Ali Akeel Wannows
General Commission of Scientific Agricultural Research-
GCSAR
Faculty of Agriculture- Damascus University,
2010
Abstract
A half diallel set of crosses among six inbred lines of maize were evaluated at the Maize Researches Department (G.C.S.A.R.) Damascus. Governorate, during 2008 and 2009 to study heterosis, combining ability, interrelationships among traits and path coefficient analysis for grain yield and its components, plant and ear height, leaf area index (LAI), specific leaf weight (SLW) and physiological maturity (Ph. M).
Inbred lines, crosses, general (GCA) and specific (SCA) combining ability mean squares were significant for all traits except SCA mean squares for leaf area index, plant height, ear height and number of kernel per row.
The ratios were detected for all traits and showed that additive gene action was more important than non-additive gene action in controlling leaf area index, physiological maturity, plant height, ear height, ear length, ear diameter, number of rows per ear and number of kernel per row, However, vice versa for specific leaf area, specific leaf weight, leaf angel, silking date, stay green, 100- kernel weight, grain yield per plant and grain yield.
GCA effects showed that the lines IL.766-06 and IL.792-06 were good general combiners for grain yield.
SCA effects showed that four hybrids were the best F1 cross combinations such as (IL.459-06 × IL.292-06) for grain yield.
Heterosis percentage for all traits was significant compared with mid and better parents.
Correlation coefficients among traits indicated that grain yield was positively and significantly associated with number of kernel per row (0.589), ear length (0.465), and leaf area index (0.497).
The path coefficient analysis was calculated to detect the relative importance of characters contributing to grain yield. Data showed that each of leaf area index, ear diameter and physiological maturity had high positive direct effects on grain yield.
Key words: Maize, Half diallel cross, Combining ability, Heterosis, Correlation and path coefficient analysis.