MULTIVARITE 0F RELATING YIELD COMPONENTS IN A SET OF CORN GENOTYPES

 
This work was conducted at the Experimental Farm of Nasser's   Faculty of Agricultural Sciences in Lahej Governorate, Yemen, during three   seasons 2003, 2004 and 2005. Five statistical procedures of relating yield components to yield; i.e., simple correlation coefficient, the path coefficient analysis, the stepwise regression, the multiple regressions and factor analysis were applied to seven yield contributing characters to determine their functional relationships to yield. Sixteen Maize genotypes were used in this study. Simple correlation coefficient revealed that, number of leaves/plant, ear height, ear length, number of rows/ear, number of kernels/row, 1000-kernel weight and shelling% had the greatest influence on grain yield/h. According to path analysis, weight of 1000-kernel had the greatest direct effect (22.23%) towards grain yield/h. While, number of kernels/row (9.33%) and ear length (9.32%) had the highest indirect effect to grain yield. Multiple linear regressions indicated that the variables which had the highest partial coefficient of determination in seed yield/h, were ear height, ear length, number of rows/ear and 1000-kernel weight (R² = 43%, 22%, 9% and 12%, respectively). The stepwise regression shows that, 1000-kernel weight, number of kernels/row, number of rows/ear and shelling% were accepted variables which had the highest coefficients of determination with seed yield (88.9%). The factor analysis grouped 7 yield contributing characters in two factors, which altogether were responsible for 70.42% of the total variability in the dependence structure.