Document Type : Research Paper
Authors
1 Crop and Horticultural Science Research Department, Agricultural and Natural Resources Research Center of Zanjan , Agricultural Research Education & Extension Organization (AREEO), Zanjan, Iran.
2 Crop and Horticultural Science Research Department, Southern Kerman Agricultural and Natural Resources Research and Education Center, Agricultural Research Education & Extension Organization (AREEO), Jiroft, Iran.
3 Dryland Agricultural Research Institute, Agricultural Research Education & Extension Organization (AREEO), Maragheh, Iran
Abstract
Introduction: As one of the most important plants of the cereal family, wheat plays a very important role in world food security. The problem of food security in the world is facing a serious challenge due to severe climate changes and a lack of supplementary irrigation resources. Understanding the responses of plants to drought is the main part of the development of stress-resistant cultivars. The relative performance of genotypes under drought stress conditions and optimal conditions is one of the necessary and preliminary points in choosing the optimal genotypes for dry conditions. This study was conducted with the aim of identifying the relationships between indicators and determining the best indicators of drought tolerance, as well as selecting the best wheat lines suitable for cold and rainy regions for future breeding programs.
Materials and Methods: In order to identify the relationships between drought tolerance indices and determine the best index as well as select the best lines for future breeding programs, 144 wheat lines in the form of Alpha Lattice design in two replications and in two conditions of rain and supplementary irrigation in The rain research station of Zanjan Agricultural Research Center were investigated. Different tolerance and sensitivity indices including relative stress index (RSI), stress sensitivity index (SSI), stress resistance index (SRI), performance index (YI), performance stability index (YSI), relative reduction index (RR), Abiotic Stress Index (ATI), Tolerance Index (TOL), Mean Production Index (MP), Stress Tolerance Index (STI), Geometric Mean Productivity (GMP), Stress Sensitivity Percentage Index (SSPI), Harmonic Mean (HM) ), the index of product of stress and non-stress environment (SNPI) and yield loss ratio (PRR) were calculated.
Research finding: The results of combined variance analysis showed that the effect of wet and rainy environment on the performance of lines was significant at the level of 1%. Also, the lines showed a significant difference in grain yield at the level of 1%. Drought stress decreased the grain yield of most lines in dry conditions. Line 82 had the highest yield in potential (2876 kg/ha) and rainfed (2000 kg/ha) conditions. Line 21 also had the lowest performance in two conditions (rainfed 825 and supplementary irrigation 1113 kg/ha respectively). Correlation results showed that grain yield in supplementary irrigation conditions had a positive and significant correlation with MP (0.56), GMP (0.56), HM (0.56), YI (0.55) and TOL (0.56) indicators. had 0.53). In the conditions of stress, MP (0.54), GMP (0.54), HM (0.54), and YI (0.53) indicators had the highest correlation with performance. Based on the first two selected components resulting from the decomposition into main components, which justified 91.35% of the changes, lines 90 and 102 were selected, which were also confirmed based on the three-dimensional diagram. The results of the cluster analysis of the lines also placed them in four groups, and lines such as 90, 131, 102 and 132 were also included in the group with high performance in both conditions. According to the results of this study, it is suggested to use the indicators of stress resistance, harmonic mean, geometric mean, and performance stability, which have the most significant positive correlation with performance in wet and dry conditions, in the selection of lines; Also, screening should be done based on the results of analysis into principal components and biplot and clustering using several indicators.
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