Stability analysis for yield and yield components through AMMI and GGE biplot techniques in germplasm of finger millet (Eleusine coracana)

PRATIK  MANNA; SWAPNIL; SANGHAMITRA  ROUT; KUMARI  RASHMI; SANJAY  SAHAY; PUSARLA  SUSMITHA; DIGVIJAY  SINGH

doi:10.56093/ijas.v96i02.173798

Authors

PRATIK MANNA Centurion University of Technology and Management, Paralakhemundi, Odisha, India
SWAPNIL Centurion University of Technology and Management, Paralakhemundi, Odisha, India
SANGHAMITRA ROUT Centurion University of Technology and Management, Paralakhemundi, Odisha, India
KUMARI RASHMI Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India
SANJAY SAHAY Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India
PUSARLA SUSMITHA Vignan’s Foundation for Science, Technology and Research, Guntur, Andhra Pradesh, India
DIGVIJAY SINGH Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, Uttar Pradesh, India

https://doi.org/10.56093/ijas.v96i02.173798

Keywords:

AMMI model, GGE biplot, Genotype × environment interaction, Principal components

Abstract

An experiment was conducted during the winter (rabi) season of 2022–23 and 2023–24 to evaluate the stability and adaptability of 35 diverse finger millet (Eleusine coracana L. Gaertn.) germplasm across three environments, namely Odisha (E1), Bihar (E2), and West Bengal (E3). The experiment was laid out in a randomised complete block design (RCBD) with three replications. Data on 15 quantitative traits were recorded to assess genotype × environment (G × E) interactions using AMMI and GGE biplot models. AMMI analysis revealed significant G × E interactions for key traits such as biological yield/plant, harvest index and grain yield/plant, indicating differential genotype responses across environments. GGE biplot analysis showed that the first two principal components (PC1 and PC2) explained 91.05% of the total yield variation, effectively capturing genotype performance and stability. E1 (Odisha) emerged as the most discriminative environment, while E2 (Bihar) was both discriminative and representative, making it suitable
for selecting widely adaptable genotypes. Genotypes Badatara, FM 1213, Chillika and FM PR 1731 exhibited stable and superior yield performance across environments. The “which-won-where” biplot identified VL 410, Badatara and FM 1213 as top performers in Odisha, Bihar and West Bengal, respectively. Positive associations among environments, inferred from acute angles between environment vectors in the GGE biplot, indicated consistent genotype performance across locations. Overall, the study demonstrated that both AMMI and GGE biplot approaches are effective for selecting high-yielding and stable finger millet genotypes.

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