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.

Assessment of Genetic Parameters and Gene Action Associated with Heterosis for Enhancing Yield Characters in Novel Hybrid Rice Parental Lines

“…Our study indicated that the studied characters were controlled by additive and non-additive gene effects. These findings are consistent with the studies that have previously been reported [8][9][10][11][12][13][14][15][16][17][18][19]. An additive gene action effect played an important role in the early selection of the trait.…”

“…The PCA summarized the data and resulted in new orthogonal parameters, named principal components. The PCA showed trends and decreases with the doubling of the data, as variance regularly completed in yield and its components' trait [12]. The advantage of PCA is determining the significance of every variability dimension of a dataset [11].…”

Genotypic Variations in Ferulic Acid, Antioxidant Capacity and Yield Components of Thai Landrace Rice Genotypes

“…In the previous research, two landrace rice varieties have grain yields in the range of this study in which Leum Pua and Hom Dong had grain yields of 1,880.5 and 2,376.1 kg/ha, respectively (Rodnuch et al, 2019). Dhakal et al (2020) also find that grain yields of 30 landrace rice varieties in Nepal range from 1,630-3,260 kg/ha. The results in this study indicate that this germplasm has variation in grain yield, and G24 is the best variety for genetic resources of high grain yield because the grain yield of this variety is high in both locations.…”

Comparative Biochemical Composition of the Sweet Cherry Fruits

“…The distance within a cluster reflects the heterogeneous nature of the landraces. A short intercluster distance implies that the cluster members are closely related (Dhakal et al, 2020). Widely distant clusters represent landraces with a wide genetic distance.…”

“…In addition, broad-sense heritability (h 2 b) and genetic advance (GA) in combination can help determine the influence of genetic and environmental effects on the expression of a particular trait. Multivariate analyses, such as principal component analysis (PCA), can describe the inherent variations across landraces by analyzing the relationships among multiple variables and thus can identify the appropriate parental lines for a hybridization program (Dhakal, 2020;Maji and Shaibu, 2012;Mahendran et al, 2015). Mahalanobis D 2 statistic, another widely applied multivariate analysis, can reliably estimate the genetic divergence in a population and the relative contribution of different components to the total variation at the interand intracluster levels (Nalla et al, 2014).…”