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Exploring variability and genetic diversity among rice genotypes in Eastern Uttar Pradesh

The genetic improvement of any crop mainly depends on the amount of genetic variability present in the population. To explore this variability, an effort was made to classify, understand the nature and magnitude of genetic variability and diversity among 112 rice genotypes for 16 quantitative traits using Mahalanobis D2 statistics. Analysis of variance revealed a wide and significant variation for all the 16 traits studied. A slight difference between PCV and GCV was found, suggesting that genetic control of traits is higher than environmental influence. Characters such as harvest index, days to first flowering, days to 50% flowering, and plant height showed high heritability coupled with high genetic advance as per cent of mean suggesting that selection for the improvement of these traits may be rewarding. Cluster analysis indicated that the 112 rice genotypes were grouped into 7 clusters, wherein cluster I had the highest number of genotypes (73) followed by cluster III with 22 genotypes. The maximum inter-cluster distance was recorded between clusters V and VII with a D2 value of 58981.07, followed by clusters II and V (51875.13). The number of spikelets per panicle (52.17%) and the number of grains per panicle (27.26%) were found to be the most contributing traits towards total genetic diversity. Hence, information on the nature and degree of divergence would help the plant breeder in the selection and hybridization procedure for choosing the right type of parents to improve yield and other traits.


INTRODUCTION
Rice (Oryza sativa L.) being the staple food for more than 70% of our national population, is also the source of livelihood for 120-150 million rural households and the backbone of Indian agriculture. The ever-increasing population in the country is creating a dire need for national food security and thus there is a demand for improving rice varieties in terms of quality and quantity. Hence, it is very important to improve rice production and productivity. Thereby, knowledge of the nature and magnitude of the genetic variation governing the inheritance of quantitative characters in rice is essential for a plant breeder. In any crop improvement program, to increase productivity breeder needs to maintain a pool of diverse desirable donor parents (Joshi and Barh, 2013). An obvious knowledge of genetic diversity is critical for the productive management and use of genetic tools for rice breeding. Hybridization between desired genotypes followed by selection depends primarily on the selection of parents having genetic divergence for different characters. Multivariate analysis like Mahalanobis D 2 statistic quantifies the degree of divergence in a population at the genotypic level and assess the relative contribution of different components to the total divergence both at the intra-cluster and inter-cluster levels. A high level of productivity in crop plants is obtained only in the presence of greater amounts of variability and genetic diversity Exploring variability and genetic diversity among (Tripathi et al., 2013). The characters with a high coefficient of variation and high heritability coupled with high genetic advance may be governed by additive genes and can be directly selected for improvement through simple plant selection. In contrast, the characters with low GCV, PCV, heritability, and genetic advance may be used in heterosis breeding.
The D 2 statistic is a valuable tool in quantifying the degree of divergence in a set of populations. Apart from estimating the genetic divergence in the base population, it also helps the breeder to classify the population into distinct clusters for their further use in crop improvement programs. Keeping this in view, an attempt was made in this study to find out the genetic variability and diversity among 112 rice genotypes and their heritability, genetic advance as percent mean which would help in the selection and further improvement of these genotypes.

MATERIALS AND METHODS
The experimental material comprised of 112 rice genotypes obtained from the IRRI AGGRi Alliance project and the experiment was conducted during Kharif, 2019 at the Agricultural Research Farm, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India, situated at 25 18′N latitudes, 83 03′E longitudes of 87 meters above MSL. The seeds of 112 rice genotypes were sown in a raised nursery bed and the seedlings were transplanted in the main field with a spacing of 20 cm between rows and 15 cm between plants in an alpha lattice design with three replications and four blocks with 4 square meter each plot. Standard agronomic practices and plant protection measures were taken as per the schedule. Observations were recorded on five randomly selected plants for different traits viz., number of effective tillers per plant, plant height (cm), panicle length (cm), number of spikelets per panicle, number of grains per panicle, spikelet fertility (%), grain weight per panicle (g), grain yield per plant (g), 1000-grain weight (g) and Kernel L/B ratio. Whereas, for traits viz., days to first flowering, days to 50% flowering, days to maturity, grain yield per plot (kg), biomass yield per plot (kg) and harvest index, the data was collected on a plot basis.
The data was recorded for all the characters whose mean values were subjected to analysis of variance to test the significance for each character as per the methodology proposed by Patterson and Williams (1976). The genotypic and phenotypic variances, as well as the genotypic and phenotypic coefficient of variations (GCV and PCV), were calculated by the formulae given by Burton (1952). Heritability in a broad sense was calculated using the formula given by Allard (1960) and genetic advance (GA) as per cent mean was estimated by the formula given by Johnson et al. (1955). Genetic divergence among the genotypes was estimated using Mahalanobis' D 2 statistics (1936) and the germplasm was grouped into several clusters by Tocher's method as described by Rao (1952).

RESULTS AND DISCUSSION
The analysis of variance for all the characters showed a fair and significant difference between the genotypes under study, suggesting the existence of appropriate heterogeneity. The magnitude of genetic variability in a population decides the effectiveness of selection. It is a fact that greater the variability among the genotypes better is the chance for further improvement in the crop. In the current study, PCV was higher than GCV with a narrow difference between PCV and GCV indicating less influence of environment on the manifestation of these characters. These results were similar to the findings of Dhurai et al. (2014), Mishu et al. (2016), Pal et al. (2016), Bagudam et al. (2018), Singh et al. (2018) and Kurmanchali et al. (2019). A high estimate of phenotypic and genotypic coefficients of variation was recorded for number of grains per panicle (34.99% and 28.22%, respectively) followed by grain weight per panicle (33.50% and 26.32%, respectively) indicating that these traits were under the major influence of genetic control and less variable due to environmental factors. Therefore, these traits can be used for further improvement in rice breeding. These findings are in close agreement with the results of Singh et al. (2018) and Perween et al. (2020).
Heritability describes the proportion of genetic diversity which is passed from parents to progeny. Burton (1952) proposed that genetic variation, along with heritability, would offer a clearer understanding of the selection efficiency predicted. Estimates of heritability along with genetic progress are more useful in forecasting the benefit under selection (Johnson et al., 1955). In the present set of material, high heritability coupled with high genetic advance as per cent of mean was recorded for harvest index (99.60% and 31.48%, respectively), days to first flowering (99.10% and 25.35%, respectively), days to 50% flowering (99.00% and 22.98%, respectively) and plant height (89.40% and 24.34%, respectively), indicating the effectiveness of selection for the improvement of these traits (Table 1) Perween et al. (2020). GCV, PCV, heritability in the broad sense (h 2 bs ) and genetic advance as percent of mean (5%) estimated for yield and quality traits are presented in Table 1.
Genetic diversity is a prerequisite for any crop improvement program and it helps in the development of superior segregants. The importance of genetic diversity in selecting parents to recover transgressive segregants has been repeatedly emphasized by many workers (Devi et al., 2017). The crosses between parents with maximum genetic divergence are responsive to genetic improvement in any crop. The multivariate analysis developed by Mahalanobis (1936)  is a numerical approach for measuring genetic diversity in a population. In the present study with 112 rice genotypes, D 2 statistics grouped the whole set of genotypes into seven different clusters based on genetic distances (Fig. 1). The composition of the various clusters obtained from the D 2 analysis is presented in Table 2, which indicates the presence of quite diverse material in the set of rice germplasm used for the study. Detailed insight into the diversity is therefore important to select desirable genotypes to be utilized in the breeding programs. The clustering pattern indicated that 73 out of 112 germplasms belong to the same cluster, i.e., cluster I. On the other hand, 22 genotypes belong to cluster III, 10 in cluster II, four in cluster ,IV whereas clusters V, VI, and VII contained one genotype each. To study the genetic diversity with regard to grain yield, yield components and quality traits in rice, Srinivas (2018) has classified 37 elite genotypes into 18 clusters with cluster I having the maximum (14) genotypes. Similarly, Singh et al. (2019) have classified 22 and 29 genotypes into 6 and 6 clusters, respectively.
In the present study, the highest intracluster distance was observed in cluster IV (4602.68) which comprised of four genotypes meaning these four genotypes are found to be more diverse in the same cluster. The highest intercluster distance (58981.07) was found between clusters V and VII indicating that hybridization between genotypes of these clusters would yield desirable segregants with the accumulation of favorable genes in segregating generations, followed by clusters II and V (51875.13) and clusters V and VI (34713.71). The smallest inter-cluster distance (5457.41) was observed between clusters I and II. The average intra and inter-cluster distances is presented in Table 3 and diagrammatically represented in Fig. 2.
Analysis of cluster means values indicated that the existence of considerable differences in the mean values of different traits ( Table 4). The highest mean values for first flowering, 50% flowering, maturity, the weight of 1000 grains, grain yield per plot and biomass yield per plot were noticed in cluster VII. The cluster V grouped genotypes with the highest mean value for the number of spikelets in a panicle, number of grains in a panicle and spikelet fertility per cent and with minimum values for plant height, weight of 1000 grains and kernel L:B ratio. Cluster VI displayed the highest mean values for plant height and length of panicle, whereas in the case of effective tillers per plant, the highest mean value was found in cluster II and that for grain weight of a panicle in cluster III. Hence, germplasm included in clusters II, III, V, VI and VII seems to be promising, with various traits under study.
The existence of a high level of genetic diversity in the analyzed material was indicated by the discrimination of germplasm lines in too many clusters. Significant genetic divergence in rice has also been documented by previous workers viz., Awasthi et al. (2005)     Kernel L/B ratio 0.00 0 also studied diversity in rice using 21, 38, 64, 32, and 70 genotypes, respectively. The contribution of different characters to total divergence is presented in Table 5.