Multivariate Analysis for Evaluating Sesame Yield and Its Contributing Factors

Abstract

Two field experiments were carried out in a commercial field at Abo Rawash village, Giza governorate, Egypt during 2004 and 2005 seasons to compare five statistical procedures including: simple correlation, path analysis, multiple linear regression, stepwise regression and factor analysis in determining the relationship between sesame seed yield and its contributing traits. Thirty sesame genotypes were used for this purpose. The studied characters were: flowering date, plant height, number of fruiting branches, stem height to the first capsule, fruiting zone length, number of capsules on main stem, number of capsules per plant, capsule density on main stem, 1000-seed weight and seed yield per plant. The simple correlation coefficients and path analysis of yield components revealed that components with the highest positive correlation to yield also had the highest positive direct effect to yield i.e., number of capsules on main stem and number of capsules per plant. Path analysis showed that, the residual effect (0.433) was high in magnitude which shows that some other important yield contributing characters which contribute to yield have to be included. Stepwise multiple regression analysis showed that 77.25% of the total variation in seed yield could be explained by the variation in number of capsules per plant and flowering date in sesame. The linear regression equation was (Y) = 10.951 -0.110 X1 + 0.114 X7, where Y, X1 and X7 represent seed yield per plant, flowering date and number of capsules per plant, respectively. Besides, coefficient of determination (R 2 ), adjusted R-squared statistic and standard error of estimate values, mean absolute error (MAE) and Durbin-Watson (DW) statistic test showed no significant differences between the full model regression and stepwise multiple regression analysis technique. However, the efficiency expressed is due to the reduction in number of variables in the fitted model from all variables (full model regression) to two variables only (stepwise multiple regression). Factor analysis indicated that three factors could explain approximately 81.9% of the total variation. The first factor which accounted for about 41% of the variation was strongly associated with fruiting zone length, number of capsules on main stem, number of capsules per plant, and capsule density. The second factor which accounts for about 25% of the variation, was strongly associated and positive effects on days to flowering, 1000-seed weight, plant height and stem height to the first capsule, whereas the third factor had positive effects on number of fruiting branches only, which accounts for about 16% of the variation. Factor analysis technique was more efficient than other used statistical techniques. It provides more information about cluster of inter-correlated variables. It could be concluded that the five of statistical analysis techniques, agreed upon that high yield of sesame plants could be obtained by selecting breeding materials with high number of capsules on main stem, number of capsules pe...

Towards Modeling and Predicting the Yield of Oilseed Crops: Multi-Machine Learning Techniques Approach

“…Independent variables (inputs) affecting seed yield were identified and considered however, CPP was the first variable required for the best results [25]. Similar results were achieved when fitting a predictor equation for seed yield [26,27]. Although traditional statistical methods (i.e.…”

“…Other variables are included after viewing interactions of main independent variables. El-Mohsen (2013) discovered a relationship between yield and agro-morphological traits with a stepwise regression process that showed 77.25% of the variance in SSY was explained by the days to flowering and CPP[27]. Also,Parimala and Mathur (2006) indicated CPP was the most effective factor for predicting yield[25] Yol et al (2010).…”