جستجوی مقالات مرتبط با کلیدواژه « genotype × environment interaction » در نشریات گروه « زراعت »

The Production of high-yielding and stable cultivars is the most important objective of crop breeding programs, including wheat. Wheat is one of the key crops cultivated in Iran. The final yield of each plant is determined by the genotype potential, the environmental effect, and the interaction effect of genotype × environment. Studies on genotype × environment interactions can help determine whether a genotype is stable in performance across a wide range of environments. Various methods (univariate and multivariate methods) have been introduced to evaluate the interaction effect, each of which examines the nature of the interaction effect from a specific point of view. The results of different methods may not be the same, but the best result is obtained when a genotype with different evaluation methods shows similar results in terms of stability. Univariate methods do not provide a complete view of the complex and multidimensional nature of genotype × environment interaction, therefore, the use of multivariate methods is suggested to solve this problem. Among the multivariate methods, genotype × genotype-environment (GGE) biplot methods are more important. Therefore, this study aimed to identify promising and stable top-performing lines of bread wheat for warm and dry climates using the GGE biplot method.

The adaptability and stability of 37 promising bread wheat lines were evaluated in 10 environments, along with three checks (Chamran2, Sarang, and Mehregan). The experiment was conducted using a randomized complete block design with three replications in two cropping seasons (2020-2021 and 2021-2022) at five research stations (Darab, Ahvaz, Dezful, Khorramabad, and Zabol). In the field, each plot was planted with a density of 450 seeds/m2. Each line was planted in plots with six four-meter lines with 20 cm line spacing. At the end of the growing season, six rows of five-meter spikes from each plot were harvested and threshed by a Wintersteiger combine. The weight of the obtained grains was measured by a digital scale and reported in hectares.Grain yield was determined using combined analyses of variance. The GGE biplot statistical method (genotype effect + genotype × environment interaction) was used to study the stability of genotypes in the studied environments. SPSSv22 software was used to analyze the experimental data using the analysis of the combined experiment. The data were analyzed with GGE-Biplot software using the GGE biplot graphic method.

The Smirnov-Kolmogorov test was conducted to examine residual errors in each environment. The results for each environment separately showed that the residual data were normal in all environments. Bartlett's test results for the environments indicated the homogeneity of error variances, allowing for a combined analysis of variance, which showed the significant main effects of the environment, genotype, and genotype × environment interaction for grain yield. The significance of the interaction effects of genotypes in this study showed that the genotypes responded differently in different environments; in other words, the difference between genotypes is not the same from one environment to another, and the stability of grain yield can be evaluated in these conditions. The environment, genotype, and genotype × environment interaction effects accounted for 70.12%, 1.24% and 9.57% of the total variation, respectively. The results showed that the three PCAs explained 54% of the total agronomical variability residing in the tested wheat genotypes. The first two PCAs accounted for 29% and 25% of the total variation, respectively. The GGE biplot analysis revealed four mega-environments and five superior genotypes. The polygonal diagram obtained from the analysis showed that the genotypes GT biplot arising G31،G21 ،G29, G27, and G32, which were located at the vertices of the polygon, were the superior genotypes. The average environmental coordinate of the GGE biplot analysis showed that genotypes G29, G28, and G16 had high grain yield and stability. The biplot of the correlation among environments revealed that the environmental vectors of Ahwaz and Zabol were near 90◦, thus these locations were different environments.  Based on the results, the environment of Zabol can be introduced as a favorable environment for selecting the best bread wheat genotypes.

Given the climate change in Iran, particularly in the hot and dry regions of the south, there is always a pressing need for using sustainable varieties with high performance. This study has clearly and easily aided in the identification of stable and superior genotypes graphically. Wheat breeders worldwide consider breeding varieties specifically adapted to different geographical and climatic agricultural regions. The general adaptability of varieties to several regions was identified in this study, indicating that the Zabol environment could be introduced as a suitable environment for selecting superior genotypes of bread wheat. Finally, it is recommended to select genotypes G29, G28, and G16 for further testing and promotion after seed multiplication and selection under farm conditions, eventually introducing them as new wheat varieties. The results obtained in this study demonstrate the efficiency of the GGE biplot technique for selecting high-yielding and stable varieties.

Genotype × environment interaction is the main limiting factor in identifying superior genotypes in plant breeding programs. This research was conducted with the aim of investigating the genotype × environment interaction and selecting high-yielding and stable quinoa genotypes using AMMI (Additive Main effects and Multiplicative Interaction) and GGE (Genotype plus Genotype by Environment interaction) biplot methods. A number of 30 different quinoa genotypes prepared from the IPK Institute of Germany with different origins were cultivated as plant materials of this experiment in the form of randomized complete block design in two environments, Buin Zahra and Takestan, during the two crop years of 2022-2023. The results showed that the variance caused by the effects of genotype, environment and genotype × environment interaction was significant for grain yield and this trait was more affected by genotypic diversity. The variation of genotype × environment interaction in the AMMI method was explained by the Two principal components. Using this method, genotypes G14, G11, G12, G23, G1, G5 and G13 were recognized as high-yielding and stable genotypes, and the Takestan environment in the second year was introduced as a stable and high-yielding environment. Also, the first two main components in GGE biplot method explained about 92% of the variation of genotype and genotype × environment interaction for grain yield. In this method, the studied environments were placed in two mega-environments. All environments had high differentiation ability for grain yield in the studied genotypes. G11 and G14 genotypes were identified as ideal genotypes. Finally, based on both AMMI and GGE bi-plot methods, genotypes G5, G11, G12, G13, G14 and G23 among the quinoa genotypes studied were identified as high-yielding and stable genotypes, and Buin Zahra environment was introduced as an ideal environment.

The increased demand for cereals that are consumed by humans and livestock can be met through the development of planting drought-tolerant genotypes. Due to the interaction of genotypes × environment, the best genotype in one environment may not be the best in other environments, and therefore, this interaction provides valuable information about the yield of each genotype in different environments and plays an important role in evaluating yield stability. Genetic modification of drought tolerance in crops is one of the most stable and cost-effective approaches to increase production and yield stability. Examining the compatibility and stability of grain yield based on various parametric and non-parametric stability statistics and evaluating tolerance to drought stress based on stress indices in promising barley genotypes of the country's temperate climate are among the goals of this research.

To assess grain yield adaptation and stability and to select high-yielding barley genotypes suitable for terminal drought stress in the temperate climate of Iran, 16 barley genotypes were cultivated during two crop years 2021-2023 in a randomized complete blocks design with three replications in three research stations including Varamin, Birjand, and Yazd under two none-stress and drought stress conditions at the end of the season (12 environments). After determining the grain yield, stress indices, including MP, GMP, TOL, HARM, STI, YI, YSI, RSI, and SSI, and the correlation of each with grain yield were calculated in this study. Stability statistics included Nassar and Huehn’s stability statistics (S(1-6)), Thennarasu’s stability statistics (NP(1-4)), deviation from regression (S²dᵢ), regression coefficient (b), Shukla’s stability variance (σ²ᵢ), environmental variation coefficient (CV), variance component (θᵢ), coefficient of variance (θ(i)), Wricke’s ecovalence (Wᵢ²), and Kang’s sum of ranks (KR). Their relationships were calculated based on Pearson’s correlation. Analysis of variance (ANOVA), mean comparison, and simple correlation were calculated using the SAS-9.0 program, stability statistics were calculated using STABILITYSOFT and principal component analysis (PCA). Stress indices and the correlation of each of these indices with grain yield were calculated using iPASTIC. The three-dimensional distribution diagram of genotypes in the ranges of A, B, C, and D was drawn using Grapher software.

The results of the combined ANOVA indicated the significance of the genotype × environment interaction. According to S(1-2) statistics, G7, G10, G11, and G3, and according to S(3-6) statistics, G7, G3, and G9 were the most stable genotypes. Among the non-parametric Thennarasu’s stability statistics according to the NP(1) criterion of G9, G3, and G5, according to NP(2) G5, G3, and G8, and according to NP(3) and NP(4) criteria, G7, G3, and G9 were recognized as the most stable genotypes. Based on Wricke (W²) and Shukla (σ²) equivalency stability statistics, G3, G9, and G13 were the most stable genotypes. Based on Eberhart and Russell's regression method, G9, G7, and G3 genotypes, with high yields, had general compatibility and good yield stability. Based on Francis and Kannenberg (CVi), genotypes G1, G2, and G15 had the lowest coefficients of environmental variation. Based on the average rank of each genotype in all stress indices (AR), G2, G7, and G3 genotypes were the most tolerant, and G14, G11, and G10 were the most sensitive genotypes to drought stress at the end of seasons, respectively. In the drought stress conditions at the end of the season, grain yield had positive and significant correlations with YI, HM, GMP,  STI, MP, YSI, and RSI indices and negative and significant correlations with the SSI index. In non-stress conditions, grain yield had positive and significant correlations with MP, GMP, STI, HM, and YI indices, but no significant correlations were observed between grain yield and SSI, TOL, YSI, and RSI indices. The PCA revealed that the first and the second principal components explained 69.71% and 30.27% of the variance of the main variables, respectively. The first main component had a positive and high correlation with yield in both stressed and non-stressed environments, as well as MP, STI, GMP, and HM indices. The second component showed a positive and high correlation with grain yield in the non-stressed environment and TOL and SSI indices; it also had negative and high correlations with RSI and YSI indices. Based on the biplot diagram, G3, G7, G8, G9, G12, and G13 presented higher grain yield potential and are more tolerant to drought stress.

In this study, grain yield had negative and significant correlations with NP(3), KR, NP(2), NP(4), S(6), and S(1) statistics, respectively, therefore these statistics can be used for identifying stable genotypes. G3, G7, and G9 with averages of 6732.9, 6730.6, and 6608.1 kg/ha, respectively, not only produced the highest grain yield but also showed the highest grain yield stability and tolerance to terminal drought stress among the studied genotypes based on the total ranking of all studied stability statistics and stress indices. Therefore, they can be used as new cultivars in drought-affected regions in temperate climates or as desirable genetic materials in barley breeding programs for drought tolerance.

Barley (Hordeum vulgare L.) ranks second in terms of cultivated area in Iran after wheat, and according to the latest statistics of the Ministry of Agriculture Jihad (2019), its cultivated area in the country is reported to be 1.547 million hectares, which the amount the production of seeds was 3.514 million tons. The objective of this study was to select the superior barley genotypes based on grain yield and its stability, and also yield components, and important yield-related agronomic traits in temperate regions of Khorasan Razavi province.

To identify the optimal genotypes of barley, three promising lines along with Nusrat, Yusuf, Gohran, and Rihan cultivars in the form of randomized complete block design with five replications in three cities of Firouzeh, Bardeskan (Anabod sector) and Khalilabad (Kander sector) during the crop year 2018-2019 were cultivated and studied. Several main traits i.e., number of spikes per unit area, number of kernels per spike, plant height, thousand kernel weight, biological yield, harvest index, and grain yield were recorded. GGE (genotype and genotype×environment) biplot analysis and genotype×function×trait biplot analysis were used to investigate the objectives of this research.   

Based on the results of stability analysis, the promising line MB95-4 was the Ideal genotype, and the closest genotype to it was the promising line MB95-11. In this study, GYT bioplate was used to identify desirable genotypes based on several traits simultaneously. Based on the results, the promising lines MB95-4 and MB95-11 promising lines were the best in combining grain yield with the evaluated traits, respectively. According to the GYT index, the promising lines MB95-4 and MB95-11 had the highest values, respectively. On the other hand, these two lines did not have negative values for combined yield with different traits. This indicated the relative superiority of these cultivars in combining grain yield with the evaluated traits. The value of the GYT index for the Goharan cultivar was close to zero (0.01) and this means that this cultivar had average values of traits in this study.

In general, based on the results of this study, the best genotypes in temperate regions of Khorasan Razavi province based on grain yield and its stability as well as yield components and important agronomic traits were MB95-4 and MB95-11 promising lines.

Durum wheat (Triticum turgidum L. var. durum) is an industrial and agricultural product that is mainly used in pasta production industries. One of the important goals of durum wheat breeding programs is to produce high-yielding cultivars that have suitable characteristics for cultivation in different regions of the country. Therefore, the purpose of this research was to investigate the genotype × environment interaction using GGE biplot graphic method in durum wheat promising lines and to identify and introduce lines with economic and stable yield for introduction and cultivation in different regions of the country.

In this research, 18 promising lines of durum wheat with two check Hana and Aran in five research stations of Karaj, Kermanshah, Khorramabad, Dezful and Darab (under two conditions of normal irrigation and interruption of irrigation during the flowering stage) in the form of randomized complete blocks design in 3 replications and in two cropping seasons (2019-2021) were cultivated and compared.

The results of two-year combined variance analysis for grain yield under normal irrigation conditions in five research stations of Darab, Dezful, Khorramabad, Karaj and Kermanshah and dry conditions at the end of the season in Darab showed that the effect of year in both conditions and all stations was significant. It has been in terms of grain yield under normal irrigation conditions, the difference between genotypes was significant in Darab, Dezful and Kermanshah stations, but not significant in Karaj and Khorramabad stations. Genotype × year interaction was significant under normal conditions in Darab and Kermanshah, but not significant in Karaj, Dezful and Khorramabad. Based on the average grain yield, lines G8, G9, G10, G14 and G18 (respectively with the average grain yield of 7725, 7597, 7742, 7661 and 7558 kg ha-1) were superior to the checks. According to the GGE biplot, three large environments were identified. The first large environment included Dezful and Khorramabad regions, and G8 and G10 genotypes were among the top genotypes in these two regions, respectively. The second largest environment was Kermanshah and Karaj, and G9 and G14 genotypes were among the top genotypes in these two regions. The third large environment included two test conditions in Darab and the superior genotype in Darab was G18 in both drought stress and non-stress conditions. GGE Biplot results showed that G8 and G10 genotypes were among the most stable lines and also had the highest grain yield. The comparison of the studied lines with the ideal genotype showed that G10 and G8 are the closest genotypes to the ideal genotype, which have the highest grain yield and stability. The results of qualitative characteristics showed that the hardness index mean of different genotypes was in the range of 57-58% and the promising lines were not significantly different from the check cultivars. G17 genotype had the lowest (14%) and G14 genotype had the highest average of grain yellow berry (45%). Also, a relatively large variation was observed in terms of the amount of wet gluten, so that genotypes G9 and G13 had the lowest and highest amount of wet gluten with 23.8 and 27.3%, respectively.

In general, according to the average grain yield and GGE biplot results in both normal and drought conditions and according to some quality characteristics of the grain, lines G8 (D-98-8) and G10 (D-98-10) were selected as the most suitable lines for both normal and dry conditions. These lines will be tested in on-farm yield trials of next crop season and finally one of them will be introduced as a new cultivar.

IntroductionWater deficit compared to other abiotic stresses is the most important factor limiting the growth and production in all crops, especially wheat. Investigating the response of crop plants under stress conditions is the best way to produce drought-tolerant cultivars and improve yield under stress conditions. The challenge of breeding for drought stress tolerance in all crop plants, is to achieve a rapid screening method of genotypes and genetic improvement of yield under these difficult environmental conditions. The objective of this experiment was to identify high-yielding and water deficit tolerant genotypes in an F4 generation population of bread wheat.Materials and methodsThe plant materials of this experiment were 90 genotypes of the F4 generation resulting from a cross between two bread wheat cultivars (Arta, a spring cultivar sensitive to salinity and drought stresses, and Arg, a tolerant cultivar to salinity and drought stresses). These genotypes along with the parents, were evaluated in split plots based on randomized complete block design with three replications under two conditions (normal irrigation and non-irrigation from the pollination stage) in the research farm of Faculty of Agriculture, Tabriz University, Tabriz, Iran, in 2014. Evaluating the genotypes were done by simultaneous application of stress tolerance index (STI) based on yield-related traits including grain yield (STI-Y), spike weight (STI-S), 1000-grain weight (STI-1000) and harvest index (STI-HI) and identifying the relationship between these indices using genotype × trait biplot analysis. The studied genotypes were ranked based on the relationships in the biplot, and the high yield and water deficit tolerant genotypes were identified.Research findingsThe results of biplot showed that the first two principal components explained 72% (46% and 26%, respectively) of total variance in the studied population. Acute and closed angle between the STI-Y, STI-S and STI-HI vectors indicated a positive correlation between these traits, while the open and obtuse angle between the STI-Y and STI-HI traits with STI-1000 showed a negative correlation between them. In contrast, an quadrant angle was observed between STI-S and STI-1000 vectors, indicating that these two traits were independent and uncorrelated. In total, according to the relationships in the biplot and based on the combination of STI-Y and STI-1000 vectors, genotypes No. 84, 45, 89 and 15 were identified and introduced as the highest grain yield and most water deficit tolerant genotypes.ConclusionThe results of this experiment led to identification of superior and promising genotypes with higher yield potential and more tolerance to water deficit stress. Genotypes No. 84, 45, 89 and 15, which were superior to the stress-tolerant parent, after the purification steps, can be introduced as suitable genotypes for cultivation under water stress conditions as well as in normal environments. These genotypes can be used as drought tolerant parental lines in future breeding programs.

IntroductionProduction of high-yielding and stable cultivars is the most important objective of crop breeding programs, including wheat. The final yield of each plant is determined by the potential of genotype (G), the effect of environment (E) and the interaction effect of genotype × environment (GE). Several methods have been presented to study the genotype × environment interaction and determine stable genotypes, which can totally be divided into two main categories, univariate and multivariate. Univariate methods do not provide a complete view of the complex and multidimensional nature of GE interaction, therefore, the use of multivariate methods is suggested to solve this problem. Among the multivariate methods, AMMI and GGE-Biplot methods are more important. The objective of the current experiment was to investigate the interaction between genotype and environment for grain yield of 20 wheat genotypes and to identify stable and high-yielding genotypes.Materials and methods The plant materials of this experiment were 20 wheat genotypes including 18 irrigated wheat lines along with two control cultivars, Rakhshan and Baharan. The experiment was carried out in a randomized complete block design with three replications in five temperate regions (Karaj, Kermanshah, Zarghan, Boroujerd and Mashhad stations) during two cropping years 2019-2020. Two multivariate methods, AMMI and GGE-Biplot, were used to investigate the interaction effect of genotype × environment and to evaluate the stability of genotypes. R software was used to analyze the experimental data using the AMMI method, and Genstat software was used to analyze the data using the GGE biplot graphic method.Research findingsThe results of combined analysis of variance showed that the interaction effect of genotype × year and genotype × year × location were significant at the 1% probability level. Based on the results of AMMI analysis, the effect of environment, genotype and genotype × environment interaction were significant. Based on AMMI1 and AMMI2 models, AMMI stability value (ASV) parameter and genotype stability index (GSI), genotype 12 with an avearage grain yield of 8.27 tons per hectare was determined as the best genotype. The study of GGE-biplot polygon led to the identification of three mega-environments, that Boroujerd had the highest power of representation and differentiation among different environments. Genotypes 12 and 9, in addition to having high yield, had higher yield stability. Genotypes 12 and 9 were placed in the center of the circle as the ideal genotype and genotypes 5, 7 and 18 were ranked next. Based on the results of both methods, genotype 12 was identified as the most stable genotype.ConclusionThe results of AMMI, AMMI stability index (ASV) and genotype stability index (GSI) compared to GGE biplot results showed that all these indices have a good potential to evaluate the performance stability of genotypes. Nevertheless, GGE biplot results are more effective and practical in examining the compatibility and stability of genotypes' performance in different environments due to the ease of interpreting graphical results.

Barley (Hordeum vulgare L.) with a cultivated area of nearly one and a half million hectares and with a production of about three million tons per year after wheat is the main crop in the country. Of this amount, about 600,000 hectares with a production of approximately two million tons are related to irrigated barley and about 900,000 hectares with a production of approximately one million tons are related to dryland barley (Ahmadi et al., 2020). In recent years, the shortage of barley production has been felt more than ever and in the comprehensive plan of the country's fodder, It has planned to increase its production in the long run. Similar to other crops, insufficient yield stability in barley is recognized as a one of the factors responsible for the gap between actual yield and potential yield (Cattivelli et al., 2008). In breeding programs, the identification of superior genotypes is difficult due to environmental variability of target locations and the interaction of these variabilities with the investigated genotypes. Therefore, it is important to evaluate the advanced agronomic lines across various environments and over multiple years to ensure their yield stability and production (Yan & Rajcan, 2002). The main objectives of this study were to evaluate grain yield stability and adaptability in some promising barley lines and characterization of barley inbred lines based on multiple traits under irrigation conditions.

Nineteen promising barley lines (G1-G19) along with one check cultivar (Behrokh), were studied during 2016-2019 at Nishabour Agricultural Research Stations. The experimental design at was a randomized complete block with three replications. Several main traits i. e., days to heading, days to maturity, plant height, thousand kernel weight and grain yield were recorded for all genotypes. GGE biplot and genotype by trait (GT) biplot methods were used to yield stability assessment of genotypes and characterization of barley inbred lines based on multiple traits.Combined analysis of variance for grain yield and other traits was analyzed using ADEL-R software. The GGE biplot and GT biplot methodologies were employed to analyze GxE interaction and characterization of barley inbred lines based on multiple traits using GEA-R software (Yan, 2001).

The yield combined analysis of variance showed that the effects of year, genotype and genotype×year were significant at 1% probability level. The results also showed that approximately 23.45% of total variance was appertained to year effect, 30.72% to genotype effect and 21.37% to genotype × year interaction. Whole the mean grain yield of the evaluated lines in all years varied from 4.446 to 6.946 ton /ha and the G17 and G12 lines had the lowest and highest grain yield, respectively. Based on the biplot of average-environment coordination (AEC) for simultaneous selection of grain yield and stability of barley genotypes, genotypes G12 and G3 with the high grain yield were the most stable genotypeS. According to resulting from biplot of barley promosing lines in comparison with ideal genotype, G12 and G3 were identified as the ideal genotype. Also the closest genotypes to them were G9, G11, G15 and G16. Based on GT-Biplot polygon, G12, G3 and G9 lines were displayed high grain yield, grain filling period and lowest days to heading. The vector view of GT biplot showed high positive correlation between grain yield with grain filling period and negative correlation with days to heading. The high heritability along with advance genetic for the grain filling period, days to heading and plant height is encouraging from a standpoint of increasing the selection efficiency. In conclusion, the GT biplot offers a useful analytic tool for examining the variation among sets of lines, for exploring multiple trait data and for aiding in multi-trait selection.

It was found that the genotypes with the highest grain yield had high the duration of the grain filling period, early in flowering time and Medium to low plant height under irrigated conditions. Based on the results, lines G12, G3 and G9 were the most stable high-yielding genotypes that had high duration of the grain filling period and thousand kernel weight, early in flowering time and low plant height in Nishabour condition.

To assess root and white sugar yield stability of 11 spring sugar beet genotypes together with three check cultivars, a field experiment was carried out using randomized complete block design with four replications in six agricultural research stations; Toroq (Mashhad), Zarghan, Khoy, Kermanshah Miandoab, and Karaj, Iran, in 2020 and 2021. Combined analysis of variance showed that genotype × environment interaction was significant (P < 0.01), and genotypes had different performance in different environmental conditions. Based on regression coefficient, deviation from regression, Shukla’s stability variance, Wrick's ecovalence, and coefficient of determination, GB-6 and GB-10 genotypes were identified with high root and white sugar yield and yield stability. Although the number of genotypes with yield stability increased by using superiority measure and coefficient of variation, three genotypes including; GB-11, GB-10, and GB-2 showed the highest yield stability for root and white sugar yield, respectively. Using GGE biplot method, GB-11, GB-10, GB-2 and GB-6 were identified with higher root and white sugar yield, than average of all genotypes, and higher yield stability, respectively. Considering the results of this research, GB-6, GB-11, GB-2, and GB-10 were identified as high-yielding genotypes with high yield stability.

The aim of the present study was to evaluate and interpret the genotype × environment interaction for advanced rainfed lentil lines. Fifteen advanced rainfed lentil lines (genotypes) along with two commercial cultivars (cv. Gachsaran and cv. Sepehr) as check were evaluated using randomized complete block design with three replications in Gachsaran, Moghan, Khorramabad and Ilam field stations in Iran in 2019-20 and 2020-21 cropping seasons. The mean seed yield of trials was 891 kg ha-1. Genotype 10 with 1031 kg ha-1 had the highest mean seed yield, and genotype 16 (cv. Gachsaran) with 728 kg ha-1 had the lowest. Since genotype × environment interaction was significant, yield stability analysis was performed. Evaluation of the seed yield stability of genotypes by rank method showed that genotypes 12 and 10 had high seed yield and yield stability. In addition, yield stability analysis using WAASB index, as a multivariate method, was also performed. The seed yield × WAASB index biplot showed that most of the lentil genotypes had higher seed yield and yield stability than the check cultivars (cv. Gachsaran and cv. Sepehr). Genotypes 12, 13, 11, 10 and 1 had higher seed yield and yield stability than the superior check cultivar (cv. Sepehr). Considering the equal contribution for mean seed yield and yield stability in the calculation of WAASBY index, genotypes 10, 13, 11, 12, 1 were ranked as the most desirable genotypes for target environments.

The most important goal in all crop breeding programs is to increase yield, and yield improvement requires the use of efficient statistical methods to identify superior genotypes. In determining the superiority of genotype, in addition to high yield, yield stability in different environments must also be considered.Biplot analyses are good tools for selecting superior genotypes and to increase efficiency in selection.

In the present study GGE biplot method was used for assessment yield and yield stability of 130 genotypes of soybean under two environmental conditions, natural conditions and disease stress (artificial induction of charcoal rot disease), were evaluated in a simple lattice design with two replications at Seed and Plant Improvement Research Institute (SPII), Karaj, Alborz province, Iran, during 2014 and 2015 (four environments).

The results of combined analysis of grain yield/plant revealed that effects of location, genotype and interaction of genotype × location were significant. The results of stability analysis using GGE-biplot method revealed that the first (Genotype) and second (genotype × environment interaction) components explained 70% and 14%, respectively, and the both components 84% of the total variation, which indicates a good validity of the biplot in explaining the variations of genotypes and genotype × environment interaction (G + GE). Polygonal biplot showed that the genotype 66 had the highest grain yield in environment E2 (disease conditions in 2014) and E4 (disease conditions in 2015), however, the genotypes 1, 3, 5, 43, 63, 66, 75, 76, 77 and 89 had a good combination of stability and yield.

Some of these genotypes such as genotype 66 did not show any signs of charcoal rot in both experimental years, they also had a good grain yield.

The interaction of genotype × environment complicates performance prediction and is a challenge for crop and breeding programs. Performance stability is critical to achievingg high and uniform performance across a wide range of environments. The aim of this study was to investigate the interaction between genotype × environment and to study the compatibility and stability of advanced seven-line grain yield of bread wheat (BC2F6) resulting from cross-breeding of Tabasi local cultivar and modified Typhoon European variety using AMMI model and some stability statistics.

The experiment was conducted in a randomized complete block design with three replications during the cropping years (2017-2018 ) and (2018-2019) in Gorgan, Tehran and Kermanshah and stability analysis was performed for 6 environments. In the field, each plot was planted with a density of four hundred seeds per square meter. Each line was planted in plots with eight four-meter lines with 25 cm line spacing. At the end of the growing season, eight rows of four-meter spikes from each plot were harvested and threshed by hand, and the weight of the obtained grains was measured by a digital scale and reported in square meters.

The results of analysis of variance of the main effects of collectible and multiplicative interaction (AMMI model) showed a significant difference in the level of one percent probability for the environment and the interaction of genotype × environment, which indicates different performance of genotypes in different environments. Therefore, sustainability can be examined. Genotype × environment interaction was divided into two main components by AMMI model. The first two components together accounted for 81.36% and the remaining components in the model accounted for 18.63% of the total variation of genotype × environment interaction. According to AMMI1 model, L4 and L6 lines and according to AMMI2 model, L4 and L7 lines were introduced as high performance and stability lines. Based on the results of Amy Stability Value Index (ASV), L4 and L5 lines and based on Genotype Selection Index (GSI), L4 and L6 lines were introduced as stable lines. The results of Rick equivalence method showed that lines L4, L7 and L3 had the lowest value of this index. According to AMMI1 model, L2 and L7 lines with E1 environment (Gorgan, 2017-2018), and E4 (Tehran, 2018-2019) and L1, L5 and L3 lines with E3 environment (Tehran, 2017-2018) and According to the AMMI2 model, L2 line with E1 environment (Gorgan, 2017-2018), E2 (Gorgan, 2018-2019) and E4 (Tehran, 2018-2019) and L3 and L5 lines with E3 environment (Tehran, 2017-2018) And L1 line had private compatibility with E6 (Kermanshah, 2018-2019) and E5 (Kermanshah, 2017-2018) environments.

Based on all methods of measuring stability in this study and considering the grain yield potential, L4 line had the highest general stability to the evaluated environments and was introduced as a stable line with high yield. Therefore, this line can be suggested for use in future breeding programs to introduce new cultivars. According to both AMMI1 and AMMI2 models, L2 line with E1 and E4 environments and L3 and L5 lines with E3 environment had the most private compatibility. If the cause of the interaction of genotype × environment is predictive factors such as soil type, cultivation operation, the interaction of genotype × environment can be reduced by selecting genotypes with their private and specific adaptation to the environment, and have maximum production.

Introduction:

The genotype × environment interaction is one of the most important factors in limiting breeding programs. The compatibility of a given genotype determines its ability and genetic capacity to produce high production and yield stability in different environments. Therefore, it is necessary to study the genotype × environment interaction in order to introduce stable genotypes in different environments. The current research was carried out to determine the compatibility and stability of yield for 10 tobacco genotypes and estimate the most stable genotype for tobacco growing areas in Iran (i.e. Guilan, Mazandaran and Golestan provinces). 

Materials and Methods:

In this study, 10 male sterile flue cured tobacco genotypes, which they completely tested in terms of quality and taste, including 7 internal modified hybrids named RVH5, RVH6, RVH8, RVH27, RVH30, RVH48, RVH70 along with 3 imported genotype DVH2101, PVH19 and NC100 in 6 tobacco growing regions of Guilan, Mazandaran and Golestan in two years of experiment (2018-2019). In total, 12 environments were studied in a randomized complete block design with 3 replications. Combined analysis was performed by considering the environments as a random factor and genotypes as a fixed factor. Then the stability analysis of genotypes was done by 10 stability statistics and the first 3 components of AMMI (Additive Main effects and Multiplication Interaction) analysis including IPCA1, IPCA2 and IPCA3. Analysis of variance was done using SAS.9 software, AMMI analysis and related graphs were performed using IRRISTAT software. 

Results and Discussion:

The results of combined analysis of variance showed that the effect of location and year × location interaction, the effect of genotype and genotype × year × location interaction were significant at the 1% probability level for yield. Genotype × environment interaction analysis using the first three main components of the AMMI analysis justified 69.47% of the total variance. The lowest coefficient of variation was related to DVH2101 and RVH27 genotypes. RVH30, RVH27 and RVH8 genotypes were selected as the most stable genotypes. According to Rick equivalence and Shoklaʹs stability variance parameters. The least squares deviation from the regression line were related to RVH27, RVH30 and RVH8 genotypes. In terms of regression coefficient, RVH27, RVH30 and DVH2101 genotypes had a line slope close to 1. Based on the coefficient of determination and Jenkins and Perkins statistics, RVH27, RVH30 and RVH8 genotypes were in the same rank in terms of stability. RVH27 and RVH30 were the most stable genotypes with superiority index. RVH27, RVH30 and DVH2101 genotypes were selected as the most stable hybrids based on the AMMI first model due to their proximity to the plot center, low interaction and high yield. Based on the second model of AMMI analysis, RVH27 and DVH2101 genotypes were selected as stable genotypes. In the third AMMI model, the RVH27 genotype was selected as the stable genotype due to its closeness to the biplot center and high yield. 

Conclusion:

Considering most of the stability statistics and AMMI analysis, the most stable genotype was RVH27 hybrid. RVH30 and DVH2101 hybrids were placed in the next categories of stability. In addition, genotypes RVH6 and NC100 showed high private compatibility to Tirtash tobacco growing area and genotype RVH27 to Rasht region.

Given the growing rate of population and its consequences, such as hungrier people and more demand for food, the Food and Agricultural Organization (FAO) has introduced potato (Solanum tubersum L.) as a food security plant. Thus, the need for expanding potato production is globally felt to manage the increase in food demands and food security (4, 10). The interaction between the genotype and the environment creates complexity in yield prediction and is a challenge for plant production and breeding programs. This study was conducted to achieve a stable high-yielding genotype that is adaptive to climatic conditions of potato-producing regions in Iran.

A total of 20 potato hybrides along with five commercial varieties (Savalan, Agria, Caesar, Luta and Satina) were evaluated in a randomized complete block design with three replicates in the agricultural research stations of five locations (Ardabil, Hamadan, Isfahan, Karaj, and Razavi Khorasan) in Iran, for two years (2016 and 2017). Each of the hybrids and control cultivars were planted in two rows with six meters long. The rows with inter row spacing of 75 cm and plant spacing of 25 cm was taken. The genotype yields were measured after the harvest. Combined analysis of variances was done and comparison of means was done by LSD at one percent probability level. The principal coordinate analysis was used to analyze yield stability.

The results of combined analysis of variance indicated that the effect of genotype, year, location and year – location, location – genotype, year – genotype and year - location – genotype interactions were significant at 1% level of probability. Therefore, the analysis of genotype - environment interaction was performed using multivariate analysis of principal coordinates. Compared to the grand mean, 10 environments under study were divided into two groups including three environments with higher performance than the total average and seven environments with lower performance than the total average. The most stable genotypes based on the MST (Minimum Spanning Tree) and distance from the center of plots were hybrids 1, 8 and Savalan cultivar in low cycles and hybrid 5 was identified in high cycles.

The hybrids 1 (35.57 ton/ha), 8 (35.05 ton/ha) and Savalan cultivar (33.52 ton/ha) which could be recommended for environments with the yield lower than the average mean of all studied environments. Also, hybrid 5 (41.21 ton/ha) was identified for environments with higher performance than the total average.

Studying the reaction of the different genotypes under different environmental conditions helps breeders to detect stable and high-yielding genotypes. In this regard, 11 new sunflower hybrids along with four cultivars (Golsa, Ghasem, Shams, and Farrokh) were evaluated in a randomized complete block design with four replications in four experimental field stations (Karaj, Sari, Kermanshah, and Dezful) during two cropping seasons. GGE biplot statistical method (genotype effect + genotype × environment interaction) was used to study the adaptation of genotypes in the studied environments. Results of combined analysis of variance indicated that the effects of environments, genotypes, and genotype × environment interaction were significant, suggesting that the genotypes responded differently in the studied environment conditions. Results of the GGE biplot method showed that the two first and second principal components of the GGE biplot explained 66.4% of the total seed yield variation. Based on the biplot polygon view, the hybrid RGK15×AGK1221 in Sari and Karaj locations and the hybrid RGK25×AGK330 and Shams cultivar in Dezful location were superior genotypes with the high specific adaptation. Besides, all environments had high discriminating ability so that could able to show differences between genotypes. Sari environment was the nearest environment to the ideal environment that had the highest discriminating ability and representativeness.

Water stress particularly during the end of crop growing season causes a great yield loss in the production of oilseed rape (Brassica napus L.) in Iran. Researchers emphasize on the use of stable and water stress tolerant genotypes to alleviate the impact of water stress on the crop yield.

In order to identify high yielding and stable oil brassica genotypes under drought stress a total of 24 genotypes from three brassica species of Brassica napus, B. rapa and B. juncea, were studied in two moisture conditions (normal and drought stress) in a randomized complete block design for two years in one location; seed and plant improvement institute. Each genotype was planted in a 6-square meter plot and irrigated at different plant growth stages including planting time, seedling establishment, stem elongation, flowering and seed filling. To apply water stress, irrigation was not implemented at seed filling stage. At full maturity, two middle rows of each plot were harvested and seed yield were measured. Following simple and combined data analyzing of variance, univariate statistics, regression coefficient, deviation from regression parameter, Shukla’s stability variance, and Wricke’s ecovalence were used to evaluate the grain yield stability of genotypes. Finally, GGE-biplot analysis was used to better interpret the interaction of genotype with environment.

The results showed that seed yield is largely influenced by environmental factors. Based on all stability parameters and biplot analysis, genotypes no. 4, 6, 8 and 10 from B. napus with mean yield of 2725, 2820, 3079 and 2729 kg/ha had higher yield than total mean, lower Wricke’s ecovalence and Shukla’s stability variance, regression coefficient equivalent to one and low deviation from regression parameter, therefore, selected as the most stable genotypes. Furthermore, genotypes no. 3 and 17 form B. napus and no. 23 (BP.18) from B. juncea with mean yield of 2660 and 2751 kg/ha showed moderate yield stability. In terms of mean yield in whole environments, line no. 23 from B. juncea with 2958 kg/ha ranked third after line no. 8 and 16 from B. napus with mean yield of 3079 and 3071 kg/ha.

Although line no. 8 from B. napus has a high yield and stability, the results of this study also implies that line no. 23 from B. juncea relatively has a good stability and performance under water stress and normal condition. Species B. juncea generally has some good agronomic characteristics such as resistance to drought, seed shattering, and pests and have early maturity that could be introduced to regions with drought stress condition.

The necessity of considering wheat production as the staple food of most people in the world reveals the urgent need to produce this strategic product. The most important aspect of producing advanced lines in addition to yield consideration is the stability of the studied traits, especially the stability of grain yield in different environments.

In this study, 23 bread wheat genotypes with 2 cultivars as control during three cropping years at Razi University of Kermanshah Agricultural Research Field were tested by randomized complete block design with three replications in two irrigated (no stress) and rainfed environments (stress) was implemented. After determining the performance of each genotype, by first performing Bartlett test and proving homogeneity of variance, combined analysis of variance was performed assuming the effect of genotypes and environment (year and location) constant. Non-parametric univariate stability statistics based on Nasser and Huhn's (1987) and Tennarasu's (1995) criteria were used for selection of stable wheat genotypes. Next, the genotype effect + genotype×environment (GGE) biplot suggested by Yan et al. (2007) was used. Other analyzes were performed using SPSS 16 and Genstat 12 software.

In this analysis, F-test was used to investigate the significant effects of variance components of grain yield based on the model (random effect of year and fixed effects of genotype and location). There was a significant difference between places, years, genotypes, interactions of year × place, year × genotype, place × genotype, year × place × genotype at the statistical probability level of 1%. Therefore, the results showed that the studied wheat genotypes showed different reactions in the studied environments. Also, the years and places studied had different effects on the performance of genotypes The Nonparametric statistics studied for selection of stable genotypes from the studied cultivars were evaluated based on the proposed criteria of Nasser and Hoon (Nasser and Huhn, 1987) and Thennarasu (1995). The results indicated that Si(1) usually had higher mathematical expectation and smaller variance than Si(2) in the Nasser and Huhn (1987) method, so the accuracy of Si(1) in selecting genotypes was higher. Stability can be far greater than Si(2) statistics. In this regard, Kaya and Taner (2003) have described the simplicity of calculating the Si(1) statistic as the reason for its preference over the Si(2) statistic. Graphical analysis was used to study the variety of cultivars, environments and the interaction of genotypes and environments. The results of GGE biplot showed that the first and second principal components accounted for 43.1% and 20.9%, respectively, of 64% of the total variation, indicating the relative validity of the biplot in justifying G + GE changes.

Overall, a closer examination of the results of nonparametric statistics indicated that genotypes 3 and 8 (Vanguard) were identified as the most stable genotypes by the two statistics Si(1) and Si(2). Whereas, Si(3) and Si(6) statistics identified genotypes 15 (pioneer) and 13 as stable genotypes. According to NPi(1) statistics, genotype 12 was the most stable genotype according to NPi(2), NPi(3) and NPi(4) statistics. This suggests that the use of nonparametric methods by Tennarasu (1995) and Nasser and Huhn (1987) may not lead to the selection of high yielding stable genotypes Soughi et al., (2016). In a study by Abdulahi et al. (2007) on the stability of safflower seed yield, they stated that the statistics of Si(1), Si(2) and Si(3) actually represent a static concept of stability and dependence. They were not significant with mean performance. Therefore, the use of multivariate methods of sustainability decomposition that actually discusses the dynamic concept of sustainability can be important. Overall, the results of multivariate stability analysis showed that GGE Biplot is a suitable method for simultaneous selection of stability and yield of cultivars and lines. In this study, GGE biplot results showed that 20, 17, 15 (pioneer), 9, 6 and 20 genotypes with average yield were among the most stable genotypes in terms of grain yield among studied genotypes., 22 and 24 were identified as the most undesirable genotypes for stability and yield.

Knowledge about genotype × environment interaction helps breeders to select the best adaptable and stable genotypes for different regions. The main objective of this research was to select higher yielding bread wheat genotypes, compared to control cultivar, that are adaptable to the climatic conditions of the tropical and subtropical rainfed regions of Iran. Thus, 15 bread wheat genotypes selected from advanced yield comparison experiments, along with the check cultivar of Aftab, were studied in a randomized complete block design with three replications for three cropping seasons (2017-2020) in four regions (Gachsaran, Gonbad, Khorramabad and Moghan). In order to analyze the genotype × environment interaction the multivariate method of Additive Main Effects and Multiplicative Interaction (AMMI) was used. The Results of combined analysis of variance indicated that 91.49, 1.54 and 5.03 percentage of the total variations are related to environment, genotype and genotype × environment interaction, respectively. Moreover, results showed that the first seven principal components of the AMMI model were significant and explained 97.94% of genotype × environment interactions. The biplot of mean grain yield and the first principle component of genotypes and environments revealed that genotypes G1, G12 and G11 with a higher grain yield than the overall mean and lowest genotype × environment interaction were stable with high grain yield. Among them, genotype 11 with suitable general adaptability can be selected as promising genotype and a candidate for introducing a new cultivar for arid and semiarid rainfed regions. In this research, Khorramabad, with high proportion of genotype × environment interaction was recognized as the ideal environment for differentiation and separation of bread wheat genotypes. The cluster analysis classified the studied environments into three groups. The inclusion of all three years experiments related to Moghan location in one group indicates the high predictability and repeatability of this region.

Evaluating of sunflower genotypes under different environmental conditions would be useful to identify genotypes with high stability and yield potential. In order to study yield stability of sunflower genotypes, 11 new hybrids along with four cultivars (namely Golsa, Ghasem, Shams and Farrokh) were evaluated in a randomized complete block design with four replications under four experimental field stations across Iran including Karaj, Sari, Kermanshah and Dezful during 2018-2020. The results of combined analysis of variance indicated that the effects of environments, genotypes and genotype × environment interaction were significant for seed yield. Existence of genotype × environment interaction, suggested that genotypes responded differently to the studied environment conditions. Therefore, there is the possibility of stability analysis. Cluster analysis based on the nonparametric stability statistics grouped genotypes in to three main clusters. According to the mean rank of all studied nonparametric stability parameters, the genotypes No. 5 and 14 with the lowest value of mean rank were distinguished as stable genotypes, meanwhile genotypes No. 1, 11, 12 and 9 with the highest values of mean rank were identified as non-stable genotypes. Also, the results indicated that the nonparametric statistics Si, NPi and NPi were associated with mean seed yield and the dynamic concept of stability. Therefore, these methods would be suitable for selecting stable and high yielding genotypes in sunflower. Finally, the genotype No. 5 with mean seed yield of 3355 kg ha-1 and high broad stability was distinguished as superior hybrid which can be used in the future breeding programs for producing the new cultivar with high yield and stability potential.