Evaluating Bivariate and Multivariate statistical analysis of yield and agronomic characters in Egyptian cotton
Journal Title: Scientia Agriculturae - Year 2015, Vol 9, Issue 3
Abstract
Two field experiments were conducted in 2009 and 2010 growing seasons at the experimental farm of the Faculty of Agriculture, Cairo University, Giza, Egypt. Sixteen Egyptian cultivars were evaluated in a balanced lattice design (4 x 4) with five replications for nine traits. The aims were to determine relationships between yield and its components and examine the efficiency of such components in building yield capacity by using six different statistical methods. Highly significant differences were detected among genotypes for all studied traits. Highly significant and positive correlation estimates were detected between seed cotton yield and each of number of fruiting branches per plant, number of opening bolls, lint cotton yield per plant, seed cotton yield per plant, lint cotton yield per plot, seed yield per plot and lint percentage. On the other hand, number of dry bolls per plant showed negative association with seed cotton yield. In multiple linear regressions, the relative contribution for all yield components explained 96% of the total variation in seed cotton yield. The stepwise regression showed that, seed yield per plot, lint cotton yield per plot and number of dry bolls, were the most contributing variables in seed cotton yield per feddan (99% of seed cotton yield variation). Stepwise multiple linear regression proved to be more efficient than the full model regression to determine the predictive equation for seed cotton yield. The criteria used in identifying the best subsets were based on monotone functions of the residual sum of squares (RSS) such as R2, adjusted R2 and Mallow’s Cp. Results revealed the best subset regression model, based on the three different criteria, were the predicted equation for seed cotton yield per fed (Y) was Y = -0.12 – 0.011 x2 – 0.011 x6 + 0.016 x7. The simplified results from best subset regression analysis indicate that the highest coefficient of determination (R2=99.9%), adjusted R2 (99.8%) and lowest Mallows' conceptual predictive (Cp) value (2.0), and has three-independent variables. The factor analysis grouped the studied variables into two groups, which explained 83.4% of the total variability in the dependence structure. The first group contributed 58.9% while, the second group was responsible for 24.5% of the total variability. Cluster analysis reflected the tendency of each group of variables in one cluster to relate closely to each other. Analysis of six statistical procedures revealed that high yield of cotton can possibly be obtained by selecting breeding materials that have high seed yield per plot (x7) and high yield per plot (x6), but have low number of dry polls per plant (x2).
Authors and Affiliations
Diaa A El-Kady, Ashraf A Abd El-Mohsen , Hashem M Abdel Latif
Keywords
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- EP ID EP105765
- DOI 10.15192/PSCP.SA.2015.9.3.150164
- Views 127
- Downloads 0
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