Finger millet is one of the most important small millets crop grown in large areas of the developing world especially in Africa and Asia. It has the ability to produce higher yield than other crops under multiple stresses such as drought, soil acidity and land marginality. Moreover, it has high nutritional value and excellent storage qualities. In India it is extensively cultivated in Karnataka, Tamil Nadu, Orissa, Andhra Pradesh, Madhya Pradesh, Jharkhand and Bihar. In Jharkhand average production is very low. The low production of this region is primarily due to lack of high yielding varieties and poor management. There is need to increase the yield by the introduction of new high yielding germplasm lines.

Creation of genetic variability and selection for important traits is a decisive exercise that any plant breeder should apply to accomplish better yield and other desirable agronomic traits. However, to carry out potent selection, the information on available genetic variation among finger millet genotypes, the nature of component traits on which selection would be effective and the influence of environmental factors on each trait need to be known [1]. Information on the nature and magnitude of variability and heritability in a population is one of the prerequisites for successful breeding program in selecting genotypes with desirable characters [2]. It is therefore, of great consequence for breeders to know the heritability of the agronomical characters to improve the yield of the crop effectively.  According to Falconer and Mackay [3], heritability is defined as the measure of the correspondence between breeding values and phenotypic values. Thus, heritability plays a predictive role in breeding, expressing the reliability of phenotype as a guide to its breeding value. It is the breeding value which determines how much of the phenotype would be passed onto the next generation [4]. There is a direct relationship between heritability and response to selection, which is referred to as genetic advance. High genetic advance with high heritability estimates offer the most effective condition for selection [5]. The utility of heritability therefore increases when it is used to calculate genetic advance, which indicates the degree of gain in a character obtained under a particular selection pressure. Thus, genetic advance is yet another important selection parameter that aids breeder in a selection program. Hence, this study was done with the objective to assess the variability, heritability and genetic advance of grain yield and some of its related components to select a more desired trait that may contribute for the improvement of finger millet.

2. MATERIALS AND METHODS

2.1 Experimental site and design

A set of 50 germplasm with four checks and one filler germplasm of finger millet obtained from coordinating unit millet, Bangluru were evaluated in augmented randomized block design at small millets experimental area of Ranchi Agricultural College, Birsa Agricultural University, Kanke, Ranchi during Kharif 2015. List of germplasm along with checks is presented Table 1. Each germplasm was represented by a single row plot of 3 m length with inter and intra-row spacing of 22.5 cm and 10 cm, respectively. Recommended agronomic practices were followed to raise a good crop. Data were recorded on five randomly selected competitive plants at different growth stages of the crop for the 16 characters viz., days to 50%  flowering, flag leaf area (sq. cm ), Plant height (cm), number of effective tillers per plant, number of ears per plant, number of fingers per ear, finger length (cm), days to maturity,  grain yield per plant (g), grain yield per plot (g), biomass yield per plant (g), harvest index (%), 1000 – seed weight (g), disease reaction (0-5 scale), lodging susceptibility. Their adjusted mean value was subjected to statistical analysis using INDOSTAT software packages.

2.3 Statistical data analysis 

2.3.1 Estimation of magnitude of variation  

The mean value of the recorded data was subjected to analysis of variance (ANOVA) using the statistical analysis procedures of Sharma (1998) [6]. The phenotypic and genotypic variances were also estimated according to the method suggested by Burton and De Vane (1953) [7] using the formula:  

σ²g      =

σ²p = σ²g+σ²e

σ²e = MSe

Where, σ²g = genotypic variance, σ²p = phenotypic variance, σ²e = environmental variance, MST =        mean sum of square due to treatment, MSE = mean sum of square due to error, r = number of replication.

The coefficient of variations at phenotypic and genotypic level variation was estimated using the formula adopted by Johnson et al. (1955) [8] as: 

PCV = [σ²p ⁄ x‾] ×100

GCV = [σ²g ⁄ x‾] × 100

ECV = [σ²e ⁄ x‾] × 100

Where, σ²p = phenotypic standard deviation (σ²g + σ²e), σ²g = genotypic standard deviation and x‾ =grand mean for the characteristic, PCV, GCV and ECV = phenotypic, genotypic and environmental coefficient of variation respectively.

Table 1. List of experimental materials and their source

2.3.2 Estimate of heritability and expected genetic advance

Heritability (h²) in broad sense for all characters was computed using the formula adopted by Allard (1960) [9].

h²= [σ²g ⁄ σ²p] ×100

σ²p = σ²g + σ²e

Where, σ ² g = genotypic variance, σ ² p = Phenotypic variance, σ²e = error variance. Genetic advance as part of the mean (GA) for each character was computed using the formula by Allard (1960) [9].

GA = (K) (σ_p) (h²) and GMA (as percent of the mean) = [(GA)] ⁄ x‾ × 100

Where, k = selection differential (at 5% selection intensity), σ_{p =} phenotypic standard deviation_, h² = heritability and x‾ = grand mean.

Table 3. Genetic estimates for different yield and yield attributing characters and disease reaction

2.3.3 Harvest index: To find out harvest index each tagged plant is harvested separately at maturity just near to the soil surface and sun dried about 15 days. The harvested plant was threshed to find out the weight of grain and then the harvest index is calculated by a formula i.e.:

Harvest index = Economical weight ⁄Biological weight ×100

Table 4. Phenotypic variability for yield and yield contributing characters and disease reaction

3. RESULT AND DISSCUSION

The result of ANOVA of thirteen yield related traits for the fifty five genotypes is presented in Table 2. The ANOVA showed significant differences among the tested genotypes for all the characters except for the neck blast and finger blast indicating the presence of variability which can be utilized through selection.

The estimate of both phenotypic (PCV) and genotypic coefficients of variation (GCV) for all the characters are presented in Table 3. The variability estimates revealed close relation between PCV and GCV. In general, variability estimates showed that the estimate of PCV were greater than those of GCV for all the traits, this suggested the role of environment in the expression of the characters. Highest GCV was obtained for grain yield per plot (42.53) followed by harvest index (31.12), grain yield per plant (29.42), biomass yield per plant (22.78), ears per plant (21.17) and effective tillers per plant (20.64). The higher estimate of PCV (42.55) in comparison to GCV (42.53) for grain yield per plot suggested environmental influence on this character. The least difference between PCV and GCV (Table 4) for plant height 1000 seed weight suggested that this character is less affected by environment. In such condition, selection can be effective on the basis of phenotype alone with equal probability of success. These results match with the findings of [10] [11] and [12] for single plant grain yield, thousand grain weight, flag leaf blade length, finger number per panicle and 1000 seed weight.

Table 2. Analysis of variance (mean sum squares) for thirteen yield attributing characters and disease reaction

*, ** & *** = significant at p = 0.05, 0.01 & 0.001 respectively

COMPETING INTERESTS

Authors    have    declared    that    no    competing interests exist.

On the basis of GCV, it is possible to determine the amount of heritable variation. It can be found that with greater degree of accuracy when heritability in conjunction with genetic advance is studied [8]. Hence, both heritability and genetic advance were determined to study the scope of improvement in various characters through selection. The heritability estimate ranged from 11.52% for neck blast to 99.94% for grain yield per plot. Among all the characters, high magnitude of heritability was recorded for grain yield per plot (99.94%), followed by 1000-seed weight (99.87%), days to maturity (99.64 %), days to 50% flowering (99.35%), plant height (99.00), flag leaf area (96.96%), biomass yield per plant (91.26%), fingers per ear (90.44%), grain yield per plant (87.54%), ears per plant (84.84%) and effective tillers per plant (79.96%) indicated that environmental effects constitute a sufficient portion of total phenotypic variation and hence selection for those characters will be less effective. Similarly high heritability for all the characters studied was reported by [12], for plant height by [10], for days to 50 per cent flowering by [13] and for thousand grain weight by [14].Genetic mean as percent mean for various characters (Table 2) revealed that grain yield per plot, harvest index and grain yield per plant showed high genetic advance. These characters exhibited high GCV, heritability together with high genetic advance indicating the predominance of additive gene effects in controlling these characters. These results match with the findings of [15] for tillers per plant, length of finger and grain yield per plant and [16] for fertile tillers per plant, length of finger and grain yield per plant.

 4. Conclusion  The PCV and GCV values were high for grain yield per plot, harvest index, grain yield per plant, biomass yield per plant, ears per plant and effective tillers per plant suggesting the possibility of improving these traits through selection. The difference between PCV and GCV values was least for 1000 seed weight indicating less influence of the environment on the expression of these characters.  High heritability coupled with high expected genetic advance as percent of mean for grain yield per plot, harvest index and grain yield per plant. Therefore, these characters could be improved more easily than the other characters.  

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