Abstract
This investigation was carried out during 2018/2019 season in three locations Homs, Al-Swaida and Tartous belongs to the General Commission for Scientific Agricultural Research in Syria, using 17 Italian, Syrian and Ethiopian wheat genotypes to estimate the potential diversity by principle component and cluster analysis, and to study the structural modeling between grain yield and other traits to define best traits as predictors and selection indexes of grain yield, and to determine the superior genotypes in grain yield. Results indicated a remarkable variation of 74% due only to the first four principle components with Eigen value > 1. PC Biplot showed that Tartous was the best location, and the genotype SD09 was superior in grain yield per plant followed by SH5 and IP39. Structural modeling results revealed that the total and fertile tillers number per plant were the best predictors for grain yield per plant, while fertile tillers per plant with grain weight per spike could be used as selection indexes of wheat grain yield because they had positive strong direct effect on grain yield per plant. Cluster analysis results confirmed the need to assess more various genotypes from different origins.
Highlights
Wheat is the most important grain crop in the world which provide people with almost 50% of the required calories [1]
Structural modeling results revealed that the total and fertile tillers number per plant were the best predictors for grain yield per plant, while fertile tillers per plant with grains weight per spike could be used as selection indexes of wheat grain yield because they had positive strong direct effect on grain yield per plant
The objectives of this investigation were to: (i) evaluate the magnitude of potential diversity between exotic and local wheat genotypes by using principal component analysis and cluster analysis, (ii) study the nature of structural modeling between grain yield and other traits via Regression and path analysis, (iii) define the superior genotypes regarding grain yield in various locations to be used in breeding programs
Summary
Wheat is the most important grain crop in the world which provide people with almost 50% of the required calories [1]. Al-Otayk [4] applied principle component analysis to study the variation in wheat germplasm, their results showed remarkable variation among them. Abd El-Mohsen [8] mentioned that prediction of grain yield via other traits can be applied by regression analysis. The objectives of this investigation were to: (i) evaluate the magnitude of potential diversity between exotic and local wheat genotypes by using principal component analysis and cluster analysis, (ii) study the nature of structural modeling between grain yield and other traits via Regression and path analysis, (iii) define the superior genotypes regarding grain yield in various locations to be used in breeding programs
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