Assessment of phenotypic diversity and cluster analysis in barnyard millet (Echinochloa frumentacea (Roxb.) Link) genotypes

Introduction

From a taxonomic standpoint, barnyard millet is a self-pollinating allohexaploid (2n = 6x = 54) placed within the genus Echinochloa, in the family Poaceae and sub-family Panicoideae (Clayton and Renvoize, 2006) [3] . It is raised as a dual-purpose crop valued for both grain and fodder, and two species are cultivated worldwide: Echinochloa frumentacea, known as Indian barnyard millet, and Echinochloa esculenta, or Japanese barnyard millet (Sood et al., 2015) [15] . These were domesticated respectively from the wild progenitors Echinochloa colona and Echinochloa crus-galli (Singh et al., 2025) [14] . India is the world's leading producer, growing the crop mainly on marginal, rainfed land where poor soil fertility and cold temperatures at high elevation make rice impractical. Its cultivation stretches across a remarkably wide range of environments, from coastal Andhra Pradesh to the hill districts of Uttarakhand and the higher elevations of the north-eastern states, underlining its broad adaptability (Hasan et al., 2025) [5] . Nutritionally, it compares well with major and minor cereals alike: every 100 g of grain provides roughly 398 kcal, 10.5% protein, 3.6% fat, 68.8% carbohydrates, and 12.6% dietary fibre, and its iron and zinc content exceeds that of major cereals (Ugare et al., 2014; Maurya et al., 2023) [16,6] . Being gluten-free and low on the glycaemic index, it also helps in managing type 2 diabetes, obesity, and cardiovascular disease (Hasan et al., 2025) [5] . Genetic diversity is the foundation of any crop improvement effort, since it provides the variability that effective selection, parental choice, and the development of better cultivars all depend on (Goron and Raizada, 2015; Bhinda et al., 2023) [4,2] . Assessing genetic divergence within germplasm collections allows breeders to identify parents distinct enough to generate wide variability and transgressive segregants in later generations (Mehta et al., 2005; Nehru et al., 2023) [7,9] . Identifying genotypes that pair genetic diversity with sound agronomic performance is therefore essential for broadening the genetic base of breeding populations and accelerating the development of high-yielding, nutrient-rich, climateresilient barnyard millet cultivars (Bhinda et al., 2023; Singh et al., 2025) [2,14] . With this context in mind, the present study was designed to assess genetic diversity among 200 barnyard millet genotypes using Ward's hierarchical clustering based on Euclidean distance, aiming to identify genetically divergent parents for future crop improvement programmes.

Materials and Methods

This study evaluated 200 barnyard millet genotypes, including released cultivars, advanced breeding lines, and diverse germplasm accessions, at the research farm of ICAR-Indian Institute of Millets Research, Hyderabad, Telangana, India, during kharif 2025. The trial was laid out in an Alpha Lattice Design with two replications, each split into ten blocks, and the crop was managed under recommended agronomic practices to ensure uniform establishment and healthy growth throughout the season. Observations on five randomly selected plants per genotype were used to record ten quantitative traits: seedling height, days to 50% flowering, days to maturity, thousand-seed weight, number of tillers, plant height, panicle length, panicle width, dehulling efficiency, and grain yield, with days to 50% flowering, days to maturity, and grain yield recorded on a plot basis rather than per plant. Trait relationships were examined using Pearson's correlation analysis (Pearson, 1948) [11] , and Ward's hierarchical clustering based on genetic distance was used to assess genetic diversity and group the genotypes into distinct clusters.

Results and Discussion

Character association Grain yield is a complex quantitative trait shaped by the combined influence of several morphological and yieldrelated characters. Understanding how these traits relate to one another is therefore important for defining effective selection strategies and raising yield potential in breeding programmes. Genotypic and phenotypic correlation coefficients between grain yield and its component traits are presented in Table 1. For most traits, the genotypic correlation coefficients exceeded the corresponding phenotypic values, suggesting that the observed associations are mainly under genetic control, with the environment playing a comparatively minor role in shaping trait expression. At both the phenotypic and genotypic levels, grain yield showed a significant, positive association with days to 50% flowering (0.198**; 0.331**, respectively), plant height (0.270**; 0.275**, respectively), and panicle width (0.207**; 0.2969**, respectively). Because these correlations are positive and significant, the traits concerned could serve as effective selection criteria for raising grain yield in barnyard millet. Similar positive links between grain yield and days to 50% flowering, plant height, and panicle width have previously been reported in barnyard millet by Prakash and Vanniarajan (2014) [13] and Prabu et al. (2020) [12] , reinforcing the case for these traits as useful indirect selection criteria for improving grain yield. Notably, dehulling efficiency was negatively correlated with thousand seed weight at both the phenotypic and genotypic levels (-0.022 NS , -0.0233 NS ), suggesting that seed size alone does not fully account for variation in dehulling efficiency. This negative association implies that selecting purely for higher thousand-seed weight could inadvertently lower dehulling efficiency, posing a challenge for breeding programmes seeking to improve both traits together. 1** Note: SH-Seedling height, DFF-days to 50% flowering, DM-days to maturity, TSW-thousand seed weight, NOT-number of tillers, PHplant height, PL-panicle length, PW-panicle width, DE-dehulling efficiency, GY-grain yield P-phenotypic correlation, G-genotypic correlation Mean performance of morphological traits Table 2 presents the mean values recorded for the ten traits: seedling height (14.0 cm), days to 50% flowering (53.2 days), days to maturity (90.4 days), plant height (143.6 cm), number of tillers (3.2), panicle length (21.9 cm), panicle width (3.00 cm), thousand-seed weight (3.80 g), dehulling efficiency (58.4%), and grain yield (398.7 g). Among the genotypes, PRJ-1 flowered and matured earliest, recording the lowest values for both days to 50% flowering (40 days) and days to maturity (79 days). PRJ-1 is normally grown as a medium-duration, high-yielding variety in the hill regions of Uttarakhand, and the change in altitude and photoperiod under the present trial conditions appears to have brought on shorter stature, earlier flowering and maturity, and lower grain yield than usual. The smallest seeds were produced by BAR 317, with a test weight of 2.63 g, while IIMR-BM-R2425-1411 gave the highest grain yield per plot (868 g), though its dehulling efficiency was comparatively low (23.75%). BAR 211 had the lowest plant height (73.9 cm) together with the shortest panicle length (14.3 cm).

Ward's hierarchical clustering

Ward's hierarchical clustering sorted the 200 genotypes into five distinct clusters (Fig. 1), reflecting substantial genetic diversity within the germplasm. Cluster 3 was the largest group, with 57 genotypes (28.5%), while Cluster 2 was the smallest, with 21 genotypes (10.5%), pointing to a small set of relatively unique, elite genotypes (Table 3). Intra-cluster distances ranged from 3.03 to 3.97, with Cluster 5 showing the greatest internal diversity (3.97), reflecting high heterogeneity among its 32 member genotypes, and Cluster 1 the greatest homogeneity (3.03). Inter-cluster distances varied more widely, from 3.77 to 6.82. Genetic divergence peaked between Cluster 4 and Cluster 5 (5.82), followed closely by Cluster 2 and Cluster 5 (5.50), while the smallest distance appeared between Cluster 1 and Cluster 3 (3.84), pointing to a relatively close genetic relationship between the two. The genotypes evaluated also showed significant phenotypic variation, as summarised in Table 4, echoing the broad genetic diversity reported earlier in barnyard millet by Mehta et al. (2005) [7] , Nehru et al. (2023) [9] , and Arya et al.

(2018) [1] . Grain yield ranged from a minimum of 131.00 in Cluster 5 to an elite maximum of 796.00 in Cluster 4. Cluster 5 held the earliest-maturing (DFF minimum: 40.00) and shortest (PH minimum: 96.14 cm) genotypes, while Cluster 4 was marked by late maturity and the tallest plants.

Conclusion

Overall, this study points to considerable genetic variability across the 200 barnyard millet genotypes evaluated, confirming that the germplasm holds valuable resources for crop improvement. The significant, positive associations of grain yield with days to 50% flowering, plant height, and panicle width indicate that these traits can act as reliable indirect selection criteria in breeding programmes aimed at raising productivity. Ward's hierarchical clustering successfully resolved the germplasm into five genetically distinct groups, highlighting the extent of genetic divergence within the collection. Taken together, these findings provide a solid foundation for the strategic use of barnyard millet germplasm in future breeding work, supporting the development of high-yielding, nutritionally enhanced, and climate-resilient cultivars for sustainable millet production.

References

1. Link) germplasm. International Journal of Bio-resource and Stress Management. 2018;9(2):192-196.
2. Bhinda MS, Joshi DC, Parihar M, Meena RP, Joshi P, Gupta A, et al. Genetics, breeding, and genomics of Indian barnyard millet (Echinochloa frumentacea): status and perspectives. In: Neglected and underutilized crops. 2023. p. 115-135.
3. Clayton WD, Renvoize SA. Genera graminum. Grasses of the world. 2006.
4. Goron TL, Raizada MN. Genetic diversity and genomic resources available for the small millet crops to accelerate a New Green Revolution. Frontiers in Plant Science. 2015;6:157.
5. Hasan N, Budakoti M, Meena RP, Bhinda MS, Rani V, Nandini C, et al. Barnyard millet. In: Millets: Crops for climate resilience and for food and nutritional security. 2025. p. 67-91.
6. Maurya NK, Singh J, Gupta E. Barnyard millet's health and nutritional significance. International Journal of Agriculture Innovation. 2023;8(2):156-160.
7. Mehta H, Tyagi PC, Mohapatra KP. Genetic diversity in barnyard millet (Echinochloa frumentacea Roxb.). Indian Journal of Genetics and Plant Breeding. 2005;65(4):293-295.
8. Mohammadi SA, Prasanna BM. Analysis of genetic diversity in crop plants-salient statistical tools and considerations. Crop Science. 2003;43(4):1235-1248.
9. International Journal of Environment and Climate Change. 2023;13(10):3094-3110.
10. Rao CR. Advanced statistical methods in biometrical research. New York: John Wiley & Sons; 1952.
11. Pearson K. Early statistical papers. Cambridge: University Press; 1948.
12. Prabu R, Vanniarajan C, Vetriventhan M, Gnanamalar RP, Shanmughasundaram R, Ramalingam J. Association studies in barnyard millet (Echinochloa frumentacea (Roxb.) Link) for early maturity and yield contributing traits at high altitude region. Electronic Journal of Plant Breeding. 2020;11(1):192-196.
13. Singh B, Pandey S, Nivedhitha S, Shekhawat N, Singh M, Jat B, et al. Comprehensive phenotyping of 1,807 Indian barnyard millet (Echinochloa frumentacea Link) accessions from Indian national genebank: unlocking diversity for core set development. Frontiers in Plant Science. 2025;16:1644491.
14. Sood S, Khulbe RK, Gupta AK, Agrawal PK, Upadhyaya HD, Bhatt JC. Barnyard millet-a potential food and feed crop of future. Plant Breeding. 2015;134(2):135-147.
15. Ugare R, Chimmad B, Naik R, Bharati P, Itagi S. Glycemic index and significance of barnyard millet (Echinochloa frumentacae) in type II diabetics. Journal of Food Science and Technology. 2014;51(2):392-395.
16. Vanniarajan C, Prabu R, Vetriventhan M, Gnanamalar RP, Shanmughasundaram R, Ramalingam J. Association studies in barnyard millet (Echinochloa frumentacea (Roxb.) Link) for early maturity and yield contributing traits at high altitude region. Electronic Journal of Plant Breeding. 2020;11(1):192-196.