CLUSTERING OF RICE GENOTYPES -A MULTIVARIATE APPROACH
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Date
2018
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Acharya N.G. Ranga Agricultural University
Abstract
Secondary data on 17 yield and yield contributing characters was collected from Agricultural Research Station (ARS), Nellore, Andhra Pradesh, at which experiment was carried out on 60 rice genotypes, during early kharif, 2016 to evaluate, categorize and classify them and for computation of Principal Components to determine the relative importance of Principal Components and characters involved in them.
Studies based on genetic divergence utilizing D2 analysis revealed that, the genotypes were grouped into 8 clusters of which clusters II was the largest cluster consisting of 21 genotypes while cluster III, IV, VII and VIII are the smallest clusters with only single genotype in each of them. The maximum intra cluster distance was found in cluster VI (D = 371.74) consisting of 8 genotypes. From the inter cluster D2 values of eight clusters, it can be seen that the highest divergence occurred between cluster V and cluster VI (1651.37) While the minimum inter cluster distance was noticed between cluster IV and cluster VII (94.06). It is observed that cluster III as well as cluster VIII had recorded highest means values for most of the characters. Out of 17 characters studied the maximum contribution (79.66 %) towards total divergence is by 5 characters only viz., days to maturity, test weight, flag leaf width, flag leaf length, days to 50% flowering.
To know the relative importance and usefulness of variables and genotypes, principal component analysis was done which explained 76% variability through first six principal components. Data were further analyzed
using principal factor analysis to offset the limitation of principal component analysis. All the variables exhibited high loading on different factors. Principal factor scores were obtained to know the performance of different genotypes in different factors that ascribed to a particular set of characters. Thus, the genotypes JGL 11118, WHITE PONNI, NLR 33671, NLR 33057 and TN 1 were having high principal factor score in PF I. Similarly, genotypes IR 109A235, IR 64, MTU1010, BG63672, NLR3217, NLR33359 and IR10C172 having high scores in PF II. Likewise, genotypes NLR 3042, NLR 40065, NLR 3296, ADT 37, NLR3350, NLR3407 and NLR30491 in PF III; NLR 3241, JGL 1798, NLR 40058, NLR40024 in PF IV; IR 11C208, IR 11C208, MDT 10, IR 11C228, ADT 43, IR64197, IR11C219 in PF V and IR 64197, IR11C186 in PF VI were found to be having high principal factor scores.
Description
D5670
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