DETERMINING THE RELATIVE CONTRIBUTION OF YIELD COMPONENT IN BREAD WHEAT USING DYFFERENT STATISTICAL METHODS
Mohammad, W., Swelam, A., Abbs, I. (2013). DETERMINING THE RELATIVE CONTRIBUTION OF YIELD COMPONENT IN BREAD WHEAT USING DYFFERENT STATISTICAL METHODS. EKB Journal Management System, 4(3), 375-382. doi: 10.21608/jpp.2013.72136
Wafaa W. Mohammad; A. A. Swelam; Iman K. Abbs. "DETERMINING THE RELATIVE CONTRIBUTION OF YIELD COMPONENT IN BREAD WHEAT USING DYFFERENT STATISTICAL METHODS". EKB Journal Management System, 4, 3, 2013, 375-382. doi: 10.21608/jpp.2013.72136
Mohammad, W., Swelam, A., Abbs, I. (2013). 'DETERMINING THE RELATIVE CONTRIBUTION OF YIELD COMPONENT IN BREAD WHEAT USING DYFFERENT STATISTICAL METHODS', EKB Journal Management System, 4(3), pp. 375-382. doi: 10.21608/jpp.2013.72136
Mohammad, W., Swelam, A., Abbs, I. DETERMINING THE RELATIVE CONTRIBUTION OF YIELD COMPONENT IN BREAD WHEAT USING DYFFERENT STATISTICAL METHODS. EKB Journal Management System, 2013; 4(3): 375-382. doi: 10.21608/jpp.2013.72136

DETERMINING THE RELATIVE CONTRIBUTION OF YIELD COMPONENT IN BREAD WHEAT USING DYFFERENT STATISTICAL METHODS

Journal of Plant Production
Article 2, Volume 4, Issue 3, March 2013, Page 375-382  XML PDF (562.06 K)
Document Type: Original Article
DOI: 10.21608/jpp.2013.72136
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Authors
Wafaa W. Mohammad1; A. A. Swelam2; Iman K. Abbs1
1Central laboratory for Design and statistical Analysis Research
2Wheat Res. Deprt., Field Crop Res. Inst A R C Egypt.
Abstract
This investigation was carried out at ،Kaffr Al-Hmam Experimental Station Sharkia Governorate during 2010/2011 and 2011/2012 to evaluate the performance of seasons eight wheat genotypes namley Sakha 93, Sakha 94, Sids1, Gemmeza 7, Gemmeza 9, Gemmeza 10, Sids10 and Giza 168. The treatments were arranged in randomized complete blocks design with three replications, in order to investigate the relationship between seed yield / plant and its factors using multivariate techniques namley; correlation, stepwise, multiple liner regression ; path –coefficient and factor analysis.
                Data showed that cultivar Sakha94 recorded the highest seed / plant, and number of spikes / plant. Moreover cultivar Giza 168 recrded the lowest grain yield plant. Factor analysis grouped the studied variables in two major factors which altogether accounted for 81.00 of the total variation. The first factor include number of spike / plant, number of grains /spike, spike grain weight,  and 1000-grain weight. The second factor included the remeaining variables. Multiple linear regression, stepwise and path analysis agreed upon the number of spike / plant, number of grains /spike, spike grain weight, 1000-grain weight as major contribution to seed yield variations.  Factor analysis technique was more efficient than other techniques. It provides more information about cluster of intercorrelated variables. Results indicated no significant between the full model regression and stepwise for coefficient determination (R2) and standard error of stimated value, however, the efficiency expressed is due, in fact, to the reduction in variables number in the equation from all raniables in full model regression to four variables in stepwise.
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