جستجوی مقالات مرتبط با کلیدواژه « gge biplot » در نشریات گروه « کشاورزی »

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.

Multiple regression analysis and path analysis of maize grain yield with other agronomic traits to determine the most effective traits of the grain yield as well as analysis of genotype×trait interaction using the GGE biplot method, to evaluate the response of the hybrids to normal and water-deficit stress conditions based on the measured traits for determining high-yielding hybrids for both conditions, were carried out. In order to determine superior hybrids under normal and drought stress conditions based on agronomic traits and with the help of genotype×trait biplot analysis and also to determine traits affecting grain yield through multiple regression and path analysis, 18 maize hybrids were evaluated in a split-plot design based on the randomized complete block design in three replications and in two consecutive years.Two levels of irrigation were allocated in the main plots and maize hybrids in the subplots.In the path analysis of the grain yield with other studied traits, the number of kernel rows, the 300-grain weight and the number of grains per row under normal conditions, and the number of kernel rows and the 300-grain weight in the drought stress conditions had significant direct effects on the grain yield.The results of GGE biplot analysis of genotype×trait in both conditions showed that in normal conditions the two first components explained 72/28% and in water-deficit stress conditions 83/60% of the total variance.In both conditions hybrid SC704 was recognized as the ideal genotype.Hybrid SC704 ranked first as the most high-yielding hybrid in both conditions, and hybrid SC647 ranked next.

Genotype-environment interaction is one of the most important limiting factors in breeding programs. A comprehensive study of genotype-environment interaction requires powerful statistical methods. Different methods for evaluating the interaction effect of genotype-environment have been proposed by different researchers. Therefore, in the present study, the role of this phenomenon on the sugar yield of sugar beet hybrids and the identification of stable hybrids were studied based on the AMMI, GGE bi-plot, and MTSI stability index methods. For this study, a total of 20 sugar beet genotypes were utilized, comprising 15 recently developed hybrids and five control cultivars (Sina, Dena, Novodora, Modex and Loriquet). Phenotypic assessments of experimental genotypes were conducted in 2021 crop year at four agricultural research stations located in Karaj, Mashhad, Shiraz and Miandoab. These selected sites differed in terms of altitude, latitude and longitude, atmospheric temperature and precipitation, and physical and chemical characteristics of soil. The experiments at each research station were carried out using a randomized complete block design with four replications. Each genotype was planted in a separate plot, consisting of three cultivation rows with a length of eight m and a distance of 50 cm interrow. Throughout the growing season, weed control, irrigation, fertilizer application, and other field management activities were performed based on the recommendations of experts. Additionally, regular monitoring and prevention of pests and diseases specific to sugar beet were conducted at each research station. Stability analysis methods of AMMI, GGE bi-plot, and MTSI stability index were used to analyze the genotype-environment interaction. The results of a combined analysis of variance confirmed the significant effects of environment and genotype on all traits at a one percent probability level. The interaction between them was significant at one and five percent probability levels for all traits except the sugar content and white sugar content. Analysis of the multiplicative effect of the AMMI model showed that the first two components are significant at the one and five percent probability levels, respectively, and together explain 92.20 percent of the interaction variations. The bi-plot of mean yield and the first principal component of the interaction confirmed the superiority of genotype no. 20 due to its high sugar yield and stability. The results obtained from the GGE bi-plot method showed that the first and second components together explain 84.16% of the variations in total sugar yield. According to the GGE bi-plot, Karaj, Shiraz, and Miandoab had a relatively similar reaction in terms of genotype yield rank and in these environments, genotype no. 20 was stable. The reaction of the Mashhad environment was different from the other three environments and in that genotype no. 9 had suitable stability. Based on the results of the MTSI index, four genotypes of 18, 2, 20, and 17 were identified as stable genotypes. In general, the selected genotypes based on the MTSI caused a favorable selection differential in all traits. Among the traits, except root yield, other traits had good selection differential and selection gain. On the other hand, genotype no. 15 had the highest value of MTSI stability index and was unfavorable genotype in terms of studied traits. In general, among breeding hybrids, in the first the hybrid obtained from crossing of (7112 × SB36) × S1– 960132 (genotype no. 2) and then the hybrid obtained from crossing of (7112 × SB36) × S1- 970063 (genotype no. 9) can be used as promising hybrids in final evaluation programs until the introduction of new hybrids. The studied sites were not very closely correlated to be suggested that a site be abandoned to reduce costs for future research; In contrast, most of the tested environments had a high differentiation capability and could make a good distinction among genotypes in terms of sugar yield in genotype-environment interaction studies of sugar beet cultivars.

The development of oilseeds cultivation is one of the important and major goals of the country in achieving self-sufficiency in this field, which will play an important role in food security. Camelina is a promising oilseed that could potentially be used as a low-input crop for production in the drylands. However, little is known about camelina’s breeding lines. Therefore, this study was performed to investigate and screen advanced camelina lines in terms of agronomic characteristics, yield, and percentage of seed oil using various statistical methods.

In this study, 19 advanced high oil lines along with cultivar Soheil were screened for agronomic characteristics, yield, and percentage of seed oil. The experiment was conducted in a randomized complete block design with three replicates under rain-fed conditions at Sarab-Changai Research Station, Lorestan Agricultural and Natural Resources Research and Education Centre, Khorramabad, Iran during the 2020-2021 cropping year in rainfed condition.

According to the results, the oil yield and grain yield traits with more than 40% had the highest coefficient of variation. Day-to-flowering and day-to-maturity traits had the least variability according to this statistic. Analysis of variance indicated significant genotypic diversity between the studied genotypes in terms of most traits. The highest grain yield (901 kg/ha), oil content (35.83%), and oil yield (324 kg/ha) were recorded for DH60 line. According to the results of means comparison, plant height of genotypes was between 70.17 and 80.33 cm, day to flowering between 124 and 136, and day to maturity between 156 and 167. The correlation between grain yield and seed oil content (0.744**) and oil yield (0.995**) was positive and highly significant. Genotypes were clustered into three groups and the groups were statistically significantly different in most of the traits.

lines DH60, DH61, and DH105 were known as desirable and candidates for further breeding and agronomic research programs based on the results of various statistical methods including mean comparisons, cluster analysis, “genotype × trait” biplot, and SIIG index. lines DH41, DH69, DH128, and DH82 were also identified as the weakest genotypes in terms of the studied traits and are not recommended for economic cultivation. The results of this study showed that camelina is a crop plant adapted to dry conditions and a short growth period that has the potential to provide diversity in dryland crop rotation.

Seed yield in soybean is a complex trait and is influenced by multiple genetic and environmental factors. In this research, 13 cultivars and advanced genotypes with two control cultivars (Sari and Katul) were cultivated in three warm and humid regions of the north of Iran, including Gorgan, Sari and Mughan, during the two cropping years of 2013 and 2014 in a randomized complete block design with three replications. Mean comparison in different years and locations showed that G15 genotype had the highest seed yield, followed by G2, G4 and G9 genotypes. Correlation coefficients   showed that the number of seeds per m2 (R² = 0.77) and the number of pods per plant (R² = 0.66) had a positive and significant correlation with seed yield, while the correlation between 100 seed weight and seed yield was insignificant (R²=0.02). G2 and G3 genotypes had the highest percentage of charcoal rot and G4 genotype showed the lowest percentage of infection. Regarding the percentage of infection with charcoal rot disease, all the investigated genotypes are below 10% and are considered tolerant genotypes for this disease. GGE biplot analysis indicated that the first and second principal components explained 70.6% of total yield variation. According to which-won-where pattern of GGE biplot, G4, G15, G12 and G7 genotypes had the longest distance from the origin of the biplot and were placed in the group of reactive genotypes to the environment and G2, G3 and G1 genotypes were classified as stable group. Genotype G4 performed well in Mughan1 and Mughan2 whereas, G15 genotype showed the best performance in Sari1 and Gorgan2, and G12 genotype was the best genotype in Sari2 environments. According to the genotypes comparison with ideal genotype, G15, G9, G2 and G1 genotypes had the shortest distance from the ideal genotype and were, hence, included in the group of stable genotypes. The simultaneous GGE biplot of seed yield and stability displayed graphically that G2 genotype had superior performance and broad adaptation to the diverse environments. Sari1(Sari 2013) was the most discriminating and representative environment and is classified as the superior environment and Gorgan 2 (Gorgan 2014) was ranked second. Based on tolerance to charcoal rot disease, average seed yield, as well as different GGE biplot graphs, G2 genotype incorporated both high mean yield and yield stability and can be released as a new soybean variety.

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.

This study was conducted to obtain high yielding adaptive lines of PachBaghela (Phaseolus vulgaris) in Guilan region, using randomized complete block design with three replications in spring season in three locations of Guilan province (Lahijan, Rasht, Shanderman) in 2016 and 2018 cropping seasons. Nine lines were evaluated along with local landrace. The results of combined analysis of variance of fresh pod yield in three regions during two years showed that the effect of genotype and the interaction of genotype × location × year were significant. Also, the results showed that the lines had a significant difference in fresh pod yield. The comparison of the average yield of fresh pods in the studied regions during two years showed that the G9 line had the highest yield and the lowest yield was related to the local landrace in the spring cultivation. Analyzing the stability of fresh pod yield by the non-parametric rank method showed that G9 and G8 lines were the most stable lines. Also, based on the GGE biplot, G9 and G8 lines were selected as the best lines with high yield and compatibility.

To investigate stability, pattern of G×E interaction, 10 faba bean genotypes (G1-G10) as well as four control cultivars; Barekat, Saraziri, Baloochi and Zereshki were evaluated using randomized complete block design with three replications in four agricultural research field stations of Gorgan, Dezful, Brojerd, and Iranshahr, for two cropping seasons (2014-15 and 2015-16). The results of composite variance analysis showed that the main effect of year, the interaction of year × location and the triple effect of year × location × genotype on grain yield were significant at the probability level of 1%. GGE -biplot analysis (genotype effect+ genotype×environment interaction) revealed that the two first and second principal explained 84.7% and 6.6% of total variation, respectively, and 91.3% of the grain yield variation. Based on a hypothetical ideal genotype biplot, the genotype G9 was better than the other genotypes across environments for stability and grain yield. View of polygon graph revealed superior mega-environments and the compatible genotypes were determined for each mega-environment; Gorgan- Brojerd (G9) and Iranshahr (G14). G9, G1, and G4 genotypes with average grain yield of 3479, 2808 and 2739 kgha-1, respectively, had the highest grain yield. Based on GEI and GGE-biplot analysis, Gorgan and Brojerd experimental environments had good differentiation ability. Finally, genotypes G1, G5, G9, and G10 were the most stable genotypes and can be used in breeding programs.

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 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 promotion and development of winter-sown sugar beet is one of the significant approaches for using seasonal rainfalls and saving irrigation water for its production. For this purpose, the present study was conducted to study the of genotype × environment interaction effect on white sugar yield of 11 winter season short-season sugar beet cultivars, and selection of superior cultivars in three regions of Moghan,(2019, 2020 and 2021) Torbat-e-Jam (2020 and-2021), and Joveyn (2020) using randomized complete block design with four replications. Combined analysis of variance showed that the environment, genotype and genotype × environment interaction had significant (P ≤ 0. 01) effect on white sugar yield. The GGE biplot method revealed that the first and second main components explained 83.64% of the total variation in white sugar yield. Based on the GGE biplot method, in Moghan 2021, cv. SVZB2019 and Dravos, and in Moghan 2019 and 2020, in Torbat-e-Jam 2020 and 2021 and in Joveyn 2020, cv. FDIR19B3021, cv. FDIR19B4028 and cv. SVZA2019 identified as the best cultivars with high white sugar yield and yield stability, respectively. In general, it was concluded that the environment played a significant role in phenotypic expression of the white sugar yield in the winter-sown short season sugar beet cultivars. Therefore, it would be necessary to select and release sugar beet cultivars adapted to climatic and agronomic conditions for winter sowing in target environments.

Investigation of the interaction of genotype × environment and identification of stable and high yielding cultivars in different environmental conditions is of great importance in plant breeding. The objectives of this study are to investigate the interaction of genotype × environment using GGE bilpot graphic method in advanced cross-breeding lines of bread wheat, to identify and introduce lines with high and stable economic performance.

In this study, the advanced stable yield of seven-line grain obtained from the reciprocal cross of bread wheat (BC2F6) in the form of a randomized complete block design with repetition in Tehran, Kermanshah and Gorgan and crop years (2017-18) and (2018-19) Was investigated. Each line was planted in plots with an eight-meter line at a distance of 25 centimeter. At the end of the season, the spikes were harvested and threshed manually from each plot, and the seeds obtained were measured by a digital scale and reported per square meter.

The results of combined analysis of variance showed a significant difference in the level of one percent probability for the effect of environment and the interaction of genotype × environment. No significant difference was observed for grain yield between the studied lines. According to the results of GGE Biplot, lines L4, L6 and L3 were the closest lines to the ideal line. Simultaneous evaluation of stability and grain yield of L4 and L6 lines identified stable high-yield lines. Examination of the polygonal diagram led to the identification of three large environments. The first large environment included E1 (Gorgan, 2017-18), E2 (Gorgan, 2018-19) and E4 (Tehran, 2018-19) environments, of which L2 was the top line of these environments and the second large environment included E6 environment (Kermanshah, 2018-19) It was that L4 and L6 lines were introduced as the top lines in these environments. The third large environment consisted of E3 (Tehran, 2017-18) and E5 (Kermanshah, 2017-18), where the L3 line showed private compatibility with the mentioned environments.

In general, based on the obtained results, L4 line is introduced as a stable line with high yield and is recommended to obtain the maximum cultivation yield of this line in the studied environments. L2 and L1 lines are introduced as the most unstable lines with the lowest performance and their cultivation is not recommended in any of the studied environments.

Sesame is one of the most important oil, industrial and medicinal plants. Sesame is cultivated in a large area of tropical and subtropical regions. In this study, fifteen sesame genotypes are cultivated to identify superior genotypes in terms of yield and stability (minimum environmental impact) in four locations of Arak, Birjand, Karaj, and Shiraz for two years. In combine variance analysis, the year effect and yaer × location, genotype × year and genotype × location × year were meaningful. The first and second main components in bipod analysis explained 84.06 and 8.40 (92.46 in total), respectively, the percentage of changes due to the impact of the environment on genotypes. According to the plots, genotypes in Arak, Birjand, and Shiraz have a high correlation with each other. The best genotypes in Arak, Birjand, Karaj, and Shiraz were Darab 14, Safiabad 1, local Ahvaz, and local Isfahan. Also in the Karaj location, Fars local cultivar, Khondab local cultivar, and Darab 1 were evaluated as superior. In general, the best genotypes in this trait in terms of yield and high stability were two genotypes, Safiabad 1 and local Khondab. In contrast, TS-3 and Yellow White genotypes were the most unstable in addition to low yield compared to other genotypes. Genotypes in the Shiraz environment had the least impact on the environment and in the Birjand environment had the maximum impact. The study areas of this experiment were divided into two megaenvironment, the first inclouds Arak, Birjand and Shiraz, and second inclouds Karaj.

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.

This study was conducted to identify stable and high-yielding rice mutant lines.

Fourteen mutant lines along with four cultivars Hashemi Tarom-Mahali, Khazar and Gilaneh were evaluated in a randomized complete block design with four replications in Rasht, Chaparsar and Fars province during two cropping years 2015-2016. Stability of genotype was assessed using AMMI indices and AMMI and GGE biplots.

The contribution of genotype, environment and genotype by environment interaction were 41.48%, 31.87% and 24.19% of total variation, respectively. The highest grain yield was obtained in G5 and G12 with 5684 and 5470 kg ha-1, respectively. The ssiMMASV, ssiSIPC, ssiDist and ssiEV identified G5, G9 and G12; and ssiASV, ssiZA and ssiWAAS, G3, G5 and G9 as the best lines. AMMI1 biplot indicated lines G12, G5, G3 and G1 as stable and high-yielding lines. Based on AMMI2 biplot, G12, G4, G17, G9 and G5 were the most stable lines near the origin of biplot. Different views of GGE biplot were also detected that G12 and G5 in addition to having a higher grain yield than overall yield mean were as stable lines. The superiority index identified lines G5, G12 and G7 as the most stable lines. Principal component analysis showed that the superiority index of Lin and Binns and all the ssi indices were suitable indices for identifying stable and high-performance genotypes.

G5 and G12 were the most stable and productive lines and can be used for introducing of the cultivar.

Due to abundance of non-living and living stresses in warm region, identification of barley genotypes with high and stable yield is one of the main goals of barley breeding programs in this region.

17 barley promising genotypes along with two check varieties were evaluated in five the Agricultural Research Stations including Ahvaz, Darab, Zabol, Gonbad, and Moghan during two consecutive cropping seasons (2018-2020). GGE biplot analysis was used for identifying the high yielding and stable genotypes across test environments.

Combined analysis of variance for grain yield data indicated a significant effect for locations, genotypes and year × location. Genotype × location and year × location × genotype. Mean comparison showed that G1, G2, G4, G5, G10, G12, G16 and G19 had high yield than other genotypes and can be considered. Grain yield stability of these genotypes were investigated for identifying of high yielding and stable genotypes across test environments. GGE Biplot analysis revealed that the first two components  accounted for 36.76% and 16.68% of the total gain yield variation, respectively. Thus these  two components can be used for explanation of grain yield of genotypes. In this study, two mega-environments were identified in warm regions of Iran. Accordingly, the first mega-environment included of the first and second year of Ahvaz and Zabol. G19 with 5114 kg/h was identified as superior genotype of this environment. The second mega-environment comprised of the the first and second year of Darab and Gonbad. G5 with 5155kg/h was identified as superior genotype of this environment. The first and second year of Moghan was located on the border between two mega environments. G3, G6, G8, G9, G14, G17 and G18 were not placed in none of the mega environments and were not suitable for cultivation in investigated environments. The vector view of ideal genotypes revealed that G4 and G5 are placed close to the ideal genotype, are most desirable than the other genotypes. The vector view of GGE biplot indicated discriminating and representative environments (first and second year of moghan) are good environments for selecting generally adapted genotypes.

In general, results indicated that genotypes G5 and G19 were identified as ideal barley genotypes for warm regions in north and south parts of Iran, respectively.