Selection of superior Persian walnut (Juglans regia L.) genotypes from seedling-originated trees in the North-Western Himalayan region based on morphological characterization using multivariate analysis.

Authors

Megha Ahir, Jitender K Chauhan, Dharam Paul Sharma, Vishal S Rana, R K Dogra, Girish Dangi

Published in

Die Naturwissenschaften. Volume 112. Issue 6. Pages 83. Oct 20, 2025. Epub Oct 20, 2025.

Abstract

The current research study was undertaken with the aim of evaluating 98 distinct walnut genotypes from the existing seedling population and assessing 26 qualitative and 21 quantitative morphological and physiological characteristics pertaining to trees, leaf, nut and kernel traits. The results revealed a significant degree of diversity among the examined walnut genotypes. A strong positive and negative association in evaluated morphological and physiological traits was identified through the analysis of correlation coefficients. Moreover, principal component analysis (PCA) applied to qualitative and quantitative characters successfully explained a notable amount of the entire variation, with the first nine and six PCs explaining 65.89 % and 78.81 % of variability, respectively. Using Ward's clustering method with the Euclidean distance metric, a hierarchical dendrogram was generated, in which two major groups were identified among the walnut genotypes, each of which subsequently partitioned into multiple smaller sub-clusters. This classification underscores the substantial genetic variability among the walnut genotypes, highlighting their potential for targeted breeding programs. Notably, genotypes distributed across different clusters present promising candidates for hybridization, facilitating the development of novel walnut varieties with desirable traits. The dendrogram illustrating the evaluated characteristics clearly demonstrates significant differentiation among the genotypes, emphasizing distinct variations. The integration of molecular techniques with conventional morphological assessments could further enhance the accuracy of genetic differentiation, thereby offering a more reliable and comprehensive approach to genotype classification and selection.

PMID:
41114817
Bibliographic data and abstract were imported from PubMed on 20 Oct 2025.

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