Abstract

Nigella sativa (L.) a member of the family Ranunculaceae is being used for edible and medicinal purposes in several countries of the world. This study revealed the analysis of genetic diversity of thirty six (36) genotypes of N. sativa based on yield traits, nutritional characteristics, and mineral nutrients. Two genotypes PK - 020561 and PK - 020646 differed significantly from other 34 genotypes with respect to grain yield (P = 0.0007), biomass (P = 0.049), oil content (P = 0.001), nitrogen (P = 0.0117), calcium (P = 0.0164) and boron (P = 0.0004). These results indicated the scope of these traits for selection of genotypes. Eigen values were greater than 1 indicating the significance of principal component analysis. Five and six clusters were observed for nutritional characteristics and mineral nutrients respectively. Based on principal component analysis, yield traits contributed 86% of the variability pertained to nutritional characteristics, whereas four factors contributed 68% of the genotypes variability based on mineral nutrients. The genotype PK - 020646 produced the highest biomass, harvest index, grain yield, oil content, protein, moisture, fibre, N, Pb, Co and Mn, whereas PK - 020561 produced the highest Ca, Mg, Pb, Zn, Mn, Na and P contents. UPGMA cluster analysis based on nutritional characteristics revealed five distinct clusters with genotypes which were markedly different. Thus, all clusters were cluster 5 and cluster 1, dominating other clusters. Based on thirteen (13) mineral components, all genotypes formed six clusters, with clusters 4 and 5 having 11 and 10 genotypes. Thus, Euclidean distance among genetically distinct clusters supports different patterns of yield and mineral nutrients. The overall grouping of clusters corresponded with principal component analysis confirming the genetic variability among genotypes. These genotypes aresuggested for their further application in the genetic improvement of N. sativa L, for their use as quality food to substitute Nigella cultivars currently being used.

Key words: Biodiversity, black seed, cluster analyses, kalonji, physico-chemical traits, principal components’ analysis.