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Study the relationships between seed cotton yield and yield component traits by different statistical techniques

By: Ashraf Abd El-Aala Abd El-Mohsen, Mohamed Mostafa Amein

Key Words: Egyptian cotton, seed cotton yield, correlation and regression analysis, stepwise multiple regression, path and factor analysis.

Int. J. Agr. Agri. Res. 8(5), 88-104, May 2016.


Two field experiments were conducted in 2013 and 2014 growing seasons at the experimental farm of the Faculty of Agriculture, Cairo University, Giza, Egypt. Twenty Egyptian cotton genotypes were evaluated in a randomized complete blocks design with three replications for six traits. The aim of this study was to determine the relationships between seed cotton yield and yield components and to show efficiency of components on seed cotton yield by using different statistical procedures. Data of seed cotton yield and yield components over the two years in the study were evaluated by statistical procedures; correlation and regression analysis, path coefficient analysis, stepwise multiple linear regression and factor analysis. Differences among all the traits were statistically highly significant. Seed cotton yield plant-1 was significantly and positively correlated with number of bolls plant-1 (r = 0.85**), boll weight (r = 0.68**), seed index (r = 0.91**) and lint percentage (r = 0.70**). Regression analysis by using step-wise method revealed that 96.51 percent of total variation exist in seed cotton yield accounted for by traits entered to regression model namely; number of bolls plant-1, boll weight and lint percentage. The path analysis indicated high positive direct effect of number of bolls plant-1 (0.57), boll weight (0.39) and lint percentage had moderate positive direct effect (0.24) on seed cotton yield plant-1. Factor analysis indicated that three factors could explain approximately 73.96% of the total variation. The first factor which accounted for about 53.21% of the variation was strongly associated with number of bolls plant-1, boll weight, seed index and lint percentage, whereas the second factor was strongly associated and positive effects on earliness index only, which accounts for about 20.75% of the variation. Stepwise multiple regression and path analysis techniques were more efficient than other used statistical techniques. Based on the five of statistical analysis techniques, agreed upon that high seed cotton yield of Egyptian cotton could be obtained by selecting breeding materials with high number of bolls plant-1 , boll weight and lint percentage.


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