Stability Assessment of Groundnut ABLs Using AMMI and GGE Biplot Analysis for Yield Performance under Multi-environments

Premika B. P.

College of Sericulture, Chintamani, University of Agricultural Sciences, Bengaluru, Karnataka, India.

Venkataravana P.

College of Sericulture, Chintamani, University of Agricultural Sciences, Bengaluru, Karnataka, India.

Sowjanya B. A. *

College of Sericulture, Chintamani, University of Agricultural Sciences, Bengaluru, Karnataka, India.

Priyadarshini S.K.

College of Sericulture, Chintamani, University of Agricultural Sciences, Bengaluru, Karnataka, India.

*Author to whom correspondence should be addressed.


The current study focused on Identification of stable line among the eight groundnut ABLs and two checks were assessed in three different environments in Karnataka during the 2020-2021 Kharif and Rabi seasons with randomized complete block design (RCBD) with three replications. The analysis of variance revealed significant differences (p≤0.01) among the genotypes, environments, and the genotype by environment interaction (G×E) for kernel yield. The AMMI analysis also showed highly significant differences (p≤0.01) for varieties, environments, and their interaction on kernel yield. The IPCA1 and IPCA2 components explained 72.72% and 25.00% of the total G×E sum of squares, respectively. The variations in kernel yield were attributed to the environment (16.44%), ABLs (81.87%), and ABLs by environment interaction (1.68%). ABLs T65, T77, T81, and T82 were identified as stable across all three environments based on ASV and SI for kernel yield per plant. These stable lines can be used as parents in breeding programs. The AMMI model and GGE biplots were effective tools for evaluating the adaptability and stability of groundnut genotypes in diverse environments.

Keywords: Groundnut, ABLs, ASV, SI, AMMI, stability analysis, GGE biplot

How to Cite

Premika B. P., Venkataravana P., Sowjanya B. A., and Priyadarshini S.K. 2024. “Stability Assessment of Groundnut ABLs Using AMMI and GGE Biplot Analysis for Yield Performance under Multi-Environments”. Journal of Experimental Agriculture International 46 (7):29-39.


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