This research aim to assess morphological diversity of the elite chickpea (Cicer arietinum L.) varieties in Ethiopia. Nineteen elite varieties of chickpea in Ethiopia were used to analyze the means and components of variability (genetic, phenotypic and environmental), and interrelationships (genetic and phenotypic) for yield and various other yield components. Such nineteen varieties were planted by the technique of Randomized Complete Block Design (RCBD) and three replications were used. Each genotype was sown in four rows with 4.8 m2 (1 m x 4.8 m) plots area, with 40 cm and 1 m spacing between plots and blocks, respectively. In each plot, one hundred and sixty seeds were planted, using 10 cm spacing between plants These nineteen elite varieties of chickpea were evaluated for the traits of hundred seed weight, biological yield, grain yield, plant height, days to 50% flowering, number of primary branches, number of secondary branches, number of pods per plant, number of seeds per plant, harvest index and days to 90% maturity. Genetic variations were evident among released chickpea cultivars as confirmed by high phenotypic and genotypic variations for quantitative and qualitative traits. Analysis of variance revealed significant differences among the genotypes for all the characters except hundred seeds weight, days of 50% flowering and grain yield. Strong and positive significant correlation was observed between grain yield, biological yield, number of seeds per plant, number of pods per plant and number of primary branches; showing that their improvement led to yield improvement in chickpea. The result suggested from the mean values of number of seeds per plant, number of pods per plant and days of maturity that chickpea genotypes ICCV-14808, Mariye and ICCV-92069 may be used as parents in further breeding program to develop high yielding cultivars. Principal component analysis revealed that quantitative traits contributed a lot to chickpea genetic variability.
Key words: Agronomic characters, correlation coefficients, elite, principal component analysis, variation
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