Identification of Drought Tolerant Genotypes in Dryland Wheat using Drought Tolerance Indices

Among different environmental stresses, drought is of great importance that induces a highly negative effect on crop production. In order to evaluate drought tolerance in dryland wheat genotypes, 36 genotypes were studied in a randomized complete block design with three replications under rainfed (drought stress) and supplemental irrigation conditions during 2016–2017 growing season in Research Farm of Moghan College of Agriculture and Natural Resources. Results showed that there are significant differences among genotypes for grain yield in both rainfed and supplemental irrigation conditions. Combined analysis of variance over two experiments showed that drought stress significantly increased grain yield. Under supplemental irrigation conditions, the genotypes 22, 34 and 21 had the highest grain yield by an average of 3.143, 3.089 and 2.921 t/ha and under rainfed condition, genotypes 20, 22 and 27 produced the highest grain yield with an average of 2.647, 2.610 and 2.558 t/ha, respectively. In order to identify drought tolerance genotypes, drought resistant indices such as tolerance index (TOL), stress susceptibility index (SSI), mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI) and yield stability index (YSI) were used. GMP, MP and STI indices were highly correlated with grain yield under both stress and non-stress conditions. Therefore, they were introduced as suitable indices to identify superior genotypes for both environmental conditions. Based on principal component analysis and three dimensional graph, genotypes 22, 34, 33 and 15 were identified as drought tolerant genotypes and 7, 17, 14 and 1 were identified as drought sensitive genotypes. Cluster analysis with Ward's method based on grain yield under supplemental irrigation (Yp) and rainfed (Ys) conditions and drought tolerance indices divided the 36 genotypes into four major groups. Therefore, this result was consistent by results from principal component analysis.