Understanding the variation in morphology and rhizome yield of Indian ginger (Zingiber officinale Rosc.) through distinctness, uniformity and stability (DUS) descriptors: A natural marker to enhance crop productivity
DOI:
https://doi.org/10.14719/pst.4509Keywords:
cluster analysis, DUS descriptors, Indian ginger, morphological traits, principal component analysisAbstract
Ginger (Zingiber officinale) is a widely consumed and economically significant plant known for its medicinal and culinary properties. Understanding the diversity within ginger germplasm is vital for its successful use in breeding programs and conservation efforts. Morphological characterization plays a key role in identifying distinct varieties, ensuring uniform traits, protecting breeders' rights and conserving genetic diversity, all of which support commercial production and drive innovation in breeding. The present investigation was carried out at the Hybrid Rice Evaluation Centre, Gudalur, Nilgiris, with 32 genotypes to study the variability for identifying the best performing ginger genotypes. Genotypes were examined for 2 qualitative and 8 quantitative traits using Distinctness, Uniformity and Stability (DUS) parameters. Out of the traits assessed, plant height, number of leaves on the main stem and dry recovery were monomorphic, with no variation within the population. However, 4 characters, normal growth habit, shoot diameter, leaf length and leaf width, exhibited 2 distinct variations within the genotypes, indicating dimorphism. Additionally, 3 traits such as the number of tillers per clump, rhizome thickness and rhizome shapes showed multiple variations among the studied genotypes, illustrating polymorphism within the population. Principal component analysis was conducted on 7 principal components with eigenvalues of more than one accounted for 98.28 % of the total variability. Among these traits, leaf length, rhizome thickness and number of tillers demonstrated the highest variation, while the remaining traits showed lower variability. Based on PCA and cluster analysis, the genotypes Nadia, Manipur Local, Aswathy, Nagaland Local, Bhaise, ACC 578, Himachal Local, Rio De Janeiro, IISR Vajra, Humnabad Local, Hassan Local, Thalavadi Local and Chikkamagalore Local 2 were identified as the most diverse genotypes.
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