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Multivariate Analyses of Grain Yield and Its Agronomic Traits in Lowland Rice (<em>Oryza sativa L.</em>) Genotypes | OMICS International | Abstract
ISSN: 2375-4338

Rice Research: Open Access
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Multivariate Analyses of Grain Yield and Its Agronomic Traits in Lowland Rice (Oryza sativa L.) Genotypes

Tefera Abebe*
Ethiopian Institute of Agricultural Research, Teppi Agricultural Research Center, P.O. Box, 34, Teppi, Ethiopia
*Corresponding Author: Tefera Abebe, Ethiopian Institute of Agricultural Research, Teppi Agricultural Research Center, P.O. Box, 34, Teppi, Ethiopia, Tel: 0940933109, Email: [email protected]

Received Date: Dec 07, 2020 / Accepted Date: Dec 21, 2020 / Published Date: Dec 28, 2020

Citation: Abebe T (2020) Multivariate Analyses of Grain Yield and Its Agronomic Traits in Lowland Rice (Oryza sativa L.) Genotypes. J Rice Res 8: 227.

Copyright: © 2020 Abebe T. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 
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Abstract

The study was conducted at Pawe Agricultural Research Center and Fogera National Rice Research and Training Center during 2015cropping season to estimate the genetic diversity and principal component analyses in rain fed lowland rice. The study was conducted using 6x6 simple lattice design with two replicates. The analysis of variance revealed significant differences (P< 0.01) among 36 genotypes for all characters measured at two locations except for number of filled spikelets per panicle, fertile tillers per plant, number of total spikelets per panicle and harvest index at Pawe and number of unfilled spikelets per panicle (p< 0.05) were significant at Fogera. Clustering of genotypes were not associated with their geographical origin instead of the genotypes were mainly grouped based on morphological significances. The Mahalanobis D2 statistics showed 36 genotypes were grouped into five distinct clusters and the chi-square test for the five clusters showed the presence of highly significant difference (p<0.01) among the clusters, which confirming that the studied genotypes were highly divergent. Principal component (pc) analyses showed the first four PCs having eigen values greater than one accounted about 79.23% of the total variation. Moreover, PCA-1 accounted about 34.06 %, PCA-2 explained 28.43 %, PCA-3 for 9.06% and PCA-4 7.68 % of the total morphological variability was assigned for the variation, respectively.

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