پژوهشنامه اصلاح گیاهان زراعی (Jun 2024)
Selection of Barley Genotypes (Hordeum vulgare L.) with High and Stable Grain Yield in Drought Stress Conditions
Abstract
Extended Abstract Background: The increased demand for cereals that are consumed by humans and livestock can be met through the development of planting drought-tolerant genotypes. Due to the interaction of genotypes × environment, the best genotype in one environment may not be the best in other environments, and therefore, this interaction provides valuable information about the yield of each genotype in different environments and plays an important role in evaluating yield stability. Genetic modification of drought tolerance in crops is one of the most stable and cost-effective approaches to increase production and yield stability. Examining the compatibility and stability of grain yield based on various parametric and non-parametric stability statistics and evaluating tolerance to drought stress based on stress indices in promising barley genotypes of the country's temperate climate are among the goals of this research. Methods: To assess grain yield adaptation and stability and to select high-yielding barley genotypes suitable for terminal drought stress in the temperate climate of Iran, 16 barley genotypes were cultivated during two crop years 2021-2023 in a randomized complete blocks design with three replications in three research stations including Varamin, Birjand, and Yazd under two none-stress and drought stress conditions at the end of the season (12 environments). After determining the grain yield, stress indices, including MP, GMP, TOL, HARM, STI, YI, YSI, RSI, and SSI, and the correlation of each with grain yield were calculated in this study. Stability statistics included Nassar and Huehn’s stability statistics (S(1-6)), Thennarasu’s stability statistics (NP(1-4)), deviation from regression (S²dᵢ), regression coefficient (b), Shukla’s stability variance (σ²ᵢ), environmental variation coefficient (CV), variance component (θᵢ), coefficient of variance (θ(i)), Wricke’s ecovalence (Wᵢ²), and Kang’s sum of ranks (KR). Their relationships were calculated based on Pearson’s correlation. Analysis of variance (ANOVA), mean comparison, and simple correlation were calculated using the SAS-9.0 program, stability statistics were calculated using STABILITYSOFT and principal component analysis (PCA). Stress indices and the correlation of each of these indices with grain yield were calculated using iPASTIC. The three-dimensional distribution diagram of genotypes in the ranges of A, B, C, and D was drawn using Grapher software. Results: The results of the combined ANOVA indicated the significance of the genotype × environment interaction. According to S(1-2) statistics, G7, G10, G11, and G3, and according to S(3-6) statistics, G7, G3, and G9 were the most stable genotypes. Among the non-parametric Thennarasu’s stability statistics according to the NP(1) criterion of G9, G3, and G5, according to NP(2) G5, G3, and G8, and according to NP(3) and NP(4) criteria, G7, G3, and G9 were recognized as the most stable genotypes. Based on Wricke (W²) and Shukla (σ²) equivalency stability statistics, G3, G9, and G13 were the most stable genotypes. Based on Eberhart and Russell's regression method, G9, G7, and G3 genotypes, with high yields, had general compatibility and good yield stability. Based on Francis and Kannenberg (CVi), genotypes G1, G2, and G15 had the lowest coefficients of environmental variation. Based on the average rank of each genotype in all stress indices (AR), G2, G7, and G3 genotypes were the most tolerant, and G14, G11, and G10 were the most sensitive genotypes to drought stress at the end of seasons, respectively. In the drought stress conditions at the end of the season, grain yield had positive and significant correlations with YI, HM, GMP, STI, MP, YSI, and RSI indices and negative and significant correlations with the SSI index. In non-stress conditions, grain yield had positive and significant correlations with MP, GMP, STI, HM, and YI indices, but no significant correlations were observed between grain yield and SSI, TOL, YSI, and RSI indices. The PCA revealed that the first and the second principal components explained 69.71% and 30.27% of the variance of the main variables, respectively. The first main component had a positive and high correlation with yield in both stressed and non-stressed environments, as well as MP, STI, GMP, and HM indices. The second component showed a positive and high correlation with grain yield in the non-stressed environment and TOL and SSI indices; it also had negative and high correlations with RSI and YSI indices. Based on the biplot diagram, G3, G7, G8, G9, G12, and G13 presented higher grain yield potential and are more tolerant to drought stress. Conclusion: In this study, grain yield had negative and significant correlations with NP(3), KR, NP(2), NP(4), S(6), and S(1) statistics, respectively, therefore these statistics can be used for identifying stable genotypes. G3, G7, and G9 with averages of 6732.9, 6730.6, and 6608.1 kg/ha, respectively, not only produced the highest grain yield but also showed the highest grain yield stability and tolerance to terminal drought stress among the studied genotypes based on the total ranking of all studied stability statistics and stress indices. Therefore, they can be used as new cultivars in drought-affected regions in temperate climates or as desirable genetic materials in barley breeding programs for drought tolerance.
