Grouping of Kabuli chickpea genotypes using multivariate statistical methods

Document Type : Original Articles

Authors

• Hadimohammad Alipoor Yamchi
• Mohammadreza Bihamta
• Seyyed Ali Peyghambari
• Mohammadreza Naghavi
• Naser Majnoon Hoseini

10.22067/ijpr.v1392i2.41258

Abstract

In order to assess and identify genetic diversity and genetic relationships of the 64 Kabuli chickpeas genotypes, an experimental design was carried out in a simple lattice design (8×8) in 2009-2010 cropping season on the Research Field of College of Agriculture and Natural Resource of Tehran University. The results of phenotypic correlation showed that grain yield per plant had significant and positive correlation with seed and pod weight, biological yield, number of filled pods, 100 seed weight, number of seeds per plant, seed diameter and main branch diameter at 1% level probability and negative correlation with day to 50% flowering and days to 50% podding. Based on factor analysis, four factors were selected that in total 79% of the total variation was explained. The first and second factors were explained high percent of variation that including 100-seed weight, plant height, pod length, pod diameter, seed length, seed diameter, number of filled pods, seed and pod weight, biological yield, grain yield and main branch diameter. Therefore, these two factors used to identify genotypes with high yield and yield components and genotypes 2, 22, 29, 36, 120, 139, 198, 239, 335, 345, 356, 357, 375, 473, 474, 534, 552, 555 and 629 with two control genotypes Jam (998) and Korosh (999) were selected as high yield and component yield genotypes. According to the result of cluster analysis, the genotypes were classified in 5 clusters. The genotypes of third and fourth cluster had high yield and earliness in compare with other clusters and genotype average. According to the result of cluster analysis, we can use third and fourth cluster genotypes and two control genotypes (Jam and Korosh) for producing new genotypes with high yield.

Keywords

• Cluster analysis
• Factor analysis
• Kabuli Chickpea
• morphological traits
• Multivariate analysis of variance

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