Investigating the Stability of Seed Yield in Promising Barley Lines Using Parametric and Non-parametric Methods in Cold Dryland Climate Regions of the Country

Abstract

Extended Abstract Background: Considering the diversity in climatic conditions, agricultural management, the extent of barley cultivation areas in Iran, and observing the different reactions of different cultivars to environmental conditions, it is of particular importance to introduce high-yielding cultivars with wide adaptability to different conditions. Due to the genotype × environment interaction effect, it is difficult to identify cultivars that have good stability and acceptable yield in various environmental conditions. Therefore, cultivars should be studied in a wide range of environmental changes in different locations and years so that the information obtained from the estimation of compatibility and yield stability of genotypes is a more reliable criterion for recommending cultivars and their efficiency. The methods for determining the genotype × environment interaction effect are divided into two groups: single variable (parametric and non-parametric) and multivariable. Each of these methods shows different aspects of the stability of genotypes, and one method alone cannot investigate the yield of a genotype in different environments from different aspects of stability. This research aimed to select promising barley genotypes with high yield and suitable stability in dry conditions in the cold climate of Iran using parametric and non-parametric univariate stability analysis methods. Methods: In this study, 25 advanced and promising lines of barley, along with Ansar, Abider, and Sararoud1 (check cultivars), were studied in dry conditions in a completely randomized block design with four replications in research stations of Maragheh, Kurdistan (Qamlo), Zanjan (Qidar), Ardabil, Kermanshah (Sararoud), Shirvan, and Hamedan for three crop seasons from 2016 to 2019. The stability of the genotypes was explored using parametric and non-parametric univariate methods. Parametric and non-parametric univariate methods were integrated using the selection ideal index genotype (SIIG) method. Finally, the correlation of the parameters with yield and the SIIG was also calculated in this research. Results: Separate analysis of variance in each of the environments showed that the genotype effect was significant in 12 out of 19 environments, which indicated the fluctuation of the yield of each genotype from one environment to another. Combined variance analysis showed that the interaction effects of year × location and genotype × year × location were significant at 1%, the year effect at 5%, and the location and genotype effects were significant at 10% probability levels. The main effect of the environment and the genotype × environment interaction effect had the largest share in the total sum of squares observed in the experiments, with 69.98% and 10.83%, respectively. Eberhart and Russel's analysis identified genotypes G1, G4, G5, G8, G9, G10, and G26 as the most stable genotypes due to having the lowest deviation from regression and a regression coefficient close to one. Considering the yield, G9 and G10 genotypes were introduced as stable genotypes with high yields. According to Finley and Wilkinson's linear regression coefficient, genotypes G4, G6, G9, G11, G12, G15, G17, G20, G27, and G28 had a regression coefficient close to one, which shows that these genotypes have general adaptability to environments. Based on Wrick's equivalence index and Shukla’s stability variance, genotypes G8, G19, G10, G20, G9, G4, G26, and G1 were identified as stable genotypes. Based on the coefficient of environmental variation, genotypes G10, G1, G8, G23, G13, G2, and G5 had the lowest coefficient of variation. Based on the Plasted and Peterson method, genotypes G10, G20, G19, and G9 were selected as stable genotypes with high yields.  In the Plaisted method, genotypes G10, G20, G19, and G9 with the least contribution in creating interaction and having the desired yields were introduced as stable and high-yielding genotypes. Based on Lin and Bains, genotypes G15, G6, G21, G19, G20, G7, and G9 had the least amount of this statistic and were introduced as the most stable genotypes. Based on Kang's total rank method, G20, G19, G10, G9, and G22 genotypes with the lowest total rank were selected as stable genotypes. Based on the parameters of Nassar and Huhn, genotypes G8, G9, G10, G1, G20, G19, and G21, and based on the parameters of Thenarasu, genotypes G8, G9, G10, G1, G19, and G22 with the lowest rank were selected as stable genotypes. Finally, based on the SIIG, genotypes G10, G9, G19, G22, and G20 had the closest value to one and produced higher yields than the overall average; therefore, they were selected as the most stable genotypes. Conclusion: Based on the SIIG, genotypes G10, G9, G19, G22, and G20 had the closest value to one and produced yields above the average; therefore, they were selected as the most stable genotypes. Moreover, the use of the SIIG is recommended due to its high correlation with all the indices used to summarize the results of parametric and non-parametric stability indices