Phenotypic data analysis of Brix, Pol%Cane and Fiber% traits and phenotypic correlation between them were done using the SAS statistical package (SAS 1990). The analysis of variance was conducted to detect the effects among blocks, progeny clones, parents and checks as also to estimate the coefficient of variation (CV%), the error (through the residue mean square) and the average of the progeny clones, parents and standard checks. The follow- ing model was assumed: Yij 1⁄4 l þ bj þ gi þ eij.where Yij is the phenotypic observation of progeny individual genotype gi (i = 1,...,220)intheblockbj (j = 1,...,4)andleijare the overall mean and error effect, respectively. Broad-sense heritabilities (h2) for each trait were estimated through the
variance components of progeny clones as h^2 1⁄4 r^2.r^2 gp where r^2g (genotypic variance) and r^2p (phenotypic vari- ance). Phenotypic variance was determined from r^2 1⁄4
2 2
Phenotypic data analysis of Brix, Pol%Cane and Fiber% traits and phenotypic correlation between them were done using the SAS statistical package (SAS 1990). The analysis of variance was conducted to detect the effects among blocks, progeny clones, parents and checks as also to estimate the coefficient of variation (CV%), the error (through the residue mean square) and the average of the progeny clones, parents and standard checks. The follow- ing model was assumed: Yij 1⁄4 l þ bj þ gi þ eij.where Yij is the phenotypic observation of progeny individual genotype gi (i = 1,...,220)intheblockbj (j = 1,...,4)andleijare the overall mean and error effect, respectively. Broad-sense heritabilities (h2) for each trait were estimated through thevariance components of progeny clones as h^2 1⁄4 r^2.r^2 gp where r^2g (genotypic variance) and r^2p (phenotypic vari- ance). Phenotypic variance was determined from r^2 1⁄42 2
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