Genotype- Environment Interaction Study Base GGE biplot Method for Kernel Yield in Sunflower (Helianthus annuus L.) Cultivars

Saremirad, Ali; Mostafavi, Khodadad; Mohammadi, Ardeshir Karami

doi:10.29252/jcb.12.34.43

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Hassani et al 46 showed that 62.9% of the GEI variations are explained by the first two PCs. In a study, Saremirad et al 47 estimated the sum of the first and second PCs to be nearly 74% with first and second PCs accounted for about 46% and 27% of the total variation, respectively.…”

Section: Resultsmentioning

confidence: 99%

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Taleghani

Rajabi

Saremirad

et al. 2023

Sci Rep

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The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this regard, often instead of depending on a single analysis, it is better to use a combination of several methods to measure the nature of the GEI from various dimensions. In this study, the GEI was investigated using different methods. For this purpose, 18 sugar beet genotypes were evaluated in randomized complete block design in five research stations over 2 years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effect's analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.

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Section: Resultsmentioning

confidence: 99%

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Taleghani

Rajabi

Saremirad

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Hassani, et al 45 showed that 62.9% of the GEI variations are explained by the first two PCs. In a study, Saremirad, et al 46 estimated the sum of the first and second PCs to be nearly 74% with first and second…”

Section: 3| Gge Biplot Graphical Analysismentioning

confidence: 99%

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Taleghani

Rajabi

Saremirad

et al. 2023

Preprint

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The aim of this study was to evaluate the effect of genotype× environment interaction (GEI) and selection of the best sugar beet genotypes. Eighteen sugar beet genotypes were evaluated in randomized complete block design in five research stations for two years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effects analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.

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Evaluation of Genotype × Environment Interaction for Seed Yield of Sunflower Hybrids using GGE Biplot Method

Gholizadeh

Ghaffari

Jabbari

et al. 2022

jcb

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Genotype- Environment Interaction Study Base GGE biplot Method for Kernel Yield in Sunflower (Helianthus annuus L.) Cultivars

Cited by 6 publications

References 26 publications

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Evaluation of Genotype × Environment Interaction for Seed Yield of Sunflower Hybrids using GGE Biplot Method

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