Senna is a popular medicinal herb used in almost all healthcare systems as a laxative. The primary issue this research addresses is the significant yield variability in Senna (Cassia angustifolia) across different environments, which hinders the identification and adoption of genotypes with consistent performance. This instability arises from strong genotype × environment interactions, making it difficult for breeders to select robust, high-yielding lines suitable for diverse agro-ecological zones. To overcome this challenge, our study employed AMMI (Additive Main Effects and Multiplicative Interaction) and GGE biplot analyses across multi-environment trials to assess yield performance, adaptability, and stability of various genotypes. These advanced statistical tools allowed us to separate main effects from interactions and visually interpret genotype responses across locations. The biplot depicts the which-won-where trend in various situations, illustrating the adaptability of the lines. PC1 and PC2's x and y axes explained 80.30 % and 9.3 % of the total variation (89.60 %), and 61.0 % and 21.30 % (82.30 %). The six environments were categorized, and then appropriate genotypes were chosen for each. Our findings identified genotypes SEN-1, 10, and 12 as the most stable and high-yielding across the tested environments. These genotypes demonstrated minimal interaction effects and superior adaptability, indicating their potential for widespread cultivation and inclusion in future breeding programs to improve Senna production. Therefore, this research not only fills a critical gap in the stability analysis of Senna but also provides concrete recommendations for genotype deployment in varied Indian agro-climates for large-scale production.
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