STEPWISE REGRESSION AND PRINCIPAL COMPONENT ANALYSES FOR QUANTITATIVE TRAITS OF RAPESEED GENOTYPES IN DIFFERENT SOWING DATES
Sažetak
The present research was carried out to determine the best selection criteria for yield improvement in rapeseed (Brassica napus L.) using stepwise regression and principal component analyses in different sowing dates. All the traits except 1000-seed weight were significant affected by sowing dates. The results of stepwise regression analysis revealed that seeds per pod had important role in first and second sowing dates, but in third and fourth sowing dates, pods per plant and days to flowering were more important than other yield components for seed yield prediction model. On the basis of cumulative percent of variation, three principal components (PC) were determined in each sowing dates. Cumulative percent of variation for three PC in fist to fourth sowing dates were 0.97, 0.96, 0.89 and 0.95, respectively. In first sowing date, firs principal component (PC1) had high positive and negative PC loading values for the traits including days to flowering, days to end of flowering, duration of flowering, pods per plant and harvest index, therefore the genotypes had high variation for these traits. PC2 of fist sowing date had also high PC loadings for pods on main raceme, seeds per pod, 1000-seed weight, biological and seed yields, therefore correlation of these traits with this PC will be high. In PC3 of first sowing date, height, pods on main raceme and pods per plant had high value of PC loadings. Due to different traits had important role in three PCs of four sowing dates, therefore different criterion can be used for improvement of seed yield in different sowing dates.
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