Multivariate Analysis of Diversity of Landrace Rice Germplasm

Multivariate analysis involves observation and analysis of more than one statistical variable at a time. The variability of 434 accessions of rice (Oryzasativa L.) germplasm from Cote d’Ivoire was evaluated for 14 agromorphological traits in upland conditions at M’be, Cote d’Ivoire (7°5´ N; 5°1´ W), using augmented experimental design, and analyzed with multivariate methods. The unweighted variable pair group method of the average linkage cluster analysis (UPGMA), canonical discriminant analysis (CAN) and principal component analysis (PCA) were used to analyze the data obtained. This enabled 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 14 agro-botanical traits using UPGMA. CAN showed the contribution of each trait to the classification of the rice accessions into different cluster groups. The first three principal components explained about 58.41% of the total variation among the 14 characters. The results of CAN and PCA suggested that traits such as plant height, leaf length, number of days to heading and maturity, tillering ability, panicle length and grain size (weight, length and width) were the principal discriminatory characteristics. It is concluded that variation exists in the germplasm, which provides opportunities for this collection to be useful for genetic improvement.