DOI: 10.36347/sjavs.2015.v02i01.010

Pages: 54-60

Quantitative trait analysis of eggplant seedlings (Solanum species) was undertaken in the North Central zone of Nigeria with a view to revealing the level of diversity and taxonomic relationships of the collected species. 60 seeds of the various eggplant accessions were randomly collected and planted in a controlled screen house environment. A total of 52 accessions were characterized using sixteen (16) quantitative characters betweenday 4 to31 of planting. In each accession, five (5) seedlings were phenotypically investigated and the mean value of the variables was noted. Data of quantitative characters were analyzed using the SPSS (Statistical Package for the Social Scientists) software programme (20.0 versions). Cluster analysis was performed using the Average Linkage (between group) method based on Euclidean distance which generated a dendrogram for the accessions. Histograms and normal distribution curves were generated which revealed the mean values and deviations of the characters. Degree of relationships amongst the variables was determined through the Pearson’s Product Moment Correlation. The results revealed substantial variations in most of the quantitative traits. Emergence of first seed leaf varied between day 4 to day 9 of planting while the percentage of germinated seeds ranged from 3.3% to 98.3% amongst the accessions. Clustering patterns which revealed a high level of intraspecific and interspecific variability were of two types: Location dependent and location independent. Interspecific similarity was also observed which indicates a common ancestral relationship of the species. Plant height was positively correlated with petiole length (0.754), plant total weight (0.903) and number of leaves (0.738). This quantitative approach of combining morphological and embryological taxonomic evidence has made available detailedtaxonomic relationships information of eggplant seedlings in Nigeria due to the dearth of such information.The implication of the find