Horse gram is the arid
rabi legume provides ray of scope of nutrient sustainability in the Indian marginal lands. The yield of horse gram is hampered by few inherent agronomic traits linked to growth and duration. The tune of variability is the basic requirement of crop improvement program
(Bhandari et al., 2017), horse gram is not an exception. Horse gram improvement is slowed by limited variability
(Chahota et al., 2013). Induced mutagenesis widens the variability
(Priyanka et al., 2021a and b). Ascertaining the breeding value of a mutant population would strengthen the breeding program. The analysis of variance (ANOVA) is used to understand the divergence for traits in a population. In the current study it displayed statistically significant difference for all the eleven quantitative traits (Table 1) which indicated the presence of wide variability. Earlier
Priyanka et al., (2019) also reported significant difference among the yield attributing traits and utility of D
2 statistic. Significant variation due to replication was witnessed which was possibly due to variation in fertility gradient at blocks as the experiment was laid out along with field fertility gradient and large number of genotypes.
The 110 mutants and two checks were grouped into ten clusters (Table 2). The cluster I was the largest with 38 mutants followed by II (31 mutants), V (19 mutants), IV (11 mutants) and III (7 mutants). The number of solitary clusters was quite high (Table 2) which indicating the potential of induced mutagenesis in evolving unique mutants as the study material were not collected from different geographical regions. An array of useful trait specific mutants was also identified for further utilization.
Priyanka et al., (2021b) established the potential of induced mutagenesis in variability evolution and formation of solitary clusters by
Varma et al., (2013).
The induced mutagenesis created the maximum variability for plant height (32.24%), 100 seed weight (25.42%) and number of pods per plant (19.18%) while it failed to induce variability for days to maturity (Fig 1). Therefore, the probability is high to improve the test traits except days to maturity by classical hybridization and selection thereon. This is in confirmatory with the earlier findings of
Priyanka et al., (2021b).
The highest intra cluster distance exhibited by cluster V (19.94) and cluster IV (19.93) (Table 3) revealed the wide genetic base and thus could be utilized for genetic advancement (Puneet, 2004). Zero intra cluster distance was observed between the clusters VI, VII, VIII and IX, indicating the close relatedness. The measurements of inter cluster distance is an indicative selection tool for identification of promising clusters. The maximum inter cluster distance was noticed between the clusters X and VIII (67.18), followed by clusters X and IX (65.57) and clusters X and III (63.94) mooted the idea of hybridization between these diverse clusters to evolve heterotic segregants. On contrary, the minimum distance was observed between the clusters VII and II (20.60) followed by cluster VI and II (21.21) indicating the close relatedness of mutants (Durga Kanaka, 2012). The estimate of average cluster mean helps in selection of trait specific mutants (Table 4). Cluster X had the lowest cluster mean values for days to first flowering (32.50), days to fifty percent flowering (35.83) and days to maturity (90.50) and hence favorably be considered to evolve genotypes with early duration. Cluster III (75.04), cluster IX (69.26) and cluster VI (57.25) possessed the highest mean values pronounced their suitability to generate high yielding genotypes. The solitary cluster VI had the maximum mean value for plant height (197.63). The other solitary clusters VII, VIII and IX possessed the highest mean values for number of seeds per pod (6.33), number of pods per plant (578.67) and number of clusters per plant (245.83) respectively. These clusters shall be considered for specific trait improvement.
In the quest, high estimates of GCV and PCV were observed for all the experimented quantitative traits except days for flowering and maturity and that indicated the existence of adequate variability (Table 5). Earlier,
Priyanka et al., (2019) reported wide variability for these traits. However
Alle et al., (2015) reported the lowest GCV and PCV for flowering and maturity. The PCV was found to be slightly higher than GCV indicating the less environmental influence and therefore phenotypic based selection could be relied upon for improvement.
High H
2 (86.66 % - 99.72%) was observed for all traits which indicated the non-significant influence of environment. Combined estimate of H
2 and GAM provides an opportunity to predict genetic gain (Table 5). High heritability coupled with high GAM was observed for all traits indicating the preponderance of additive gene action. Therefore, these traits could be improved by phenotype based selection. High H
2 and GAM were reported for yield attributing traits except flowering and maturity traits
(Joshi et al., 2007).