Stepwise regression and principal component analyses for quantitative traits of rapeseed genotypes at different sowing dates
2016
Rameeh, Valiollah (Agricultural and Natural Resources Research Center of Mazandran, Sari (Iran Islamic Republic). Agronomic and Horticulture Crops Research Department)
The present research was done to assess the best selection criteria for yield improvement in rapeseed (Brassica napus L.) using stepwise regression and principal component analyses at different sowing dates. All the traits except 1000-seed weight were significantly affected by sowing dates. The results of stepwise regression analysis revealed that seeds per pod had an important role at the first and second sowing dates, but at the third and fourth sowing dates, pods per plant and days to flowering were more important than other yield components for a seed yield prediction model. On the basis of a cumulative percent of variation, three principal components (PCs) were determined for each sowing date. The cumulative percentages of variation for three PCs at the first to fourth sowing dates were 0.97, 0.96, 0.89 and 0.95, respectively. At the first sowing date, the first principal component (PC1) had high positive and negative PC loading values for the studied traits such as days to flowering, days to the end of flowering, duration of flowering, pods per plant and harvest index. Therefore, there was high variation in these traits among the genotypes. PC2 of the first sowing date had also high PC loadings for pods on the main raceme, seeds per pod, 1000-seed weight, biological and seed yields, therefore the correlation of these traits with this PC will be high. In PC3 of the first sowing date, height, pods on the main raceme and pods per plant had the high value of PC loadings. Based on stepwise regression analysis, seeds per pod at the first and second sowing dates and days to flowering and pods per plant at the third and fourth sowing dates had an important role for improving seed yield.
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