Genotype-by-environment interaction analysis for cotton seed yield using various biometrical methods under irrigation regimes in a semi-arid region

ABSTRACT

The main objective of this study was to identify high-yielding and stable cotton genotypes under normal irrigation regimes and drought stress conditions using some biometrical methods including combined analysis of variance (ANOVA), joint regression analysis (JRA), the additive main effect and multiplicative interaction (AMMI), and genotype (G) main effect plus genotype-by-environment (GE) interaction (GGE) biplot. Cotton seed yield (CSY) was found to be significantly affected by genotypes, environments, and Genotype-by-environment interaction (GEI) using combined ANOVA, JRA, and AMMI. AMMI was superior, explaining 79% of the total variability caused by GEI under drought stress conditions compared to 66% and 25% for the GGE biplot and JRA, respectively. The CSY was found to be significantly lower under drought stress conditions vs normal irrigation regimes, ranging from 9.03% (G24) to 29.91% (G15) across the five tested environments. The JRA, AMMI, and GGE biplot methods were positively correlated for classifying the genotypes for static stability. The GGE biplot was the most effective and acceptable for identifying stable genotypes and optimal environments in both irrigation regimes. All the methods compared were concordant in separating, ranking, and identifying that the G5 and G20 genotypes were highly stable across the environment as being higher-yielding and drought-tolerant.

KEYWORDS:

• Drought stress
• GGE biplots
• joint regression analysis
• AMMI models
• multi-environmental trials

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/03650340.2023.2287759

Additional information

Funding

This work was supported by the Cotton Research Institute, Agriculture Research Center, Giza, Egypt.