Multivariate Analysis of Diversityof Landrace Rice Germplasm - Sanni, K.A.; Fawole, I.; Ogunbayo, A.; Tia, D.; Somado, E.; Futakuchi, K.; Sié, M.; Nwilene, F.E.; Guei, R.G. (Wiley, 2012)
Multivariate analysis is based on a statistical principle involving observation and analysis of more than one statistical variable at a time. The variability of 434 accessions of rice (Oryza sativa L.) germplasm from Côte d'Ivoire was evaluated for 10 agro-morphological traits in upland conditions at M'bé, Côte d'Ivoire (7°5′ N, 5°1′ W) in 2002 using augmented experimental design and analyzed with multivariate methods. The unweighted pair group method of the average linkage (UPGMA) cluster analysis, canonical discriminant analysis, and principal component analysis (PCA) were used to analyze the data obtained. This enabled the assessment of the extent and pattern of variation of the germplasm and identification of the major traits contributing to the diversity. Seven cluster groups were obtained from the 10 agro-botanical traits using the UPGMA. Canonical discriminant analysis showed the contribution of each trait to the classification of the rice accessions into different cluster groups. The first three principal components explained about 72.24% of the total variation among the 10 characters. The results of canonical discriminant analysis and PCA suggested that traits such as plant height, number of days to heading and maturity, tillering ability, and grain size (weight, length, width, and shape) were the principal discriminatory characteristics. It was concluded that variation exists in the germplasm, which provides opportunities for this collection to be useful for genetic improvement.