Studies on variability for yield and yield attributing traits in finger millet [Eleusine coracana (L.) Gaertn.]

Introduction

Finger millet (Eleusine coracana (L.) Gaertn.) is an important cereal crop of the family Poaceae and is extensively cultivated in the semi-arid regions of Asia and Africa. It is believed to have originated in the Ethiopian highlands and was later introduced into the Indian subcontinent, where it became an important part of traditional agricultural systems (Dida et al., 2008) [1] . Finger millet is a self-pollinated allotetraploid species with a chromosome number of 2n=4x=36. It is widely recognized for its ability to adapt to diverse agroclimatic conditions, particularly drought-prone areas and soils with low fertility (Upadhyaya et al., 2007) [2] . Its tolerance to unfavourable climatic conditions makes it a valuable crop for sustainable agriculture, especially under rainfed farming systems. Among the small millets, finger millet is considered nutritionally superior because of its high content of carbohydrates, dietary fibre, essential amino acids, and minerals, particularly calcium and iron (Devi et al., 2014) [3] . The grains contain approximately 7-10 per cent protein and constitute an important source of nutrition for rural communities. In addition, its low glycaemic index and high antioxidant content have increased its significance as a healthpromoting cereal crop (Chandra et al., 2016) [4] . India is the world's leading producer of finger millet. During 2024-25, the crop was cultivated over an area of 12.48 lakh hectares, producing 19.77 lakh tonnes with an average productivity of 1,584 kg ha -1 . In Gujarat, finger millet occupied approximately 0.10 lakh hectares, with a production of 0.08 lakh tonnes and an average productivity of 835 kg ha -1 (Anonymous, 2025) [5] . The crop is mainly cultivated in the tribal and hilly areas of South Gujarat, particularly in the Dangs and Valsad districts. However, the productivity of finger millet in Gujarat remains considerably below the national average, highlighting the need for genetic enhancement and the development of high-yielding varieties. The effectiveness of any crop-breeding programme largely depends on the availability of adequate genetic variability within the breeding material. Genetic variation provides the basic foundation for selection and creates opportunities to develop superior genotypes with desirable agronomic characteristics. A detailed understanding of the nature and magnitude of variability within a population is therefore necessary for designing an efficient breeding programme. The presence of substantial variation increases the possibility of identifying and selecting superior recombinants. Moreover, estimates of heritability and genetic advance provide valuable information regarding the inheritance of traits and their expected response to selection (Burton and DeVane, 1953) [6] .

Grain yield is a complex quantitative trait controlled by several component characters and strongly affected by environmental conditions. Consequently, the estimation of genetic variability parameters is essential for developing effective selection strategies. Therefore, the present study was conducted to assess genetic variability, heritability, and genetic advance for yield and yield-contributing traits in finger millet genotypes evaluated across four different locations.

Materials and Methods

The experimental material consisted of 35 finger millet genotypes evaluated at four locations: the Centre for Millets Research, SDAU, Deesa (E1); the Agronomy Instructional Farm, SDAU, Sardarkrushinagar (E2); the Maize Research Station, SDAU, Bhiloda (E3); and the Agricultural Research Station, SDAU, Ladol (E4), during Kharif 2024. The experiment was conducted in a randomized block design with three replications. Each genotype was planted in a single row measuring 3.0 m in length, with a spacing of 30 cm × 10 cm. Standard agronomic and plant-protection practices recommended for the crop were followed throughout the growing season. Data were collected from five randomly selected competitive plants in each plot for all the characters, except days to 50 per cent flowering and days to maturity, which were recorded on a plot basis. Protein content was estimated using the Kjeldahl method (AOAC, 2005). Iron and zinc concentrations were determined through atomic absorption spectrophotometry following wet-acid digestion, while calcium content was measured using the versenate titration method described by Jackson (1967) [7] .

Statistical analysis

Analysis of variance was performed to determine the extent of variation among the genotypes. The analysis was conducted separately for each environment and subsequently pooled across environments using the randomized block design model described by Panse and Sukhatme (1978) [8] . Different genetic variability parameters were estimated using the standard formulae proposed by the respective researchers.

Coefficients of variation

The magnitude of variation present within a crop population can be effectively determined by estimating the genotypic and phenotypic coefficients of variation. The genotypic coefficient of variation represents the extent of genetic differences among the genotypes and indicates the heritable component of the observed variation. Conversely, the phenotypic coefficient of variation reflects the total variation expressed by a trait, including both genetic and environmental effects. These parameters are commonly employed in plant-breeding research to quantify the available variability and identify traits with greater potential for improvement through selection. In the present investigation, the genotypic coefficient of variation and phenotypic coefficient of variation were estimated according to the method proposed by Burton (1952) [9] .

Where, σ 2 p = Phenotypic variance, σ 2 g = Genotypic variance and X ̅ = Population mean.

Heritability (broad sense) h 2 (b)

In most plant-breeding experiments, broad-sense heritability [h² (b)] is estimated more frequently than narrow-sense heritability. Narrow-sense heritability represents the ratio of additive genetic variance to the total phenotypic variance. However, the overall genotypic variance consists of both additive and nonadditive genetic components. Therefore, estimating narrowsense heritability requires the separation of additive genetic variance from the total genetic variance, which is often difficult, time-consuming, and mathematically complex. In contrast, broad-sense heritability is defined as the ratio of total genetic variance to phenotypic variance and does not require the separation of additive and non-additive genetic effects. It is an important parameter that indicates the proportion of total phenotypic variation within a population that is attributable to overall genetic differences. In the present study, broad-sense heritability [h² (b)] was estimated using the formula proposed by Allard (1960) [10] :

Where, H (b) 2 = Heritability (broad sense), σ 2 p = Phenotypic variance and σ 2 g = Genotypic variance.

Expected genetic advance (GA) (Genetic gain):

Genetic advance represents the expected improvement in the mean genotypic value of selected plants over that of the original population. It was estimated according to the method proposed by Allard (1960) [10] , assuming a selection intensity of 5 per cent and using a constant 'k' value of 2.06.

𝐺. 𝐴. = ℎ (𝑏) 2 × 𝐾 × 𝜎 𝑝

Where, G.A. = Genetic advance, h 2 (b) = Heritability (broadsense), K = Selection intensity at 5 percent (2.06) and σp = Phenotypic standard deviation

Genetic advance expressed as percent of mean:

Genetic advance as a percentage of the mean was calculated using the formula proposed by Johnson et al. (1955) [11] .

Where, GA = Expected Genetic advance, X ̅ = Mean of character under study Table 2 showed the categorization of genetic estimations. Sivasubramanian and Madhavamenon (1973) [12] categorized GCV and PCV, Robinson et al. (1949) [13] categorized heritability and Johnson et al. (1955) [11] categorized GAM.

Results and Discussion

The mean squares due to replications were non-significant for all the traits in each environment, demonstrating uniformity within the experimental sites. In contrast, the mean squares due to genotypes were highly significant for all the characters across all environments. This indicated the presence of considerable genetic variability among the evaluated genotypes, which could be effectively utilized for selecting and improving grain yield and its associated traits (Table 3).

In the pooled analysis, the mean squares due to replications were non-significant for all the characters except days to 50 per cent flowering, further indicating the general homogeneity of the experimental conditions. Significant genotypic mean squares for all the traits confirmed the existence of substantial genetic variation among the genotypes. Environmental and genotype × environment interaction effects were significant for most morphological traits, except 1000-grain weight. However, these effects were non-significant for biochemical traits, including protein, calcium, iron, and zinc contents. The significant environmental and genotype × environment interaction effects indicated that environmental conditions influenced the expression of different genotypes and that the genotypes responded differently across the test locations. Therefore, further evaluation of these traits across multiple environments is necessary to identify stable and consistently performing genotypes (Table 4).

For traits such as main ear-head length, finger length, finger width, 1000-grain weight, protein content, calcium content, iron content, and zinc content, genotypic variance closely followed phenotypic variance. This narrow difference indicated that environmental factors contributed relatively little to the observed variation and that selection based on phenotypic performance would likely be effective. In contrast, environmental variance exceeded genotypic variance for days to 50 per cent flowering, days to maturity, plant height, productive tillers per plant, number of fingers per ear head, grain yield per plant, fodder yield per plant, and harvest index. Consequently, direct phenotypic selection may be less effective for improving these traits. A combination of selective breeding, multi-location evaluation, and intensive phenotypic selection may therefore be more suitable for their improvement (Table 5). Low estimates of GCV and PCV were observed for days to 50 per cent flowering, with values of 3.77 and 5.89 per cent, respectively, and days to maturity, with corresponding values of 3.50 and 5.88 per cent. Similar low estimates of GCV and PCV for flowering and maturity traits were reported by John (2006) [14] , Ulaganathan and Nirmalakumari (2011) [15] , and Suryanarayana et al. (2014) [16] . Low GCV accompanied by moderate to high PCV was recorded for plant height (8.65 and 14.21%), productive tillers per plant (9.16 and 34.15%), and number of fingers per ear head (9.32 and 23.75%). These findings were consistent with the results reported by John (2006) [14] , Ulaganathan and Nirmalakumari (2011) [15] , Anuradha et al. (2017) [17] , and Patel et al. (2020) [18] ( [19] , Anuradha et al. (2017) [17] , Devaliya et al. (2018) [20] , Keerthana et al. (2019) [21] , Madhavilatha et al. (2021) [22] , Bharathi et al.

(2022) [23] , Madhusri et al. (2022) [24] , and Udamala et al. (2022) [25] . High estimates of both GCV and PCV were observed for iron content (33.92 and 34.02%) and zinc content (38.57 and 38.69%). Comparable findings were also reported by Savitha et al. (2013) [26] and Udamala et al. (2022) [25] (Table 6 High heritability accompanied by high genetic advance as a percentage of the mean was observed for main ear-head length, 1000-grain weight, protein content, calcium content, iron content, and zinc content. This combination indicated that these traits were predominantly governed by additive gene action and could therefore be effectively improved through direct selection. Similar observations for different traits were reported by John (2006) [14] , Haradari et al. (2011) [27] , Priyadharshini et al. (2011) [28] , Ulaganathan and Nirmalakumari (2011) [15] , Lule et al. (2012) [29] , Reddy et al. (2013) [30] , Wolie and Dessalegn (2013) [31] , Suryanarayana et al. (2014) [16] , Saundaryakumari and Singh (2015) [32] , Mahanthesha et al. (2017) [19] , Devaliya et al. (2018) [20] , Keerthana et al. (2019) [21] , Anuradha et al.

(2020) [33] , Gopal et al. (2021) [34] , Madhavilatha et al. (2021) [22] , Bharathi et al. (2022) [23] , Madhusri et al. (2022) [24] , Udamala et al. (2022) [25] , Ketema et al. (2025) [35] , Pavithra et al. (2025) [36] , and Ujjainkar et al. (2025) [37] . Grain yield per plant exhibited low heritability combined with moderate genetic advance as a percentage of the mean. This suggested that the trait was strongly influenced by environmental factors, although some additive genetic effects were also involved. Therefore, direct phenotypic selection may not be sufficiently effective, and improvement may be achieved more successfully through progeny selection and recurrent selection in advanced generations. These findings were consistent with those reported by Savitha et al. (2013) [26] . Moderate heritability associated with moderate genetic advance as a percentage of the mean was observed for plant height, finger width, and harvest index. In contrast, days to 50 per cent flowering and days to maturity exhibited moderate heritability together with low genetic advance. These combinations suggested the involvement of both additive and non-additive genetic effects in the expression of the traits. Breeding approaches that efficiently utilize additive genetic variation, such as the development of transgressive segregants, may therefore be combined with appropriate hybridization and selection procedures to achieve substantial improvement. Finger length and fodder yield per plant exhibited moderate heritability coupled with high genetic advance as a percentage of the mean. This indicated that these traits were largely controlled by additive gene effects, although environmental factors also contributed to their expression. Selection based on phenotypic performance could therefore be useful, and improvement may be achieved through simple breeding methods such as mass selection or pedigree selection. In contrast, productive tillers per plant and number of fingers per ear head showed low heritability and low genetic advance as a percentage of the mean. These findings indicated a strong environmental influence and suggested that improvement through direct selection would be difficult.

Conclusion

The present investigation demonstrated significant genetic variability among the finger millet genotypes for all the traits studied, indicating adequate opportunities for genetic improvement through selection. Significant genotype × environment interactions for most morphological characters revealed that the genotypes responded differently across environments, emphasizing the need for multi-environment evaluation to identify stable and widely adapted genotypes. High heritability accompanied by high genetic advance as a percentage of the mean was observed for main ear-head length, 1000-grain weight, protein content, calcium content, iron content, and zinc content. These results indicated the predominance of additive gene action and suggested that direct selection for these traits would be effective in future breeding programmes. In contrast, grain yield per plant, productive tillers per plant, and number of fingers per ear head were strongly affected by environmental factors. Improvement of these traits would therefore require progeny selection, recurrent selection, and evaluation across multiple locations. Overall, traits exhibiting high heritability and substantial genetic advance can be effectively utilized for developing highyielding and nutritionally superior finger millet cultivars. The promising genotypes identified in the study may also serve as valuable parents in future hybridization and crop-improvement programmes.

Acknowledgement

AcknowledgementsThe authors express their sincere gratitude to Sardarkrushinagar Dantiwada Agricultural University (SDAU) for providing the necessary funding and research facilities. We are also deeply grateful to the Hill Millet Research Station, NAU, Waghai for generously sharing the research materials.

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