Genetic diversity analysis of Basmati rice (Oryza sativa) genotypes for grain yield and quality traits

Abstract

The present study was carried out to determine the genetic diversity and association among 36 Basmati rice (Oryzasativa L.) genotypes based on morphological and quality traits. Different genotypes were evaluated in randomizedblock design (RBD) at Rice Research Station, Kaul (India) during rainy (kharif) season 2016 and 2017. Principlecomponent analysis indicated that first five principle components (PC) accounted for more than 80% (PC1=35.33%,PC2=19.84%, PC3= 11.30, PC4=8.08 and PC5=6.81%) of the total variation. Principle component 1 was loaded withnumber of tiller per plant, panicle length, panicle weight, number of spikelets per panicle, thousand grain weight,grain yield per plant and harvest index while plant height, biological yield per plant, hulling per cent, milling percent, head rice recovery per cent, alkali spreading value and amylose content were present in principle component 2. Cluster analysis divided the genotypes into five clusters and genotypes with earliness and yielding traits were present in cluster 1. A high positive and significant correlation of grain yield per plant was seen with number of tillers per plant, panicle length, panicle weight, number of spikelets per panicle, biological yield per plant, harvest index, alkali spreading value and amylose content. This study would be helpful in identifying the diverse and donor parents for important traits which can be used for genetic improvement programs of Basmati rice.

Diversity and Multi-variate Analysis of Basmati and Non-Basmati Rice Genotypes over Two Seasons using Organo-leptic Traits

“…The third PC contributed to 10.9% of the total variation and positive loadings to this PC were given by the traits viz., DFF, PL, NPT, TGW, SPY, KL, KB, L/B ratio, KLAC, LER, AC, GC, ASV, WUR and VER. This PC was contributed positively by most of the physico-chemical and yield-related traits as reported by Kesh, Battan [23] who gave PC1 with positive loadings by most of the traits particularly the grain quality traits.…”

“…This finding implies that all genotypes for yield-related characteristics and physico-chemical traits exhibit a spectrum of genetic variability. Earlier researchers [22,23] had also noted a significant level of genetic variability in the experimental material for the studied grain yield and quality traits. Pooled estimates of mean, range, variability, heritability and genetic advance as per cent of mean for all the studied yield and grain quality traits in both the seasons were given in The experiment led to the classification genotypes as per their ASV as low (8), low to medium (17), medium (23) and high (3).…”

“…The multivariate method of principal component analysis is widely used for assessing genetic diversity and identifying the most important traits that account for the greatest variation among the genotypes [23]. In PCA, data standardization made characteristics equally contribute to the divergence studies regardless of the units used [38].…”

Genetic potential and diversity of rice land races for grain Fe content

“…The statistical analyses (pooled ANOVA)of this investigation demonstrated a considerable and wide range of variance across all the traits (Genotypes and environments) linked with Fe content in both brown and polished rice.On other hand, mean sum squares of environment X Genotype exhibited signi cance association except for HP and HRR. Earlier researchers had also noted a signi cant level of variation in rice landraces (Nagamani et al, 2022;Kesh et al, 2022) for grain nutrients. The study of coe cient of variation for morpho-quality traits viz., HP, MP, HRR, KL, KB, LBR, FEB and FEP were recorded between the range of 2.47-15.87%.…”

“…Principal component analysis (PCA) is a multivariate statistical analysis for examining and simplifying complex and large datasets. Based on the correlation among the traits and clusters, the pattern of variation also studied using PCA to determining the most signi cant characteristics that account for the greatest diversity among the native landraces (Kesh et al, 2022).A total of 8 principal components (PCs) were extracted and it revealed four most informative PCs with Eigen values of 2.13, 1.9, 1.30 and 1.02which responsible for 79.62% of total variation (Table 5). Among them, PC1 accounted for 26.66% of the total variability and majorly contributed byMP and LBR.…”