Multivariate Analysis of North East Indian Rice Landraces

Author: A.V. Nageshwara Reddy, D. Purushotama Rao, Pankaj Kumar Shah, H.P. Chaturvedi and G. Padmavathi

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Abstract

North East India is renowned for its rich biodiversity, cultural diversity, and remarkable agricultural heritage. Within this region, a multitude of rice landraces—traditional rice varieties developed over generations—flourish, each possessing distinct characteristics and adaptations to their local environments. The study aims to comprehensively understand the genetic and phenotypic diversity present in these landraces. By employing multivariate analysis techniques, researchers can extract valuable insights from large datasets comprising various agronomic, morphological, and molecular traits associated with rice landraces. In a ground breaking experiment researchers conducted an evaluation of 52 genotypes during Kharif-2021 RBD, with five replications at the esteemed Agricultural Research Station (ARS) in Bapatla, affiliated with ANGRAU. Data was meticulously recorded and analyzed, focusing on nine quantitative characters to assess the genetic diversity of 52 rice landraces. To achieve this, the researchers employed the K-means cluster analysis and PCA methodologies. The non-hierarchical cluster analysis (k-mean) technique was utilized to divide the rice landraces into five distinct clusters, while the Elbow method was used to determine the optimal number of clusters. The analysis revealed a diverse clustering pattern, with Cluster I, II, III, IV, and V comprising of 21, 5, 6, 15 and 5 accessions, respectively. Moreover, the PCV unveiled the existence of three principal compounds, contributing to more than 54.58% of the cumulative variance in rice landraces for yield-contributing characters. PC1 demonstrated a significant variation of 25.48% in the study, while PC2 contributed 15.55% to the total variability, and PC3 exhibited 13.55% of the variance not explained by PC1 and PC2. Overall, the study revealed a plethora of valuable insights into the genetic diversity of rice landraces, paving the way for future research in the field.

Keywords

Rice, Landraces, K-means, principal component analysis, Multivariate analysis

Conclusion

The rice landraces studied here exhibit a remarkable level of genetic diversity, as revealed by both clustering pattern and principal compound analysis. The three principal components with eigen values greater than one contribute significantly to the variance in the population, making them attractive targets for plant selection. Among the clusters, Cluster II contains five genotypes with particularly desirable mean values for all traits, making them excellent candidates for use in breeding and crossing programs. By leveraging these insights, plant breeders can effectively harness the natural diversity present in these landraces to develop improved rice varieties.

References

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How to cite this article

A.V. Nageshwara Reddy, D. Purushotama Rao, Pankaj Kumar Shah, H.P. Chaturvedi and G. Padmavathi (2023). Multivariate Analysis of North East Indian Rice Landraces. Biological Forum – An International Journal, 15(7): 240-247.