INTRODUCTION Finger millet (Eleusine coracana L. Gaertn) is a tetraploid with chromosome number 2n=4x=36 belonging to family poaceae and commonly known as Ragi, Nagli, Nachani and Mandua. It is evolved from a cross between two diploid species, E. indica (L.) Gaertn. (AA) and E. flocifolia or E. tristachya (BB) as genome donors (Chennaveeraiah and Hiremath, 1973). It is native to Ethiopian highlands of central Africa and was domesticated into Indian subcontinent nearly 3000 years ago (Anon., 1995). The cultivation of finger millet in India under diverse agro-climatic conditions associated with natural and human selection has resulted in larger variability in crop, thus India became the secondary centre of diversity. In world, finger millet is a widely cultivated in the semi-arid areas of Eastern and Southern Africa and South Asia. It serves as staple food for a huge section of poor and farming community in many areas of India. It ranked as fourth millet in world after sorghum, pearl millet and foxtail millet, while in India it ranks third after sorghum and pearl millet in millets (Upadhaya et al., 2007). An enormous number of small farmers cultivate finger millet with limited water resources and in numerous nations this crop is frequently known as “poor people’s crop”. In India, ragi is cultivated in an area of 1.15 million hectare with a production of 1.99 million tonne giving an average productivity of 1724 kg/ha majorly in Karnataka, Andhra Pradesh, Tamil Nadu, Maharashtra, Uttarakhand and Odisha (Anon., 2022). Karnataka is the leading producer of finger millet which contributes 68.26 % to the total area and 68.34 % to the total production of ragi cultivated in India (Anon., 2022). Ragi is acknowledged for its health advantageous effects, like antidiabetic, anti-diarrheal, antitumerogenic, anti-inflammatory, atherosclerogenic, antiulcer, antimicrobial, antioxidant propertiesand commonly known as “nutritious millet” as contains a high quantity of calcium (0.38%), protein (6%–13%), carbohydrates (65%–75%), minerals (2.5%–3.5%) and dietary fiber (18%) (Ulagnathan and Nirmalakumari, 2014). It provides 8-10 times more calcium than that of rice and wheat. As concerned to global food security for growing population, it is mandatory to enhance the production and productivity of ragi crop. In plant breeding programme, genetic improvement through conventional breeding approaches like hybridisation, selection depends mainly on availability of diverse germplasm with presence of enormous genetic variability. The maximum utilization of any species for breeding and its adaptation to different environments depend on the level of genetic diversity it holds. Genetic distance among parents may be attributed to their differences for number of genes and their functional relations in a given environment (Nei, 1976). Evaluation of genetic divergence and relatedness among breeding materials has significant implications for the improvement of crop plants. Knowledge on genetic diversity in finger millet could help breeders and geneticists to understand the structure of germplasm, predict which combinations would produce the best offsprings and facilitate to widen the genetic basis of breeding material for selection (Singh et al., 2013). Progenies derived from diverse parents selected on basis of genetic divergence analysis are expected to show a broad spectrum of genetic variability, providing a greater scope for isolating transgressive segregant in advanced generation (Singh and Mishra, 1996) and promising heterotic effect may be observed in early generation. Mahalanobis D2 statistics-based clustering is powerful tools in quantification of genetic divergence among germplasm accessions with respect to productive per se traits. Assessment of genetic diversity in finger millet using Mahalanobis D2 statistics has previously been done by many researchers (Kadam, 2008; Karad and Patil, 2013; Mahanthesha et al., 2017; Negi et al., 2017). Therefore, the present study was undertaken to estimate the genetic divergence in a set of 145 finger milletgermplasm and explore potential to evaluate the relationship of these genotypes based on quantitative trait data. MATERIALS AND METHODS The current investigation on assessment of genetic divergence for yield and its contributing traits in finger millet germplasm accession was carried out during kharif, 2019 at Zonal Agricultural Research Station (ZARS), V. C. Farm, Mandya represents Southern Dry Zone of Karnataka (Zone 6) which is located at 12°32’ N latitude and 76°53’ E longitude and an altitude of 716 m above MSL. The material for present study was provided by ICAR- Indian Institute of Millets Research (IIMR) Rajendranagar, Hyderabad. The experiment was carried out in a randomized complete block design (RCBD) with two replications consisted of 145 germplasm accessions along with three checks viz. VL 352, GPU 67, and MR 6. The recommended package of practices was followed during crop growth period to raise a good crop. The observations on 11 quantitative traits viz., plant height (cm), productive tillers plant-1,days to 50% flowering, days to maturity, fingers ear-1, finger length (cm), ear head length (cm), peduncle length (cm), seed yield plant-1 (g), straw yield plant-1and 1000-seed weight (g) were recorded on five randomly selected plants from each germplasm accession in each replication. Estimation of genetic divergence was done by multivariate analysis using Mahalanobis (1936). All the D2 values were calculated using Tocher’s method as described by Rao (1952). The inter and intra cluster distances were calculated by the formula given by Singh and Chaudhary (1977). RESULTS AND DISCUSSION Selection of parents based on the extent of genetic divergence has been successfully utilized in different crop species by different researchers (Sharma et al., 2021). The investigated genotypes were organized into 10 clusters using Tocher’s method (Rao, 1952). The clusters were constructed in such a way that the genotypes within a cluster had lower D2 value than those of the other diverse clusters. The distribution pattern of genotypes into diverse clusters was furnished in Table 1. Out of 10 clusters, maximum number of accessions were organized into cluster I (135 accession including 3 checks) followed by cluster V with 5 accessions. The clusters II, III, IV, VI, VII, VIII, IX, X (i.e., 8 clusters) were constituted by a single accession that revealed the presence of high degree of heterogeneity among accessions. Based on 11 quantitative traits, Euclidean genetic distances were estimated between different genotypes of finger millet. The average intra and inter cluster distances D2 values were presented in Table 2 and Fig. 1. The maximum intra cluster D2 values was recorded for cluster V (108.52) followed by cluster I (91.89). The intra cluster distances ranged from zero (solitary clusters) to 108.52 (Cluster V). Zero intra cluster distance was observed in cluster II, III, IV, VI, VII, VIII, IX and X represented by single accession. These results indicate that genotypes contributing to same clusters have a low diversity and the selection within a cluster is not considered as satisfactory. A broad range of inter cluster distance were recorded from 37.95 (among cluster IV and VI) to 448.27 (among cluster II and VIII). The maximum inter cluster D2 value (448.27) was observed between cluster II (accession 3855) and VIII (accession 4866) followed by D2 value (436.08) among cluster II and IV, revealed that maximum heterosis can be anticipated from the crosses with parents belonging to theses most diverse clusters i.e., cross among 3855 × 4866 and 3855× 6526 accessions as parents could be used further in hybridization programme for enhancing the productivity of finger millet. Similar results were reported by Kadam (2008); Kumar et al. (2010) ; Devaliya et al. (2017). Therefore, the accessions from diverse clusters mentioned above can be used as parent in future breeding programme to develop desirable segregates. Cluster means of the traits: The cluster mean for all quantitative traits under investigation were illustrated in Table 3. Cluster III was recorded the highest mean values of seed yield plant-1 (18.75), straw yield plot-1 (0.89), earhead length (15.80), peduncle length (34.80), productive tillers plant-1 (5.30) and plant height (107.00). Accession from cluster VII i.e., 6754 was found the lowest mean values for seed yield plant-1 (9.06), straw yield plot-1 (0.26), earhead length (6.30), peduncle length (20.20), days to 50% flowering (58.50), finger length (4.55), finger number ear-1 (4.90) and plant height (59.70). Genotype of cluster III revealed the possibility of selecting directly one genotype that can be used for yield enhancement as it showed highest cluster mean for yield and important contributing traits. The present findings were in agreement with the findings of Kadam (2008); Kumar et al. (2010); Mahanthesha et al. (2017); Devaliya et al. (2017). Relative contribution of characters towards genetic divergence: The numbers of times that each one of the 11 characters showed up 1st rank and their corresponding per cent contribution to genetic divergence were furnished in Table 4. Among all traits under investigation, 1000 seed test weight (34.39%), straw yield plot-1(17.09%), finger length (12.21%), days to maturity (9.28%), days to 50% flowering and seed yield plant-1 (8.08%) were reported the higher contribution towards the genetic divergence. Similar results were observed by Negi et al. (2017) for days to 50% flowering, straw yield plot-1 and peduncle length. The findings for plant height and productive tillers plant-1 were in accordance with Mahanthesha et al. (2017).