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.

Considering the speed of environmental changes and the extent of the data obtained from breeding evaluations, there is a need to employ fast and reliable data analysis methods. biplot graphical methods greatly contribute to the advancement of breeding programs by offering the possibility of quick, comprehensive, and accurate analysis of information, along with easy interpretation of the results. A number of 100 oilseed sunflower Genotypes were evaluated in terms of plant height, number of leaves, leaf length, leaf width, petiole length, stem diameter, chlorophyll content, days to flowering, days to maturity, head diameter, relative water content, seed oil percentage, and seed yield in the form of simple 10 x 10 lattice design under normal and limited irrigation conditions during 2012 and 2013. To identify the superior Genotypes in each irrigation conditions, the Genotype-trait biplot analysis method was used. Under both irrigation conditions, a high correlation was observed between the traits including leaf length, leaf width, stem diameter, head diameter, seed oil percentage, and petiole length with seed yield. In terms of all studied traits under both irrigation conditions, Genotype with code number of 8 was the best and Genotypes with code numbers of 59, 42, and 72 and Genotypes with code numbers of 19, 59, and 17 were the worst Genotypes under normal and drought stress conditions, respectively. Based on the results, the traits including stem diameter and head diameter can be used as indirect selection criteria for seed yield improvement under normal and drought stress conditions, respectively.

A mini collection comprising 32 sainfoin Genotypes was cultivated using a randomized complete block design with four replicates. Various parameters were recorded, including the number of plants per area (NPA), total dry yield (TDY), thousand seed weight (TSW), number of main stems (NMS), petiole length (PL), length of inflorescence (LI), number of leaflets per leaf (NLL), leaves per main stem (LMS), number of leaves per stem (NLS), stem dry weight (SDW), leaf dry weight (LDW), inflorescence dry weight (IDW), and number of florets per inflorescence (NFI). The first and second components of the biplot accounted for 88% of the variability in the dataset, with 70% attributed to the first component and 18% to the second. A pentagon was identified, featuring two distinct sections with Genotypes 16 and 25, as well as Genotype 14, serving as vertex entries. Notably, Genotype 14 (Azna) excelled in three traits: NLL, LMS, and NLS. Additionally, vertex Genotypes 16 and 25 demonstrated superior performance in other measured traits, including economically significant traits such as total dry yield. In accordance with the ideal entry, Genotypes 13, 14, and 19, along with Genotypes 16 and 25, exhibited greater favorability compared to other sainfoin Genotypes regarding variability in the measured traits. Based on the ideal tester, total dry yield, number of leaves per stem, and number of florets per inflorescence were identified as key factors for assessing variation among Genotypes. An examination of Genotypes based on total dry yield indicated that Genotypes 16 and 25, followed by Genotypes 14 and 16, were the most desirable.

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.

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.

Canonical discriminant analysis (CDA) in combination with cluster analysis and Genotype by trait (GT) biplot analysis methods were used to assess 9 wheat cultivars having different degrees of tolerance along with 36 F1 hybrids derived from partial diallel crosses, using stress tolerance indices, in two levels (well watered and cessation of irrigation at pollination stage). Cluster analysis classified Genotypes into three clusters and the results used to CDA as classifying variable. The first canonical variable was under the influence of high canonical coefficients for STI, HM, MP, GMP, Yp and Ys. Canonical coefficients for SSI and TOL in the second canonical variable were remarkable. The first canonical variable differentiated Genotypes based on yield potential and stress tolerance and the second canonical variable distinguished stress tolerant Genotypes from the sensitive ones. The scatter plot of two significant canonical variables characterized three distinct groups and all pairwise Mahalanobis distances among groups were significant. The Genotypes in group 1 had high yield in stress and non-stress conditions and the Genotypes of group 3 had more stress tolerance and less susceptibility to stress. To identify tolerant and high yielding Genotypes in both stress and non-stress conditions, GT biplot analysis based on combination of STI and SSI was used and superior Genotypes which simultaneously have higher yield potential and stress tolerance were introduced.

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.

Cantaloupe is a common crop and relatively important in Iran which show high genetic diversity. The objective of this study was the investigation of interrelationships among different traits in Iranian cantaloupe cultivars and comparison of different cantaloupe Genotypes based on different traits using biplot method. Forty nine Genotypes including seven Iranian endemic cantaloupes Rishbaba, Shahabadi, Samsori, Dastjerdi, Magasi, Tiltorogh and Savei and their direct and reciprocal crosses were evaluated during two years using triple lattice design. Results indicated the positive and significant correlations between yield and yield components in both of years. Three traits including fruit weight, flesh thickness and fruit width indicated positive and significant correlation with yield among of traits. The ranks of the Genotypes relative to yield were indicated the differences between Genotypes in two years. The Genotypes ranks according to total soluble solids and early maturity indicated that Magasi × Savei (G57) cross and Dastjerdi (G4) parent were the most superior Genotype in two years; so Magasi × Savei (G44) cross and Dastjerdi (G4) parent can be considered for the production of cultivars with high sugar and early mature cultivars, respectively. Results also showed that the yield were independent from total soluble solids and early maturity so in cantaloupe breeding programs we can individually modified yield trait in breeding projects. The two traits, total soluble solids and early maturity had the negative correlation with each other; so these traits could be considered simultaneously for breeding.

Salinity stress is one of the major abiotic stresses in arid and semi-arid regions of the world, such as Iran. In this research to finding interrelationships between different traits and performance; some wheat Genotypes were evaluated by using biplot method. In this study, 110 bread wheat Genotypes were evaluated in two conditions (normal and saline stress) at the research field of the National Salinity Research Center (NSRC). The salinity of water used in irrigation in saline and normal conditions was 10 and 2 dS. m-1 respectively. Also the average of soil salinity rate in saline and normal condition was 9. 5 and 2. 7 dS. m-1 respectively. The results revealed that there was a strong positive association between biological yield and harvest index with seed yield in both normal and saline conditions. Therefore, it seems that biological yield and harvest index could be used as a suitable criterion in selecting for increased seed yield in wheat. In this research among 110 studied bread wheat Genotypes, promising advanced lines Salt22, Salt29 and Salt30 were identified as the most salinity-tolerant Genotypes that these lines can be utilized for salt-affected areas and as donor parents in wheat breeding programs for further improvement of germplasm for salinity tolerance.