Comparative Analysis of Statistical Methods for Yield Stability and Genotype by Environment Interaction of Barley

Gunfrie Molla

dc.contributor.author	Gunfrie Molla
dc.date.accessioned	2023-03-23T07:39:48Z
dc.date.available	2023-03-23T07:39:48Z
dc.date.issued	2023-02-24
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/15189
dc.description.abstract	Background: Barely is one of the most significant cereal crops. The selection of stable and high yielding barley genotypes and ideal discriminative environments is an important strategy for the development of new cultivars. The objective of this study was comparative analysis of statistical methods used to analyze genotype by environment interaction and yield stability of the barely. Methods: Data were obtained from Sirinka agricultural institute which included ten barley genotypes evaluated at six environments during the three main cropping years of 2001- 2003 in a randomized complete block design with three replications. Environmental variance, Shukla stability variance, Wricke’s ecovalence, coefficient of variability, Tai’s stability variance, Eberhart-Russell and Finlay-Wilkinson were used from univariate parametric stability methods, and AMMI and GGE were used from multiplicative statistical methods. Results: The ANOVA revealed, environment main effect accounted for 47.9% of total variation, compared to 25.2 and 17.1% for genotype and GE interaction effects, respectively. Spearman’s rank correlation analysis indicated that Eberhart- Russell’s, ecovalence, Shukla’s stability variance and Tai’s deviation from linear response were significantly correlated (P < 0.01) in ranking of genotypes for stability, suggesting that they can be used interchangeably. The first two IPCAs of SREG2 and AMMI2 was explained 75.9 and 85.7% of the GEI and GGE total variations, respectively. GGE biplot indicated that, G6 (KP-90134.2) followed by G9 (KP-90138.12), G3 (CIP-386423.13) and G7 (CIP-387676.24) as high-yielding and stable genotypes. Based on the results, E6 (Kon-03) was both discriminating and representative, it’s suggested as good test environments for selecting generally adaptable genotypes, whereas E3 (Srinka-02) was discriminating but non representative test environment; it’s suggested for selecting specifically adaptable genotypes. Conclusion: The main conclusions were GGE biplot was more effective to identify stable and high-yielding genotypes than the other methods. Positive increase in yield and yield stability is attributable predominately to genetic improvement in barley breeding. The genotypes G6 (KP 90134.2), G9 (KP-90138.12), G3 (CIP-386423.13) and G7 (CIP-387676.24) could serve as a good genetic source for both high-yielding and stability in barely breeding programs. Keywords: Stability, GGE biplot, AMMI model	en_US
dc.language.iso	en_US	en_US
dc.subject	Statistics	en_US
dc.title	Comparative Analysis of Statistical Methods for Yield Stability and Genotype by Environment Interaction of Barley	en_US
dc.type	Thesis	en_US