Horsegram is extensively cultivated in southern states
i.e. Karnataka, Andhra Pradesh and Maharashtra and to some extent in parts of West Bengal, Bihar, Himachal Pradesh, Orissa, Chhattisgarh and foot hills of Uttar Pradesh
(Purushottam et al., 2017). In India, horsegram covers an acreage of 0.5 million hectares with production and productivity of 0.26 million tones and 520 kg/ha respectively (Anonymous, 2015).
Horsegram cultivating lands are resource challenging/demanding which requires development of climatic resilience genotypes. The climatic challenges include extremities of drought and cold, salinity and diseases. Because of these constraints, the yield potential of horsegram is not fully realized. Additionally, it is being grown extensively in
rabi season due to photosensitivity nature. Development of multiple tolerant and photo-insensitive varieties with improved yield would extend its cultivation in non-traditional areas thereby yield gap can be compensated.
Presence of wide spectrum of genetic variability is essential for any crop improvement strategy. Genetic diversity can be exploited through collection and evaluation of germplasm lines and genotypes of a crop, which is pre-requisite for any breeding programme
(Ramakrishnan et al., 2014). The analysis of variance revealed highly significant differences for all the traits included in the study (Table 1). However, it is not a reliable measure for prediction of divergence among genotypes
(Geetha et al., 2011). Knowledge on quantitative divergence of the traits contributing to yield is of primary concern for a plant breeder, which was widely fulfilled by multivariate (D
2) analysis.
D
2 statistic is a powerful tool to measure the genetic divergence within set of genotypes
(Murty et al., 1966; Dasgupta et al., 2005). Based on estimates of D
2 statistic, 252 accessions were grouped into 25 clusters (Table 2) with generalized distance ranging from 0.00 to 52.79. Traits
viz., number of pods per plant (34.14%), hundred seed weight (18.82%), days to fifty per cent flowering (16.97%), number of clusters per plant (10.21%) contributed maximum towards genetic divergence whereas, least contribution was made by days to maturity (0.10%) and number of primary branches per plant (0.35%). It was noted that most of the yield related traits had exhibited considerable contribution towards cluster divergence hence, probability of improving these traits turns to be feasible by hybridization and selection (Fig 1). The results were in conformity with the findings of
Geetha et al., (2011).
Among the 25 clusters, maximum accessions were grouped under cluster I (83 genotypes) followed by cluster II (77 genotypes), cluster IV (33 genotypes) and cluster III (30 genotypes). On the contrary, 20 solitary clusters
viz., cluster V to XXV except cluster XIV were formed, which could be given priority for improvement of specific traits. Clusters with single genotype were high owing to reason that experimental accessions were collected through various explorations across Indian States over years. These geographical variations might have yielded more number of solitary clusters. The intra and inter cluster distance among 25 clusters were presented in Table 3. The highest intra cluster distance was noticed in cluster XIV (23.63) followed by cluster IV (22.37), cluster III (17.26), cluster II (15.73) and cluster I (15.52) while the remaining 20 clusters were found to be unique cluster possessing no intra cluster value. The genotypes present within cluster tend to be genetically less diverse compared to other clusters. Maximum inter cluster distance was observed between solitary clusters
viz., cluster XXI and XVII (52.79) followed by cluster XXI and XV (50.97) and cluster XXI and VII (50.97). Promoting hybridization between diverse genotypes would be highly heterotic thereby evolve superior varieties
(Singhal et al., 2010). The least inter cluster distance was recorded between cluster XIX and VII, cluster XVIII and X and cluster XVIII and XII with a distance of 13.02, 12.33 and 12.53 respectively.
The average cluster mean values for 12 traits would help to identify genotypes for improving specific yield components (Table 4). Cluster XIII with single genotype and Cluster XIV with 9 genotypes recorded lower mean value for days to fifty percent flowering whereas, cluster IX and XIII for days to maturity. These genotypes could be utilized as a donor for evolving short duration types. Among the germplasm accessions, cluster XXI (PLS 6105) was found to be high yielding line with mean yield of 65.51 g. Cluster XXII (PLS 6004) recorded the highest group mean for yield components
viz., number of clusters per plant, number of pods per plant and pod width. Being a unique cluster, the genotype can be used in crossing programme to evolve high yielding varieties as it exhibits superior
per se performance for several yield components. A solitary cluster XI (PLS 6224) recorded the highest mean for number of pods per cluster (4.35).
Neelam et al., (2014) reported significant correlation between number of pods per plant and single plant yield. Hence, accession PLS 6224 can be utilized for yield improvement by breeding for increased number of pods per plant. Several other unique clusters were also contributed for yield components
viz., cluster XIX (8513/4-3) recorded the highest mean yield with 5.32 g for hundred seed weight, cluster XVIII (PLS 6221) possessed highest mean value for number of seeds per pod (7.12 g) and cluster X (PLS 6030) for number of primary branches per plant (10). These genotypes can be used as a donor to derive agronomically superior varieties. On a nutshell, it was concluded that horsegram accessions taken for study revealed wide range of genetic diversity and many unique clusters with promising accessions were formed contributing to the improvement of specific yield components (Table 5). These unique clusters can be given due importance in the future breeding programme to evolve superior types. These diverse genotypes shall be utilized in different research institutes.