Multivariate Analysis of Phenotypic Diversity of Rice (Oryza sativa L.) Landraces from Lamjung and Tanahun Districts, Nepal

Abstract

The magnitude and nature of genetic divergence play a vital role in the selection of the desirable landraces for its utilization in the breeding program. A study was carried out with 30 rice landraces at the Institute of Agriculture and Animal Science, Lamjung Campus, during June–November 2018 to determine relation among individuals, estimate the relative contribution of various traits of rice using principal component analysis, and identify the potential parents for hybridization using Mahalanobis distance (D2). The principal component analysis revealed that five among the thirteen principal components were significant (eigenvalue >1) and contributed to 29.96%, 20.26%, 13.56%, 11.68%, and 9.22% of the total variance, respectively. PC1 included the traits that were related mostly to the yield, yield attributing, and grain characteristics. Landraces from Anadi group, Jetho Budo, Jarneli, and Rato Masino performed well in PC1 while landraces such as Mansara, Pakhe Sali, and Aanga performed well in PC2. The landraces were grouped into six clusters where 12 landraces were grouped into cluster I. Cluster analysis showed maximum and minimum intracluster distance in cluster VI (D2 = 35.77) and cluster I (D2 = 18.59), respectively. The maximum intercluster distance was obtained between clusters V and VI (D2 = 40.18) followed by clusters III and VI (D2 = 36.17) and clusters IV and VI (D2 = 35.74). Cluster III showed the highest mean value for grain width, flag leaf breadth, yield, and minimum mean value for plant height while mean values of total grain per panicle, filled grain percentage, and thousand-grain weight were maximum in cluster IV. Mean values of effective tiller and kernel width were found maximum in clusters V and VI, respectively. Landraces from clusters V and VI or clusters III and VI or clusters IV and VI can be used in the hybridization program to develop the superior hybrids by exploiting heterosis in segregating generation.