Multivariate analysis in late rice genotypes (Oryza sativa L.) under transplanted condition

Introduction

Rice (Oryza sativa L.) is the most important food source of the world along with wheat and maize. It is the staple food of more than half of the world population particularly in Asia. (Muthayya et al.2014) [7] . The estimated world rice production for the year 2019-20 was 496.67 million metric tons. World population is estimated to increase from 6.2 billion in the year 2000 to nearly 8.2 billion by the year 2030, whereas rice requirement may reach to about 765 million tons (FAO 2020). Yield of rice has increased with a growth rate of 2.5 per cent per year since the green revolution. In India, low-land rice area is about 14.4 million hectares, which accounts 32.4 per cent of the total area under rice crop in the country and its production is highly variable. They are usually transplanted in levelled and bonded fields that retain surface water but the depth and duration of the soil varies greatly from year-to-year within a growing season. Depending upon the depth of water it can further classified to shallow water (<50 cm), semideep water (50-100 cm.) and deep water (>100 cm). Present agricultural research programs focus on development of high yielding varieties and adoption of advanced production technologies to make the country self-sufficient with respect to yield requirement. Genetic improvement mostly depends upon the amount of variability present in the population. Nature and degree of genetic divergence for agronomic traits is an efficient tool for effective selection of parents for hybridization programme and also a key segment in broadening the genetic base of the crop plants (Banumathy et al., 2010) [1] . Rice crop exhibits greater genetic diversity in cultivar's field and land races through the world. The study of genetic diversity assumes a critical role in the selection of diverse parents having wider variability to different traits (Nayak et al., 2004) [4] . With an aim to attain maximum heterosis and transgressive segregants in the successive generations from the crosses between diverged parents genetic diversity among genotypes can be estimated through various multivariate statistical analyses. Mahalanobis D 2 statistics is a potent statistical tool to quantify the genetic distance between genotypes based on replicated data of multiple variables (Mahalanobis, 1936) [5] .

Materials and Methods

The experimental materials consist of 32 late duration genotypes of rice including three check entries. The test genotypes were evaluated on research plot of EB-I section of the Department of Plant Breeding and Genetics of Odisha University of Agriculture and Technology, Bhubaneswar during Kharif, 2019. The trial was carried out in a Randomized Complete Block Design with three replications. Observations were recorded on five competitive plants which were randomly selected and tagged from each experimental unit in all the replications. The plant wise data on the 12 metric characters were recorded except for the traits like days to 50% flowering which was recorded on plot basis. The mean values were used for statistical analysis. After testing differences among the genotypes for each character, Mahalanobis' D 2 statistic was used for assessing the genetic divergence among all the genotypes by the inclusion of twelve characters. Rao (1952) [10] described the multivariate analysis of genetic divergence using D 2 statistics. Since, the formula for computation requires inversion of higher order determinant, transformation of the original correlated un-standardized character mean to standardized uncorrelated variable was done by pivotal condensation to simplify the computational procedure. All the possible pairs of D2 values were calculated from the 33 genotypes with respect to 12 different characters. The clustering of D² values was done using Tocher's method as described by Rao (1952) [10] , while the intra and inter-cluster distances were calculated using the formula given by Singh and Choudhary (1985) [12] . The relative contribution (in per cent) of each character to total divergence was assessed from average D2 for individual character as suggested by Singh, 1981 [12] .

Results and Discussion

Genetic variability with respect to all traits is the important component and the measure to determine the norm and direction of manipulation. Hence analysis of variance for the characters is essential to study the genetic variability. Analysis of variance showed significant variation among the genotypes under study for all 12 quantitative characters under study at 1% level of significance. Simultaneous variation between 32 rice genotypes with respect to 12 quantitative characters were tested following Mahalanobis' D 2 statistics for assessment of magnitude of genetic divergence among them. Considering the magnitudes of D 2 following the Torcher's method all genotypes were grouped into six clusters. Cluster composition of 32 genotypes was presented in Table 1. Cluster I & Cluster II were two largest clusters with eleven entries each. Cluster III and cluster IV included six and two genotypes respectively. Cluster V & Cluster VI were mono-genotypic clusters. The genotypes showing proximity in their character expression were grouped into one cluster and those showing diversity were placed in different clusters justifying their individual identity. In terms of distribution also, those genotypes belong to same geographical region may be placed in separate clusters and those belongs to different geographical regions may be placed in same cluster. Hence divergence among genotypes of same geographical region may be due to selection criteria, differences in adoption, selection pressure and environmental conditions (Nayak et al., 2004 and Mandal et al., 2022) [4,6] . [11,3,2,14,9] .

Relative contribution of characters to genetic diversity

The contribution of various characters to genetic diversity was presented in Table 3. In D 2 analysis 12 characters were considered simultaneously for making clusters, usually the members having common features of growth and yield behaviour have clustered together. However, the contribution of different characters to genetic divergent was not same. Therefore, an understanding of relative contribution of different characters for diversity would be of great importance for scheduling future programme. In present study highest contribution towards genetic divergence was made by grain yield per plant (45.32%) followed by harvest index (21.05%). This result was in broad agreement with the findings of Tandekar and Koshta, 2014 [141] and Sandhya et al., 2014 [11].

All other characters contributed very little towards genetic divergence. It was suggested that the genotypes selected from different clusters based on inter-cluster distance and mean performance can be utilized in further breeding programme as suggested by Sandhya et al., 2014; [11]. Bibi et al., 2015 [3] ; Beevi & Venkatesan, 2015 [2] ; Tandekar & Koshta [14] , 2014 and Radha et al. 2018 [9] . So, mean performance of grain yield and harvest index should be given priority while selecting the parent for hybridization programme. [4] and Suresh et al.2014 [12] . Over all observations revealed that the genotypes under study differ with respect to 12 quantitative characters providing ample scope for selection and utilization in further breeding programme.

Conclusion

Thirty-two genotypes were grouped into six clusters out of which two were mono-genotypic clusters. Grain yield per plant and harvest index contributed maximum towards genetic divergence. Based on maximum genetic distance and cluster mean genotypes present in Cluster I, Cluster II and Cluster IV can be utilized in hybridization programme.

Acknowledgement

AcknowledgementThe author is highly grateful to Odisha University of Agriculture and Technology, Bhubaneswar for providing academic, technical and financial throughout the research program.

References

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