This study gave empirical evidence on sixteen agro-morphological data that were collected from one hundred and twenty three rice germplasm comprising of Oryza sativa and Oryza glaberrima lines including checks. The data was collected from thirteen villages in two States in Nigeria and were characterized using ANOVA model. Among the studied traits, high coefficients of variation were observed for number of unfilled grain per head (45.8%), grain weight (29.1%), 1000 grain weight (23.0%), tiller number at three weeks after planting (22.5%), and tiller number at maturity (20.9%). Seven out of the sixteen phenotypic traits measured were statistically significant at (P = 0.001 and P = 0.05), and 7 phenotypic variables also showed significant differences when subjected to univariate statistics at (P = 0.001 and P = 0.05). The association of all morphological traits was estimated by phenotypic correlation coefficient and showed that eight dependent variables were positively related. Cluster analysis using Ward's method classified the 123 populations into seven distinct groupings. A large number of genotypes was placed in cluster 5 (65 genotypes) followed by cluster 1 (20), cluster 4 (14) and cluster 3 (9), cluster 2 (8) and cluster 6 (7). Cluster 6 includes five checks with few sativa lines, cluster 5 with large grouping of sativa lines with only FARO 56 in that group. Cluster 1 consists of only the O. glaberrima. Clusters 2, 3 and 4 consisted of only O. sativa groups indicating no association between clustering pattern and eco-geographical distribution of genotypes. The maximum inter-cluster distance was observed between clusters indicating the possibility of high heterosis if individuals from these clusters are cross-bred. Principal component analysis resulted in the first two components with Eigen value greater than 1 accounting for 78% of the total variation. The results of Principal Component Analysis (PCA) were closely in line with those of the cluster analysis. These results can now be used by breeders to develop high yielding rice varieties and new breeding protocols for rice improvement.
Key words: Principal component analysis, germplasm characterization, correlation coefficient, correlation matrix, cluster analysis, genetic variability.
Copyright © 2021 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0