Introduction

Grain yield is a complex trait and is the result of interaction of many variables (Singh et al., 2015a). Though a wide range of genetic variability has been reported for yield traits in the past, but still there exists untapped genetic variability in germplasms which is of paramount importance in selecting the potential parents so as to get maximum heterosis and superior recombinants (Rashmi et al., 2017). Genetic improvement for quantitative traits depends on the nature and amount of variability present in the genetic stock and the extent to which the desirable traits are heritable (Namrata et al., 2016). The knowledge about genetic variability of yield contributing characters, inter relationship among them and their relation with yield are necessary for an effective breeding programme (Nayak et al., 2016). Knowledge concerning heritability helps plant breeders to predict the nature of the succeeding generation, to make an appropriate selection and to assess the magnitude of genetic improvement through selection (Tuhina Khatun et al., 2015). In addition, high genetic advance coupled with high heritability offers the most effective condition for selection for a specific character (Larik and Rajput, 2000). The association of different characters was essential to determine their contribution towards yield. Correlation studies provide an opportunity to study the magnitude and direction of association among different traits with grain yield and their direct and indirect effect on grain yield (Solanki et al., 2017). In this context, an attempt was made to assess genetic variability for yield characteristics and unravel the correlation of different grain yield traits among a set of thirty eight rice germplasms.

Materials and Methods

2.1.  Experimental material, location, and experimental design

Thirty eight rice germplasms viz., C-1, C-2, C-3, C-6, C-7, SANGSANGBA, MOIRANG PHOU, MOIRANG PHOU KHONG NEMBT, KUMBI PHOU, LANG PHOU, LOKTAK PAT PHOU, TUMAT AMUBI, BARASALI, IR78933-B24-B-B-4, HURLC-40, CR-3488-1-2-1-2, CRR599-51, OR2172-7-1, CRR599-4-1, R1570-418-1-149-1, NDR-1159, UPRI 2012-15, RPBIO4918-70-11, IR 83142-60, HURLC 22, IR82635-B-B47-1, BVS 1, IC 256649, CR 3635-1-1, JALNIDHA, LAL KHADHAN, IC 426013, HURL-54, DHAULAKISH, CSR-36, IC 337598, TETEP (Check), received from DBT Networking Project, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi were evaluated at Agricultural Research Farm, BHU, Varanasi, which is located in North-eastern zone of Uttar Pradesh at 25.18°N latitude and 83.03°E longitude and at altitude of 75.5 m from sea level. This eastern Indo-Gangetic Plains is very fertile due to annual low level floods in the Ganges. The experiment was laid out in randomized block design (RBD) with three replications. The nursery was sown on 14^th June, 2016 on uniform raised beds applied with a fertilizer dose of 1.0 kg N, 1.0 kg P₂O₅ and 0.5 kg K₂O per 50 m² area. Twenty five days old seedlings were transplanted in main research plot with one seedling hill^-1. Each plot consisted of five rows of 1.5 m length with spacing (15×20) cm². The recommended agronomic practices were followed to raise a good and healthy crop.

2.2.  Data collection

Observations were recorded on ten randomly selected plants of each germplasm in each replication for eleven quantitative traits viz., days to 50% flowering, days to maturity, plant height (cm), panicle length (cm), number of spikelet, effective tillers per plant, filled grains panicle^-1, total grains panicle^-1, spikelet fertility per cent, Test weight (gm) and Total yield per plant (gm).

2.3.  Statistical analyses

Univariate analysis of the individual character (ANOVA) including the estimation of mean, range and coefficient of variation (CV%) was conducted and interrelationships among traits values were estimated using SAS software version 9.1 (SAS Institute, 1998). Differences were called statistically significant at p<0.05. Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were calculated by the formula given by Burton and Devane (1953). Heritability in broad sense (h²) and genetic advance as percent of mean were estimated by the formula as suggested by Allard (1960). The correlation coefficient among pairs of characters was calculated according to the formula suggested by Searle (1961). To test the significance of correlation coefficients, the estimated values were compared with the table value (statistical table by Fisher and Yates, 1963) at n-2 degrees of freedom (where n denotes the number of genotypes tested) at 5% and 1% level of significance. Path coefficient analysis was done to partition the total correlation into direct and indirect effects due to the dependent variable. Wright (1934) suggested this analysis and it was further elaborated by Dewey and Lu (1959).

Results and Discussion

3.1.  Analysis of variance

The studies of variance among thirty-eight genotypes for eleven quantitative traits revealed that the genotypes differed significantly for all the traits (Table 1), which suggested that the materials selected for the studies might be of diverse origin. Several workers have reported the presence of variability and amongst the genotypes of rice for different quantitative traits. These findings are in accordance with the findings of Shobha et al. (2001); Yadav et al. (2002); Satyanarayana et al. (2005); Mustafa and Elsheikh (2007); Khare et al. (2015), Islam et al. (2016); Shah et al. (2017); Rashmi et al. (2017); Tripathi et al. (2017).

Table 1: Analysis of variance (ANOVA) for eleven quantitative traits in thirty eight rice genotypes

3.2    Per se performance

Genotype C-2 was found earliest in flowering (74 days) and maturity (102 days) suggesting that this genotype can be used as a donor in hybridization programme for evolving early maturity or short duration rice variety (Table 2). Genotype IC256649 and IR 82635-B- B47-1 was recorded as the shortest and tallest, respectively. The highest mean performance for effective tillers per plant was observed in genotype IR8314-60 (15 tillers) followed by CRR-599-4-1 (14 tillers) and R-1570-418-1-149-1 (13 tillers). Genotype IC 337598 exhibited highest mean performance for panicle length followed by genotype JALNIDHA and HURLC-54. Genotype CRR 599-4-1 (36.89 g) recorded highest mean performance for grain yield plant^-1 followed by IR 83142-60 (29.24 g) and BARASALI (27.47 g) indicating that these genotypes can be used in hybridization programme in order to achieve desirable hybrids for yield.

3.3    Variation and genetic parameters among accessions

The amount of genetic variability present, decides the effectiveness of selection. The extent of variability as measured by phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) provides information regarding the relative amount of variation for various traits. Here, the estimates of PCV were higher than their corresponding GCV for all the traits studied, but the difference is very less indicating the presence of environmental influence to some degree in the phenotypic expression of the traits (Table 3). Similar results were reported by Souroush et al. (2004), Satyanarayana et al. (2005); Patra et al. (2006); Subudhi et al. (2011); Singh et al. (2013), Singh et al. (2014); Khare et al.

Table 2: Variability parameters for eleven quantitative characters in thirty eight rice germplasms

Table 3: Estimates of Phenotypic Correlation between yield and its related trait in thirty eight genotypes of rice

(2015), Nayak et al. (2016) and Rashmi et al. (2017).

The highest PCV and GCV were recorded for grain yield plant^-1 (38.27, 37.71) followed by filled grains panicle^-1 (28.32, 28.26) and effective tillers plant^-1 (24.40, 23.39), respectively (Table 2). Result suggested that these traits like grain yield plant^-1 and filled grains panicle^-1 were under the major influence of genetic control and less variable due to environmental factor. Therefore, such traits could be relied upon and selection can be practiced for further improvement. The present findings were in consonance with earlier reports made by Singh et al. (2013); Chouhan et al. (2014); Sawarkar and Senapati (2014); Singh et al. (2015); Tuhina Khatun et al. (2015); Anis et al. (2016).

3.4.  Heritability and Genetic Advance estimates

Heritability and genetic advance are important selection parameters. Highest heritability was recorded for filled grain panicle^-1 (100 %) followed by spikelet fertility, days to maturity and test weight (98%) whereas lowest heritability was recorded for panicle length (Table 2). Similar reports were also made by Satyanarayana et al. (2005) and Rashmi et al. (2017). This indicated that selection of these traits would be more effective as compared to others. The breeder should be cautious in making selection based on heritability as it indicates both additive and non-additive gene action. Thus, heritability values coupled with genetic advance would be more reliable and useful in formulating selection procedure as it indicates that most likely the heritability is due to additive gene effects. Genetic advance as percent of mean (5%) was recorded highest for grain yield panicle^-1 (76.57) followed by filled grains panicle^-1 (58.09) and effective tillers per plant (46.21). While lowest for panicle length (10.15) followed by days to maturity (22.72) and number of fertile spikelet (28.02). High heritability coupled with high genetic advance as percent was recorded for filled grain panicle^-1, spikelet fertility percentage and test weight indicating effectiveness of selection for the improvement of these traits while high heritability coupled with low genetic advance as percent of mean were observed for grain yield plant^-1, effective tiller plant^-1 which is indicating of non-additive gene action (Table 2). The high heritability is being exhibited due to favorable influence of environment rather than genotype and selection for such traits may not be rewarding. These results are in conformity with the findings of Krishna et al. (2010); Singh et al. (2012); Sawarkar and Senapati (2014); Tripathi et al. (2017).

3.5.  Correlation coefficient analysis

Correlation coefficient analysis measures the mutual relationship between various plant characters and component characters on which selection can be based for genetic improvement in yield. A positive correlation between desirable characters is favorable to the plant breeder because it helps in simultaneous improvement of both the characters. The results on correlation analysis showed that spikelet fertility percentage has positive and highly significant association with filled grain per panicle, effective tiller per plant, plant height and test weight. Similar results were also reported by Joshi et al. (2015); Haradari and Hittalmani (2017). Whereas, characters like days to 50% flowering showed negative and significant correlation and days to maturity, total grain per panicle and number of fertile spikelet have showed negative and non-significant. This was in accordance with the findings of Pankaj et al. (2010). Grain yield plant^-1 was positively and significantly correlated withplant height, effective tillers plant^-1, test weight, filled grains panicle^-1 and spikelet fertility (Table 3). Similar results were also reported by Akhter et al. (2004); Singh et al. (2013); Sawarkar and Senapati (2014).

3.6.  Path coefficient analysis

Traits like days to 50% flowering, plant height, number of effective tiller, panicle length, test weight and filled grain per panicle exhibit positive direct effect while days to maturity, total grain panicle^-1, fertile spikelet and spikelet fertility percentage had negative direct effect on grain yield plant^-1 (Table 4). Ganesan (2001); Panwar et al. (2007); Haradari and Hittalmani (2017) have also reported similar type of finding in rice. The strong positive association of days to 50% flowering with grain yield plant^-1 was mainly through its direct effect. However, it was also due to the indirect effect through plant height, effective tiller, spikelet fertility percentage, filled grains per panicle, total grains panicle^-1, panicle length and test weight. The direct effect of days to 50% flowering on grain yield plant^-1 was positive and negligible. Many researchers reported similar findings like Panwar et al. (2007). Whereas, the indirect effect of days to 50% flowering via days to maturity was found to be negative. Therefore, selection based on these traits will be effective in improving grain yield and other related traits. The magnitude of residual effect (0.49) indicated that there may be few more characters which could have been include for estimation of direct and indirect contributors towards their relationship with grain yield plant^-1.

Table 4: Estimates of Phenotypic path matrix of eleven quantitative traits on grain yield plant-1 in thirty eight genotypes of rice

Conclusion

The genotypes exhibited a wide range of variability for most of the traits. This indicated that there is ample scope for selection of promising genotypes for yield improvement. On the basis of per se performance, the genotypes CRR599-4-1 and IR 83142-60 were found to be the best for yield and yield contributing traits. Therefore, these genotypes can be successfully utilized as parents in hybridization programme for evolving rice variety.  

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