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.

Introduction and Objective: 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. Materials and Methods: 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. Results: 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. Conclusion: 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.

Extended Abstract Background: 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. Methods: 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.  Results: 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. Conclusion: 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.

In the present investigation, 81 safflower genotypes were studied in a 9×9 simple lattice design for several plants per plot (NPP), plant height (PH), the height of the first lateral branch (HFL), the height of the first lateral capitulum (HFC), stem diameter (SD), number of lateral branches per plant (NLB), number of main branches per plant (NMB), number of capitula per plant (NCP), number of seeds per main capitulum (SMC), number of seeds per lateral capitulum (SLC), seed yield (SY) and thousand seed weight (TSW). The genotype by trait (G×T) interaction biplot tool was used to indicate the pattern of G×T interaction two-way interaction data in a graph with 73% description of observed variation whereas the first principal component (PC) effect explained 49%, and the second PC, 24%, of the observed interaction variability. The vector view displayed that NCP with NMB, and SMC with SY were positively associated while there was a negative association between HFC with TSW, and between NLB with NPP. The polygon-view graph is divided into eleven sectors, and the sector of genotype G80 was a winner for most traits. Genotype G58 followed by genotypes G30, G33 and G72, were the most favorable genotypes in regard to SY while regarding this trait as a reference, SMC was identified as the most related trait which is followed by SLC, SD. Applying G×T biplot to the safflower multiple trait data demonstrated that this model visually showed the associations among seed yield with the number of seeds per main and lateral capitula followed by the number of capitula per plant and thousand seed weight, and provide ease of visual genotype comparisons and choosing. We found that choose of seed yield alone was not only dependent on the number of seeds per main and lateral capitula but also related to the other traits in safflower breeding.

Selection of high yielding and stable genotypes is especially important for rainfed conditions. The objective of this study was to use the genotype by yield × trait (GYT) biplot method to select superior bread wheat genotypes based on yield-trait combinations under rain-fed conditions. To this end, 16 bread wheat genotypes were assessed in a randomized complete block design with four replications in two cropping seasons, 2017-2019. Comparison of average grain yield of genotypes in the experimental years showed that genotypes G1 (Aftab), G15 and G8 had the highest grain yield with an average yield 3433, 3269 and 3233 kg/ha, respectively. The result of polygon view of GYT biplot showed that G8 was the best genotype in combining grain yield with 1000-kernel weight, number of grain per spike and spike length. Similarity, G1 also had a relative superiority in combining grain yield with number of spikelet per spike, awn length and early maturity. Average tester coordinate (ATC) view of GYT biplot indentified genotypes G8, G1 and G15 with all positive yield-trait combinations as the best genotypes and genotypes G14, G4, G13 and G7 as the weakest genotypes. Genotypes G15 and G16 were also balanced for yield-trait combinations. Ranking of the genotypes based on GYT index showed that genotypes G8, G1 and G15 had the best genotypes in combining grain yield with the evaluated traits in the experimental years. Based on the results of this study, high correlations were observed between 1000-kernel weight, number of grain per spike and grain weight per spike in combination with grain yield, indicating high efficiency of combining these traits with grain yield to improve productivity of bread wheat genotypes under rainfed conditions.

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.

In this study, the methods of genotype × trait (GT) and genotype by yield× trait biplot (GYT) were used to identify interrelationships between different traits and the best wheat genotypes selection based on multiple traits. During the 2017-2018 cropping season, fifteen cultivars/ promising lines along with a durum wheat cultivar, Zahab, were tested on field’ s farms at two sites, Delfan and Khorramabad (Imanabad region). The results of ANOVA for the evaluated traits showed significant differences among genotypes for plant height, day to heading and maturity, grain yield and harvest index. According to the mean grain yield of the genotypes at both experimental sites, the maximum grain yield was obtained for Varan, Sadra, Ivan and Azar2/Sabalan/… genotypes with mean grain yield of 3374, 3207. 5, 3054 and 2866. 5 kg/ha, respectively. Based on GT-biplot polygon, Varan and Sadra cultivars were displayed high GY, SSM, TKW and PH at two experimental sites. The vector view of GT biplot showed high correlation between grain yield with 1000-kernel weight, plant height and harvest index at experimental sites. Based on results of GYT biplot, Varan and Sadra cultivars were the best genotypes in combining grain yield with all traits evaluated. The high correlations between combinations of 1000-kernel weight and plant height with grain yield were observed, suggesting the importance of these traits in combining with grain yield to increase genotypes productivity.

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 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.

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.