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. 2025 Jan 18;25(1):76.
doi: 10.1186/s12870-024-05530-6.

Integrating univariate and multivariate stability indices for breeding clime-resilient barley cultivars

Ammar Elakhdar  1 , Ahmed A El-Naggar  2 , Sally El-Wakeell  2 , Anas H Ahmed  2
Affiliations

Integrating univariate and multivariate stability indices for breeding clime-resilient barley cultivars

Ammar Elakhdar et al. BMC Plant Biol. .

Abstract

Studying genetic variability through the phenotypic performance of genotypes is crucial in the breeding program. Therefore, evaluating both yield performance and stability across diverse environments is essential in yield trials to identify high-yield potential and stable cultivars. In this study, we employed 12 univariate and 10 multivariate stability models to analyze how genotype (G), environment (E), and their interaction (G × E) affect the yield performance of 32 barley genotypes across 10 environments. The environmental main effect explained 81.3% of the total variation, compared to 18.6% for genotypes and G × E interaction effects. Using the GGE biplot 'which-won-where' polygon, we categorized environments into five groups and genotypes into six groups, identifying eight genotypes with mean grain yield (GY) superior to the overall mean (4.43 tons ha- 1). Spearman's correlation analysis indicated significant positive correlations (P < 0.01) between GY and various stability parameters such as linear regression coefficients (bi), Perkins and Jinks's stability parameters (Bi), environmental variance (Sxi2) and Tai's environmental effects (αi), among others, as univariate stability measures. Additionally, nonparametric measures such as Nassar and Huhn's (SI 6 and SI 3) and Thennarasu's (NP I (3) and NP I (4)), TOP-rank stability and the yield stability index (YSI), showed significant correlations with GY. Both univariate and multivariate stability models highlighted genotypes G32, G1, and G27 as the most stable, exhibiting minimal yield variation across environments. Furthermore, G15, followed by G13, G7, and G9, demonstrated high stability based on multivariate measures. Accordingly, it might be safe to utilize the stability parameters of different groups concerning static and dynamic concepts of stability to avoid the possibility of estimating the same concept of stability. This study emphasizes the importance of utilizing a combination of univariate and multivariate stability models to assess genotype stability comprehensively and select "ideal genotypes" that offer both high yield potential and stability.

Keywords: AMMI; Barley; Environmental variance; GGE biplot; Multi-environment trials.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Geographical distribution of the environments studied. The ellipses show the five delineated mega-environments (MEs) based on GGE biplot information
Fig. 2
Fig. 2
A panel of diagnostic plots for grain yield. (A) Histogram of residuals indicating a normal distribution of the residuals. (B) The normal probability plot displays the distribution of the residual values and their linear pattern. (C) Violin plot showing the distribution of grain yield across environments
Fig. 3
Fig. 3
AMMI biplot showing GE interactions for 32 barley genotypes evaluated across 10 test environments. (B) A polygon view pattern ‘which-one-where’ of GE interaction for the barley genotypes studied
Fig. 4
Fig. 4
GGE biplot analysis for the yield performance of 32 barley genotypes evaluated across 10 test environments. (A) The mean vs. stability view for the grain yield and the environment. (B) Discriminating power and representativeness of the test environments. (C) Ranking of 32 barley genotypes relative to the ideal genotype
Fig. 5
Fig. 5
(A) The stability of the genotypes was determined based on Tai’s [18] model. (B) A plot of the rank standard deviation (δr) against the rank (kr) for genotypes across 10 environments
Fig. 6
Fig. 6
The best stability statistics were selected. (A) Hierarchical clustering is based on Ward’s method for grouping the stability parameters. (B) A correlation heatmap displays the correlation between genotypes and stability variables to identify the best stability parameters for the simultaneous selection of high yield and stable performance
Fig. 7
Fig. 7
Hierarchical clustering between the 22 stability parameters. The heatmap colors indicate the Spearman correlation coefficient among the stability parameters. A darker color indicates a greater correlation. The number of Latina indicates the subgroup
See this image and copyright information in PMC

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