PJB-2017-350
Multivariate statistical analysis for yield and yield components in bread wheat planted under rainfed conditions
Mirza Faisal Qaseem, Rahmatullah Qureshi, Noshin Illyas, Jalal-Ud-Din and Ghulam Shabbir
Abstract
Crop yields of major cereals including wheat are not increasing at the targeted growth rates to feed the rising demands stemming from increase in the human population. Variability in germplasm is always the key to improvement and to assess the extent of variation is never ending process in a plant breeding program. Out of several methods available for assessing the variability, multivariate analysis is one of the most important and widely used methods. Present study was designed to find diversity patterns among 30 wheat lines of hexaploid wheat from CIMMYT nursery planted for two years (i.e. 2013-14 and 2014-15) in two replicates to seek genetic variability amongst genotype in rainfed conditions. Thirteen quantitative traits were determined phenotypically. Three genotypes viz., 27, 28 and 29 performed well with higher values of yield and yield components that may be considered as tolerant to rainfed conditions. The Principal Component Analysis (PCA) grouped all germplasm into four major components explaining over 75% of genetic variation. Cluster analysis classified studied traits into seven groups. Days to heading (DH) and days to maturity (DM) had highest positive loading in first component, while most of yield components had positive loading in second component. It can be concluded that wheat yield can be improved in Pakistan by employing breading programme in terms of crosses and heterosis in determined different parent clusters
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