USING OF MULTIVARIATE ANALYSIS FOR EVALUATING WHEAT GRAIN YIELD AND ITS COMPONENTS UNDER WATER STRESS CONDITIONS
Abd El-Shafi, M., El-Hassia, S. (2009). USING OF MULTIVARIATE ANALYSIS FOR EVALUATING WHEAT GRAIN YIELD AND ITS COMPONENTS UNDER WATER STRESS CONDITIONS. EKB Journal Management System, 23(1), 12-21. doi: 10.21608/fjard.2009.197011
M. A. Abd El-Shafi; S. A. El-Hassia. "USING OF MULTIVARIATE ANALYSIS FOR EVALUATING WHEAT GRAIN YIELD AND ITS COMPONENTS UNDER WATER STRESS CONDITIONS". EKB Journal Management System, 23, 1, 2009, 12-21. doi: 10.21608/fjard.2009.197011
Abd El-Shafi, M., El-Hassia, S. (2009). 'USING OF MULTIVARIATE ANALYSIS FOR EVALUATING WHEAT GRAIN YIELD AND ITS COMPONENTS UNDER WATER STRESS CONDITIONS', EKB Journal Management System, 23(1), pp. 12-21. doi: 10.21608/fjard.2009.197011
Abd El-Shafi, M., El-Hassia, S. USING OF MULTIVARIATE ANALYSIS FOR EVALUATING WHEAT GRAIN YIELD AND ITS COMPONENTS UNDER WATER STRESS CONDITIONS. EKB Journal Management System, 2009; 23(1): 12-21. doi: 10.21608/fjard.2009.197011

USING OF MULTIVARIATE ANALYSIS FOR EVALUATING WHEAT GRAIN YIELD AND ITS COMPONENTS UNDER WATER STRESS CONDITIONS

Fayoum Journal of Agricultural Research and Development
Article 2, Volume 23, Issue 1, January 2009, Page 12-21  XML PDF (352.9 K)
Document Type: Research articles.
DOI: 10.21608/fjard.2009.197011
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Authors
M. A. Abd El-Shafi1; S. A. El-Hassia2
1Agronomy Department, Faculty of Agriculture, Cairo University, Egypt
2Mathematic Department, Faculty of Science, Amer El-Mohtar University, Lybia
Abstract
Twenty bread wheat genotypes differed in yield performance were
grown at Kafr El-Hamam (El-Sharkea Governorate) during two seasons
(2005/2006 and 2006/2007) under water stress conditions. Five
statistical procedures (simple correlation, multiple linear regression,
stepwise regression, factor analysis and principal components analysis)
were used to study the relationship between wheat grain yield and its
components under water stress conditions. The simple correlation
coefficients revealed that the highest positive correlations to grain yield
were no. of spikes/m2, no. of grains/spike, biological yield t/ ha and
harvest index.
Stepwise multiple regression analysis showed that 92.90% of the
total variation in grain yield could be explained by the variation in
harvest index, biological yield and grains weight/spike. The linear
regression equation was (Y) = -2.201 + 0.092 X9 + 0.300 X8 -0.160 X6,
where Y, X9 , X8 and X6 represent, grain yield t/ ha, harvest index,
biological yield and grains weight/spike, respectively. Factor analysis
indicated that four factors could explain approximately 76.5% of the
total variation, which were 33.90% for grains weight/spike, 1000-grains
weight and biological yield (factor 1), 18.50% for plant height and
harvest index (factor 2), 14.60% for no. of grains/spike (factor 3) and
9.50% for no. of spikes/ m2. The principal components analysis had
grouped the estimated wheat variables into four main components,
which accounted 77.00% from the total variation of grain yield.
However, harvest index, biological yield, no. of spikes/m2, grains
weight/spike, no. of grains/spike and 1000-grains weight were the most
important variables greatly affected grain yield. It could be concluded
that the multiple statistical procedures which used in this study showed
that the grains weight/spike, harvest index and biological yield were the
most important yield variables to be considered under water stress
conditions.

Keywords
Water stress; Wheat; Simple correlation; Multiple linear regression, Stepwise regression; Factor analysis; Principal components analysis
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