Menshawy,, A., El-Hag, A., Khedr, A. (2005). GENETIC DIVERGENCE ANALYSIS AMONG 56 BREAD WHEAT GENOTYPES. Journal of Plant Production, 30(1), 61-70. doi: 10.21608/jpp.2005.237089
A. M.M. Menshawy,; A. A. El-Hag; A. H. Khedr. "GENETIC DIVERGENCE ANALYSIS AMONG 56 BREAD WHEAT GENOTYPES". Journal of Plant Production, 30, 1, 2005, 61-70. doi: 10.21608/jpp.2005.237089
Menshawy,, A., El-Hag, A., Khedr, A. (2005). 'GENETIC DIVERGENCE ANALYSIS AMONG 56 BREAD WHEAT GENOTYPES', Journal of Plant Production, 30(1), pp. 61-70. doi: 10.21608/jpp.2005.237089
Menshawy,, A., El-Hag, A., Khedr, A. GENETIC DIVERGENCE ANALYSIS AMONG 56 BREAD WHEAT GENOTYPES. Journal of Plant Production, 2005; 30(1): 61-70. doi: 10.21608/jpp.2005.237089
GENETIC DIVERGENCE ANALYSIS AMONG 56 BREAD WHEAT GENOTYPES
1National Wheat Research Program, Field Crops Res. Institute, ARC.
2Intensification Research Dept., Field Crops Research Institute, ARC.
Abstract
Genetic diversity analysis could be used to identify those genotypes, which are useful not only for sampling in subsequent studies but also for parental selection in breeding programs. To test these applications in bread wheat (Triticum aestivum L.), relationships among 56 wheat genotypes were measured using cluster analysis of earliness components and grain yield characters. Entries were planted in replicated field trails in three sowing dates i.e. 1Oct.,29 Oct. and 26 Nov. Data were obtained for days to heading, days to maturity, grain filling period, grain filling rate and grain yield in 2003/2004 season at Sakha Agric. Res. Station.
The genetic divergence among genotypes on Euclidean distance revealed some sort of dissimilarities between genotypes for earliness, plant height and grain yield characters. Grain filling rate and days to heading were the most important source of variation among genotypes. At 75 Euclidean distances, the 56 genotypes were distributed in three different clusters. At a similarity lower than 50 Euclidean distance, the 56 genotypes were distributed in six different clusters. Moreover, at 25 Euclidean distance, the studied genotypes divided into 13 clusters, while, entry 15 formed a single cell cluster.
Clustering method was effective in detecting the yielding and earlier clusters, which have almost similar genotypes in such attributes. The selected clusters based on clustering pattern at 25 Euclidean distance appeared to be more accurate than those at 50 Euclidean distance.
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