Evaluation of Spring Wheat Recombinant Inbred Lines under Drought Stress

• Published in: Ikūfīziyūlūzhī-i giyāhān-i zirāʻī (Tabrīz. Online) Vol. 6; no. 1(21) بهار; pp. 37 - 56
• Main Authors: M. Moghaddaszadeh-Ahrabi, M. Moghaddam Vahed, S. Aharizad, S.A. Mohammadi
• Format: Journal Article
• Language: Persian
• Published: Islamic Azad University, Tabriz Branch 01.07.2012
• Subjects: Agronomic traits; drought stress; Drought tolerance indices; Grain yield; Spring wheat recombinant inbred lines; Stepwise regression
• ISSN: 2538-1822; 2476-731X

Iran is one of arid and semi-arid regions of the world. Wheat as a strategic agricultural products faces water deficiency in most areas of the country. Therefore, identification of the resistant varieties to drought stress is one of main aims for breeders. To assess effect of drought stress at heading on 72 spring wheat recombinant inbred lines derived from American Yecora Rojo (high yielder, dwarf and early maturity) as paternal parent and Iranian No. 49 line (tall and late maturiting) as maternal parent cross were studied. The experiment was conducted at the Research Station of the University of Tabriz using a randomized complete block design with two replications during 2009 growing season. Based on the results from combined analysis of variance significant difference was observed among lines for all of traits studied, except for harvest index, grain number per spike and days to heading. There was significant difference between normal and drought stress conditions. Since the interaction between line and conditions was insignificant for all traits, it does therefore, provide the possibility of comparing the lines without regard to irrigation levels. Based on the means of, the traits it was found that the lines 96, 122, 123 and 155 were superior. MP, GMP and STI indices were recognized to be suitable indices to identify superior lines. With respect to these indices, lines 96, 122, 123, 138, 149 and 155 were found superior as compared with remaining lines. Based on stepwise regression analysis of grain yield with other traits, respectively grain number per spike, number of spikes/m2 and 1000 kernel weight were inserted into final model as effective variables on grain yield, which made 81/9 percent of the grain yield variation. Path analysis of grain yield and related traits, based on stepwise regression, demonstrated the significant positive direct effect for grain number per spike, number of spikes/m2 and 1000 kernel weight on grain yield. Cluster analysis, also showed that, lines under study were grouped into two clusters; superior lines for studied traits assigned to group one.