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Research Article Open access CC BY 4.0

Character Interrelationship and Principle Component Analysis of Some Black Gram (Vigna mungo L. Hepper) Genotypes under Moisture Stress Deficit Environment

Dibosh Bordoloi, Akashi Sarma, Debojit Sarma

International Journal of Environment and Climate Change · pp. 1190–1204 · Published 27 Jun 2022

10.9734/ijecc/2022/v12i1030916

Abstract

Two pot experiments were conducted with ten black gram genotypes during winter in the net house of the Department of Plant Breeding and Genetics, Assam Agricultural University, Assam. The present experiment was conducted to assess the character interrelationship and grouping of genotypes based on their performance under water stress. The experiment was conducted in a controlled environment in a complete randomized block design with three replications and two environments (Non-stress & Stress environments). The plant was exposed to drought stress during the first flower bud initiation (i.e., 33 days after sowing). The genotypes were screened on the basis of the results of multivariate analysis viz., pearson correlation, path analysis, modified path analysis, cluster analysis based on usual Euclidean distance and principal component analysis. The Pearson correlation, path analysis and modified path analysis identified the proline content and pod per plant as the critical yield determinants under both the environments. Besides, other significant yield contributing traits for water deficit stress were leaf area, chlorophyll content, pod length and seeds per pod. Usual Euclidean distance-based clustering categorized the genotypes into three groups with substantial variation in cluster composition of both stress and non-stress environment. Based on the relationship of characters and genotypes to the PC 1, it can be concluded that the genotypes AKU 10-6 and SBC 40 can be selected for Number of seeds per pod, Chlorophyll content, number of pods per plant and grain yield per plant characters for breeding purposes in both the environments.

Correlation path analysis modified path analysis cluster analysis and PCA analysis

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