Aggregate selection helps making selection decisions for improved profitability and permits to exploit information on correlated traits to improve accuracy. In the present study, the efficiency of trait selection was assessed by the Smith-Hazel (SHI) and the Pesek-Baker (PBI) models in 35 wheat genotypes under drought stress at heading and well-irrigated conditions at the Research Farm of Shiraz University, Iran in 2011 and 2012 years. The analysis of correlated response (CR) revealed that thousand grain weight (TGW) with CR = 24.05 (in 2011) and 9.15 (in 2012) and harvest index (HI) with CR = 18.37 (in 2011) and 9.08 (in 2012) had higher indirect effects on grain yield (GY) increase under drought stress. In both years, biological yield (BY), grain number/spike (GN), TGW, and GY had the highest genetic gain (ΔG) in SHI model. The top ten landraces in PBI for the trials in 2011 were also placed in the top rankings for grain yield under drought stress conditions. These results and estimation of the efficiency of selection (ΔH) revealed that PBI (ΔH = 19.95 and 16.5 in the first and 11.15 and 11.06 in the second year) was more efficient than SHI (ΔH = 9.58 and 8.97 in the first and 9.74 and 8.59 in the second year) in view of identifying superior genotypes based on aggregate trait selection. Overall, repeatability estimates for grain yield (33%–57.8%) was relatively low showing that individual plant selection has low efficiency for wheat improvement whilst aggregate trait selections leads to relatively high genetic gain (1.63–2.75) for grain yield.

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