicates in breeding program, such as during the testing and selection of superior genotypes (Rao et al, 2011;Atnaf et al, 2013;Hagos and Abbay, 2013;Kumar et al, 2014). The significant presence of GEI has been showed in previous studies in rice (Samonte et al, 2005), sorghum (Rakshit et al, 2010), maize (Tonk et al, 2011), potato (Gedif et al, 2014), and soybean (Kandil et al, 21012;Adie et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

Krisnawati

Adie

2019

ipas

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Soybean in Indonesia is grown in diverse agro-ecological environments. The performance of soybean yield often varies due to significant genotype × environment interaction (GEI), therefore the yield stability of performance is an important consideration in the breeding program. The aim of the research was to exploring the GEI pattern and yield stability of soybean promising lines in the tropics using GGE (Genotype and Genotype by Environment Interaction) biplot method. A total of 16 soybean promising lines were evaluated in ten environments during 2016 growing season. The experiment was arranged in a randomized completely block design with four replicates. The analysis of variance revealed that environments (E) explained the highest percentage of variation (51.45%), meanwhile the genotypes (G) and genotype × environment interactions (GEI) contributed for 3.24%, and 14.59% of the total variation, respectively. Seed yield of 16 soybean promising lines ranged from 2.41 to 2.83 t.ha-1 with an average of 2.74 t.ha-1. Joint effects of genotype and interaction (G+GE) which was partitioned using GGE biplot analysis showed that the first two components were significant, explaining 60.88% (37.89% PC1 and 22.98% PC2) of the GGE sum of squares. Indonesia can be divided into at least four putative mega environments for soybean production. The GGE biplot identified G10 as high yielding and stable promising line, thus recommended to be developed in multi-environment in tropical regions of Indonesia.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

Krisnawati

Adie

2019

ipas

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“…In the AMMI analysis, the first principal component (PCI) and second principal component (PCII) explained 34.06% and 13.49% of the total G×E variation (47.55%), respectively (Table 3). The large G×E effects depicted that the performance of soybean genotypes was different at different locations (Atnaf et al, 2013). The graphical method GGE biplot was employed to approximate and display the GGE of a multi environment trials (MET).The GGE refers to genotypic main effects (G) and G×E interaction (GE) plays a crucial role in cultivar evaluation in multi-locational trials and the GGE biplot has many visual interpretations in comparison to AMMI that particularly allows visualization of any crossovers in the G×E interaction (Karimizadeh et al, 2013).…”

Section: Resultsmentioning

confidence: 99%

AMMI and GGE biplot analysis of multi environment yield trial of soybeanin North Western Himalayan state Uttarakhand of India

Bhartiya

Aditya

Singh

et al. 2016

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The investigation was carried out to study Genotype × Environment (G×E) interaction for seed yield in 36 soybean genotypes including check PS1092 over 3 diverse environments represented by different altitudes in Uttarakhand. Grain yield performances of soybean genotypes were evaluated during Kharif 2013 season using a randomized complete block design. The AMMI analysis indicated that environment, genotypes and genotype by environment interactions had significantly affected seed yield and accounted for 9.76, 28.97 and 47.55% of the total variation, respectively. GGE biplot clearly displayed interrelationships between test locations as well as genotypes and facilitated visual comparisons based on Principal Component Analysis (PCA). The first two principal components PCI and PCII were used to create a two-dimensional GGE biplot that accounted for 45.68 and 38.88% variations respectively and based on discriminating and representative ability, E2 (Majhera) was most suitable location for selecting generally adapted genotypes. Soybean genotype C1 (PS1539) was identified as ideal genotype with high yield and low G×E interaction i.e. high stability.

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“…This 66.73% attributes was Figure 3. The GGE biplot is an effective method for which-won-where pattern and superiorly performing stable genotypes displaying via GEI vs. ME's visualizing (Yan et al, 2007;Atnaf et al, 2013;Massaine et al, 2018). The results of GGE biplot showed that G3, G5, G4, G12, G9, and G10 were the highest and poorest located at the vertexes of polygon responding either positively or negatively for seed yield (Figure 3).…”

Section: Analysis Of Variancesmentioning

confidence: 99%

GGE biplot phenotypic stability analysis of soybean [Glycine max (L.) Merril] genotypes

Tefera¹

2018

J. Plant Breed. Crop Sci.

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The study on genotypes by environment interaction (GEI) and stability analysis was conducted to determine the G, E, and GEI variance magnitudes. The experiment was carried out at three locations in two consecutive years on 26 soybean genotypes using randomized complete block design (RCBD) design with three replications. The objectives were to (i) estimate the magnitudes of G, E, and GEI effects, (ii) stability analysis of 26 genotypes, and (iii) to identify the highest yielding genotypes for both specific and wide adaptability. The combined analysis of variance (ANOVA) of seed yield data was confirmed strongly significant (p0.001) for G, E, and GEI variances. At Kamash, the yield was increased by 47.6% as compared to Begi might be due to soil factors differences. The soybean plants therefore grew more produced, more yield where soil fertility is the highest as compared to poorest areas. The G, E, and GEI effects contributed 15.1, 51.6, and 30.2%, respectively. Such that the main variability is due to E and GEI variances being the largest proportions of the total treatment sum of square (TTSS). The genotypes main effect and genotypes by environment interaction (GGE) biplot is therefore the most appropriate recently used model's for stability analysis in efficiently utilizing and exploiting the existed GEI SS. The first two PC (PC1 and PC2) axes were used to create the two dimensional GGE biplots that explained 40.35 and 26.38% of GGE TSS, respectively. The biplots polygons vertex genotypes were categorized as the strongest and weakest as well as stable and unstable genotypes. The result of GGE biplot for G3 and G5 providing the best niche at A15, B15 and B16, G5, and G4 the highest at A16 and K16, while G4 and G12 are also best at K15. The highest and specifically performing polygon vertex genotypes contributed maximum MS for GEI SS. The highest scores for PC1, near zero absolute values for PC2, and the highest means were recorded from G5, G6, G19, G17, and G25 contributing nothing or little MS for GEI SS. These consistently performing genotypes showed high stability based on GGE biplots analysis growing vigorously in producing maximum means without changing their ranking across all sites for this economically interesting trait.

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GGE biplots to analyze soybean multi-environment yield trial data in north Western Ethiopia

Cited by 31 publications

References 20 publications

GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

AMMI and GGE biplot analysis of multi environment yield trial of soybeanin North Western Himalayan state Uttarakhand of India

GGE biplot phenotypic stability analysis of soybean [Glycine max (L.) Merril] genotypes

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