فهرست مطالب zeinab sabzi

Due to the droughts of the last few years, the low need of barley crop to water and rich soil, the need of the country in the supply of Community Food and the importance of barley in the food industry, therefore, it is necessary to introduce high-yielding and drought-resistant genotypes.

The research was conducted at the Ilam Agricultural Research Station in 2020-2021. Identification of tolerant and sensitive genotypes was based on grain yield and drought tolerance indices. Accordingly, 16 barley genotypes were implemented in the form of random complete blocks with four repetitions in two conditions: dim and supplementary irrigation. After harvesting, grain yield was measured and stress tolerance indices were calculated.

The results of correlation between drought tolerance indices and grain yield in rainfed and irrigated conditions showed that tolerance index (STI), mean productivity (MP), harmonic mean (HM) and geometric mean productivity (GMP) were positively correlated, and had significance with grain yield traits in both environmental conditions. So, they were suitable indicators for selecting high yielding genotypes in both rainfed and irrigated environments. Based on SIIG index, genotypes 11 and 9 show the highest values, while genotypes 3 and 4 had the lowest values. Also, factor analysis based on principle component analysis showed that the first two factors explain 98.5%((the first factor 68.7% and the second factor 29.8%) of the total diversity.

Based on the obtained results, genotypes No. 9 and 11 have the highest resistance to drought stress and yield, which are suitable for cultivation in areas under moisture stress, and genotypes No. 3 and 13 are suitable for planting in areas without moisture stress.

The identification of the most favorable cultivar(s) with high yield and stable performance is usually done based on the analysis of the genotype × environment (GE) interaction. The yield stability of 16 barley lines with two check varieties was studied in a randomized complete block design with four replications across three years at five locations in a multi-environment trial layout. The dataset was analyzed with a GGE (genotype main effect (G) + GE interaction) biplot method. Results indicated that the first two principal components (PCs) explained 81, 78 and 71% of the GGE sum of squares for 2017, 2018 and 2019 growing seasons, respectively. According to the average environment coordinate abscissa, G2, G13 and G18 were the best genotypes in terms of grain yield in years 2017 and 2018 while genotypes G2, G7 and G14 were the highest yielding genotypes in 2019. When both yield performance and stability were considered simultaneously, the G2 and G13 genotypes in 2017 and G2, G8 and G13 in 2018, were closer to the ideal genotype. In 2019, G2, G7 and G14 were the best in terms of grain yield and stability. In the "which-won-where pattern", the five locations in 2017 fell into four sectors with different winning genotypes as G2, G5, G14 and G13. In 2018, the five locations fell into three sectors in which G2, G4 and G17 were the highest yielding genotypes while in 2019, locations were positioned in four sectors and G2, G7, G10 and G13 were chosen as the winning genotypes. However, for practical use of the “which-won-where” pattern, the mean performance of genotypes over three years in the five test locations was taken into account. Although the results revealed six mega-environments, by neglecting small differences, we can assume only one mega-environment in which G2 (the check variety Khorram) was the best performing genotype.

Analysis of the genotype×environment (GE) interaction of barley (Hordeum vulgare L.) can aid in detecting genotype performance better under diverse and harsh environments. Sixteen advanced breeding lines and two cultivars were tested across five locations at Gachsaran, Gonbad, Ilam, Lorestan and Mughan districts during three years of 2017 to 2019. Stability analysis was determined using 19 different variance and regression methods with 26 univariate statistics because each method explores stability from different aspects and all of them can reflect a comprehensive stability characteristic of genotypes. The result showed that environment, genotype and GE contributed 92, 2 and 8% of the total variation and there is no strongest genetic control. According to the GE sum squares-based parameters, genotypes G13, G12 and G15 were more stable. The coefficient of line slope and residual variance of common and adjusted linear regression, manifested G1, G2, G12 and G18 as the most stable and responsive genotypes. The selective value of genotype (SVG) identified G6, G10 and G11 as the most stable genotypes while G2, G5 and G13 were the most stable genotypes based on superiority measure (SM). According to H parameter, genotypes G2, G13 and G18 were identified as the most stable genotypes while the dynamic CV and dynamic regression introduced G3 and G15 as the most stable genotypes. The relative superiority (RS), proposed G1, G2 and G5 as the most stable genotypes. Finally, H statistic, RS and SM could be recommended for stability analysis in future breeding programs for the simultaneous selection of yield and stability.