The GGE (Genotype main effect, G and Genotype by environment interaction, GEI) biplot graphical tool was applied to analyze multi-environment trials (MET) data. In this study, eight improved and local rice Genotypes including two rice cultivars as check were evaluated with the objective of selecting stable and high-yielding varieties by GGE biplot analysis. According to which-won-where pattern of GGE biplot the vertex Genotypes were BC25, BC4, RI18446-13, Hassani, Abjiboji and RI18435-13. These Genotypes were the best or the poorest Genotypes in some or all of the test environments since they had the longest distance from the origin of the biplot. The performance of Genotypes BC9, BC25, RI18436-46 and Saleh were highly stable and had the highest grain yield, while Genotype BC4 was high yielding with intermediate stability. In addition, performance of Genotype RI18446-13 was lowly stable with the high grain yield and Genotype RI18435-13 was poor based on both stability and yield. But the performance of Genotype Hassani was intermediate stable with low grain yield, while Genotypes Abjiboji and RI18430-74 were highly stable with low yielding.Totally, the results of this research showed that BC4 line (derived from a backcross between Abjiboji cultivar as recurrent parent and Saleh cultivar as donor parent) with high grain yield (5.0-5.5 t.ha-1), suitable maturity time (110-115 days), intermediate amylose content (20-21 %) and desirable plant height (105-110 cm) was the superior Genotype of this experiment which is recommended to cultivate in environmental conditions of the north provinces of Iran.

This study was carried out to adaptability evaluation of 50 grapevine varieties introduced from Russia from 2008 to 2013 in Qazvin and Urmia provinces. The experimental design was randomized complete blocks design with three replications. Three superior grapevine clones of white Bidaneh were as control in both areas. The Corden bilateral training system was used in 2×3. 5 m space planning and two vines in each experimental unit in the both areas. Measurement traits were: yield per plant, length and width of berry, total soluble solids of juice (TSS), juice pH, juice titratable acid (TA) and harvest time of. Combined analysis of variance and adaptability analysis was performed on the base of GGE biplot principal components analysis of the environment scaling method. Statistical analysis was done by GenStat ver. 12 computer software. The effects of the environments, varieties and environments × varieties were significant in the combined analysis of variance. The adaptability of Zenbil 13-366 and Ljana was higher than other varieties on the base of yield components in Qazvin. Ruski Ramphi had higher adaptability than other varieties in Urmia region. Yoski biser, Bobili magaracha and Ramphi Izdangareh had the most inappropriate situation on the base of yield components in two areas.

In Present research 14 maize hybrids were studied in form of randomized compelet block design with 4 replicates across 6 environments including 3 agriculture research centers (Karaj , Mashhad and Jiroft) in two years (1390-1391).Result of combined variance analysis showed that Genotype ´year´location interaction is meaningful in %1 probability level. According to this result for studying interaction effect and determining stable hybrids, the GGE biplot multivariate method was used. Results showed that the first two principal components regression model explained 92% of the observed changes. By using biplot polygons, six Top Genotypes and two mega environments were identified. Also based on, both yield and stability biplot, Genotype number 2, were identified as stable Genotype with average function. biplot analysis of correlations between environments a positive correlation between the Jiroft and Karaj and a negative correlation between Mashhad and Karaj environments were existed Evaluation of Genotypes relative performance revealed that Genotype No.5 in Karaj and Genotypes 14 and 2 in Mashhad and Jiroft, had the highest performance.

Sesame is one of the most important oil, industrial and medicinal plants that is cultivated in a large area of tropical and subtropical regions. In this study, fifteen sesame Genotypes are cultivated to identify the superior Genotypes in terms of yield and stability (minimum environmental impact) in four locations (Arak, Birjand, Karaj, and Shiraz) for two years. In combine variance analysis, effect of location, Genotype and interaction effect of location × Genotype were not significant.. The first and second main components of bipod analysis explained 84.06% and 8.40% (92.46% in total), of environment derived changes on Genotypes, respectively. According to the plots, Arak, Birjand, and Shiraz Genotypes had a high correlation in term of grain yield. The best Genotypes in Arak, Birjand, Karaj, and Shiraz were Darab 14, Safiabad 1, local Ahvaz, and local Isfahan. Also the Karaj location, Fars local cultivar, Khondab local cultivar, and Darab 1 were evaluated as superior. In general, Safiabad 1 and local Khondab were the best Genotypes terms of yield and high stability. In contrast, TS-3 and Yellow White Genotypes received the most impact from their environment, in addition to low yield. environment had the least and most impact on Shiraz and Birjand Genotypes. Finally,the study area of this experiment were divided into two megaenvironments : Arak, Birjand and Shiraz megaenvironment and Karaj megaenvironment.

In the present study, 18 lines and cultivars of chickpea were evaluated in three cold dryland stations including Maragheh, Kurdestan and Shirvan for three years (2014-2016). Results showed that the environments, Genotypes and their interaction effects were significant. interaction analysis using the AMMI model indicated that the two principal components (IPCAs) significantly accounted for 67.9% of the total variation. However, these two components accounted for about 65% of the changes in the GGE biplot method. Evaluation of Genotype yield stability using AMMI statistics was very similar to AMMI2 biplot. This similarity was also partially observed between the AMMI and GGE biplot methods. In both models, the environments were subdivided into groups with separate superior Genotypes and the behavior of some sites in the selection of Genotypes was similar across years, while in some other sites, it was different. Therefore, it is concluded that the reproducibility pattern of superior Genotypes in each site is challenging under dryland conditions. Control cultivars of Samin, Gazvin and Jam showed good grain yield and stability. Although, the ability to select high yielding and stable Genotypes among the lines was poor compared to control cultivars, Genotypes NO. 1, 3, 6, 12 and 14, due to their high yield, can be used in specific adaptation and suitable genetic sources for introducing drought and cold tolerant cultivars in the studied fields.

Genotype evaluation and mega-environmental identification are among the most important objectives of multi-environmental trials. Although the measured yield is a combined result of effects of Genotype (G), environment (E), and Genotype by environment interaction (GE), only Genotypes and GE interaction are relevant to Genotype evaluation and mega-environmental identification. This study presents a GGE (ie, G+GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2). The GGE biplot graphically displays G plus GE of a multienvironmental trial in a way that facilitates visual Genotype evaluation and mega environmental identification. This strategy was employed to analyse winter canola (Brassica napus L.) multi-environmental trial data. The stabilities for yield of 24 canola Genotypes were determined using replicated trials for two years and nine locations. The first two principal components (PC1 and PC2) of the SREG model accounted for 65% of the total GE interaction. There were six winning Genotypes and three megaenvironments according to the SREG model and polygon view of biplot. According to the ideal-Genotype biplot, Genotype DP.94.8 was better than all the other Genotypes and had the general adaptability for all the environments; it exhibited both high mean yield and high stability of performance across environments. The estimated relative yield of Genotypes in Karaj revealed that Genotype SLM046 yielded the highest in Karaj area.According to Genotype+GE interaction sources of variations, the Genotypes Olara, Consul and SLM046 were the most suitable varieties for the canola producing regions in Iran and had the specific adaptability for each and any of the environments. The results of this study should help in determining the test sites or environments for winter canola comparisons in Iran. Based on the obtained results, Genotype Consul for Arak, Genotype Olara for Shahrekord and Genotype SLM046 for Isfahan, Eslamabad, Sanandaj, Zarghan, Zanjan, Karaj, and Hamadan were the ones proposed for cultivation.

Extended Abstract Background: Oilseeds are among the most important sources of energy all over the world. Soybean (Glycine max L.) is an important crop and its oil has nutritional and high economic value. As an annual, self-pollinating, diploid plant belonging to the Leguminosae pea family, soybean falls into the most important oil plants in the world, containing 18-22% oil and 40-50% protein, depending on the Genotype and environmental factors. Soybean has been the food of Asian people, especially China, for centuries, and Chinese people consume it along with rice as their main food. The United States of America is the largest producer of soybeans and produces almost two-thirds of the world's crop. Improving seed yield is always a major goal in soybean breeding programs. The economic performance of soybeans can be increased by using new and high-yield varieties. It is essential to evaluate promising advanced soybean Genotypes under different environmental conditions for identifying and selecting superior Genotypes with high and stable yield potential. Genotype × environment interaction effects are important limiting factors in the introduction of new cultivars. The Genotype × environment interaction is a major challenge in the study of quantitative characters because it reduces yield stability in different environments and complicates the interpretation of genetic experiments, making predictions difficult. Therefore, it is crucial to know the type and nature of the interaction effect and reach the verities that have the least role in creating interaction effects. Various 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 GGE-biplot graphic method is a technique with suitable efficiency to investigate the Genotype × environment interaction effect and provides good information about the studied Genotypes and environments graphically. This study aimed to investigate the Genotype × environment interaction effect using the GGE-biplot graphic method to evaluate Genotypes, environments, and relationships between Genotypes and environments. Finally, this research seeks to identify stable soybean Genotypes with high grain yields under different environmental conditions. Methods: In total, 27 new soybean lines along with Saba and Amir cultivars were evaluated under different environmental conditions in a randomized complete block design with three replications in four experimental field stations (Karaj, Gorgan, Sari, and Moghan) during the 2022 cropping season. The plots consisted of four rows of 5 m in length with 50 cm spacing between the rows. The GGE biplot statistical method (the Genotype effect + Genotype × environment interaction) was used to study the stability of Genotypes in the studied environments. Plants were harvested at maturity, and then the seed yield was recorded for each Genotype at each test environment. Results: The results of the combined analysis of variance indicated that the effects of environments (E), Genotypes (G), and Genotype × environment (G×E) interaction were significant for seed yield, suGGEsting that the Genotypes responded differently in the studied environmental conditions, making the stability analysis possible. The results of the Genotype × environment interaction analysis using the GGE-biplot method indicated that the two first and second principal components of the GGE-biplot explained 84.8% of the total seed yield variation, indicating the high validity of the biplot in explaining the variations of Genotypes and the Genotype × environment interaction (G + GE). This study identified two mega-environments, the first of which included Gorgan and Mughan, and the second mega-environment included Sari and Karaj. Based on the polygon view of the biplot, the Genotype G1 in Sari and Karaj environments, and the Genotypes G21 and G22 in Gorgan and Moghan environments were superior Genotypes with high specific adaptation. The results of the average environment coordinate biplot showed that the G1, G22, G5, and G9 Genotypes produced the highest seed yield, respectively. On the other hand, the G28, G25, G16, and G19 Genotypes produced the lowest seed yield, respectively. Based on the hypothetical ideal Genotype biplot, the G22, G5, G16, G12, G14, and G9 Genotypes were better than the other Genotypes for seed yield and stability and showed high general adaptation to all environments. Moreover, the Karaj and Moghan environments were the nearest environments to the ideal environment with the highest discriminating ability and representativeness. Therefore, the Karaj and Moghan environments can be used as a suitable test location for selecting superior soybean Genotypes. Conclusion: Based on the results of this study, the G22, G5, G16, G12, G14, and G9 Genotypes are superior for seed yield and stability in this study. Therefore, these hybrids can be used for further testing, including adaptation tests. Besides, the results show that the Karaj and Moghan environments can be used as suitable test locations for selecting superior soybean Genotypes. Generally, our results demonstrate the efficiency of the GGE-biplot graphical method to investigate the G × E interaction effect and provide good information about the studied Genotypes and environments.

Evaluating of the maize Genotypes under different stresses would be useful to identify Genotypes with stable and high yield potential. The objective of this study was to estimate yield stability of the grain maize hybrids and identifying high yielding stable hybrids under different water stress conditions in Moghan, Ardabil, Iran. So, seven maize hybrids were assessed by randomized complete block design with three replications under four irrigation conditions including normal irrigation (E1), water deficit at vegetative (E2), water deficit at flowering and (E3) water deficit at grain filling (E4) stages during three years (totally 12 environments). Combined analysis of variance showed that the effects of environments, Genotypes and Genotype-by-environment (GE) interaction were significant, suGGEsting that the hybrids responded differently in the studied environment conditions. Therefore, there was the possibility of stability analysis. Results of stability analysis by GGE biplot method revealed that two first and second principal components of the GGE biplot explained 94.7% of the total yield variation. In stability ranking graph of the GGE biplot, SC700, TWC600 and SC724 hybrids were the most stable hybrids, respectively, and the higher grain yield hybrids than the average grain yield were SC704, SC724, SC703, SC720 and SC647 hybrids, respectively. Based on a hypothetical ideal Genotype biplot, the hybrid SC704 was better than the other hybrids across environments for grain yield and stability and had the high general adaptation to all environments. Furthermore, the hybrid SC704 at E1, E2 and E4 environments and hybrid SC647 in E3 environment were superior hybrids with the high specific adaptation. Also, comparison of the studied environments showed that the E1 and E4 environments were quite similar in ranking, grouping and assessing stability of the hybrids, whereas the E2 and E3 environments were different from the other environments.

Accurate interpretation of the Genotype-environment interaction provides the ability to the identification of stable Genotypes for breeders. To study Genotype-environment interaction, 12 Genotypes of sunflower were cultivated in five regions including Arak, Birjand, Kashmar, Karaj and Shiraz were evaluated in the 2015-2016 growing season. To do yield stability analysis the graphical GGE biplot method was used. The results showed that the Record and Zaria Genotypes in Karaj, SHF81-90 and Sor Genotypes in Birjand and Kashmar, Gabur Genotype in Shiraz and Armaverski in Arak were stable with the highest kernel yield. environments of Birjand, Kashmar, Karaj, Arak and Shiraz were the best environments respectively. Genotypes rankings based on the ideal cultivar and also cultivars ranking graph based on the mean yield and stability revealed that Genotypes SHF81-90, Lakomka and Sor were the best and stable Genotypes. The relationship view of biplot indicated a high correlation between environments of Karaj, Kashmar and Birjand. biplot graphical method determined four mega-environments: Karaj as the first mega-environment, Kashmar and Birjand as the second mega-environments, Shiraz as the third mega-environment and Arak as the fourth mega-environment.

Extended Abstract Introduction and Objectives: The evaluation of the Genotype × environment interaction effect provides valuable information regarding the yield of plant cultivars in different environments and plays an important role in evaluating the stability of the yield of breeding cultivars. Genotype × environment interaction effect, especially in stressful environments, are important limiting factors in the introduction of new cultivars; therefore, it is very important to know the type and nature of the interaction effect and reach the verities that have the least role in creating interaction effects. Various 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. The purpose of this research was to evaluate the Genotype × environment interaction effect in experiments conducted in different environments, to determine the relationships between Genotypes and environments and to introduce the most stable red bean Genotypes. Material and Methods: In this research, 14 red bean lines along with Yakut, Ofog and Dadfar control cultivars were cultivated in the form of a randomized complete block design with three replications in Khomein, Borujerd, Shahrekord and Zanjan research stations for 2 crop years under the same conditions. After combined variance analysis, according to the significance of Genotype × environment interaction, AMMI and GGE-biplot analysis methods were used to determine the compatibility and stability of Genotypes. After AMMI analysis, the stability parameters of AMMI were calculated. In addition to the AMMI stability parameters, the simultaneous selection index was also calculated for each of the indices, which was the sum of the rank of the Genotypes based on each of the AMMI stability indices and the average seed yield rank of the Genotypes in all environments. Results: The significance of the double and triple interaction effects of Genotype with year and place (environment) in this study showed that Genotypes showed different responses in different environments, and in other words, the difference between Genotypes is not the same from one environment to another, and in these conditions, the stability of grain yield can be evaluated. The contribution of about 2.5 times the interaction effect of Genotype × environment from the total sum of squares, compared to the effect of Genotype, indicated the possibility of the existence of mega-environmental groups in which some Genotypes show their maximum performance potential in those environmental groups. Genotypes G12, G5 and G17 had the highest seed yield among the Genotypes with yields of 3288, 3136 and 3111 kg per hectare, respectively. AMMI analysis showed that the first to seventh main components were significant at the 1% probability level, and despite the significance of all model components, the first and second main components had the largest contribution to the expression of Genotype × environment interaction (66.5%). Based on AMMI1 biplot Genotypes G4, G5, G16, G17 and G12 had the highest values (positive and negative) of IPCA1. In contrast, Genotypes G8, G3, G2, G7 and G11 had IPCA1 values close to zero. However, only the Genotype G11 showed a performance higher than the average total yield and therefore it can be introduced as a stable Genotype with high general compatibility. Based on AMMI2 biplot, Genotypes G2, G7, G3 and to some extent G8 and G13 were introduced as stable Genotypes, but only G13 Genotype had a higher yield in all environments, so this Genotype can be introduced as a stable Genotype with good yield.; also, every two years, the same place under investigation had a high correlation with each other, so that Bro1 and Bro2 environments on the one hand and Kho1 and Kho2 environments and finally Zan1 and Zan2 showed a high positive correlation (the same effect) to create a mutual effect. In total of the simultaneous selection indices calculated based on AMMI analysis, Genotypes G11, G17, G7, G13 and G12 were introduced as stable Genotypes with high yield. GGE-biplot analysis based on average yield and stability showed that Genotypes G1, G2, G3, G8 and G7 had the highest general stability compared to other Genotypes despite having the lowest yield. On the other hand, G12, G5 and G17 Genotypes had the highest yield with less stability. No ideal environment was observed. But Kho1, Kho2 and Sha1 environments are closer to the ideal environment than other environments and they can be used to distinguish the studied Genotypes to some extent. On the other hand, G12 Genotype can be considered as a desirable Genotype that has high average yield and high yield stability. In the same way, G17, G5 and G11 Genotypes were in the next stage compared to the ideal Genotype and to some extent they can also be considered as desirable Genotypes. Conclusion: According to all the results, G12 Genotype can be considered as a desirable Genotype that has a high average yield and also has yield stability, and G17, G5 and G11 Genotypes were in the next stage.