A framework for selection of high-yielding and drought-tolerant genotypes of barley: Applying yield- based indices and multi-index selection models

A framework for selection of high-yielding and drought-tolerant genotypes of barley: Applying yield- based indices and multi-index selection models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A framework for selection of high-yielding and drought-tolerant genotypes of barley: Applying yield- based indices and multi-index selection models Habibollah Ghazvini, Alireza Pour-Aboughadareh, Seyed Shahriyar Jasemi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3917144/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Apr, 2024 Read the published version in Journal of Crop Health → Version 1 posted You are reading this latest preprint version Abstract Drought stress is one of the major environmental stresses that dramatically reduces agricultural production around the world. Barley ( Hordeum vulgare L.) plays an important role in both food and feed security. The objective of this study was to identify the superior drought-tolerant genotypes using grain yield and several yield-based indices of tolerance and susceptibility by applying various multivariate selection models. To achieve this objective, a set of promising new barley genotypes was evaluated in three drought-prone regions of Iran (Mashhad, Karaj, and Hamadan) during two consecutive growing seasons (2019–2020 and 2020–2021). The results of additive main effect and multiplicative interaction (AMMI) analysis showed significant effects for genotypes (G), environments (E), and their interaction (G×E). Based on the AMMI model, G3, G7, G9, and G13 were identified as the four highest-ranked genotypes in terms of grain yield. Based on the Smith-Hazel, factor analysis and genotype-ideotype distance index (FAI), and genotype–ideotype distance index (MGIDI) selection models, genotypes G4 and G13 showed the greatest tolerance to drought stress conditions in the three regions. Moreover, the most significant selection gain was found for the stress tolerance index, yield index, and grain yield under drought stress conditions (Ys). The results of the genotype (G) + genotype × environment (GGE) biplot analysis coincided with those obtained, in which the G4 and G13 genotypes showed specific adaptability in drought environments. In addition, among the environments tested, the Karaj region was selected as an ideal target environment with significant discriminatory power and representative ability. In conclusion, the collective analysis using the AMMI, GGE biplot, and multi-index selection models identified genotypes G4 and G13 as superior genotypes. Consequently, these genotypes may be candidates for commercial introduction. Drought stress genotype-by-environment interaction MGIDI network correlation selection model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Barley ( Hordeum vulgare L.) is the fourth most important cereal crop in the world after wheat, rice and corn. Moreover, it has been reported that this cereal is one of the most economically important crops in terms of seed quality (Giraldo et al. 2019 ). From the point of both human’s and animal’s feed and also industry products, barley is a key cereal due to the presence of various minerals such as phosphorus and calcium, small amounts of vitamins, especially B vitamins, moderate amount of protein and dietary fiber in the seeds. In addition, the high carbohydrate content of barley seeds has led to the use of barley in soups, stews, bread and other foods (Fatemi et al. 2022 ). The frequency of available water for plants is the most important variable in determining global yield limits. It has been reported that limiting this water is responsible for 60–70% of the variance in final yield (Nykiel et al. 2022 ). Since water scarcity is a major source of yield loss worldwide, developing more drought-tolerant varieties in the current decade is more critical to food security than in previous times. Drought tolerance is one of the main breeding objectives in arid and semi-arid regions, as well as in other water-poor regions (Magalhães and Magalhães 2019 ). Extreme drought stress during crop growth and development cycles has a negative impact on barley productivity. Therefore, it is crucial for breeders and agronomists to identify genotypes that are tolerant to it (Khan et al. 2014). It has been reported that stress-tolerant genotypes can be selected by growing advanced breeding materials under normal and stressed conditions (Khan et al. 2014). Consequently, using better selection approaches is often a challenge for breeders in identifying superior drought-tolerant cultivars. One of the main efforts to develop new varieties is to decipher the genotype-by-environment interaction (GEI) effect in multi-environment trials (METs) (Eben et al. 2021; Linus et al. 2023 ). Correct interpretation of this effect in METs can help select the best genotypes with high performance and stability under different environmental conditions (Bocianowski et al. 2019 ; Vaezi et al. 2019 ). Grain yield is a quantitative trait and its variation controlled by various genes. In MTEs, the GEI has bottleneck effects on yield improvement. Indeed, this effect hinder the identification of high-yield and stable genotypes and the introduction of superior genotypes for specific agro-climatic areas (Eben et al. 2021). Thus, to counteract the negative effects of GEI on the outcomes of breeding programs, extensive knowledge on this effect is necessary for breeders to improve selection accuracy in breeding programs. Among the proposed models, the genotype (G) + genotype × environment (GGE) biplot is one of robust approaches that are widely used to examine the effect of GEI on grain yield in MET experiments. Both statistical models are based on principal component analysis (PCA), which allows understanding of the relationship between genotypes, environments, and GEI to identify stable and high-yielding genotypes for target environments or across environments (Linus et al. 2023 ). During breeding practice, breeders often measure multiple growth traits and face the problem of selecting desirable genotypes based on multiple traits. Several performance-based indices are used to select stress-tolerance varieties, such as the stress susceptibility index (SSI; Fischer and Maurer ( 1978 )), relative stress index (RSI; Fischer and Wood ( 1979 )), tolerance index (TOL; Rosielle and Hambling ( 1981 )), mean productivity index (MP; Rosielle and Hambling ( 1981 )), yield stability index (YSI; Bouslama and Schapaugh ( 1984 )), harmonic mean (HM; Bidinger et al. ( 1987 )), geometric mean productivity (GMP; Fernandez (1992)), stress tolerance index (STI; Fernandez (1992)), and yield index (YI; Gavuzzzi et al. (1997)) have been proposed. Selection based on each of the indices is usually difficult, hence the use of a model that integrates all indices or traits into an index is ideal for the selection of superior genotypes in METs. Accordingly, several selection indices have been proposed, such as the Smith–Hazel index (Smith 1963; Hazel 1943 ), the factor analysis and genotype-ideotype distance index (FAI-BLUP) (Rocha et al. 2018 ), the multiple trait selection index (MTSI) (Olivoto et al. 2019 ), the genotype–ideotype distance index (MGIDI) (Olivoto and Nardino 2021), and the selection index for the ideal genotype (SIIG) (Zali et al. 2023 ) have been proposed to select superior genotypes based on traits measured in METs. In this sense, the main objective of this study was to use several tolerance indices based on grain yield and selection models to identify drought-tolerant barley genotypes in three drought-prone regions of Iran. Overall, this study provides new information on the use of multi-indices selection, helping breeders make better decisions toward effective multivariate selection in barley breeding programs to improve drought tolerance programs. Materials and Methods Plant materials and multi-environment trails A set of 17 promising genotypes of barley (Table 1 ) along with a local verity (cv. Jolge as the reference genotype) were tested at three agricultural research stations in the cold regions of Iran [including Mashhad, Karaj, and Hamadan] during two cropping seasons (2019–2020 and 2020–2021). Among test environments, Karaj (50°93' E, 35°78' N) and Hamadan (48°53' E, 34°88' N) have the Mediterranean conditions with spring rains, while Mashhad (59°60' E, 36°20' N) has semi-desert conditions (Supplementary Figure S1 ). More information of the weather conditions in each test environment are presented in Supplementary Table S1 . At each environment, basic fertilizers such as P 2 O 5 and N were applied at 100 and 32 kg ha –1 , respectively, before sowing. At all test stations, randomized complete block designs with three replications were used to evaluate genotypes under two irrigated conditions and drought stress conditions. Each genotype was planted in separate plots in 6 m 2 including six lines of 6 meters in length and spaced 20 cm apart. An experimental planter (Wintersteiger, Ried, Austria) was used for sowing. The sowing density in each plot was 450 seeds per m 2 . The number of irrigations was one time in autumn and five times in spring at stem elongation (ZGS 31), booting (ZGS 41), anthesis (ZGS 69), mild development (ZGS 71), and dough development (ZGS 83). Drought stress treatment was applied after anthesis, and irrigation was stopped for all stressed plot until seed repining stage (ZGS 92). In each irrigation period, 1000 m3 of water was applied. Granstar (Thifensulfuron-methyl and Tribenuron-methyl as active substances) and Pumasuper (fenoxaprop-P-ethyl 69 gL − 1 and safener (mefenpyr-diethyl) as active substances) herbicides were used to control the broad-leaved and narrow-leaved weeds at the tillering stage. The studied genotypes were classified into two- and six-row groups based on their spike types. Moreover, based on the growth type, they were grouped into three groups: spring, winter, and facultative. Based on the time of physiological maturity in each test environment, a combine harvester (Wintersteiger, Ried, Austria) was used to harvest the experimental plots. Grain yield was determined for each genotype in the test environment and finally converted to ton ha − 1 . Table 1 The pedigree of the 17 evaluated promised barley genotypes along with the reference genotype across three drought-prone locations during two years in Iran. Genotype Code Pedigree Spike Type Growth Type G1 Jolge (Reference genotype) Six-row Winter G2 Bahman/3/Makouee//Zarjow/80-5151 Six-row Winter G3 Alger//CI10117/Choyo/3/Makouee/4/STB-12 Six-row Winter G4 Comp.Cr229//As46/Pro/3/Srs/4/Express/5/Goharan/6/Goharan Six-row Facultative G5 Zarjow/80-5151//Makouee/3/Makouee Six-row Winter G6 Makouee//Zarjow/80-5151/3/Bahman Six-row Winter G7 Radical/Birgit//Pamir-154/3/Goharan Six-row Facultative G8 Cali92/Robust//ND16301 Two-row Spring G9 Radical/Birgit//Pamir-154/3/Goharan Six-row Facultative G10 Yousef/4/82S:510/3/Arinar/Aths//DS 29 Six-row Facultative G11 Courlis/Rhn-03//Karoon Six-row Spring G12 Mahtab/Goharan Six-row Spring G13 Comp.Cr229//As46/Pro/3/Srs/4/Express/5/Goharan/6/Goharan Six-row Spring G14 Pamir-147/Sonata/8/Alpha/Durra/7/P101/5/3896/1–15/3/3896/28//584/28/4/5050/6/Tipper Two-row Winter G15 Courlis/Rhn-03//Karoon Six-row Winter G16 Bda/Rhn-03//ICB-107766/3/Yousef Six-row Facultative G17 Sonata/8/Api/CM67//Hma-03/4/Cq/Cm//Apm/3/RM1508/5/Attiki/6/Aths/7/SP(6H) /Apro//Ca1Mr/3/ROD586/Apm/4/Aths/9/Sararood Two-row Winter G18 Nadawa/Rhn-03//Birka Six-row Spring Data analysis The grain yield data collected from the 12 environments (combination of three locations and two cropping seasons) were subjected to the additive main effect and multiplicative interaction (AMMI) analysis based on the following model (Gauch 1988 ): \({\mu _{ij}}=\mu +{\alpha _i}+{\tau _j}+\sum\nolimits_{{k={\text{1}}}}^{p} {{\lambda _k}{\alpha _{ik}}} {t_{jk}}+{\rho _{ij}}+{\varepsilon _{ij}}\) where, is the additive effects of genotype, is the environmental mean, is the singular value for the k th interaction principal component axis (IPCA), is the j th element of the kth eigenvector, and is the i th element of the k th eigenvector. A residual remains if not all p IPCA are used, where p ≤ min (g–1; e − 1). Based on grain yield data under irrigated and drought stress conditions, several drought tolerance indices (Table 2 ) were calculated for the tested genotypes using iPASTIC software (Pour-Aboughadareh et al. 2019 ). To assess the relationships between the estimated drought tolerance indices and grain yields, as well as to test reputation, the Pearson’s-based network correlations were determined. In addition, to select the genotypes with the highest drought tolerance based on calculated drought tolerance and susceptible indices as well as grain yield data, three selection indices were calculated, such as Smith-Hazel, FAI-BLUP, MGIDI. In this approach, each drought tolerance index was considered a quantitative trait. A Venn diagram was used to comparison results obtained by each selection index. The GGE biplot model was used to plot graphs of the following models: (1) ‘which-won-where’ pattern of GGE, (2) mean performance vs. stability pattern, (3) discriminativeness power and representativeness ability of test environments, and (4) ranking of genotypes tested across all environments. All statistical analyses and graphical plots were calculated using the packages ‘metan’ (Olivoto and Lucio 2020 ), ‘tidyverse’ (Wichkham et al. 2019) in R software (R Core Team 2018 ). Table 2 Mathematical formulas of drought tolerance and susceptible indices No. Index Formula Pattern of selection Reference 1 Mean Productivity \(MP=\frac{{Y}_{P}+{Y}_{S}}{2}\) Maximum value Rosielle and Hamblin (1981) 2 Geometric Mean Productivity \(GMP=\sqrt{{Y}_{S}\bullet {Y}_{P}}\) Maximum value Fernandez (1992) 3 Harmonic Mean \(HM=\frac{2\left({Y}_{S}{\bullet Y}_{P}\right)}{\left({Y}_{S}{+Y}_{P}\right)}\) Maximum value Bidinger et al. ( 1987 ) 4 Stress Susceptibility Index \(SSI=\frac{1-\left({Y}_{S}{/Y}_{P}\right)}{1-\left(\stackrel{-}{{Y}_{S}}{/\stackrel{-}{Y}}_{P}\right)}\) Minimum value Fischer and Maurer ( 1978 ) 5 Stress Tolerance Index \(STI=\frac{{Y}_{S}{\bullet Y}_{P}}{{\left({\stackrel{-}{Y}}_{P}\right)}^{2}}\) Maximum value Fernandez (1992) 6 Yield Index \(YI=\frac{{Y}_{S}}{{\stackrel{-}{Y}}_{S}}\) Maximum value Gavuzzi et al. ( 1997 ) 7 Yield Stability Index \(YSI=\frac{{Y}_{S}}{{Y}_{P}}\) Maximum value Bouslama and Schapaugh ( 1984 ) 8 Relative Stress Index \(RSI=\frac{\left({Y}_{S}{/Y}_{P}\right)}{\left(\stackrel{-}{{Y}_{S}}{/\stackrel{-}{Y}}_{P}\right)}\) Maximum value Fischer and Wood ( 1979 ) Yp and Ys indicate the grain yield of genotypes under normal and drought stress conditions, respectively. Moreover, \({\overline {Y} _P}\) and \({\overline {Y} _S}\) indicate the mean grain yield of all genotypes under irrigated and drought stress conditions, respectively. Results AMMI model The result of AMMI analysis for grain yield data indicated significant effects for environments (E), genotypes (G) and their interaction (GEI) (Table 3 ). The main effect of E, G and GEI justified 76.69%, 1.43%, and 9.60% of the total variation, respectively. The GEI effect was divided into four significant IPCAs, each of which accounted for 30.62%, 22.18%, 15.5%, and 12.72%, respectively. The result of first four AMMI selection per environment showed that genotype G4 appeared in seven environments (E1, E2, E3, E4, E7, E8, and E10); genotype G13 was recommended in five environments (E1, E4, E7, E8, and E10); genotype G7 was one of the top ranks in five environments (E1, E3, E9, E10, and E12); genotype G1 appeared in the top four in five environments (E2, E5, E6, E7, and E8); and genotype G9 was observed in the top four in four environments (E1, E6, E11, and E12). Table 3 The results of AMMI analysis for grain yield data Source of variation df Sum of square Mean square F -value Variability explained (%) Total 647 1728.8 2.67 Treatments 215 1516.6 7.05 14.99** Genotypes 17 24.8 1.46 2.10** 1.43 Environments 11 1325.8 120.53 143.23** 76.69 Block 24 20.2 0.84 1.79** Interactions 187 165.9 0.89 1.89** 9.60 IPCA1 † 27 50.8 1.88 4** 30.62 IPCA2 25 36.8 1.47 3.13** 22.18 IPCA3 23 25.8 1.12 2.38** 15.5 IPCA4 21 21.1 1 2.13 12.72 Residual 91 31.4 0.34 0.79 Error 408 192 0.47 ** Significant at p < 0.01 † IPCA indicates interaction principal component axes Drought tolerance and susceptible indices The results of the calculated drought tolerance and drought susceptible indices for each genotype tested in each environment are presented in Table 5 . Based on the average two-year data, genotypes G4, G13 and G16 with the highest values of the indices Yp (except G16), Ys, MP, GMP, YI, YSI, HM, STI and RSI indices and the lowest values of the SSI index were identified as the most drought tolerant genotypes in the Karaj environment (Table 5 A). Based on the results obtained in the Hamadan environment, significant differences were found between the genotypes tested for some drought indices. For example, genotypes G2, followed by G11 and G15 showed the highest grain yield under irrigated conditions, while the highest grain yield values under drought stress were recorded for G1 (reference genotype), G17 and G12. Based on the MP and GMP indices, G1 (reference genotype), G3 and G11 showed the highest values and were identified as drought-tolerant genotypes. The HM index identified G1 (reference genotype), G3 and G12 as drought-tolerant genotypes. In addition, based on YSI, RSI and SSI indices, genotypes G12, G13 and G17 were identified as superior genotypes compared to other genotypes. Of the genotypes tested, G1 (reference genotype), G12 and G17 recorded the highest YI index values and were identified as drought-tolerant genotypes (Table 5 B). Table 4 The selected top-ranked genotypes using the AMMI model for each test environment Environment Conditions Location Year Grain yield (ton ha − 1 ) 1 2 3 4 E1 Irrigated Karaj 2019–2020 7.28 G7 G9 G4 G13 E2 Irrigated Hamadan 2019–2020 7.32 G2 G4 G1 G6 E3 Irrigated Mashhad 2019–2020 6.78 G8 G6 G7 G4 E4 Irrigated Karaj 2020–2021 6.55 G4 G13 G11 G2 E5 Irrigated Hamadan 2020–2021 9.96 G3 G2 G11 G1 E6 Irrigated Mashhad 2020–2021 6.41 G1 G9 G15 G3 E7 Drought Karaj 2019–2020 5.81 G13 G16 G4 G1 E8 Drought Hamadan 2019–2020 4.25 G1 G4 G13 G8 E9 Drought Mashhad 2019–2020 6.55 G8 G6 G12 G7 E10 Drought Karaj 2020–2021 5.34 G7 G4 G13 G16 E11 Drought Hamadan 2020–2021 4.64 G9 G17 G3 G5 E12 Drought Mashhad 2020–2021 5.44 G7 G17 G12 G9 Table 5 Estimated values for drought tolerance and susceptible indices and their ranks (in bracket) in the evaluated genotypes of barley in the different regions in Iran [Karaj (A), Hamadan (B), and Mashhad (C)] Code Karaj (A) Yp Ys MP GMP HM SSI STI YI YSI RSI G1 7.15 (6) 5.72 (7) 6.44 (6) 6.36 (6) 6.28 (6) 1.11 (13) 0.85 (6) 1.02 (8) 0.80 (12) 1.00 (12) G2 7.02 (10) 4.89 (18) 5.95 (13) 5.80 (15) 5.66 (15) 1.54 (17) 0.74 (13) 0.88 (17) 0.69 (18) 0.85 (18) G3 7.21 (4) 4.93 (16) 6.07 (11) 5.89 (12) 5.73 (14) 1.56 (18) 0.75 (11) 0.89 (16) 0.70 (17) 0.86 (17) G4 7.59 (2) 6.44 (3) 7.01 (2) 6.98 (2) 6.95 (2) 0.80 (4) 1.03 (2) 1.15 (2) 0.85 (4) 1.06 (4) G5 6.68 (13) 5.38 (9) 6.03 (12) 5.98 (11) 5.93 (11) 1.00 (12) 0.75 (12) 0.97 (9) 0.81 (9) 1.01 (9) G6 6.58 (15) 5.27 (12) 5.93 (15) 5.88 (13) 5.83 (12) 0.99 (10) 0.73 (14) 0.95 (12) 0.80 (11) 1.00 (11) G7 7.05 (9) 5.64 (8) 6.34 (8) 6.21 (9) 6.08 (9) 0.84 (6) 0.84 (7) 1.02 (7) 0.83 (7) 1.02 (7) G8 6.47 (17) 5.13 (14) 5.79 (17) 5.71 (17) 5.64 (17) 1.00 (11) 0.69 (17) 0.91 (15) 0.80 (13) 0.98 (13) G9 7.17 (5) 5.80 (6) 6.48 (5) 6.43 (5) 6.37 (5) 0.95 (9) 0.87 (5) 1.04 (6) 0.82 (8) 1.01 (8) G10 5.79 (18) 4.89 (17) 5.34 (18) 5.31 (18) 5.28 (18) 0.82 (5) 0.60 (18) 0.87 (18) 0.84 (5) 1.04 (6) G11 7.14 (7) 6.03 (4) 6.59 (4) 6.55 (4) 6.52 (4) 0.90 (7) 0.91 (4) 1.08 (4) 0.84 (6) 1.05 (5) G12 6.49 (16) 5.12 (15) 5.81 (16) 5.72 (16) 5.64 (16) 0.90 (7) 0.69 (16) 0.92 (13) 0.81 (9) 1.00 (10) G13 7.61 (1) 6.76 (1) 7.19 (1) 7.14 (1) 7.09 (1) 0.55 (3) 1.07 (1) 1.21 (1) 0.90 (3) 1.11 (3) G14 6.68 (13) 6.03 (5) 6.35 (7) 6.30 (7) 6.26 (7) 0.50 (2) 0.83 (8) 1.08 (5) 0.92 (2) 1.14 (2) G15 7.08 (8) 5.37 (10) 6.22 (10) 6.14 (10) 6.06 (10) 1.19 (14) 0.80 (10) 0.96 (10) 0.77 (14) 0.95 (14) G16 6.77 (11) 6.46 (2) 6.61 (3) 6.60 (3) 6.59 (3) 0.26 (1) 0.92 (3) 1.15 (2) 0.95 (1) 1.17 (1) G17 6.70 (12) 5.16 (13) 5.93 (14) 5.87 (14) 5.81 (13) 1.24 (15) 0.73 (15) 0.92 (14) 0.77 (14) 0.95 (15) G18 7.22 (3) 5.35 (11) 6.29 (9) 6.21 (8) 6.14 (8) 1.33 (16) 0.82 (9) 0.96 (10) 0.74 (16) 0.93 (16) Hamadan (B) G1 9.03 (4) 4.96 (2) 6.99 (1) 6.65 (1) 6.33 (1) 0.91 (5) 0.61 (1) 1.11 (2) 0.56 (5) 1.07 (5) G2 9.47 (1) 4.09 (15) 6.78 (4) 6.20 (9) 5.67 (13) 1.17 (18) 0.55 (7) 0.93 (14) 0.44 (18) 0.84 (18) G3 9.00 (5) 4.79 (6) 6.90 (2) 6.55 (2) 6.22 (2) 0.94 (6) 0.58 (2) 1.08 (6) 0.55 (6) 1.04 (6) G4 8.67 (10) 4.79 (5) 6.73 (6) 6.40 (4) 6.09 (5) 0.90 (4) 0.58 (3) 1.09 (5) 0.57 (4) 1.08 (4) G5 8.74 (8) 4.26 (12) 6.50 (10) 6.09 (10) 5.71 (10) 1.07 (12) 0.50 (13) 0.96 (12) 0.50 (12) 0.95 (13) G6 8.71 (9) 4.04 (17) 6.38 (14) 5.91 (16) 5.48 (16) 1.11 (16) 0.50 (14) 0.91 (16) 0.47 (16) 0.90 (16) G7 8.23 (15) 4.14 (13) 6.19 (17) 5.82 (17) 5.48 (17) 1.04 (11) 0.46 (17) 0.93 (13) 0.51 (11) 0.98 (10) G8 8.18 (17) 3.86 (18) 6.02 (18) 5.59 (18) 5.19 (18) 1.07 (13) 0.45 (18) 0.87 (18) 0.49 (!4) 0.92 (14) G9 8.98 (6) 4.50 (8) 6.74 (5) 6.34 (7) 5.96 (8) 1.03 (10) 0.54 (8) 1.01 (8) 0.51 (10) 0.98 (11) G10 8.24 (14) 4.39 (11) 6.31 (15) 5.99 (13) 5.70 (11) 0.97 (8) 0.50 (11) 0.98 (11) 0.54 (8) 1.03 (9) G11 9.17 (2) 4.51 (7) 6.84 (3) 6.43 (3) 6.04 (7) 1.08 (14) 0.55 (4) 1.01 (7) 0.49 (13) 0.95 (12) G12 8.36 (13) 4.86 (3) 6.61 (8) 6.36 (5) 6.13 (3) 0.87 (3) 0.55 (5) 1.09 (3) 0.59 (2) 1.14 (2) G13 8.42 (11) 4.82 (4) 6.62 (7) 6.35 (6) 6.10 (4) 0.86 (2) 0.55 (5) 1.09 (4) 0.59 (3) 1.12 (3) G14 8.37 (12) 4.43 (9) 6.40 (12) 6.08 (11) 5.77 (9) 0.98 (9) 0.50 (12) 0.99 (10) 0.54 (9) 1.03 (8) G15 9.13 (3) 4.05 (16) 6.59 (9) 6.05 (12) 5.57 (14) 1.15 (17) 0.50 (15) 0.91 (17) 0.45 (17) 0.87 (17) G16 8.19 (16) 4.41 (10) 6.30 (16) 5.98 (15) 5.69 (12) 0.95 (7) 0.51 (10) 0.99 (9) 0.55 (7) 1.04 (7) G17 7.78 (18) 5.00 (1) 6.39 (13) 6.22 (8) 6.06 (6) 0.71 (1) 0.53 (9) 1.12 (1) 0.66 (1) 1.26 (1) G18 8.80 (7) 4.11 (14) 6.46 (11) 5.99 (14) 5.56 (15) 1.09 (15) 0.49 (16) 0.92 (15) 0.48 (15) 0.91 (15) Mashhad (C) G1 6.93 (2) 5.97 (9) 6.45 (5) 6.41 (5) 6.36 (6) 1.49 (16) 0.94 (6) 0.99 (10) 0.87 (16) 0.95 (16) G2 6.25 (17) 5.26 (18) 5.76 (17) 5.71 (17) 5.66 (18) 1.71 (17) 0.75 (18) 0.87 (18) 0.84 (18) 0.92 (18) G3 6.28 (16) 5.77 (16) 6.03 (16) 5.98 (16) 5.93 (16) 0.88 (6) 0.83 (16) 0.97 (15) 0.94 (4) 1.03 (4) G4 6.80 (5) 6.02 (8) 6.41 (7) 6.37 (7) 6.34 (7) 1.24 (14) 0.94 (7) 1.00 (8) 0.88 (14) 0.97 (14) G5 6.75 (7) 5.87 (14) 6.31 (11) 6.28 (11) 6.25 (11) 1.41 (15) 0.91 (11) 0.98 (14) 0.87 (15) 0.96 (15) G6 6.45 (13) 5.85 (15) 6.15 (15) 6.12 (15) 6.09 (15) 1.00 (8) 0.87 (15) 0.97 (15) 0.91 (10) 1.00 (11) G7 6.69 (8) 6.62 (1) 6.65 (1) 6.64 (1) 6.63 (1) 0.10 (2) 1.02 (1) 1.11 (1) 1.00 (1) 1.10 (1) G8 6.90 (3) 6.26 (5) 6.58 (2) 6.57 (2) 6.55 (2) 1.00 (9) 1.00 (2) 1.04 (6) 0.90 (12) 0.99 (12) G9 6.81 (4) 6.32 (3) 6.56 (3) 6.55 (3) 6.53 (3) 0.77 (4) 0.98 (3) 1.05 (3) 0.93 (5) 1.03 (5) G10 6.52 (12) 5.87 (12) 6.20 (14) 6.17 (14) 6.15 (14) 1.08 (11) 0.88 (14) 0.98 (12) 0.91 (10) 1.00 (10) G11 6.06 (18) 5.40 (17) 5.72 (18) 5.71 (18) 5.69 (17) 1.18 (13) 0.75 (17) 0.90 (17) 0.89 (13) 0.98 (13) G12 6.77 (6) 6.26 (6) 6.51 (4) 6.49 (4) 6.48 (4) 0.82 (5) 0.97 (4) 1.05 (5) 0.93 (6) 1.03 (5) G13 6.53 (11) 5.92 (11) 6.22 (13) 6.21 (13) 6.19 (13) 1.01 (10) 0.89 (13) 0.99 (11) 0.91 (8) 1.01 (7) G14 6.42 (14) 6.29 (4) 6.35 (8) 6.33 (9) 6.32 (9) 0.22 (3) 0.92 (10) 1.05 (3) 0.99 (3) 1.09 (3) G15 6.62 (9) 6.07 (7) 6.35 (10) 6.32 (10) 6.30 (10) 0.89 (7) 0.92 (9) 1.01 (7) 0.92 (7) 1.01 (8) G16 6.61 (10) 5.93 (10) 6.27 (12) 6.24 (12) 6.22 (12) 1.11 (12) 0.90 (12) 1.00 (9) 0.91 (8) 1.01 (8) G17 6.37 (15) 6.34 (2) 6.35 (9) 6.34 (8) 6.33 (8) 0.06 (1) 0.93 (8) 1.06 (2) 0.99 (2) 1.10 (1) G18 6.98 (1) 5.87 (13) 6.43 (6) 6.40 (6) 6.37 (5) 1.72 (18) 0.95 (5) 0.98 (12) 0.84 (17) 0.93 (17) Ys, Yp, HM, MP, GMP, SSI, STI, YI, YSI and RSI indicate grain yield under the irrigated conditions, grain yield under drought stress conditions, mean productivity, geometric mean productivity, stress susceptibility index, stress tolerance index, yield index, yield stability index, relative stress index, respectively. In the Mashhad environment, the highest grain yield under the irrigated conditions was recorded in the genotype G18, followed by G1 and G8, whereas genotypes G7, G9, and G17 had the highest performances under drought stress conditions. Based on HM, MP, and GMP indices, genotypes G7, G8, and G9 showed the highest values. Of these, G7 along with G14 and G17 showed the highest values for YI, YSI, and RSI as well as the lowest value for SSI. The STI index screened genotypes G7, G8, and G9 as the drought-tolerant genotypes (Table 5 C). According to average data for the three environments tested, genotypes G1 (the reference genotype), G4, and G18 showed the highest grain yield under the irrigated conditions. Under drought stress conditions, G4 followed by G13, and G18 showed the highest grain yield compared to other genotypes. Moreover, the MP, GMP, STI, and HM identified G1 (the reference genotype), G4, and G13 as the drought-tolerant genotypes. The SSI and YSI similarly identified G14, G16, and G17 as the three top-rank genotypes. Based on The RSI index, G13, G14, and G17 were identified as drought-tolerant genotypes, while YI index identified G4, G13, and G17 as the tolerant genotypes compared to other genotypes (Table 6 ). Table 6 Estimated values for drought tolerance and susceptible indices and their ranks (in bracket) in the evaluated genotypes of barley over three tested regions Code Yp Ys MP GMP HM SSI STI YI YSI RSI G1 7.71 (1) 5.55 (5) 6.63 (3) 6.47 (3) 6.33 (3) 1.01 (10) 0.80 (3) 1.04 (4) 0.74 (10) 1.00 (10) G2 7.58 (6) 4.75 (18) 6.16 (15) 5.90 (17) 5.67 (18) 1.35 (18) 0.68 (17) 0.89 (18) 0.66 (18) 0.87 (18) G3 7.50 (8) 5.16 (12) 6.33 (11) 6.14 (13) 5.96 (14) 1.13 (15) 0.72 (13) 0.98 (11) 0.73 (14) 0.98 (12) G4 7.68 (2) 5.75 (2) 6.72 (1) 6.58 (1) 6.46 (1) 0.91 (6) 0.85 (1) 1.08 (2) 0.77 (7) 1.03 (6) G5 7.39 (10) 5.17 (11) 6.28 (13) 6.12 (14) 5.96 (13) 1.09 (13) 0.72 (14) 0.97 (12) 0.73 (15) 0.97 (13) G6 7.25 (12) 5.05 (16) 6.15 (16) 5.97 (15) 5.80 (15) 1.10 (14) 0.70 (16) 0.94 (16) 0.73 (13) 0.96 (15) G7 7.32 (11) 5.47 (8) 6.39 (5) 6.22 (8) 6.06 (10) 0.91 (7) 0.77 (6) 1.02 (9) 0.78 (6) 1.03 (7) G8 7.18 (15) 5.08 (15) 6.13 (17) 5.95 (16) 5.79 (16) 1.06 (12) 0.71 (15) 0.94 (17) 0.73 (12) 0.97 (14) G9 7.65 (4) 5.54 (6) 6.59 (4) 6.44 (4) 6.29 (4) 1.00 (9) 0.80 (4) 1.04 (6) 0.75 (9) 1.01 (9) G10 6.85 (18) 5.05 (17) 5.95 (18) 5.83 (18) 5.71 (17) 0.95 (8) 0.66 (18) 0.95 (15) 0.76 (8) 1.02 (8) G11 7.46 (9) 5.31 (10) 6.38 (9) 6.23 (7) 6.08 (7) 1.04 (11) 0.74 (11) 1.00 (10) 0.74 (11) 0.99 (11) G12 7.21 (13) 5.41 (9) 6.31 (12) 6.19 (10) 6.08 (8) 0.90 (5) 0.74 (9) 1.02 (8) 0.78 (5) 1.06 (5) G13 7.52 (7) 5.83 (1) 6.68 (2) 6.57 (2) 6.46 (2) 0.81 (4) 0.84 (2) 1.09 (1) 0.80 (4) 1.08 (3) G14 7.15 (16) 5.58 (4) 6.37 (10) 6.24 (6) 6.11 (6) 0.80 (2) 0.75 (8) 1.04 (5) 0.81 (1) 1.09 (2) G15 7.61 (5) 5.16 (13) 6.39 (8) 6.17 (11) 5.98 (12) 1.16 (16) 0.74 (10) 0.96 (13) 0.71 (16) 0.94 (16) G16 7.19 (14) 5.60 (3) 6.39 (6) 6.28 (5) 6.17 (5) 0.80 (3) 0.78 (5) 1.05 (3) 0.80 (3) 1.07 (4) G17 6.95 (17) 5.50 (7) 6.22 (14) 6.14 (12) 6.07 (9) 0.76 (1) 0.73 (12) 1.03 (7) 0.81 (2) 1.10 (1) G18 7.67 (3) 5.11 (14) 6.39 (7) 6.20 (9) 6.02 (11) 1.21 (17) 0.75 (7) 0.96 (14) 0.69 (17) 0.92 (17) Ys, Yp, HM, MP, GMP, SSI, STI, YI, YSI and RSI indicate grain yield under the irrigated conditions, grain yield under drought stress conditions, mean productivity, geometric mean productivity, stress susceptibility index, stress tolerance index, yield index, yield stability index, relative stress index, respectively. Network correlation analysis To investigate the interrelationships among grain yield data and drought tolerance/susceptible indices, we used network correlation analysis based on each environment and the average data. Based on the data obtained in the Karaj environment (Fig. 1 A), there was observed a positive and significant association between Yp with Ys, HM, GMP, MP, STI, and YI. The Ys index positively and significantly correlated with MP, GMP, HM, STI, YSI, YI, and RSI indices. The STI index showed a positive and significant correlation with all indices except SSI. Correlations between YSI and YI and with other indices were positive and significant. The result of correlation analysis based on the Hamadan data indicated that there was a positive and significant correlation between Yp with MP and GMP indices. However, Ys positively and significantly correlated with all indices except SSI. Correlation among indices STI, HM, GMP, and MP was significant and positive, while SSI showed a negative correlation with all indices except Yp (Fig. 1 B). The most important positive and significant correlations based on the obtained data in the Mashhad environment were: Yp with Ys, MP, GMP, HM, STI, and YI; Ys with all indices except SSI; YI with all indices; and YSI with YI (Fig. 1 C). Based on the average data, correlation between Yp with MP, GMP, and STI was positive and significant. Moreover, Ys showed positive and significant with all indices except SSI. The RSI only showed a significant and positive correlation with Ys, HM, YI, and YSI (Fig. 3 D). Identification of the drought-tolerant genotypes using selection models Based on the data from Karaj environment, a highly significant genotype effect was found for the Yp, Ys, MP, GMP, HM, YI and STI indices (Table 7 ). The broad-sense heritability (h 2 ) ranged from 0.16 (SSI) to 0.76 (STI). Moreover, the total selection gain (SG) using the filtered indices was higher (48.19%), and STI showed the highest SG value. The MGIDI index generally identified G4, G13, and G16 as superior genotypes compared to others. The SH index showed a similar result to the MGIDI index and genotypes G4, G13, and G16 were selected. The FAI index indicated that the h 2 ranged from 0.16 (SSI) to 0.75 (STI) and the SG for filtered indices was estimated as 46.56%. The selected genotypes using the FAI index were G4, G11, and G13. The Venn diagram showed that genotypes G4 and G13 were superior based on all selection indices (Fig. 2 ). Table 7 Heritability and selection gain (SG) for drought tolerance and susceptible indices in the test environment using SH, MGIDI, and FAI-BLUP models Index Pattern Karaj Hamadan Average environments h 2 Selection gain (SG) h 2 Selection gain (SG) h 2 Selection gain (SG) MGIDI SH FAI MGIDI SH FAI MGIDI SH FAI MGIDI SH FAI MGIDI SH FAI MGIDI SH FAI Yp Increase 0.68 0.68 0.68 2.8 2.8 3.64 0.76 0.75 0.75 1.76 -2.98 1.76 0.73 0.73 0.73 1.71 0.52 0.60 Ys Increase 0.61 0.61 0.61 6.46 6.45 5.52 0.63 0.63 0.63 3.63 4.05 3.63 0.65 0.65 0.65 2.99 3.10 3.15 MP Increase 0.75 0.75 0.75 6.18 6.18 6.11 0.68 0.68 0.68 2.36 -0.003 2.36 0.72 0.72 0.72 2.52 1.88 1.96 GMP Increase 0.73 0.73 0.73 6.4 6.4 6.27 0.64 0.64 0.64 2.41 0.96 2.41 0.73 0.73 0.73 2.81 2.24 2.35 HM Increase 0.71 0.71 0.71 6.47 6.47 6.28 0.63 0.67 0.62 2.67 1.88 2.67 0.74 0.73 0.73 3.07 2.56 2.71 STI Increase 0.76 0.76 0.76 13.7 13.66 13.4 0.52 0.52 0.52 3.40 0.99 3.40 0.73 0.72 0.71 5.49 4.39 4.98 YI Increase 0.6 0.6 0.6 6.18 6.18 5.34 0.63 0.63 0.63 3.68 4.04 3.68 0.72 0.68 0.68 3.30 3.36 3.52 SSI Decrease 0.16 0.16 0.16 -1.16 -1.16 -0.59 0.67 0.67 0.67 -3.50 -8.28 -3.50 0.53 0.61 0.54 1.43 -0.98 1.06 YSI Increase 0.23 0.2 0.2 0.43 0.43 0.25 0.69 0.69 0.69 2.93 7.71 2.93 0.50 0.50 0.50 0.74 1.40 1.27 RSI Increase 0.19 0.19 0.19 0.39 0.39 0.24 0.70 0.70 0.70 2.73 8.08 2.73 0.64 0.64 0.64 1.39 2.48 2.32 Ys, Yp, HM, MP, GMP, SSI, STI, YI, YSI and RSI indicate grain yield under the irrigated conditions, grain yield under drought stress conditions, mean productivity, geometric mean productivity, stress susceptibility index, stress tolerance index, yield index, yield stability index, relative stress index, respectively. In the Hamadan environment, the genotypic effect was significant for all indices except the STI. In the MGIDI model, the h2 ranged from 0.52 (STI) to Yp (0.76) (Table 7 ). Moreover, the SG was estimated as 22.07%, and the highest value recorded for Ys index. The selected genotypes using the MGIDI index were G1, G3, and G4. In the SH analysis, the SG was different and it ranged from − 8.28% (SSI) to 8.08% (RSI) with a total average of 15.45%. The genotypes selected using the SH were G12, G13, and G17. The total SG value in the FAI model was 22.07 and the indices Yp and YI showed the lowest (1.76%) and highest (3.68%) values, respectively. Similar to the MGIDI index, genotypes G1, G3, and G4 were selected as the superior genotypes (Fig. 3 ). In the Mashhad environment, the genotypic effect was not significant for all calculated indices. Hence, we used the Karaj and Hamadan environments data to create the average data matrix. Based on the average data, the h2 varied between 0.50 (YSI) and 0.74 (HM). The SG value ranged from 0.74% (YSI) to 5.49% (STI) with a total of 21.07% (Table 7 ). Using the MGIDI index, three genotypes G4, G9, and G13 were selected. In the SH model, the SG value ranged from 0.52% (Yp) to 4.39% (STI) with a total of 22.83%. The identified genotypes in this model were G4, G13, and G16. In the FAI analysis, Yp and STI indicated the lowest and highest values for the SG (0.60% and 4.98%). The total estimated SG for filtered indices in the model was 22.86%. Genotypes G4, G9 and G13 were selected as superior genotypes using the FAI model. Using the Venn diagram, genotypes G4 and G13 were selected as the common genotypes in three selection models (Fig. 4 ). GGE biplot analysis The results of the GGE biplot analysis based on average two-year data have been summarized into four biplots (Fig. 5 ). The first two components justified 72.78% of the total grain yield variation in the irrigated and drought stress conditions across three test environments. The polygon view of point the GGE analysis showed six environments clustered into three main sectors. The first sector included both irrigated and drought conditions of Karaj (E1 and E4) along with the drought stress conditions of the Hamadan (E5) (Fig. 5 A). The best genotypes for this sector were G13 and G16. Moreover, the genotype G4 showed a broad adaptability for these environments. The second sector consisted of the Mashhad environment (both irrigated and drought stress conditions (E3 and E6, respectively) with a winner genotype G17. The irrigated conditions of the Hamadan (E2) along with G2 were placed into the third sector. Based on the “mean vs. stability” biplot, the genotypes G13 followed by G4, G16, G11, G9, and G1 (the reference genotype) showed that highest average of grain yield across different test environments. Of these, G1 and G9 showed indicated performances closet to the grand mean. Moreover, genotypes G4 and G13 with the high average of grain yield showed the most stability due to their position in the biplot (Fig. 5 B). As another result of the GGE biplot analysis, a positive and significant correlation was found among drought stress environments (E4, E5, and E6). Furthermore, E4 (Karaj-drought stress condition) with a longer vector and small angel with the average environment coordinate (AEC) showed the highest discrimination power and representative ability compared to other environments (Fig. 5 C). To select the ideal genotypes across the test environments, a comparative view of the GGE biplot was used. Based on this biplot, genotypes G4 and G13 were near to the average environment axis (AEA) and closest to the central circle were identified as the ideal genotypes and showed the specific adaptability to drought conditions in the Karaj (E4) and Hamadan (E5) environments (Fig. 5 D). Discussion In this study, a set of promising barley genotypes was evaluated on the basis of grain yield and several yield-based indices in 12 environments (a combination of three locations, two years, and two growth conditions [irrigated and drought stress]). The experimental data obtained were subjected to various statistical analyses to identify genotypes with different traits, such as high yield and stability, as well as a high drought tolerance index. The results of the AMMI model indicated that the environmental effect explained the greatest variation in grain yield than the genotypic effect (76.69% vs. 1.43%) and the genotype-by-environment interaction (76.69% vs. 9.60%) (Table 3 ). The main reason for this result may relate to the high variability of environmental factors (Li et al. 2021 ), where there were significant differences in terms of temperature and precipitation parameters in different test environments (Table S1 ). Several studies have reported a large effect of environment on grain yield variability and its importance in selecting the best barley genotypes (Ahakpaz et al. 2021 ; Bakshi and Shahmoradi 2022; Pour-Aboughadareh et al. 2022 , 2023 ; Bakshi et al. 2023). Moreover, GEI effects were further divided into four IPCAs, showing the magnitude of GEI, which in turn indicated that each genotype responded differently in the two irrigated and drought stress environments with regard to grain production (Lamba et al. 2023 ). Drought stress negatively affected all aspects of the plant growth and development and finally reduces the plant production. The considerable decrease in grain yield due to various levels of drought stress has been shown in previous studies (Dong et al. 2017 ; Alghabari et al. 2018; Etminan et al. 2019 ; Saddiq et al. 2021 ; Hossain et al. 2023 ; Yang et al. 2023 ). In the present study, the overall mean across the 18 barley genotypes tested in the Karaj, Hamadan, and Mashhad, as well as across three environments was reduced significantly by 41.9%, 4%, 21.41%, and 22.07%, respectively, under drought stress environments as compared to the irrigated conditions (Table 4 ). Several stress tolerance indices have been proposed for the identification of tolerant genotypes under stress conditions. Our results indicated that various stress tolerance indices gave similar ranking patterns in the selection of tolerant genotypes in different test environments (Table 5 ). As mentioned heretofore, one of the main goals of this study was to determine the efficiency of the multi-index models in selecting the superior drought-tolerant genotypes. To achieve this goal, three multivariate models––including SH, FAI-BLUP, and MGIDI––were utilized to identify most drought-tolerant barley genotypes based on several stress-tolerance indices. In all models, the high values for Yp, Ys, HM, MP, GMP, YI, YSI, RSI, and STI were targeted, while the low value for the SSI index was targeted as its weight. Moreover, the selection pressure in all models was considered as 15%. The SH index selected genotypes G4, G13, and G16 in the Karaj environment; genotypes G12, G13, and G17 in the Hamadan environment; and genotypes G4, G13, and G14 in over two environments. Hence, the G13 was identified as a common genotypes with high rate of drought tolerance (Fig. 2 ). Considering the FAI model, genotypes G4 and G13 were simultaneously selected as the superior genotypes in the Karaj, Hamadan, and over two environments (Fig. 3 ). Moreover, the last-mentioned genotypes were identified as the most drought-tolerant genotypes in the MGIDI model (Fig. 4 ). The Venn diagram rendered based on each environment and average data revealed that among evaluated genotypes, G4 and G13 had the best performance and tolerance to drought stress compared to other genotypes. In accordance to our results, Pour-Aboughadareh and Poczai (2021 a, b), Sellami et al. ( 2021 ), Al-Ashkar et al. ( 2023 ), Costa et al. ( 2023 ), Klein et al. ( 2023 ), Hussain et al. (2023), and Zali et al. ( 2023 ), confirmed the efficiency of the MGIDI, FAI-BLUP, and SH selection models in various crops such as wheat, lentil, mango, chickpea, and barley respectively. As another result obtained for these analyses, the most significant selection gain were found for the STI, YI and Ys index. For indices in which the gains were smaller, selection gains in other indices can be balanced (Klein et al. 2023 ). Moreover, to find the most suitable indices for drought stress tolerance, correlation coefficients were determined among grain yield under both conditions and other indices. According to obtained results, in all environments, Yp and Ys positively correlated with each other and with STI, MP, and GMP indices. This result was supported by Dorostkar et al. ( 2015 ), Kamrani et al. ( 2017 ), Shabani et al. ( 2018 ), Etminan et al. ( 2019 ), Pour-Aboughadareh et al. (2020 a, b), and Lamba et al. ( 2023 ), who suggested that based on these associations, high performing genotypes can be identified using aforesaid indices under both irrigated and drought stress conditions. In this study, we used the GGE biplot analysis to assess the stability and adaptability of selected genotypes in different environments. The “which-won-where” view of GGE analysis clustered the six environments (three irrigated and three drought stress conditions) into three main sectors (Fig. 5 A). As we expected, the genotype G13 and G4 showed specific adaptability in the both irrigated and drought stress conditions in the Karaj (E1 and E4) and Hamadan (E5) environments. Furthermore, genotypes G13 and G4 showed the highest grain yield and stability compared to other genotypes (Fig. 5 B). Among test environments, all drought stress conditions (E4, E5, and E6) showed a positive and significant correlation with each other due to the cosine angles among their vectors (Fig. 5 C). Of these, E4 along with E1 (irrigated environment in the Karaj) with a longer vector and small angel with the average environment coordinate (AEC) showed the highest discrimination power and representative ability. Hence, according to Yan’s theory (Yan 2001 ), these environments can be chosen as the ideal target environment for investigating new varieties for their full yield potential. In general, these results supported by the comparison view of the GGE biplot, where genotypes G4 and G13 were identified as the ideal genotypes and revealed a specific adaptability to drought conditions in the Karaj and Hamadan environments (Fig. 5 D). Likewise, several studied reported the high efficiency of the GGE biplot approach in the identification of ideal genotypes of barley in MET experiments (Jalate 2001; Ahmadi et al 2012 ; Mortazavian et al. 2014 ; Kendal 2016 ; Vaezi et al. 2019 ; Daba et al. 2023 ; Linus et al. 2023 ; Pour-Aboughadareh et al. 2023 ). Little information is available in the literature on selecting the best genotypes based on multiple indicators or multiple traits in MET. Therefore, the most important goal of any breeding program is to evaluate genotypes under different environmental conditions and identify those superior genotypes that perform better under changing climatic conditions in a wide range of environments. In this study, the AMMI and GGE biplot models were used along with several tolerance indices based on grain yield to evaluate a set of promising new barley genotypes. In addition, three selection indices were used to identify the best drought-tolerant genotypes based on grain yield and other indices. Of the genotypes tested, genotypes G4 and G13 were identified as the most drought-tolerant genotypes with significant yield and stability. Conclusion Evaluation of drought tolerance in different environments led us to conclude that a complex of selection models and multivariate approaches can be used to identify superior barley genotypes with high yield and stability. In conclusion, the results obtained in the current study revealed that STI, MP, YI, and GMP indices positively and significantly correlated with both grain yield under the irrigated and drought stress conditions. Hence, these indices could be used as suitable selection yield-based indices in future studies. Moreover, Multi-index selection models (MGIDI, SH, and FAI-BLUP) provided more details on the selection gain for each drought tolerance index, so that the STI, Ys, and YI were identified as efficient tools in screening drought-tolerant genotypes. The collective analysis using the AMMI, GGE biplot, and multi-index selection models identified genotypes G4 and G13 as the superior genotypes. Thus, these genotypes can be candidates for commercial introduction. The pedigree of these genotypes showed that they are two sister lines (Comp.Cr229//As46/Pro/3/Srs/4/Express/5/Goharan/6/Goharan), which originated from a hybridization program between a foreign line (Comp.Cr229//As46/Pro/3/Srs/4/Express) and a local cultivar (Goharan). The “Goharan” cultivar with pedigree "Rhn-03//L.527/NK1272" was previously selected from barley genotypes received from the International Center for Agricultural Research in the Dry Areas (ICARDA) and was introduced as a high-yielding and stable cultivar for the moderate climate in Iran (Nikkhah et al. 2018). One of the significant characteristics of this cultivar is to its tolerance to terminal drought stress. Therefore, we considered that the selected genotypes (G4 and G13) could be recommended for further evaluation and commercial introduction in drought-prone regions in Iran and other areas with similar weather conditions. Declarations Author Contributions : Conceptualization, H.G. and A.P.; methodology, H.G. and A.P; software, A.P. and J.B.; validation, H.G., J.B.; formal analysis, A.P. and J.B.; investigation, H.T., M.C., S.J., and H.G.; resources, H.G.; data curation, H.G. and A.P.; writing—original draft preparation, A.P.; writing—review and editing, A.P. and J.B. All authors have read and agreed to the published version of the manuscript. Funding : This research received no external funding. Data Availability Statement : The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. Acknowledgments : The authors acknowledge the Seed and Plant Improvement Institute (SPII), Agricultural Research, Education and Extension Organization (AREEO), Iran, for providing plant genetic material and supporting the research facilities. Conflicts of Interest : The authors declare no conflict of interest. References Ahakpaz F, Abdi H, Neyestani E, Hesami A, Mohammadi B, Nader Mahmoudi K, Abedi-Asl G, Jazayeri Noshabadi MR, Ahakpaz F, Alipour H (2021) Genotype-by-environment interaction analysis for grain yield of barley genotypes under dryland conditions and the role of monthly rainfall. 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Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2024 Read the published version in Journal of Crop Health → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3917144","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270609128,"identity":"c02b2f9f-14aa-43ab-aa9e-e565eed37425","order_by":0,"name":"Habibollah Ghazvini","email":"","orcid":"","institution":"Agricultural Research, Education and Extension Organization (AREEO)","correspondingAuthor":false,"prefix":"","firstName":"Habibollah","middleName":"","lastName":"Ghazvini","suffix":""},{"id":270609129,"identity":"3d869f4a-646c-4891-9f7d-651d232ea20a","order_by":1,"name":"Alireza Pour-Aboughadareh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYPCCAwwMEmDEkMAP4icUkKJFsgFEGpCixeAASACPFv4G3oOPK2ruyMlHNz+88XOPTZ7x+dWJHx4YMMjzix3AqkXiAF+y4Zljz4wN7xwztux5llZsduPtZgmgwwxnzk7A4SQeM8kGtsOJG2ckmEnwHDicuO3G2Q0gLQkGt7FrkQdr+QfSkv5N8s+B/4mbZ5zd/AOfFgOQlsa2w4nzJXLMpHkOHEjcwN+7Da8thoeBfmnse2ZsIJFTbC1zIDlxxg3ebRYJBhI4/SJ3vPfgw4ZvwBCbkb7x5psDdon9/Wc33/xRYSPPL43D+8w8MBfCRCTAKiWwKwcDqBb5BpgA/wHsCkfBKBgFo2DEAgCKNGln47MlFAAAAABJRU5ErkJggg==","orcid":"","institution":"Agricultural Research, Education and Extension Organization (AREEO)","correspondingAuthor":true,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Pour-Aboughadareh","suffix":""},{"id":270609130,"identity":"ae816376-d3f7-4b12-8e78-a423174016b0","order_by":2,"name":"Seyed Shahriyar Jasemi","email":"","orcid":"","institution":"Agricultural Research, Education and Extension Organization (AREEO)","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Shahriyar","lastName":"Jasemi","suffix":""},{"id":270609131,"identity":"511cfb5a-50d3-4360-9b24-90d8218a615c","order_by":3,"name":"Mehrdad Chaichi","email":"","orcid":"","institution":"Agricultural Research, Education and Extension Organization (AREEO)","correspondingAuthor":false,"prefix":"","firstName":"Mehrdad","middleName":"","lastName":"Chaichi","suffix":""},{"id":270609132,"identity":"6049f9a3-c9be-4c67-ad24-03269d59031a","order_by":4,"name":"Hamid Tajali","email":"","orcid":"","institution":"Agricultural Research, Education and Extension Organization (AREEO)","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Tajali","suffix":""},{"id":270609133,"identity":"251ce8ef-258a-4f74-ae96-ec043943852a","order_by":5,"name":"Jan Bocianowski","email":"","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Bocianowski","suffix":""}],"badges":[],"createdAt":"2024-02-01 11:16:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3917144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3917144/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10343-024-00981-1","type":"published","date":"2024-04-25T22:04:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50662388,"identity":"b2a08a01-3a2b-4d98-8072-9dc9c82393ab","added_by":"auto","created_at":"2024-02-05 12:16:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":808868,"visible":true,"origin":"","legend":"\u003cp\u003eThe network correlation among grain yields (Yp and Ys) and other drought tolerance and susceptible indices based on the Karaj (A), Hamadan (B), Mashhad (C), and over three locations (D), respectively. Ys, Yp, HM, MP, GMP, SSI, STI, YI, YSI and RSI indicate grain yield under the irrigated conditions, grain yield under drought stress conditions, mean productivity, geometric mean productivity, stress susceptibility index, stress tolerance index, yield index, yield stability index, relative stress index, respectively.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3917144/v1/226fd529622a30c22aa213c8.jpeg"},{"id":50662158,"identity":"422be991-1b74-4889-8455-34e73e36cf8b","added_by":"auto","created_at":"2024-02-05 12:08:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":719417,"visible":true,"origin":"","legend":"\u003cp\u003eSelected barley genotypes using the MGIDI (A), FAI-BLUP (B), and Smith-Hazel models in the Karaj environment. The red circle represents the point separating the desired genotypes, which is marked with a red point. Venn diagram (D) for selected genotypes based on three used models.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3917144/v1/cde2de22d49dfa36933a7730.jpeg"},{"id":50662389,"identity":"3b4489ab-a62a-433c-81b5-add13fca0397","added_by":"auto","created_at":"2024-02-05 12:16:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":703854,"visible":true,"origin":"","legend":"\u003cp\u003eSelected barley genotypes using the MGIDI (A), FAI-BLUP (B), and Smith-Hazel models in the Hamadan environment. The red circle represents the point separating the desired genotypes, which is marked with a red point. Venn diagram (D) for selected genotypes based on three used models.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3917144/v1/9f8c79333c3a4a5b4cc1b1b2.jpeg"},{"id":50662161,"identity":"c57299c9-1827-4130-93d7-775ab583eec4","added_by":"auto","created_at":"2024-02-05 12:08:03","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":701746,"visible":true,"origin":"","legend":"\u003cp\u003eSelected barley genotypes using the MGIDI (A), FAI-BLUP (B), and Smith-Hazel models based on the average data (Karaj and Hamadan). The red circle represents the point separating the desired genotypes, which is marked with a red point. Venn diagram (D) for selected genotypes based on three used models.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3917144/v1/7987e8f42deef4766e8b2921.jpeg"},{"id":50662163,"identity":"efcb4538-bff4-475a-b4af-4934c6e82fae","added_by":"auto","created_at":"2024-02-05 12:08:04","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":685516,"visible":true,"origin":"","legend":"\u003cp\u003eView of the GGE ‘which-won-where’ biplot of winning genotypes for grain yield in each sector (A). Biplot for simultaneous selection of grain yield and stability of barley genotypes tested (B). A view of the ‘discriminating power and representativeness’ of the GGE biplot (C). Comparison of promising barley genotypes with the ‘ideal’ genotype in terms of grain yield and stability at four test locations (D). E1, E2, and E3 indicate the irrigated conditions in the Karaj, Hamadan, and Mashhad locations, respectively. E4, E5, and E6 indicate the drought stress conditions in the Karaj, Hamadan, and Mashhad locations, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3917144/v1/555f71dbd9db86b8dd36485a.jpeg"},{"id":55689502,"identity":"a5354da6-fa5f-42c0-b303-53e3f289a42d","added_by":"auto","created_at":"2024-05-01 22:04:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1848364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3917144/v1/9aa0e03c-bbcb-4ed0-98a7-13bd9cf76c27.pdf"},{"id":50662162,"identity":"caa9e6eb-eeb7-42af-9d45-c3343ad3d80d","added_by":"auto","created_at":"2024-02-05 12:08:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":173156,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3917144/v1/2ccd23ce281a87499bdd60b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A framework for selection of high-yielding and drought-tolerant genotypes of barley: Applying yield- based indices and multi-index selection models","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBarley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) is the fourth most important cereal crop in the world after wheat, rice and corn. Moreover, it has been reported that this cereal is one of the most economically important crops in terms of seed quality (Giraldo et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From the point of both human\u0026rsquo;s and animal\u0026rsquo;s feed and also industry products, barley is a key cereal due to the presence of various minerals such as phosphorus and calcium, small amounts of vitamins, especially B vitamins, moderate amount of protein and dietary fiber in the seeds. In addition, the high carbohydrate content of barley seeds has led to the use of barley in soups, stews, bread and other foods (Fatemi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe frequency of available water for plants is the most important variable in determining global yield limits. It has been reported that limiting this water is responsible for 60\u0026ndash;70% of the variance in final yield (Nykiel et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since water scarcity is a major source of yield loss worldwide, developing more drought-tolerant varieties in the current decade is more critical to food security than in previous times. Drought tolerance is one of the main breeding objectives in arid and semi-arid regions, as well as in other water-poor regions (Magalh\u0026atilde;es and Magalh\u0026atilde;es \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Extreme drought stress during crop growth and development cycles has a negative impact on barley productivity. Therefore, it is crucial for breeders and agronomists to identify genotypes that are tolerant to it (Khan et al. 2014). It has been reported that stress-tolerant genotypes can be selected by growing advanced breeding materials under normal and stressed conditions (Khan et al. 2014). Consequently, using better selection approaches is often a challenge for breeders in identifying superior drought-tolerant cultivars.\u003c/p\u003e \u003cp\u003eOne of the main efforts to develop new varieties is to decipher the genotype-by-environment interaction (GEI) effect in multi-environment trials (METs) (Eben et al. 2021; Linus et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Correct interpretation of this effect in METs can help select the best genotypes with high performance and stability under different environmental conditions (Bocianowski et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vaezi et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Grain yield is a quantitative trait and its variation controlled by various genes. In MTEs, the GEI has bottleneck effects on yield improvement. Indeed, this effect hinder the identification of high-yield and stable genotypes and the introduction of superior genotypes for specific agro-climatic areas (Eben et al. 2021). Thus, to counteract the negative effects of GEI on the outcomes of breeding programs, extensive knowledge on this effect is necessary for breeders to improve selection accuracy in breeding programs. Among the proposed models, the genotype (G)\u0026thinsp;+\u0026thinsp;genotype \u0026times; environment (GGE) biplot is one of robust approaches that are widely used to examine the effect of GEI on grain yield in MET experiments. Both statistical models are based on principal component analysis (PCA), which allows understanding of the relationship between genotypes, environments, and GEI to identify stable and high-yielding genotypes for target environments or across environments (Linus et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring breeding practice, breeders often measure multiple growth traits and face the problem of selecting desirable genotypes based on multiple traits. Several performance-based indices are used to select stress-tolerance varieties, such as the stress susceptibility index (SSI; Fischer and Maurer (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1978\u003c/span\u003e)), relative stress index (RSI; Fischer and Wood (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1979\u003c/span\u003e)), tolerance index (TOL; Rosielle and Hambling (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)), mean productivity index (MP; Rosielle and Hambling (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)), yield stability index (YSI; Bouslama and Schapaugh (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1984\u003c/span\u003e)), harmonic mean (HM; Bidinger et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1987\u003c/span\u003e)), geometric mean productivity (GMP; Fernandez (1992)), stress tolerance index (STI; Fernandez (1992)), and yield index (YI; Gavuzzzi et al. (1997)) have been proposed. Selection based on each of the indices is usually difficult, hence the use of a model that integrates all indices or traits into an index is ideal for the selection of superior genotypes in METs. Accordingly, several selection indices have been proposed, such as the Smith\u0026ndash;Hazel index (Smith 1963; Hazel \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1943\u003c/span\u003e), the factor analysis and genotype-ideotype distance index (FAI-BLUP) (Rocha et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the multiple trait selection index (MTSI) (Olivoto et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the genotype\u0026ndash;ideotype distance index (MGIDI) (Olivoto and Nardino 2021), and the selection index for the ideal genotype (SIIG) (Zali et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have been proposed to select superior genotypes based on traits measured in METs. In this sense, the main objective of this study was to use several tolerance indices based on grain yield and selection models to identify drought-tolerant barley genotypes in three drought-prone regions of Iran. Overall, this study provides new information on the use of multi-indices selection, helping breeders make better decisions toward effective multivariate selection in barley breeding programs to improve drought tolerance programs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and multi-environment trails\u003c/h2\u003e \u003cp\u003eA set of 17 promising genotypes of barley (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) along with a local verity (cv. Jolge as the reference genotype) were tested at three agricultural research stations in the cold regions of Iran [including Mashhad, Karaj, and Hamadan] during two cropping seasons (2019\u0026ndash;2020 and 2020\u0026ndash;2021). Among test environments, Karaj (50\u0026deg;93' E, 35\u0026deg;78' N) and Hamadan (48\u0026deg;53' E, 34\u0026deg;88' N) have the Mediterranean conditions with spring rains, while Mashhad (59\u0026deg;60' E, 36\u0026deg;20' N) has semi-desert conditions (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). More information of the weather conditions in each test environment are presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. At each environment, basic fertilizers such as P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e and N were applied at 100 and 32 kg ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively, before sowing. At all test stations, randomized complete block designs with three replications were used to evaluate genotypes under two irrigated conditions and drought stress conditions. Each genotype was planted in separate plots in 6 m\u003csup\u003e2\u003c/sup\u003e including six lines of 6 meters in length and spaced 20 cm apart. An experimental planter (Wintersteiger, Ried, Austria) was used for sowing. The sowing density in each plot was 450 seeds per m\u003csup\u003e2\u003c/sup\u003e. The number of irrigations was one time in autumn and five times in spring at stem elongation (ZGS 31), booting (ZGS 41), anthesis (ZGS 69), mild development (ZGS 71), and dough development (ZGS 83). Drought stress treatment was applied after anthesis, and irrigation was stopped for all stressed plot until seed repining stage (ZGS 92). In each irrigation period, 1000 m3 of water was applied. Granstar (Thifensulfuron-methyl and Tribenuron-methyl as active substances) and Pumasuper (fenoxaprop-P-ethyl 69 gL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and safener (mefenpyr-diethyl) as active substances) herbicides were used to control the broad-leaved and narrow-leaved weeds at the tillering stage. The studied genotypes were classified into two- and six-row groups based on their spike types. Moreover, based on the growth type, they were grouped into three groups: spring, winter, and facultative. Based on the time of physiological maturity in each test environment, a combine harvester (Wintersteiger, Ried, Austria) was used to harvest the experimental plots. Grain yield was determined for each genotype in the test environment and finally converted to ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe pedigree of the 17 evaluated promised barley genotypes along with the reference genotype across three drought-prone locations during two years in Iran.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePedigree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpike Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrowth Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJolge (Reference genotype)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBahman/3/Makouee//Zarjow/80-5151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlger//CI10117/Choyo/3/Makouee/4/STB-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComp.Cr229//As46/Pro/3/Srs/4/Express/5/Goharan/6/Goharan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacultative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZarjow/80-5151//Makouee/3/Makouee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMakouee//Zarjow/80-5151/3/Bahman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadical/Birgit//Pamir-154/3/Goharan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacultative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCali92/Robust//ND16301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadical/Birgit//Pamir-154/3/Goharan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacultative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYousef/4/82S:510/3/Arinar/Aths//DS 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacultative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCourlis/Rhn-03//Karoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMahtab/Goharan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComp.Cr229//As46/Pro/3/Srs/4/Express/5/Goharan/6/Goharan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePamir-147/Sonata/8/Alpha/Durra/7/P101/5/3896/1\u0026ndash;15/3/3896/28//584/28/4/5050/6/Tipper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCourlis/Rhn-03//Karoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBda/Rhn-03//ICB-107766/3/Yousef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacultative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSonata/8/Api/CM67//Hma-03/4/Cq/Cm//Apm/3/RM1508/5/Attiki/6/Aths/7/SP(6H) /Apro//Ca1Mr/3/ROD586/Apm/4/Aths/9/Sararood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNadawa/Rhn-03//Birka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix-row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe grain yield data collected from the 12 environments (combination of three locations and two cropping seasons) were subjected to the additive main effect and multiplicative interaction (AMMI) analysis based on the following model (Gauch \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1988\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\mu _{ij}}=\\mu +{\\alpha _i}+{\\tau _j}+\\sum\\nolimits_{{k={\\text{1}}}}^{p} {{\\lambda _k}{\\alpha _{ik}}} {t_{jk}}+{\\rho _{ij}}+{\\varepsilon _{ij}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003ewhere, is the additive effects of genotype, is the environmental mean, is the singular value for the k\u003csup\u003eth\u003c/sup\u003e interaction principal component axis (IPCA), is the j\u003csup\u003eth\u003c/sup\u003e element of the kth eigenvector, and is the i\u003csup\u003eth\u003c/sup\u003e element of the k\u003csup\u003eth\u003c/sup\u003e eigenvector. A residual remains if not all p IPCA are used, where p\u0026thinsp;\u0026le;\u0026thinsp;min (g\u0026ndash;1; e\u0026thinsp;\u0026minus;\u0026thinsp;1). Based on grain yield data under irrigated and drought stress conditions, several drought tolerance indices (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were calculated for the tested genotypes using iPASTIC software (Pour-Aboughadareh et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To assess the relationships between the estimated drought tolerance indices and grain yields, as well as to test reputation, the Pearson\u0026rsquo;s-based network correlations were determined. In addition, to select the genotypes with the highest drought tolerance based on calculated drought tolerance and susceptible indices as well as grain yield data, three selection indices were calculated, such as Smith-Hazel, FAI-BLUP, MGIDI. In this approach, each drought tolerance index was considered a quantitative trait. A Venn diagram was used to comparison results obtained by each selection index. The GGE biplot model was used to plot graphs of the following models: (1) \u0026lsquo;which-won-where\u0026rsquo; pattern of GGE, (2) mean performance vs. stability pattern, (3) discriminativeness power and representativeness ability of test environments, and (4) ranking of genotypes tested across all environments. All statistical analyses and graphical plots were calculated using the packages \u0026lsquo;metan\u0026rsquo; (Olivoto and Lucio \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u0026lsquo;tidyverse\u0026rsquo; (Wichkham et al. 2019) in R software (R Core Team \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMathematical formulas of drought tolerance and susceptible indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePattern of selection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(MP=\\frac{{Y}_{P}+{Y}_{S}}{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRosielle and Hamblin (1981)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeometric Mean Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(GMP=\\sqrt{{Y}_{S}\\bullet {Y}_{P}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFernandez (1992)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarmonic Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(HM=\\frac{2\\left({Y}_{S}{\\bullet Y}_{P}\\right)}{\\left({Y}_{S}{+Y}_{P}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBidinger et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1987\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress Susceptibility Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(SSI=\\frac{1-\\left({Y}_{S}{/Y}_{P}\\right)}{1-\\left(\\stackrel{-}{{Y}_{S}}{/\\stackrel{-}{Y}}_{P}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFischer and Maurer (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1978\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress Tolerance Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(STI=\\frac{{Y}_{S}{\\bullet Y}_{P}}{{\\left({\\stackrel{-}{Y}}_{P}\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFernandez (1992)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYield Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(YI=\\frac{{Y}_{S}}{{\\stackrel{-}{Y}}_{S}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGavuzzi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYield Stability Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(YSI=\\frac{{Y}_{S}}{{Y}_{P}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBouslama and Schapaugh (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1984\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative Stress Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(RSI=\\frac{\\left({Y}_{S}{/Y}_{P}\\right)}{\\left(\\stackrel{-}{{Y}_{S}}{/\\stackrel{-}{Y}}_{P}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFischer and Wood (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1979\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eYp and Ys indicate the grain yield of genotypes under normal and drought stress conditions, respectively. Moreover, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\overline {Y} _P}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\overline {Y} _S}\\)\u003c/span\u003e\u003c/span\u003eindicate the mean grain yield of all genotypes under irrigated and drought stress conditions, respectively.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAMMI model\u003c/h2\u003e \u003cp\u003eThe result of AMMI analysis for grain yield data indicated significant effects for environments (E), genotypes (G) and their interaction (GEI) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The main effect of E, G and GEI justified 76.69%, 1.43%, and 9.60% of the total variation, respectively. The GEI effect was divided into four significant IPCAs, each of which accounted for 30.62%, 22.18%, 15.5%, and 12.72%, respectively. The result of first four AMMI selection per environment showed that genotype G4 appeared in seven environments (E1, E2, E3, E4, E7, E8, and E10); genotype G13 was recommended in five environments (E1, E4, E7, E8, and E10); genotype G7 was one of the top ranks in five environments (E1, E3, E9, E10, and E12); genotype G1 appeared in the top four in five environments (E2, E5, E6, E7, and E8); and genotype G9 was observed in the top four in four environments (E1, E6, E11, and E12).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of AMMI analysis for grain yield data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSum of square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eVariability explained (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1728.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1516.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.99**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.10**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEnvironments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1325.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e143.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e76.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eInteractions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.89**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPCA1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e30.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIPCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e22.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIPCA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.38**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIPCA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e12.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e** Significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eIPCA indicates interaction principal component axes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDrought tolerance and susceptible indices\u003c/h2\u003e \u003cp\u003eThe results of the calculated drought tolerance and drought susceptible indices for each genotype tested in each environment are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Based on the average two-year data, genotypes G4, G13 and G16 with the highest values of the indices Yp (except G16), Ys, MP, GMP, YI, YSI, HM, STI and RSI indices and the lowest values of the SSI index were identified as the most drought tolerant genotypes in the Karaj environment (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Based on the results obtained in the Hamadan environment, significant differences were found between the genotypes tested for some drought indices. For example, genotypes G2, followed by G11 and G15 showed the highest grain yield under irrigated conditions, while the highest grain yield values under drought stress were recorded for G1 (reference genotype), G17 and G12. Based on the MP and GMP indices, G1 (reference genotype), G3 and G11 showed the highest values and were identified as drought-tolerant genotypes. The HM index identified G1 (reference genotype), G3 and G12 as drought-tolerant genotypes. In addition, based on YSI, RSI and SSI indices, genotypes G12, G13 and G17 were identified as superior genotypes compared to other genotypes. Of the genotypes tested, G1 (reference genotype), G12 and G17 recorded the highest YI index values and were identified as drought-tolerant genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe selected top-ranked genotypes using the AMMI model for each test environment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrain yield (ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKaraj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHamadan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMashhad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKaraj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHamadan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMashhad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKaraj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHamadan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMashhad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKaraj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHamadan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMashhad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated values for drought tolerance and susceptible indices and their ranks (in bracket) in the evaluated genotypes of barley in the different regions in Iran [Karaj (A), Hamadan (B), and Mashhad (C)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c11\" namest=\"c2\"\u003e \u003cp\u003eKaraj (A)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.15 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.72 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.44 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.36 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.28 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.80 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.02 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.89 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.95 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.80 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.66 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.54 (17)\u003c/p\u003e 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colname=\"c3\"\u003e \u003cp\u003e5.27 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.93 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.88 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.83 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.80 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.05 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.64 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.34 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.21 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.08 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.84 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.83 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.02 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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colname=\"c11\"\u003e \u003cp\u003e0.98 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.17 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.80 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.48 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.43 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.37 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.04 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.79 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.89 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.31 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.28 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.87 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.84 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.14 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.03 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.59 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.52 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.08 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.84 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.05 (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.49 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.81 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.72 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.64 (16)\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.11 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.07 (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.47 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.09 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.78 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.20 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.67 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.55 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.44 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.84 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.00 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.79 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.90 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.22 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.08 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.55 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.67 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.79 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.73 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.40 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.09 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.57 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.08 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.74 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.26 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.50 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.09 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.71 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.50 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.95 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.71 (9)\u003c/p\u003e \u003c/td\u003e 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(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.17 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.84 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.43 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.04 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.55 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.49 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.95 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.36 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.86 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.61 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.36 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c3\"\u003e \u003cp\u003e4.05 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.59 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.05 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.57 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.15 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.91 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.45 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.19 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.41 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.30 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.98 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.69 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.55 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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colname=\"c11\"\u003e \u003cp\u003e1.26 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.80 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.11 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.46 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.99 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.56 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.48 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.91 (15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMashhad (C)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.93 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.97 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.45 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.41 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.36 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.87 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.95 (16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.25 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.26 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.76 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.71 (17)\u003c/p\u003e \u003c/td\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.41 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.37 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.34 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.88 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.97 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.87 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.31 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.28 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.25 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.87 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.96 (15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.45 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.15 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.12 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.09 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.91 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.69 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.62 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.65 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.64 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.63 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.11 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.90 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.26 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.58 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.57 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.55 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.04 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.90 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.81 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.32 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.56 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.53 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.52 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.87 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.20 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.15 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e5.69 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.18 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.89 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.98 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.77 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.26 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.51 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.49 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.48 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.53 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.92 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.22 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.21 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.19 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.91 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.42 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.29 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.35 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.33 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.32 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09 (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.62 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.07 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.35 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.32 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.30 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.92 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.61 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.93 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.27 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.24 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.22 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.91 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.37 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.34 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.35 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.34 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.33 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.98 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.87 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.43 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.40 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.37 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.72 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.84 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.93 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eYs, Yp, HM, MP, GMP, SSI, STI, YI, YSI and RSI indicate grain yield under the irrigated conditions, grain yield under drought stress conditions, mean productivity, geometric mean productivity, stress susceptibility index, stress tolerance index, yield index, yield stability index, relative stress index, respectively.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the Mashhad environment, the highest grain yield under the irrigated conditions was recorded in the genotype G18, followed by G1 and G8, whereas genotypes G7, G9, and G17 had the highest performances under drought stress conditions. Based on HM, MP, and GMP indices, genotypes G7, G8, and G9 showed the highest values. Of these, G7 along with G14 and G17 showed the highest values for YI, YSI, and RSI as well as the lowest value for SSI. The STI index screened genotypes G7, G8, and G9 as the drought-tolerant genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). According to average data for the three environments tested, genotypes G1 (the reference genotype), G4, and G18 showed the highest grain yield under the irrigated conditions. Under drought stress conditions, G4 followed by G13, and G18 showed the highest grain yield compared to other genotypes. Moreover, the MP, GMP, STI, and HM identified G1 (the reference genotype), G4, and G13 as the drought-tolerant genotypes. The SSI and YSI similarly identified G14, G16, and G17 as the three top-rank genotypes. Based on The RSI index, G13, G14, and G17 were identified as drought-tolerant genotypes, while YI index identified G4, G13, and G17 as the tolerant genotypes compared to other genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated values for drought tolerance and susceptible indices and their ranks (in bracket) in the evaluated genotypes of barley over three tested regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.71 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.55 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.63 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.47 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.33 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.04 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.74 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.58 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.75 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.16 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.90 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.67 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.66 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.16 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.33 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.14 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.96 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.98 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.68 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.75 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.72 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.58 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.46 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.08 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.77 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.39 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.17 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.28 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.12 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.96 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.97 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.25 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.05 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.15 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.97 (15)\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e0.80 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.04 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.81 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.61 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.16 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.39 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.98 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.71 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94 (16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.19 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.60 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.39 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.28 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.17 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.80 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.07 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.95 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.50 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.22 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.14 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.07 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.03 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.81 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.67 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.11 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.39 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.20 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.02 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.92 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eYs, Yp, HM, MP, GMP, SSI, STI, YI, YSI and RSI indicate grain yield under the irrigated conditions, grain yield under drought stress conditions, mean productivity, geometric mean productivity, stress susceptibility index, stress tolerance index, yield index, yield stability index, relative stress index, respectively.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNetwork correlation analysis\u003c/h2\u003e \u003cp\u003eTo investigate the interrelationships among grain yield data and drought tolerance/susceptible indices, we used network correlation analysis based on each environment and the average data. Based on the data obtained in the Karaj environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), there was observed a positive and significant association between Yp with Ys, HM, GMP, MP, STI, and YI. The Ys index positively and significantly correlated with MP, GMP, HM, STI, YSI, YI, and RSI indices. The STI index showed a positive and significant correlation with all indices except SSI. Correlations between YSI and YI and with other indices were positive and significant. The result of correlation analysis based on the Hamadan data indicated that there was a positive and significant correlation between Yp with MP and GMP indices. However, Ys positively and significantly correlated with all indices except SSI. Correlation among indices STI, HM, GMP, and MP was significant and positive, while SSI showed a negative correlation with all indices except Yp (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The most important positive and significant correlations based on the obtained data in the Mashhad environment were: Yp with Ys, MP, GMP, HM, STI, and YI; Ys with all indices except SSI; YI with all indices; and YSI with YI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Based on the average data, correlation between Yp with MP, GMP, and STI was positive and significant. Moreover, Ys showed positive and significant with all indices except SSI. The RSI only showed a significant and positive correlation with Ys, HM, YI, and YSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the drought-tolerant genotypes using selection models\u003c/h2\u003e \u003cp\u003eBased on the data from Karaj environment, a highly significant genotype effect was found for the Yp, Ys, MP, GMP, HM, YI and STI indices (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The broad-sense heritability (h\u003csup\u003e2\u003c/sup\u003e) ranged from 0.16 (SSI) to 0.76 (STI). Moreover, the total selection gain (SG) using the filtered indices was higher (48.19%), and STI showed the highest SG value. The MGIDI index generally identified G4, G13, and G16 as superior genotypes compared to others. The SH index showed a similar result to the MGIDI index and genotypes G4, G13, and G16 were selected. The FAI index indicated that the h\u003csup\u003e2\u003c/sup\u003e ranged from 0.16 (SSI) to 0.75 (STI) and the SG for filtered indices was estimated as 46.56%. The selected genotypes using the FAI index were G4, G11, and G13. The Venn diagram showed that genotypes G4 and G13 were superior based on all selection indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeritability and selection gain (SG) for drought tolerance and susceptible indices in the test environment using SH, MGIDI, and FAI-BLUP models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"21\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eKaraj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c15\" namest=\"c9\"\u003e \u003cp\u003eHamadan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c21\" namest=\"c16\"\u003e \u003cp\u003eAverage environments\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eSelection gain (SG)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003eSelection gain (SG)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c18\" namest=\"c16\"\u003e \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e \u003cp\u003eSelection gain (SG)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMGIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMGIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMGIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMGIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eMGIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eMGIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c21\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"21\" nameend=\"c21\" namest=\"c1\"\u003e \u003cp\u003eYs, Yp, HM, MP, GMP, SSI, STI, YI, YSI and RSI indicate grain yield under the irrigated conditions, grain yield under drought stress conditions, mean productivity, geometric mean productivity, stress susceptibility index, stress tolerance index, yield index, yield stability index, relative stress index, respectively.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the Hamadan environment, the genotypic effect was significant for all indices except the STI. In the MGIDI model, the h2 ranged from 0.52 (STI) to Yp (0.76) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Moreover, the SG was estimated as 22.07%, and the highest value recorded for Ys index. The selected genotypes using the MGIDI index were G1, G3, and G4. In the SH analysis, the SG was different and it ranged from \u0026minus;\u0026thinsp;8.28% (SSI) to 8.08% (RSI) with a total average of 15.45%. The genotypes selected using the SH were G12, G13, and G17. The total SG value in the FAI model was 22.07 and the indices Yp and YI showed the lowest (1.76%) and highest (3.68%) values, respectively. Similar to the MGIDI index, genotypes G1, G3, and G4 were selected as the superior genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Mashhad environment, the genotypic effect was not significant for all calculated indices. Hence, we used the Karaj and Hamadan environments data to create the average data matrix. Based on the average data, the h2 varied between 0.50 (YSI) and 0.74 (HM). The SG value ranged from 0.74% (YSI) to 5.49% (STI) with a total of 21.07% (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Using the MGIDI index, three genotypes G4, G9, and G13 were selected. In the SH model, the SG value ranged from 0.52% (Yp) to 4.39% (STI) with a total of 22.83%. The identified genotypes in this model were G4, G13, and G16. In the FAI analysis, Yp and STI indicated the lowest and highest values for the SG (0.60% and 4.98%). The total estimated SG for filtered indices in the model was 22.86%. Genotypes G4, G9 and G13 were selected as superior genotypes using the FAI model. Using the Venn diagram, genotypes G4 and G13 were selected as the common genotypes in three selection models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGGE biplot analysis\u003c/h2\u003e \u003cp\u003eThe results of the GGE biplot analysis based on average two-year data have been summarized into four biplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The first two components justified 72.78% of the total grain yield variation in the irrigated and drought stress conditions across three test environments. The polygon view of point the GGE analysis showed six environments clustered into three main sectors. The first sector included both irrigated and drought conditions of Karaj (E1 and E4) along with the drought stress conditions of the Hamadan (E5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The best genotypes for this sector were G13 and G16. Moreover, the genotype G4 showed a broad adaptability for these environments. The second sector consisted of the Mashhad environment (both irrigated and drought stress conditions (E3 and E6, respectively) with a winner genotype G17. The irrigated conditions of the Hamadan (E2) along with G2 were placed into the third sector. Based on the \u0026ldquo;mean vs. stability\u0026rdquo; biplot, the genotypes G13 followed by G4, G16, G11, G9, and G1 (the reference genotype) showed that highest average of grain yield across different test environments. Of these, G1 and G9 showed indicated performances closet to the grand mean. Moreover, genotypes G4 and G13 with the high average of grain yield showed the most stability due to their position in the biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs another result of the GGE biplot analysis, a positive and significant correlation was found among drought stress environments (E4, E5, and E6). Furthermore, E4 (Karaj-drought stress condition) with a longer vector and small angel with the average environment coordinate (AEC) showed the highest discrimination power and representative ability compared to other environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). To select the ideal genotypes across the test environments, a comparative view of the GGE biplot was used. Based on this biplot, genotypes G4 and G13 were near to the average environment axis (AEA) and closest to the central circle were identified as the ideal genotypes and showed the specific adaptability to drought conditions in the Karaj (E4) and Hamadan (E5) environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, a set of promising barley genotypes was evaluated on the basis of grain yield and several yield-based indices in 12 environments (a combination of three locations, two years, and two growth conditions [irrigated and drought stress]). The experimental data obtained were subjected to various statistical analyses to identify genotypes with different traits, such as high yield and stability, as well as a high drought tolerance index. The results of the AMMI model indicated that the environmental effect explained the greatest variation in grain yield than the genotypic effect (76.69% vs. 1.43%) and the genotype-by-environment interaction (76.69% vs. 9.60%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The main reason for this result may relate to the high variability of environmental factors (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), where there were significant differences in terms of temperature and precipitation parameters in different test environments (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Several studies have reported a large effect of environment on grain yield variability and its importance in selecting the best barley genotypes (Ahakpaz et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bakshi and Shahmoradi 2022; Pour-Aboughadareh et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bakshi et al. 2023). Moreover, GEI effects were further divided into four IPCAs, showing the magnitude of GEI, which in turn indicated that each genotype responded differently in the two irrigated and drought stress environments with regard to grain production (Lamba et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrought stress negatively affected all aspects of the plant growth and development and finally reduces the plant production. The considerable decrease in grain yield due to various levels of drought stress has been shown in previous studies (Dong et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alghabari et al. 2018; Etminan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Saddiq et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hossain et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study, the overall mean across the 18 barley genotypes tested in the Karaj, Hamadan, and Mashhad, as well as across three environments was reduced significantly by 41.9%, 4%, 21.41%, and 22.07%, respectively, under drought stress environments as compared to the irrigated conditions (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Several stress tolerance indices have been proposed for the identification of tolerant genotypes under stress conditions. Our results indicated that various stress tolerance indices gave similar ranking patterns in the selection of tolerant genotypes in different test environments (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs mentioned heretofore, one of the main goals of this study was to determine the efficiency of the multi-index models in selecting the superior drought-tolerant genotypes. To achieve this goal, three multivariate models\u0026ndash;\u0026ndash;including SH, FAI-BLUP, and MGIDI\u0026ndash;\u0026ndash;were utilized to identify most drought-tolerant barley genotypes based on several stress-tolerance indices. In all models, the high values for Yp, Ys, HM, MP, GMP, YI, YSI, RSI, and STI were targeted, while the low value for the SSI index was targeted as its weight. Moreover, the selection pressure in all models was considered as 15%. The SH index selected genotypes G4, G13, and G16 in the Karaj environment; genotypes G12, G13, and G17 in the Hamadan environment; and genotypes G4, G13, and G14 in over two environments. Hence, the G13 was identified as a common genotypes with high rate of drought tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Considering the FAI model, genotypes G4 and G13 were simultaneously selected as the superior genotypes in the Karaj, Hamadan, and over two environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, the last-mentioned genotypes were identified as the most drought-tolerant genotypes in the MGIDI model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Venn diagram rendered based on each environment and average data revealed that among evaluated genotypes, G4 and G13 had the best performance and tolerance to drought stress compared to other genotypes. In accordance to our results, Pour-Aboughadareh and Poczai (2021 a, b), Sellami et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Al-Ashkar et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Costa et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Klein et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Hussain et al. (2023), and Zali et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), confirmed the efficiency of the MGIDI, FAI-BLUP, and SH selection models in various crops such as wheat, lentil, mango, chickpea, and barley respectively. As another result obtained for these analyses, the most significant selection gain were found for the STI, YI and Ys index. For indices in which the gains were smaller, selection gains in other indices can be balanced (Klein et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, to find the most suitable indices for drought stress tolerance, correlation coefficients were determined among grain yield under both conditions and other indices. According to obtained results, in all environments, Yp and Ys positively correlated with each other and with STI, MP, and GMP indices. This result was supported by Dorostkar et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Kamrani et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Shabani et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Etminan et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Pour-Aboughadareh et al. (2020 a, b), and Lamba et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who suggested that based on these associations, high performing genotypes can be identified using aforesaid indices under both irrigated and drought stress conditions.\u003c/p\u003e \u003cp\u003eIn this study, we used the GGE biplot analysis to assess the stability and adaptability of selected genotypes in different environments. The \u0026ldquo;which-won-where\u0026rdquo; view of GGE analysis clustered the six environments (three irrigated and three drought stress conditions) into three main sectors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). As we expected, the genotype G13 and G4 showed specific adaptability in the both irrigated and drought stress conditions in the Karaj (E1 and E4) and Hamadan (E5) environments. Furthermore, genotypes G13 and G4 showed the highest grain yield and stability compared to other genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Among test environments, all drought stress conditions (E4, E5, and E6) showed a positive and significant correlation with each other due to the cosine angles among their vectors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Of these, E4 along with E1 (irrigated environment in the Karaj) with a longer vector and small angel with the average environment coordinate (AEC) showed the highest discrimination power and representative ability. Hence, according to Yan\u0026rsquo;s theory (Yan \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), these environments can be chosen as the ideal target environment for investigating new varieties for their full yield potential. In general, these results supported by the comparison view of the GGE biplot, where genotypes G4 and G13 were identified as the ideal genotypes and revealed a specific adaptability to drought conditions in the Karaj and Hamadan environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Likewise, several studied reported the high efficiency of the GGE biplot approach in the identification of ideal genotypes of barley in MET experiments (Jalate 2001; Ahmadi et al \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mortazavian et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kendal \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vaezi et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Daba et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Linus et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pour-Aboughadareh et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLittle information is available in the literature on selecting the best genotypes based on multiple indicators or multiple traits in MET. Therefore, the most important goal of any breeding program is to evaluate genotypes under different environmental conditions and identify those superior genotypes that perform better under changing climatic conditions in a wide range of environments. In this study, the AMMI and GGE biplot models were used along with several tolerance indices based on grain yield to evaluate a set of promising new barley genotypes. In addition, three selection indices were used to identify the best drought-tolerant genotypes based on grain yield and other indices. Of the genotypes tested, genotypes G4 and G13 were identified as the most drought-tolerant genotypes with significant yield and stability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEvaluation of drought tolerance in different environments led us to conclude that a complex of selection models and multivariate approaches can be used to identify superior barley genotypes with high yield and stability. In conclusion, the results obtained in the current study revealed that STI, MP, YI, and GMP indices positively and significantly correlated with both grain yield under the irrigated and drought stress conditions. Hence, these indices could be used as suitable selection yield-based indices in future studies. Moreover, Multi-index selection models (MGIDI, SH, and FAI-BLUP) provided more details on the selection gain for each drought tolerance index, so that the STI, Ys, and YI were identified as efficient tools in screening drought-tolerant genotypes. The collective analysis using the AMMI, GGE biplot, and multi-index selection models identified genotypes G4 and G13 as the superior genotypes. Thus, these genotypes can be candidates for commercial introduction. The pedigree of these genotypes showed that they are two sister lines (Comp.Cr229//As46/Pro/3/Srs/4/Express/5/Goharan/6/Goharan), which originated from a hybridization program between a foreign line (Comp.Cr229//As46/Pro/3/Srs/4/Express) and a local cultivar (Goharan). The \u0026ldquo;Goharan\u0026rdquo; cultivar with pedigree \"Rhn-03//L.527/NK1272\" was previously selected from barley genotypes received from the International Center for Agricultural Research in the Dry Areas (ICARDA) and was introduced as a high-yielding and stable cultivar for the moderate climate in Iran (Nikkhah et al. 2018). One of the significant characteristics of this cultivar is to its tolerance to terminal drought stress. Therefore, we considered that the selected genotypes (G4 and G13) could be recommended for further evaluation and commercial introduction in drought-prone regions in Iran and other areas with similar weather conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: Conceptualization, H.G. and A.P.; methodology, H.G. and A.P; software, A.P. and J.B.; validation, H.G., J.B.; formal analysis, A.P. and J.B.; investigation, H.T., M.C., S.J., and H.G.; resources, H.G.; data curation, H.G. and A.P.; writing\u0026mdash;original draft preparation, A.P.; writing\u0026mdash;review and editing, A.P. and J.B. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The authors acknowledge the Seed and Plant Improvement Institute (SPII), Agricultural Research, Education and Extension Organization (AREEO), Iran, for providing plant genetic material and supporting the research facilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhakpaz F, Abdi H, Neyestani E, Hesami A, Mohammadi B, Nader Mahmoudi K, Abedi-Asl G, Jazayeri Noshabadi MR, Ahakpaz F, Alipour H (2021) Genotype-by-environment interaction analysis for grain yield of barley genotypes under dryland conditions and the role of monthly rainfall. 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Plants 12: 1843. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants12091843\u003c/span\u003e\u003cspan address=\"10.3390/plants12091843\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Drought stress, genotype-by-environment interaction, MGIDI, network correlation, selection model","lastPublishedDoi":"10.21203/rs.3.rs-3917144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3917144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought stress is one of the major environmental stresses that dramatically reduces agricultural production around the world. Barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) plays an important role in both food and feed security. The objective of this study was to identify the superior drought-tolerant genotypes using grain yield and several yield-based indices of tolerance and susceptibility by applying various multivariate selection models. To achieve this objective, a set of promising new barley genotypes was evaluated in three drought-prone regions of Iran (Mashhad, Karaj, and Hamadan) during two consecutive growing seasons (2019\u0026ndash;2020 and 2020\u0026ndash;2021). The results of additive main effect and multiplicative interaction (AMMI) analysis showed significant effects for genotypes (G), environments (E), and their interaction (G\u0026times;E). Based on the AMMI model, G3, G7, G9, and G13 were identified as the four highest-ranked genotypes in terms of grain yield. Based on the Smith-Hazel, factor analysis and genotype-ideotype distance index (FAI), and genotype\u0026ndash;ideotype distance index (MGIDI) selection models, genotypes G4 and G13 showed the greatest tolerance to drought stress conditions in the three regions. Moreover, the most significant selection gain was found for the stress tolerance index, yield index, and grain yield under drought stress conditions (Ys). The results of the genotype (G)\u0026thinsp;+\u0026thinsp;genotype \u0026times; environment (GGE) biplot analysis coincided with those obtained, in which the G4 and G13 genotypes showed specific adaptability in drought environments. In addition, among the environments tested, the Karaj region was selected as an ideal target environment with significant discriminatory power and representative ability. In conclusion, the collective analysis using the AMMI, GGE biplot, and multi-index selection models identified genotypes G4 and G13 as superior genotypes. Consequently, these genotypes may be candidates for commercial introduction.\u003c/p\u003e","manuscriptTitle":"A framework for selection of high-yielding and drought-tolerant genotypes of barley: Applying yield- based indices and multi-index selection models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-05 12:07:58","doi":"10.21203/rs.3.rs-3917144/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73603264-3071-4688-948e-eca0b56df66b","owner":[],"postedDate":"February 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-01T22:04:32+00:00","versionOfRecord":{"articleIdentity":"rs-3917144","link":"https://doi.org/10.1007/s10343-024-00981-1","journal":{"identity":"journal-of-crop-health","isVorOnly":false,"title":"Journal of Crop Health"},"publishedOn":"2024-04-25 22:04:32","publishedOnDateReadable":"April 25th, 2024"},"versionCreatedAt":"2024-02-05 12:07:58","video":"","vorDoi":"10.1007/s10343-024-00981-1","vorDoiUrl":"https://doi.org/10.1007/s10343-024-00981-1","workflowStages":[]},"version":"v1","identity":"rs-3917144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3917144","identity":"rs-3917144","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}