Trait Association and Path Analysis Studies of Yield Attributing Traits in Rice (Oryza sativa L.) Germplasm

Sneha Gupta, Sameer Upadhyay, Ganesh Kumar Koli, Sanket Rajendra Rathi, Prashant Bisen, Bapsila Loitongbam, Pawan Kumar Singh and Brajesh Sinha

  • Page No:  508 - 517
  • Published online: 03 Dec 2020
  • DOI : HTTPS://DOI.ORG/10.23910/1.2020.2156

  • Abstract
  •  iamsameerupadhyay@gmail.com

Forty-eight Rice germplasm were undertaken for estimating variability, heritability, genetic advance, correlation and path analysis for yield and yield attributing traits. The experiments were conducted in a randomized block design with three replications during two-season viz., July-Oct, 2017 and July-Oct, 2018 at the Agriculture Research Farm, Institute of Agricultural Sciences, Banaras Hindu University Varanasi. Analysis of variance revealed that genotypes contain significant differences for all the traits. High estimates of GCV and PCV were observed for traits like filled grains panicle-1 followed by total grains panicle-1, grain yield plant-1 and number of effective tillers plant-1. High heritability coupled with high genetic advance as % of mean was observed for the traits viz. plant height, days to 50% flowering and test weight. These characters exhibited less influence of environmental variance in their inheritance and hence could be improved by means of simple selection. Path analysis revealed that characters like filled grains panicle-1, total grains panicle-1 and number of effective tillers plant-1 had prominent direct positive effects on grain yield plant-1. Genotypes LC-53, LC-55 and LC-50 and LC-59 were found to be superior for yield and contributing traits during July-Oct, 2017 and July-Oct, 2018 respectively while LC-90 was found to be consistently overling under both seasons. Genotypes LC-54 and LC-56 were earliest in flowering and maturity suggesting that they can be used as a donor in hybridization programme for evolving early maturing rice variety.

Keywords :   Rice, correlation, path coefficient, heritability, genetic advance

  • Introduction

    Rice belongs to the genus Oryza, subtribe Oryzineae of the family Poaceae. It is one of the few crop species endowed with richest genetic diversity. The genus comprises of 24 recognized species, out of which only 2 are domesticated (O. sativa and O. glaberrima) and the rest 22 are wild representing 11 genomic groups, 6 are diploid (n = 12: AA, BB, CC, EE, FF and GG) and 5 are polyploid (n=24: BBCC, CCDD, HHJJ, HHKK and KKLL). Rice found in Asia, America and Europe belongs to Oryza sativa and varieties grown in West Africa belong to Oryza glaberrima. The sativa rice species are commonly divided into three subspecies viz.; Indica, Japonica and Javanica. Globally rice is grown in an area of 167.24 Million hectares, with a production of 769.65 Million tonnes and a productivity of 46 q ha-1 (Anonymous, 2017). India is the second largest producer of rice after china, accounting 18% of the world rice production. In India rice is cultivated in an area of 43.78 Million hectares (20% cropped area), with a production of 168.5 million tonnes and productivity of 38.4 q ha-1 and contributes 25% to agricultural GDP (Anonymous, 2017). The world population is predicted to reach nine billion by the year 2050 and food insecurity could become a serious global problem (Alexandratos and Bruinsma, 2012). Therefore, it is crucial to augment the productivity of major cereal crops such as rice to satisfy ever increasing demand of a population.
    Rice is a highly domesticated crop, and domestication processes are accompanied by genetic erosion, which in turn cause a reduction in genetic diversity among traditional varieties leading to gradual loss of landraces from the fields (Khare et al., 2014). Modern rice cultivars have been developed through the hybridization of elite lines and subsequent selection for yield and quality traits. The genetic potential and magnitude of heterogeneity are still present in local landraces that need to be characterize in available upland rice germplasm.
    The base for any crop improvement programme relies on availability of amount and direction of genetic association of the traits, the base population (Girma et al., 2018), and adoption of appropriate selection techniques (Tiwari et al., 2011; Rani et al., 2016, Adhikari et al., 2018). Variability refers to the presence of differences among the individuals of plant population due to their genetic composition and the environment in which they are raised. The existence of variability is pre requisite for improvement of genetic material because effective selection utilises the genetic variability present among the individuals to be bred enabling the plant breeder to more rapidly produce new varieties or improve existing ones (Meena and Bahadur, 2014; Ranganatha et al., 2013; Yared and Misteru, 2016). The variability for yield in crop species will get exhausted sooner or later if not checked, which in turn limits the prospects of further improvements in the crop species. Thus, knowledge of genetic variability present in a crop species for the character under improvement is of paramount importance for the success of any crop breeding programme.
    Yield as such is a complex entity and it is the resultant of different traits thus marking the importance of component traits (Hossain et al., 2015; Tiwari et al., 2019). Different traits are selected as per requirement and breeding programmes are carried out. Different genetic variability parameters, namely, Genotypic Coefficient of Variability (GCV), Phenotypic Coefficient of Variability (PCV), heritability and genetic advance for yield attributing traits are a major concern for any plant breeder and for crop improvement programs. The genetic improvement of quantitative traits expected in a crop species depends upon heritability pattern of the trait, nature and amount of variability present in the existing germplasm (Iraddi et al., 2013; Rashmi et al., 2017). Estimation of heritability along with genetic advance, conjointly, is helpful in predicting the gain under selection than heritability estimate alone (Moosavi et al., 2015). It is important that selection should be based on high heritability coupled with genetic advance. The correlation coefficient helps to identify characters that have little or no importance in the selection programme. The existence of correlation may be attributed to the presence of linkage or pleiotropic effect of genes or physiological and development relationship or environmental effect or in combination of all. Correlation study should aim in selecting traits showing positive association with grain yield. Correlation in grouping with path analysis would give a better insight into cause and effect relationship between different pairs of characters.
    The spectrum of variability in germplasm for grain yield traits depends on the genetic diversity of the combining parents. Hence, estimation of genetic diversity for yield traits among accessions is important for planning the future crossing programmes. In view to the above facts continuous efforts are required to conserve the germplasm and exploiting their worth through analysing the yield potential and judging its component characters and therefore, the present investigation involves trait association and path analysis studies among forty eight upland rice germplasm.


  • Materials and Methods

    The investigation was carried out during two crop seasons viz. July-Oct, 2017 and July-Oct, 2018 to study correlation and path coefficient in forty eight rice germplasm (Table 1).


    The experiment was conducted at the Agricultural Research Farm, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi which is situated at 25018’ North latitude and 83003’ East longitude and at altitude of 123.23 m from sea level. The experimental unit had fertile alluvial-loam soil and is characterized as soil of Indo-Gangetic Plains. The germplasm were grown in a Randomized Block Design with three replications. Each plot consisted of three rows of 1.5 m length with spacing (15×20) cm2. with recommended package of practices. Five random plants were tagged and the observations were recorded on 11 yield contributing traits viz., days to 50% flowering, days to maturity, plant height, panicle length, effective tillers plant-1, filled grains panicle-1, unfilled grains panicle-1, total grains panicle-1, spikelet fertility %, test weight and grain yield plant-1. ANOVA was done for partitioning the total variation into variation due to treatment and replication as per Panse and Sukhatme (1967). It is worked out to test the significance of ‘F’ and‘t’ test  and further carried out according to the procedure of Randomised Block Design for analysis of each character as per methodology of Fisher and Yates (1938). Genetic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) were worked out as per Burton and De-Vane (1953) and classified as per categorisation suggested by Sivasubramanian and Menon (1973). Further Correlation analysis was performed. Path analysis was carried out following the method suggested by Dewey and Lu (1959).


  • Results and Discussion

    Significant difference has been revealed through analysis of variance (ANOVA) of forty-eight genotypes for eleven traits representing the presence of inherent genetic difference (Table 2).


    The mean sum of square due to treatment showed high significant differences for all the traits under study for both the season viz. July-Oct, 2017  and July-Oct, 2018  except panicle length. These findings are in accordance with Patra et al., 2006, Mustafa and Elsheikh (2007) and Rashmi et al., (2017).

    The study revealed genotypes LC-53 (54.99 g) followed by LC-55 (53.45 g) and LC-90 (50.16 g) while the genotype LC-50 (41.74 g) followed by LC-90 (36.52 g) and LC-59 (36.32 g) as the best for yield and yield contributing traits during July-Oct, 2017  and July-Oct, 2018  respectively (Table 3.1 and 3.2).


    Therefore, these germplasm can be successfully utilized as parent (s) in future breeding programme. Genotype LC-56 was found to be earliest in flowering and maturity during July-Oct, 2017 while in July-Oct, 2018, genotype LC-54 and LC-56 were found to be earliest. These genotypes can be used as a donor parent (s) in hybridization programme for evolving early maturing rice variety.

    3.1.  Genetic variability

    The estimates of Phenotypic Coefficient of variation (PCV) were found to be higher than Genotypic Coefficient of variation (GCV) for all the traits under consideration. The high estimates of PCV and GCV were observed for traits like filled grains panicle-1 followed by grain yield plant-1, total number of grains panicle-1 and number of effective tillers during July-Oct, 2017 and 2018. (Table 4) suggesting that these traits were controlled mainly by genetic factors and is less affected by environment hence; they can be utilized for further improvement of crop. The estimates were found to be in accordance with Souroush et al., 2004.


    3.2.  Heritability and genetic advance

    High heritability (h2) coupled with high genetic advance (GA) as % of mean was observed for plant height, number of effective tillers, number of filled grains panicle-1, number of unfilled grains panicle-1, total number of grains  panicle-1, spikelet fertility %, test weight and grain yield plant-1 for both the seasons viz. July-Oct, 2017  and July-Oct, 2018  (Table 4). This indicated the preponderance of additive gene action therefore simple selection would be effective for these traits. These results are in conformity with findings of Krishna et al., 2010; Chouhan et al., 2014 and Pratap et al., 2018.

    3.3.  Correlation between grain yield and other yield factors

    During July-Oct, 2017 , significant positive correlation with grain yield was exhibited by filled grains panicle-1 (0.436) followed by total grains panicle-1 (0.369) and test weight (0.272) (Table 5.1). However, during July-Oct, 2018 , significant positive correlation with grain yield was manifested effective tillers plant-1 (0.336) followed by filled grains panicle-1 (0.229) and total grains panicle-1 (0.214) (Table 5.2). Similar findings were observed by Singh et al., 2015, Tuhina-Khatun et al., 2015, Anis et al., 2016.


    3.4.  Phenotypic path coefficient analysis

    DuringJuly-Oct, 2017 , filled grains panicle-1 (0.557) exhibited maximum positive direct effect on grain yield plant-1 followed by number of effective tillers (0.436) and test weight (0.332), indicating as the major yield contributing traits in rice. Days to flowering (-0.082), unfilled grains panicle-1 (-0.247) and spikelet fertility % (-0.332) exhibited negative direct effect on grain yield plant-1 (Table 6.1). Similar results were observed by Ganesan (2001), Panwar et al., 2007; Haradari and Hittalmani (2017).

    In July-Oct, 2018 , maximum positive direct effect on grain yield plant-1 was manifested by total grains  panicle-1 (0.889) followed by effective tillers (0.771), days to flowering (0.697) and spikelet fertility % (0.335). Traits like days to maturity (-0.437), filled grains panicle-1 (-0.392) and unfilled grains panicle-1 (-0.080) exhibited negative direct effect (Table 6.2). These observations support the earlier finding by Nayak et al., 2016.


    3.5.  Mutual association among different yield components

    Days to 50% flowering exhibited highly significant positive correlation with days to maturity, panicle length, filled grains panicle-1, unfilled grains panicle-1 and total grains panicle-1. Days to maturity showed highly significant positive correlation with panicle length, filled grains panicle-1, unfilled grains panicle-1 and total grains panicle-1. Plant height observed highly significant positive correlation with panicle length, filled grains panicle-1, total grains panicle-1 and spikelet fertility %.

    Panicle length reported highly significant positive correlation with filled grains panicle-1, unfilled grains panicle-1 and total grains panicle-1. Filled grains panicle-1 observed highly significant positive correlation with total grains panicle-1 and spikelet fertility %. Unfilled grains panicle-1 observed highly significant positive correlation with total grains panicle-1.


  • Conclusion

    Filled grain panicle-1, total grains panicle-1, test weight and number of effective tillers plant-1 can be used as selection indices for improvement as it have direct positive effect on grain yield plant-1. Genotypes LC-53, LC-55, LC-90 and LC-50 were found to be the propitious genotypes for yield and yield contributing traits during both seasons. Genotype LC-54 and LC-56 were earliest in flowering and maturity. Hence, these genotypes can be employed as parents in the hybridization programme to obtain potent transgressive segregants.


  • Acknowledgement

    Authors acknowledge the support from Institute of Agricultural Sciences, Banaras Hindu University for providing necessary resources for smooth conduction of the experiment.


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